# Law, Economics & Statistics

Law, Economics & Statistics

Spring 2015

Homework Assignment #2

Directions:

1.    Firestone tire recall

Cost /unit    Pr(tire tread separation)    Loss if separation    Expected Loss    Total Cost
Keep tires    \$0    0.004    \$1,100,000    4400    4400
Recall Tires    \$800    0.001    \$1,000,000    1000    1800

In 2002, Ford had to make the decision whether to recall firestone tires on its SUVs. The tires had a somewhat higher probability of failing than other tires: they were more likely to have the tread separate from the rim at high speeds, thus causing serious accidents. 700,000 cars were affected. Changing all four tires on each car cost \$800.
(a)    Under a strict liability rule, what is optimal for Ford to do? (Recall or not). Explain.
Under the strict liability rule, victim has no incentive for caution and injured always liable. The total cost= 1800*700000=1260000000
(b)    Under a no liability rule, what is optimal for Ford to do? (Recall or not). Explain
Under a no liability rule, victim bear the cost and injured has no incentive of caution. Ford could keep tires on this situation. The total cost=4400*700000=3080000000. By the way, Ford also could recall because the total cost of recalling tire is only 1260000000.
(c)    Assuming perfect information, what will consumers who own a Ford product with firestone tires do under a no liability rule?
By this information, consumers would be choose firestone tire because they need to higher cost if accident occurs.

2.     Parking Meter Heist (adapted from Finkelstein & Levin, Statistics for Lawyers, pp.7-10). Data posted on blackboard.

In the late 1970s, NYC owned and operated about 70,000 parking meters. Daily collections averaged about \$50,000. In May 1978, Brinks, Inc. took over the contract to collect coins from Wells Fargo. On April 9, 1980, 5 collectors were arrested for stealing a portion of the parking meter change they were supposed to collect; they were later convicted of fraud (the \$4500 they had stolen that day). The City terminated Brinks’ contract on April 9, and a new firm, CDC, took over coin collection.  Three additional points may be useful:

There was a gasoline shortage from May 1979 – December 1979.
There was gasoline rationing from June 1979 – September 1979.
There was a suburban commuter rail strike from June 1980 – August 1980

These facts may be useful because they may have impacted the number of cars that were parking during these time periods.

In the data (available on Blackboard), “1-A” refers to a small group of meters (47 of them) right near City Hall (downtown) for which City workers always did the collection (never an outside firm). “# Cols” refers to the number of collection days, as the number of days parking was not free changed depending on the # of weekend days and holidays in a particular time period.

A.    As attorney for the City, what damage calculation would you make, assuming that the City seeks ONLY damages from the last 10 months before Brinks’ contract was cancelled (ie, June 1979-April 1980).

B.    As attorney for Brinks’. What objections would you make to the City’s method of damage estimation?

3.     Texas Reapportionment (adapted from Finkelstein & Levin, Statistics for Lawyers, pp.22-25). Data posted on blackboard.

The constitutionality of state legislative reapportionments depends on whether the small divergences from a strict population equality (one person, one vote – ie, equal voting power) is “based on legitimate considerations incident to the effectuation of a rational state policy.” Reynolds v. Sims, 377 U.S. 533, 579 (1964). The data posted on blackboard provide a reapportionment plan for the Texas House of Representatives, which has 150 members from 90 single-member districts and 11 multi-member districts.

A.    Treating the multi-member districts as if they were separate districts of equal size (each with one member), calculate summary statistics to describe the variation in size: variance, standard deviation, coefficient of variation and interquartile range.
B.    Which statistic(s) do you think should be used in a legal setting? I.e., how would you effectuate the legal language quoted above? Justify your answer.