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Asymmetric effects of monetary policy on firm scale in China: A quantile regression approach
Accepted Manuscript Asymmetric effects of monetary policy on firm scale in China: A quantile regression approach
Liting Fang, Lerong He, Zhigang Huang PII: DOI: Reference:
S1566-0141(18)30419-9 https://doi.org/10.1016/j.ememar.2018.11.013 EMEMAR 589
To appear in:
Emerging Markets Review
Received date: Accepted date:
3 November 2018 26 November 2018
Please cite this article as: Liting Fang, Lerong He, Zhigang Huang , Asymmetric effects of monetary policy on firm scale in China: A quantile regression approach. Ememar (2018), https://doi.org/10.1016/j.ememar.2018.11.013
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ACCEPTED MANUSCRIPT Asymmetric Effects of Monetary Policy on Firm Scale in China: A Quantile Regression Approach Liting Fanga, Lerong He*, Zhigang Huang
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Lerong He* School of Business & Management The College at Brockport State University of New York Brockport, NY, 14420, USA & School of Economics & Management Fuzhou University Fuzhou, China Email: [email protected]
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Liting Fang School of Economics & Management Fuzhou University Fuzhou, 350116, China Email: [email protected]
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*Corresponding author
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Zhigang Huang School of Economics & Management Fuzhou University Fuzhou, 350116, China Email: [email protected]
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Acknowledgements: This paper was presented at the 2017 Cross Country Perspectives of Finance conferences held in Chengdu, China and Chiang Mai, Thailand. We are grateful for insightful comments provide by the special issue editors Drs. Gady Jacoby and Zhenyu Wu, two anonymous reviewers, and paper discussants and conference participants at these two conferences. The paper is supported by the National Natural Science Foundation of China Grant # 71473039 “The Asymmetric Effect of Monetary Policy Transmission Mechanism on Micro Level”, Grant #71673048 “Information Disclosure and Market Competition under Information Asymmetry: Theory, Evidence and Policy” and Grant # 71703025 “Nonparametric Quantile Regression Models: Theory and Applications”. Financial supports from the Ministry of Education in China Project of Humanities and Social Sciences (No. 17YJC910004) and the 4th Fujian 100 Entrepreneurship and Innovation Grant are also greatly appreciated.
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Asymmetric Effects of Monetary Policy on Firm Scale in China: A Quantile Regression Approach Abstract
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This study explores asymmetric effects of monetary policy on firm scale at different firm size levels. We find that Chinese firms respond to raising benchmark lending interest rates and deposit reserve requirements by decreasing their scales. Our quantile regression results also indicate that larger firms respond more strongly to both policy instruments by adjusting their scales to a greater degree than smaller firms. Moreover, SOEs react less strongly to policy changes than non-SOEs at all firm size distribution. The impact of monetary policy on firm scale is also stronger after commercial banks are given greater leeway in setting interest rates.
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Keywords: Monetary Policy; Quantile Regression; Firm Size; Asymmetric Effect; China
ACCEPTED MANUSCRIPT 1 Introduction Monetary policy refers to actions of central banks or other regulatory bodies to manage monetary supply and influence real and nominal interest rates in an economy. Expansionary monetary policies aim at increasing monetary supply to stimulate economic growth, while contractionary
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monetary policies are implemented to decrease monetary supply to control inflation (Mishkin,
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1995). Central banks typically utilize three primary monetary policy tools to achieve these goals,
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modifying the interest rate, changing the deposit reserve requirements, and engaging in open market operations such as selling or buying government bonds. These policy tools influence
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bank lending, firm investment, and consumer demand in the micro level, and ultimately affect
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aggregated consumption and economic outputs in the macro level through a series of monetary transmission mechanisms (Bernanke and Gertler, 1995).
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The effect of monetary policy however is often asymmetric. For example, contractionary
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monetary policy typically has a stronger impact on economic outcomes than expansionary monetary policy (Cover, 1992; Weise, 1999). There is also evidence that business cycles
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influence the effectiveness of monetary policy (Bernanke, et al., 1996; Hoppner et al., 2008).
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Heterogeneities in countries, regions, and industries may also affect the efficacy of monetary policy on economic outputs (e.g., Karras, 1999; Peersman, 2004; Peersman and Semets, 2005).
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Apart from macro level contingencies, firms with various characteristics often respond to monetary policy shocks in a distinct way (Ehrmann, 2000; Laopodis, 2010). In particular, firm size is identified as a crucial contingency factor, with smaller firms often being more vulnerable to monetary policy than their larger counterparts (Gertler and Gilchrist, 1994; Thoma, 1994). The main objective of our paper is to utilize firm- level data to examine the effectiveness of monetary policy instruments in influencing firm scale in China. More specifically, we explore
ACCEPTED MANUSCRIPT asymmetric effects of monetary policy on firm scale at different firm size levels, i.e., whether small and medium sized enterprises (SMEs) react more strongly to monetary policy tools compared to larger industrial firms. We also examine how regulatory environment and firm ownership structure may moderate asymmetric responses of firms of different sizes to monetary
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policy. To the best of our knowledge, no prior empirical literature has systematically examined
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these research questions in the Chinese context.
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Our study contributes to extant literature in the following ways. First, we provide empirical evidence on the efficacy of monetary transmission mechanisms in influencing micro firm
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behavior in an important emerging market context that is subject more to the grapping hand of
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the government than the invisible hand of the market. It thus augments prior empirical studies in this regard that are mostly conducted in developed nations with an efficient financial market to
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drive monetary demand and supply (e.g., Gertler and Gilchrist, 1994; Ehrmann, 2000; Laopodis,
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2010; Thoma, 1994). In addition, the utilization of Chinese context allows us to examine additional contingencies that may affect the functioning of monetary policy tools but are
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unavailable in prior studies. For example, we explore the influences of state ownership and
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financial policy reforms on the effectiveness of monetary policy in shaping firm expansion and contraction decisions. Our study thus also complements prior empirical literature on Chinese
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monetary policy that have predominantly used aggregated macroeconomic data (e.g., Chen et al., 2017; He and Wang, 2012; Sun, 2013) to provide fine-grained micro- level evidence on the role of monetary policy in encouraging or discouraging firm growth. In this respect, our paper also possesses significant practical implications by guiding the design of monetary policy in influencing growth and stability of Chinese economy. Second, we predict and demonstrate that the impact of monetary policy on firm scale is not
ACCEPTED MANUSCRIPT constant across the firm size distribution as typically assumed by prior studies. We show that the influence of monetary policy on firm scale is quantitatively larger in magnitude for larger firms compared to smaller firms by utilizing a novel research technique, quantile regression method (Koenker and Gilbert, 1978; Koenker and Hallock, 2001). Our results demonstrate that monetary
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policy generates distinct effects on firm size at different parts of the firm size dis tribution.
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Therefore, prior micro- level studies that have widely adopted conditional mean regression
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methods based on the assumption of a constant average policy effect on firm scale conceal significant parameter heterogeneity (e.g., Chen et al., 2017; Fernald et al., 2014). In contrast, the
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utilization of the quantile regression method in this study enables us to yield a more
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comprehensive characterization of the relationship between monetary policy and firm scale. In this regard, our paper also makes a significant methodological contribution toward a deeper
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understanding of monetary policy effects.
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The rest of this paper is organized as follows. Section 2 introduces Chinese institutional background and provides a brief review of related literature. Section 3 develops our main
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hypotheses. Section 4 describes data and methods, including a discussion of the quantile
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regression technique and its merits. Section 5 presents main empirical findings and Section 6 provides results of additional analyses. We conclude the paper in section 7 with a discussion.
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2. Institutional Background and Literature Review The Chinese monetary policy is implemented by the People’s Bank of China (PBC). According to the Law of the People’s Bank of China enacted in 1995 and amended in 2003, the role of the PBC is to formulate and implement monetary policies under the leadership of the State Council. The aim of these monetary policies is “to maintain the stability of the value of the currency and
ACCEPTED MANUSCRIPT thereby promote economic growth.” 1 To realize the dual goals of financial stability and economic growth, the PBC relies on a mixture of quantity- and price-based monetary policy instruments, including various policy interest rates, reserve requirements, open market operations, foreign-exchange intervention, and window guidance on bank loans (He and Wang,
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2012; Sun, 2013). Among these instruments, reserve requirements have been applied most
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frequently by the PBC to absorb and control excess liquidity in the banking sector. In addition,
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PBC sets benchmark deposit and lending rates, and allows commercial banks to adjust interest rates around these benchmark levels within a limited band (Fernald et al., 2014). 2 The PBC also
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engage in open market operations including issuance of new central bank bills and notes to
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adjust market liquidity and avoid excess volatility in market interest rates (He and Wang, 2012). Occasionally, PBC adopts non-standard administrative methods such as window guidance to
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control lending levels and targets of commercial banks. In broad stroke, the PBC applies a wide
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range of monetary policy instruments to achieve its multiple objectives of economic growth, currency stability, and managed exchange rates. This is in sharp contrast with its western
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counterparts that typically employ the conventional policy interest rates and rely heavily on open
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market operations to influence economic activities. Extant literature has utilized aggregated macroeconomic data to examine the effect of
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Chinese monetary policy. For example, He and Wang (2012) investigate the response of market interest rates to monetary policy instruments using monetary and bond market data. They find that market interest rates are most sensitive to changes in benchmark deposit interest rates,
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Accessed from http://www.pbc.gov.cn/english/130733/2941519/2015082610501049304.pdf.
Starting fro m July 2013, the PBC removed its controls on the retail lending interest rates, but allowed financial institutions to determine loan interest rates based on the benchmark lending rate. Later in October 2015, PBC also removed control on the retail deposit rate, although it continues to announce both benchmark deposit and lending rates serving as the floor and ceiling of retail rates (Chen, et al., 2017; Fu and Liu, 2015).
ACCEPTED MANUSCRIPT followed by changes in reserve requirements, but not sensitive to open market operations. Sun (2013) examines the influence of Chinese monetary policy on real economic outputs and inflation using three events of policy shocks and broadly confirms the effectiveness of monetary transmission mechanisms. In addition, Fernald et al. (2014) compare several monetary policy
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tools in China and find that reserve requirements and benchmark interest rates are most effective
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in controlling aggregated economic activities and inflation in China, while other measures such
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as shocks to M2 or target lending levels only play limited roles. A recent study by Chen et al. (2017) shows that the transmission of monetary policy impulses to the rest of the economy in
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China is similar to the transmission process in developed economies in terms of output growth
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and inflation. However, Chinese bank loans are not sensitive to policy changes as those in developed economies.
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Although it is valuable to examine the aggregated impact of monetary policy on Chinese
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economy from the macro angle, the aforementioned studies are unable to address the monetary policy effect on individual firm behavior. A few exceptions include Peng and Lian (2010) who
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document that raising interest rates increase firm operation costs and subsequently result in
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inflation, and such effects are more salient during the contractionary period of the economic cycle than in the expansionary period. In addition, Fu and Liu (2015) find that Chinese listed
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firms adjust their investment levels in response to monetary policy instruments through both monetary and credit channels, particularly in e xpansionary monetary policy periods. These limited firm- level studies however have yet to explore the influence of firm heterogeneity on the effectiveness of monetary policy, but only focus on variations in macroeconomic contexts particularly economic cycles. Importantly, all prior studies have applied conditional mean regression methods including quadratic curve models, smooth transition models, and vector auto
ACCEPTED MANUSCRIPT regression (VAR) models to conduct their analysis. These approaches are built on the premise that the average effect of monetary policy on firm investment and scale is constant. We propose and demonstrate that such an assumption is questionable. In contrast, monetary policy tools also influence the dispersion of firm scale, thus generating distinct effects at different parts of the firm
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size distribution. As a result, the conditional mean regression method used in prior studies is
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unable to distinguish between heterogeneous impacts of monetary policy on firms at different
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size distribution, which will be a focus of this study. 3. Hypotheses Development
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Monetary policy could affect firm scale through a variety of transmission mechanisms, including
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the interest rate channel, the bank lending channel, and the balance-sheet channel (Bernanke and Gertler, 1995). First, contractionary monetary policy leads to an increase in real interest rates,
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and subsequently raises firm cost of capital, thus causes a decline in firm investment (Mishkin,
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1995). Second, contractionary monetary policy that decreases bank reserves and bank deposits will cause a reduction in bank loans, reduce the likelihood of firms obtaining sufficient loans for
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investment spending, and consequently prompts firms to refrain from increasing investment
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(Lucas, 1990). Third, raising interest rates under a contractionary policy will increase firm interest expenditure, and subsequently reduce its cash flow. In response to the cash flow problem,
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financially constrained firms may curtail investment or reduce inventory holding to ease the liquidity concern (Kashyap et al., 1994). Lower short-term or long-term investments and lower inventory holding will all deteriorate firm assets, and adversely affect firm scale. Taken together, contractionary monetary policy is expected to negatively influence firm scale. These monetary transmission mechanisms are built on the premise of an advanced market economy where commercial banks make independent lending decisions based on supply and
ACCEPTED MANUSCRIPT demand conditions in the financial market. The banking industry of China however is highly regulated and only partially liberalized in recent years. As a result, Chinese banks and other financial institutions have yet to develop risk-assessment expertise, and still do not possess the right incentives to lend on commercial principles (Prasad, 2009). Consequently, they often make
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loan decisions not based on market conditions but on administrative guidance of the central
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government or relationship with their clients (Li, 2006). This is particularly the case of the five
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largest state-owned banks that account for two-thirds of assets and liabilities in China’s banking system. Therefore, Chinese bank loans may not respond to monetary policy shocks in the way as
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predicted by traditional monetary transmission mechanisms in developed countries. For example,
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Chen et al (2017) find that the growth rate of bank loans in China is often detached from conditions on the interbank market set by the central bank, and Chinese monetary policy seems
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to have limited effect on containing asset price bubbles. As a result, the effect of Chinese
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monetary policy on firm scale becomes a pending empirical question. We make the following baseline hypothesis following the prediction of monetary transmission mechanisms:
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H1: Contractionary monetary policy has a negative influence on firm scale.
