As EVs become a more popular option amongst consumers the price can become a barrier for consumers. State and Federal governments have utilized a variety of incentives to encourage consumers to make the switch. In this paper, I will be testing my hypothesis on Electric Vehicle Incentives within the United States. I make the argument that incentives encourage consumers to make the purchase as it is fiscally obtainable with rebates and tax credits. Some consumers would make the purchase regardless of incentives. I will be comparing states that have high and low incentives and the percentage of registered EVs in the respective state. I will be using EV data from 2020 and 2019 to construct my models. I conclude from the models that incentives and EV adoption have no significant correlation, The exception in the dataset is the state of California where EV adoption is correlated with incentives.

1. Janelle Ntim
ECO 231 - Applied Business Statistics
Professor Trevor O'Grady
Fall 2021
Final Project
Ev Adoption In The United States and Government Backed Electric Vehicle Incentives
Abstract:
As EVs become a more popular option amongst consumers the price can become a barrier for
consumers. State and Federal governments have utilized a variety of incentives to encourage
consumers to make the switch. In this paper I will be testing my hypothesis on Electric Vehicle
Incentives within the United States. I make the argument that incentives encourage consumers to
make the purchase as it is fiscally obtainable with rebates and tax credits. For some consumers
they would make the purchase regardless of incentives. I will be comparing states that have high
and low incentives and the percentage of registered EVs in the respective state. I will be using
EV data from 2020 and 2019 to construct my models. I conclude from the models that incentives
and EV adoption have no significant correlation, The exception in the dataset is the state of
California where the EV adoption is correlated with incentives.
Introduction/Background
Economic incentives are policy tools to help encourage favorable behavior. Economic
incentives are used for any variety of reasons that could be in the case of EVs to get consumers
to consider an EV as a comparable option to any other vehicle on the market. The incentive for
EVs are to make them more attractive and competitive in the automotive industry. States play a
large role in encouraging EV adoption. I hypothesize that EVs are more likely to have a higher
rate of adoption compared to states that have little incentives for consumers. The question I will
be answering in this paper is, Do states that have higher amounts of laws & regulations and
incentives also have higher amounts of EV’s registered in the respective state? Does state
government involvement in EV adoption have any impact at all on the percentage of EVs per
state?
In the article “Do electric vehicle incentives matter? Evidence from the 50 U.S. states”
the authors SherilynWee, Makena Coffman, and Sumner La Croix assembled a data set with
U.S. state-level policies for EVs from 2010 to 2015. The results from their model show that there
is a $1000 increase in value of EV policies resulting in a 5 to 11% more new EV registrations.1
The results from the study presented evidence that there is a positive relationship between the
two variables,policy incentives and EV adoption. The study used data gathered from the U.S.
Department of Energy’s Alternative Fuel Database Center (AFDC). In my study i also used data
gathered from the same database to construct my models and run my regression analysis. In this
study the authors study specific policy instruments. The BEV (Battery Electric Vehicles) and
PHEV (Plug-In Hybrid Electric Vehicles) policy instruments affected consumers between 2010
1
Wee, Sherilyn, Makena Coffman, and Sumner La Croix. 2018. “Do Electric Vehicle Incentives Matter?
Evidence from the 50 U.S. States.” Research Policy 47 (9): 1601–10.
https://doi.org/10.1016/j.respol.2018.05.003.

