Results from Initial model

Several tests were undertaken to examine the effects of changes in model parameters. One important adjustment was required in Musti and Kockelman’s (2009) market entrance model: The value of the coefficient on maximum age of a vehicle in the household’s fleet for the buy and sell options was negative (-0.0955), making it less likely that a household would get rid of a vehicle or buy a new one as its oldest vehicle

aged. With no other time-varying inputs to increase the chance of a buy or sell, most

households ended up locked into their initial fleets. Households could have a 30+ year-

old vehicle and less than 1% chance of selling. To address this unrealistic result, the

coefficient was made positive (+0.01), and the alternative specific constants were

decreased slightly (to -3 for acquire and -4 for dispose) to keep the general probabilities

close to normal. This adjustment was used for all the results presented here and produced

reasonable results for the smaller data sets, but seemed to cause the fleet to grow too

large with the full 5,000-household set. One of the options for removing old vehicles,

scrapping according to a hazard function’s prediction, is implemented in the section 9.2.

9.1.1 Increasing Fuel Cost

Increasing fuel costs can affect vehicle purchase decisions and thereby the vehicle fleet.

Table 9.1 shows how the model predicts vehicle holdings (by type) will change over 20

years. For the base case, compact cars, pickups, luxury cars and SUVs dropped

dramatically, while sales of large cars, subcompacts and vans increased. CUV was the

89

gallon, affected the type of vehicle held by making larger, less fuel efficient vehicles less

desirable. The effects of the simulations were similar to each other, however, when

compared to the initial (empirical) holdings. Therefore, it is the comparison of different

model specifications that there is a clear trend towards subcompacts, and away from

vans, pickups and SUVs, but compacts only saw a modest rise. The increase in fuel price

did not have any effect on other aspects of the fleet (e.g. scrappage, new cars sales), as

fuel prices were only important in terms of which vehicle type to choose. Further

discussion can be found in section 9.2.2.

Initial (from data)

Base Case Shares ($2.50/gallon) Year 20 High-Fuel Cost Scenario ($5/gallon) Year 20 Subcompact 12.7% 26.6% 36.6% Compact 23.8% 10.2% 11.1% Midsize 16.2% 14.5% 14.3% Large 3.7% 7.1% 7.3% Luxury 4.6% 1.0% 1.0% CUV 6.2% 7.5% 6.8% SUV 15.7% 6.3% 4.3% Pickup 11.6% 8.8% 5.4% Van 5.6% 18.0% 13.2%

Table 9.1: Vehicle holdings by type after 20 years (Initial Model)

9.1.2 Adjusting Auction Price Variability

Table 9.1 summarizes results from a simulation for the base case with an arbitrarily set

$500 scrappage price and all parameters specified as previously noted. These suggest a

percentage of households choosing to buy or sell vehicles similar to that in Musti and

90

scrapped is quite low considering most of the automobiles were held at the beginning of

the simulation. This, as well as the high average vehicle age, shows an obvious bias

towards older used vehicles and, more importantly, a bias against putting them on the

market. Increasing the simulation duration (to 30 and 40 years, for example) allowed

increases in average vehicle age, but these were less than the number of simulated years.

Despite this, an average of 11 vehicles that went into each auction (i.e., 4% of those, not

including scrapped vehicles) were returned to their previous owners as unsold.

Base Case 25% Price Variability

Per Year Total Per Year Total

Buyers in Auction 413 8258 415 8301

Vehicles in Auction 264 5,270 257 5,141

Auction Rounds 229 4,573 722 14,447

Vehicles Unsold 11 219 9.5 189

Total Vehicles 12,294 12,534

New Vehicles Purchased 4,255 4,495

Vehicles Scrapped 1,048 1,146

Average Veh Age in Year 20 20.9 yrs 20.6 yrs

Table 9.2: Base case (15% price variability) and 25% price variability simulation results (Initial Model)

The left side of Table 9.1 show results when price was allowed to vary by up to 15%

above or below initial auction price. When this was increased to 25%, as seen on the

right side, 9% more of the used vehicles fell below the scrappage price and were removed

from the market. This resulted in 6% more new vehicles being purchased and fewer used

vehicles being returned from auction to their previous owners. However, the wider

allowance on market price range did not encourage more convergence on a market price

with the current parameters. For the base case, only 7.0% of vehicles that went into an

91

maximum price, or scrapped. With the higher price deviations, this number fell to only

6.5%. (Excluding scrapped vehicles, 8.7% and 8.3% were sold at market price with the

base and higher variability models, respectively.) Adding price variability does increase

the number of auction rounds if left unchecked. Changing the variability to 25% from

15% increased the range of prices by 66%. The number of auction rounds increased, as a

result, by over 200%.

9.1.3 Subsidized Scrappage

Since governments sometimes choose to induce car turnover (thereby improving fleet emissions or safety) by offering scrappage subsidies (e.g., the Obama Administration’s “Cash for Clunkers” program or those described in Esteban [2007]), scrappage value is a variable parameter of interest. A simulation was done in which the scrappage incentive (per qualifying vehicle) was increased from $500 to $2500 (for all vehicles). Table 9.3’s results show several changes from results of the $500 base case, as described earlier.

Scrappage rates increased by over 85%, while about 20% more new cars were sold. 65%

fewer vehicles went unsold, most likely being taken out of the market for scrappage

reasons. The average vehicle age and the number of buyers and vehicles in the auctions

were very close to the base scenario’s outcomes – suggesting that the incentives did little

92

Base Case $2500 Scrappage

Per Year Total Per Year Total

Buyers in Auction 413 8258 410 8,201

Vehicles in Auction 264 5,270 257 5,130

Auction Rounds 229 4,573 334 6,674

Vehicles Unsold 11 219 3.9 77

Total Vehicles 12,294 13,137

New Vehicles Purchased 4,255 5,098

Vehicles Scrapped 1,048 1,950

Average Veh Age in Year 20 20.9 yrs 19.7 yrs

Table 9.3: Average simulation results with $2500-per-vehicle scrappage incentive (Initial Model)

These simulation runs exhibit a trend of an increasing number of used vehicles in the

market over time. The only reason the likelihood of the dispose choice can increase over

time comes via increases in the maximum vehicle age (in the household’s fleet), since

there is no mileage or person-age (or other) factor to consider here, and no other

attributes are changing over time. When the disposal choice is made, however, the oldest vehicle in a household’s possession might not be chosen to be put on the

market.Comparing the average age of all vehicles to those in the (yearly, used) market

shows how younger vehicles were being selected for disposal, since they bring in more

money when sold used. Therefore, simulated households appear to be undervaluing new-

ness in their vehicles, in exchange for the added value from sale of newer cars.