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Prior literature suggests that monetary transmission mechanisms may affect firms of different sizes in a distinct way (Gertler and Gilchrist, 1994; Mishkin, 1995). Because smaller
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firms possess a high degree of idiosyncratic risks, the problems of information asymmetry and adverse selection tend to be more severe for them. Banks are thus more reluctant to lend to these riskier smaller firms under contractionary monetary policy. In addition, sma ller firms may not possess sufficient collateral assets as their larger counterparts, which further reduces their credibility to banks and impairs their financing capability. Using a sample of U.S. manufacturing firms, Gertler and Gilchrist (1994) find that smaller firms account for a significantly
ACCEPTED MANUSCRIPT disproportionate share of production reduction and inventory decline after the tightening of monetary policy. Applying German business survey data, Ehrmann (2000) likewise confirms that smaller firms are subject more to the influence of contractionary monetary policy than larger firms, particularly during the economic downturn.
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This underlying logic may not necessarily hold in China, given the nation’s banking industry
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is heavily influenced by the grabbing hand of the government instead of the invisible hand of the
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market. For example, large Chinese firms often enjoy preferential status in obtaining bank loans due to their importance to local economies, while smaller firms are often more susceptible to
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heavy red tape and extralegal fees and have to extensively rely on trade credits and other
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financing channels for operation and growth (Brandt and Li, 2003; Li, 2008). In addition, the Chinese government often offers preferential credits to promote large inefficient firms
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particularly those in labor-intensive industries in an attempt to achieve its employment goal
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(Chen et al., 2016). Because larger firms rely more heavily on government-backed bank financing, they are also more vulnerable to the influence of governmental policy changes when
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government-controlled banks have to follow administrative guidance of the central government
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to make loan decisions. In contrast, smaller firms in China do not enjoy such privileged treatments from the first place and are generally more inclined to seek alternative credit channels
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for growth and other financial needs (Li, 2008). Their investment decisions therefore may actually depend less on changes in monetary policy. Taken together, we expect that firms of different sizes are likely to vary in their responses to monetary policy. In particular, larger Chinese firms may react more strongly to monetary policy tools by adjusting their scales to a greater degree, which leads to the following hypothesis: H2: The influence of contractionary monetary policy on firm scale is greater in larger firms
ACCEPTED MANUSCRIPT than in smaller firms. 4. Data and Methods 4.1 Data and Variables We obtain quarterly financial information of Chinese listed firms between 2011 and 2016 from
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CSMAR database provided by GuoTaiAn Information System. These data cover all Chinese
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public firms listed on Shanghai and Shenzhen Stock Exchanges. Macroeconomic data are
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withdrawn from the People’s Bank of China and National Bureau of Statistics of China for each quarter of our sample period. We require complete information on firm scale and a set of
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independent covariates. After deleting firms in the financial industry and firms with missing
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information, the final sample consists of 24,281 observations between 2011 and 2016. Table 1 presents the yearly distribution of our sample.
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***Insert Table 1 about Here***
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Considering that multiple monetary policy instruments are often used simultaneously in China, we capture changes in the Chinese monetary policy from two angles by adopting one
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price-based policy instrument, the benchmark lending interest rate (denoted as Interest Rate-L)
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and one quantity-based policy instrument, the reserve requirement ratio (denoted as Reserve Ratio). Figure 1 depicts the quarterly movements of these two monetary policy instruments
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within our sample period, where we also draw the benchmark deposit interest rate (Interest RateD) as a contrast. Figure 1 reveals that all three policy instruments generally move in the same direction within this time period although not at the same pace. The magnitude of changes is slightly greater for reserve requirement ratios, while trends for both types of benchmark interest rates are smoother. This pattern is consistent with the conclusion made by Sun (2013) and He & Wang (2012) that PBC tends to rely more extensively on reserve requirements than benchmark
ACCEPTED MANUSCRIPT interest rates as policy instruments. ***Insert Figure 1 about Here*** Our dependent variable is firm size measured as the logarithm of the end of the quarter total assets (denoted as Log Assets). Our models also include a series of control variables essential for
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firm investment decision following prior studies (Fu and Liu, 2015; Huang et al., 2012). We
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control for firm financial structure using firm leverage measured as the debt to asset ratio
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(denoted as Leverage) by dividing the end-of-quarter long-term debt to end-of-quarter total assets. We measure government subsidies (denoted as Subsidy) as the quarterly amount of
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government subsidies divided by total sales. We measure firm performance using the return on
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assets ratio (denoted as ROA) by dividing quarterly net income to total assets. We control for current period firm investment level using the log value of net fixed assets (Log Fixed). We also
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control for firm age (denoted as Age) measured as the current year minus the firm founding year
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and plus one. In addition, we control for firm ownership structure using the proportion of stateowned shares in total shares outstanding (denoted as State %) and the proportion of largest
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shareholder equity ownership in total shares outstanding (denoted as Largest %). Our models
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also include a set of macroeconomic indicators. First, we control for the rate of economic growth using the quarterly real GDP growth rate (denoted as GDP Growth). Because the real estate
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bubbles in the past 10 years may severely affect firms’ investment intension, we also include a real estate price index (denoted as Real Estate Index) as a control. This index is calculated as inflation adjusted average real estate price using quarterly real estate price data of the 100 most representative Chinese cities, with year 2010 as the base year at the value of 100. To capture regional differences, we include the widely recognized NERI market development index (denoted as Marketization Index) compiled by Wang et al. (2016), which is an annual composite
ACCEPTED MANUSCRIPT index created for each of the 31 Chinese provinces as well as four central government controlled municipalities. We use the location of the firm’s headquarter to identify the regional institutional environment the firm is facing. Finally, our models also include a set of industry dummy variables to capture industry effects. Measures of these variables are summarized in Appendix 1.
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Table 2 provides descriptive statistics of our main variables. We can tell from Table 2 that
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the benchmark lending rates fluctuate between 4.35% and 6.56% during our sample period, with
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the median being 5.60%. The reserve requirement ratio varies between 16.50% and 21.50% during this period, with a median of 19.00%. The average firm size measured by the logarithm of
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total assets is 22.177, and the average value of log fixed assets is 19.754. An average firm in our
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sample has a debt to asset ratio of 0.513, and a return to asset ratio of 0.017. The average rate of government subsidy accounts for 0.3% of firm sales, and an average firm in our sample is 19
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years old. Largest shareholders on average hold 38.729% of firm equity, and the average state
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ownership is 30.92% of total shares outstanding. During our sample period, the average GDP growth rate is 9.14% and the average provincial marketization index is 7.777. In addition, the
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real estate index ranges from 104.273 to 139.615 with an average of 116.342.
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***Insert Table 2 about Here***
4.2 Research Methods
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We first apply the OLS method to pooled cross-sectional data and use it as a baseline to examine the impact of monetary policy on firm scale. The model is specified as below: 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼 + 𝛽𝑀𝑃𝑖,𝑡 + 𝛾𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
(1)
The subscript i denotes the firm and t denotes time. The dependent variable is Log Asset. MP represents monetary policy measured either by benchmark lending rates or reserve requirement ratios. Control’ is a vector of control variables specified above, µt represent time dummy
ACCEPTED MANUSCRIPT variables, and εit is the random error. This pooled-OLS method, however, inevitably leads to omitted variable bias and is unable to control for the endogeneity problem (Wooldridge, 2002). We next apply the panel data fixed effect model to control for endogeneity caused by unobserved firm specific effects that may be simultaneously correlated with our dependent
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variable (firm scale) and independent variable (monetary policy). The fixed effect model is
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equivalent to imposing firm specific indicator variables in the regression along with other
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independent variables. 3 It is actually a difference model testing the impact of changes in monetary policy on changes in firm scale. Thus, it provides a within-sample estimate that
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compares firm scale with itself overtime instead of with other firms in the sample. This model is
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specified as below:
∆𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼𝑖 + 𝛽∆𝑀𝑃𝑖,𝑡 + 𝛾∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
(2)
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Here the subscript i denotes the firm and t denotes time. ΔLog Asset is the difference
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between firm scale for firm i at time t and time-series mean firm assets within each crosssectional unit. αi is firm-specific fixed effects which is normally distributed with zero mean.
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ΔControl represent changes in all control variables specified above. µt represent time dummy
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variables, and εit is the random error. Although the fixed effect model eliminates firm specific heterogeneity, it does not control
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for the dynamic structure in the data. In particular, firm scale may possess rigidity. That is, the current period asset may be correlated with assets in adjacent periods, while both static asset models identified above unrealistically assume firm assets have no temporal correlation. We next follow the dynamic panel data method proposed by Arellano and Bond (1991) and Blundell and Bond (1998) to estimate a dynamic autoregressive model in which the dependent variable is Log 3
The Hausman -Wu test confirms the appropriateness of using a fixed effects model over the rando m-effects panel data model. Our results suggest that fixed effect models provide more consistent estimates for both monetary policy measures.
ACCEPTED MANUSCRIPT Asset in firm i at time t. Persistence in firm scale is captured by including two lagged dependent variables (Log Asset i,t-1, Log Asset i,t-2 ). The model is specified as: 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝑀𝑃𝑖,𝑡 + 𝜃1 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖 ,𝑡−1 +𝜃2 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−2 + 𝜆𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
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(3)
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Here the subscript i denotes the firm and t denotes time. αi. represent the firm fixed effects.
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Persistence in firm assets is reflected in the adjustment parameters θ1 and θ 2 . Other variables are specified as above. 4 This model is also a change model providing a within-sample estimate that
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captures the effect of monetary policy change on firm scale change after controlling for the
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influence of lagged dependent variables.
All three aforementioned models apply conditional mean regression methods built on the
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assumption that the average effect of monetary policy on firm scale is constant, and are
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computed by minimizing the mean square error to predict the conditional average of the outcome variable. Given monetary policy may also affect the dispersion of firm scale and generate distinct
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effects on different parts of the firm size distribution, the conditional mean regression method
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used above may conceal significant parameter heterogeneity in the link between monetary policy and firm scale. We next apply the quantile regression method to solve this problem (Koenker and
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Hallock, 2001) 5 . Compared with conditional mean regressions, quantile regression can better reflect the impact of the independent variables on the position, scale and shape of the distribution
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We estimate the model using the Arellano-Bond method (1991) as extended in Blundell and Bond (1998) to construct an appropriate IV and GMM estimator. More specifically, lags of the dependent variable and first differences of the exogenous variables are used as instruments for the first-difference equation. This is achieved using Stata 14 code xtabond. 5
Quantile regression is a method to estimate conditional quantile functions of dependent variables, and the parameters are estimated by minimizing the sum of the weighted absolute values of the residuals in a weighted least squares regression. A mo re co mplete d iscussion of quantile regression methods can be found in Koenker and Gilbert (1978) and Koenker and Hallock (2001).
