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

________________________________________________________________________ Members (log) -0.031 0.969 0.024 -1.27 .205 Hired Guns -0.053 0.948 0.024 -2.16 .031 Own Lobbyists 0.031 1.032 0.015 2.14 .032 Media (log) -0.012 0.988 0.015 -0.80 .426 Age (log) 0.154 1.167 0.138 1.30 .193

House PAC (log) -0.093 0.911 0.023 -3.67 .000

Chair PAC (log) -0.016 0.984 0.023 -0.66 .510 Com PAC (log) 0.079 1.082 0.058 1.47 .141 MajCom PAC (log) 0006 1.006 0.050 0.12 .905 Constant -3.031 0.489 -6.20 .000

______________________________________________________________________ N=2633 Prob >chi2 = 0.0005 Pseudo R2 = .0220

In this model, both hired guns and own lobbyists are carrying a statistical influence in the same direction as the previous regression. Media is no longer

statistically significant but one measure of political influence: House contributions. The relationship between PAC contributions made to House members and the probability of success is negative though, contrary to what was expected. Possibly again, the perception is that the larger groups who are giving vast contributions to all members are not

providing as high quality information as groups who are solely focused on providing

36

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information to committee or subcommittee. Members is another variable with a number of missing data as is shown in Table 26. Dropping members from the model adds another 1682 observations. Table 27 reveals the results of this final limited model with only interest group variables.

Table 27: Probability an Interest Group’s Recommended Change is Included in the Bill Markup with Interest Group Variables Only (dropping budget & members) ________________________________________________________________________ Variable Coefficient Odds Ratio Std. Error z P > |z|

________________________________________________________________________

Hired Guns -0.000 1.000 0.005 -0.02 .988 Own Lobbyists 0.009 1.009 0.006 1.42 .156 Media (log) 0.006 1.006 0.007 0.97 .334

Age (log) 0.184 1.203 0.053 4.16 .000

House PAC (log) 0.006 1.006 0.012 0.56 .577

Chair PAC (log) -0.022 0.978 0.009 -2.43 .015

Comm PAC (log) -0.039 0.962 0.020 -1.87 .061

MajCom PAC (log) 0.041 1.042 0.019 2.26 .024

Constant -1.474 0.187 -7.89 .000

______________________________________________________________________ N=4315 Prob >chi2 = 0.0000 Pseudo R2 = .0067

This regression reveals statistical significance for one measure of interest group legitimacy and two measures of political influence. Age is positively related to the likelihood the interest group’s recommendation will be incorporated in the bill markup. The older a group is, the more likely it will see success. Looking at political influence,

House PAC is no longer statistically significant, but contributions made to the committee

subcommittee are. But to confuse things, making a contribution to the chair is negatively related to interest group influence whereas contributions made to majority party members seated on the committee or subcommittee is positively related.

These results are contradictory almost. To be sure these results are not being offset by any problems with the model specification the variables have been tested for multicollinearity using the Collin test in STATA. The resultant VIF values for the political influence variables do indicate multicollinearity. To alleviate this problem, the only measures of political influence to be kept in the model are chair PAC and comm

PAC. Table 28 reveals the results of the corrected model.

Table 28: Probability an Interest Group’s Recommended Change is Included in the Bill Markup with Interest Group Variables Only (dropping budget & members,

corrected)

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

________________________________________________________________________

Hired Guns -0.000 1.000 0.005 -0.01 .991 Own Lobbyists 0.010 1.010 0.006 1.64 .101 Media (log) 0.006 1.006 0.006 0.91 .361

Age (log) 0.185 1.204 0.044 4.18 .000 Chair PAC (log) -0.017 0.983 0.009 -1.96 .050

Comm PAC (log) 0.003 1.003 0.007 0.43 .668 Constant -1.484 0.187 -7.95 .000

______________________________________________________________________ N=4315 Prob >chi2 = 0.0000 Pseudo R2 = .0057

The model is better specified but the counterintuitive relationship between chair

PAC and influence remains. This is not the fully specified model and it remains to be

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preliminary speculation is reluctance on the part of any chair to be engaging in any type of behavior which might be construed as “vote buying.” While it is not necessarily the case that a chair who receives money from an interest group and then yields them influence on a committee markup is engaging in any unethical behavior, the appearance may lead others to think otherwise. If this is the case, the negative relationship between

chair PAC and the likelihood of getting a recommendation included in the bill markup is

logical.

