Dealer Trade Network: Trade Per Unit Inventory

The full post is located on Rpubs at

http://rpubs.com/napairolero/71798

This paper explores Chrysler dealer trade data aggregated over 3.5 months between Oct 2014 and Jan 2015. VIN numbers were scraped biweekly from Chrysler dealer websites in Minnesota, North Dakota, South Dakota, Nebraska, Iowa, Wisconsin and Illinois. Raw inventory was tracked as individual models moved from dealer to dealer through trade. The data gives a glimpse of the Chrysler dealer trade network. The trading of new vehicles between dealers is crucially important to the functioning of this market, and thus questions related to profitability and efficiency should take this network structure into account.

This paper explores one facet of the data, trade per unit inventory. Each dealers trade volume is normalized by inventory size to enable comparisons across dealers of different sizes. One might expect the smallest dealers to trade most, relative to inventory size. The only way to satisfy demand with limited selection would be to trade more, per unit in stock. On the other hand, smaller inventories make it more difficult to trade. Limited selection also implies that distant dealers are unlikely to find their desired model at any one particular location with high frequency. This makes the development of trade relationships difficult. The data provides some evidence for an inverted U relationship between inventory size and trade per unit inventory. However, there are a few dealers in the smallest group that trade more relative to inventory size. The overall relationship is noisy. Inventories are divided into 5 quantiles, from smallest to largest. The mean and median of the second quantile is lower than that of the third quantile. The median of the first quantiles is less than the second and third, however the mean is higher. Histograms show that trade per unit Inventory in the smallest group (1st quantile) is highly right skewed, raising the mean. A few dealers in this group are able (or willing) to trade a lot, per unit inventory. Without these few (and thus the skew), the mean of the first quantile would be less than the second and third. The mean and median of trade per unit inventory drops abruptly after the third quantile.

The exploratory analysis raises several interesting theoretical questions. What is the optimal trade policy for smaller dealers to pursue? In the smallest inventory group, most dealers are unable or unwilling to trade relatively more often, but a few dealers do. Why is this histogram bimodal? If these dealers are unable to trade, in what ways can strategy limit the trade frictions caused by limited selection? Should small dealers pursue a few strong relationships or many one trade events? Should they be willing to go greater distances to trade than larger dealers?

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