Project Details
Description
"Since the adoption of the reform and opening-up policies in the late 1970s, an increasing number of Chinese students have traveled abroad to study. At the same time, the Chinese government has set up various programs to promote the return of these highly skilled migrants. The overseas returnees were hired by universities, governments, and businesses. Nicknamed “haigui” or “sea turtles,” they brought new knowledge, technologies, managerial skills, and international academic and business networks that were not available in China. How do Chinese firms benefit from the arrival of overseas returnees? In recent years, China is in ransition from a manufacturing center to a global innovation hub. How much did the returnees contribute to this process? Our proposed project will investigate these topics from three angles: performance, innovation, and internationalization of firms. We also plan to perform a heterogeneous analysis based on different types of Chinese firms and different types of returnees. The first challenge to implementing this research design is that firm-level data usually do not include employment information about returnees. We plan to overcome this by adopting a big data approach using information from LinkedIn, an online platform used for professional networking and career development. Professionals and students who seek networking or new job opportunities often open an account on LinkedIn. Because LinkedIn has information on more than 500 million high-skilled individuals around the world, we will be able to identify Chinese returnees by tracking their overseas study and work experience and their work experience in China. We will then merge the relevant firm names in LinkedIn with the Orbis and other databases, which provide production, patent, exports and outward foreign investment information on more than one million Chinese industrial firms.
The second challenge is reverse causality. Returnees may choose to join firms with future growth potential. A simple correlation between the arrival of returnees and subsequent firm growth will not imply causality. We will solve this problem using a shift-share type instrumental variable approach. We will use the local economic conditions in a foreign country to model the outmigration probability of overseas Chinese in that country. We will then use a predetermined migration network to translate the outflow of returnees from foreign countries into inflow to Chinese cities and firms. Th instrument variable approach will be able to solve the reverse causality problem as long as the instruments are relevant and exogenous."
The second challenge is reverse causality. Returnees may choose to join firms with future growth potential. A simple correlation between the arrival of returnees and subsequent firm growth will not imply causality. We will solve this problem using a shift-share type instrumental variable approach. We will use the local economic conditions in a foreign country to model the outmigration probability of overseas Chinese in that country. We will then use a predetermined migration network to translate the outflow of returnees from foreign countries into inflow to Chinese cities and firms. Th instrument variable approach will be able to solve the reverse causality problem as long as the instruments are relevant and exogenous."
Status | Active |
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Effective start/end date | 1/07/22 → 1/12/24 |
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