A Time Series Analysis of Department of Justice Antitrust Filings: Partisan Politics versus Public Choice Theory Tom Fomby and Dan Slottje Department of.

A Time Series Analysis ofDepartment of Justice Antitrust Filings:

Partisan Politics versusPublic Choice Theory

Tom Fomby and Dan Slottje

Department of Economics

SMU

OUTLINE

I. Posner’s Seminal (1970) JLE PaperII. The DOJ Count DataIII. Using Count Models: Poisson Regression and QML - Negative Binomial RegressionIV. The Core Equations to adjust for trend and autocorrelationV. Tests of Over/Under-DispersionVI. Empirical Analysis of CountsVII. 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

0

20

40

60

80

100

120

00 10 20 30 40 50 60 70 80 90 00

TOTAL CRIM CIVIL

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 FOROVER/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 POLITICSEQUATIONS

PUBLIC CHOICEEQUATIONS

RETROSPECTIVEPOSNER 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.