Dmitriy Skougarevskiy

The New Gold Standard? Empirically Situating the Trans-Pacific Partnership in the Investment Treaty Universe

The Trans-Pacific Partnership (TPP) has been labeled a ‘new, high-standard trade agreement’. But just how ‘new’ and ‘high’ are the standards it sets? To answer that question we combine traditional legal analysis with computational text comparisons situating the TPP in the universe of international investment agreements (IIAs). We find that the TPP investment chapter offers few truly novel features — 81% of its text is taken from prior American treaties. Compared to the majority of IIAs, however, the TPP goes beyond existing practice: it sets high levels of investment protection, explicitly safeguards host state sovereignty and establishes a sophisticated investment arbitration architecture. Nevertheless, the TPP is unlikely to revolutionize the IIA universe. Its innovations are open to circumvention given that older treaties remain in force parallel to the TPP. Moreover, as disagreement persists with Europe and BRICS countries, the TPP is unlikely to serve as a template for future multilateralization.

Text of Trade Agreements (ToTA)-A Structured Corpus for the Text-as-Data Analysis of Preferential Trade Agreements: Text of Trade Agreements

With multilateral negotiations at the World Trade Organization (WTO) in deadlock, rulemaking on international economic governance has shifted to preferential trade agreements (PTAs). To facilitate the scholarly investigation of the fast�?growing universe of PTAs, this article introduces a machine�?readable and structured full text corpus of 448 WTO�?notified trade agreements stored on a Github repository—the Text of Trade Agreements (ToTA) corpus. The article (1) provides a summary analysis of the ToTA corpus, (2) illustrates how text�?as�?data techniques can be used to investigate PTA design using ToTA, including through an interactive website accompanying this research, and (3) concludes with an overview of research applications involving this PTA text corpus in economics, political science, and law. The current codebook is attached herein as an appendix. The dataset, codebook, and code, as updated, are available online at the Github website.

National or European Politicians? Gauging MEPs Polarity When Russia is Concerned

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.