Sinterización en Horno de plasma

The dummy variable measuring whether a bill is regulatory in nature also carries a statistical influence. Interest groups are much more likely to see their recommendations included in the bill markup if they are testifying on regulatory policy. This likely is the case because the information they are providing in testimony is more technical in nature. For example, testifying on the Interstate Banking and Branching Act of 1994, Arthur Wilmarth of George Washington University recommended in testimony the prohibition of any bank from holding more than 10% of the total deposits held by insured depository institutions in the US, or from holding more than 25-30% of the total deposits held by such institutions in any state. He justifies his recommendation in testimony. Similarly, in his testimony on the Comprehensive One-Call Notification Act of 1994, Walter Garner

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of the National Utility Contractors Association urges the committee to consider an additional program element whereby the states would establish guidelines requiring facility operators to ensure that new underground construction and installations can be reasonably located at a later date as built. The recommendation was technical. Technical information in the form of a policy recommendation plausibly has a better chance of impacting bill markups.

Before proceeding to the last analyses, I would like to look at the preceding regression analyses, but with the narrower definition of influence. All of the influence models have been examined using all of those recommendations that were supported by the subsequent bill markups, whether they were seeking changes or whether they testified in support of already existing portions of the bill. Recommendations accepted using this definition comprise 34.6% of the observations. Based on our understanding of influence, particularly as it is conceptualized by Cox and McCubbins (1993) in relation to negative agenda control, it is exerted both offensively and defensively. The results restricting influence to explicitly changing some part of the bill are presented in Table 30. Using this narrower definition of impact, only 4.3% of the recommendations are incorporated in the bill markups. The results present some interesting changes.

Table 30: Probability an Interest Group’s Recommended Change is Included in the Bill Markup (narrower definition of impact)

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Variable Coefficient Odds Ratio Std. Error z P > |z|

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Partisanship -0.130 0.878 0.035 -3.77 .000 Ratio Maj : Min -1.746 0.174 0.848 -2.06 .040 Ideology -1.328 0.265 0.548 -2.42 .015 Tenure -0.144 0.866 0.032 -4.53 .000 Regulatory 0.188 1.207 0.191 0.99 .324 Hired Guns -0.050 0.952 0.024 -2.07 .038 Own Lobbyists -0.010 0.990 0.018 -0.53 .593 Media (log) -0.012 0.988 0.016 -0.74 .459 Age (log) -0.013 0.987 0.107 -0.13 .900 Chair PAC (log) 0.010 1.010 0.025 0.42 .672 Comm PAC (log) 0.001 1.001 0.020 0.04 .968 Business -0.219 0.803 0.283 -0.78 .438

Trade Assn -0.517 0.596 0.226 -2.28 .022

Membership -0.219 0.803 0.264 -0.83 .406 Fed Govt -0.368 0.692 0.273 -1.35 .178

Union n/a n/a n/a n/a n/a

Agriculture -2.141 0.118 0.401 -5.33 .000 Banking -1.985 0.137 0.405 -4.90 .000 Commerce -1.155 0.315 0.383 -3.02 .003 Resources -2.185 0.060 0.660 -4.27 .000 Transportation -2.160 0.115 0.407 -5.30 .000 106th -1.726 0.178 0.481 -3.59 .000 108th -2.891 0.056 0.538 -5.38 .000 Submitted 0.093 1.097 0.190 0.49 .626 Constant 4.876 1.750 2.79 .005 ________________________________________________________________________

N = 4213 Prob > chi2 = 0.000 Pseudo R2 = .0891

First, the direction of the relationship between the ratio of majority party members to minority party members seated on a committee or subcommittee and interest group

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impact changes. Here the relationship is negative, indicating it is easier for interest groups to set their policy in committees where the margin of seats between the majority party and minority party is slimmer. It could be that for highly politicized bills on highly politicized committees, interest groups that are aligned ideologically with the majority party are more likely to offer testimony seeking to preserve the bill in its original form as it heads into markup, and then in markup their position prevails. While on the other hand, interest groups seeking to change the bill in its original form have a more difficult time gaining votes from the majority party members on a committee or subcommittee; they are more likely to prevail when the committee seats are more evenly distributed.

The other interesting change when defining the dependent variable more narrowly is the appearance of tenure as being statistically relevant. The more senior chairs in the House are less likely to yield influence to interest group recommendations when the group is trying to change the bill. This is what is expected. Over time members in the House gain policy expertise through committee work. Individuals who have more years of service likely have more expertise and therefore are reluctant to change bills they likely had a hand in creating. This variable wouldn’t impact the more broadly defined dependent variable because chairs want interest groups to testify in support of their bills.

The political resource of contract lobbyists also impacts the likelihood of success with the narrower dependent variable. There is a negative relationship between the number of contract lobbyists hired, and the group’s ability to change the bill. Committee members likely identify the presence of contract lobbyists as partially a political ploy and partially as a signal to the salience of the issue at hand. While most of the work

occurring in committee goes unnoticed, this is not true of political and salient issues. It seems reasonable that committee and subcommittee members would be more reluctant to incorporate an interest group’s recommendations under these conditions. Although the change would please the group it would likely not please others and members of Congress are careful with their votes when the issue is politicized.

The only other difference between the two regression models is that interest group legitimacy as measured by age is supported by the first model but not by the second.

Logistic Regression Comparison by Session of Congress

The last analysis examines the influence model for each House session separately. Since all three sessions operate under the conditional party government model this helps to see whether interest group impact operates similarly when the Democrats control the House of Representatives as when the Republicans do. The results of these regressions are found in Table 31.