Hot money flows and production uncertainty: Evidence from China

Accepted Manuscript Does “hot money” affect real economy? Evidence from production outputs in China

Yihao Zhang, Fang Chen, Jian Huang, Catherine Shenoy PII: DOI: Reference:

S0927-538X(18)30307-X doi:10.1016/j.pacfin.2018.09.006 PACFIN 1070

To appear in:

Pacific-Basin Finance Journal

Received date: Revised date: Accepted date:

31 May 2018 8 September 2018 21 September 2018

Please cite this article as: Yihao Zhang, Fang Chen, Jian Huang, Catherine Shenoy , Does “hot money” affect real economy? Evidence from production outputs in China. Pacfin (2018), doi:10.1016/j.pacfin.2018.09.006

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ACCEPTED MANUSCRIPT Does “Hot Money” Affect Real Economy? Evidence from Production Outputs in China Yihao Zhanga, Fang Chenb, Jian Huangc, Catherine Shenoyd a.

School of Business, Nanjing University, Nanjing, Jiangsu, China 210093 College of Business, University of New Haven, West Haven, CT 06516 c. College of Business and Economics, Towson University, Towson, MD 21252 d. School of Business, University of Kansas, Lawrence, KS 66045

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Abstract

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Common wisdom generally associates hot money (short-term international capital) with macroeconomic risk originating from an economy’s financial sector, while its impact on the real economy is ignored. Using the Chinese data from 2000 to 2016, we document the pattern of hot money flows, and empirically examine the relation between hot money and industry, service and agriculture production outputs in China. We find the unidirectional causality from hot money to the industry output, and the bidirectional causality between hot money and the service output. Specifically, with a 1% increase in hot money inflow, there is a 0.29% increase in the industry output and a 0.25% increase in the service output. We further confirm that the short-run variation in hot money is the Granger cause of variation in the industry and service outputs. Moreover, the investigation of six capital-concentrated subsectors provides additional cross-sectional evidence of hot money’s heterogeneous effects. Overall, we provide the first piece of empirical evidence on the impact of short-term international capital on real production outputs in China, pointing to a significant microeconomic risk factor of escalated output volatility. These findings lend regulatory insights into risk management and capital control in China.

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Key Words: short-term international capital flow; production output; risk management JEL Classification: F21

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Emails: Yihao Zhang, [email protected]; Fang Chen, [email protected]; Jian Huang, [email protected]; Catherine Shenoy, [email protected]. Other than the first author, the rest authors are listed alphabetically. The usual disclaimer applies.

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“Mass inflows of short-term capital are causing asset bubbles and currency appreciation in developing countries, which make macroeconomic policy difficult and raise the risk of future crises…” - Stephany Griffith-Jones and Kevin Gallagher, The Financial Times, December 17, 2010

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1. Introduction

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With its dramatic growth from only $82 billion in the early 1970s to a record $1,680 billion approaching the financial crisis in 2008, hot money is considered as a major

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macroeconomic risk factor and is tightly linked with financial booms and crises in emerging

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economies (Chari and Kehoe, 2003). Hot money formally refers to the flow of speculative funds or capital across the border to earn a short-term profit by capitalizing on market differences. 1

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With the development of financial innovation, hot money is changing into a professional

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speculative capital game characterized by high liquidity and secrecy. Particularly in China, the largest emerging economy, it is reported that starting from 2003,

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there has been a huge short-term capital inflow that cannot be explained by trade surplus or

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foreign direct investment (Prasad and Wei, 2005). The aggregate hot money that flowed into China from 2003 to the first quarter of 2008 surged to about 1.75 trillion dollars (Martin and

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Morrison, 2008). Then right after the global financial crisis of 2008, the hot money inflows had another sharp increase (Zhang and Huang, 2011). Moreover, hot money accounts for a large part of the foreign exchange reserves in China. A report published in 2016 by the Chinese Academy of Social Sciences shows the hot money that flowed into China from 2001 to the third quarter of

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We exclude the regular capital flows due to normal trading or bank financing wh ich are strict ly regulated and accurately tracked in Ch ina. Our measure of short-term international capital flow specifically refers to the speculative capital flo w via varying open and unregulated channels. We thus use the terms of “hot money” and “short-term international capital (or STIC)” interchangeably in this article. More discussion is provided in Section 2. 1

ACCEPTED MANUSCRIPT 2015 reached 2.79 trillion dollars, almost 70% the total foreign exchange reserves in China over the same period. The massive inflow of hot money has exposed emerging economies such as China to risks in the financial sectors, imposing inflation pressures, asset price bubbles, and increased

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pressures on monetary policy operations and foreign exchange reserve management (Li et al.,

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2012). Recent studies have shown its impacts on asset pricing especially in the stock and real

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estate markets (Feng et al., 2017; Guo and Huang, 2010a; 2010b) and the exchange rate market in China (Lee et al., 2017). More severely, the volatile short-term capital flow has been

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associated with financial crises in countries with weak economic fundamentals (Kaminsky and

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Reinhart, 1998). Specifically, the excess short-term capital flow can lead to a financial bubble. As the bubble fades and short-term capital leaves, economic panic may ensue, leading to a

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financial crisis. Such a process has been observed during the 1997 Asian Financial Crisis,

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especially in Thailand, Indonesia, and South Korea. Despite the significant effect of hot money, its effect on the real economy is largely

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ignored mainly for the following two reasons. First, the common wisdom generally believes that

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hot money is restricted in the financial sectors of an economy, while only long-term international capital, especially foreign direct investments (FDI), is believed to increase the productivity of

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domestic firms (Bitzer and Gorg, 2009; Girma et al., 2001&2008; Javorcik, 2004; Keller and Yeaple, 2003; Markusen and Venables, 1999). These conjectures regarding the lack of hot money’s impact on the real production outputs have not been examined empirically or theoretically. Second, short-term international capital flow is difficult to measure in emerging economies due to government controls, low currency convertibility, regulatory evasion, and the diversity of channels for short-term capital flow.

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ACCEPTED MANUSCRIPT To fill in the gap, this paper assesses the impact of short-term international capital (“hot money”) on real production outputs in China. According to Keynesians, short-term money supply induces changes in output. Our research question is: Does the short-term international capital flow influence production outputs? If so, is there any heterogeneity among its effects on

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industry, agriculture, and service sectors? To the best of our knowledge, we provide the first

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piece of empirical evidence on the impact of short-term international capital on real production

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outputs in China.

