Abstract

Scholarship on the relationship between national political leadership and economic development has not received the attention it deserves. In the large volume of literature on economic growth, there is only limited examinations on the relationship between political leaders and elites and its implications for economic growth and development. This research attempts to fill this gap in current scholarship by analyzing leaders’ characteristics in shaping the patterns of economic growth in several national settings. Because the COVID-19 pandemic has further underscored the importance of political leadership in addressing national (and global) challenges to societal well-being and security, studying national leaders and its implications are more important and meaningful than ever before.

In the field of political economy, scholars generally use mathematical and statistical models to analyze data and to test hypotheses about the results. This research intends to analyze the impact of leaders on economic growth by using leader data from the Archigos (Goemans et al. 2009) database from 1835 to the end of 2015. The data is modelled by the AutoGluon model developed by Amazon through machine learning, deep learning. AutoML and AutoGluon automatically extract features from the data and then uses multiple classifiers to train the data. We will use models to evaluate the impact of leaders on economic growth.

Biography of speaker

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