Regulator Performance, Regulatory
Environment and Outcomes
An Examination of Insurance Regulator Career
Incentives on State Insurance Markets
by
Martin F. Grace
Georgia State University
[email protected]
Richard D. Phillips
Georgia State University
[email protected]
2007 Annual Meeting of the
American Risk and Insurance Association
Quebec City, Canada
Revolving Door
The door can swing in two directions
Industry  Government
Government  Industry
DAVID LAURISKI, chosen as the Labor Department’s Assistant Secretary
of Mine Safety and Health, previously spent 30 years in the mining
industry, during which time he advocated loosening of coal dust
standards. Once in office, he issued controversial rules (later blocked
by the Senate) that would have reduced coal-dust testing in mines.
Lauriski resigned from his position in late 2004 and took a job with a
mine-industry consulting company. The Charleston Gazette later reported
that Lauriski had been negotiating for private-sector jobs as early as six
months before leaving office.
Source: A Matter of Trust (Revolving Door Working Group: Washington D.C., 2005)
Revolving Door
o Debate exists whether revolving doors between a regulatory agency
and the industry it oversees should be open or closed
o Traditional concern is about capture by regulated industry
o Stigler (Bell Journal 1971)
o However, agency can be captured by other than industry interests
o Peltzman (J. of Law and Economics 1976)
o More recent literature suggests
o Regulators may pursue own private incentives
o Laffont (various papers with various authors)
o Revolving doors may provide incentives that actually increase overall
efficiency of the regulated market place
o Che (RAND 1995)
o Salant (RAND 1995)
Research Objectives of this Paper
o Who are the regulators of the insurance industry?
o
o
o
o
Where do they come from?
What professional backgrounds do they have?
How do they become insurance commissioner?
Where do they go when they are no longer the insurance
commissioner?
o Can we find evidence that the decisions of insurance regulators, in
states that provide them a means, are influenced by their
o Pre-agency employment
o Post-agency career ambitions, or by
o The manner through which they attained the office?
o Can we explain the rather surprising result that rate regulation, on
average, appears to have little effect on insurance prices yet in some
states and at some times the effect appears large?
Prior Literature
o Regulatory Incentives
o
o
o
o
Peltzman (JLE 1976)
Laffont and Martimort (RAND 1999)
Che (RAND 1995)
Salant (RAND 1995)
o Selection Mechanism
o Besley and Coate (J. of the European Econ. Assoc. 2003)
o Rate Regulation and Automobile Insurance
o Harrington (Brookings-AEI 2002)
o Cummins, Phillips, and Tennyson (JIR 2001)
o And a number of other papers not cited here…
Revolving Door
Sample of the Empirical Literature
o Gormley (American Journal of Political Science 1979)
o Revolving door between industry and government for FCC regulators
o Cohen (American Journal of Political Science 1986)
o Revolving door between government and industry for FCC regulators
o Gely and Zardkoohi (American Law & Economic Review 2001)
o Revolving door between U.S. cabinet positions and lobbying firms
o Boylan (American Law & Economic Review 2005)
o Revolving door between U.S. attorneys and judgeships and big-firm
partnerships
Industrial Focus of Our Research
o Personal auto insurance is an ideal setting
o Some states provide authority for regulator to approve rates
prior to their use (Prior Approval States)
o Some states provide limited or no authority to approve rates
(Competitive States)
o Some states have elected commissioners – others are appointed
o One insurance commissioner per state
o Proxy for the price of automobile insurance is well established in
the literature
Database of State Insurance
Commissioners: 1985 - 2002
o Biographical information on all state insurance commissioners
o Information collected included
o Gender
o Educational background
o Prior agency professional
background
o Post agency career choice
Total number of insurance comm’s
- Number of interim comm’s
- Number of comm’s w/missing data
Number of comm’s in study
Comm’s from regulated states
Comm’s from competitive states
o Data Sources
o NCOIL’s The Insurance Legislative Fact Book and Almanac (various years)
o Trade press and national and local newspapers in Lexis-Nexis and
Factiva
o Google/Internet Searches
Anecdotal Comments about the
Commissioners
o One state had three insurance commissioners, in a row,
convicted of either
o Lying to a federal agent
o Accepting illegal campaign contributions, and/or
o Bribery
Most recent two seem to be clean!
o Six commissioners in our sample resigned for ethical
reasons or were serving jail time. Since our sample two
more have been forced from office over ethical issues.