To identify the channels for hot money to influence the output, we develop a derivation

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from the C-D production model, in which hot money affects the output through the monetary

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supply mechanism. Based on the model and the theory that hot money can increase industry output by improving the domestic financial system of the recipient country and directly promote

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productivity partly taking the form of FDI, we predict that the short-term international capital

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flow has a positive relation with industry, agriculture and service outputs. We first conduct a unit root test and find that the hot money and the output variables are

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stationary at their first differences. Next, the Granger causality test shows evidence of

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unidirectional causality from short-term international capital flow to industry output, and bidirectional causality between short-term international capital flow and the service output. By

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contrast, the result shows no causality between short-term international capital flow and agriculture output. Further, we run the Johansen cointegration relationship test to confirm that the short-term international capital flow has comovement with the industry output as well as service output. It is noteworthy that with a 1% increase in hot money inflow, there is a 0.29% increase in the industry output, while only a 0.25% increase in the service output. With the Vector Error

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ACCEPTED MANUSCRIPT Correction Model (VECM) test, we find that the short-run variation in the short-term international capital is the Granger cause of variation in the industry and service outputs. A relevant question is whether there are heterogeneous impacts of hot money on the outputs across the subsectors. We repeat the Granger causality test, Johansen cointegration

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relation test and the VECM test for six capital-concentrated subsectors: banking, energy,

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chemical engineering, nonferrous metal, steel, and construction materials. The results indicate

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the bidirectional causality between short-term international capital flow and the banking industry output, and the unidirectional causality from short-term international capital flow to the

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nonferrous metal industry output and to the energy industry. The Johansen cointegration further

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reveals that short-term international capital flow has a negative comovement with the banking output, and a positive comovement with the nonferrous metal and energy outputs. The VECM

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test shows that the short-term international capital flow negatively influence s the banking output

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adjustment in the short run but not the nonferrous metal and energy outputs. While the existing research on the short-term international capital concentrates on the

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impacts of hot money on financial market (e.g., Guichard, 2017; Larry, 2004; McKinnon and

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Huw, 1996), our study extends this stream of research by identifying the relation between hot money and the outputs of the economic sectors –- industry, service, and agriculture, and by

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providing possible channels through which hot money affects the real economy. Further, our findings provide regulatory insights into risk management and capital control from the microeconomic perspective. The excessive liquidity, inflation aggravation, asset bubbles, and a cyclical rise in bank loans induced by hot money inflow and the financial vulnerabilities and crises led by hot money outflow, all contribute to macroeconomic risk to recipient countries. Our focus on the relationship of short-term international capital flows and

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ACCEPTED MANUSCRIPT real economy belongs to a microeconomic risk of escalated output volatility. This provides evidence for the capital control in China from a different perspective, and corresponds to Griffith-Jones and Gallagher’s call for “curbing hot capital flows to protect the real economy” as early as 2010. These findings also lend support to the Chinese government’s efforts in coping

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with the capital outflow when “hot money cools on China (Curran et al., 2015).” Finally, by

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proposing an ad hoc model on the monetary supply channel and raising the possible mechanisms

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via which hot money can affect the real production outputs, we provide further insights into how to put capital control in place to govern capital flows into and out of China, and to minimize its

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impact on the real production outputs.

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The rest of the paper is organized as follows: Section 2 reviews the literature on international capital flow, and develops our hypothesis regarding the link between STIC and

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production outputs. We present empirical evidence in Section 3 and conclude in Section 4.

2. Literature Review and Background Long-term international capital

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The existing literature differs in their way of gauging international capital. The first stream of literature does not differentiate short-term and long-term international capital, and

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examines the motivation and impact of international capital flow on recipient economies using the lumped international capital. On one hand, the positive economic effects hypothesis posits that the international capital flow promotes economic development, through the reduction of capital costs (Henry, 2000), technology spillover (Feldstein, 2000), the increase of domestic deposits (Mishra et al, 2001), and the acceleration of domestic financial innovations (Claessens

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ACCEPTED MANUSCRIPT et al., 2001). The economic effect is more obvious in industrial countries than in non-industrial countries due to their differing abilities to absorb foreign capital (Prasad et al, 2006). On the other hand, the negative economic effects hypothesis posits that economies are more prone to overheating and even financial crises. Too much capital may overheat the

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economy via channels such as monetary expansion and inflation pressure (Larry, 2004),

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domestic currency appreciation and deteriorating terms of trade (Athukorala and Rajapatirana,

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2003), and a negative effect on aggregate demand (Celasun et al, 1999). Consequently, excess capital inflow might make the financial system fragile (Mckinnon and Pill, 1996), and prone to

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the financial crisis (Calvo, 1998). These effects can be especially amplified, where poor

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corporate governance and financial regulation allow corporates and banks to take excessive risks and expand through international leverage (Guichard, 2017; Lane, 2015).

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This stream of literature has also been devoted to disentangling global factors (such as

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global risk aversion, global liquidity, interest rates, or growth in advanced economies) from local factors in receiving countries (such as domestic macroeconomic fundamentals and s tructural

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policy settings) (Avdjiev et al., 2017; Eichengreen et al., 2017; Guichard, 2017; Koepke, 2015).

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For example, Avdjiev et al. (2017) split gross debt inflows by the recipient sector (government, the central bank, banks, and NFCs), and find that the impact of global risk aversion varies with

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the borrowing sectors and the type of countries. They show that when risk aversion increases, foreign capital borrowed by banks and NFCs in both advanced and emerging economies and by sovereigns in EMEs declines, but that debt inflows to advanced economies and sovereigns increase. Eichengreen et al. (2017) find that, as with non-FDI inflows, non-FDI outflows respond negatively to increases in global risk aversion and that FDI outflows from emerging markets also respond negatively to increases in global risk aversion while FDI inflows into emerging markets

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ACCEPTED MANUSCRIPT are mostly driven by pull factors. They also show that the sensitivity of outflows from EMEs to global risk aversion has increased over time. The second stream of literature differentiates long-term international capital from the short-term capital, and particularly focuses on the impact of FDIs on economic development and

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industrial output. Many theoretical studies on the FDI-productivity link are not conclusive. On

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the one hand, theories suggest that host productivity may be promoted by FDIs through linkages,

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imitation, new products or processes, technology spillover and worker training (Grossman and Helpman, 1991; Kokko, 1992; Markusen and Venables, 1999; Romer, 1990). On the other hand,

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it is suggested that host productivity may not benefit from FDI, since multinational corporations

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may restrict diffusion of technology, especially advanced ones, to their subsidiaries abroad by reducing linkage effects or keeping skills and know-how a secret (Caves, 1996; Das, 1987; and

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Teece, 1977). Bitzer and Gorg (2009) investigate the productivity effects of inward and outward

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FDI using industry-and country- level data for 17 OECD countries over the period 1973 to 2001. Their results show that there are productivity benefits from inward FDI, but outward FDI is

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negatively related to productivity. Urata and Kawai (2000) find that labor costs, infrastructure,

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and industry clustering are key determinants of FDI for small and medium-sized Japanese firms. In the context of China, attracted by the increase in both the volume and volatility of

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China’s international capital flows, recent long-term international capital literature identifies the relation of FDI with industrial output in China. Zhao and Zhang (2010) posit that FDI has positive direct and spillover effects on China’s industrial productivity level and growth, and the contribution of FDI to productivity is enhanced by its interaction with China’s human capital. While labor- intensive industries benefit more from FDI direct effects, capital- intensive industries gain more from FDI spillover effects. Along the same line, Li and Qi (2016) measure the

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ACCEPTED MANUSCRIPT magnitude and direction of the different effects on the path of FDI and document a negative FDI scale effect, a negative FDI composition effect, and a positive FDI technique effect. Zhang (2014) suggests that FDI has large positive effects on China's industrial performance; such effects are much greater on low-tech manufacturing than medium and high-tech industries, and

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the contribution is enhanced by FDI’s interaction with local human capital. All of the above

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results support the FDI-industry nexus in China like other emerging markets, such as Mauritius,

Short-term international capital (hot money)

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2.2.

