Short-term global capital flow and listed firms’ financing cost in China
(2.School of Economics, Xiamen University 361005)
(3.School of International Trade and Economics, University of International Business and Economics 100029)
(4.China Financial Futures Exchange 200122)
【Abstract】Over the past decades, the global financial system has become more integrated than ever, and finance has gradually replaced trade as one of the most important channels of risk contagion across countries. A clear understanding of the impact of global capital flow on its recipient countries thus has great significance to both policymakers and market regulators. Despite this, there remains a lack of rigorous empirical studies of how global capital flow impacts recipient countries, especially emerging markets. The literature uses time-series analysis (VAR) of aggregated variables to study the influences of short-term global capital flow (SGCF) on recipient countries. However, VAR analysis may not provide much insight into the channel of how SGCF affects these countries, as it not only overlooks the cross-sectional differences but also suffers from the identification problem. We use a panel data approach to investigate the impact of SGCF on Chinese firms’ cost of debt. A panel regression not only takes care of the cross-sectional difference of firms, but also controls for fixed effects. This advantage allows us to identify the heterogeneous impact of SGCF across firms. The uniqueness of the Chinese market makes it particularly suitable for such panel data analysis. China is one of the few countries in the world to adopt a managed floating exchange rate system and impose strict control on cross-border capital flows. This causes a significant amount of SGCF to sneak into China’s bank deposits through faked international trade by artificially inflating export prices and deflating import prices, which implies that custom cities with higher volumes of foreign trade are easier destinations of SGCF. In addition, the markets of bank loans are regionally segmented across different cities, and SGCF is expected to have a heterogeneous influence on the cost of debt of firms in different cities. Therefore, we hypothesize that the cost of debt of firms in these cities should be more sensitive to SGCF than those with lower volumes of foreign trade; this SGCF impact on cost of debt is consistent with changes in local bank deposits. To test these hypotheses, we consider the single term of SGCF and its interaction with the city’s previous-period foreign trade volume. We find that SGCF has a significant and heterogeneous impact on firms’ cost of debt. Consistent with our hypotheses, firms headquartered in cities with higher volumes of foreign trade are affected more by SGCF. In comparison, the general influences of SGCF based on aggregated VAR analysis are insignificant. The relation between cost of debt and SGCF may be reversed; that is, local credit conditions may shift the direction and increase the magnitude of SGCF. We deal with this issue by using the lag-period inflation rate of the United States as the instrument variable (IV) of SGCF. The empirical result of the IV analysis reveals that the endogeneity issue is not a concern for the crucial relation between SGCF and foreign trade volume. To further explore the channels of how SGCF affects the cost of debt of firms in different cities, and to verify our hypothesis that SGCF has a heterogeneous influence on the bank deposits of various cities, we run a regression analysis of bank deposits with SGCF and its interaction with local trade volume serving as the explanation variables. Our results show that those cities with larger trade volumes indeed see more bank deposit increases through capital flow. Furthermore, we study whether the impact of SGCF on the cost of debt can vary across industries. We find that the cost of debt of the manufacturing industry is more sensitive to SGCF than that of other industries. Further analyses show that this is because firms in the manufacturing industry rely more on bank loans as their funding resources than firms in other industries. Finally, we divide the sample into subsamples based on three periods and try other control variables to ensure the robustness of our results. We also carefully compare the empirical findings of this paper with the theoretical studies of SGCF and the sterilization operations of the People’s Bank of China.
【Keywords】 short-term global capital flow; international hot money; financing; international trade;
. ① Recent relevant literature includes Jotikasthira et al. (2012), Forbes and Warnock (2012), Mendoza et al. (2009), and Gourio et al. (2013).
. ① There are direct and indirect methods for the estimation of short-term global capital flows in the academic community. The direct method was first proposed by Cuddington (1986) and obtained the scale of short-term global capital flows by directly adding several items in a country’s balance of payments. However, Su and Tong (2011) pointed out that on the one hand, the coverage of direct method is too small, and the definition of hot money is somewhat arbitrary; on the other hand, the direct method ignores the hot money that may be hidden in the current account. The indirect method can avoid the above problems, so scholars use the indirect method to estimate short-term global capital flows, which was first proposed by the World Bank (1985).
. ① It is worth mentioning that the dollar inflation rate as a time series may also be related to the unobservable factors affecting the financing cost of enterprises, so that the instrumental variable of the dollar inflation rate is not exogenous. But we believe that, first, the dollar inflation rate reflects the development of the US domestic economy, which mainly affects the cost of capital for hot money. Therefore, the impact on the financing cost of Chinese enterprises is mainly reflected in the short-term global hot money flow. Second, in all regression analysis of this paper, we have controlled the main time series variables such as China’s loan interest rate, local financial development level index, local economic development GDP, and local inflation rate CPI, which may affect the financing costs of local enterprises. Therefore, in addition to the international hot money we want to consider, the dollar inflation rate should not be related to other unobservable factors that may affect corporate financing cost.
