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Domestic interest rate, foreign direct investment, and corruption

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Abstract

It has been argued that foreign direct investment can exert upward or downward pressure on the domestic interest rate depending on foreign investors’ relative weights on internal and external finance with respect to the domestic economy. Additionally, a country’s level of corruption can influence firms’ ability to obtain external finance. We find that across countries a 1 percent increase in FDI inflows (outflows) is more likely to reduce the domestic interest rate by as much as 0.7 (1) percent. This empirical association between domestic interest rates and FDI flows is non-monotonically contingent on a country’s level of corruption.

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Notes

  1. See, for e.g., Calvo et al. (1996), Sun (2002), Agosin and Machado (2005), Magud et al. (2014), Harrison and McMillan (2003), Harrison et al. (2004).

  2. Using data from over 135,000 manufacturing firms in 135 countries from 2005 to 2014, the Enterprise Surveys record different qualifiers that signal foreign firms and banks in host countries do engage in credit-based interactions. For example, of all foreign firms surveyed, an average of approximately 90 percent have a checking or savings accounts, 35 percent have a bank loan or line of credit, 25 percent use banks to finance investments, and 29 percent use banks to finance working capital. These averages are from the authors’ computations.

  3. Different international organizations have conducted surveys to assess the factors that impinge on investors’ decision to undertake business in their respective host countries. A country’s level of institutional uncertainty is the common denominator across the principal subsets of factors identified by these surveys. For example, in 1996, a World Bank survey of 3685 firms in 69 countries reveals that (of 15 obstacles) corruption ranks as the second most important obstacle to business worldwide (Brunetti et al. 1997, p. 59). In 2007, the Worldwide Survey of Foreign Affiliates, conducted jointly by UNCTAD and the World Association of Investment Promotion Agencies, involving 96 chief executive officers of foreign affiliates around the world were asked to indicate the policy area that governments should improve upon to render their locations more attractive to FDI; the most important policy area was the regulatory and institutional environment—which includes curbing corruption (UNCTAD 2007).

  4. In welfare economics, a first-best equilibrium is a competitive market equilibrium free from market distortions, whereas a second-best equilibrium is the market equilibrium when distortions are present. The traditional view of universal benefit of a reduction in corruption is a first-best view because this perspective fails to account for many market imperfections or distortions that are present across countries. Taking these distortions into account requires one to recognize that under certain circumstances corruption need not have a monotonic effect on the FDI-interest rate relationship. A second-best view recognizes that reductions in corruption need not be optimal in light of other market frictions.

  5. Detailed summaries of the FDI-corruption literature with evidences of nonmonotonic interactions are included in Cuervo-Cazurra (2008), Brada et al. (2012), and Brada et al. (2017), and the references cited therein.

  6. Given the structure of Eq. (2.1), readers should note that \(\gamma _j(Z_{it}) : \mathbb {R}^p \rightarrow \mathbb {R}\) for \(j=0,1,2,3\) but \(\gamma _j(Z_{it}) : \mathbb {R}^p \rightarrow \mathbb {R}^l\) for \(j=4\).

  7. The wild bootstrap that we use to estimate the standard errors mitigates, among other things, imprecision of the estimates that may arise as a result of estimation using a small sample.

  8. Note that many of these explanatory variables have been linked theoretically to FDI flows (see, for example, Calvo et al. 1996), and some variables such as domestic investment and economic growth, have been found to be correlated empirically with FDI flows across countries; thus, these explanatory variables should mitigate omitted variable bias in the estimated effects of FDI on the lending rate.

  9. The first year that this corruption index is available is 1984; hence, the availability of the corruption index places a restriction on the beginning year of our analysis.

  10. It has been documented (e.g.,World Bank 1997a, b; UNCTAD 2000) that governments may try to attract FDI by offering tax credits, infrastructure subsidies and import duty exemptions to foreign investors.

  11. Using only discrete variables in Z reduces our local-linear least squares smooth coefficient estimator to a local-constant least squares smooth coefficient estimator. We refer to Li and Racine (2007) for technical details.

  12. We highlight the stability of the dynamic specifications in both Tables 2 and 3, which can be deduced from the magnitude and statistical significance of the reported percentiles for the one-period lagged interest rate regressor.

  13. The thirteen countries are Albania, Angola, Azerbaijan, Bahrain, Bolivia, Cameroon, Kenya, Kuwait, Latvia, Libya, Malawi, Mexico and Poland.

  14. This graph can be retrieved from the appendix.

  15. A careful analysis of the relationship between FDI inflows coefficient estimates and partial effects reveals no dependence between the type and magnitude of the FDI inflows coefficient estimates and those of their partial effects.

  16. A careful analysis of the relationship between FDI outflows coefficient estimate and partial effects shows no general association between the type and size of the FDI outflows coefficient estimates and those of their partial effects.

  17. This joint kernel density can be retrieved from the appendix.

  18. The kernel densities coupled with the density equality test of Li et al. (2009), as well as significance plots, for these coefficients all reveal that the densities of estimated coefficients for these explanatory variables are statistically different from their counterparts in Table 3.

  19. For the intial-valued regressions our primary results remain largely unchanged, indicating that our main results are not driven by substantial biases.

  20. We construct exchange rate volatility following (Clark et al. 2004) as the standard deviation of the first difference of the logarithm of the bilateral real exchange rate in the five preceding years; this variable is from the IMF’s International Financial Statistics. The additional TI and WGI regressions have smaller sample sizes relative to our main model due to their initial coverage years.

  21. We also consider different measures of institutional uncertainty from the ICRG index; specifically, we replace corruption in our main model (one at a time) with Investment Profile, Law and Order, Democratic Accountability, and Bureaucracy Quality. Though there are some differences in these heterogeneous estimates, the overall qualitative features of these distributions are remarkably similar across measures of institutions. Our general conclusion of non-monotonic effects of corruption carry over into other measures of institutional quality.

  22. These are widely used proxies for financial and economic development in the empirical literature; moreover, in pooled data of developed and developing countries, such as our data, a strong correlation between corruption and each of these proxies is an empirical regularity.

  23. This seemingly ad-hoc choice set is driven by data paucity. However, the interest rates that we do not control for are highly correlated with those in this set.

  24. In China, Héricourt and Poncet (2009) show that joint ventures between foreign and private domestic firms is a source of finance for the latter in the presence of legal and financial domestic obstacles.

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  1. Department of Economics, The University of the West Indies at Mona, Kingston 7, Jamaica

    Nadine McCloud

  2. Department of Agricultural Economics, Purdue University, West Lafayette, IN, 47907-2056, USA

    Michael S. Delgado

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  1. Nadine McCloud
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Correspondence to Michael S. Delgado.

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McCloud, N., Delgado, M.S. Domestic interest rate, foreign direct investment, and corruption. Rev World Econ 158, 467–491 (2022). https://doi.org/10.1007/s10290-021-00435-0

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