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India, and Nigeria (Fauzel et al., 2015; Kumar and Dehradun, 2016).

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Although China has adopted various measures to restrict volatile international short-term capital flows that are speculative in nature, investors continue to circumvent Chinese laws and

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regulations (Guo and Huang, 2010a; 2010b). Consequently, China is not isolated from hot

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money inflows as capital controls cannot reach all capital account transaction categories and several of these categories are loosely managed (Xie, 2004). The hot money usually takes

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advantage of varying open and unregulated channels, such as cargo trade, service trade, and

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direct investments. The violators cover up their illegal purposes with seemingly legal transactions, breaking or sidestepping the regulations as necessary, in their chameleon channels

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which can be summarized as follows. The first transmission channel of hot money is through the trad ing. This channel conveniently allows hot money to enter by reporting fraudulently high export prices and low import prices, providing advance export payments and entrepôt trade. For example, Zhang and Huang (2011) find that most short-term capital flows into China are via illegal channels such as misinvoicing in the trade transaction.

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ACCEPTED MANUSCRIPT The second is through highly organized underground banks. The underground banks transfer hot money into or out of foreign countries through foreign currency cash smuggling across country borders, personal foreign exchange transactions, non-resident foreign exchange settlement, and financial institutions’ business transactions.

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The third transmission channel of hot money is through fraudulent foreign investment.

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For example, Martin and Morrison (2008) find that more than half of the hot money flowed into

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China takes the form of over-reported or forged FDIs. The methods for overseas hot money to flow into and out of China under the guise of foreign investments are diversifying daily. The

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main forms of transmission used by foreign- invested enterprises include transfer pricing, false

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profit reporting, short-term foreign debt financing, and fraudulent domestic enterprise financing. Next, we discuss the hot money’s impact on macroeconomic risk globally and

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domestically. Chari and Kehoe (2003) find that economic booms and crises in emerging

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economies are tightly linked to hot money flows. Similarly, McKinnon (2014) points out that hot money has a first-order impact on global financial stability while waves of hot money crowd into

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Emerging Markets (EM) with convertible currencies. When each EM central bank intervenes to

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prevent its individual currency from appreciating, collectively they lose monetary control, inflate, and cause an upsurge in primary commodity prices internationally. When the good times roll in

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the economy, speculators and investors can make whopping profit margins. For example, Ferreira and Matos (2008) suggest that foreign institutional investors are more prone to chase shares with recent positive stock return performance in “hot” markets. However, when the outflow of hot money begins and the asset bubble bursts, the effect can be devastating, and the entire economy can be destabilized. Although the inflow of hot money builds up gradually over time, the outflow tends to occur simultaneously, with each

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ACCEPTED MANUSCRIPT player struggling to be the first to exit (Domowitz et al., 1997). As a good example, Sarno and Taylor (1999) show that a sudden capital outflow was a major factor in the 1997 East Asian financial crisis. The massive flow of hot money brings dramatic impacts to China too. The speculative

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capital inflows to China have fueled inflation, driven up stock prices, and accelerated a

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worrisome bubble in the real estate market (Guo and Huang, 2010a; 2010b). Wang et al. (2017)

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further explain that the relation between short-term international capital inflows and asset prices is self- fulfilling and mutually reinforcing. The unsterilized portion of the monetary base further

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exacerbates asset price bubbles, which suggests that short-term international capital inflows and

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excess liquidity gradually escalate the severity of asset price bubbles. Further, hot money also increases the fluctuation in business cycles in China. Guo and

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Huang (2010a;2010b) document a considerable degree of long-run cointegration and

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bidirectional causality effects between hot money and business cycle volatility. The liquidity shock stemming from hot money is shown to be the primary factor responsible for the

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significantly enhanced fluctuation in business cycles during the most recent global financial

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crisis period. A similar effect has been documented in Malaysia, Thailand, and Singapore (see,

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for example, Yang and Hamori, 2016).

Hot money and production outputs As shown in the previous sections, relevant studies either focus on long-term

international capital or the impacts of hot money on asset prices, especially stock and house prices in China (Feng et al., 2017; Guo and Huang, 2010a; 2010b; Lee et al., 2017). Little is known about hot money’s impact on the real economy in China. With its impact on monetary

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ACCEPTED MANUSCRIPT supply, short-term international capital flow can play a significant role in the real economy. Yang and Liu (2011) apply a monetary supply model with the international capital flow to the reform of the foreign exchange system in China and investigate the influence of international capital flow on monetary supply. However, they do not distinguish long-term capital flow from

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short-term capital flow or “hot money.” According to Keynesians, short-term money supply

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induces changes in output (e.g., Blanchard, 1987). International empirical evidence supports the

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view that hot money affects money supply. For example, Fuertes et al. (2016) examine hot money and bank credit in 18 emerging markets during 1988-2012 and reveal that hot money has

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gained importance as a way of financing relative to bank credit during the 2000s.

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To formally establish the link between hot money and production output via the mechanism of monetary supply, we set up an ad hoc model to explain this mechanism and derive

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our prediction. The flow chart of the mechanism is provided as follows (see the Appendix for a

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full description of the model).

1) STIC inflows into China which is a fixed exchange regime country. 2) The Chinese central bank intervenes in the foreign exchange market, and increases

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monetary supply.

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→

3) Interest rate drops and bank credit becomes more available.

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4) Production outputs in the real economy increase.

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→

Derived from the ad hoc model, when short-term international capital inflow in previous periods increases, industry output at period t increases. Besides the above model-derived “monetary supply mechanism,” there are other possible mechanisms. First, hot money can improve the domestic financial system of the recipient country, which in turn increases production output. For example, Levine (2001) finds that international

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ACCEPTED MANUSCRIPT capital flows promote international financial integration, economic development, and industry output by encouraging improvements in the domestic financial system such as the banking system. Second, hot money, which partly takes the form of FDI, can directly promote productivity. There is much literature on the well-established FDI-productivity link. For example,

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using an extensive dataset, Kose et al. (2009) examine the link between productivity and

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financial openness for a large sample of countries. Specific ally, they find that FDI and portfolio

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equity liabilities boost productivity growth (measured by TFP) while external debt is negatively correlated with TFP growth. All the above mechanisms point to a positive link between hot

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money and production outputs.

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In the next section, we empirically test the conjecture derived from the above discussion and further investigate whether the impact of hot money is heterogeneous across the three major

3. Data and Empirical Tests Data

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3.1.

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sectors.

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We use quarterly data from the WIND database, a leading financial database provider in China (www.wind.com.cn). Our sample consists of 68 quarterly observations of short-term

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international capital balances and industrial, agriculture and services output from the first quarter of 2000 to the fourth quarter of 2016.

3.1.1. Short-term international capital balance It is difficult to measure the short-term international capital balance in China due to government controls, low currency convertibility, regulatory evasion, and the diversity of

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ACCEPTED MANUSCRIPT channels for short-term capital flow (Zhang et al., 2007). Hot money flows are often mixed or concealed in current account as imports, exports, or other international trade because the Chinese government imposes strict capital account controls and looser current account controls (Guo and Huang, 2010a; 2010b). Hot money cannot easily flow in or out of China through investment or

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other financial channels (Feng et al., 2017). While it is difficult to get an accurate measure of hot

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money flow due to its complexity and secrecy, we can estimate its size. There are three

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estimation methods: direct computation (Cuddington, 1986; Kant, 1996), foreign reserve income

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estimation (Cline, 1987), and computation based on abnormal trade surplus (Dooley, 1986). The direct computation uses the balance of payments and is defined as:

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Short-term International Capital Inflow = Net Errors & Omission in Balance of International Payments + Short-term Capital Inflow in Non-bank Private Sector + Short-term Capital Inflow

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through Other Normal Channels

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FDI and actual trade surplus.