2. 1
and 2015 by U.S. state.2
The authors used a “high-dimensional fixed-effect regression model”.
The authors choose this model because the model “allows us to compare new model registrations
in a state to national model registrations by incorporating two higher-dimensional fixed effects
and state-specific time trends to control for unobserved heterogeneity.” The overall result of the
study showed that “This implies that a $1000 increase in a state’s EV subsidies leads to a 7.5%
increase in registrations of an EV model in that state.”3
The authors and I make the same claim
that EV incentives such as subsidies lead to more EVs being adopted.
Data Overview
What is the unit of analysis (individuals, states, counties, products, etc.)?
The unit of analysis is states within the United States of America. There are 51 observations.
US states from 2020 and Presidential election results from 2000. I assembled the data myself
using multiple sources from ​
​
MIT Election Data and Science Lab, the U.S. Department of
Energy: Alternative Fuels Data Center, National Transportation Atlas Database and The U.S.
Department Of Transportation: Bureau Of Transportation Statistics. I dropped one observation
from the data set depending on the graph. During my analysis I did run graphs that omitted
California. The data from California caused the data to be skewed so we compared the two to see
how much of an impact did the data from California have on the analysis.
Include summary statistics of the main outcome variable(s) and left-hand side variables used in
the analysis. At a minimum, you should report the means and standard deviations of the key
variables and the sample size.
Stata id Description Obs Mean Std Min Max
evlr
EV Laws and Regulations
Category 1=(1-5) 2=(6-10)
3=(11-15) 4=(16-20) 5=(21+)
51 3.27451 1.234154 1 5
stinct
State Incentives Category 1=(1-5)
2=(6-10) 3=(11-15) 4=(16-20)
5=(21+)
51 2 0.7018184 1 4
licdrv State Licensed drivers (2019) 51 4,483,916 4933903 424155 2.72e+07
ttllawinctprg
Total Laws Incentives and
Programs Category 0 = (46-50)
1 = (51+)
51 0.9411765 0.2376354 0 1
utilprvtincent
Utility/Private Incentives
Category 1=(1-5) 2=(6-10)
3=(11-15) 4=(16-20) 5=(21+)
51 5.058824 7.393001 0 48
election2000
2000 Election Results
(Democratic Candidate - 1 &
Republican Candidate - 0)
51 0.4117647 0.4970501 0 1
3
Wee, Sherilyn, Makena Coffman, and Sumner La Croix. 2018. “Do Electric Vehicle Incentives Matter?
Evidence from the 50 U.S. States.” Research Policy 47 (9): 1601–10
2
Wee, Sherilyn, Makena Coffman, and Sumner La Croix. 2018. “Do Electric Vehicle Incentives Matter?
Evidence from the 50 U.S. States.” Research Policy 47 (9): 1601–10

3. 2
evsetotal Total EVSE 51 2234.549 4993.417 87 35421
evpercent
Percentage of Registered EVs in
State
51 0.0025311 0.0024955
0.000243
5
0.0136108
Empirical Methods and Model Estimation
The main regression i used to test my hypothesis is; ( evpercentPredicted = .002171 +
.000691*evlr + -.0007913 *stinct + -.0018589 *ttllawinctprg + .0001188*utilprvtincent +
6.97e-11 *licdrv + .0008381*election2000 ). I tested the following variables in my analysis
evpercent: The percentage of EVs registered in a given state; evlr: EV laws & regulations; stinct:
State incentives; ttllawinctprg: total laws incentives and programs offered in the respective state;
utilprvtincent: utility programs within each state ; licdrv: Licensed drivers; and election2000:
election results from 2000 indicating the Candidate that received the most votes. The second
regression equation I estimates was for EV adoption and EV laws & regulations;
(evpercentPredicted = -.0008571 + 0010347*evlr )
The assumption that is needed to understand my comparison between states utility
programs and state level incentives is that I assume states that went to the democratic candidate
in 2000 would most likely have incentive programs that encourage EV adoption opposed to
states that went to the republican candidate. I believe the data will support my logic and
assumption. The control variable in the regression is the 2000 election due to the fact that in
2000 EVs were not being promoted by government officials as they have been in the past decade.
Using the control variable removes bias.
Results and Discussion

4. 3
Graph 1. EV Adoption Rate and States 2000 Election Results (California omitted)
Graph 1 has omitted California as the data point has skewed the results, I have included a graph,
Graph.2 , that does include the point to show the overall results. Each point on the graph
represents an individual state. I hypothesized that states who went for the democratic candidate
had a higher rate of registered EVs in the state. Graph 1 supports my hypothesis about the
political leanings of states. I can now reject the null hypothesis. EV Adoption Rate and States
2000 Election Results without all 50 states now includes California which does not accurately
show the distribution between the two variables. The regression line shows that there is a
positive correlation between the EVs and the 2000 election results. For Graph 1 and 2 the
Democratic Candidate is coded as 1 and the Republican Candidate is coded as 0. Both Graph 1
and 2 show the same model but Graph 1 is much more detailed and can better show the
distribution of EVs amongst the states as it doesn’t have the data point from California. I tested
the significance of my hypothesis by analyzing the regression output that accounts for multiple
variables such ev laws & regulations, state incentives, licensed drivers, total laws incentives and
programs, utility/private incentives, 2000 election results, and percentage of registered evs in
state.
Graph 2. EV Adoption Rate and States 2000 Election Results 50 States
In my regression analysis With all of the variables the model predicts 49% of the estimated
percentage of variation explained by only the independent variables. The Adjusted R-Square
shows the percentage of the estimated the percentage of variation explained by only the

