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Are business cycles in emerging market economies alike?

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Abstract

This paper explores the predictions of real business cycle theory on the roles of total factor productivity (TFP) and financial frictions to explain business cycles in emerging market economies (EMEs). I obtain evidence about TFP, price of capital, risk premium, and collateral constraint shocks by estimating structural vector autoregressions (SVARs) on a Brazilian sample from 1999Q1 to 2018Q4 and a Mexican sample from 1997Q1 to 2018Q4. On each sample, two SVARs are estimated. One SVAR identifies shocks by imposing restrictions on their short-run impact. The other SVAR is grounded on restrictions that shocks have long-run effects on business cycles in EMEs. Estimates of the SVARs show the TFP shock is the main driver of business cycle movements in Brazil and Mexico. However, this evidence is produced by the SVAR under the long-run restrictions, which indicates the identification of shocks matters to the explanation of business cycles in EMEs. Next, the Brazilian and Mexican business cycles are markedly different, as the contributions of shocks to aggregate fluctuations vary across the two countries. Hence, findings of this paper suggest although not all business cycles are alike in EMEs, “the cycle is the trend” view on aggregate fluctuations in EMEs remains valid. The empirical results of this paper are in support of the economic policies that aim to robustify the productivity process of EMEs.

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Notes

  1. The working capital constraint forces firms to borrow internationally to pay for labor in order to produce. As a result, the working capital constraint creates a transmission mechanism in that shocks to international financial markets affect output in a SOE.

  2. See, for example, Itskhok and Moll (2019) and Benguria et al. (2022).

  3. Bianchi and Mendoza (2018) are the representative paper for this macroprudential policy.

  4. This paper had considered including other EMEs. However, there is a availability of data issue for other developing countries that forced them out of the empirical analysis. See Appendix for more details.

  5. Longer Brazilian and Mexican samples are available. However, SVARs estimated on the longer samples produce badly behaved impulse response functions (IRFs) and FEVDs due to non-stationary endogenous variables. Discussions about the sample size are included in the Appendix.

  6. Construction of the world real interest rate follows Neumeyer and Perri (2005) and Uribe and Yue (2006).

  7. See the Appendix for details about the sources and construction of data.

  8. The small sample critical value for the Brazilian trade balance-output ratio equals \(-\)3.160 at the 10% significance level.

  9. The potential of cointegration among the levels of variables is checked by applying the Johansen cointegration tests. The test results show that there are no cointegration among the I(1) variables in the Brazilian and Mexican samples. Refer to the Appendix for more details on cointegration.

  10. For reference, see Akinci (2013); Neumeyer and Perri (2005) and Uribe and Yue (2006).

  11. See, among others, Aguiar and Gopinath (2006); Arellano (2008) and Eaton and Gersovitz (1981).

  12. Mendoza (2010) and Schmitt-Grohè and Uribe (2016) are examples of studying the Kiyotaki and Moore collateral constraint in a SOE-RBC model.

  13. I follow the common practice in the EME empirical literature of estimating SVARs with two lags. See, for example, Akinci (2013); Bruno and Shin (2015) and Kim et al. (2020).

  14. The Appendix details the transformation from real GDP growth, the first difference of the log world real interest rate, the first difference of log Tobin’s Q, and the trade balance-output ratio to real GDP, the world interest rate, Tobin’s Q, and the trade balance.

  15. The remaining IRFs are available in the Appendix.

  16. In their SOE-RBC model, a positive TFP shock loosens the KM collateral constraint that enables a small open economy to borrow more foreign debt. As a result, the demand for capital increases its price.

  17. I interpret point estimates of FEVDs greater than 20% as economically important and confidence bands surrounding the median estimate of a FEVD greater than 40% interval as large uncertainty.

  18. Sensitivity checks using a different lag length, and a different ordering of variables are included in the Appendix. Results from sensitivity checks confirm the robustness of the empirical findings.

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Acknowledgements

I am grateful and indebted to Jim Nason for his guidance and support. I extend my gratitude to two anonymous referees, Giuseppe Fiori, Daisoon Kim, and Xiaoyong Zheng, for their thoughtful comments and suggestions. I thank Givi Melkadze, Margaret Jacobson, and participants at the Business Fluctuations session at the 91st Southern Economics Association conference for helpful comments. My thanks also go to Tasha Bigelow for editorial assistance.

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  1. Department of Economics, Illinois Wesleyan University, 205 E Beecher St, Bloomington, IL, 61701, USA

    Bira Zhahadai

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My name is Bira Zhahadai. I am a assistant professor from the department of economics at Illinois Wesleyan University. This paper is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. I have no conflicts of interest to disclose. Please address all correspondence concerning this paper to me at bzhahada@iwu.edu. All remaining errors are my own.

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Zhahadai, B. Are business cycles in emerging market economies alike?. Int Econ Econ Policy 20, 537–561 (2023). https://doi.org/10.1007/s10368-023-00567-8

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