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The Chinese Statistics Bureau modified this method in 2006 by using new reserves minus

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The foreign reserve income estimation method adds the capital gain or loss induced by

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foreign reserve income and currency changes to the direct computation method. It is defined as: Short-term International Capital Inflow = New Foreign Currency Reserve – Capital Gains or

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Losses of Reserves – Earnings – (Net Export + FDI)

(2).

The foreign reserve income estimation method is mainly based on the trade surplus, earnings, and the size of FDI. If the domestic trade surplus increases sharply, a large amount of hot money flows into China through seemingly “legal” channels. We assume the trade surplus would not have increased without a large amount of short-term international capital, but would rather have increased at a normalized pace. Based on monthly data, Zhang and Shen (2008)

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ACCEPTED MANUSCRIPT modified the State Statistics Bureau method by omitting the concealed short-term international capital inflow in actual trade surplus. Because of these concerns, we modify the method of calculating short-term international capital flow (Zhang and Shen, 2008). We develop the following method: STICt = STICt-1 + ΔSTICt

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(3),

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international capital balance at the end of the quarter.

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where STICt is the short-term international capital net stock in month t, defined as the short-term

ΔSTICt = (ΔForeign Exchange Reservet - FDIt –Trade Surplust – Foreign Exchange Reserve

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Proceedst ) × Nominal RMB/$

(5),

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ΔForeign Exchange Reservet = Foreign Exchange Reservet – Foreign Exchange Reservet-1

(4).

where Trade Surplust is the weighted moving average of foreign trade surplus in the previous 12

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months.

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Zhang and Shen (2008) use a moving average, and this paper improves their method by using weighted moving average with the weight of 1.15. We estimate monthly trading surp lus for

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2001 to 2004 as the moving average of trade surpluses in the previous 12 months. We divide the

weights.2 Thus,

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corresponding actual trading surplus by the estimates and use the average of these ratios as

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Foreign Exchange Reserve Proceedst = Foreign Exchange Reservet-1 × (Annualized Foreign Exchange Reserve Proceeds Rate)/12

(6).

Figure 1 shows the short-term international capital (STIC) during the first quarter of 2000

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Short-term international cap ital flow is more much volatile than long -term flow. Consequently, it is much harder for domestic countries to respond to and deal with the short-term cap ital flo w. Based on this attribute of capital flows and the focus of this study, Function (2) emphasizes the effect of short-term international cap ital flow on h ighpowered money, and puts the effect of long-term capital flow together with other factors into Ct.

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ACCEPTED MANUSCRIPT through the fourth quarter of 2016. The STIC balance changes are consistent with the development of the Chinese economy and the global economy. During 2000-2010, STIC flowed into China aggressively, due to global factors such as the global financial crisis, and the domestic issues such as RMB appreciation, real estate property price surging, state-owned enterprise

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restructuring, and loose capital controls in China. After 2010, STIC inflow slowed down or even

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reversed, due to the reasons such as the volatile stock/real estate/currency markets, and the

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shrinking arbitrage opportunities from the state-owned enterprise restructuring in China. --Insert Figure 1 here--

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The long-term international capital balance (FDI) from the first quarter of 2000 through

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the fourth quarter of 2016 is shown in Figure 2. There is a sustainable increase in the long-term international capital balance. Comparing Figure 1 with Figure 2, short-term international capital

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is more volatile.

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3.1.2. Production output

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--Insert Figure 2 here--

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Following the classification method provided by the WIND database, we use the industrial, agriculture and services sectors in our analysis. Since there is strong seasonality in the

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sector outputs, we produce seasonally adjusted series using the X12 seasonal adjustment procedure (Pakko, 2008). Figure 3 shows the sector outputs are an increasing trend during the period of 2000 to 2016. --Insert Figure 3 here-Within the industry sector, we further examine five subsectors: energy, chemical

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ACCEPTED MANUSCRIPT engineering, nonferrous metal, steel, and building materials. 3 They are also the major sectors in the Chinese State Council’s Industrial Revitalization Plan. 4 Within the service industry, we select the banking subsector. These six subsectors are characterized by high capital concentrations. For the proxies of the subsector outputs, we use bank loan balance for banking, coal

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output for energy, ethane output for chemical engineering, aluminum output for the nonferrous

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metals, steel output for steel, and cement output for building materials. Figure 4 shows the

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increasing trend of the seasonally-adjusted outputs of the six subsectors from 2000 to 2016. --Insert Figure 4 here--

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We list the definition of the variables in Table 1 and provide the descriptive statistics and

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correlation of the variables in Table 2 and Table 3 respectively.

--Insert Tables 1-3 here--

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In Table 2, we show that service has the largest output, followed by industry, and

Empirical tests

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3.2.

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agriculture output is the lowest among all three sectors.

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First, we test all variables for time series stationarity with unit root tests using an augmented Dickey-Fuller test (ADF). The variables include STIC, industry output, agriculture

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output, service output, steel output, loan output, coal output, ethane output, aluminum output,

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The output from these six subsectors accounts for more than 30% o f the production output. Moreover, since the end of 2008, the Chinese State Council started a stimulus package including Industrial Revitalizat ion Planning. The plan covers logistics and nine industries: text ile, steel, auto, shipping, equipment manufact uring, electronic informat ion, light manufacturing, petrochemicals, and nonferrous metal. Statistics show that the added value of these nine industries accounts for 90% of the total national industrial added value, and 1/3 of GDP. 4 The standard classification of industries can be grouped into two systems. The first classification system serves the purpose of management, which is based on internal structure and development condition of the national economy. Examples are ISIC (UN), NAICS (North A merica), and GB/T4754-2002 (P.R. Ch ina). The second system is for the purpose of investment, which includes investment analysis, performance evaluation, and allocation of assets such as Global Industries Classificat ion Standard (GICS), FTSE. The classification system used in this study, WIND, is designed based on GICS, and modified slightly to match China’s actual condition. 16

ACCEPTED MANUSCRIPT and cement output. The results are shown in Table 4. For all the variables, the ADF test shows that all variables are found to be non-stationary at levels but stationary at their first differences, implying the first-order differences cointegration and Granger causality. We will employ the first-order differences in the following cointegration and Granger causality test to meet their

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--Insert Table 4 here--

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stationarity requirement.

3.2.1. Granger causality test for the relation between short-term international capital and

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sector outputs

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Next, we use Granger causality, a well-known test for bivariate, to test the influence of the past of one time series on the present and future of another time series (Hiemstra and Jones,

M

1994). Specifically, a standard join test (F-test) is used to determine whether lagged Y has

PT

result is presented in Table 5.

