The Incidence of Cash for Clunkers Evidence from the 2009 Car Scrappage Scheme in Germany$
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The Incidence of Cash for Clunkers
Evidence from the 2009 Car Scrappage Scheme in
GermanyI
Ashok Kaul, Gregor Pfeifer, Stefan Witte
Working Paper
This version: October 2013
First version: April 2012,II
Abstract
Governments all over the world have invested tens of billions of dollars in car scrappage
programs to fuel their economies in 2009. We investigate the German case using a unique
micro transaction dataset covering the years 2007-2010. Our focus is on the incidence
of the premium, i.e., we ask how much of the e 2,500 buyer subsidy is actually captured
by the buyer. A simple heuristic model suggests that the incidence will depend on the
market segment. For cheaper cars, the supply-side is likely to capture some small part
of it while it will offer additional discounts for more expensive cars. Using regression
analysis, we find these hypotheses confirmed. Subsidized buyers of cheap cars paid more
than comparable buyers who did not receive the subsidy, e.g., for cars costing e 12,000, car
dealers reaped about 7% of the scrappage premium, leaving 93% with the buyer. For more
expensive vehicles (cars costing e 32,000), subsidized buyers were granted extra discounts
of about e 1,100 on top of the government premium they received. The results are robust
to extensive sensitivity checks.
I
We are thankful for valuable remarks from Martin Becker, Nadja Dwenger, Marc Es-
crihuela Villar, Rainer Haselmann, Stefan Kloessner, Dieter Schmidtchen, and Michael
Wolf, as well as the participants of the ACDD conference 2012 in Strasbourg, the Econo-
metric Society Australasian Meeting 2012 in Melbourne, the Warsaw International Eco-
nomic Meeting 2012, the annual meeting of the German Economic Association 2012 in
Goettingen, and the IIPF conference 2012 in Dresden. Corresponding author is Ashok
Kaul. E-mail address: ashok.kaul@econ.uzh.ch; Tel.: +41 (0)44 634 37 36; Fax: +41
(0)44 634 49 07.
II
University of Zurich, Department of Economics Working Paper No. 68 (http://www.
econ.uzh.ch/static/workingpapers.php?id=745).1. Introduction
As a reaction to the 2007 financial crisis, governments all over the world
launched car scrappage programs to stimulate the economy in 2009. While
the U.S. spent $3 billion on their “Cash-for-Clunkers” agenda, Germany af-
forded the most expensive program of all countries with a total volume of
about $7 billion (e 5 billion), a third of the worldwide budget spent on scrap-
page schemes in this period. Before 2009, similar programs have previously
been implemented, particularly in the 1990s. Since such interventions are
popular amongst policy makers and consumers, we expect similar programs
to be adopted in the future.
In the present contribution, we ask the question how much of the e 5
billion was actually captured by which market side, i.e., we analyze the in-
cidence of the German scrappage program.1 To the best of our knowledge,
this is the first analysis trying to evaluate the incidence of a scrappage re-
bate. While the subsidy was meant to benefit the consumer, economic theory
suggests that the economic incidence of a subsidy is independent of the statu-
tory incidence.2 Instead, the division of the beneficial amount between buyers
and sellers depends on the relative elasticities of demand and supply. The
1
The paper is closely related to the empirical literature on tax incidence (for the fundamen-
tals and an extensive literature review, see Kotlikoff and Summers (1987) and Fullerton
and Metcalf (2002), since a subsidy is essentially a negative tax.
2
This so-called tax equivalence theorem is a basic fundamental within the incidence con-
text. Ruffle (2005) for instance, shows that this theorem empirically holds. However,
other research (e.g., Busse et al. (2006), Chetty et al. (2009), and Sallee (2011)) implies
that, contrary to standard theories of incidence, the statutory incidence of a policy does
affect the economic incidence.
2German scrappage program, called Abwrackprämie (scrappage premium) or
Umweltprämie (environmental premium), started in late January 2009. To
receive the lump-sum subsidy of e 2,500 (about $3,500), buyers had to prove
scrappage of an old car and registration of a new one. By September 2009,
the budget was exhausted, having subsidized the purchase of 2 million new
cars. Car dealers in general managed the scrapping of the old car and dealt
with the responsible federal agency and, hence, could identify two different
groups of customers, buyers receiving the subsidy or not. That is why, in our
model framework, we argue that we expect our incidence results to be in line
with an optimal long-run pricing strategy of the supply side reflecting differ-
ent price elasticities of demand and market conditions in different car price
segments. We therefore expect the effect to be heterogenous over car prices.
To be more precise, we assume that for cheaper cars, the bulk or even all
of the subsidy amount remains with the buyer, implying incidence amounts
of slightly below or at around 100%. For subsidized buyers of large cars we
assume extra discounts on top of the scrappage subsidy amount, implying
incidence amounts of more than 100%.
In the empirical analysis, we use a unique sample of transaction data for
Germany from the years 2007 to 2010. Our focus is on the discount received
by subsidized buyers in comparison to non-subsidized buyers controlling for
covariates. We apply linear regression methods to model the percentage dis-
count from the manufacturer’s suggested retail price (MSRP) as a function
of the scrappage dummy. In a first step, we find that the average effect of
3the premium on discount was slightly positive, implying that customers cap-
tured more than the total amount of the subsidy. Augmenting that model
and allowing for heterogeneity across price segments when comparing sub-
sidized to non-subsidized purchases, we find that these differ significantly.
Subsidized buyers of the first quartile (cheap cars) received less discount
than non-subsidized buyers, implicating a demand-associated incidence of
less than 100%. Somewhere in the second quartile, the difference was zero,
implying just no pass-through of the subsidy to the dealers at all or, put
differently, an incidence of exactly 100%. Above the median MSRP, the dis-
count for subsidized buyers was higher than the discount for non-subsidized
ones, translating into incidence amounts of more than 100%. Consequently,
the empirical results confirm our model assumptions.
Previous work on incidence focused mostly, but not only, on taxes, e.g.,
in Evans et al. (1999), Chetty et al. (2009), Friedman (2009), Hastings and
Washington (2010), Rothstein (2010), and Marion and Muehlegger (2011).
