@article{JankinBaturo, author = {Jankin, Slava and Baturo, Alexander}, title = {Blair Disease? Business Careers of the Former Democratic Heads of State and Government}, series = {Public Choice}, volume = {166}, journal = {Public Choice}, number = {3-4}, issn = {0048-5829}, doi = {10.1007/s11127-016-0325-8}, pages = {335 -- 354}, abstract = {Examining the careers of democratic heads of state and government from 1960-2010, we find that one in every seven turns to the private sector after office. Distinguishing between the factors that attract leaders to business and those that render leaders attractive, we find that the global CEO compensation rates, cultural norms, having served in office in Anglo-Saxon countries as well as their personal background, matter. We also find that certain economic outcomes and policies in office such as economic growth and reduction in state spending are often associated with post-tenure business careers. We do not find evidence, however, that leaders are able to implement policies with future careers in mind, which would in turn raise concerns over accountability.}, language = {en} } @article{JankinBenoitConwayetal., author = {Jankin, Slava and Benoit, Kenneth and Conway, Drew and Laver, Michael}, title = {Crowd-Sourced Text Analysis: Reproducible and agile production of political data}, series = {American Political Science Review}, volume = {110}, journal = {American Political Science Review}, number = {2}, issn = {0003-0554}, doi = {10.1017/S0003055416000058}, pages = {278 -- 295}, abstract = {Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of non-experts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers' attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.}, language = {en} }