ED

significant linear predictive power for current X in a test of the relation between Y and X. The

--Insert Table 5 here--

CE

Focusing on rejections of the null hypothesis that there is no Granger causality at the 5% significant level, the Granger tests show evidence of unidirectional causality from short-term

AC

international capital flow to the industry output. Moreover, the result indicates bidirectional causality between short-term international capital flow and the service output. In contrast, the result shows no causality between short-term international capital flow and agriculture output. Therefore, the following tests will focus on the impact of short-term international capital flow on industry and service sectors only.

17

ACCEPTED MANUSCRIPT 3.2.2. Cointegration test for short-term international capital and sector outputs Since the test in Section 3.2.1 shows the causality between short-term international capital and the industry and service outputs, we further use the Johansen cointegration test to examine the long-run relationship between short-term international capital and the industry and

T

service outputs. The test results are sensitive to the lag length chosen. We select the number of

IP

lags based on the Pantula criteria (Pantula et al., 1994). When the selection cannot be made by

CR

AIC or SC, we apply L.R. statistic (Neyman-Pearson). Based on the above criteria, we construct three VAR (1) models to test the cointegration relations between STIC (short-term international

US

capital) flow and Y (Industrial, Agriculture or Service), respectively.

(7),

AN

𝑌𝑖,𝑡 = 𝐶 + 𝛼𝑌𝑖,𝑡−1 + 𝛽𝑆𝑇𝐼𝐶𝑡−1 + 𝜖

where Y is the output, i = sector (industry, agriculture, or service), C is a constant, and ε is the

M

residual.

ED

The results of the Johansen cointegration test are shown in Table 6. The test result shows

PT

that there is a cointegration relation between STIC and the industrial output, as well as a cointegration relationship between STIC and the Service sector. The results imply that the short-

--Insert Table 6 here—

AC

CE

term international capital flow contributes to both the industry output and the service output.

The cointegration equation shows that the magnitude of the contribution of STIC to the industry output is larger than that of the service sector. Specifically, with a 1% increase in hot money inflow, there is a 0.29% increase in the industry output, while only a 0.25% increase in the service outputs. Thus, STIC has the strongest effect in the industry sector, possibly indicating that hot money flows into the industry sector more than either the service sector or the agriculture sector.

18

ACCEPTED MANUSCRIPT The regulation and productivity can explain the flow of hot money. First, due to the strict capital control in China, hot money inflow is commonly mixed with trading and FDI items which are mostly related to manufacturing. Further, the service sector, especially the finance industry, is highly regulated, while the Chinese government encourages the marketization of the industrial

T

sector, attracting hot money into relevant industries. Finally, the productivity in the industry

IP

sector is the highest among all the three sectors and the productivity is positively related to the

CR

return on investment. For example, Li et al. (2012) finds that the productivity is RMB 15,268.8/per capita, RMB 11,300.7/per capita, and RMB 2,485.2/per capita in the industry,

AN

US

service, and agriculture sectors, respectively.

3.2.3. VECM test for short-term international capital and sector outputs

M

Engle and Granger (1987) indicate that an error-correction representation always exists in

ED

the presence of cointegration. Since variables STIC and the industry and services output move together in the long run, we further run Vector Error Correction Model (VECM) to examine the

PT

impact of the hot money on the short-term industry and service output adjustment.

CE

The VECM model used is:

Yi ,t  ECM t 1  Yi ,t 1  STICt 1  C  

(8),

AC

where i=sector (industry or service), C is the constant, and  is the random error term. The dependent variable of the VECM is expressed regarding the first difference. The right- hand side of each equation includes a one-period lagged output first difference and a one-period lagged STIC first difference. The coefficient of error correction term (EC T) is derived from the long run cointegration relationship and reflects the adjustment speed as the output variables return to their long-term values after a shock. The one-period lagged variables are the indication of the short-

19

ACCEPTED MANUSCRIPT term causal effects (Masih and Masih, 1999). The result is presented in Table 7. The coefficient of ECT is -0.01 for the industry output and 0.002 for the service output. Both are statistically significant at the 5% level. The small magnitude of coefficients of ECT implies a slow self-adjustment from the long run cointegration

T

relationship. The coefficient of a one-period lagged STIC first difference is 0.05 for the industry

IP

output and 0.03 for the service output. Both are statistically significant, suggesting that short-run

CR

variation in the short-term international capital is the Granger cause of variation in the industry and service outputs.

US

--Insert Table 7 here--

AN

We further conduct variance decomposition of STIC on the outputs in an attempt to gauge to what extent shocks to certain outputs are explained by its innovations and STIC.

M

Consistently, we use the first-order difference for STIC, industry output and service output. The

ED

result is presented in Table 8. For the industry output, the explanatory power of the variance of STIC on that of the industry output increases over time, peaking at the 10th period when the

--Insert Table 8 here--

CE

PT

variance of STIC accounts for 16.79% of the variance of industry output.

For the service output, the explanatory power of the variance of STIC on that of the

AC

service output first increases over time, peaking at the 6th period, and then decreases. In the 6 th period, the variance of STIC accounts for 9.29% of the variance of industry output. We use impulse response analysis to examine the speed of adjustment of industry and service outputs to long-run equilibrium following a shock in STIC, which is another indicator to gauge the impact of STIC on the outputs. The result is presented in Figure 5. The result shows that STIC affects both industry and service outputs differently. Specifically, the impact of STIC

20

ACCEPTED MANUSCRIPT on the industry output increases and peaks during the second period, while the impact of STIC on the service output peaks in the second period and the eighth period. --Insert Figure 5 here--

T

3.2.4. Short-term international capital and subsector outputs

IP

To further investigate the heterogeneous impact of hot money, we repeat the Granger

CR

causality test, the cointegration relation test, and the VECM test for six capital- intensive subsectors: banking, energy, chemical engineering, nonferrous metal, steel, and building

US

materials, which are believed to be sensitive to capital inflows.

AN

The Granger test for the subsectors is presented in Table 9 Panel A. The result indicates bidirectional causality between short-term international capital flow and the bank ing industry

M

output (loan). Also, the results show evidence of unidirectional causality from short-term

ED

international capital flow to nonferrous metal industry output (aluminum) and energy industry

PT

output (coal).

--Insert Table 9 here--

CE

The Johansen cointegration tests of three subsectors with the Ganger causality with the short-term international capital flow are shown in Table 9 Panel B. The result confirms that there

AC

is a statistically significant cointegration relation between STIC and output in the banking, nonferrous metal, and energy subsectors. Specifically, STIC flow has a negative comovement with the banking output and a positive comovement with the nonferrous metal and energy output. We apply a Vector Error Correction Model (VECM) to examine the contribution of the short-term international capital flow to the adjustment speed of three subsector outputs. The result is reported in Table 9 Panel C. The coefficient of one-period-lagged STIC for the banking

21

ACCEPTED MANUSCRIPT (loan) is -1.37 and significant at the 1% level, implying that the short-term international capital flow negatively influences the banking output in the short run. We do not find such impact of the short-term international capital flow on the nonferrous metal (aluminum) and energy output (coal).

T

Finally, we examine the pattern of the impact of STIC on the banking, nonferrous metal

IP

and energy subsector outputs following a shock in STIC. The results are presented in Figure 6.

CR

The result shows that STIC affects the banking (loan) most in the first period and energy (coal) outputs in the third period. For nonferrous metal (aluminum) outputs, the strongest effect from

US

STIC shock is in the second period and the tenth period.

