Evaluation for Cash for Clunkers Ayanda Francis and Rose Anthony

Evaluation for Cash for Clunkers
                   Ayanda Francis and Rose Anthony

                                            Abstract

        The Car Allowance Rebate System (C.A.R.S.), colloquially called Cash for Clunkers,
started June 24, 2009 under the Obama administration. It was suppose to last until November
1st, 2009 but failed to do so because of funding issues. Our research explores whether C.A.R.S
truly had an effect stimulating us out of a recession. We will be exploring the long term effects
of C.A.R.S by exploring regional differences. To do this, we explored the variation between
regions and compare it to the effectiveness of the program and concluded that the program
was not effective.

Keywords: C.A.R.S program, stimulus evaluation

Anthony 2

I. Introduction

        In 2009, President Obama unveiled the Car Allowance Rebate System in an attempt to
stimulate the economy. It seemed to be the perfect solution, allowing the auto industry to
increase their own sales while helping the environment by increasing the amount of fuel
efficient C.A.R.S. on the road. For three months, approved vehicles were eligible to be traded
for newer more fuel-efficient and environmentally friendly compact C.A.R.S. by a system of
vouchers. The program became commonly referred as “cash for clunkers” because of its
ambitious goals.

        Unfortunately the program was cut short due to public dissent and underfunding.
Experts agree that it was not cost effective costing $3 billion dollars which was not recovered in
the short term. However, it is still unclear what the long term effects or the overall efficiency of
the program were. Exploring the long term effects of programs like these are vital because
stimulus policies are often used by the government to alleviate economic strife. By analyzing
policies such as this one, we can better learn on how to use help policy makers use this tool.

II. Literature Review

        The Car Allowance Rebate System (C.A.R.S.), colloquially called Cash for Clunkers,
started June 24th 2009 under the Obama administration. The program was scheduled to last
until November 1st 2009, but it ended more quickly because the amount of funding necessary
was underestimated and unsustainable. By the end the program, around 700,000 C.A.R.S. had
been sold generating “$4-$7 billion in revenue and creating 60,000 in jobs” (Tyrrell and
Dernbach, 2010). It is clear that during the program’s duration, there was an increase in car
sales, but the question of how much of this increase was due to C.A.R.S. still remains. The
larger, and perhaps more pressing question, is if the impact of the program will be a lasting
one.

       Many are skeptical about the program’s merits, arguing that all the effects on the
economy are artificial and are simply replacing future sales with current sales. Copeland and
Kahn (2013) believe that the sales dispersion is due to the fact that the program lacks a
component that targets the production process. The program simply focuses sales, in fact
production accounts for “less than half of the induced increase in vehicle sales”(Copeland and
Kahn, 2013).They estimate that the results would be rendered trivial by January 2010.
 Inventory is not depleted rapidly by sudden jumps in the market because producers are
accustomed to these changes and have developed mechanisms for smoothing out the process.
The stimulus also caused a shifted in the business term from September- December to July-

Anthony 3

August. Though this shift seems slight, this shift allowed customers to buy 2009 models of
C.A.R.S. instead of 2010 (Copeland and Kahn, 2013). For Copeland and Kahn, it is clear that
C.A.R.S.’ success is simply due to lucky timing.

        Gayer and Parker (2012) seem to agree with this notion. They demonstrate that though
55 day period though the program increase market shares all around with about 31.4 percent
of total vehicle sales during this period, ultimately it only created a short term GDP boost.
Gayer and Parker argue that the temporary relief the program provided was at the cost of “
shifting roughly $2 billion into the third quarter of 2009 from the subsequent two quarters”
while destroying valuable economic capital by ending C.A.R.S. before its time (Gayer and
Parker, 2012). They also state that even though “roughly 2,050 additional job[s]” were created
by the stimulus, the cost was too great, “0.7 jobs for each million dollars.., resulting in a cost of
$1.4 million per job created” (Gayer and Parker, 2012). They did conclude that the stimulus did
improve fuel efficiency and reduce carbon emission and perhaps the program suffered from
attempting too many issues.

        Tyrrell and Dernbach (2010) have a more optimistic view concluding that the program
achieved exactly what it had set to do. They believe though the program had its faults, it
provided sufficient and rapid response to the recession. It increased GDP by “$4 to $7 billion …
saving or creating more than 60,000 jobs in automobile manufacturing and sales, as well as in
related industries.” They cite “2010 analysis by Toledo-based Maritz Automotive Research
Group” as evidence against the phenomenon of shifting profits claiming that the profits
produced during the programs’ term did not negatively affect future sales. However, they did
show that the participants of the program were fairly limited and although the program did
take C.A.R.S. with lower performing fuel standard out, “new inefficient vehicles or old fuel-
efficient C.A.R.S.” were not targeted as heavily.

