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Bee Yan Aw

Researcher at Pennsylvania State University

Publications -  44
Citations -  5032

Bee Yan Aw is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Productivity & Total factor productivity. The author has an hindex of 27, co-authored 44 publications receiving 4808 citations. Previous affiliations of Bee Yan Aw include World Bank.

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Productivity and Turnover in the Export Market: Micro-level Evidence from the Republic of Korea and Taiwan (China)

TL;DR: In this article, the authors analyzed the link between a producer's total factor productivity and its decision to participate in the export market, using manufacturing data from the Republic of Korea and Taiwan (China).
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R&D Investment, Exporting, and Productivity Dynamics

TL;DR: In this paper, a dynamic structural model of a producer's decision to invest in R&D and export, allowing both choices to endogenously affect the future path of productivity using plant-level data for the Taiwanese electronics industry, was found to have a positive effect on the plant's future productivity.
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Productivity and the export market: A firm-level analysis

TL;DR: In this article, an empirical model is developed to distinguish the roles of resource-level differences from productivity differences in explaining output differences between exporters and non-exporters of Taiwanese electronic products.
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Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing

TL;DR: In this article, the authors measured total factor productivity for entering, exiting, and continuing cohorts of firms and quantified the contribution of firm turnover to industry productivity improvements in Taiwan's manufacturing sector.
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Export Market Participation, Investments in R&D and Worker Training, and the Evolution of Firm Productivity

TL;DR: In this article, the effect of these investments on the firm's future producti veity trajectory is modelled while controlling for the selection bias introduced by endogenous firms, and the authors propose a probabilistic model that recognises the interdependence of the decisions.