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Data, development, and growth


Economic development and growth theory have long grappled with the consequences of cross-border flows of goods, services, ideas, and people. But the most significant growth in cross-border flows now comes in the form of data. Like other flows, data flows can demonstrate imbalances among exports and imports. Some of these flows represent ‘raw’ data while others represent high-value-added data products. Does any of this make a difference in national economic development trajectories? This paper argues that the answer is yes. After reviewing the core logic of ‘high development theories’ from the twentieth century, I analyze the sometimes implicit applications of these arguments to data as they are evolving in the existing literature. I then put forward a different argument which takes better account of unique characteristics of the political economy that emerges at the intersection of data, machine learning, and the platform firms that use them. I explore the implications of this new argument for some policy choices that governments face with regard to data localization, import substitution, and other decisions relevant to growth in both advanced and emerging economies.

Corresponding author
* Corresponding author: Steven Weber, Professor, School of Information and Department of Political Science University of California Berkeley
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Funding for this work was provided by the Hewlett Foundation via the Center for Long Term Cybersecurity at UC Berkeley; and by the Carnegie Corporation via the ‘Bridging the Gap’ grant program.

Acknowledgments: Thank you to Jesse Goldhammer, Stephane Grumbach, Phil Nolan, AnnaLee Saxenian, Naazneen Barma, Elliot Posner, two anonymous reviewers, and the Editors of Business and Politics for comments and critiques.

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