The Promise of Topic Modeling for Studying Political Leaders' Role for Economic Performance


This paper provides empirical evidence for the role of political leadership in economic performance. Although “good political leadership” is often cited as important, empirical tests are suspect because good leadership is typically an ex-post conclusion. We posit that good leaders are committed to economic performance and that this commitment will be evident in their public announcements. Taking advantage of recent developments in machine learning, we apply Latent Dirichlet Allocation (LDA) to quantify the priorities expressed in U.S governors’ State of the State Addresses. We validate the approach by showing that these thematic contents mirror objective measures of actual future state budgets. More importantly, we find strong evidence that consistency on priorities predicts measures of economic performance. The approach developed and expounded upon in this paper shows that a leader’s commitment to economic performance can be measured objectively and that this commitment has real and measurable consequences. (JEL C38, O21, P16, R50, H52)
Keywords: Political leadership; Development; Economic Performance; Topic modeling; Canonical Correlation Analysis; State of the State Addresses.