5. 4
independent variables and dependent variable which is EV adoption. The adjusted R-square
attempts to yield a more honest value to estimate the R-squared for the population. EV
percentage of the R-square is 55%, that actually affects the dependent variable. I could continue
to add predictors to the model which could potentially continue to improve the ability of the
predictors to explain the dependent variable, but the p value for all the independent variables are
showing me that the independent variables do not have any significant impact on the dependent
variable.
Table 1. EV percent Regression Output
(evpercentPredicted = .002171 + .000691*evlr + -.0007913 *stinct + -.0018589 *ttllawinctprg
+ .0001188*utilprvtincent + 6.97e-11 *licdrv + .0008381*election2000)
The regression analysis shows that for every one unit increase in the predicted value of
state level incentives decreases in the predicted value of EV adoptions by .0007913. This
indicates that EV adoption and State level incentives have a negative relationship and are not
correlated. For every one unit increase in the predicted value of total laws, incentives & utility
programs decrease by the value of EV adoption by 0.0018589. This also shows that all of the
incentives and programs combined do not influence consumers to purchase an EV. Utility
programs on their own do have a positive impact on the adoption of EVs. For every one unit
increase in the predicted value of a utility program it increases by the value of EV adoption
.0001188. The election results, which is the control variable that is there to predict the political
ideology of the citizens of the state, has a positive impact on EV adoption in the respective state.
For every one unit increase in the 2000 election results there is an increase by the value of EV
adoption by .0008381.
The only variable that could potentially have any correlation is the dependent variable
evlr, EV laws & regulations. Table 2 does however show the simple linear regression analysis for
EV adoption and EV Laws & Regulation. Based on p-value the EV laws & regulations are
statistically significant in explaining EV adoption in states. In this case EV laws & regulations
explains 25% of the variance in EV adoption in states. For each unit increase in EV laws &
regulations, EV adoption scores increase by .0010347(0.1%) percentage points. Based on
p-value, the independent variable is statistically significant in explaining EV adoption in states

6. 5
with higher laws & regulations. A statistically significant independent variable has a p-value is <
.00001.
Table 1. EV adoption and EV Laws and Regulation Regression Output
(evpercentPredicted = -.0008571 + 0010347*evlr )
Graph 3. EV adoption and EV Laws and Regulation 50 States
In Graph 3. I test the hypothesis that EV laws & regulations have a positive correlation with EV
adoption. The null hypothesis is that there is no relationship. The model in Graph 3. substantiate
my hypothesis that there is a positive relationship between the two variables. I can reject the null

7. 6
hypothesis in this instance. Laws and regulations were broken up into 5 categories, 1=(1-5)
2=(6-10) 3=(11-15) 4=(16-20) 5=(21+). I choose to do this as a way to better visualize the data in
a more structured and easier way.
Graph 4. EV Adoption Rate and States Level Incentives 50 States
I tested my 3 hypotheses on the relationship between EVs and incentives on the state level. The
null hypothesis refutes my claim by stating that there is no correlation between the EV adoption
rates and state level incentives. State level incentives range from 0 to 50 incentives. State level
incentives range between 1 and 14, with a mean of 6.64, omitting California. Graph 4. Shows the
relationship between the two variables including all states. Like I've mentioned before California
is an outlier that has a strong impact on the output of the model overall and this is also seen in
Graph 4. In Graph 5. It is the same model but with California omitted. The model in Graph 5.
Shows that there is no relationship between the two variables. Which allows me to assert that my
hypothesis about the relationship between the two variables is unfounded and accept the null
hypothesis. I utilized a twoway scatter plot for both Graph 4. and Graph 5. with a linear
regression line. The line is upward sloping yet the points are scattered and don’t trend in any
direction.