Anecdotal Comments about the
Commissioners (2)
o 52% of commissioners had no industry experience prior to their
appointment or election
o Biographical statistics
o
o
o
o
Just over 75 % are male
48% are lawyers
22%went to graduate school
Some of undergraduate majors represented in our sample*
o
o
o
o
Business
Education
Psychology
Mortuary Science!
o Two states have had only one commissioner during the entire time
period of our study
* - Note - data on majors is incomplete
i
Empirical Test
m
pit  b
where pit
Xm
PA
Xr
nt
ηi
eit
bm, br, g
m
Xit
r
 gPAit  b
r
PAit Xit
 i  nt  it
= unit price of personal automobile liability insurance in
year t and state i
= is a vector of explanatory market variables in year t and
state i
= is an indicator for prior approval statute in year t and state
i
= is a vector of indicator variables controlling for the
professional background, the prior and post agency
employment choices of the insurance regulators, and the
manner by which the regulator attained the position in
year t and state i
= is the year specific error term
= is the state specific error term
= random error term
= estimated coefficients
Estimation Methodology
o Two sources of potential endogeneity
o Decision by the states to allow the regulator to intervene may be
jointly determined with insurance prices
o Prior literature mixed whether this selectivity bias exists
o Cummins, Phillips and Tennyson (2001)
o Harrington (2002)
o Econometric technique: Heckman’s (1978) two-stage sample
selection model extended to a treatment effects model
o Estimate first-stage Probit regression
o Calculate inverse Mills terms for regulated and unregulated state-year
observations
Estimation Methodology (2)
o Two source of potential endogeneity, continued
o Commissioner’s post employment choices may be jointly
determined with the decisions she makes while in office
o Econometric technique: Similar to Heckman’s two-stage sample
selection model except post-agency choice is modeled using
multinomial logistic regression.
o See Lee (1983) for details.
o Test null hypothesis of exogoneity using F-test restrictions on
o Regulation inverse Mills terms jointly equal zero
o Post-agency career choice inverse Mills terms jointly equal zero
o Regression methodology
o Two-way fixed effects estimated using weighted least squares
Data Sources and Sample
o Data Sources
o State insurance markets
o AIPSO
o NAIC
o Political Environment
o Berry et al (1998) Political Ideology Index
o Demographic and Economic Environment
o U.S. Bureau of the Census
o Include all state-year observations from 1985 – 2002
o Exclude the District of Columbia
o Eliminate state-year observations where we do not have complete data
on the insurance commissioner
o Final sample is 708 state-year observations
Personal Automobile Unit Price Ratio
All Coverages by Regulatory Regime: 1985 - 2002
Premium Ratio
1.24
1.20
1.16
1.12
1.08
Competitive States
Regulated States
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
1.04
Year
Premium Ratio is defined as the ratio of direct premiums earned divided by direct loss and
loss adjustment expenses incurred plus policyholder dividends paid. Source: NAIC
Summary Statistics
Regulated vs. Unregulated States: 1985-2002
Regulated
Variable
Unit Price Ratio
Ave. Loss Per Car Year ($2004)
Ave. Prem Per Car Year ($2004)
Percent Car Years in Residual Market
Total Written Car Years (000's)
Percent Liability Losses Incurred in No-fault
Percent Losses Incurred in Liability Coverages
Percent Premiums by Direct Writers
Percent Private Sector Employees in Ins. Ind.
Percent State GDP from Insurance
Percent of National Insurance GDP from State
Indicator = 1 if commissioner is elected
Liberal Ideology Index
Real Per Capita Income ($2004)
Population Density
Percent Miles Driven in Urban Areas
Poverty Rate
Competitive
Mean
1.191
$ 688.12
$ 816.97
6.12%
2,767
8.86%
71.39%
62.10%
1.89%
2.40%
2.02%
0.293
54.064
$ 28,688
256.78
56.04%
12.33%
Mean
1.194
$ 607.41
$ 721.29
0.83%
3,440
8.33%
69.04%
63.93%
1.87%
2.37%
2.45%
0.105
45.466
$ 27,840
120.74
50.05%
12.14%
Note - there are 363 unregulated and 345 regulated state-year observations.
