Abstract
We examine the hypothesis that corruption in a country negatively influences the macroeconomy through an increase in the country-specific interest rate (interest rate shock). An empirical study estimated the contribution of the interest rate shocks to the variance in output growth at 5.1% in Mexico within the framework of stochastic growth models for small open economies. We replicate this study with the same dataset and investigate which parameters affect the contribution of the interest rate shocks to business cycles. Then, we estimate the same model for different emerging economies to investigate the relationship between the corruption level and macroeconomic contribution of the interest rate shocks. For this purpose, we use Transparency International’s Corruption Perceptions Index (CPI) to measure the corruption level. Finally, we investigate the correlation between the CPI and the estimated series of the interest rate shock. Our findings are as follows. First, the average size of the interest rate shocks is positively associated with the contribution of these shocks to the variability of output growth. Second, the average size of the interest rate shocks is also positively associated with the corruption level. Third, the estimated interest rate shock and the corruption level are positively correlated with each other. As we treat the corruption level as an exogenous variable in the model, these findings lead us to accept the hypothesis. The “Appendix” further clarifies a well-known hypothesis that the cycle is the trend in an emerging economy.
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Notes
Viewed in December 2016. http://www.transparency.org/cpi2015.
The prediction that corruption decreases investment is consistent with some empirical studies. Mauro (1995) argues that Bangladesh can increase its investment rate by approximately five percentage points by improving its bureaucratic efficiency to the level of Uruguay. Wei (2000) shows that the effect of an increase in corruption from the level of Singapore to that of Mexico on an inflow of foreign direct investment is equivalent to a tax increase of eighteen to fifty percentage points.
A stationary autoregressive process of the first order can be written as a moving-average process of the infinite order. Therefore, the autoregressive process assumed in Eq. (10) is more flexible in describing the dynamics of the interest rate shock than it might appear.
The dataset is available at https://sites.google.com/site/andresfernandezmartin8/research. Viewed on 31 October 2016.
The sources of quarterly data on GDP and its components and the annual data on population are the OECD and the World Bank, respectively. The annual data are interpolated by spline interpolation. The sample periods for ARG, BRA, CHL, COL, KOR, TUR and ZAF are 2004Q2–2016Q2, 1996Q2–2016Q3, 1995Q2–2016Q3, 2000Q2–2016Q3, 1994Q1–2016Q3, 1998Q2–2016Q3 and 1995Q1–2016Q3, respectively.
The quarterly series of \(\epsilon^{R}\) is converted to annual data by taking the average of four quarters in each year.
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Acknowledgements
The author would like thank an anonymous referee for constructive comments. The author would also like to thank the Japan Society for the Promotion of Science (JSPS) for financial support. This research is supported by the JSPS KAKENHI Grant Number 16K03683. The author would like to thank Enago (www.enago.com) for the English language review.
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Appendix
Appendix
A well-known hypothesis in the literature states that the cycle is the trend in an emerging economy. Aguiar and Gopinath (2007) estimate a stochastic growth model driven by productivity shocks for Canada and Mexico, finding that the contribution of permanent productivity shocks to the variance in output growth is greater in Mexico than in Canada. As a permanent productivity shocks generates a stochastic trend, this finding implies that a stochastic trend is a primary force driving business cycles in an emerging economy like Mexico. However, García-Cicco et al. (2010) augment the model by considering not only productivity shocks but also non-productivity shocks and estimate the augmented model for Argentina over a long sample period of 1900–2005. They find that permanent productivity shocks account only for a small proportion of the variance in output growth, which leads them to reject the hypothesis that the cycle is the trend. Chang and Fernández (2013) reject the same hypothesis by estimating models like the one laid out by García-Cicco et al. (2010) with the dataset used by Aguiar and Gopinath (2007).
The fourth section estimates the benchmark model of Chang and Fernández (2013) for eight emerging economies. Table 9 shows the forecast-error variance decompositions for a 40-quarter horizon. Temporary productivity shocks account for the largest proportion of the variance in output growth in six of the eight countries, which is consistent with the rejection of the hypothesis. In contrast, permanent productivity shocks account for 84.6 and 66.7% of the variance in output growth for COL and ZAF, respectively. The large contribution of permanent productivity shocks to the variance in output growth supports the hypothesis. Not surprisingly, the choice of the sample affects whether we can accept or reject the hypothesis that the cycle is the trend in an emerging economy.
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Suzuki, T. Corruption, interest rates and business cycles: comparison of emerging economies. Econ Change Restruct 51, 303–316 (2018). https://doi.org/10.1007/s10644-017-9206-5
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DOI: https://doi.org/10.1007/s10644-017-9206-5