AN

--Insert Figure 6 here--

M

4. Conclusion and Risk Management Implications

ED

The dramatic flows of hot money into and out of emerging economies have exposed these financial systems to vulnerability and the possibility of financial crises. While common wisdom

PT

generally associates hot money with macroeconomic risk originating from an economy’s

CE

financial sector, we propose and empirically document the impact of hot money on the real economy in China. Specifically, we document the unidirectional causality from hot money to the

AC

industry output, and the bidirectional causality between hot money and the service output, while we do not find that such effects exist in the agriculture sector. Further investigation of six capital- intensive subsectors provides cross-sectional evidence of hot money’s heterogeneous effects. These findings have meaningful policy implications for risk management in China given that the scale of rapidly growing short-term international capital outflow has reached $1 trillion

22

ACCEPTED MANUSCRIPT U.S. dollars in the recent two years. 5 This is in sharp contrast with the rapid growth of hot money before 2010. Based on the positive link between hot money and production outputs documented in this study, the observed pattern of sharp increase/decrease of hot money can escalate microeconomics risks and induce greater production output volatility followed by an economic

T

recession. Because the annual growth rate of China’s GDP has fallen to 6.9% in 2017 from 9.6%

CR

international capital (“hot money”) flows as follows.

IP

in 2007, it is critical for China’s policymakers to take precautions against the risk of short-term

First, Chinese authorities (e.g., State Administration of Foreign Exchange, or SAFE)

US

should adopt differentiated capital flow management policies. On the one hand, greater attention

AN

should be paid to the dynamics of speculative capital flows as they are volatile and, therefore, susceptible to a sudden stop and reversal. For example, more systemic reforms can be put in

M

place to eliminate distortions and motives for speculative capital flows. Macro-prudential

ED

policies and proactive financial supervisory oversight should be put in place to safeguard against the increasing volatility of international capital flows. 6 On the other hand, policymakers can

PT

continue to cultivate the incentives that have helped to attract FDI which is believed to be long-

CE

term oriented and thus more stable.

Relatedly, although Prasad and Wei (2005) suggest that China should move towards

AC

greater exchange rate flexibility to deal with domestic and external shocks, the capital account liberalization in China should be implemented cautiously and gradually, due to the weaknesses in the domestic financial system.

5

Between June 2014 and January 2017, Ch ina’s foreign exchange reserves have fell by 25% fro m $3.9932 trillion to $2.9982 trillion. The main reason is the net excessive outflow o f short-term international cap ital fro m China with shrinking arbitrage opportunities. 6 Macro-prudential policies are much wider than capital flow management (CFM ) measures as they include measures to reduce financial instability not all linked to international capital flo ws (Claessens, 2014; Guichard, 2017). 23

ACCEPTED MANUSCRIPT Finally, as one of the largest economies, China also needs to pay close attention to its policies’ international spillover effect, and actively take responsibility for international cooperation and coordination of policies. The collaboration can aim to deal with international speculative capital, including efforts in the three main areas as proposed by Giordani et al.

T

(2014): a) monetary policy, b) capital control management and more broadly financial policies,

IP

and c) the global financial safety net (GFSN). Similarly, on the international level, to better

CR

preserve financial and exchange rate stability, the big four central banks–-Fed, ECB, Bank of England, and Bank of Japan–-can consider moving jointly to phase in a common minimum target

US

for their basic short-term interbank rates.

AN

Overall, large fluctuations in China’s capital flows can be detrimental to its international trade and the smooth operation of financial markets. However, given that China runs a strict

M

financial management system, chances that massive movement of hot money will induce a

ED

financial crisis are rather slim. Regardless, it is critical for policy- makers to take precautions

AC

CE

PT

against the speculative factors in their future policy decisions.

24

ACCEPTED MANUSCRIPT Appendix: Model of the relation between short-term international flow and production outputs The exchange rate regime will affect the monetary consequences of the international capital inflows. In most models, consumption and investment booms will be accompanied by a

T

rise in money demand. In a small open economy operating under a free float, capital inflows will

IP

be associated with a nominal exchange rate appreciation and no change in either reserves or the

CR

monetary aggregates. Under a fixed exchange rate regime, money market equilibrium will be achieved via an accumulation of international reserves at the central bank and arise in the money

US

supply. For intermediate cases, the degree of monetary expansion following a rise in capital

AN

inflows will be smaller to the extent that the inflows are sterilized, or that the nominal exchange rate is allowed to appreciate ( Calvo et al., 1996).7

M

China’s fixed or band foreign exchange regime and high costs are associated with

ED

sterilized intervention result in the positive influence of international capital inflow on the monetary supply of the People's Bank of China (Zhang et al., 2007). Via the channel of monetary

PT

supply, we set up an ad hoc model to derive our prediction that when short-term international

CE

capital flow (STIC) in previous periods increases, industry output at period t increases. The model is to derive a mechanism to explain the influence of short-term international

AC

capital flow (STIC) on industry output. According to the multiplier theory of monetary supply, if short-term monetary supply, M2, is exogenous, then monetary supply is determined by base

7

Calvo et al. (1996) present the most important aspects of the largest recipients of capital inflows in Asia and Latin America, including Argentina, Brazil, Chile, Colo mbia, Indonesia, Malaysia, Mexico, Ph ilippines and Thailand. They find six effects of capital inflows on other key economic variab les: First, a substantial portion of the surge in capital inflows has been channeled to the accumulation of foreign exchange reserves. Second, in most countries the capital inflo ws have been associated with widening current account deficits. Third, as one would expect fro m the fall in national saving, there has been a rise in private consumption spending. Fourth, in almost all th e countries examined there is rap id gro wth in the money supply in both nominal and real terms. Fifth, the surge in portfolio flows to the Asian and Lat in A merican countries was accompanied by sharp increases in stock and real estate prices. Sixth, the evidence on the behavior of the real exchange rate presents a mixed picture. 25

ACCEPTED MANUSCRIPT money supply, H, and a monetary multiplier, λ. Broadly-defined monetary supply at time t, is

M 2t   * Ht a , for   0

(9),

where M 2t is short-term monetary supply at time t, and H t  a is base monetary supply at time (ta). We assume, in a country with fixed exchange rates, the change in short-term international

IP

T

capital, STIC, is the inflow and outflow of short-term international capital. This change induces a

Ht  STICt  Ct

CR

variation of the monetary base, H.

(10),

US

where C is all other factors influencing the monetary base, including long-term international capital flow.

AN

Using Equation (10) in Equation (9), we obtain:

(11).

M

M 2t   *(STICt a  Ct a )

ED

The real interest rate, i, is the cost of capital. We assume the interest rate is mainly influenced by monetary supply. Equation (12) shows the relation:

 M2

(12),

, and θ is a constant and θ>0.

CE

where f ( M 2) 

PT

it  f (M 2t b ) , f (M 2 )  0

AC

We use a Cobb-Douglas production function to examine production output, Y. Production output can be written as:

Yt  AKt L ,   0 ,   0 ,  + =1

(13),

where A is technological capability, K is capital stock, L is labor, and α and β are constants. We assume A and L are constant in the short term. The capital stock of a country at period t, Kt, is determined by capital stock Kt-1 , investment It, and depreciation Kt 1 , where η is depreciation rate. Kt is shown as: 26

ACCEPTED MANUSCRIPT Kt  (1  ) Kt 1  It

(14).