ACCEPTED MANUSCRIPT of the dependent variable, and better capture tail characteristics of the distribution as well (Li, 2016). More specifically, we first identify a class of estimators, one for each desired quantile (e.g., 0.25, 0.50, 0.75). We then characterize the conditional distribution of the outcome variable by showing the effect of each input variab le on the outcome measure at each given quantile
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𝑄𝜏 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 |𝑋𝑖𝑡 ) = 𝛼𝜏 + 𝛽𝜏 𝑀𝑃𝑖𝑡 + 𝛾𝜏 𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
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(Koenker, 2004).6 The panel data quantile regression model is expressed as: (4)
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Here Qτ (Log Asset it|Xit) is the τth quantile regression function on firm size. In our main analysis, we report estimates of quantile functions at the median of the size distribution (50th
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percentile) and the interquartile regressions (25th and 75th percentile) by estimating three
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quantile regression functions: Q(0.25), Q(0.5), and Q(0.75). MP refers to monetary policy instruments. ατ are a set of constant terms at each quantile and β τ are coefficient estimates
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5. Main Empirical Results
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corresponding to each quantile . Other variables are defined as above.
5.1 Conditional Mean Regression Results
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We present testing results for Hypothesis 1 in Table 3 in a pecking order. We first present results
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of pooled-OLS models in Columns 1 and 2, which provide information on the correlation between monetary policy and firm scale but ignore unobserved firm heterogeneity, dynamic
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structure, and endogenous variables. We next present static fixed effects models in Columns 3 and 4, which control for firm specific heterogeneity but do not consider the dynamic structure. We next present dynamic panel data estimates in Columns 5 and 6 to control for the persistence in firm scale, firm level fixed effects, and also address the endogenous variable problem. Columns 1, 3, 5 report results of benchmark loan interest rates and columns 2, 4, and 6 report 6
Specifically, we utilize the panel quantile regression estimate (PQR) in STATA 14.
ACCEPTED MANUSCRIPT results of reserve requirement ratios. ***Insert Table 3 about Here*** Table 3 demonstrates a consistently significant negative association between interest rates and firm scale in the pooled OLS, fixed-effect panel data, and dynamic panel data models, with
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coefficients ranging from -0.026 to -0.129, all significant at 0.01 level. These results suggest that
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firms reduce their scales in response to increasing interest rates under contractionary monetary
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policy, thus confirms the prediction of H1. In addition, the impact of the reserve requirements on firm scale is significantly negative at least at 0.10 level, with coefficients ranging from -0.005 to
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-0.026. These results provide additional support for H1 that firm scales decrease when reserve
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requirements increase under contractionary monetary policy. Overall, Table 3 provides consistent support for our H1 that both quantity and price based monetary policy instruments are
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effective in affecting firm scale in China. This conclusion holds after we control for firm level
5.2 Quantile Regression Results
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fixed effects and persistence of firm scale over time.
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As we argue earlier, results from mean regression models only reflect the average impact of
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monetary policy on firm size, but cannot tell whether firms of various sizes may react to monetary policy by adjusting their scales to a different degree. We thereby present quantile
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regression results in Table 4 to elaborate the asymmetric effect of monetary policy on firm size. Columns 1, 2, 3 report results of interest rates and columns 4, 5, 6 present results of reserve requirement ratios. ***Insert Table 4 about Here*** Table 4 suggests that both lending interest rates and reserve requirements have a significantly negative impact on firm scale at all three firm size quantile levels (25th, 50th, and
ACCEPTED MANUSCRIPT 75th) when examined separately. These results are consistent with results reported in the mean regression models, i.e. firms reduce their scales in response to tightening monetary policy. Importantly, we observe that the impact of monetary policy at different quantile points is not the same. Taken interest rates as an example, the coefficient is -0.098 at the 25th percentile, it
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decreases to -0.117 at the 50th percentile, and further reduces to -0.142 at the 75th percentile of
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firm size. These results suggest that larger firms respond more strongly to rising interest rates by
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decreasing their scales at a greater magnitude. The impact of reserve requirements shows the same pattern, with the negative coefficients of large firms being much larger in magnitude (β=-
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0.035) compared to those of median sized (β=-0.031) and smaller firms (β=-0.023). In general,
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these results support our H2 that larger firms reduce their scales to a greater degree in case of contractionary monetary policy.
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We next plot the size effects of interest rates and reserve requirements in Figure 2 to reveal
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the asymmetric monetary policy effect. For each dependent variable, we estimated nineteen separate quantile regressions for quantiles ranging from 0.05 to 0.95 at a 5% incremental step.
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The left side presents results of interest rates and the right side illustrates results of reserve
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requirement ratios. The horizontal x-axis in each figure shows the quantile scale and the vertical y-axis indicates coefficients of monetary policy effects on firm size. The solid line shows the
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estimate for each quantile τ and the shaded area represents the 90% confidence intervals from the regression estimates.
*** Insert Figure 2 about Here *** Figure 2 shows a clear decreasing trend between the size quantile and coefficients of monetary policy on firm size for both interest rates and deposit reserve ratios. We find that the effect of interest rates on firm scale is quantitatively smaller at lower percentiles than at higher
ACCEPTED MANUSCRIPT percentiles. Similarly, the effect of reserve requirements on firm size is also quantitatively smaller at lower percentiles than at higher ones. These results once again support H2 that larger firms react more strongly to monetary policy by adjusting their scales to a greater degree. 4.3 Inter-quantile Regression Results
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Our findings presented above suggest that monetary policy affects firm scale differently at
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different firm size levels. We next test whether these differences are statistically significant by
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performing inter-quantile regressions. Using the difference between the 75th quantile and the 50th quantile as an example, the estimates can be expressed as below:
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𝑄(75/50) = 𝑄0.75 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 ) − 𝑄0.50 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 )
M
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= (𝛼0.75 − 𝛼0.50 ) + (𝛽0.75 − 𝛽0.50 )𝑀𝑃𝑖𝑡 + (𝛾0.75 − 𝛾0 .50 )𝐶𝑜𝑛𝑡𝑟𝑜𝑙 ′ 𝑖𝑡 + 𝜀𝑖𝑡
(5)
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Here Q(75/50) is the difference- in-quantile estimate between the 75th quantile and the 50th quantile. Q0.75 (LogAsset) refers to the 75th quantile estimation, and Q0.50 (LogAsset) is the 50th
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quantile estimation. All other variables are defined as in Formula (4). We also report inter-
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quantile estimates for Q(50/25) and Q(75/25) in Table 5, with columns 1 to 3 on the impact of
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interest rates and columns 4 to 6 on the impact of reserve requirements. *** Insert Table 5 about Here ***
Table 5 suggests that compared to firms at the 25th percentile of size distribution, firms at the 75th percentile size distribution react much more strongly to interest rate changes by reducing their scales, with the coefficient being -0.044 and significant at 0.05 level. There are also statistically significant differences between firms at the 75th percentile size distribution and the 50th , as well as between firms at the 50th percentile size distribution and the 25th. In terms of
ACCEPTED MANUSCRIPT reserve requirement ratios, the difference between the 50th percentile and the 25th percentile Q(50/25) is statistically significant, which suggests that median-sized firms respond more strongly to increasing reserve requirements by decreasing their scales than smaller firms. However, no statistically significant difference is observed between other quantile pairs.
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Generally speaking, our inter-quantile regression results support our prediction in H2 that larger
of interest rate changes. 6. Additional Analyses
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6.1 Ownership Structure and Monetary Policy Effect
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firms react more strongly to monetary policy than their smaller counterparts, particularly in case
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A unique Chinese context is that a significant proportion of listed firms are state-owned enterprises (SOEs), in which the state retains sufficient shares and control. Unlike their private
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counterparts, maximizing shareholder value may not be the ultimate goal of SOEs who often
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pursue other social and political objectives such as providing employment and maintaining social stability (Chang and Wong, 2009). Moreover, SOEs are often sheltered from the market for
PT
corporate control as a result of “soft budget constraints” (Kornari et al., 2003). Consequently,
CE
they are often bailed out by the government through subsidies, additional bank loans, and other financial support in the event of financial distress instead of undergoing bankruptcy or takeover
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(Conyon and He, 2011). SOEs may also enjoy exclusive protection in some industries where government regulations create high entry barriers for private competitors. The combination of soft budget constraints and limited product market competition provides few incentives for SOE managers to make investment decision and adjust firm scales based on credit availability and cost of capital in the financial market. Wang et al. (2015) thus suggest that monetary policy instruments are less effective in influencing SOEs’ expansion or contraction decisions due to the
ACCEPTED MANUSCRIPT lack of market mechanisms to discipline SOEs. Similarly, we expect that the effect of Chinese monetary policy on firm scale may be less salient among SOEs than in non-SOEs. To test the moderating role of ownership structure on the influence of monetary policy a nd the policy’s differential impact on SOEs and non-SOEs at different size distribution, we add
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interaction variables of SOE with monetary policy to Equation (4) and report our quantile
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regression results in Table 6. A firm is classified as a SOE when the largest equity owner is the
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state and vice versa. Columns 1, 2, 3 of Table 6 report results of benchmark interest rates by adding SOE×Interest Rate-L and columns 4, 5, and 6 report results of reserve requirements by
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adding SOE×Reserve Ratio. Using interaction variables to test the moderating role of SOE is
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based on the assumption that relationships between control variables and the dependent variable are the same between the subgroups of SOEs and non-SOEs, while this assumption may not
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always hold. Therefore, we also perform split-sample analysis to allow other covariates to vary
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between the subgroups of SOEs and non-SOEs. These results are reported in Table 7, with columns 1 to 6 on interest rates and columns 7 to 9 on reserve ratios. We utilize quantile
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policy on firm size.
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regression methods in all these models to take into account the differential impact o f monetary
*** Insert Tables 6 and 7 about Here ***
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Table 6 shows that both interest rates and reserve requirements have significant negative effects on firm scale for all size quantiles of SOEs and non-SOEs. These results again confirm our prediction in H1 that both price- and quantity-based monetary policy instruments are effective in China. We also observe the asymmetric size effect, with absolute values of coefficients at lower quantiles being generally smaller than those at higher size quantiles. Consistent with H2, we find that larger firms respond to contractionary policy by reducing their
ACCEPTED MANUSCRIPT scales to a greater degree than smaller firms for both SOEs and non-SOEs. Importantly, Table 6 indicates that the coefficients of SOE×Interest Rate-L and SOE×Reserve Ratio are positively significant in all three quantiles. These results suggest that the impact of monetary policy on firms scale is less salient in SOEs than in non-SOEs across all quantiles of firm size distribution,
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thus are consistent with our prediction.
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Our split sample results in Table 7 reveal the same pattern. We find that increasing interest
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rates and reserve requirements result in a reduction of firm scale for all size quantiles of SOEs and non-SOEs as predicted by H1. Table 7 also reveals that larger firms respond more strongly
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to both types of monetary policy instruments than their smaller counterparts. In addition, the
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absolute values of coefficients are consistently smaller in SOEs than in non-SOEs for each pair of the size quantile. These results indicate that SOEs are less responsive to changes in monetary
M
policy instruments than private firms in all size distribution.
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To better uncover the distinct responses of SOEs and non-SOEs to monetary policy, we plot the impact of both policy instruments on firm size for these two types of firms in Figure 3. The
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left side presents the impact of interest rates and the right s ide depicts the influence of reserve
CE
requirements. We estimate separate quantile regressions for firm size quantiles ranging from 0.10 to 0.90, and present the firm size effect for 10% incremental steps. The horizontal x-axis in each
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figure shows the quantile scale and the vertical y-axis indicates coefficients of monetary policy effects on firm size. Figure 3 shows that both SOEs and non-SOEs respond to raising interest rates and reserve requirements by decreasing their sizes across all quantiles of size distribution. However, the coefficient lines of SOEs are much smoother than those of non-SOEs for both policy instruments, which confirms our prediction that SOEs make smaller adjustment to their scales than non-SOEs in case of monetary policy changes.