        Our research will explore whether C.A.R.S truly had an effect stimulating us out of a
recession. We will be exploring the long term effects of C.A.R.S. by exploring regional
differences. To do this, we explore the variation between regions and compare it to the
effectiveness of the program. We will be keeping the number of vouchers constant in order to
better explore the implementation process. C.A.R.S is an important event to study because
stimuli are important and common fiscal tool of the government.

III. Data

        The empirical research question we are testing is “Did the cash for clunkers program
have an effect in stimulating the economy after the recession?” To properly measure the effect
of the stimulus package, we will be comparing the auto industry sales for each state before the

Anthony 4

use of the Cash for Clunkers allotted vouchers and the auto sales after the program. We will
also be looking at regional differences to see if they have an effect on the variation in the
effectiveness of the program. Our model is listed below.

         The dependent variable in this case is the change in the amount of income created from
the auto sales industry before and after the stimulus package was introduced, and will be
measured in millions of dollars. It is stated as the logarithm of sales to allow the data to be
more manageable. The independent variable is the package itself, and it will be measured in the
amount of money allotted to each state to use as vouchers denoted by lvouch. Again logarithm
is used to make the model more manageable. A variable for the median income, lincome,
allows us to take into account the socioeconomic differences between states. To fully take
account of the socioeconomic differences between states, we also included unemployment
rates, listed as urate, and poverty rates, listed as prate. The model also accounts for each region
by including four dummy variables. W accounts for the Western region of the US, NE for the
North Eastern region and MW for the Mid-Western region of the US. The southern region is
the base group in this model.

        Including changes in auto sales in the model is vital because they serve as a good
representation of the success of the program because the main purpose of the package was to
stimulate the national economy by increasing consumption of auto industry products.
‘Vouchers’ is also a necessary variable because the allotted funding was the main incentive
given from the government to achieve their economic goals. The income variable is also
important in that it will take into account the effect that income has on the affinity to purchase
new C.A.R.S., which left unmeasured would skew our model. Poverty rates highlight the
percentage of the population that would not, in most cases, take part in the program because
the program did suffer income bias. Unemployment rates take into account which regions were
most effect by the economic recession.

       The source of the voucher data is the Department of Transportation’s list of voucher
amounts per state. Because this is a government stimulus program, and the voucher funds and
allotments were made through this department, the Department of Transportation has the
most thorough and reliable information on all aspects of this variable. For the data on sales, the
National Automobile Dealers Association is the most comprehensive and trustworthy source.
The NADA is one of the largest automobile associations in the US and has several decades of
data on changes in the automobile industry from industry, state, and national levels. The

Anthony 5

income data and the poverty rates were taken from the US census bureau’s data on state
incomes. The data on unemployment rates were taken from the US Bureau of Labor Statistics.

         This model satisfies several of the Gauss-Markov assumptions. We have collected data
that is linear in parameter because the graph of the points for each variable is linear. Thus, the
data satisfies assumption one. Because the data for the sales is an aggregate of all the auto
sales throughout each state, it is random in parameter. The vouchers variable is the collection
of all the voucher amounts allotted to all the states, so this too is random. This satisfies
assumption two.

       Table 1: Statistical Correlation Table
Variable          Unemployment Poverty               Sales           Income          Vouchers
Unemployment            1.000              ----           ----           ----            ----
Rate
Poverty Rate           0.3447            1.000            ----             ----            ----
Log of Auto             0.322            0.1741          1.000             ----            ----
Sales
Log of Median          -0.2147          -0.6729          0.0432          1.000
Income
Log of                 0.3515            0.3758          0.5664         -0.1808          1.000
Vouchers

        There is no perfect collinearity between any of the regressors because though vouchers
incentives an increase in sales, they are not a direct component of either the sales figures. This
satisfies assumption three.

        Though we have gathered the data and chosen the variables that encompass the
majority of the data for the Cash for Clunkers program, there is no variable without bias.
However, because this program had a finite beginning and ending dates as well as the fact that
there were measures taken by the government to ensure that the majority of the population of
the Cash for Clunkers participants would keep a record of their participation, the bias
associated with these issues has been minimized. Also, the variables we have chosen
encompass the main components of the Cash for Clunkers program, so there is little risk in
omitting a variable and influencing the error term in this manner. As for possible measurement
errors, there is a low likelihood that we have unknowingly measured our sample in a manner
that differs from the larger population. These variables are all measured naturally in monetary
terms, and we have done the same in our model. Homoscedasticity is difficult to test, but for
this model we ran a robustness test to see the variation in the error terms.

