VARIABLES

The previous section provided visual evidence that Wordscores can broadly discern speeches in favor of a given bill from those in opposition. This section pushes the analysis by testing whether speech scores can reliably predict voting behavior. To be clear, I am not claiming that speech causes votes. Rather the purpose is to more rigorously test whether the dif- ferences in symbol-usage captured by Wordscores are good indicators of each legislator’s attitude.

To gain even more confidence that the symbols used in congressional speech are reliable indicators of policy attitude, I also include each legislator’s_dw-nominate1st-dimension

ideal point score as a predictor of how they voted on the Estate Tax. First, this sets up a more difficult test for the Wordscores estimates because legislators’ nominate scores were strongly associated with how they voted on repealing the Estate Tax. In 2002, 2003, and 2005, legislator ideal-point estimates on the nominate1st dimension are correlated with votes on the Estate Tax at the .87 level, indicating that this vote largely comported with legislators’ broader voting patterns. Finding that Wordscores estimates are still significantly predictive of votes on the Estate Tax when we control for their broader voting records is even more evidence that language contains unique and reliable information about where political actors stand on specific issues. This also provides another test of the claim that studying speech allows us to identify dimensions of attitude that are specific to the issue at hand. While a legislator’s voting record can indicate a general tendency in their liberal or conservative disposition, there should be some differences between these general tendencies an how they saw this specific issue. Again, the point is not that left-right ideology is irrelevant, but that studying linguistic symbols provides leverage on issue-specific dispositions that shape how legislators behave.

Table 4.1 contains the results of a logistic regression where votes on repeal of the Es- tate Tax in 2002, 2003, and 2005 were modeled as a function of legislator speech scores produced using the 2001 legislative debate as the reference texts and their broader ideo- logical leanings as captured by theirdw-nominate ideal point estimate. As some of the representatives spoke more than once in a given year, or gave speeches in more than one of the years studied, robust standard errors were used because their observations cannot be seen as entirely independent. The data in table 4.1 are a further demonstration that members’ language reveals a great deal about their beliefs. The speech scores were scaled such that larger values should indicate stronger support for the bill eliminating the Estate Tax, so the significant and positive relationship in this model is as expected. nominate scores are scaled with larger values indicating a more conservative voting record, so the positive relationship with supporting repeal of the Estate Tax is also as expected. More- over, table 4.1 demonstrates that speech carries unique information about the attitudes of legislators about particular bills that is not captured by their broader ideological voting

Table 4.1: Predicting Vote for Repealing the Estate Tax based on Member Ideology and Language used in Floor Speech

Predictor Coefficient Odds

(robust s.e.) Ratio

Pro-Repeal Language 16.43∗∗ 13,700,000.00∗∗ (3.65) (50,000,000.00) Conservatism 7.66∗∗ 2123.00∗∗ (2.65) (5632.00) 2003 dummy 1.79 5.97 (1.49) (8.91) 2005 dummy 3.24∗ 25.63∗ (1.30) (33.48) Constant −10.78∗∗ (2.06) pseudo-r2 .90 N=173 Clusters (Member) = 108 *=p<.05; **=p<.01

Note: Unit of analysis is each Representative’s vote on repealing the Estate Tax in 2003, 2003, and 2005. Dependent variable coded: 1 = member voted for repeal, 0 = member voted against repeal. Data limited to those Representatives who gave a speech during floor debate. Language score created using previous legislative speech to anchor wordscores analysis of later debates: higher scores indicate that members’ language was similar to previous speeches that opposed the Estate Tax. Conser-

vatism captured by dw-nominate 1st dimension estimate: higher values indicate

more conservative voting record.

patterns. Even for an issue that fell quite cleanly along ideological lines, language cap- tures differences of opinion that are specific to the issue at hand. Wordscores appears to be capturing a dimension in speech that, while related to legislator ideology, is also more proximate to this issue.

Table 4.2 shows that speech scores are also good predictors of voting behavior in the debate over China’s Most-Favored Nation trade status. Here the observations are speeches and votes in 1999 and 2001 using congressional debate from 1998 as the reference texts. Larger speech estimates should indicate greater support for good trade relations with China, which is borne out by these data. Because this issue did not fall along ideological or partisan lines, legislators’nominatescores have been dropped from this analysis. Once again, these results support the expectation that attitudes about specific issues translate into consistent symbolic choices. Regardless of whether an issue falls along ideological

Table 4.2: Predicting Vote for Granting China Most-Favored Nation Trade Status a Function of Language and Member Ideology

Predictor Coefficient Odds

(robust s.e.) Ratio

Pro-Trade Language 11.80∗∗ 132,971.00∗∗ (2.22) (296,055.00) Member Conservatism −.14 .87 (.61) (.53) 2001 dummy .25 1.23 (.45) (.57) Constant −5.52∗∗ (1.11) pseudo-r2 .51 N=146 Clusters (Member) = 102 **=p<.01

Note: Unit of analysis is each Representative’s vote on granting Most-Favored-Nation trade status to China in 1999 and 2001. Dependent variable coded: 1 = member voted to extend most-favored-nation trade status, 0 = member voted against most-favored- nation trade status. Data limited to those Representatives who gave a speech during

floor debate. Language score created using previous legislative speech to anchor

wordscores analysis of later debates: higher scores indicate that members’ language was similar to previous speeches that opposed the Estate Tax. Conservatism captured by dw-nominate1st dimension estimate: higher values indicate more conservative voting record.

lines or not, the language elites use to defend their positions provide a good basis for inferring where they stand. Overall, this section provides even more concrete evidence that language is a window into the attitudes of political actors. The next section will show that these patterns in language are also present in discourse that occurs outside of Congress.