Let's Go Fly a Kite: Correlates of Involvement in the House Bank Scandal

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<ul><li><p>Let's Go Fly a Kite: Correlates of Involvement in the House Bank ScandalAuthor(s): Charles Stewart IIISource: Legislative Studies Quarterly, Vol. 19, No. 4 (Nov., 1994), pp. 521-535Published by: Comparative Legislative Research CenterStable URL: http://www.jstor.org/stable/440172 .Accessed: 14/06/2014 10:37</p><p>Your use of the JSTOR archive indicates your acceptance of the Terms &amp; Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp</p><p> .JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org.</p><p> .</p><p>Comparative Legislative Research Center is collaborating with JSTOR to digitize, preserve and extend accessto Legislative Studies Quarterly.</p><p>http://www.jstor.org </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/action/showPublisher?publisherCode=clrchttp://www.jstor.org/stable/440172?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>CHARLES STEWART III Massachusetts Institute of Technology </p><p>Let's Go Fly a Kite: Correlates of Involvement In the House Bank Scandal </p><p>This research note examines some hypotheses about why members wrote overdrafts on the House bank, the central behavior of the House bank scandal. We use the negative binomial model, a type of event-count model, to compare the effects of vari- ables related to political power (party, seniority, and electoral security) with personal variables (age and wealth). The results are consistent both with partisan interpretations (Democrats bounced more checks, even when their lower average wealth is controlled for) and with more personalistic interpretations (younger members and members of modest means also wrote more overdrafts). </p><p>The 1992 congressional election produced the greatest turn- over in the House since the one that immediately followed World War II. A central element of the election story was the House bank scandal, which used a broad brush to paint an unflattering picture of the institution. </p><p>Although when viewed dispassionately the behavior that con- stituted the scandal was tame, it became a filter through which issues of democratic accountability were examined in 1992. Not surprisingly, the scandal was immediately subject to much press attention before and immediately after the election, an attention that has been mir- rored by scholars (see Groseclose and Krehbiel 1992; Jacobson and Dimock 1993). </p><p>The scandal was viewed by many-partisans, scholars, and pundits alike-as an object lesson about power and corruption. Specif- ically, the fact that Democrats tended to bounce' more checks than Republicans (Table 1) was used to illustrate the possibility that long- time control of a legislature by one political party could tempt mem- bers of that party to use a public institution for private gain. This note explores the empirical foundation of this inference more dispassion- ately than did the partisans, who understandably were more interested in political gain than in careful social science analysis. </p><p>LEGISLATIVE STUDIES QUARTERLY, XIX, 4, November 1994 521 </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>Charles Stewart III </p><p>TABLE 1 Overdrafts in the House of Representatives Bank, </p><p>1 July 1988 to 3 October 1991 (in percentages of members) </p><p>Number of Overdrafts All Members Democrats Republicans </p><p>0 36.1 31.6 43.0 1-10 32.7 33.0 32.0 11-50 13.5 12.5 15.0 51-100 6.8 9.1 3.5 101-200 4.8 6.1 3.0 201-300 1.2 1.3 1.0 301-400 1.2 1.3 1.0 401-500 0.8 1.0 0.5 501-1000 2.8 4.0 1.0 Number of Cases 498 297 200 </p><p>Note: The data include all members who served during the period. Bernard Sanders, an independent, had five overdrafts. Source: Congressional Quarterly Weekly Report, 18 April 1992, 1006-7. </p><p>Although Democrats were more likely to be implicated in the scandal than Republicans, the zero-order relationship between partisanship and scandal involvement may be spurious. Democrats may have bounced more checks than Republicans not because of their partisanship, per se, but because of the differences in individual char- acteristics associated with representatives of the two parties. For instance, if members of substantial means were less likely to write overdrafts and if Republicans were on average wealthier than Demo- crats, then the observed difference in the partisan tendency to write overdrafts could really be due to these differences in the average Dem- ocratic and Republican members, not in how the bank treated the par- ties themselves. </p><p>This note shows that there is room for both group-level and individual-level explanations of involvement in the scandal. Senior members with fewer financial resources kited more checks than junior or wealthy members. Still, even with these measurable, individual- level characteristics controlled for in the equation, Democrats wrote more overdrafts than Republicans. These findings have implications for how House members were held accountable for their involvement in the scandal, which is the focus of the concluding discussion. </p><p>522 </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>House Bank Scandal 523 </p><p>FIGURE 1 Scandal Time Line </p><p>Ethics Committee Report Coverage </p><p>100th Congress 101st Congress </p><p>102d Congress Ethics Committee </p><p>Election Election GAOReport Report lection I I I I I </p><p>I,....