Within the scrappage context however, most papers analyze either sales
(quantities) or environmental aspects—and ignore the incidence of the sub-
sidy, i.e., the price dimension.3 To the best of our knowledge, there exists
only one piece that—amongst others—tries to combine scrappage scheme and
3
For instance, see Adda and Cooper (2000), Licandro and Sampayo (2006), Li et al.
(2013), and Mian and Sufi (2012) for sales effects, and Hahn (1995), Deysher and Pickrell
(1997), Kavalec and Setiawan (1997), Szwarcfiter et al. (2005), and Knittel (2009) for
environmental impacts. This literature mostly finds that the increases in sales during the
program are offset, sometimes completely, by a decrease in later sales as well as the fact
that from an environmental perspective, these programs did not pay off.
4pass-through questions. Using the car price as the dependent variable, Busse
et al. (2012) estimate whether the U.S. programs rebates did pass through
fully to buyers, without going into a thorough incidence or price discrimi-
nation analysis. Instead, they further evaluate whether the rebate crowded
out or stimulated manufacturer incentives, and whether the scrapping of a
large number of vehicles affected prices in the used-vehicle market.4 There
is also some important research regarding incidence within the automobile
market, albeit irrespective of the scrappage context. Busse et al. (2006) an-
alyze cash incentives directed at either the dealer or the customer. They
show that customer rebates are passed to the buyer to an extent of 70% to
90%. Dealer rebates—which are mostly unknown to customers—are passed
through only at about 30% to 40%. Sallee (2011) investigates the case of the
Toyota Prius, a car that was tax-subsidized for its fuel efficiency. Despite a
binding production constraint on the supply side, Sallee finds that the in-
centives are fully captured by the customers. He suggests that this is due
to a long-term pricing policy of the manufacturer. Verboven (2002) shows
that our approach of combining the two concepts of price discrimination and
incidence, and analyzing how the one translates into the other, indeed is ob-
vious and feasible. He uses existing tax policies toward gasoline and diesel
cars in European countries to analyze quality-based price discrimination and
4
They find that consumers received the full amount of the rebate, that the program stim-
ulated manufacturer rebates, and that the scrapping of old vehicles did not raise prices
in the used-vehicle market.
5the implied tax incidence.
Our paper contributes to the literature in several ways. First, it fills the
existing gap of evaluating and quantifying the incidence of car scrappage
subsidies, programs that have played an important role in many countries
during the recent financial crisis. Due to exactly that popularity, it is very
likely that such interventions will be put in place again in the future. We an-
alyze the most expensive such program ever launched and therefore focus on
a program with an extremely high potential to analyze this question. Second,
we present a simple heuristic model which helps explaining the mechanisms
at work. Since we develop a very simple and robust estimation strategy that
explicitly takes heterogeneity over different prices into account, we augment
the “standard” model as it is used in related research so far. This kind of
evaluation can easily be applied to similar programs in other countries now
and in the future.
The rest of the paper proceeds as follows. Section 2 gives a short overview
of the German scrappage program and the dataset we use. Section 3 presents
the estimated model. Section 3.1 provides model assumptions, Section 3.2
descriptive evidence, and Sections 3.3 and 3.4 outline the empirical approach
and show the results of the regression. Section 3.5 shows that the data
cover only a limited price range and Section 3.6 presents numerical values
for the price discrimination and the incidence over this price range. Section
3.7 summarizes the main results of the analysis. Section 4 presents a large
variety of sensitivity checks. Section 5 concludes.
62. Program and Dataset Description
2.1. Program
Incentives for car replacement designed as consumption subsidies are sup-
posed to have three major benefits: (1) They are potentially environmental-
friendly by replacing old fuel-consuming cars with new ones with better emis-
sion standards. (2) They help the automotive manufacturing industry which
plays a particularly important role in Germany. Problems in this sector
would not only come along with the risk of layoffs and the corresponding
negative spill-overs, but also harm consumer confidence severely. (3) They
induce consumers to spend a multiple of the voucher’s value, and thereby
create a multiplier effect in the economy.
The idea for a scrappage program in Germany was introduced by the Ger-
man vice-chancellor Steinmeier in an interview on December 27, 2008. Only
two weeks later, the government passed an economic stimulus package in-
cluding a scrappage program. The program officially started on January 14,
2009 and first key points were published on January 16, 2009 by the respon-
sible agency BAFA5 . The subsidy of e 2,500 could be requested by private
individuals who scrapped an old car which was at the time of scrappage at
least nine years old, and which had been licensed to the applicant for at least
12 months prior to the application. The new car had to be a passenger car
5
Bundesamt für Wirtschaft und Ausfuhrkontrolle (Federal Office of Economics and Export
Control).
7fulfilling at least the emission standard Euro 46 and be licensed to the appli-
cant. While the money was transferred only after the purchase, applicants
could be sure to receive the subsidy if the (simple) requirements were met
and provided that the budget was not yet exhausted. While the money was
granted to car buyers, car dealers in general organized the scrappage and
dealt with the federal agency. Many reported that they even treated the
amount of the subsidy as a down-payment.
The program turned out to be very popular, and the original budget risked
to be used up in April. The government raised the budget to e 5 billion,7
just a few days after switching from a paper-based to an online application
scheme. By September 2, 2009, the budget was depleted, having subsidized
the purchase of 2 million new cars. By the end of 2009, the bulk of requests
had been processed by the agency. National new car registration counts
show that registrations for lower-priced segments (Mini, Small, Medium, and
MPV) roughly doubled in 2009.
2.2. Data
We analyze a unique set of micro transaction data with 8, 156 observa-
tions. The data cover information from six randomly chosen car dealers in the
6
European emission standards define the acceptable limits for exhaust emissions of new
vehicles sold in EU member states. Actually, for the German case, this prerequisite was
redundant since all new cars bought in 2009 were Euro 4 equipped anyway.
7
To the best of our knowledge, this is the biggest budget provided for scrapping schemes
in this period. For an overview see http://www.acea.be/images/uploads/files/
20100212_Fleet_Renewal_Schemes_2009.pdf, last accessed on May 30, 2012.
8center of (West) Germany over six different brands providing information on
the purchase of new cars over a time frame of four years (2007–2010). One of
those dealers covers two distinct brands, and one brand is represented by two
different dealers.8 As we will show in more detail in Section 3.2, this data is
very representative for Germany as a whole. Table A1 in the appendix gives
a summary of the distribution.