ACCEPTED MANUSCRIPT *** Insert Figure 3 about Here *** 6.2 Regulatory Reform and Monetary Policy Effect During our study period, the PBC has initiated some major monetary policy reforms. Before July 2013, the PBC not only issued the benchmark lending interest rates, but also imposed a band for
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financial institutions to set their retail lending rates (Fernald et al., 2014). This range restriction is
IP
removed after July 2013 when financial institutions have greater leeway in determining their
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retail loan interest rates based on the published benchmark lending rates. The enforcement of a rate band in early years seriously constrain variations in commercial banks’ retail lending rates,
US
and consequently affect incentives of banks to engage in more or less active lending. As a result,
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we expect that the effect of the monetary transmission mechanism on firm investment will be limited in this period. In contrast, the removal of the band allows retail interest rates to fluctuate
M
to a greater degree, thus enables commercial banks to better adjust their lending rates and scales
ED
based on firm risks and other characteristics. Consequently, the impact of monetary policy instruments on firm investment and other outcomes shall also be larger during this period. Taken
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together, we expect that monetary policy mechanisms will be more effective in affecting firm
CE
scale after the removal of the floating restriction. We next apply both the interaction variable approach and the split sample approach to test
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the moderating role of this regulatory reform. We first create a dummy variable Reform, which is equal to 1 if the period is after the second quarter of 2013 and 0 if it is before this cutoff point. We report results of our interaction models in Table 8 with columns 1, 2, and 3 adding Reform×Interest Rate-L to test the influence of lending interest rate changes a nd columns 4, 5, and 6 adding Reform×Reserve Ratio to examine the impact of reserve requirements. We also report our split-sample results for the pre-reform period and the post-reform period in Table 9,
ACCEPTED MANUSCRIPT with columns 1 to 6 showing results of interest rates, and columns 7 to 9 reporting results of reserve requirements. We again apply quantile regression methods in all models to investigate the asymmetric effect of firm size. *** Insert Tables 8 and 9 about Here ***
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Table 8 indicates that the impact of interest rates on firm scale is more salient for firms at the
IP
75% quantile during the post-reform period compared to the earlier years, while no significant
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difference between the pre-reform and the post-reform periods is identified for the other two size quantiles. These results suggest that the impact of interest rates on firm scale is more salient for
US
larger firms during the post-reform period than in the pre-reform period. We also observe that
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raising reserve ratios has no significant influence on firm scale before the regulatory reform, but results in significant reduction in firm scale in the post reform period for all three firm size
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quantiles. These results suggest that reserve requirement is more effective in controlling firm
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scale after the regulatory reform. Our split sample results in Table 9 reveal a similar but clearer trend on the influence of regulatory reform on monetary policy effect. In the post-reform period,
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when lending interest rates or reserve requirement rates increase, firms at all three size quantiles
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reduce their scales. Such a negative relationship however is not consistent in the pre-reform period. These findings indicate that both price- and quantity-based monetary policy instruments
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are more likely to achieve their intended goals during the post-reform period when the government allows commercial banks more leeway in setting their lending rates based on market conditions. 7. Discussion and Conclusion This paper investigates the asymmetric effect of monetary policy on firm scale at different firm size levels. Using panel data of C hinese listed firms between 2011 and 2016, we document that
ACCEPTED MANUSCRIPT Chinese firms respond to raising interest rates and reserve requirement ratios by decreasing their scales. Importantly, such a response is stronger for larger firms compared to smaller firms in case of both monetary policy instruments. Our results also indicate that SOEs react more mildly to monetary policy instruments than privately controlled firms do. In addition, monetary policy
IP
commercial banks having greater leeway in setting their lending rates.
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tools are more effective in influencing firm scales during the post-reform period when
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Our paper reveals that monetary transmission mechanisms do seem to work in China to influence firm expansion and contraction decisions at least for our sample companies during our
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sample period. This result echoes findings of prior research that has adopted macroeconomic
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data or bank data to study monetary transmission mechanisms in China (e.g., e.g., Chen et al., 2017; He and Wang, 2012; Ouyang and Shi, 2010; Ouyang and Wang, 2009; Sun, 2013). We
M
also highlight heterogeneity in the effectiveness of monetary policy instruments. Contrary to
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evidence in western economies that smaller firms are more heavily influenced by monetary policy (Gertler and Gilchrist, 1994; Ehrmann, 2000), we instead find that larger firms in China
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respond more strongly to monetary policy by adjusting their scales to a greater degree than
CE
smaller firms do. These results could probably be attributed to the fact that smaller Chinese firms tend to rely less on formal financial institutions for investment needs and are thus less sensitive
AC
to governmental policy changes. We also observe that monetary policy has a smaller influence on SOEs than on privately controlled firms. This is likely a result of SOEs’ soft budget constraints that relax their liquidity constrains and consequently mitigate the influence of monetary policy instruments. In addition, our results indicate that the effect of monetary policy on firm scale is stronger in the post-reform period when financial institutions have more leeway in setting their lending rates.
ACCEPTED MANUSCRIPT From the methodological point of view, our study is the first to apply the quantile regression method to examine the differential impact of monetary policy on firm scale at different firm size distributions. In contrast to conditional mean regression models that predict the conditional average of the outcome variable, quantile regression generates estimates from a complete range
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of conditional quantile functions to characterize the whole distribution of the response variable
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(Li, 2015). Pertinent to our paper, the utilization of quantile regression method allows us to
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describe the relationship between monetary policy indicators and firm scales for firms at different size distribution. It thus offers a more complete picture of the size impact of monetary
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policy than prior studies applying standard conditional mean regressions. From this aspect, our
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study also augments prior empirical studies utilizing the quantile regression method to further highlight merits and applicability of this method in economic and finance literature, which we
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hope may stimulate more research along this line (e.g., Conyon and He, 2017; Klomp and Haan,
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2012; Lee and Li, 2012).
Our study also possesses significant practical implications. Our results reveal that Chinese
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firms’ investment decisions are still heavily influenced by the grabbing hand of the government
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instead of the invisible hand of the market. Chen et al (2017) observe that Chinese bank loans do not respond to monetary policy shocks in the way as banks at developed countries. Specifically,
AC
they point out that the growth rate of bank loans in China is somehow detached from conditions on the interbank market set by the central bank. While our empirical results indicate that monetary policy tools are generally more effective in the post-reform period when the government relaxes its control on commercial banks’ retail lending rates, the liberalization of the Chinese financial market still has a long way to go for the market to play a more central role in influencing business behavior and economic outcomes.
ACCEPTED MANUSCRIPT Our research is also subject to a number of limitations that warrant further investigations. First, the outcome variable in this study is firm scale measured using firm total assets. Monetary policy instruments can influence many other firm decisions and outcomes, such as long-term investment, R&D expenditure, and capital structure. Future research could further explore
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additional process and outcome variables to help untangle the impact of monetary policy on
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micro firm behavior. Second, our study uses data from China. The extant literature suggests that
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there may exist significant cross-country differences in the monetary policy effect. Therefore, it may also be valuable for future research to investigate the effect of monetary policy in a broader
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multi-country setting to explore how country heterogeneity and firm heterogeneity may
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intertwine with each other to influence the functioning of monetary transmission mechanisms. To sum up, our paper provides the first micro- level evidence on the asymmetric effects of
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monetary policy on firm scales at different firm size levels by utilizing the quantile regression
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method. We suggest that quantile regression approach has important merits in overcoming the limitations of the conditional mean regression to provide a more thorough investigation of the
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influence of firm heterogeneity on monetary policy effect. We hope that our findings will
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stimulate more research on the asymmetric effect of monetary policy instruments on firm
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behavior and economic outcomes in China and beyond.
ACCEPTED MANUSCRIPT
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ACCEPTED MANUSCRIPT Table 1. Sample Distribution by Years
Year Firm # Observation #
2011 996 3,849
2012 1,046 3,921
2013 1,064 4,157
2014 1,063 3,949
2015 1,069 4,200
2016 1,070 4,239
PT CE AC
CR
Max 6.560 21.500 27.104 27.320 0.337 2.885 38.000 31.806 74.350 84.998 36.018 10.407 139.615
US
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Mean 5.521 18.882 22.177 19.754 0.513 0.003 19.344 0.017 38.729 30.920 9.140 7.777 116.362
M
Median 5.600 19.000 22.095 19.998 0.506 0.007 19.000 0.011 36.350 32.252 8.679 8.210 116.379
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Variable Interest_L (%) Reserve Ratio (%) Log Assets Log Fix Assets Leverage Subsidy Firm Age ROA Largest % State % GDP Growth (%) Marketization Index Real Estate Index
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Table 2. Descriptive Statistics of Key Variables
Min 4.350 16.500 17.441 5.594 0.091 0.000 11.000 -28.756 9.440 0.000 -10.823 2.500 104.273
SD 0.766 1.613 1.390 2.361 0.281 0.205 4.202 4.811 16.116 24.102 11.943 1.942 9.790
ACCEPTED MANUSCRIPT Table 3. The Impact of Monetary Policy on Firm Scale: Conditional Mean Regression Models
Interest Rate_L Reserve Ratio
Log Assets Fix Effects (3) (4) -0.030*** (0.010) -0.005* (0.003)
Log Fix Assets Largest % State % GDP Growth Marketization Index
AC
R2 Observations
CE
Real Estate Index
-0.000 (0.000) 0.000* (0.000) 0.112*** (0.004) 0.007*** (0.001) 0.193*** (0.002) 0.002*** (0.000) 0.104*** (0.025) -0.003*** (0.000) -0.042*** (0.010) 0.002*** (0.001) Yes 16.333*** (0.131) 0.528 24,281
US
-0.000 (0.000) 0.000* (0.000) 0.104*** (0.005) 0.007*** (0.001) 0.193*** (0.002) 0.002*** (0.000) 0.107*** (0.025) -0.002*** (0.000) -0.043*** (0.010) 0.002*** (0.001) Yes 16.584*** (0.163) 0.528 24,281
AN
ROA
M
Firm Age
0.006*** (0.000) 0.004*** (0.000) 0.002 (0.002) 0.038*** (0.001) 0.360*** (0.003) 0.009*** (0.001) -0.017 (0.035) -0.000 (0.001) 0.086*** (0.004) 0.012*** (0.001) Yes 12.794*** (0.223) 0.471 24,281
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Subsidy
0.006*** (0.000) 0.004*** (0.000) 0.001 (0.002) 0.038*** (0.001) 0.358*** (0.003) 0.009*** (0.001) -0.009 (0.035) 0.001* (0.001) 0.086*** (0.004) 0.007*** (0.001) Yes 13.591*** (0.233) 0.472 24,281
PT
Leverage
CR
L2.LogAssets
Time Dummy Constant
-0.026*** (0.008)
0.078*** (0.021) -0.005 (0.009) 0.005*** (0.002) 0.000 (0.000) 0.067*** (0.005) 0.005*** (0.001) 0.157*** (0.010) 0.000 (0.001) 0.000 (0.000) -0.002*** (0.000) 0.000 (0.016) 0.001*** (0.000)
IP
L1.LogAssets
Log Assets Dynamic Panel Data (5) (6)
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Variables
Log Assets OLS (1) (2) -0.129*** (0.016) -0.026*** (0.006)
-0.010* (0.006) 0.091*** (0.021) -0.002 (0.009) 0.005*** (0.002) 0.000 (0.000) 0.065*** (0.008) 0.005*** (0.001) 0.158*** (0.010) -0.000 (0.001) 0.000 (0.000) -0.002*** (0.000) -0.000 (0.016) 0.001*** (0.000)
Yes
Yes
15.874*** (0.495)
15.601*** (0.483)
/ 20,331
/ 20,331
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors in
parentheses.
