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"What's In It For Me?": Why Members of Congress Pursue Oversight
Unformatted Document Text:  NOMINATE scores are closely correlated; the two measures respective first dimensionpoint estimates correlate at least at 0.96 for all Congresses during the period examinedin this paper. 57 To test H5: Rules Committee Deference, I examined the proportion of committee bills that were reported to the floor with a closed or modified closed rule during the previousCongress. Kenneth Moffett generously provided these data to the author. 58 Compiling data to test H6: Divided Government and H7: Oversight Mandate involved simple binary determinations. 59 While many of data sources listed above are collected at the individual MC level, the hypotheses I am interested in testing are at the (sub)committee level, as they involvethe number of oversight hearings held by committees and subcommittees. Therefore, Icombed through editions of the biennial Congressional Directory to create a new dataset ofthe membership rosters of each House committee and subcommittee. 60 I then determined (sub)committee-level mean values for the following variables: margin of victory, seniority,passage of sponsored bills, and first & second dimension Common Space coordinates, andentered these means as the covariate values. I estimate the effects of these variables using a series of Generalized Estimating Equa- tion (GEE) models for negative binomial regression. GEE models are appropriate formodeling correlated data, and there is a reasonable a priori expectation that the numberof hearings held by each subcommittee may be correlated in two ways. First, the numberof hearings held during a given Congress by subcommittees that are nested within thesame parent committee are likely to be correlated, in part because there is significantoverlap in the membership of subcommittees that are contained within the same commit-tee, but also because pressure from on high (i.e., from the full committee chair) is likelyto be felt to some degree by all subcommittees within that chair’s committee. Second,since the same subcommittees are used as repeated observations over time, one may ex- 57 Poole, Keith and Howard Rosenthal. “Common Space Data.” available on-line at: vote- view.ucsd.edu/readmeb.htm 58 Cf. Moffett, Kenneth. 2006. “Partisan Differences and Managing the Legislative Process in the Post-Reform House.” Ph.D. dissertation; and Moffett, Kenneth. n.d. “Parties and Procedural Choice inthe House Rules Committee.” working paper. 59 A note on the temporal order of variables: Electoral Security, MC Bill Passage, and Rules Comm. Deference are measured in the period prior to measurement of the dependent variable.MC Seniority, Divided Government and Oversight Mandate measure personal and structural featuresthat are present at the beginning of a given Congress; i.e., they are established prior to a sub-unit’s decision to hold hearings in that Congress. The Common Space coordinates used to measureSubunit Preference Heterogeneity, however, cannot be said to be temporally prior to the dependent vari-able. Unlike DW-NOMINATE and other ideal point estimates, with Common Space scores each MC isassigned one set of coordinates for his entire congressional career. In other words, an MC’s CommonSpace coordinates at t and t + n are equal for any value of n. Since Poole & Rosenthal (1997) observeonly a trivial amount of variance in most MCs’ NOMINATE scores over the course of these MCs’ careers,I argue that MCs’ policy preference scores can be thought of almost as unchanging, baseline character-istics. Thus, in my view problems associated with the cotemporaneous measurement of Common Spacecoordinates and number of subunit hearings are minimal. 60 To my knowledge, these data on MCs’ subcommittee memberships are not available elsewhere. This new dataset – which includes thousands of MC subcommittee assignments over the 1995-2006 period,with covariates related to each “seat-holder’s” personal, partisan, and district characteristics – may beuseful to scholars, as currently available committee-level datasets have proven to be (cf. Stewart, Charlesand Jonathan Woon. “Congressional Committee Assignments, 103rd to 110th Congresses, 1993-2007.”).Therefore, I intend on posting these data online in the not-so-distant future. 33

Authors: Feinstein, Brian.
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NOMINATE scores are closely correlated; the two measures respective first dimension
point estimates correlate at least at 0.96 for all Congresses during the period examined
in this paper.
57
To test H5: Rules Committee Deference, I examined the proportion of committee bills
that were reported to the floor with a closed or modified closed rule during the previous
Congress. Kenneth Moffett generously provided these data to the author.
58
Compiling data to test H6: Divided Government and H7: Oversight Mandate involved
simple binary determinations.
59
While many of data sources listed above are collected at the individual MC level, the
hypotheses I am interested in testing are at the (sub)committee level, as they involve
the number of oversight hearings held by committees and subcommittees. Therefore, I
combed through editions of the biennial Congressional Directory to create a new dataset of
the membership rosters of each House committee and subcommittee.
60
I then determined
(sub)committee-level mean values for the following variables: margin of victory, seniority,
passage of sponsored bills, and first & second dimension Common Space coordinates, and
entered these means as the covariate values.
I estimate the effects of these variables using a series of Generalized Estimating Equa-
tion (GEE) models for negative binomial regression. GEE models are appropriate for
modeling correlated data, and there is a reasonable a priori expectation that the number
of hearings held by each subcommittee may be correlated in two ways. First, the number
of hearings held during a given Congress by subcommittees that are nested within the
same parent committee are likely to be correlated, in part because there is significant
overlap in the membership of subcommittees that are contained within the same commit-
tee, but also because pressure from on high (i.e., from the full committee chair) is likely
to be felt to some degree by all subcommittees within that chair’s committee. Second,
since the same subcommittees are used as repeated observations over time, one may ex-
57
Poole, Keith and Howard Rosenthal.
“Common Space Data.”
available on-line at:
vote-
view.ucsd.edu/readmeb.htm
58
Cf. Moffett, Kenneth. 2006. “Partisan Differences and Managing the Legislative Process in the
Post-Reform House.” Ph.D. dissertation; and Moffett, Kenneth. n.d. “Parties and Procedural Choice in
the House Rules Committee.” working paper.
59
A note on the temporal order of variables:
Electoral Security,
MC Bill Passage,
and
Rules Comm. Deference are measured in the period prior to measurement of the dependent variable.
MC Seniority, Divided Government and Oversight Mandate measure personal and structural features
that are present at the beginning of a given Congress; i.e., they are established prior to a sub-
unit’s decision to hold hearings in that Congress. The Common Space coordinates used to measure
Subunit Preference Heterogeneity, however, cannot be said to be temporally prior to the dependent vari-
able. Unlike DW-NOMINATE and other ideal point estimates, with Common Space scores each MC is
assigned one set of coordinates for his entire congressional career. In other words, an MC’s Common
Space coordinates at t and t + n are equal for any value of n. Since Poole & Rosenthal (1997) observe
only a trivial amount of variance in most MCs’ NOMINATE scores over the course of these MCs’ careers,
I argue that MCs’ policy preference scores can be thought of almost as unchanging, baseline character-
istics. Thus, in my view problems associated with the cotemporaneous measurement of Common Space
coordinates and number of subunit hearings are minimal.
60
To my knowledge, these data on MCs’ subcommittee memberships are not available elsewhere. This
new dataset – which includes thousands of MC subcommittee assignments over the 1995-2006 period,
with covariates related to each “seat-holder’s” personal, partisan, and district characteristics – may be
useful to scholars, as currently available committee-level datasets have proven to be (cf. Stewart, Charles
and Jonathan Woon. “Congressional Committee Assignments, 103rd to 110th Congresses, 1993-2007.”).
Therefore, I intend on posting these data online in the not-so-distant future.
33


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