The data represent detailed information on the car (brand, vehicle class,
model) and on the transaction, i.e., the MSRP, the actual selling price9 , and
hence the granted discount. They also include dealer specifics, like the cor-
responding seller as well as buyer specifics, like age, sex and whether the
respective customer was a company employee or purchased a demonstration
car (see below for further explanations). Most importantly, we have infor-
mation on whether a car was purchased with (CC ) or without (non-CC ) a
Cash-for-Clunkers subsidy within the year 2009.
Note that the MSRP is not a short-term pricing tool for manufacturers.
In general, catalogs and price lists are published once a year without an a
posteriori adjustment of the MSRP. Manufacturers also have much better
means of varying selling prices at their disposal, i.e., dealer and consumer
cash incentives such as those discussed by Busse et al. (2006). In contrast
to the MSRP, these incentives can be changed at very low cost, and are
8
For data privacy reasons, we never report the name of a respective dealership or brand.
9
Note that trade-in values do not affect the data. Trade-ins are treated as fixed-value assets
which are shifted to the used car department of a dealership. Actual trade-in values were
therefore treated as cash-substitutes and consequenlty did not affect the reported prices.
9unpredictable for the buyers, as well as the dealers who normally do not know
which programs will be issued by the manufacturer next month. In contrast
to the MSRP which is the same all over Germany, incentive programs can also
vary geographically. Manufacturers therefore have good reasons to keep the
MSRP stable and vary incentives in order to meet changing local conditions
without jeopardizing their long-term pricing strategy.10
Table 1 shows how the number of purchases is distributed over the years
2007-2010. Year 2009 is split into non-subsidized (Non-CC ) and subsidized
(CC ) purchases. On average, we observe about 1, 600 sales a year, with twice
that amount in 2009 (1, 649 non-subsidized plus 1, 541 subsidized ones).11
Table 2 provides summary statistics of essential variables. The average car
cost about e 25, 600, and was discounted approximately 17%. Roughly 30%
of all buyers were female. About 16% of all purchases were of demonstration
cars (so-called “floor models”) and 12% refer to sales to employees of auto
manufacturers (called “company employees” henceforth). The average buyer
age was 47 years, but we only observe 1, 425 (out of 8, 156) data points
featuring customer age information.12
10
This is why we consider this variable strictly exogenous, meaning that the MSRP did
not react due to the implementation or the process of the scrappage program.
11
CC purchases are concentrated in the months February to October, and then decline
(see Table A2 in the appendix). This is in line with the distribution of applications for
the subsidy as reported by the BAFA.
12
The remarkably high percentage discount over 50% (max) was due to the fact that
demonstration cars as well as company employees benefit from huge (and) additional
discounts. The high discount of more than e 50, 000 was observed for a demonstration
car of the most expensive category (luxury car segment).
10Table 1: Number of Purchases over Time by Car Dealers and CC
Year of Purchase and Clunker’s Premium
Dealership 2007 2008 2009 2010
Non-CC CC
Dealer 1 315 443 587 317 504
Dealer 2 250 235 268 330 381
Dealer 3 263 314 277 359 286
Dealer 4 633 484 346 135 270
Dealer 5 81 67 60 43 43
Dealer 6 12 158 111 357 227
1649 1541
Total 1554 1701 3190 1711
Note: Non-CC are non-subsidized purchases, CC subsidized ones.
Table 2: Summary Statistics: All Data
Variables Mean SD Med Min Max N
Discount in Percent 16.91 8.68 16.40 0.00 53.37 8,156
Discount in 1000 EUR 4.18 3.23 3.44 0.00 51.81 8,156
MSRP in 1000 EUR 25.62 14.37 21.50 8.19 198.66 8,156
Clunker’s Premium (CC) 0.19 0.39 0 0 1 8,156
Demonstration Car (DC) 0.16 0.37 0 0 1 8,156
Company Employee (CE) 0.12 0.32 0 0 1 8,156
Female 0.29 0.45 0 0 1 8,156
Age at Purchase 47.23 14.93 48 18 89 1,425
Note: MSRP is the manufacturer suggested retail price. CC is a dummy variable indicating whether the
buyer of a car received the scrappage subsidy. DC is a dummy variable indicating whether a buyer bought
a demonstration car. CE is a dummy variable indicating whether the buyer was an employee of a car
manufacturing company. Female is a dummy of female buyers, the summary statistics therefore report
the share of women, age at purchase is the age of the buyer at the time of purchase.
113. Analysis
In a first step, we present a heuristic model regarding the German scrap-
page program and its anticipated effects on the subsidys incidence. We then
provide descriptive evidence and, thereafter, our regression analysis. We
start with a standard specification to estimate the average impact of receiv-
ing the subsidy on the percentage discount. In this model, we include all rel-
evant control variables as discussed above plus fixed effects for time, brand,
dealership, and seller. Afterwards, we augment this basic specification by
additionally interacting the scrappage dummy with the MSRP, allowing for
heterogeneity across the car price range. This preferred specification reflects
our model assumptions. Taking into account the distribution of purchases
and the share of subsidized purchases over the price range, we show for which
interval of MSRP our results are reliable. To illustrate the estimated differ-
ences, we show the magnitude of price discrimination in percentage points
and Euros as well as the incidence over what we consider the relevant price
range. We close this section by summing up and discussing our main findings.
3.1. Model Assumptions
There exists a sizable public finance literature on (tax) incidence in mar-
kets with imperfect competition and one could justify just about any differ-
ence in terms of incidence across subsidy participants and non-participants
12as seeming credible.13 We therefore develop sound assumptions regarding our
anticipated evaluation outcomes. Those assumptions are made on grounds
of knowledge of the institutional design of the program, the car market in
general and of different market conditions and price elasticities of demand
across car segments. In essence, we expect our results regarding the incidence
of the scrappage subsidy to be in line with an optimal long-run pricing strat-
egy of manufacturers and dealers in the car market. This pricing strategy is
supposed to reflect different price elasticities of demand and market condi-
tions in different car or price segments. Thus, we expect our results to be
heterogenous over car prices. For the following argumentation, it is pivotal to
remember that dealers were able to reliably identify two different groups of
customers within the year of 2009. Since they managed the scrapping of the
old car and dealt with the responsible federal agency BAFA, dealers always
were able to distinguish between subsidized and non-subsidized buyers.