***, **, * denotes statistical significance at the 1, 5 and 10 percent levels, respectively.
T-Test
mreg = mcomp
0.462
6.317 ***
6.605 ***
7.880 ***
2.984 ***
0.597
4.629 ***
1.936 *
0.495
0.346
2.216 **
6.410 ***
4.647 ***
2.549 **
7.368 ***
4.821 ***
0.726
Biographical Statistics of Insurance Comm’s
Regulated vs. Unregulated States: 1985 - 2002
Variable
Characteristics and Background
Indicator = 1 if commissioner is elected
Tenure in Office
Consumer Advocate
College Graduate
Graduate from Law School
chool
Degree from Graduate School
Male
Regulated
Competitive
22.52%
4.64
3.60%
90.99%
49.55%
20.72%
74.77%
6.36%
3.95
0.91%
90.91%
38.18%
28.18%
78.18%
***, **, * denotes statistical significance at the 1,5, or 10 percent levels, respectively
T-Test
mreg = mcomp
3.51
1.46
1.36
0.02
1.71
1.29
0.60
***
*
*
**
*
Pre-and-Post Agency Choices of Ins. Comm’s
Regulated vs. Unregulated States: 1985-2002
Variable
Career Prior to Tenure as Insurance Commissioner
Career Insurance Bureaucrat
Career Bureaucrat Not Just Insurance
Career Politician
Private Sector Insurance Industry
Private Sector Not Exclusively Insurance
Mixture Gov't and Private Sector
Employment After Tenure as Insurance Commissioner
Returned to Insurance Department
Seek Higher Office
Private Sector Insurance
Private Sector Other Than Insurance
Indicted, Jailed or Resign Amid Ethical Charges
Died
Retired
Still in office in 2002
T-Test
mreg = mcomp
Regulated
Competitive
18.92%
15.32%
13.51%
17.12%
12.61%
22.52%
18.18%
24.55%
7.27%
16.36%
6.36%
27.27%
0.14
1.73 **
1.53 *
0.15
1.60 *
0.82
2.70%
8.11%
45.05%
9.91%
3.60%
0.90%
5.41%
11.71%
5.45%
6.36%
50.00%
15.45%
1.82%
0.00%
6.36%
1.82%
1.04
0.50
0.74
1.24
0.82
1.00
0.30
2.99 ***
***, **, * denotes statistical significance at the 1,5, or 10 percent levels, respectively
State Insurance Commissioners Career
Choice Transition Matrix
Career Prior
to Ins.
Comm.
Lateral
Private
Move
Sector
in
Seek Private Other
State Higher Sector Than
Gov't Office
Ins.
Ins.
Indicted
Still in
office
in
Retired 2002
Num
Ins.
Dep't
Career Ins.
Bureaucrat
41
7
5
0
15
3
1
0
4
6
Career
Bureaucrat
Not Just Ins.
44
1
9
1
23
7
1
0
0
2
Career
Politician
23
0
3
8
7
1
0
0
2
2
Private Sector
Ins.
37
0
0
0
23
5
1
0
5
3
Private Sector
Not Just Ins.
21
0
3
1
9
5
2
0
1
0
Mixture Gov't
& Private Ind.
55
1
8
6
28
7
1
1
1
2
221
9
28
16
105
28
6
1
13
15
Totals
Died
Estimated Marginal Effects from Multinomial
Logistical Regression of Post-Agency Career Choice
Lateral
Move into
Gov't
Seek
Higher
Political
Private
Sector Ins.
Graduate from Law School
-0.103 *
0.038
0.218 *** -0.086
Degree from Graduate School
-0.011
0.036
-0.036
0.052
Male
-0.004
-0.036
0.072
-0.012
Mixture Gov't and Private Sector
-0.072
0.098
0.016
-0.012
Career Politician
-0.125 ***
0.622 *** -0.390 **
Prior Industry Experience
-0.257 ***
0.007
0.151
0.062
-2.104
3.332
Varialbe
/
Post Employment
Choice
Private
Sector Not Ins.
-0.117 **
Percent State GDP from Insurance
0.501
-1.974
Percent of National Insurance GDP
from State
-1.784
-0.500
3.315 *
-0.161
Ave. Size of the Residual Market
Final Two Years in Office
-0.732
-0.159
0.785
-0.019
0.044
-0.345 *
0.077
Ave. Loss Per Car in State to Ave.
Loss Per Car in Nation
0.265 **
***, **, * denotes statistical significance at the 1, 5 and 10 percent levels, respectively.
Probit Regression Results of the Choice
of Regulatory Regime: 1985 - 2002
Variable
Intercept
Percent Private Sector Employees in Insurance Industry
Ave. Loss Per Car in State to Ave. Loss Per Car in Nation
Lagged(Percent Car Years in Residual Market)
Lagged(Percentage Increase in Ave. Loss Per Car)
Percent Premiums by Direct Writers
Indicator = 1 if commissioner is elected
Percent Liability Losses Incurred in No-fault Coverage
Liberal Ideology Index
Real Per Capita Income ($2004)
Population Density
Percent Miles Driven in Urban Areas
Poverty Rate
Log-Likelihood Function Value
Pseudo R-Squared
0.035
-41.069
0.351
6.617
1.115
-1.172
1.200
-0.757
0.007
0.000
0.001
1.327
0.009
-448.82
20.84%
***, **, * denotes statistical significance at the 1, 5 and 10 percent levels,
respectively.
***
***
*
***
***
***
***
State Unit Price of Automobile Ins.