The other significant variable is age, which is positively related to interest group success. Older, more established groups are more likely to get their recommendations marked up into the bill. Political visibility comes with age and experience.

Logistic Regression with Committee and Interest Group Variables

The next model combines the interest group variables and committee and chair variables. It also includes dummy variables to measure the parent committee for the committees and subcommittees used in the research sample. For example, the

Agriculture Subcommittees on Livestock and Horticulture are both measured by their parent Agriculture Committee. The Transportation and Infrastructure Subcommittee on Aviation is measured as a Transportation and Infrastructure Committee.37 The results of this fully specified model are shown in Table 29. In addition, dummy variables

representing the type of interest group and the session of Congress are included to control

37

This is done since many subcommittees have no instances of accepting an interest group’s recommended change and were predicting failure perfectly. When this occurs, STATA drops them. There were also many subcommittees being dropped due to correlation with one another. Using the parent committees for the dummy variables resolved this issue.

for the effects they may exert in a pooled time-series analysis. Lastly, a variable to measure whether the testimony is submitted in writing (rather than in person) is added.

Table 29: Probability an Interest Group’s Recommended Change is Included in the Bill Markup

________________________________________________________________________

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

________________________________________________________________________

Partisanship -0.071 0.931 0.014 -4.96 .000 Ratio Maj : Min 0.740 2.096 0.357 2.07 .038 Ideology -0.774 0.461 0.236 -3.28 .001 Tenure -0.016 0.984 0.015 -1.11 .269 Regulatory 0.445 1.560 0.084 5.31 .000 Hired Guns -0.004 0.996 0.005 -0.73 .463 Own Lobbyists 0.011 1.011 0.007 1.59 .111 Media (log) 0.003 1.003 0.008 0.44 .661 Age (log) 0.153 1.165 0.048 3.17 .002

Chair PAC (log) -0.007 0.993 0.009 -0.73 .463 Comm PAC (log) 0.003 1.003 0.008 0.39 .693 Business 0.011 1.011 0.137 0.08 .934 Trade Assn 0.079 1.082 0.112 0.70 .481 Membership -0.208 0.812 0.131 -1.59 .113 Fed Govt 0.163 1.177 0.130 1.26 .209 Union -1.216 0.297 0.327 -3.72 .000 Agriculture -1.004 0.366 0.173 -5.80 .000 Banking -0.554 0.575 0.161 -3.44 .001 Commerce -0.701 0.496 0.162 -4.31 .000 Resources -1.579 0.206 0.209 -7.56 .000 Transportation -0.793 0.452 0.168 -4.72 .000 106th 0.370 1.447 0.207 1.79 .074 108th 0.307 1.360 0.215 1.43 .152 Submitted -0.098 0.907 0.091 -1.07 .283 Constant -1.394 0.758 -1.84 .066 ________________________________________________________________________

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This model confirms the importance of partisanship. The more partisan a

committee or subcommittee, the less likely an interest group is to get its recommendation included in the bill markup. The mean level of partisanship for all committees and subcommittees in the sample is 3.526. The predicted probability of an interest group having its recommendation taken, with all other variables set at their mean values is 0.184. The predicted probability at the mean partisanship level during for businesses is 0.186 and the odds rise to 0.195 for trade associations. Membership groups face a 0.157 probability of success at mean levels of partisanship and agencies of the federal

government a probability of 0.208. Graph 6 shows the predicted probabilities at other values of Partisanship using CLARIFY (Tomz, Wittenberg & King 2003).

.1 .1 5 .2 .2 5 .3 P rob ab ilit y -5 0 5 10 15 Partisanship Business Trade_Assn Membership Fed_Govt by Partisanship