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Institutions and FDI from BRICS countries: a meta-analytic review

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Abstract

Emerging multinational corporations are grown-up from environments where institutional quality (IQ) is generally poor and market mechanisms are inefficient. Having experience at home, firms from emerging markets have transformed institutional disadvantages into advantages, especially through investing in developing countries. In terms of investing abroad, is the host country's institutional quality a point of concern for emerging multinational corporations? Despite the economic benefits of well-organized institutions, several studies empirically tested the role of institutional quality on foreign direct investment (FDI) from emerging markets to provide inconclusive results. This study conducted a meta-analysis and empirical examination of the IQ-FDI nexus from five emerging markets, namely Brazil, Russia, India, China, and South Africa, collectively referred to as BRICS countries. We found that emerging multinational corporations invested in risky markets where institutional quality is generally modest. Our results suggest that state-owned enterprises persuasively invested in Africa for natural resources when they evaluate a positive and significant effect size of IQ on FDI. We also found that collected estimates contain genuine evidence concerning the effect of a host country’s institutional framework on FDI from BRICS countries.

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Notes

  1. Recent data from the United Nations Conference on Trade and Development (2018) lists China as the seventh largest source of FDI in the world (the Russian Federation being the twelfth) from 2000–2017. There is also significant growth in outward FDI from other emerging markets such as India and Brazil.

  2. PCC ranges from − 1.0 to + 1.0 where, in the case of this study, − 1.0 (+ 1.0) represents a 100% negative (positive) association between institutional factors and FDI from emerging markets given a set of control variables. For instance, if the value of PCC is 0.1, it means that IQ explains about 10% of the OFDI variance, holding constant other variables. PCC values are rarely reported directly in primary studies; therefore, we computed PCC values by using Eq. (1), as well as the t-statistics and degrees of freedom reported in each primary study in the sample (Greene, 2008).

  3. It should be noted that meta random-effects and meta fixed-effects are different from the longitudinal/panel meta random-effects or panel fixed-effects models.

  4. The last two estimators are based on longitudinal/panel meta random-effects and panel fixed-effects models and estimated through xtreg command in Stata15.

  5. We are thankful for anonymous reviewers who drew our attention to these non-linear techniques to test publication selection bias.

  6. They suggest that WAAP have smaller bias estimates than RE when the results of some of the selected studies are positive and statistically significant (i.e., selective reporting). If there is no selective reporting, the difference between RE’s and WAAP’s statistical properties is practically negligible.

  7. According to Cohen’s (1988) guideline, if the absolute value of a partial correlation coefficient is 0.1 or less, it is considered a “small effect.” Similarly, the effect is “medium” if PCC values are between 0.3 and 0.5, and “large” when PCC values are greater than 0.5.

  8. Ho in the PEESE test is that the coefficient of the inverse of the standard error of PCC β1 is zero.

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Acknowledgements

This research was financially supported by Cape Breton University grant number [RISE8173]; a grant-in-aid from the Japan Society for the Promotion of Science (JSPS KAKENHI Grant Number 20H01489); the Zengin Foundation for Studies on Economics and Finance; and the Japan Center for Economic Research. We thank Hristos Doucouliagos, Tom D. Stanley, and participants of MAER-NET 2018 Colloquium at Deakin University, Melbourne, Australia October 4–5, 2018, for their helpful comments and suggestions on an earlier draft of the paper. We also would like to thank Tammy Bicket and Cindy Butler for their editorial assistance. The usual disclaimer applies.

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  1. Shannon School of Business, Cape Breton University, Sydney, NS, Canada

    Amar Anwar

  2. International SEPT Competence Center, Universität Leipzig, Leipzig, Germany

    Amar Anwar

  3. Institute of Economic Research, Hitotsubashi University, Tokyo, Japan

    Ichiro Iwasaki

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Appendix A1

Method for evaluating the quality level of a study

To determine the quality level of the studies subjected to our meta-analysis, we used the ranking of IDEAS and Thomson Reuters Impact Factor. IDEAS, the largest bibliographic database, provides a ranking of economics journals and working papers. The list is freely available at http://ideas.repec.org. For our meta-study, we divided journals into 10 clusters and assigned each of these journal clusters a score (weight) from 1 (the lowest journal cluster) to 10 (the highest). Thomson Reuters Impact Factor was used for academic journals not ranked by IDEAS. We referred to Thomson Reuters Impact Factor and other journal rankings and identified the same level of IDEAS ranking-listed journals that correspond to these non-listed journals; we assigned each of them the same score as its counterparts. Meanwhile, for academic books and book chapters, we assigned a score of 1 in principle, but if at least one of the following conditions was met, each of the relevant books or chapters uniformly received a score of 4, which is the median value of the scores assigned to the above-mentioned IDEAS ranking-listed economics journals. The ranking process is as follows: (1) The academic book or book chapter clearly states that it has gone through the peer-review process; (2) its publisher is a leading academic publisher that has external evaluations carried out by experts; or (3) the research level of the study has been evaluated by the authors to be high.

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Anwar, A., Iwasaki, I. Institutions and FDI from BRICS countries: a meta-analytic review. Empir Econ 63, 417–468 (2022). https://doi.org/10.1007/s00181-021-02145-w

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