International Statistic Information Center of National Bureau of Statistic. China Statistical Yearbook 2006 (中国统计年鉴), Beijing: China Statistics Press, (2006).
Liu, L. Journal of Financial Research (金融研究), (10) (2008).
Meng, X. & Li, C. Forum of World Economics & Politics (世界经济与政治论坛), (6) (2006).
Song, B. & Gao, B. Research on Financial and Economic Issues (财经问题研究), (3) (2007).
Su, J. & Tong, L. Economic Perspectives (经济学动态), (11) (2011).
Wang, Q. Studies of International Finance (国际金融研究), (6) (2006).
Wang, S. & He, H. The Journal of World Economy (世界经济), (7) (2007).
Zhang, Y. & Shen, X. Journal of Financial Research (金融研究), (11) (2008).
Zhang, Y. Economic Research Journal (经济研究), (7) (2015).
Acemoglu, D., and S. Johnson, 2005, “Unbundling Institutions”, Journal of Political Economy, 113 (5): 949–995.
Cuddington, J. T., 1986, Capital Flight: Estimates, Issues, and Explanations, Princeton, NJ: International Finance Section, Department of Economics, Princeton University.
Edison, H., and C. M. Reinhart, 2001, “Stopping Hot Money”, Journal of Development Economics, 66 (2): 533–553.
Forbes, K. J., and F. E. Warnock, 2012, “Capital Flow Waves: Surges, Stops, Flight, and Retrenchment”, Journal of International Economics, 88 (2): 235–251.
Fratzscher, M., 2012, “Capital Flows, Push Versus Pull Factors and the Global Financial Crisis”, Journal of International Economics, 88 (2): 341–356.
Gourio, F., M. Siemer, and A. Verdelhan, 2013, “International Risk Cycles”, Journal ofInternational Economics, 89 (2): 471–484.
Guo, F., and Y. S. Huang, 2010, “Does ‘Hot Money’ Drive China’s Real Estate and Stock Markets?”, International Review of Economics and Finance, 19 (3): 452–466.
Jotikasthira, C., C. Lundblad, and T. Ramadorai, 2012, “Asset Fire Sales and Purchases and the International Transmission of Funding Shocks”, Journal of Finance, 67 (6): 2015–2050.
Kim, J. B., M. L. Z. Ma, and H. P. Wang, 2015, “Financial Development and The Cost of Equity Capital: Evidence From China”, China Journal of Accounting Research, 8 (4): 243–277.
Kunt, A. D., and R. Levine, 1996, “Stock Markets, Corporate Finance, and Economic Growth: An Overview”, World Bank Economic Review, 10 (2): 223–239.
Martin, M. F., and W. M. Morrison, 2008, “China’s ‘Hot Money’ Problems”, Library of Congress Washington DC Congressional Research Service.
Mendoza, E. G., V. Quadrini, and J. V. R. Rull, 2009, “Financial Integration, Financial Development, and Global Imbalances”, Journal of Political Economy, 117 (3): 371–416.
Montiel, P., and C. M. Reinhart, 1999, “Do Capital Controls and Macroeconomic Policies Influence the Volume and Composition of Capital Flows? Evidence from the 1990s”, Journal ofInternational Money and Finance, 18 (4): 619–635.
Obstfeld, M., 2012, “Financial Flows, Financial Crises, and Global Imbalances”, Journal ofInternational Money and Finance, 31 (3): 469–480.
Rijckeghem, C. V., and B. Weder, 2001, “Sources of Contagion: Is It Finance or Trade”, Journal of International Economics, 54 (2): 293–308.
Sarno, L., and M. P. Taylor, 1999, “Hot Money, Accounting Labels and the Permanence of Capital Flows to Developing Countries: An Empirical Investigation”, Journal of DevelopmentEconomics, 59 (2): 337–364.
World Bank, 1985, World Development Report, Washington D. C.
World Bank Policy Research Report, 1997, Private Capital Flows to Developing Countries: theRoad to Financial Integration, Oxford Press.
Wright, L., 2008, “Hot Money in China: Where’s It Going and How’s It Troubling”, ChinaStakes, July 14.
Wurgler, J., 2000, “Financial Markets and the Allocation of Capital”, Journal of Financial Economics, 58: 187–214.
Zhang, G. Y., and H. G. Fung, 2006, “On the Imbalance between the Real Estate Market and the Stock Markets in China”, Chinese Economy, 39: 26–39.