8. 7
Graph 5. EV Adoption Rate and States Level Incentives 49 States (California omitted)
Graph 6. EV Adoption Rate and Utility/Private Incentives 49 States (California omitted)

9. 8
My hypothesis on utility and private initiatives for the adoption of EVs is that there is a positive
correlation between the two variables. In Graph 6. California is omitted showing the other 49
states and the model looks very different from Graph 7. In Graph7. The linear regression line is
on a positive slope indicating that there is a positive correlation between the two variables.
Utility/Private Incentives range between 0 and 122, with a mean of 4.2, while omitting
California. With California the mean is 5.058824 and the rand is between 0 and 48. Graph 6.
Clearly shows that there is zero correlation between the variables. Graph 7. Can be very
misleading due to the 48 Utility/Private incentives added to the dataset.
Graph 7. EV Adoption Rate and Utility/Private Incentives 50 States
Graph 8 and 9 test my control variable states 2000 presidential election results for each state
against state level incentives. My hypothesis for this model is that political leanings in an
individual state will have an impact on state level incentives. I believe that the model will
substantiate a correlation between the two variables. In 2000 during the election individual states
had not drafted EV incentives and policies. This removes any potential biases that could happen
if I were to look at the political leanings from data from the 2020 election. Graph 8 shoes that
there are a similar amount of state incentives regardless of political leaning in the 2000 election.
With the figures from the Graph 8 and Graph 9 I can accept the null hypothesis that there is
correlation between the state's political leanings in 2000 and the implementation of state
incentives. This allows me to confidently claim that political leanings do not impact the adoption
of EV incentives at the state level. States that hold differing ideologies encourage the adoption of
EVs through economic incentives.

10. 9
Graph 8. States 2000 Election Results and States Level Incentives 49 States (California omitted).
Democratic Candidate - 1 Republican Candidate - 0
Graph 9. States 2000 Election Results and States Level Incentives 50 States. Democratic
Candidate - 1 Republican Candidate - 0
The overall conclusion that I can make is that there is no correlation between EV adoption
through economic incentives and mechanisms that encourage adoption of EVs. I have accepted

11. 10
the null hypothesis. The significance of this study provides evidence that incentives are just not
enough to get people to make a purchase of an EV. There are external factors such as access to
charging stations in the area that also contribute to consumers' decision to purchase an EV. I can
confidently say that there is not correlation between incentives and EV adoptions. I can however
confidently argue that EV adoption and EV Laws and Regulation has a positive correlation,
Graph 3.

12. 11
References:
Alternative Fueling Station Locator: Alternative fueling stations in the United States and
Canada. Alternative Fuels Data Center: Maps and Data - Alternative Fueling Station Locator.
the U.S. Department of Energy.
https://afdc.energy.gov/stations/#/analyze?country=US&status=E&status=P
Blanco, Sebastian “Zap Tax Credit, Zap Demand? Salespeople Split on How Critical EV
Incentive Is.” Automotive News 92, no. 6804 (2017).
Electric Vehicle Registrations by State: U.S. light-duty electric vehicle population as of
December 2020. Alternative Fuels Data Center: Maps and Data - Electric Vehicle Registrations
by State. the U.S. Department of Energy. https://afdc.energy.gov/data/10962
Electric Vehicle Laws and Incentives by State: Displays states and their respective law and
incentive counts related to electric vehicles. Alternative Fuels Data Center: Maps and Data -
Electric Vehicle Registrations by State. the U.S. Department of Energy.
https://afdc.energy.gov/data/10373
Lewis,Michelle “Electric vehicles projected to make up 31% of the global fleet by 2050”
Electrek. Oct. 26th 2021.
https://electrek.co/2021/10/26/electric-vehicles-projected-to-make-up-31-of-the-global-fleet-by-2
050/#:~:text=October%2026-,Electric%20vehicles%20projected%20to%20make%20up%2031,t
he%20global%20fleet%20by%202050&text=Electric%20vehicles%20will%20grow%20from,E
nergy%20Information%20Administration%20(EIA).
MIT Election Data and Science Lab, 2017, "U.S. Senate 1976–2020",
https://doi.org/10.7910/DVN/PEJ5QU, Harvard Dataverse, V5,
UNF:6:cIUB3CEIKhMi9tiY4BffLg== [fileUNF]
MIT Election Data and Science Lab, 2017, "U.S. President 1976–2020",
https://doi.org/10.7910/DVN/42MVDX, Harvard Dataverse, V6,
UNF:6:4KoNz9KgTkXy0ZBxJ9ZkOw== [fileUNF]
Wee, Sherilyn, Makena Coffman, and Sumner La Croix. “Do Electric Vehicle Incentives
Matter? Evidence from the 50 U.S. States.” Research Policy 47, no. 9 (2018): 1601–10.
https://doi.org/10.1016/j.respol.2018.05.003.
“State Highway Travel” Bureau of Transportation Statistics: National Transportation Atlas
Database. The U.S. Department Of Transportation.
https://www.bts.gov/browse-statistical-products-and-data/state-transportation-statistics/state-high
way-travel