Two Way Fixed-Effects Regression Results
Price Regulation Endogenous
Post Agency Choice Endogenous
Variable
Intercept
% Liability Losses Incurred in No-fault
% Losses Incurred in Liability Coverages
% Premiums by Direct Writers
Ave. Loss Per Car in State to National Ave.
Log(Written Car Years)
PA Indicator
PA x Consumer Advocate
PA x Elect
PA x Prior Mix Gov't and Private Sector
PA x Prior Politico
PA x Prior Insurance Industry
PA x Private Sector Other Industries
PA x Probability Post Lateral Move to Gov't
PA x Probability Post Higher Office
PA x Probability Post Private Sector Ins.
PA x Post Private Sector Other Than Ins.
R-squared
No
No
Model 1
2.298 ***
-0.578 ***
0.839 ***
-0.195 ***
-0.143 ***
-0.100 ***
0.017 *
-0.044 ***
0.032
0.668
No
No
Model 2
2.146 ***
-0.598 ***
0.853 ***
-0.153 ***
-0.134 ***
-0.091 ***
0.001
-0.051 ***
0.053 **
-0.005
0.022 **
-0.013
-0.018 *
-0.005
0.063 ***
0.017
0.008
0.698
Yes
No
Model 3
2.147 ***
-0.597 ***
0.854 ***
-0.151 ***
-0.134 ***
-0.092 ***
0.004
-0.050 ***
0.052 **
-0.005
0.023 **
-0.013
-0.018 *
-0.005
0.063 ***
0.017
0.008
0.699
F-Statistics for Selectivity Tests
bRegulated = bUnregulated IMR = 0
0.040
bPost Bureaucrat IMR = bPost Higher Office IMR = bPost Insurance Industry IMR = bPost Other Industry IMR = 0
***, **, * denotes statistical significance at the 1,5, or 10 percent levels, respectively
No
Yes
Model 4
1.914 ***
-0.581 ***
0.870 ***
-0.182 ***
-0.125 ***
-0.072 **
0.008
-0.057 ***
0.044 **
0.001
0.015
-0.009
-0.011
0.006
0.069 ***
0.010
0.005
0.706
Yes
Yes
Model 5
1.964 ***
-0.587 ***
0.872 ***
-0.193 ***
-0.122 ***
-0.076 **
0.004
-0.060 ***
0.050 **
0.002
0.014
-0.009
-0.011
0.007
0.072 ***
0.011
0.005
0.706
3.230 ***
0.690
3.310 ***
Estimated Effect of Regulator Career
Incentives on the Price of Insurance in
Regulated States
Price Regulation Endogenous
No
No
No
No
Post Agency Choice Endogenous
Model 2
Model 1
Variable
Estimated Effect of Prior Approval Regulation by Regulator Type
bPrior Approval + bPrior Approval x Consumer Advocate = 0
-0.050 **
bPrior Approval + bPrior Approval x Elected Commissioner = 0
0.054 **
bPrior Approval + bPrior Approval x Prior Mixed Public-Private = 0
-0.004
bPrior Approval + bPrior Approval x Prior Politico = 0
0.023
bPrior Approval + bPrior Approval x Prior Insurance Industry = 0
-0.012
bPrior Approval + bPrior Approval x Prior Other Industry = 0
-0.017
bPrior Approval + bPrior Approval x Post Lateral Move to Gov't = 0
-0.004
bPrior Approval + bPrior Approval x Post Higher Office = 0
0.064 ***
bPrior Approval + bPrior Approval x Post Private Sector Insurance = 0
0.018 *
bPrior Approval + bPrior Approval x Post Private Sector Other Than Ins. = 0
0.004
Yes
No
Yes
No
Model 3
Yes
Model 4
Yes
Model 5
-0.047
0.056
-0.001
0.026
-0.010
-0.014
-0.001
0.066
0.021
0.003
***, **, * denotes statistical significance at the 1, 5 and 10 percent levels, respectively.
*
**
*
***
-0.049
0.052
0.009
0.023
-0.001
-0.003
0.014
0.077
0.018
0.006
*
*
***
*
-0.055 **
0.055 *
0.006
0.018
-0.005
-0.007
0.012
0.076 ***
0.015
0.007
Conclusions
o The overall estimated impact of rate regulation appears small
o We find significant evidence of a revolving door from government to
industry
o Prices appear jointly determined with the post-agency career aspirations of the
insurance commissioners in regulated states
o Particularly strong for commissioners with aspirations to seek higher political office
o Weaker evidence for commissioners seeking post-industry employment
o We find no evidence the prior employment background of insurance
regulators leads to price outcomes significantly different than what one
would expect from a similarly situated state with a competitive rating law
o We find some evidence the selection mechanism motivates commissioners
inconsistent with the results of Besley and Coate (2003)
o We find some evidence avowed consumer advocates are successful
reducing the price of insurance