With regard to the lower price segment, two facts are crucial. On the one
hand, the scrappage program shifted demand heavily toward smaller cars.
This gave dealers market power, and thus allowed for price making. Sudhir
(2001) states that the supply side has a motivation to be aggressive in the
small-car market (the entry level segments) to increase profits and market
13
To name just very few examples, Stern (1987) provides theoretical work on tax incidence
showing that there is the possibility of either over- or undershifting of (different) taxes (to
consumers); Delipalla and O’Donnell (2001) deliver a related application to the cigarette
industry; Anderson et al. (2001), again, show that incidence amounts of more than 100%
are theoretically possible.
13share. In contrast to non-subsidized customers, buyers receiving the subsidy
and aiming to join this market segment were presumably relatively more
price-inelastic since the subsidy was available only for a very short period
of time and because people were keen on seizing the opportunity of receiv-
ing a e 2,500 check. These people wanted to buy now not only because the
subsidy was not available for a long time but also since nobody could ex-
actly know when it would expire. In contrast, the non-subsidized customers
could be more patient. In addition, close substitutes for small cars were not
available since downgrading was hardly possible and the demand shock es-
sentially affected all brands alike. Altogether, this suggests that there was
room for price discrimination based on observables (the scrappage premium
information) in the lower price segment. On the other hand, it is well-known
that competition in the market for small cars is quite high (Sudhir (2001))
and competition presumably increased in 2009 due to the scrappage subsidy.
However, competition limits the scope for price discrimination. In partic-
ular, in a competitive market where brand loyalty is not (yet) established,
price elasticity is presumably not spectacularly different across buyer groups.
Moreover, within a certain class of cars, the potential buyer certainly could
choose from different brands and dealerships leaving her with a supposably
higher intra-segment price elasticity of demand.14 Dealers and manufacturers
14
Berry et al. (1995) state that the most elastically demanded cars are that in small market
segments. Cross-price elasticities (large for cars with similar characteristics tend to be
bigger for cheap cars as compared to expensive ones.
14contemplating higher markups for subsidy receivers therefore had to trade off
higher margins against lower sales in the short run. Also, long-run pricing
considerations may have played a role in the pricing policy (also compare
Sallee (2011)). Together, these suggest why there could be some price dis-
crimination against scrappage premium receivers within the small-car market
but also why this group of buyers should still receive the bulk or even all of
the subsidy.15
Things were very different in the upper price segment. Here, the mar-
ket was slack and unsold cars were piling up. From an upper-segment car
dealer’s perspective, an interesting and unique potentially profit-maximizing
strategy was possible due to the subsidy. Customers buying in the large car
segment, could be divided into two observable groups (as in the lower price
segment just with different “characteristics”) with distinct price elasticities
15
Busse et al. (2006), who analyze car market cash incentives, find that between 70%
and 90% of the customer promotion amount remains with the buyer, i.e., the seller
reaps only a small fraction of the promotion. Since a customer promotion is quite
comparable to a buyer subsidy granted by the government, the two instruments are
in fact comparable. This supports our assumption that the subsidy should remain—in
large part or even entirely—with the buyer in the small car market. Sallee (2011) finds
that a customer-directed tax subsidy for the Toyota Prius, a car that would fall into
the small to medium-size car market, is fully captured by customers, although sellers
face a binding production constraint. He suggests that this is due to a long-term pricing
policy of the manufacturer. In the case of the German scrappage premium, a production
constraint was also binding in the small car segment since the subsidy caused a run on
these cars. We therefore would argue, again, that in this kind of car price segment,
the subsidy amount should (almost) be fully captured by the consumer. While Sallees
explanation builds mainly on long-run pricing policy of manufacturers, we conjecture
that in the German case, increased competition due to the demand shock induced by
government intervention additionally could lead to the fact that the supply-side would
only capture a small or even negligible fraction of the subsidy in the small car segment.
15of demand, namely regular large car buyers who did not (and did not tend
to) receive the subsidy, and subsidized buyers who would typically not buy a
large car. Non-subsidized customers in the upper price segment should not
receive exceptional rebates since that would interfere with the well-known
cooperative pricing strategy of car manufacturers toward brand-loyal long-
term customers (Sudhir (2001)).16 This would unnecessarily erode margins
without increasing long-term demand in that customer segment. In fact, in-
terviews with car dealers suggest that a selection effect could have worked
in their favor. Subsidized buyers were typically not customers buying pricey
cars, and would usually not upgrade from a clunker to a new expensive car.17
Therefore, their price elasticity of demand for large cars was quite high. To
this buyer group, in contrast to the subsidized buyers within the small car
segment, “substitutes” indeed have been available since downgrading to a
medium priced car was easily possible. All this should lead the supply side
to offer exceptionally high discounts (on top of the subsidy amount) to this
customer group. Moreover, offering high rebates to subsidized customers
would not interfere with long-run pricing considerations of manufacturers
since the new customer group was a one-time target without any significant
downside risk with regard to their long-run car demand for manufacturers of
large cars.
16
Also compare Goldberg (1995) regarding brand loyalty in the car market.
17
Since facing a flat subsidy, buyers should not be willing to trade in a “clunker” worth
more than e 2,500 and have to accept diminishing benefits from purchasing more ex-
pensive cars.
16In summary, we expect our results to be heterogenous over car prices.
Firms in the small car segments tend to be aggressive, and room for price
discrimination based on observable characteristics—such as the information
of receiving the scrappage premium—is limited due to strong competition.
We therefore assume that for cheaper cars, the bulk or even all of the subsidy
amount remains with the buyer, implying incidence amounts of slightly below
or at just 100%. With regard to the larger car segments, aggressive pricing is
usually avoided since such pricing behavior reduces margins without increas-
ing demand of regular customers who tend to be very brand-loyal in that
market segment. However, granting huge discounts to a new group of cus-
tomers, who could be distinguished from the old and loyal ones based on the
scrappage premium information, offered a one-time opportunity to increase
profits for large car manufacturers and dealers by increasing sales. Hence,
we assume that subsidized buyers of large cars eventually received extra dis-
counts on top of the scrappage subsidy amount, implying incidence amounts
of more than 100%.
3.2. Descriptive Evidence
Figure 1 shows the number of observed purchases for the different vehicle
classes over the observation period.18 Mainly cheap vehicle classes like A
(Mini), B (Small), C (Medium), and M (MPV) benefited from the program.