ACCEPTED MANUSCRIPT Table 4. The Impact of Monetary Policy on Firm Scale: Quantile Regression Models Q75 (3) -0.142*** (0.028)
Reserve Ratio
EP T
Log Assets Q50 (5)
Q75 (6)
-0.023*** (0.006) 0.004*** (0.001) 0.003*** (0.001) -0.006*** (0.001) 0.036*** (0.002) 0.514*** (0.006) 0.005*** (0.000) 0.006 (0.033) 0.001 (0.000) 0.080*** (0.003) 0.007*** (0.001) Yes 9.906*** (0.252) 0.334 24,281
-0.031*** (0.006) 0.011*** (0.001) 0.005*** (0.001) 0.011*** (0.002) 0.047*** (0.002) 0.385*** (0.007) 0.009*** (0.000) -0.046 (0.042) 0.001** (0.001) 0.080*** (0.003) 0.009*** (0.001) Yes 12.193*** (0.255) 0.294 24,281
-0.035*** (0.009) 0.018*** (0.001) 0.005*** (0.001) 0.015*** (0.003) 0.055*** (0.002) 0.299*** (0.007) 0.014*** (0.001) -0.211*** (0.052) 0.002*** (0.001) 0.077*** (0.005) 0.010*** (0.001) Yes 13.938*** (0.315) 0.279 24,281
SC
0.018*** (0.001) 0.005*** (0.001) 0.014*** (0.002) 0.056*** (0.002) 0.296*** (0.007) 0.014*** (0.001) -0.204*** (0.040) 0.003*** (0.001) 0.078*** (0.005) 0.005** (0.002) Yes 14.651*** (0.486) 0.279 24,281
NU
0.011*** (0.000) 0.005*** (0.001) 0.009*** (0.002) 0.047*** (0.002) 0.383*** (0.005) 0.009*** (0.000) -0.031* (0.018) 0.002*** (0.000) 0.081*** (0.003) 0.006*** (0.001) Yes 12.664*** (0.293) 0.294 24,281
MA
0.004*** (0.000) Subsidy 0.004*** (0.001) Firm Age -0.007*** (0.002) ROA 0.037*** (0.001) Log Fix Assets 0.513*** (0.007) Largest % 0.005*** (0.000) State % 0.000 (0.027) GDP Growth 0.002*** (0.000) Marketization Index 0.080*** (0.003) Real Estate Index 0.004*** (0.001) Time Dummy Yes Constant 10.431*** (0.307) Pseudo R2 0.334 Observations 24,281
ED
Leverage
Q25 (4)
PT
Log Assets Q50 (2) -0.117*** (0.016)
RI
Interest Rate_L
Q25 (1) -0.098*** (0.015)
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
AC C
in parentheses.
ACCEPTED MANUSCRIPT Table 5. Monetary Policy and Firm Scale: Inter-quantile Consistency Tests
Reserve Ratio
Largest % State % GDP Growth Marketization Index Real Estate Index Time Dummy Constant Observations
Log Assets Q (75/50) Q (50/25) (5) (6)
-0.012 (0.012) 0.015*** (0.001) 0.002*** (0.001) 0.021*** (0.003) 0.019*** (0.003) -0.215*** (0.006) 0.009*** (0.001) -0.217*** (0.047) 0.002* (0.001) -0.003 (0.006) 0.002 (0.002) Yes 4.032*** (0.432) 24,281
-0.004 (0.009) 0.007*** (0.000) 0.001 (0.001) 0.004* (0.002) 0.008*** (0.002) -0.086*** (0.003) 0.005*** (0.001) -0.165*** (0.043) 0.001 (0.001) -0.002 (0.005) 0.001 (0.002) Yes 1.745*** (0.345) 24,281
SC
Log Fix Assets
NU
ROA
0.007*** (0.000) 0.001** (0.000) 0.017*** (0.002) 0.011*** (0.002) -0.130*** (0.005) 0.003*** (0.001) -0.031 (0.040) 0.001 (0.001) 0.001 (0.003) 0.002** (0.001) Yes 2.233*** (0.162) 24,281
MA
Firm Age
0.007*** (0.000) 0.001 (0.001) 0.005** (0.002) 0.008*** (0.002) -0.087*** (0.006) 0.005*** (0.001) -0.173*** (0.034) 0.001 (0.001) -0.003 (0.006) -0.001 (0.001) Yes 1.987*** (0.250) 24,281
ED
Subsidy
0.014*** (0.000) 0.002*** (0.001) 0.021*** (0.003) 0.019*** (0.002) -0.217*** (0.006) 0.009*** (0.001) -0.204*** (0.054) 0.002** (0.001) -0.002 (0.006) 0.001 (0.001) Yes 4.220*** (0.284) 24,281
EP T
Leverage
Q (75/25) (4)
PT
Log Assets Q (75/50) Q (50/25) (2) (3) -0.025* -0.018* (0.013) (0.011)
RI
Interest_L
Q (75/25) (1) -0.044** (0.018)
-0.008** (0.004) 0.007*** (0.000) 0.001** (0.000) 0.017*** (0.001) 0.011*** (0.002) -0.129*** (0.006) 0.004*** (0.000) -0.052 (0.035) 0.001* (0.000) -0.000 (0.004) 0.002** (0.001) Yes 2.287*** (0.166) 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
AC C
in parentheses.
ACCEPTED MANUSCRIPT Table 6. The Influence of SOE on Monetary Policy Effect: Interaction Models Q75 (3)
-0.159*** (0.021) 0.091*** (0.019)
-0.158*** (0.025) 0.070*** (0.017)
-0.190*** (0.038) 0.079*** (0.027)
Reserve Ratio
ROA Log Fix Assets Largest % State % GDP Growth Marketization Index Real Estate Index
0.336 24,281
0.296 24,281
AC C
Time Dummy Constant Pseudo R2 Observations
Log Assets Q50 (5)
Q75 (6)
-0.045*** (0.010) 0.034*** (0.009) -0.527*** (0.176) 0.003*** (0.000) 0.004*** (0.001) -0.007*** (0.002) 0.037*** (0.001) 0.511*** (0.005) 0.005*** (0.001) -0.001*** (0.001) 0.001 (0.001) 0.080*** (0.003) 0.007*** (0.001) Yes 10.402*** (0.336)
-0.053*** (0.010) 0.039*** (0.009) -0.564*** (0.168) 0.011*** (0.001) 0.005*** (0.001) 0.008*** (0.002) 0.046*** (0.002) 0.383*** (0.006) 0.009*** (0.001) -0.003*** (0.001) 0.002*** (0.000) 0.082*** (0.004) 0.009*** (0.001) Yes 12.715*** (0.291)
-0.057*** (0.012) 0.033*** (0.012) -0.380* (0.227) 0.018*** (0.000) 0.005*** (0.001) 0.013*** (0.002) 0.053*** (0.002) 0.297*** (0.005) 0.014*** (0.001) -0.005*** (0.000) 0.002** (0.001) 0.076*** (0.005) 0.009*** (0.002) Yes 14.424*** (0.392)
0.335 24,281
0.296 24,281
0.282 24,281
SC
Firm Age
-0.195 (0.162) 0.018*** (0.000) 0.005*** (0.001) 0.012*** (0.002) 0.054*** (0.003) 0.294*** (0.006) 0.014*** (0.001) -0.004*** (0.001) 0.003*** (0.001) 0.078*** (0.005) 0.006*** (0.002) Yes 14.770*** (0.308)
NU
Subsidy
-0.220** (0.095) 0.011*** (0.000) 0.005*** (0.001) 0.007*** (0.002) 0.047*** (0.002) 0.380*** (0.006) 0.009*** (0.000) -0.003*** (0.001) 0.002*** (0.001) 0.083*** (0.003) 0.005*** (0.001) Yes 12.947*** (0.152)
MA
Leverage
-0.389*** (0.098) 0.004*** (0.001) 0.004*** (0.001) -0.008*** (0.002) 0.037*** (0.002) 0.510*** (0.006) 0.005*** (0.001) -0.001*** (0.000) 0.001** (0.001) 0.079*** (0.003) 0.004*** (0.001) Yes 10.547*** (0.207)
ED
SOE
EP T
SOE× Reserve Ratio
Q25 (4)
PT
SOE×InterestRate_L
Log Assets Q50 (2)
RI
Interest Rate_L
Q25 (1)
0.283 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
in parentheses.
ACCEPTED MANUSCRIPT Table 7. The Influence of SOE on Monetary Policy Effect: Split Sample Models Log Assets
Interest Rate_L
Q25 (1) -0.079*** (0.016)
SOEs Q50 (2) -0.095*** (0.020)
Q75 (3) -0.100*** (0.019)
Log Assets
Q25 (4) -0.141*** (0.028)
Non-SOEs Q50 (5) -0.181*** (0.032)
Q75 (6) -0.194*** (0.038)
Q25 (7)
SOEs Q50 (8)
T P
I R
Reserve Ratio
Q75 (9)
-0.016** -0.018** -0.023* (0.007) (0.008) (0.012) Leverage 0.006*** 0.012*** 0.019*** 0.001 0.009*** 0.016*** 0.006*** 0.012*** 0.018*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) (0.001) Subsidy 0.006*** 0.006*** 0.008*** 0.002*** 0.002*** 0.002** 0.006*** 0.006*** 0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) Firm Age 0.004* 0.009*** 0.014*** -0.030*** -0.007 -0.005 0.004** 0.010*** 0.016*** (0.002) (0.003) (0.003) (0.004) (0.004) (0.005) (0.002) (0.002) (0.003) ROA 0.046*** 0.056*** 0.057*** 0.026*** 0.037*** 0.047*** 0.046*** 0.055*** 0.056*** (0.002) (0.003) (0.003) (0.004) (0.003) (0.005) (0.002) (0.003) (0.003) Log Fix 0.510*** 0.398*** 0.327*** 0.479*** 0.352*** 0.224*** 0.511*** 0.400*** 0.330*** (0.007) (0.006) (0.007) (0.012) (0.011) (0.008) (0.006) (0.008) (0.007) Largest % 0.007*** 0.010*** 0.014*** 0.004*** 0.007*** 0.009*** 0.007*** 0.010*** 0.014*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) (0.001) State % -0.111* -0.311*** -0.436*** -0.248*** -0.456*** -0.586*** -0.103 -0.318*** -0.423*** (0.063) (0.059) (0.058) (0.075) (0.092) (0.094) (0.073) (0.068) (0.065) GDP Growth 0.002*** 0.002*** 0.003*** 0.001 0.002 0.003** 0.001* 0.002** 0.003*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Market. Index 0.076*** 0.093*** 0.102*** 0.092*** 0.084*** 0.022** 0.076*** 0.092*** 0.102*** (0.004) (0.005) (0.005) (0.008) (0.007) (0.010) (0.004) (0.005) (0.007) Real Estate Index 0.002* 0.004*** 0.004*** 0.007*** 0.006** 0.009*** 0.005*** 0.007*** 0.007*** (0.001) (0.001) (0.001) (0.002) (0.003) (0.003) (0.001) (0.002) (0.002) Time Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 10.262*** 12.396*** 13.915*** 11.526*** 14.065*** 16.996*** 9.790*** 11.781*** 13.358*** (0.245) (0.299) (0.290) (0.470) (0.482) (0.535) (0.262) (0.298) (0.513) Pseudo R2 0.316 0.306 0.302 0.317 0.261 0.231 0.316 0.306 0.302 Observations 16,046 16,046 16,046 8,235 8,235 8,235 16,046 16,046 16,046 Note: ***,** and * represent significance at levels of 1%, 5% and 10% respectively. Robust standard errors in parentheses.