The capital stock I t at period t is jointly determined by the real interest rate, I, at the same period, and capital stock Kt 1 at period t-1, as follows: It   Kt 1   i ,   0 ,   0

T

 and  are constants.

IP

where

(15),

CR

Inserting Equation (15) into Equation (14) obtains: Kt (i)  (1    ) Kt 1   i

(16).



 ( STICt a b  Ct a b )

] L

(17).

AN

Yt  A[(1     ) Kt 1 

US

From Equations (11), (12), (13) and (16), we derive Equation (17):

M

We then transform both sides into a logarithm, and take derivatives on both sides to obtain:

PT

ED

 ln Yt  ln Yt Kt it M 2t b    0 STICt a b Kt it M 2t b STICt a b Kt M 2t b 2

(18).

Since Equation (18) is positive, when short-term international capital flow (STIC) at the

AC

CE

period (t-a-b) increases, production output at period t increases.

27

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Tsai, I. C., Chiang, M. C., Tsai, H. C., Liou, C. H., 2014. Hot money effect or foreign exchange exposure? Investigation of the exchange rate exposures of Taiwanese industries. Journal of International Financial Markets, Institutions & Money. 31, 75-96.

IP

Umar, M. and Sun, G., 2016. Determinants of different types of bank liquidity: Evidence from BRICS countries. China Finance Review International. 6(4), 380-403.

CR

Urata, S., Kawai, H., 2000. The determinants of the location of foreign direct investment by Japanese small and medium-sized enterprises. Small Business Economics. 15(2), 79-103.

US

Wang, C.H., Hwang, J.T., Chung, C. P., 2017. Do short-term international capital inflows drive China’s asset markets? The Quarterly Review of Economics and Finance. 60, 115-124.

AN

Xie, P., 2004. China’s monetary policy: 1998-2002. Stanford Center for International Development Working Paper. No. 217. Stanford University. Yang, L., Hamori, S., 2016. Hot money and business cycle volatility: Evidence from selected Asian countries. Emerging Markets Finance & Trade. 52, 351-363.

M

Yang S.G., Liu Z.H., 2001. Analysis on the international capital flow’s effect on China monetary policy and its policy. World Economy. 6, 61-66.

ED

Zhang, K. H., 2014. How does foreign direct investment affect industrial competitiveness? Evidence from China. China Economic Review. 30, 530-539.

PT

Zhang, L., Huang, Z., 2011. Do capital flows fuel asset bubbles in China? Workshop Financial Stability in Emerging Market. 1-16.

CE

Zhang, Y., Shen, X.H., 2008. An empirical research on appreciation of RMB, rising of stock price and hot money inflow. Journal of Financial Research. 11, 87-98.

AC

Zhang, Y., Pei, P., Fang, X. M., 2007. An empirical analysis on short-term international capital inflow and its motivation in China: Basing on triple-arbitration model. Studies of International Finance. 9, 41-52. Zhao, Z.X., Zhang, K. H., 2010. FDI and industrial productivity in China: Evidence from panel data in 2001-06. Review of Development Economics. 14 (3), 656-665.

32

ACCEPTED MANUSCRIPT Table 1 Variable Definitions

Definition

Units

Explanation

Industry

Industry output

100 million RM B

The outputs of three main industries and most sectors

Agriculture

Agriculture output

100 million RM B

(except chemical engineering and banks) have strong

Service

Services output

100 million RM B

seasonality, so we use seasonally adjusted variables.

Coal

Coal output

10 thousand tons

Quarterly loan balance is second-order stationary, while

Aluminum

Aluminum output

10 thousand tons

the quarterly short-term international balance is the first-

Steel

Steel output

10 thousand tons

order stationary. Therefore, we cannot use them in the

Cement

Cement output

10 thousand tons

integration test. Instead, we use monthly loan balance

Ethane

Ethane output

10 thousand tons

Loan

Loan balance (monthly)

100 million RM B

100 million RM B

M

capital balance (quarterly) Short-term international

100 million RM B

AC

CE

PT

capital balance (monthly)

ED

STIC1

IP

CR

US

AN

Short-term international STIC

T

Variable

33

and monthly short-term international capital balance during the period.

STIC1 of the third month of the contemporary quarter.

See section 4.2 for details.

ACCEPTED MANUSCRIPT Table 2 Descriptive Statistics

Minimum

39,568

37,404

77,411

8,740

8,132

16,253

Service

41,107

34,333

100,085

Coal

62,804

64,802

527

377

Steel

15,853

14,834

Cement

38,950

34,872

Ethane

275

264

1,213,637

STIC

-77

27,578

97,394

19,337

26,672

1516

38

475

30,085

3,018

9,084

74,157

14,298

18,063

456

111

118

858,558

3,190,889

283,738

864,683

8,468

27,378

-96,396

26,826

IP

9,436

AC

CE

PT

ED

Loan

22,295 4,255

US

Aluminum

10,870 3,578

AN

Agriculture

Std. Dev.

T

Maximum

M

Industry

Median

CR

Mean

34

ACCEPTED MANUSCRIPT Table 3 Correlation

Agriculture

Service

C

AL

Steel

Cement

C2H2

0.995

Service

0.982

0.992

Coal

0.951

0.925

0.886

Aluminum

0.964

0.980

0.994

0.847

Steel

0.994

0.987

0.972

0.962

0.953

Cement

0.989

0.983

0.968

0.948

0.955

Ethane

0.983

0.973

0.955

0.959

0.937

0.986

0.977

Loan

0.968

0.982

0.997

0.858

0.995

0.956

0.954

0.938

STIC

-0.627

-0.684

-0.756

-0.384

-0.791

-0.591

-0.601

-0.559

Loan

IP

T

Agriculture

CR

Industry

US

AN

M ED PT CE AC

35

0.991

-0.793

ACCEPTED MANUSCRIPT Table 4 Unit Root Test

ADF Test Critical values (1%, 5%)

First Difference ADF test

Critical values (1%, 5%)

STIC

2.48 (0,0,1)

(-2.60, -1.95)

-5.29 (1,1,0)***

(-4.10, -3.48)

Agriculture

-2.37 (1,1,0)

(-4.10, -3.48)

-8.05 (1,1,0)***

(-4.10, -3.48)

Industry

-2.56 (1,1,1)

(-4.10, -3.48)

-3.36 (1,0,0)**

(-3.53, -2.90)

Service

28.96 (0,0,0)

(-2.60, -1.95)

-5.34 (1,1,0)***

(-4.11, -3.48)

Steel

4.16 (0,0,0)

(-2.60, -1.95)

-6.38 (1,0,0)***

(-2.60, -1.95)

Loan

32.77 (0,0,0)

(-2.60, -1.95)

-4.49 (1,1,1)***

(-4.11, -3.48)

Coal

0.16 (1,1,1)

(-4.10, -3.48)

-9.17 (1,0,0)***

(-3.53, -2.91)

Ethane

-0.69 (1,0,0)

(-3.53, -2.91)

-7.98 (0,0,0)***

(-2.60, -1.95)

Aluminum

-1.15 (1,1,0)