18
The classification A, B, C, D, E, F, J, M, S is in accordance to the EU classification.
For an overview see http://ec.europa.eu/competition/mergers/cases/decisions/
m1406_en.pdf, last accessed on January 26, 2012.
171200
A - Mini Cars
B - Small Cars
C - Medium Cars
D - Large Cars
E - Executive Cars
F - Luxury Cars
J - SUV
M - MPV
S - Sports Coupés
1000
Number of Purchases
800
600
400
200
0
Year of Purchase
2007 2008 2009 CC 2009 other 2010
Note: A, B, C, D, E, F, J, M, S are auto segments according to the EU car classification. 2009 CC are car
purchases in 2009 involving the scrappage subsidy, 2009 other are non-subsidized purchases. SUV stands
for Sport Utility Vehicle, MPV for Multi Purpose Vehicle
Figure 1: Number of Purchases over Time by EU Vehicle Class
18We do not find many additional purchases in vehicle classes D (Large), E
(Executive), F (Luxury), and S (Sports Coupés).19 Overall, it seems that
subsidized purchases were made over and above the regular purchases, and
were not pulled forward from the following purchase period.20 Note that the
pattern of this sample depiction is almost identical to what new vehicle reg-
istration counts for the whole of Germany looked like.21 This indicates that
we are dealing with very representative transaction data, and have sufficient
external validity to transpose our results from the research sample to the
target population (from which the sample was drawn), i.e., car dealerships
in Germany.22
Figure 2 shows the development of the discount over time per vehicle
class. As mentioned previously, inexpensive vehicle classes experienced an
increase in car purchases, while the more pricey segments faced a staggering
or declining demand. We can see that some of the segments which experi-
enced a positive demand shock (Mini and Small) are the ones which receive a
smaller discount throughout 2009 when purchased as a CC car compared to
non-CC cars. For the other, more expensive segments, the opposite happens:
19
This is not surprising since expensive cars are predominantly purchased by corporate
customers, so they obviously played a minor role within the scrapping context.
20
Böckers et al. (2012) analyze the pull-forward effects of smaller vehicle classes in Ger-
many. Heimeshoff and Müller (2011) provide estimates of how many additional cars
were sold due to scrappage programs in 23 OECD countries.
21
Figure A1 in the appendix shows the new car registrations for non-commercial cars in
Germany for the years 2008-2010.
22
We could not have conducted the same analysis by just using the registration count
data, since most of the relevant information is missing therein, for instance the amount
of discount and the indicator for whether a subsidy was received.
19A - Mini Cars B - Small Cars C - Medium Cars
25 22 24
20 20 22
18 20
15
18
Discount in Percent of MSRP
16
10 14 16
D - Large Cars E - Executive Cars F - Luxury Cars
20 25 20
20 15
15
15 10
10 10 5
J - SUV M - MPV S - Sports Coupés
20 25 30
18 20
16 20
14 15
12 10
10
2007q1 2008q1 2009q1 2010q1 2011q1 2007q1 2008q1 2009q1 2010q1 2011q1 2007q1 2008q1 2009q1 2010q1 2011q1
Quarter of Purchase
Non-CC transactions CC transactions
Note: Average discount in percent of MSRP over quarters of years across EU vehicle classes. SUV stands
for Sport Utility Vehicle, MPV for Multi Purpose Vehicle.
Figure 2: Percentage Discount over Time by EU Vehicle Class
20CC customers received a comparatively higher discount.23
The same pattern arises within vehicle classes (see Figure A2 in the ap-
pendix), namely that subsidized cars are cheaper than non-subsidized ones.
We therefore control for MSRP in our regression model rather than interac-
tions of “make, model, and turn” as in Busse et al. (2006). More importantly,
using MSRP allows to control for differences in optional equipment since any
additional feature is included in the catalog price.
In a next step, we deepen this discussion a little further by moving from
a graphical to a numerical focus, and present essential figures. First, we take
a closer look at 2009 (Table A4 in the appendix gives summary statistics for
that year only). The average MSRP in 2009 was about e 2, 500 lower com-
pared to the 2007-2010 mean due to a difference in composition: more small
and smallest cars were bought in that period. The average discount in 2009
(17.7%) is relatively stable when compared to the discount in the 2007-2010
sample (16.9%). About 14% and 13% of the 2009 purchases were of demon-
stration cars and made by company employees respectively. Table 3 shows
the difference for relevant variables between subsidized and non-subsidized
purchases within the year 2009. Non-CC cars received a discount of 17.67%,
23
Summary statistics for the MSRP over vehicle classes are given in Table A3 in the
appendix. It shows that prices rise monotonically over the vehicle classes A through
to F. The mean price of MPVs is similar to Medium Cars; SUVs cost on average as
much as Large Cars; Sports Coupés are comparable to Executive Cars. The standard
deviation of the prices of the last three categories are about twice as big as those of their
respective reference category. The last three vehicle classes are therefore consistent with
the described pattern.
21whereas CC cars received 16.51%.24 The corresponding absolute values are
e 4, 686 and e 3, 235 respectively. These differences are significant at the
1% level. Yet, we have to take the MSRP into consideration: Non-CC cars
on average cost e 26, 720, whereas CC cars amounted to about e 19, 062.25
This means that customers who called upon the subsidy on average asked for
smaller (cheaper) cars than customers who purchased without the subsidy
denoting differences in the group compositions of CC and non-CC customers.
We therefore have to control for MSRP in our regression analysis rather than
for vehicle class. Furthermore, about 25% of the non-CC group, and about
39% of the CC group was comprised of women. The shares of demonstration
cars and company employees are 19% vs. 10% and 17% vs. 8% (non-CC vs.
CC ) respectively. The last information is important because the unequal
share of the two high-discount categories might be driving the difference in
percentage discount. Both categories make up for a smaller share in the CC
group compared to the reference group, which implies that the average dis-
count of CC purchases would rather be biased downward.26 In the following
analysis, we therefore control for both groups.
Both the descriptive and graphical evidence suggest that price discrimina-
24
Table A5 in the appendix gives an overview of the development of the percentage discount
over the years including a CC/non-CC distinction.