C S
U N
D E
T P
C A
E C
A M
Q25 (10)
Non-SOEs Q50 (11)
Q75 (12)
-0.033** (0.014) 0.000 (0.001) 0.002*** (0.000) -0.029*** (0.004) 0.027*** (0.004) 0.478*** (0.009) 0.004*** (0.001) -0.300*** (0.071) -0.000 (0.001) 0.092*** (0.005) 0.012*** (0.002) Yes 10.847*** (0.465) 0.316 8,235
-0.047*** (0.013) 0.009*** (0.001) 0.002*** (0.001) -0.002 (0.004) 0.036*** (0.003) 0.360*** (0.010) 0.007*** (0.001) -0.478*** (0.075) 0.001 (0.001) 0.078*** (0.006) 0.011*** (0.002) Yes 13.255*** (0.508) 0.260 8,235
-0.045*** (0.016) 0.016*** (0.001) 0.003*** (0.001) -0.004 (0.005) 0.046*** (0.003) 0.227*** (0.007) 0.009*** (0.002) -0.651*** (0.085) 0.001 (0.001) 0.022*** (0.008) 0.015*** (0.003) Yes 16.075*** (0.638) 0.229 8,235
ACCEPTED MANUSCRIPT Table 8. The Influence of Regulatory Reform on Monetary Policy Effect: Interaction Models Q75 (3)
-0.029 (0.048) -0.062 (0.048)
-0.065* (0.034) -0.040 (0.036)
-0.004 (0.069) -0.113* (0.068)
Reserve Ratio Reform×Reserve Ratio
ROA Log Fix Assets Largest % State % GDP Growth Marketization Index Real Estate Index
AC C
Time Dummy Constant Pseudo R2 Observations
0.335 24,281
0.295 24,281
Log Assets Q50 (5)
0.008 0.003 (0.008) (0.010) -0.059*** -0.059*** (0.011) (0.013) 1.287*** 1.287*** (0.230) (0.265) 0.004*** 0.011*** (0.000) (0.000) 0.004*** 0.005*** (0.000) (0.000) -0.008*** 0.010*** (0.002) (0.002) 0.037*** 0.047*** (0.001) (0.002) 0.512*** 0.384*** (0.008) (0.007) 0.005*** 0.009*** (0.001) (0.000) -0.000 -0.000 (0.000) (0.000) 0.002*** 0.003*** (0.001) (0.001) 0.082*** 0.081*** (0.003) (0.003) 0.001 0.003*** (0.001) (0.001) Yes Yes 9.978*** 12.115*** (0.192) (0.188)
SC
NU
Firm Age
0.817* (0.421) 0.018*** (0.001) 0.005*** (0.000) 0.013*** (0.003) 0.055*** (0.003) 0.297*** (0.005) 0.014*** (0.001) -0.002*** (0.000) 0.004*** (0.001) 0.078*** (0.006) 0.003** (0.001) Yes 14.025*** (0.499)
MA
Subsidy
0.330 (0.221) 0.011*** (0.000) 0.005*** (0.000) 0.009*** (0.001) 0.048*** (0.001) 0.383*** (0.006) 0.008*** (0.000) -0.000 (0.000) 0.003*** (0.000) 0.082*** (0.003) 0.004*** (0.001) Yes 12.554*** (0.288)
ED
Debt to Asset
0.462 (0.301) 0.004*** (0.001) 0.004*** (0.001) -0.008*** (0.002) 0.037*** (0.002) 0.512*** (0.005) 0.006*** (0.000) -0.000 (0.000) 0.002*** (0.000) 0.081*** (0.004) 0.002* (0.001) Yes 10.222*** (0.342)
EP T
Reform
Q25 (4)
PT
Reform×InterestRate_L
Log Assets Q50 (2)
RI
Interest Rate_L
Q25 (1)
0.280 24,281
0.335 24,281
0.295 24,281
Q75 (6)
0.016 (0.014) -0.081*** (0.015) 1.759*** (0.313) 0.018*** (0.000) 0.006*** (0.001) 0.013*** (0.003) 0.055*** (0.002) 0.297*** (0.007) 0.014*** (0.001) -0.002*** (0.000) 0.005*** (0.001) 0.079*** (0.005) 0.002 (0.002) Yes 13.804*** (0.306)
0.280 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
in parentheses.
ACCEPTED MANUSCRIPT Table 9. The Influence of Regulatory Reform on Monetary Policy Effect: Split Sample Models Log Assets
Before Reform Interest Rate_L
Q25 (1) -0.082* (0.049)
Q50 (2) -0.062 (0.057)
Q75 (3) -0.089 (0.072)
Log Assets
After Reform Q25 (4) -0.094*** (0.011)
Q50 (5) -0.100*** (0.020)
Q75 (6) -0.105*** (0.024)
Before Reform Q25 (7)
Q50 (8)
T P
I R
Reserve Ratio
Q75 (9)
After Reform Q25 (10)
Q50 (11)
Q75 (12)
0.001 0.001 0.002 -0.051*** -0.056*** -0.060*** (0.007) (0.010) (0.014) (0.010) (0.008) (0.012) Leverage -0.000 0.004*** 0.010*** 0.008*** 0.016*** 0.022*** -0.000 0.004*** 0.010*** 0.008*** 0.016*** 0.023*** (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) Subsidy 0.003*** 0.003*** 0.005*** 0.004*** 0.005*** 0.005*** 0.003*** 0.004*** 0.005*** 0.004*** 0.004*** 0.005*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Firm Age -0.008*** 0.007** 0.016*** -0.005** 0.010*** 0.012*** -0.008*** 0.007** 0.017*** -0.005** 0.010*** 0.011*** (0.002) (0.003) (0.006) (0.002) (0.003) (0.004) (0.003) (0.003) (0.006) (0.002) (0.002) (0.003) ROA 0.033*** 0.041*** 0.045*** 0.041*** 0.050*** 0.058*** 0.032*** 0.041*** 0.045*** 0.041*** 0.050*** 0.057*** (0.002) (0.004) (0.005) (0.003) (0.003) (0.003) (0.002) (0.002) (0.004) (0.003) (0.002) (0.003) Log Fix 0.461*** 0.359*** 0.273*** 0.527*** 0.399*** 0.316*** 0.462*** 0.360*** 0.274*** 0.527*** 0.398*** 0.315*** (0.008) (0.008) (0.011) (0.009) (0.007) (0.007) (0.010) (0.008) (0.008) (0.009) (0.005) (0.008) Largest % 0.003*** 0.007*** 0.014*** 0.009*** 0.011*** 0.015*** 0.003*** 0.007*** 0.014*** 0.009*** 0.011*** 0.015*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) State % 0.234*** 0.101 -0.279*** -0.223*** -0.108** -0.309*** 0.239*** 0.088 -0.278*** -0.222*** -0.109*** -0.312*** (0.047) (0.072) (0.090) (0.055) (0.043) (0.051) (0.057) (0.057) (0.048) (0.038) (0.041) (0.046) GDP Growth -0.000 0.001 0.001 0.003*** 0.004*** 0.005*** 0.000 0.001 0.001 0.003*** 0.003*** 0.005*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Market. index 0.085*** 0.083*** 0.049*** 0.074*** 0.079*** 0.080*** 0.085*** 0.084*** 0.050*** 0.074*** 0.080*** 0.082*** (0.004) (0.005) (0.009) (0.003) (0.004) (0.005) (0.006) (0.005) (0.007) (0.005) (0.005) (0.006) Real Estate Index 0.005 0.007 0.007 0.002 0.004*** 0.003 0.007* 0.008 0.009 0.001 0.002* 0.002 (0.004) (0.005) (0.007) (0.001) (0.001) (0.002) (0.004) (0.006) (0.006) (0.002) (0.001) (0.002) Time Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 11.469*** 13.156*** 15.279*** 10.158*** 12.261*** 14.177*** 10.686*** 12.577*** 14.349*** 10.706*** 12.930*** 14.937*** (0.655) (0.678) (1.009) (0.281) (0.262) (0.410) (0.429) (0.585) (0.630) (0.351) (0.312) (0.469) 2 Pseudo R 0.287 0.233 0.223 0.356 0.322 0.301 0.287 0.233 0.223 0.356 0.322 0.306 Observations 9,819 9,819 9,819 14,462 14,462 14,462 9,819 9,819 9,819 14,462 14,462 14,462 Note: ***,** and * represent significance at levels of 1%, 5% and 10% respectively. Robust standard errors in parentheses.
C S
U N
D E
T P
C A
E C
A M
ACCEPTED MANUSCRIPT
Figure 1. Movements of Monetary Policy Instruments
25
6
20
T
15
IP
4
3
10
2
CR
Interest rate
5
Deposit reserve ratio
7
1
0
Interest_L
AN
Interest_D
US
201103 201106 201109 201112 201203 201206 201209 201212 201303 201306 201309 201312 201403 201406 201409 201412 201503 201506 201509 201512 201603 201606 201609 201612
0
5
Deposit reserve ratio
M
Notes: X axis indicates periods ranging from 03/ 2011 to 12/ 2016. The Y axis represents interest rates or reserve requirement ratios. The solid line depicts lending interest rates, the dotted line represents reserve requirement ratios, and the dashed lines indicates deposit interest rates.
.05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 .75 .8 .85 .9 .95 Quantile
0.00 -0.02 -0.04 -0.06
Reserve_Ratio
CE AC
-0.10 -0.20
-0.15
Interest_L
-0.05
PT
0.00
ED
Figure 2. The Impact of Monetary Policy on Firm Scale
.05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 .75 .8 .85 .9 .95 Quantile
Notes: The left figure is the coefficient estimates of lending interest rates, and the right figure is coefficient estimates of reserve requirement ratios. The solid line indicates estimates for each quantile and the shaded area represents the 90% point-wise confidence interval from the regression estimates.
ACCEPTED MANUSCRIPT
PT
ED
M
AN
US
CR
IP
T
Figure 3. The Impact of Monetary Policy on Firm Scale: SOEs vs. Non-SOEs
AC
CE
Notes: The left figure is the coefficient estimates of lending interest rates, and the right figure is coefficient estimates of reserve requirement ratios. The solid line indicates estimates for each quantile for the subsample of SOEs and the dotted line represents estimates for each quantile for the subsample of non-SOEs.
ACCEPTED MANUSCRIPT Appendix 1. Variable Definition
= =
AC
CE
PT
ED
M
=
T
GDP Growth % Marketization Index Real Estate Index
IP
= =
CR
State % Largest %
Benchmark lending interest rate % by quarter. Benchmark deposit interest rate% by quarter. Required reserve ratio by quarter Logarithm of the end of the quarter total assets Quarterly debt to asset ratio quarterly amount of government subsidies divided by total sales Log value of net fixed assets. Quarterly return on assets ratio The proportion of independent directors on the board. Firm age measured as current year minus the firm founding year plus one. The proportion of state-owned shares in total shares outstanding The proportion of largest shareholder equity ownership in total shares outstanding Quarterly real GDP growth rate NERI market development index, an annual composite index created for each of the 31 Chinese provinces and four municipalities. Inflation adjusted average real estate price using quarterly real estate price data of the 100 most representative Chinese cities.
US
= = = = = = = = = =
AN
Interest Rate-L Interest Rate-D Reserve Ratio Log Assets Leverage Subsidy Log Fixed ROA Largest % Age
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN
US
CR
IP
T
Highlights: Explore asymmetric effects of monetary policy on firm scale at different firm size levels. Chinese firms respond to raising benchmark lending interest rates and deposit reserve requirements by decreasing their scales. Larger firms respond more strongly to both policy instruments by adjusting their scales to a greater degree than smaller firms do. SOEs react less strongly to monetary policy changes than privately controlled listed firms at all firm size distribution. The impact of monetary policy on firm scale is stronger after the policy reform allowing commercial banks more leeway in setting their interest rates. Firm heterogeneity and regulatory environment both affect the effectiveness of monetary policy in influencing firm behavior.
Liting Fang, Lerong He, Zhigang Huang PII: DOI: Reference:
S1566-0141(18)30419-9 https://doi.org/10.1016/j.ememar.2018.11.013 EMEMAR 589
To appear in:
Emerging Markets Review
Received date: Accepted date:
3 November 2018 26 November 2018
Please cite this article as: Liting Fang, Lerong He, Zhigang Huang , Asymmetric effects of monetary policy on firm scale in China: A quantile regression approach. Ememar (2018), https://doi.org/10.1016/j.ememar.2018.11.013
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ACCEPTED MANUSCRIPT Asymmetric Effects of Monetary Policy on Firm Scale in China: A Quantile Regression Approach Liting Fanga, Lerong He*, Zhigang Huang
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Lerong He* School of Business & Management The College at Brockport State University of New York Brockport, NY, 14420, USA & School of Economics & Management Fuzhou University Fuzhou, China Email: [email protected]
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Liting Fang School of Economics & Management Fuzhou University Fuzhou, 350116, China Email: [email protected]
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*Corresponding author
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Zhigang Huang School of Economics & Management Fuzhou University Fuzhou, 350116, China Email: [email protected]
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Acknowledgements: This paper was presented at the 2017 Cross Country Perspectives of Finance conferences held in Chengdu, China and Chiang Mai, Thailand. We are grateful for insightful comments provide by the special issue editors Drs. Gady Jacoby and Zhenyu Wu, two anonymous reviewers, and paper discussants and conference participants at these two conferences. The paper is supported by the National Natural Science Foundation of China Grant # 71473039 “The Asymmetric Effect of Monetary Policy Transmission Mechanism on Micro Level”, Grant #71673048 “Information Disclosure and Market Competition under Information Asymmetry: Theory, Evidence and Policy” and Grant # 71703025 “Nonparametric Quantile Regression Models: Theory and Applications”. Financial supports from the Ministry of Education in China Project of Humanities and Social Sciences (No. 17YJC910004) and the 4th Fujian 100 Entrepreneurship and Innovation Grant are also greatly appreciated.
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Asymmetric Effects of Monetary Policy on Firm Scale in China: A Quantile Regression Approach Abstract
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This study explores asymmetric effects of monetary policy on firm scale at different firm size levels. We find that Chinese firms respond to raising benchmark lending interest rates and deposit reserve requirements by decreasing their scales. Our quantile regression results also indicate that larger firms respond more strongly to both policy instruments by adjusting their scales to a greater degree than smaller firms. Moreover, SOEs react less strongly to policy changes than non-SOEs at all firm size distribution. The impact of monetary policy on firm scale is also stronger after commercial banks are given greater leeway in setting interest rates.
