Testing veto players theory in parliamentary systems requires researchers to have accurate estimates of policy preferences of political parties. In empirical studies, however, political scientists use remarkably context-free and invariant estimates of party positions. As a consequence, they must make assumptions about the nature of policy conflict. In the extreme, the lack of accurate estimates reduces sample sizes and increases the risk of selection bias in studies that use the ideological range among veto players as a key explanatory variable. We introduce a new automated method to identify policy relevant content in political texts. Our method “smart tags” sentences in election manifestos using portfolio-specific keywords from a legislative database. Policy positions can then be estimated on the tagged subset of relevant sentences. We apply our method to analyze the legislative positions of political parties for several portfolios in the German parliament. We show that these estimates vary over time and across portfolios – variation which cannot be produced using existing methods. We believe our method simplifies preference estimation and increases construct validity of policy preference measures used to evaluate policy-focused theories.