25
The distribution of the MSRP of subsidized cars is concentrated among lower prices. Its
median is e 17, 000, and the 75th percentile is at about e 22, 000.
26
Table A6 in the appendix shows the percentage discount by different types of purchases.
Standard purchases earned lower discounts (14%) than company employees (26%) or
demonstration cars (23%).
22Table 3: Summary Statistics: Comparison within 2009 by CC
Non-CC CC Diff
Variables Mean SD Mean SD Mean
Discount in Percent 17.67 8.73 16.51 6.67 -1.16
Discount in 1000 EUR 4.69 3.80 3.24 2.15 -1.45
MSRP in 1000 EUR 26.72 15.27 19.06 7.56 -7.66
Demonstration Car (DC) 0.19 0.39 0.10 0.29 -0.09
Company Employee (CE) 0.17 0.38 0.08 0.28 -0.09
Female 0.25 0.43 0.39 0.49 0.14
Note: Non-CC are non-subsidized purchases, CC subsidized ones. The last column gives the difference
in means between CC and non-CC purchases. MSRP is the manufacturer suggested retail price. DC is
a dummy variable indicating whether the buyer bought a demonstration car. CE is a dummy variable
indicating whether the buyer was an employee of a car manufacturing company. Female is a dummy of
female buyers, the summary statistics therefore report the share of women.
tion across consumers of different market segments as well as price discrimi-
nation between subsidized and non-subsidized buyers may have been present.
Subsidized customers who bought (very) small up to medium cars received
a smaller discount compared to non-subsidized customers; when purchasing
bigger cars the opposite seems to be true, namely that subsidized buyers
received a higher discount than non-subsidized ones. Before drawing further
conclusions however, we need to control for various aspects such as the ex-
act MSRP, the year of purchase, the kind of dealer and brand, as well as
high-discount groups.
3.3. Basic Specification
In our most basic specification, we follow the “standard model” of, e.g.,
Busse et al. (2006) and estimate the incidence effect as a weighted average.
Hence, in this first step, we neglect potential heterogenous impacts of re-
23ceiving the subsidy on the percentage discount of car prices. After we get
an idea of the average influence of the government intervention, we then—
in the next section—explicitly consider our heuristic model framework and
allow for heterogeneity across car price segments by augmenting this basic
specification.
We start by estimating the following regression model:
discount = α + βCC + γM SRP + θ0 X + (1)
The dependent variable (discount) is the discount in percent of the MSRP
granted for a single car purchase in percent.27 The key explanatory variable of
interest is CC, the Cash-for-Clunkers dummy, i.e., an indicator as to whether
a car was purchased with the scrappage subsidy (CC = 1) or without it
(CC = 0). M SRP denotes the manufacturer’s suggested retail price or
catalog price (in e 1,000). The vector X contains a set of other controls.
Brands and dealers are modeled as seven brand-dealer dummies, i.e., there
is a dummy for each combination of brand and dealer. Dummies for buyers
who are employees of car manufacturing companies (“company employees”,
27
So it is
M SRP − Selling P rice
discount = 100 ∗ (2)
MSRP
with the selling price including the subsidy amount.
24CE) and demonstration cars (DC ) are included. Also a dummy for each
individual seller is included, as well as a sex dummy for buyers and year and
month dummies to capture seasonalities and macroeconomic effects. The
error term is represented by .
The estimated coefficients are α, β, γ and the vector θ. The key coefficient
of interest in this specification is β. It measures the percentage difference
in discount a subsidized buyer received in comparison to an non-subsidized
buyer. A positive (negative) estimate of β indicates that subsidized buyers
received a higher (lower) discount than non-subsidized buyers, controlling
for the covariates mentioned above. The coefficient γ measures how dealers’
discount policies differs across price segments. To be precise, γ measures
how the discount changes as the MSRP increases by e 1, 000, holding other
things constant.
Column (1) of Table 4 reports the results of estimating the specification
in Equation (1). The estimated coefficient β measuring the effect of receiv-
ing the scrappage subsidy on the discount granted for a car purchase is 0.4.
It is positive and statistically different from zero at the 10%-level.28 This
suggests that the overall pass-through of the subsidy was negative, i.e., deal-
ers grant a 0.4 percentage points bigger discount for CC purchases than for
28
Similar to Busse et al. (2006) who identify the very car based on make, model, and its
very specification, we also ran the regressions with make-model interactions rather than
the MSRP on the right-hand side. In this case, the coefficient of CC gets bigger (0.59
if we control for brands and dealerships, 0.63 if we do not). However, none of these
coefficients is statistically different from the 0.40 of the reported value.
25non-subsidized ones, controlling for the discussed covariates. Although the
coefficient is quantitatively small (compared to a mean value of about 17%,
see Section 2.2), the result is surprising since a capturing of a subsidy of more
than 100% is not consistent with the related empirical literature.29 The value
of 0.05 for γ suggests that the percentage discount grows at a rate of about
0.05 percentage points with every e 1,000 of MSRP. This means that a dif-
ference of e 20,000 implies a higher discount of one percentage point. Before
discussing the controls in vector θ, consider the full model which takes into
account that the effect is heterogeneous over the price range.
3.4. Full Specification
Specification (1) has a shortcoming, namely that it restricts the effect of
receiving the subsidy on the discount to be uniform across price segments.
As discussed in Section 3.1 however, we expect our results to be heterogenous
across car prices. In Section 3.2 we already got an idea how market conditions
and the discounts themselves were different over different vehicle classes and
price segments.
To account for this heterogeneity, we interact the dummy CC with the
MSRP (CC ∗ M SRP ) and estimate the extended regression model in Equa-
29
Busse et al. (2006) find that 70%-90% remain with the customers, Sallee (2011) finds
that customers capture 100% of the subsidy.
