A Time Series Analysis of
Department of Justice Antitrust Filings:
Partisan Politics versus
Public Choice Theory
Tom Fomby and Dan Slottje
Department of Economics
SMU
OUTLINE
I. Posner’s Seminal (1970) JLE Paper
II. The DOJ Count Data
III. Using Count Models: Poisson Regression and
QML - Negative Binomial Regression
IV. The Core Equations to adjust for trend and
autocorrelation
V. Tests of Over/Under-Dispersion
VI. Empirical Analysis of Counts
VII. A Retrospective View of Posner (1970)
VIII. Conclusions
Posner’s (1970) Paper
“A Statistical Study of Antitrust Enforcement” JLE
• Annual 1890 – 1969
• Hypotheses
i.Size of Economy (GNP) (+)
ii. Size of Budget of Agency (+)
iii. Economic Contractions: Scapegoat Hypothesis
(Monopoly Causes Contractions) (+)
iv. Periods of War: Antitrust could be divisive (-)
v. Politics: Party in White House ( - Republicans,
+ Democrats)
vi. Four Years Following Switch of Presidential Party (?)
vi. Presidential Election Year ( ?)
vii. Interactions between Economic and Political Factors
• All Informal Findings Were Negative (no association)
Shortcomings of Posner Paper
• Relatively Short Data Span. Antitrust Policy has
continued for 34 years hence.
• Statistical Analysis Very Informal. For example,
he compared simple proportions heuristically and
used no formal statistical tests. No analysis of
trend and autocorrelation in data.
• Possibly the use of additional data and more
sophisticated statistical methods could shed
additional light on factors affecting antitrust
activity of DOJ.
DOJ DATA
• TOTAL FILINGS = CRIMINAL +
CIVIL
• ANNUAL: 1891- 2002
• SERIOUS FUNDING OF DOJ DID
NOT BEGIN UNTIL 1925
• DATA SPAN WE CHOOSE TO
ANALYZE IS 1925 – 2002
GRAPH: DOJ FILINGS
120
100
80
60
40
20
0
00
10
20
30
TOTAL
40
50
60
CRIM
70
80
90
CIVIL
00
DEPENDENT VARIABLES
(COUNTS)
• TOTAL = TOTAL NUMBER OF
CASES
• CRIM = CRIMINAL CASES
• CIVIL = CIVIL CASES
POLITICAL EXPLANATORY
VARIABLES
• PARTY = 1 if Republican, 0 if Democrat
• ELECTYR = 1 if Presidential ElectionYear,
0 otherwise
• SWITCH = 1 for First Four Years after
Party Switch, 0 Otherwise
ECONOMIC (PUBLIC CHOICE)
EXPLANATORY VARIABLES
• DUNEMP = First Difference of
Unemployment Rate
• DINF96 = Change in Inflation Rate (1996 dollars)
• ERI = DUNEMP – DINF96
• GDOJ96 = Growth in DOJ budget (1996 dollars)
• GGNP96 = Growth in GNP (1996 dollars)
• WAR = 1 for War Year, 0 Otherwise
• RECESS = 1 for negative growth year,
0 0therwise
CORE EQUATIONS
FOR TREND AND AUTOCORRELATION
• TOTAL = f(C, TIME, TIME2, LOG(DOJFILE(1)), LOG(DOJFILE(-2)))
Q(12) = 3.5232 (P = 0.991)
• CRIM = g(C, TIME, LOG(CRIM(-1)))
Q(12) = 9.7698 (P = 0.636)
• CIVIL = h(C, TIME, TIME2,
LOG(CIVIL(-1)))
Q(12) = 3.5116 (P = 0.991)
TESTS FOR
OVER/UNDER-DISPERSION
• TOTAL: Cameron and Trivedi (1990)
t = 2.758 (p = 0.0073)
Wooldridge (1997)
t = 2.563 (p = 0.0123)
QMLE parameter = 0.043
• CRIM: Cameron and Trivedi (1990)
t = 3.227 (p = 0.0018)
Wooldridge (1997)
t = 0.901 (p = 0.3702)
QMLE parameter = 0.074
• CIVIL: Cameron and Trivedi (1990)
t = 3.968 (p = 0.0002)
Wooldridge (1997)
t = 3.083 (p = 0.0028)
QMLE parameter = 0.081
PARTISAN POLITICS
EQUATIONS
PUBLIC CHOICE
EQUATIONS
RETROSPECTIVE
POSNER EQUATIONS
CONCLUSIONS - I
• We analyze Total, Criminal, and Civil Antitrust
filings by the Department of Justice over the years
1925 – 2002.
• We find that Partisan Politics (Party) doesn’t seem
to affect any of the filings of the DOJ. In other
words, when it comes to Antitrust enforcement,
Democrats and Republicans are alike in terms of
their activism/passivity, other factors held
constant.
CONCLUSIONS - II
• Turnover in administrations doesn’t appear to
bring with it reactionary change vis-à-vis the
previous administration. (Switch)
• Election year politics (Electyr) doesn’t seem to
affect the number of antitrust cases brought by the
DOJ. That is, Antitrust activity of the
Presidential election year appears to be no
different than that of non-election years.
CONCLUSIONS - III
• The impact of economic (Public Choice) variables on DOJ
Antitrust activity comes through a select few variables and
then only affects Total and Criminal filings and not Civil
filings. Possibly Criminal cases have a higher profile than
Civil cases and as a result are more important in conveying
messages to the Public about the Administration’s concern
over economic variables of interest to the public.
• Evidently DOJ officials engage in Antitrust activity with a
reticence that depends on the most recent changes in the
unemployment rate and inflation rate. We measure this
reticence with what we call the “Economic Reticence
Index” (ERI = dunemp – dinf96).
CONCLUSIONS - IV
• DOJ officials appear to be more reticent in engaging in
Antitrust activity when unemployment is increasing
(possibly in fear of creating more unemployment) and less
reticent in bringing Antitrust cases when inflation is
increasing (possibly thinking that inflation is being caused
by “monopoly power”).
• There is tenuous evidence that DOJ officials may weigh
unemployment somewhat more heavily than inflation
when deciding on the vigor with which to pursue Antitrust
activity.
CONCLUSIONS - V
• Finally, we examine an interesting historical question. If
Prof. (Judge) Posner had had, in 1969, the econometric
methodology of today, would he have found the same ERI
effect that we found here? Or, even in the presence of
advanced econometric methodology, would the limited
span of the data he had available at the time have
prevented him from finding any meaningful relationships
at all as implied by the lack of associations he reported in
his 1970 paper?
• A related question: Do advanced econometric methods
help steepen the learning curve in economics?
CONCLUSIONS - VI
• In fact, if Prof. Posner had had the current
econometric methodology available at the
time he wrote his 1970 paper, he too would
have found the ER effect (some 34 years
earlier than this paper). To whit, yes,
advanced econometric methodology, can
help steepen the learning curve of
economics.