Adverse Consequences of Tort and Statutory Law Public
Edwards, Griffin Sims (2011)
Abstract
The effect of a law, whether it be through legislatures or
courts, is often difficult
to identify given unintended consequences that arise. One example
is the seminal ruling
of Tarasoff v. Regents that enacted a duty that required
mental health providers to warn
potential victims of any real threat to life made by a patient.
Using a fixed effects model
and exploiting the variation in the timing and style of duty to
warn laws across states, I
find that mandatory duty to warn laws cause an increase in
homicides of 5%. These
results are robust to model specifications, falsification tests,
and help to clarify the true
effect of state duty to warn laws.
Another is the ruling in Chevron v. Natural Resources Defense
Council. Previous
research has found both theoretically and empirically that
Chevron favors agencies and
their interpretation of statutes, but the magnitude of
Chevron's impact remains unclear
due to possible selection issues biasing the post- Chevron
world. I account for the
possibility that incentives change both to the challenger of an
agency and the agency
itself post- Chevron by estimating a break in the trend of
agency deference on the date
Chevron was decided. This allows me to exploit the exogenous
cases that were pending
when Chevron was decided while still employing the full
sample of rulings. Both
parametric and nonparametric specifications of the trend in agency
deference suggest that
Chevron increased agency deference by about 20 percentage
points meaning that agency
will win a challenge around 80% of the time.
The third law on which I focus deals with an organized criminal
firm's ability to
extract monopoly rents from victim firms. Using a U.S. state panel
and data on federal
racketeering cases charged, I find that all else equal, a 0.1
percentage point increase in the
amount of non-English speakers in a state will increase the
expected number of
racketeering cases per state per year by 0.8. This is weakly
supported by the fact that
states with fewer small businesses, and thus a higher probability
of earning monopoly
rents, experience less racketeering activity.
Table of Contents
Table of Contents
1
Doing Their Duty: An empirical analysis of the unintended effect
of
1
Tarasoff v Regents on homicidal activity
2
A Selection-Corrected Estimate of Chevron's Impact on Agency
Deference
50
3
The Power of the Racketeer: An Empirical Approach
83
Tables and Figures
1
Summary of State Duty to Warn Laws
34
2
Summary Statistics
36
3
FE estimation of the effect of state duty to warn laws on the log
of state
40
NCHS homicide rates
4
FE estimation of the effect of state duty to warn laws on the log
of state non-
41
stranger UCR-SHR homicide rates
5
FE estimation of the heterogeneous effect of state duty to warn
laws on the
42
log of state NCHS homicide rates
6
FE estimation of the effect of state duty to warn laws on the log
of state
44
NCHS homicide rates with lower court timing
7
FE estimation of the effect of state duty to warn laws on the log
of state non-
45
stranger UCR-SHR homicide rates with lower court timing
8
FE estimation of the effect of state duty to warn laws on the log
of UCR
46
manslaughter rates
9
The effect of state duty to warn laws on the log of UCR
manslaughter rates
47
with lower court timing
10 Current State Duty to Warn Laws Figure
48
11 Duty to Warn Laws Year of First Enactment Figure
49
12 Chevron Summary Statistics
69
13 Replication of Richards et al. (2006) measures of the Chevron's
Effect on
71
Judicial Voting for Agency Deference
14 Parametric Estimates of Chevron's Effect on Judicial Voting for
Agency
73
Deference
15 Non-Parametric Estimates of Chevron's Effect on Agency Deference
Over a
74
Range of Bandwidths
16 Non-Parametric Estimates of Chevron's Effect on Placebo
Outcomes
75
17 Parametric Specification of Chevron's Effect on Agency
Deference Graph
77
Nonparametric Specification of Chevron's Effect on Agency
Deference
18
78
Graph
19 The Effect of Bandwidth Selection Graph
79
20 Non Parametric Jumps Among Control Variables Graphs
80
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