26tion (3).30
discount = α + βCC + γM SRP + δCC ∗ M SRP + θ0 X + (3)
Results are presented in column (2) of Table 4. Estimating this specifica-
tion, all the essential coefficients—β, γ, and δ—are statistically significantly
different from zero at the 1% level. The results confirm our expectations:
controlling for individual- and dealer-specifics as well as time trends and
high-discount groups, we find a strong relationship between the MSRP, the
subsidy and the discount in percent. We see that β, the coefficient for CC,
is negative, with −4.4 being rather large,31 and highly significant. The esti-
mate for δ is 0.24 and hence positive, implying that the more expensive a car
was, the more additional discount was granted if the buyer benefited from
the subsidy. The coefficient of M SRP (γ) is 0.03 and thus a little smaller
than in Specification (1), but qualitatively not different.32
Keeping everything else constant, the results allow to depict two different
functions: one for subsidized and one for non-subsidized buyers, denoting the
latter as “baseline function”. Recall that the estimated coefficient for the CC
dummy is −4.4 which means the y-intersect is 4.4 percentage points lower
30
As discussed previously, we cannot simply interact CC with a set of vehicle class dummies
because within each such class, the two groups (subsidized and non-subsidized purchases)
differ.
31
Note that the dummy itself has no meaningful interpretation as it measures the difference
from the overall constant for a price of zero. Interpreting this value as such would be an
inadmissible extrapolation.
32
Clustered standard errors would not change these results, see Section 4.
27for the CC-function than for the function of non-subsidized purchases. The
coefficient of the interaction term is 0.24, so this function is steeper than the
baseline function with a slope of 0.034 (coefficient for MSRP); with every
additional e 1,000 of MSRP, the expected discount of subsidized purchases
becomes 0.24 + 0.034 = 0.274 percentage points bigger. For non-subsidized
cars, it grows at the rate 0.034 percentage points per e 1,000 of MSRP. All
the relevant coefficients are statistically significant at the 1% level.
Throughout the different specifications, the controls in vector θ remain
stable. For instance, the coefficients of the controls for company employees
(CE) and demonstration cars (DC ) hardly change.33
Note that we do not report the estimated coefficient for sex (taking the
value one if the buyer was female, zero otherwise). In all specifications,
female turns out to be both economically and statistically insignificant.34
Due to the interaction terms, the interpretation of the results is facilitated
if we do not discuss single coefficients, but rather the expected percentage
discount as a (linear) function of the MSRP. For the group of non-subsidized
buyers (CC = 0), this function has a y-intersect (M SRP = 0) at the constant
of 18.05 and a slope coefficient equal to 0.0335.35 For the group of subsidized
33
These percentage values experienced some downward adjustment compared to the de-
scriptive statistics (see Section 2.2), but are still considerably lower compared to a “nor-
mal” consumer who bought a “normal” car, i.e., when the purchase involved neither a
company employee nor a demonstration car.
34
This finding is in line with Goldberg (1996) who shows there is no evidence for discrim-
ination against female car buyers.
35
More precisely, the y-intersect depends on the constant as well as the coefficients of
any (binary) control variable. To focus on the relevant part of the function, and since
28Table 4: Linear Regression Estimation Results of Different Specifications
Dependent Variable: Discount in Percent of MSRP
VARIABLES (1) (2)
CC 0.398* -4.401***
(0.233) (0.503)
CC*MSRP 0.244***
(0.0228)
MSRP 0.0453*** 0.0335***
(0.00818) (0.00800)
DC 11.01*** 10.88***
(0.277) (0.276)
CE 11.50*** 11.56***
(0.313) (0.312)
Constant 17.69*** 18.05***
(1.670) (1.673)
Observations 8,156 8,156
Adjusted R-squared 0.488 0.496
Year Dummies Yes Yes
Month Dummies Yes Yes
Sex Dummy Yes Yes
Seller Dummies Yes Yes
Dealer Dummies Yes Yes
Intersect n/a 18.06
Note: *** significant at the 1%-level, ** significant at the 5%-level,
* significant at the 10%-level. Robust standard errors (HC3) in
parentheses. CC: dummy for subsidized (Cash-for-Clunkers) trans-
action, MSRP: manufacturer’s suggested retail price in e 1000, DC:
dummy for demonstration car, CE: dummy for employees of auto
manufacturing companies. Year = 2008 (2009) (2010) are dummy
variables for the given years, 2007 is the base year. Intersect indi-
cates where the estimated value for subsidized purchases is equal to
the one of baseline function.
29buyers (CC = 1), the function has a y-intercept of 18.05 − 4.401 = 13.65
and a slope coefficient equal to 0.2440 + 0.0335 = 0.2775. The latter line is
therefore steeper than the former but starts lower. Thus, the two functions
intersect at
Ilin = −β/δ (4)
where β measures the downward shift of the CC curve for MSRP zero,
and δ the difference between the slope of the CC and the non-CC functions.
Equation 4 therefore gives the MSRP where both functions intersect. This
value is reported at the bottom of Table 4 (Intersect), it is about e 18,000
for specification (2).
A general conclusion is that subsidized buyers of the first quartile faced
negative price discrimination, i.e., they paid more (experienced a lower dis-
count) if they received the subsidy. Since the scrappage program shifted
demand heavily to the lower-priced segments, car dealers could impose a
price markup by granting less discount. In contrast, subsidized buyers in
the third (and fourth) quartile faced positive price discrimination, meaning
they had to pay less (received more discount) if buying with the subsidy. In
this much slacker part of the car market, dealers used additional discounts
in order to seize a one-time opportunity of selling to very elastic (subsidized)
customers instead of losing them to competitors or lower car segments. At
consideration of these additional controls does not alter the results, we neglect this point
in the discussion.
30an aggregate level, the positive price discrimination in the upper part of the
distribution overcompensates the negative effect in the lower part.36 Within
the second quartile finally, the difference between the CC and the non-CC
function is just zero. This implies that within the second quartile of MSRP,
car dealers did not price discriminate at all, and CC customers received the
full amount of the subsidy of e 2, 500.
3.5. The Relevant Price Range
But how relevant is the region we are considering? Moreover, are subsi-
dized and non-subsidized purchases sufficiently balanced, meaning whether
the shares of CC and non-CC purchases are rather equal and therefore com-
parable? If this was not the case, our results might be misleading. Figure 3
gives an insight into the distribution and adds the share of CC purchases by
MSRP.37 The dash-dotted line shows the CC share as a falling function of
MSRP. This is what we expected, given that the lump-sum subsidy matters
relatively more for cheaper cars. However, in a region below e 12,000, the
share is larger than 60%, reaching up to 80% for cars of an MSRP of about
e 9,000. We claim that this part of the distribution lacks common support
because its composition is too unbalanced. The graph of the distribution
(dotted density plot) is very steep on the left side, which means that there
36
The reported coefficient β on the CC dummy from specification (1) of Table 4 can be
interpreted as a weighted average.