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Keywords: Monetary Policy; Quantile Regression; Firm Size; Asymmetric Effect; China
ACCEPTED MANUSCRIPT 1 Introduction Monetary policy refers to actions of central banks or other regulatory bodies to manage monetary supply and influence real and nominal interest rates in an economy. Expansionary monetary policies aim at increasing monetary supply to stimulate economic growth, while contractionary
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monetary policies are implemented to decrease monetary supply to control inflation (Mishkin,
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1995). Central banks typically utilize three primary monetary policy tools to achieve these goals,
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modifying the interest rate, changing the deposit reserve requirements, and engaging in open market operations such as selling or buying government bonds. These policy tools influence
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bank lending, firm investment, and consumer demand in the micro level, and ultimately affect
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aggregated consumption and economic outputs in the macro level through a series of monetary transmission mechanisms (Bernanke and Gertler, 1995).
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The effect of monetary policy however is often asymmetric. For example, contractionary
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monetary policy typically has a stronger impact on economic outcomes than expansionary monetary policy (Cover, 1992; Weise, 1999). There is also evidence that business cycles
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influence the effectiveness of monetary policy (Bernanke, et al., 1996; Hoppner et al., 2008).
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Heterogeneities in countries, regions, and industries may also affect the efficacy of monetary policy on economic outputs (e.g., Karras, 1999; Peersman, 2004; Peersman and Semets, 2005).
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Apart from macro level contingencies, firms with various characteristics often respond to monetary policy shocks in a distinct way (Ehrmann, 2000; Laopodis, 2010). In particular, firm size is identified as a crucial contingency factor, with smaller firms often being more vulnerable to monetary policy than their larger counterparts (Gertler and Gilchrist, 1994; Thoma, 1994). The main objective of our paper is to utilize firm- level data to examine the effectiveness of monetary policy instruments in influencing firm scale in China. More specifically, we explore
ACCEPTED MANUSCRIPT asymmetric effects of monetary policy on firm scale at different firm size levels, i.e., whether small and medium sized enterprises (SMEs) react more strongly to monetary policy tools compared to larger industrial firms. We also examine how regulatory environment and firm ownership structure may moderate asymmetric responses of firms of different sizes to monetary
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policy. To the best of our knowledge, no prior empirical literature has systematically examined
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these research questions in the Chinese context.
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Our study contributes to extant literature in the following ways. First, we provide empirical evidence on the efficacy of monetary transmission mechanisms in influencing micro firm
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behavior in an important emerging market context that is subject more to the grapping hand of
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the government than the invisible hand of the market. It thus augments prior empirical studies in this regard that are mostly conducted in developed nations with an efficient financial market to
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drive monetary demand and supply (e.g., Gertler and Gilchrist, 1994; Ehrmann, 2000; Laopodis,
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2010; Thoma, 1994). In addition, the utilization of Chinese context allows us to examine additional contingencies that may affect the functioning of monetary policy tools but are
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unavailable in prior studies. For example, we explore the influences of state ownership and
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financial policy reforms on the effectiveness of monetary policy in shaping firm expansion and contraction decisions. Our study thus also complements prior empirical literature on Chinese
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monetary policy that have predominantly used aggregated macroeconomic data (e.g., Chen et al., 2017; He and Wang, 2012; Sun, 2013) to provide fine-grained micro- level evidence on the role of monetary policy in encouraging or discouraging firm growth. In this respect, our paper also possesses significant practical implications by guiding the design of monetary policy in influencing growth and stability of Chinese economy. Second, we predict and demonstrate that the impact of monetary policy on firm scale is not
ACCEPTED MANUSCRIPT constant across the firm size distribution as typically assumed by prior studies. We show that the influence of monetary policy on firm scale is quantitatively larger in magnitude for larger firms compared to smaller firms by utilizing a novel research technique, quantile regression method (Koenker and Gilbert, 1978; Koenker and Hallock, 2001). Our results demonstrate that monetary
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policy generates distinct effects on firm size at different parts of the firm size dis tribution.
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Therefore, prior micro- level studies that have widely adopted conditional mean regression
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methods based on the assumption of a constant average policy effect on firm scale conceal significant parameter heterogeneity (e.g., Chen et al., 2017; Fernald et al., 2014). In contrast, the
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utilization of the quantile regression method in this study enables us to yield a more
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comprehensive characterization of the relationship between monetary policy and firm scale. In this regard, our paper also makes a significant methodological contribution toward a deeper
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understanding of monetary policy effects.
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The rest of this paper is organized as follows. Section 2 introduces Chinese institutional background and provides a brief review of related literature. Section 3 develops our main
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hypotheses. Section 4 describes data and methods, including a discussion of the quantile
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regression technique and its merits. Section 5 presents main empirical findings and Section 6 provides results of additional analyses. We conclude the paper in section 7 with a discussion.
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2. Institutional Background and Literature Review The Chinese monetary policy is implemented by the People’s Bank of China (PBC). According to the Law of the People’s Bank of China enacted in 1995 and amended in 2003, the role of the PBC is to formulate and implement monetary policies under the leadership of the State Council. The aim of these monetary policies is “to maintain the stability of the value of the currency and
ACCEPTED MANUSCRIPT thereby promote economic growth.” 1 To realize the dual goals of financial stability and economic growth, the PBC relies on a mixture of quantity- and price-based monetary policy instruments, including various policy interest rates, reserve requirements, open market operations, foreign-exchange intervention, and window guidance on bank loans (He and Wang,
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2012; Sun, 2013). Among these instruments, reserve requirements have been applied most
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frequently by the PBC to absorb and control excess liquidity in the banking sector. In addition,
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PBC sets benchmark deposit and lending rates, and allows commercial banks to adjust interest rates around these benchmark levels within a limited band (Fernald et al., 2014). 2 The PBC also
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engage in open market operations including issuance of new central bank bills and notes to
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adjust market liquidity and avoid excess volatility in market interest rates (He and Wang, 2012). Occasionally, PBC adopts non-standard administrative methods such as window guidance to
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control lending levels and targets of commercial banks. In broad stroke, the PBC applies a wide
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range of monetary policy instruments to achieve its multiple objectives of economic growth, currency stability, and managed exchange rates. This is in sharp contrast with its western
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counterparts that typically employ the conventional policy interest rates and rely heavily on open
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market operations to influence economic activities. Extant literature has utilized aggregated macroeconomic data to examine the effect of
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Chinese monetary policy. For example, He and Wang (2012) investigate the response of market interest rates to monetary policy instruments using monetary and bond market data. They find that market interest rates are most sensitive to changes in benchmark deposit interest rates,
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Accessed from http://www.pbc.gov.cn/english/130733/2941519/2015082610501049304.pdf.
Starting fro m July 2013, the PBC removed its controls on the retail lending interest rates, but allowed financial institutions to determine loan interest rates based on the benchmark lending rate. Later in October 2015, PBC also removed control on the retail deposit rate, although it continues to announce both benchmark deposit and lending rates serving as the floor and ceiling of retail rates (Chen, et al., 2017; Fu and Liu, 2015).
ACCEPTED MANUSCRIPT followed by changes in reserve requirements, but not sensitive to open market operations. Sun (2013) examines the influence of Chinese monetary policy on real economic outputs and inflation using three events of policy shocks and broadly confirms the effectiveness of monetary transmission mechanisms. In addition, Fernald et al. (2014) compare several monetary policy
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tools in China and find that reserve requirements and benchmark interest rates are most effective
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in controlling aggregated economic activities and inflation in China, while other measures such
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as shocks to M2 or target lending levels only play limited roles. A recent study by Chen et al. (2017) shows that the transmission of monetary policy impulses to the rest of the economy in
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China is similar to the transmission process in developed economies in terms of output growth
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and inflation. However, Chinese bank loans are not sensitive to policy changes as those in developed economies.
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Although it is valuable to examine the aggregated impact of monetary policy on Chinese
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economy from the macro angle, the aforementioned studies are unable to address the monetary policy effect on individual firm behavior. A few exceptions include Peng and Lian (2010) who
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document that raising interest rates increase firm operation costs and subsequently result in
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inflation, and such effects are more salient during the contractionary period of the economic cycle than in the expansionary period. In addition, Fu and Liu (2015) find that Chinese listed
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firms adjust their investment levels in response to monetary policy instruments through both monetary and credit channels, particularly in e xpansionary monetary policy periods. These limited firm- level studies however have yet to explore the influence of firm heterogeneity on the effectiveness of monetary policy, but only focus on variations in macroeconomic contexts particularly economic cycles. Importantly, all prior studies have applied conditional mean regression methods including quadratic curve models, smooth transition models, and vector auto
ACCEPTED MANUSCRIPT regression (VAR) models to conduct their analysis. These approaches are built on the premise that the average effect of monetary policy on firm investment and scale is constant. We propose and demonstrate that such an assumption is questionable. In contrast, monetary policy tools also influence the dispersion of firm scale, thus generating distinct effects at different parts of the firm
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size distribution. As a result, the conditional mean regression method used in prior studies is
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unable to distinguish between heterogeneous impacts of monetary policy on firms at different
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size distribution, which will be a focus of this study. 3. Hypotheses Development
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Monetary policy could affect firm scale through a variety of transmission mechanisms, including
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the interest rate channel, the bank lending channel, and the balance-sheet channel (Bernanke and Gertler, 1995). First, contractionary monetary policy leads to an increase in real interest rates,
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and subsequently raises firm cost of capital, thus causes a decline in firm investment (Mishkin,
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1995). Second, contractionary monetary policy that decreases bank reserves and bank deposits will cause a reduction in bank loans, reduce the likelihood of firms obtaining sufficient loans for
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investment spending, and consequently prompts firms to refrain from increasing investment
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(Lucas, 1990). Third, raising interest rates under a contractionary policy will increase firm interest expenditure, and subsequently reduce its cash flow. In response to the cash flow problem,
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financially constrained firms may curtail investment or reduce inventory holding to ease the liquidity concern (Kashyap et al., 1994). Lower short-term or long-term investments and lower inventory holding will all deteriorate firm assets, and adversely affect firm scale. Taken together, contractionary monetary policy is expected to negatively influence firm scale. These monetary transmission mechanisms are built on the premise of an advanced market economy where commercial banks make independent lending decisions based on supply and
ACCEPTED MANUSCRIPT demand conditions in the financial market. The banking industry of China however is highly regulated and only partially liberalized in recent years. As a result, Chinese banks and other financial institutions have yet to develop risk-assessment expertise, and still do not possess the right incentives to lend on commercial principles (Prasad, 2009). Consequently, they often make
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loan decisions not based on market conditions but on administrative guidance of the central
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government or relationship with their clients (Li, 2006). This is particularly the case of the five
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largest state-owned banks that account for two-thirds of assets and liabilities in China’s banking system. Therefore, Chinese bank loans may not respond to monetary policy shocks in the way as
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predicted by traditional monetary transmission mechanisms in developed countries. For example,
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Chen et al (2017) find that the growth rate of bank loans in China is often detached from conditions on the interbank market set by the central bank, and Chinese monetary policy seems
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to have limited effect on containing asset price bubbles. As a result, the effect of Chinese
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monetary policy on firm scale becomes a pending empirical question. We make the following baseline hypothesis following the prediction of monetary transmission mechanisms:
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H1: Contractionary monetary policy has a negative influence on firm scale.
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Prior literature suggests that monetary transmission mechanisms may affect firms of different sizes in a distinct way (Gertler and Gilchrist, 1994; Mishkin, 1995). Because smaller
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firms possess a high degree of idiosyncratic risks, the problems of information asymmetry and adverse selection tend to be more severe for them. Banks are thus more reluctant to lend to these riskier smaller firms under contractionary monetary policy. In addition, sma ller firms may not possess sufficient collateral assets as their larger counterparts, which further reduces their credibility to banks and impairs their financing capability. Using a sample of U.S. manufacturing firms, Gertler and Gilchrist (1994) find that smaller firms account for a significantly
ACCEPTED MANUSCRIPT disproportionate share of production reduction and inventory decline after the tightening of monetary policy. Applying German business survey data, Ehrmann (2000) likewise confirms that smaller firms are subject more to the influence of contractionary monetary policy than larger firms, particularly during the economic downturn.
