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The Determinants of FDI Choices: The Importance of Investment Climate and Clustering

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Applied Regional Growth and Innovation Models

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

Recent work on factors that determine the choices made by foreign investors within and across countries has been growing rapidly – see Mukim and Nunnenkamp (2012) for a comprehensive review. However, the cross-country literature on how the investment climate affects decisions made by foreign investors using robust methodological approaches is lacking. Indicators of foreign investment regulation that has been standardised across countries could lend itself for different types of empirical analysis. This paper will use data made available by the Investing Across Borders database of the World Bank Group, to study how the investment climate across sectors and countries affects the choice of new investment projects across different countries.

This paper was written when the author was a Research Fellow at Columbia University. This research has been conducted and funded as part of the World Bank Group’s Investing Across Borders project. The author is grateful to Kusi Hornberger, John Anderson, Murat Seker and two anonymous referees for helpful comments and suggestions, and to Ray Mataloni of the US Bureau of Economic Analysis for providing access to US Foreign Affiliate Data.

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Notes

  1. 1.

    Owing to lack of data on firm-level inputs and outputs, or measures of productivity, this paper will be unable to test the predictions of their model.

  2. 2.

    Guimaraes et al. (2003) provide an overview of the problems and how different researchers have attempted to deal with them in the past.

  3. 3.

    More formally, this implies that the \( {\varepsilon_{ijk }} \)s are independent across individual investors and choices; all locations would be symmetric substitutes after controlling for observables.

  4. 4.

    The database also includes project investments made across states in the US – these are also excluded from the analysis.

  5. 5.

    The bilateral distance data is described and made available by the CEPII on its website: http://www.cepii.fr/anglaisgraph/bdd/distances.htm

  6. 6.

    To verify that the large number of zeroes do not reflect an underlying always-zero population, I also compute zero-inflated models and find that the results are markedly similar to those of the negative binomial model.

  7. 7.

    This also implies a unit percentage change for regressors that are in logarithms of the original independent variables.

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Author information

Authors and Affiliations

  1. World Bank, 1818 H Street NW, 20433, Washington, DC, USA

    Megha Mukim

Authors
  1. Megha Mukim
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Corresponding author

Correspondence to Megha Mukim .

Editor information

Editors and Affiliations

  1. Department of Spatial Economics, VU University, Amsterdam, The Netherlands

    Karima Kourtit

  2. Department of Spatial Economics, VU University, Amsterdam, The Netherlands

    Peter Nijkamp

  3. Building and Planning Level 5 Architecture Building, University of Melbourne Faculty of Architecture,, Parkville, VIC, Australia

    Robert Stimson

Appendix: Industry Concordances

Appendix: Industry Concordances

IAB

FDI markets

BEA

Agriculture & forestry

 

1110; 1120; 1130; 1140; 1150

Banking

Financial services

5221; 5223; 5224; 5231; 5238; 5242; 5243; 5249; 5252

Construction, tourism and retail

Building & construction materials; hotels & tourism

4410; 4420; 4431; 4440; 4450; 4461; 4471; 4480; 4510; 4520; 4530; 4540; 2360; 2370; 2380

Electricity

Alternative/renewable energy

2211; 2212; 3336

Health and waste

Healthcare

2213; 5620; 6210; 6220; 6230; 6240

Light manufacturing

Automotive components; beverages; biotechnology; business machines & equipment; ceramics & glass; chemicals; consumer electronics; engines & turbines; food & tobacco; industrial machinery, equipment & tools; medical devices; paper, printing and packaging; pharmaceuticals; plastics; rubber; semiconductors; textiles; wood products

3111; 3112; 3113; 3114; 3115; 3116; 3117; 3118; 3119; 3121; 3122; 3130; 3140; 3150; 3160; 3210; 3221; 3222; 3231; 3256; 3259; 3322; 3326; 3254; 3261; 3262; 3271; 3272; 3273; 3274; 3279; 3311; 3312; 3314; 3315; 3321; 3323; 3324; 3325; 3327; 3328; 3329; 3331; 3332; 3333; 3334; 3335; 3336; 3342; 3343; 3344; 3345; 3346; 3351; 3352; 3353; 3361; 3362; 3363; 3364; 3365; 3366; 3369; 3370; 3391; 3399

Media

Leisure & entertainment

5111; 5112; 5121; 5122; 5151; 5152

Mining, oil and gas

Coal, oil and natural gas; metals; minerals

2111; 2121; 2123; 2114; 2115; 2116; 2117; 2132; 2133; 3244; 3243; 3242; 4863; 4868

Telecommunications

Communications; software & IT services

5171; 5172; 5174; 5179; 5182; 5191

Transportation

Aerospace; transportation

3361; 3363; 3364; 3365; 3366; 3369; 4810; 4821; 4833; 4839; 4850; 4863; 4880

  1. Notes: IAB investing across borders, BEA bureau of economic analysis

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Mukim, M. (2014). The Determinants of FDI Choices: The Importance of Investment Climate and Clustering. In: Kourtit, K., Nijkamp, P., Stimson, R. (eds) Applied Regional Growth and Innovation Models. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37819-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-37819-5_4

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