Anthony 6

Table 2: Total Summary Statistics by Period

    Variable                Observation                Mean              Standard Deviation
Unemployment rate              250                     4.8004                1.133162
(2004-2008)
Log of Auto Sales                250                  13816.82                18719.36
(2004-2008)
Log of Median                    250                  48035.33                7672.195
Income(2004-2008)
Poverty Rate(2004-               250                   12.6128                3.014556
2008)
Unemployment rate                150                 8.4166667                 1.98739
(2009-2011)
Log of Auto Sales                150                  10960.79                11785.17
(2009-2011)
Log of Median                    150                  50088.97                7486.003
Income(2009-2011)
Poverty Rate(2009-               250                   14.256                 3.405079
2011)
Vouchers                         50                   306.4265                1755.402

We included data from the four years before the program, 2004-2008, so that we could
compare the result. There are 250 observations for the years before the program began, 2004-
2008, because each state is an observation, there are 50 observations for each variable per
year. Since there is one year less in the period for the second group, 2009-2011, there are only
150 observations. These years are after the program has been in place. The variable “vouchers”
only has 50 observations because it is a onetime fund given for the program to each state. This
is significant because it will limit our result if it is included in the model. We have factored this
into account by creating a separate model without this variable.

       A simple summary of the variables contains no surprises. As expected, the
unemployment rate has nearly doubled under the recession period. Poverty rates have slightly
increased and the auto sales have decreased. It is interesting to note that the median income in
group 2 has increased when we compare it to group 1.

Anthony 7

III. Results

                       Dependent Variable: Log of Sales
Independent           Model (1)-      Model (2)-          Model (3)-   Model (4)-
Variables             Simple 1        Multiple 2          Adjusted3    Prevoucher4

Log of Vouchers       .4878***        .4227***            ------       ------
                      (5.97)          (4.31)

Unemployment          ------          .1582**             .17947***    .2169***
rate                                  (2.43)              (4.35)       (3.67)
Poverty Rate          ------          -.0041              .0820**      .1228***
                                      (-0.07)             (2.51)       (3.63)
Log (income)          ------          1.209               1.622**      2.4328***
                                      (1.07)              (2.27)       (3.90)

Midwest               ------          -.2307              ------       ------
                                      (-0.71)
North Eastern         ------          -.1822              ------       ------
                                      (-0.47)
Western               ------          -.2142              ------       ------
                                      (-0.63)
Intercept             6.8878***       -7.0756             -11.40***    -19.79233***
                      (21.57)         (-0.56)             (5.97)       (5.97)

No. of obs.           50              50                  150          250

R-square              .4259           .5172               .2009        0.1293

Adjusted R-           .4139           .4367               .1844        0.1187
squared

*Significant at 10%, **5%, ***1%
_______________________________________
1: Simple Regression:
2: Multiple Regression:

3: Adjusted Model:
4: Pre-voucher Era:

Anthony 8

         In order to compare the results of the program, the data has been separated into two
different groups. Group one consists of data from 2004-2008 while group two consist of data
after the program has ended, 2009-2011. The STATA outputs for each regression for are
located in the appendix. The single regression model for group 2 reflects that around 41.3% of
the data fit in this model. The model also shows that there is a slight positive correlation
between vouchers and sales. The voucher is statistically significant in this model. The multiple
regression model for group 2 reflects the data a bit better with around 43.6% of the data fit in
this model. The model also shows those income and unemployment rate are statistically
significant at 1% and 5% respectively. There is no statistical significance between regions of the
US because the US is most homogenous because all factors of the market can freely move
between states. Poverty rates also display no significance.

        A separate model has also been included because of the variable vouchers is limited to
50 observations because it was a onetime funded amount. As shown in the model below,
removing the variable from the regression does alter the model significantly because vouchers
are statistically significant. Once removed unemployment rates, poverty rates, and income all
become statistically significant, though only unemployment rates are significant at 1%. The
model though is a worst fit because it only accounts for 18.4% of the data.

         Compared to the multiple regression model of group 2 though, the adjusted model is a
better fit. Under the pre-voucher model only 11.9% of the data is accounted for. However the
model shows that all factors, unemployment rates, poverty rates, and income all become
statistically significant at 1%.

Anthony 9

Robustness Test

Independent             Vouchers and                 Vouchers and                Vouchers and
Variables               Unemployment                 Unemployment and            Unemployment and
                                                     Income                      Income and Poverty
                                                                                 Rate
F Statistic             9.11                         7.45                        6.12

The F tests indicate joint significance among the individually significant variables. It compares
the explained variability to the unexplained variability A high F statistic indicates that the
results are significant because the F- critical value is 2.20. In this case, we can see that vouchers
and unemployment have the highest value at 9.11 which demonstrates that the model
becomes less accurate when poverty rates and income are added. However, this was already
demonstrated in Table 2, where vouchers and unemployment have more statistical significance
compared to poverty rates and income.

