The European Parliament (EP) is viewed as a normal parliament. Voting patterns of its
members (MEPs) are mainly aligned with transnational political groups, not national
cleavages. Yet, it has been proven by many that MEP voting patterns are an outcome of
conflicting pressures and a distorted indicator of their individual political orientations. In this
study we rely on MEP written questions to the European Commission to measure the policy
positions and their determinants. Using the universe of 100,000+ such questions in 2002–
2015 linked with MEP country and European Political Group affiliation data, we test whether
one issue of high sensitivity to their domestic audiences — Russia — makes the MEPs take
their nationality seriously and pay more attention to it regardless of their transnational
partisan affiliations. We rely on supervised machine learning to uncover sentiment of every
question asked on a negative-positive scale. Then we contrast the sentiment of questions
related to Russia with the rest of questions conditional on party and national affiliation of the
MEP asking the question. We find that (i) MEP question involving Russia is twice as negative
in tonality as an average question, (ii) more variation in modality of Russia-related questions is
explained by MEP national affiliation than her EPG. Our findings are robust to alternative
methods of sentiment extraction and to controlling for time-invariant unobserved
heterogeneity of MEPs.
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