--I ---- ...-........ - -....-.-... .......,,, ....,.. . I.I </p><p>1987 1988 1989 1990 1991 1992 1993 </p><p>Background </p><p>The scandal was widely reported in the news media at the time it developed, so only the most basic facts will be rehearsed here. Offi- cial congressional and news reports made it clear that this scandal was a continuation of the bank's Keystone Kops history. (See U.S. Con- gress 1992; Kuntz 1992.) The bank-really the House's payroll office-allowed members of the House to write checks on insufficient funds, so long as the checks would be covered by the member's next pay check. The scandal broke when the press covered a General Accounting Office (GAO) audit in late 1991 that criticized the prac- tice. The House Ethics Committee issued two reports in April 1992 revealing the names of all members of the House (including three- fifths of the sitting members) who had written at least one overdraft with the bank sometime during a 39-month period from 1988 to 1991 (see U.S. Congress 1992; Congressional Quarterly Weekly Report 1992). The worst offender among sitting members was Robert Mrazek (D.-NY), who overdrew his account 920 times. Figure 1 sketches the timing of the key events as the scandal unfolded. </p><p>One detail of the scandal bears on the empirical analysis that follows. According to the Ethics Committee report and press accounts that quoted members of Congress, they may have begun writing over- drafts in one of two ways. First, a few members generously interpreted House rules to mean that they had a right to draw their pay one month before it was actually placed in their accounts by the sergeant-at-arms. </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>Charles Stewart III </p><p>Second, many members initially bounced checks through inadver- tence. However, the member might then have received a call from a bank employee saying that the check could be made good if the mem- ber just deposited enough funds so that the overdraft was less than the member's next monthly pay check. This call would alert the member to the same behavior followed by the first group, opening up the possibil- ity of further, purposeful overdrafts. </p><p>Both paths suggest that check kiting was (to use the statistical term) contagious. That is, within the period covered by the Ethics Committee's report, if a member did not kite a check one day, she or he was unlikely to kite one the next. However, kiting one check increased the probability of kiting not just one more check the next day, but many more. This is a feature of the scandal that we will be able to address below in the statistical estimation. Unfortunately, there is no reliable way from official sources-or unofficial ones-to tell which avenue a member followed in getting caught up in the scandal. </p><p>Data </p><p>The data in this paper were drawn from numerous official sources. The dependent variable is the number of overdrafts by mem- bers during the period (July 1988 to October 1991) covered by the House Ethics Committee investigation into the scandal. This 39-month period spans parts of three congresses and encompasses two elections. Unfortunately, the committee's report does not subdivide the number of overdrafts by time periods. Because the Ethics Commit- tee report centered on the 101 st Congress, and because only the 101st was completely covered by the report, the analysis in this paper will consider only those members who were members for the entire 101st Congress.2 If members who died, resigned, or otherwise served for only part of the 101st Congress are excluded, the effective maximum sample size is 431. </p><p>The explanatory variables can be grouped into two categories, one associated with institutional position and political power and one associated with personal circumstances. Four member characteristics examined here are associated with institutional position and power: electoral security, party, committee leadership, and chamber seniority. Electoral security is measured as the average two-party vote share received by the member in the 1988 and 1990 elections. Combining two elections should give a more stable estimate of the member's medium-run