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Lizhi Liu

Lizhi Liu

Winter 2015 Graduate Fellowship Recipient
Stanford Center on Global Poverty and Development


Lizhi Liu is a PhD candidate in political science with a primary research interest in Chinese political economy and international relations. She also holds a master degree in statistics. Her dissertation examines the political and social implications of the economic side of the Internet – eCommerce. More specifically, she studies how the rise of eCommerce affects state-business relations and local governance structure.

Fellowship research abstract

The Political Economy of eCommerce Growth in China

Alibaba Group, the Chinese eCommerce giant, filed to sell up to $24.3 billion in stock in September, 2014, making it the largest U.S. initial public offering (IPO) in history. Underpinning this remarkable event is China’s extraordinary growth in eCommerce over the past decade. By the end of 2014, China was already the world’s biggest online market, with annual sales as high as $450 billion and 332 million online shoppers. Given the sharp distinction between eCommerce and traditional business, my research project aims to answer the following questions: how do state-business relations evolve as eCommerce thrives and expands in China? What does this change mean to authoritarian resilience? Does the rapid growth of eCommerce curtail or reinforce state authority? I propose a general model of clientelism and connectivity to study the impact of eCommerce on state-business relations. Based on this model, I look into two aspects of state-business relations. The first aspect pertains to the regional variations in government support for eCommerce. The second aspect concerns the causal impact of eCommerce on merchant support for the regime. More specifically, I examine whether adopting eCommerce stimulates political participation and how it changes merchants’ attitudes towards the local state. To empirically test the implications of my theoretical framework, I will employ a multi-method approach, combining case studies and large-N quantitative analysis.