Anthony 10

IV. Conclusions

        For the most part, the program did not have a significant role in helping the economy.
The program created minimal relief which is seen in seen by the slight correlation between auto
sales and the number of vouchers, but this is expected because the vouchers serve as an
incentive for the sales of auto. This result is not unexpected given previous studies conducted
the C.A.R.S. program. Overall, our analysis seems to fit the literature which states although
there was a slight increase in sales, these sales were in fact “borrowed” from future sales. Look
at the fitted regression of our data below, graph 1, demonstrates this. As the economy
progresses sales stabilize, back to pre-voucher levels, the fitted value flattens.

         Graph 2: Fitted Regression by Year
21
20
19
18

                                                                                    .
17

     0                   20000                40000               60000
                                     Sales

                                 2009            2010
                                 2011

        The significant of income in terms of auto sales, as show in Graphs 3 & 4 in the
appendix, provides some interesting insight. Previous studies have shown that the program was
limited by socioeconomic standing of the participant which our model takes care to put in
account by including individuals those who may not have the means to purchase a new car,
those who are in poverty. However, from our result, it appears that this was not as

        It is also important to note, our conclusions do not include the total amount of money
lost through the program, by pulling the certain C.A.R.S. out earlier than expected. A future
study might seek to examine this and compare the “clunker” damage to a specific type of more

Anthony 11

fuel efficient car before and after the program by exploring the increase in sales before and
after the program, if the availability of data suffices. The study could also include the
environmental impact by also factoring the cost of carbon and calculating how much pollution
emission the new car prevented.

Anthony 12

        References

Copeland, A., & Kahn, J. (2013). The Production Impact of 'Cash-for-Clunkers': Implications for
        Stabilization Policy. Economic Inquiry, 51(1), 288-303.
Executive Office Of The President Council Of Economic Advisers . (2009, September 10). Economic
        Analysis Of The Car Allowance Rebate System (“Cash For Clunkers”). Retrieved from
        http://www.whitehouse.gov/administration/eop/cea/CarAllowanceRebateSystem
Gayer, T.,Parker, E. (2013, October 31). Cash for Clunkers: An Evaluation of the Car Allowance Rebate
           System. Retrieved from http://www.brookings.edu/research/papers/2013/10/cash-for-
        clunkers-evaluation-gayer
Mian, A., & Sufi, A. (2012). The Effects of Fiscal Stimulus: Evidence from the 2009 Cash for Clunkers
        Program. Quarterly Journal Of Economics, 127(3), 1107-1142.
National Automobile Dealers Association. (2013). NADA Data 2012:State-of-the-Industry Report [PDF].
        Retrieved from http://www.nada.org/Publications/NADADATA/default.htm
National Highway Traffic Safety Administration. (2009, August 26 ). C.A.R.S. Program Statistics Retrieved
        from http://www.nhtsa.gov/staticfiles/administration/pdf/C.A.R.S._stats_DOT13309.pdf
Tyrrell, M. (2011). The 'Cash For Clunkers' Program: A Sustainability Evaluation. The University Of Toledo
        Law Review, 42467.
US Census Bureau. (2012). Median Household Income by State - Single-Year Estimates [XLS-98K].
        Retrieved from http://www.census.gov/hhes/www/income/data/statemedian/
Multiple
http://www.census.gov/prod/2012pubs/acsbr11-01.pdf
        U.S. Census Bureau, American Community Survey, 2007 and 2008; Current Population Survey,
        Annual Social and Economic Supplements, 2011. Web: www.census.gov .
http://www.bls.gov/lau/data.htm
http://data.bls.gov/cgi-bin/surveymost

Bishaw, Alemayehu. Poverty: 2010 and 2011. N.p.: U.S. CENSUS BUREAU, 2012. Print.
"Local Area Unemployment Statistics." Bureau of Labor Statistics. United States Department of Labor,
 n.d. Web. 18 Apr. 2014. .

Anthony 13

       Appendix

       STATA Output for Collinearity

STATA Output for Group 1

Anthony 14

STATA Output for Simple Regression (from 2009-2011)

STATA Output for Group 2

Stata Output for Adjusted model

Anthony 15

Graph 1: Correlation between Vouchers and Sales

Anthony 16

Graph 3: Income vs. Sales 2004-2008.

Graph 4 : Income vs. Sales 2009-2011