electoral security than would the vote in any single elec- tion. For the members in the analysis not running in the 1990 general </p><p>524 </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>House Bank Scandal </p><p>election, I used only their 1988 vote share. If a member was unopposed in one of the elections, I used only the vote share from the other elec- tion. For the 15 members of the House who were unopposed in both 1988 and 1990, I set the average vote share at 100%. </p><p>Party is a dummy variable coded as 1 if the member was a Democrat, 0 otherwise. Seniority is measured as the number of con- secutive years the member had served in the House at the start of the 101st Congress. Experimentation with functional forms revealed that the logarithm of seniority fit the data better, so that was the variable actually entered into the analysis. I include two dummy variables mea- suring committee leadership; one indicating committee chairs and one indicating ranking minority members. </p><p>To the degree that involvement in the scandal was prompted by the arrogance of power, we should expect the number of bounced checks to be positively associated with electoral security, being a Dem- ocrat, seniority, and service as a committee leader. </p><p>I examined two personal characteristics of members, their per- sonal wealth and age. Personal wealth was measured in two variables derived from the 1989 financial disclosure forms filed with the House Clerk. The first, the logarithm of estimated unearned income was used to measure members' investment income.3 The second income varia- ble was the logarithm of honorarium income, net of contributions to charities. Since the logarithm of zero is undefined, I added $100 to both of these variables before taking the logarithm. This method induces a small amount of bias into the estimated coefficients, but allows me to include in the analysis members with either no unearned income or no honoraria. </p><p>Age is likely to be a surrogate for many personal factors that are difficult, if not impossible, to measure reliably. In the general pop- ulation younger workers, with families to raise and few accumulated assets, tend to be more in debt and to consume a higher fraction of their incomes than older workers; it is unlikely that members of the House are fundamentally different from the general population in this regard. Hence, older members (controlling for seniority) should bounce fewer checks than younger ones. </p><p>If bouncing checks was sometimes associated with financial distress, then members with lower unearned incomes should have bounced more checks than wealthier members.4 The relationship between honoraria and check kiting is less intuitively obvious. How- ever, upon studying the financial disclosure forms it was clear that the wealthiest members rarely generated substantial net incomes from honoraria, while younger, less financially secure members participated </p><p>525 </p><p>This content downloaded from on Sat, 14 Jun 2014 10:37:15 AMAll use subject to JSTOR Terms and Conditions</p><p>http://www.jstor.org/page/info/about/policies/terms.jsp</p></li><li><p>Charles Stewart III </p><p>TABLE 2 Descriptive Statistics for the Independent Variables </p><p>Standard Variable Mean Deviation </p><p>Overdrafts 53 146 Average Vote Share in 1988 and 1990 69 12 Democrat 0.60 Committee Chair 0.052 Ranking Member 0.052 Senioritya 12 8 Age 52 10 Net Honorariab $11,996 $ 15,651 Unearned Incomeb $70,915 $199,593 a Subsequently logged b Subsequently logged after adding $100 </p><p>in the honorarium circuit more vigorously. Hence, high honorarium income should be associated with greater check kiting. </p><p>Table 2 reports the means and the standard deviations of the variables, and Table 3 reports the cross-correlation matrix of the inde- pendent variables. </p><p>Estimation </p><p>The dependent variable, checks kited, has a large number of observations at zero, is highly skewed, and is constrained to take inte- ger values greater than or equal to zero (see Table 1 again). Ordinary least squares is not the proper procedure for estimating the parameters of this model, in view of the distribution of the dependent variable and the behavior it attempts to capture. However, recent methodological work has adapted a suite of techniques to political science that are appropriate to address this problem. These techniques fall into the cat- egory of event-count models (Maddala 1983, pp. 51-54; King 1987, 1988, 1989a,b,c; King et al. 1990).5 The best-known event count tech- nique is Poisson regression, which is increasingly used in political sci- ence. The technique used in this paper is a generalization of Poisson regression called the negative binomial model.6 </p><p>Readers interested in investigating event-count model...</p></li></ul>