Corruption Distance and Foreign Direct Investment

Abstract

We study the effects of “corruption distance,” defined as the difference in corruption levels between country pairs on bilateral foreign direct investment (FDI). Using a “gravity” model and the Heckman (1979) two-stage framework on a data set of forty-five countries from 1997 to 2007, we find that corruption distance adversely influences both the likelihood of FDI and the volume of FDI. A novel finding in this study is that we identify the asymmetric effect of corruption distance and find that the positive corruption distance, defined as the corruption distance from a high corruption source to a low corruption host country, is the prominent one that affects the behavior of bilateral FDI.

KEY WORDS:

• foreign direct investment
• corruption
• developing countries

Acknowledgments

We thank Jakob de Haan, Hong Ma, participants of Tsinghua University winter meeting 2012, seminar participants in the University of California, Merced, and SUNY Buffalo State, and two anonymous referees, for their comments and suggestions.

Notes

1. The 2008 global financial crisis caused a pronounced drop on FDI flows that just started to recover by 2010 when around $1.24 trillion were registered (UNCTAD STAT).

2. See for example, Blonigen (2005) for a review.

3. For example, Johanson and Wiedersheim-Paul (1975) and Johanson and Vahlne (1977, 1990).

4. Ghemawat (2001) suggests four dimensions of distance, namely cultural, administrative, geographic, and economics. The corruption distance is one type of administrative distance.

5. Industrial countries include Australia, Austria, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States. Developing countries are Algeria, Argentina, Azerbaijan, Brazil, Bulgaria, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Kazakhstan, Korea South, Malaysia, Mexico, Nigeria, Pakistan, Peru, Philippines, Poland, Russia, Slovak Republic, Thailand, Turkey, Venezuela, and Vietnam.

6. The main results do not differ significantly when we exclude from our analysis the missing observations. Those results are available from authors upon request.

7. For a detailed description of the ICRG data see Knack and Keefer (1995).

8. The inverse Mills ratio is given by the probability density function over the cumulative distribution function estimated in the first stage, which includes both zero and non-zero observations. Intuitively, the ratio captures the effect of truncating the sample and is included to control for selection biases in the second stage regression, which uses only positive (but not “zero”) FDI observations.

9. The fixed effect specification would generate biased estimates under the censored specification (Greene 2004a, 2004b).

10. For a review of the literature on trade and FDI, see Blonigen (2005).

11. Ledyaeva et al. (2013), present compelling evidence on the positive effects on commonality of corruption and democracy in source countries with Russian regions.

12. For a discussion of the “South-South FDI” see Aleksynska and Havrylchyk (2013) and the references there in.

Additional information

Funding

Qian gratefully acknowledges the financial support from the School of Natural and Social Sciences at SUNY Buffalo State.