37
To calculate the share of CC in Figure 3, we rounded the MSRP to e 1,000 and calculated
the share of subsidized purchases in 2009 for each e 1,000 price interval.
31were relatively few purchases at a price range of about e 8,000, but already
quite a few at a price of e 10,000 to e 12,000. Cutting off this fringe, we see
that from an MSRP of e 12,000 on, the data points are comfortably dense
enough, and the distribution between CC and non-CC purchases is rather
balanced with about 60% or less. At the other end of the distribution, the
share of CC purchases drops below one third at a price of about e 32,000.
We choose this point as an upper bound for the following discussion. At this
point, we still observe a sufficiently balanced distribution between CC and
non-CC purchases which then steadily shrinks along with the density. In
the following discussion, we therefore focus on a price range from e 12,000
to e 32,000 which we judge to be the most relevant interval of our data with
a solid balance of CC and non-CC purchases.
3.6. Price Discrimination and Incidence
As a next step, we quantify the exact amount of price discrimination and
the corresponding incidence over the price interval for which our results were
found to be relevant. Table 5 yields an overview regarding that quantification
for the linear model (specification (2)). It provides the percentage (PD (%))
and the respective absolute (PD (e )) discount received for a certain MSRP
(what we refer to as “price discrimination”) as well as the corresponding part
(Inc (%)) of the e 2,500 subsidy which remained with the consumer (what
32.08
.8
22
Discount in Percent of MSRP
Kernel Density of MSRP
.06
Share of CC-purchases
.6
20
.04
.4
18
.02
.2
16 14
0
0
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
MSRP in 1000 EUR
non-CC linear CC linear
Density Plot Share of CC
Note: non-CC linear is the function of non-subsidized purchases based on specification (2), CC linear
is the function of the subsidized ones. The density plot refers to the MSRP in 2009. To calculate the
CC -share, we rounded the MSRP to e 1,000 and calculated the share of subsidized purchases in 2009 for
each e 1,000 price interval. The bold vertical lines indicate the boundaries of the interval we consider
the relevant price range. The thin vertical line at e 18,000 indicates the price where we observe just no
difference in discount received between both buyer groups (the intersect).
Figure 3: Linear Model with Distribution and CC -Share
33we refer to as “incidence”).38 Recall that the point where the two groups
do not differ at all was at e 18,000. At that point the incidence is 100%, a
result that reflects the findings of Sallee (2011), for a car that would fall into
our second quartile of MSRP. For a cheap car with a MSRP of e 12,000, the
linear model yields a price discrimination of −1.48% or e −178, i.e., dealers
skimmed off about 7% of the subsidy amount (e 2,500). This translates into
an incidence amount of 93%, which would be an upper bound when compared
to the values Busse et al. (2006) find (70 − 90%).
Results for higher-priced cars are more remarkable: a car which cost
e 28,000 and therefore is at the lower end of the fourth quartile of MSRP,
would benefit from an additional discount of 2.42% or e 678, which means
that buyers received an additional discount (on top of the subsidy they re-
ceived) of around 27%. A car purchase at the very end of our relevant MSRP
range (e 32,000) caused an extra 3.4% or approximately e 1,100, which is
44% of the scrappage subsidy amount. Speaking of incidence this means
that 127% and 144% of the subsidy amount for a e 28,000 and a e 32,000
automobile “remained” with the buyer respectively. Incidence amounts lo-
cated above the 100%-threshold, in our case clearly distant from that, are
empirically rarely found.
38
The Euro values were calculated from the corresponding percentage values and the
MSRP, not from a separate estimation with discount in Euro as a dependent variable.
34Table 5: Price Discrimination and Incidence over different MSRPs
MSRP PD (%) PD (e ) Inc (%)
12,000 -1.48 -178 93
14,000 -0.99 -139 94
16,000 -0.50 -80 97
18,000 -0.01 -2 100
20,000 0.47 94 104
24,000 1.45 348 114
28,000 2.42 678 127
32,000 3.40 1088 144
Note: The table presents price discrimination for a given MSRP in percentage points of MSRP (PD (%))
and Euro (PD (e )) based on the linear model from specification (2) as well as the respective Incidence
(Inc (%)) which indicates what percentage part of the subsidy remained with the consumer.
3.7. Results
The main result of this paper is that the incidence of the subsidy strongly
and significantly varies across price segments. We focused most of our dis-
cussion on three price segments that roughly correspond to the first, second,
and third price (MSRP) quartile.39
In the first quartile that mainly covers mini cars and to some extent small
cars, subsidized buyers received slightly lower discounts than non-subsidized
ones controlling for covariates. In the second quartile—mainly consisting of
small and medium cars—discounts between the two buyer groups did not
differ much, implying that the full subsidy amount remained with the buyer.
The most striking result was found for sales in the upper half of the price
39
We also consider the lower part of the fourth quartile of MSRP since we argue that our
relevant price range reaches e 32,000.
35distribution. We focused particularly on the third price quartile (mainly
medium and large cars), where subsidized and non-subsidized sales were quite
balanced. In this segment, scrappage premium receivers were granted much
higher discounts than regular customers. The incidence in this price segment
was such that subsidized buyers received huge extra discounts from sellers
over and above the government premium.
Our result for the lower price segments—loosely speaking for the bottom
half of the distribution—is in line with the results in Busse et al. (2006) and
Sallee (2011). Busse et al. (2006) find that between 70% and 90% of the cus-
tomer promotion amount remains with the buyer, i.e., the seller reaps only
a small fraction of the promotion. Since a customer promotion is quite com-
parable to a buyer subsidy granted by the government, the two instruments
are in fact comparable, and so are our results of roughly 90% of the subsidy
amount remaining with the buyer in the first quartile of MSRP. Sallee (2011)
finds that a customer-directed tax subsidy for the Toyota Prius, a small car
that would fall into our second price quartile, is fully captured by customers,
although sellers face a binding production constraint. In the case of the Ger-
man scrappage premium, a production constraint was also binding in the
small-car segment since the subsidy caused a run on these cars. Our results
in the second price quartile are therefore fully in line with Sallee’s results.
While his explanation builds mainly on long-run pricing policy of manufac-
turers, we conjecture that in the German case, increased competition due to
the demand shock induced by government intervention additionally explains
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