(-4.10, -3.48)

-5.16 (0,0,0)***

(-2.60, -1.95)

Cement

-2.71 (1,1,1)

(-4.10,-3.48)

-10.54 (0,0,0)***

(-2.60,-1.94)

M

AN

US

CR

IP

T

(C, T, K)

ED

Variable

AC

CE

PT

***, ** and * represent significance at 1%, 5% and 10%.

36

ACCEPTED MANUSCRIPT Table 5 Granger Causality Test for the Relation between Short-term International Capital and Sector Outputs

F value 4.03

d(Industry) does not Granger Cause d(STIC)

d(Service) does not Granger Cause d(STIC)

0.17

0.84

0.29

0.75

5.17

0.01**

3.90

0.03*

CR

AN

d(STIC) does not Granger Cause d(Service)

US

d(Agriculture) does not Granger Cause d(STIC)

AC

CE

PT

ED

M

***, ** and * represent significance at 1%, 5% and 10%.

37

0.02*** 0.13

2.09

d(STIC) does not Granger Cause d(Agriculture)

P value

IP

d(STIC) does not Granger Cause d(Industry)

T

Null Hypothesis

ACCEPTED MANUSCRIPT Table 6 Cointegration Test for Short-term International Capital and Sector Outputs

Cointegration

Cointegration Function

Cointegration Trace Statistic

34.18

Industryt = 0.29STIC t +2383.58Trend (0.22)

6.55

(317.37)

Servicet = 0.25STIC t -4574.14Trend (Service, STIC) (1077.21)

AC

CE

PT

ED

M

AN

***, ** and * represent significance at 1%, 5% and 10%.

38

None**

12.52

At most 1

37.41

25.87

None**

7.69

12.52

At most 1

US

(0.45)

25.87

CR

(Industry, STIC)

Function

T

(Standard Errors)

IP

Variables

5% Critical Value

ACCEPTED MANUSCRIPT Table 7 VECM Test for Short-term International Capital and Sector Outputs

D(STIC(-1))

0.54***

ECT

0.05**

T

-0.01**

Variables D(Service)

D(Industry(-1))

D(Service(-1))

0.002***

D(STIC(-1))

IP

D(Industry)

ECT

0.39***

0.03**

AC

CE

PT

ED

M

AN

US

***, ** and * represent significance at 1%, 5% and 10%.

CR

Variables

39

constant 535.9*** constant 887.9***

ACCEPTED MANUSCRIPT Table 8 Variance Decomposition of STIC to Industry and Service Outputs

Industry

STIC

Service

STIC

01

100.00

0.00

100.00

0.00

02

97.65

2.35

97.07

03

96.00

4.00

94.56

04

95.00

5.00

93.06

6.94

05

94.39

5.61

92.28

7.72

06

94.04

5.96

07

93.85

6.15

08

93.76

6.24

09

93.75

6.25

10

93.79

6.21

AC

CE

PT

ED

M

AN

US

CR

IP

T

Period

40

2.93 5.44

91.95

8.05

91.89

8.11

92.00

8.00

92.26

7.77

92.51

7.49

ACCEPTED MANUSCRIPT Table 9 Short-term International Capital and Subsector Outputs

Panel A. Granger Causality Test for Subsectors

F value 7.78

d(Loan) does not Granger Cause d(STIC)

P value 0.00***

IP

d(STIC) does not Granger Cause d(Loan)

T

Null Hypothesis

0.02**

0.52

0.60

3.4

0.04

4.27

0.02**

d(Aluminum) does not Granger Cause d(STIC)

0.19

0.83

d(STIC) does not Granger Cause d(Cement)

0.59

0.56

0.30

0.75

d(STIC) does not Granger Cause d(Coal)

2.72

0.07*

d(Coal) does not Granger Cause d(STIC)

2.00

0.14

d(STIC) does not Granger Cause d(Ethane)

0.22

0.8

3.72

0.03

CR

4.41

d(STIC) does not Granger Cause d(Steel)

US

d(Steel) does not Granger Cause d(STIC)

M

AN

d(STIC) does not Granger Cause d(Aluminum)

CE

PT

ED

d(Cement) does not Granger Cause d(STIC)

d(Ethane) does not Granger Cause d(STIC)

AC

***, ** and * represent significance at 1%, 5% and 10%.

41

ACCEPTED MANUSCRIPT Panel B. Cointegration Test for Subsectors Cointegration

Cointegration Function

Trace

Cointegration 5% Critical Value

Variables

(Standard Errors)

Statistic

Loan t = -0.20STIC t

Function

18.97

15.49

None**

2.30

3.84

At most 1

38.48

25.87

None**

12.52

At most 1

20.26

None**

9.16

At most 1**

(0.04) Aluminumt = 0.005STICt -29.4Trend

T

(Loan, STIC)

(9.15)

6.81

Coal t = 41205.58+1.63S TICt

33.77

(Coal, STIC) (11221.4) (0.49)

CR

(0.01)

IP

(Aluminum, STIC)

15.33

US

***, ** and * represent significance at 1%, 5% and 10%.

ECT -0.01***

Aluminum

-0.01

Coal

-0.01

ED

Loan

Industry(-1)

M

Variable

AN

Panel C. VECM Test for Subsectors

0.50***

-1.37***

0.001

-0.001

PT

-0.03

AC

CE

***, ** and * represent significance at 1%, 5% and 10%.

42

STIC(-1)

-0.10

ACCEPTED MANUSCRIPT Figure 1

US

Figure 2

CR

IP

T

Short-term International Capital Balance (in RMB 100 billion)

PT

ED

M

AN

Long-term International Capital Balance (in RMB billion)

Note: We use FDI balance as the long-term international capital flow. For the convenience, we use

AC

CE

relative ratio here by assuming FDI prior to the year of 2000 was zero.

Figure 3

Industry, Agriculture and Service Outputs (in RMB trillion)

43

ACCEPTED MANUSCRIPT Figure 4 Subsector Outputs

CR

IP

T

Panel A. Steel, Coal, and Cement Outputs (in 10 million tons)

ED

M

AN

US

Panel B. Aluminum and Ethane Outputs (in million tons)

AC

CE

PT

Panel C. Loan Balance (in trillion RMB)

44

ACCEPTED MANUSCRIPT Figure 5 Impulse Responses of the Effects of STIC on Industry and Service

US

CR

IP

T

Panel A. Response of d(Industry) to d(STIC)

AC

CE

PT

ED

M

AN

Panel B. Response of d(Service) to d(STIC)

45

ACCEPTED MANUSCRIPT Figure 6 Impulse Responses of the Effects of STIC on Loan, Aluminum, and Coal

US

CR

IP

T

Panel A. Response of d(Loan) to d(STIC)

CE

PT

ED

M

AN

Panel B. Response of d(Aluminum) to d(STIC)

AC

Panel B. Response of d(Coal) to d(STIC)

46

ACCEPTED MANUSCRIPT Does “Hot Money” Affect Real Economy? Evidence from Production Outputs in China

Highlights: Our model shows that hot money affects the production outputs via monetary supply

T



CE

PT

ED

M

AN

US

CR

Hot money has heterogeneous impacts on six capital-concentrated subsectors

AC



IP

 We find the causality between hot money and the industry and service optputs

47