FDI spillover effects in incomplete datasets

• 成果类型: Article
• 署名作者: Eapen, Alex
• 署名单位: Australian National University
• 刊物名称: JOURNAL OF INTERNATIONAL BUSINESS STUDIES
• ISSN/ISSBN: 0047-2506
• DOI: 10.1057/jibs.2013.32
• 发表日期: 2013
• 页码: 719-744
• 关键词: knowledge and productivity spillovers; incomplete datasets; IDENTIFICATION PROBLEMS; Monte Carlo simulation; weighted instrumental variable estimator

摘要

Scholars studying foreign direct investment (FDI) spillovers usually examine whether productivity gains in domestic firms can be attributed to the presence of foreign firms in their industry. However, empirical estimation is often based on datasets that omit certain kinds of firms in the economy. We argue that identifying FDI spillover effects in such incomplete datasets is problematic, owing to measurement error and selection problems. Using Monte Carlo simulations, we show that spillover effect estimates from incomplete datasets are potentially biased. We discuss the theoretical implications of this, and demonstrate a weighted instrumental variable approach that could yield better spillover effect estimates in incomplete datasets.