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This underlying logic may not necessarily hold in China, given the nation’s banking industry
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is heavily influenced by the grabbing hand of the government instead of the invisible hand of the
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market. For example, large Chinese firms often enjoy preferential status in obtaining bank loans due to their importance to local economies, while smaller firms are often more susceptible to
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heavy red tape and extralegal fees and have to extensively rely on trade credits and other
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financing channels for operation and growth (Brandt and Li, 2003; Li, 2008). In addition, the Chinese government often offers preferential credits to promote large inefficient firms
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particularly those in labor-intensive industries in an attempt to achieve its employment goal
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(Chen et al., 2016). Because larger firms rely more heavily on government-backed bank financing, they are also more vulnerable to the influence of governmental policy changes when
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government-controlled banks have to follow administrative guidance of the central government
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to make loan decisions. In contrast, smaller firms in China do not enjoy such privileged treatments from the first place and are generally more inclined to seek alternative credit channels
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for growth and other financial needs (Li, 2008). Their investment decisions therefore may actually depend less on changes in monetary policy. Taken together, we expect that firms of different sizes are likely to vary in their responses to monetary policy. In particular, larger Chinese firms may react more strongly to monetary policy tools by adjusting their scales to a greater degree, which leads to the following hypothesis: H2: The influence of contractionary monetary policy on firm scale is greater in larger firms
ACCEPTED MANUSCRIPT than in smaller firms. 4. Data and Methods 4.1 Data and Variables We obtain quarterly financial information of Chinese listed firms between 2011 and 2016 from
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CSMAR database provided by GuoTaiAn Information System. These data cover all Chinese
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public firms listed on Shanghai and Shenzhen Stock Exchanges. Macroeconomic data are
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withdrawn from the People’s Bank of China and National Bureau of Statistics of China for each quarter of our sample period. We require complete information on firm scale and a set of
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independent covariates. After deleting firms in the financial industry and firms with missing
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information, the final sample consists of 24,281 observations between 2011 and 2016. Table 1 presents the yearly distribution of our sample.
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***Insert Table 1 about Here***
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Considering that multiple monetary policy instruments are often used simultaneously in China, we capture changes in the Chinese monetary policy from two angles by adopting one
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price-based policy instrument, the benchmark lending interest rate (denoted as Interest Rate-L)
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and one quantity-based policy instrument, the reserve requirement ratio (denoted as Reserve Ratio). Figure 1 depicts the quarterly movements of these two monetary policy instruments
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within our sample period, where we also draw the benchmark deposit interest rate (Interest RateD) as a contrast. Figure 1 reveals that all three policy instruments generally move in the same direction within this time period although not at the same pace. The magnitude of changes is slightly greater for reserve requirement ratios, while trends for both types of benchmark interest rates are smoother. This pattern is consistent with the conclusion made by Sun (2013) and He & Wang (2012) that PBC tends to rely more extensively on reserve requirements than benchmark
ACCEPTED MANUSCRIPT interest rates as policy instruments. ***Insert Figure 1 about Here*** Our dependent variable is firm size measured as the logarithm of the end of the quarter total assets (denoted as Log Assets). Our models also include a series of control variables essential for
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firm investment decision following prior studies (Fu and Liu, 2015; Huang et al., 2012). We
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control for firm financial structure using firm leverage measured as the debt to asset ratio
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(denoted as Leverage) by dividing the end-of-quarter long-term debt to end-of-quarter total assets. We measure government subsidies (denoted as Subsidy) as the quarterly amount of
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government subsidies divided by total sales. We measure firm performance using the return on
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assets ratio (denoted as ROA) by dividing quarterly net income to total assets. We control for current period firm investment level using the log value of net fixed assets (Log Fixed). We also
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control for firm age (denoted as Age) measured as the current year minus the firm founding year
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and plus one. In addition, we control for firm ownership structure using the proportion of stateowned shares in total shares outstanding (denoted as State %) and the proportion of largest
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shareholder equity ownership in total shares outstanding (denoted as Largest %). Our models
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also include a set of macroeconomic indicators. First, we control for the rate of economic growth using the quarterly real GDP growth rate (denoted as GDP Growth). Because the real estate
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bubbles in the past 10 years may severely affect firms’ investment intension, we also include a real estate price index (denoted as Real Estate Index) as a control. This index is calculated as inflation adjusted average real estate price using quarterly real estate price data of the 100 most representative Chinese cities, with year 2010 as the base year at the value of 100. To capture regional differences, we include the widely recognized NERI market development index (denoted as Marketization Index) compiled by Wang et al. (2016), which is an annual composite
ACCEPTED MANUSCRIPT index created for each of the 31 Chinese provinces as well as four central government controlled municipalities. We use the location of the firm’s headquarter to identify the regional institutional environment the firm is facing. Finally, our models also include a set of industry dummy variables to capture industry effects. Measures of these variables are summarized in Appendix 1.
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Table 2 provides descriptive statistics of our main variables. We can tell from Table 2 that
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the benchmark lending rates fluctuate between 4.35% and 6.56% during our sample period, with
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the median being 5.60%. The reserve requirement ratio varies between 16.50% and 21.50% during this period, with a median of 19.00%. The average firm size measured by the logarithm of
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total assets is 22.177, and the average value of log fixed assets is 19.754. An average firm in our
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sample has a debt to asset ratio of 0.513, and a return to asset ratio of 0.017. The average rate of government subsidy accounts for 0.3% of firm sales, and an average firm in our sample is 19
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years old. Largest shareholders on average hold 38.729% of firm equity, and the average state
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ownership is 30.92% of total shares outstanding. During our sample period, the average GDP growth rate is 9.14% and the average provincial marketization index is 7.777. In addition, the
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real estate index ranges from 104.273 to 139.615 with an average of 116.342.
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***Insert Table 2 about Here***
4.2 Research Methods
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We first apply the OLS method to pooled cross-sectional data and use it as a baseline to examine the impact of monetary policy on firm scale. The model is specified as below: 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼 + 𝛽𝑀𝑃𝑖,𝑡 + 𝛾𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
(1)
The subscript i denotes the firm and t denotes time. The dependent variable is Log Asset. MP represents monetary policy measured either by benchmark lending rates or reserve requirement ratios. Control’ is a vector of control variables specified above, µt represent time dummy
ACCEPTED MANUSCRIPT variables, and εit is the random error. This pooled-OLS method, however, inevitably leads to omitted variable bias and is unable to control for the endogeneity problem (Wooldridge, 2002). We next apply the panel data fixed effect model to control for endogeneity caused by unobserved firm specific effects that may be simultaneously correlated with our dependent
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variable (firm scale) and independent variable (monetary policy). The fixed effect model is
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equivalent to imposing firm specific indicator variables in the regression along with other
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independent variables. 3 It is actually a difference model testing the impact of changes in monetary policy on changes in firm scale. Thus, it provides a within-sample estimate that
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compares firm scale with itself overtime instead of with other firms in the sample. This model is
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specified as below:
∆𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼𝑖 + 𝛽∆𝑀𝑃𝑖,𝑡 + 𝛾∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
(2)
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Here the subscript i denotes the firm and t denotes time. ΔLog Asset is the difference
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between firm scale for firm i at time t and time-series mean firm assets within each crosssectional unit. αi is firm-specific fixed effects which is normally distributed with zero mean.
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ΔControl represent changes in all control variables specified above. µt represent time dummy
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variables, and εit is the random error. Although the fixed effect model eliminates firm specific heterogeneity, it does not control
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for the dynamic structure in the data. In particular, firm scale may possess rigidity. That is, the current period asset may be correlated with assets in adjacent periods, while both static asset models identified above unrealistically assume firm assets have no temporal correlation. We next follow the dynamic panel data method proposed by Arellano and Bond (1991) and Blundell and Bond (1998) to estimate a dynamic autoregressive model in which the dependent variable is Log 3
The Hausman -Wu test confirms the appropriateness of using a fixed effects model over the rando m-effects panel data model. Our results suggest that fixed effect models provide more consistent estimates for both monetary policy measures.
ACCEPTED MANUSCRIPT Asset in firm i at time t. Persistence in firm scale is captured by including two lagged dependent variables (Log Asset i,t-1, Log Asset i,t-2 ). The model is specified as: 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝑀𝑃𝑖,𝑡 + 𝜃1 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖 ,𝑡−1 +𝜃2 𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−2 + 𝜆𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
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(3)
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Here the subscript i denotes the firm and t denotes time. αi. represent the firm fixed effects.
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Persistence in firm assets is reflected in the adjustment parameters θ1 and θ 2 . Other variables are specified as above. 4 This model is also a change model providing a within-sample estimate that
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captures the effect of monetary policy change on firm scale change after controlling for the
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influence of lagged dependent variables.
All three aforementioned models apply conditional mean regression methods built on the
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assumption that the average effect of monetary policy on firm scale is constant, and are
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computed by minimizing the mean square error to predict the conditional average of the outcome variable. Given monetary policy may also affect the dispersion of firm scale and generate distinct
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effects on different parts of the firm size distribution, the conditional mean regression method
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used above may conceal significant parameter heterogeneity in the link between monetary policy and firm scale. We next apply the quantile regression method to solve this problem (Koenker and
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Hallock, 2001) 5 . Compared with conditional mean regressions, quantile regression can better reflect the impact of the independent variables on the position, scale and shape of the distribution
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We estimate the model using the Arellano-Bond method (1991) as extended in Blundell and Bond (1998) to construct an appropriate IV and GMM estimator. More specifically, lags of the dependent variable and first differences of the exogenous variables are used as instruments for the first-difference equation. This is achieved using Stata 14 code xtabond. 5
Quantile regression is a method to estimate conditional quantile functions of dependent variables, and the parameters are estimated by minimizing the sum of the weighted absolute values of the residuals in a weighted least squares regression. A mo re co mplete d iscussion of quantile regression methods can be found in Koenker and Gilbert (1978) and Koenker and Hallock (2001).
ACCEPTED MANUSCRIPT of the dependent variable, and better capture tail characteristics of the distribution as well (Li, 2016). More specifically, we first identify a class of estimators, one for each desired quantile (e.g., 0.25, 0.50, 0.75). We then characterize the conditional distribution of the outcome variable by showing the effect of each input variab le on the outcome measure at each given quantile
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𝑄𝜏 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 |𝑋𝑖𝑡 ) = 𝛼𝜏 + 𝛽𝜏 𝑀𝑃𝑖𝑡 + 𝛾𝜏 𝐶𝑜𝑛𝑡𝑟𝑜𝑙′𝑖𝑡 + 𝜇𝑡 + 𝜀𝑖𝑡
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(Koenker, 2004).6 The panel data quantile regression model is expressed as: (4)
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Here Qτ (Log Asset it|Xit) is the τth quantile regression function on firm size. In our main analysis, we report estimates of quantile functions at the median of the size distribution (50th
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percentile) and the interquartile regressions (25th and 75th percentile) by estimating three
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quantile regression functions: Q(0.25), Q(0.5), and Q(0.75). MP refers to monetary policy instruments. ατ are a set of constant terms at each quantile and β τ are coefficient estimates
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5. Main Empirical Results
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corresponding to each quantile . Other variables are defined as above.
5.1 Conditional Mean Regression Results
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We present testing results for Hypothesis 1 in Table 3 in a pecking order. We first present results
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of pooled-OLS models in Columns 1 and 2, which provide information on the correlation between monetary policy and firm scale but ignore unobserved firm heterogeneity, dynamic
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structure, and endogenous variables. We next present static fixed effects models in Columns 3 and 4, which control for firm specific heterogeneity but do not consider the dynamic structure. We next present dynamic panel data estimates in Columns 5 and 6 to control for the persistence in firm scale, firm level fixed effects, and also address the endogenous variable problem. Columns 1, 3, 5 report results of benchmark loan interest rates and columns 2, 4, and 6 report 6
Specifically, we utilize the panel quantile regression estimate (PQR) in STATA 14.
ACCEPTED MANUSCRIPT results of reserve requirement ratios. ***Insert Table 3 about Here*** Table 3 demonstrates a consistently significant negative association between interest rates and firm scale in the pooled OLS, fixed-effect panel data, and dynamic panel data models, with
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coefficients ranging from -0.026 to -0.129, all significant at 0.01 level. These results suggest that
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firms reduce their scales in response to increasing interest rates under contractionary monetary
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policy, thus confirms the prediction of H1. In addition, the impact of the reserve requirements on firm scale is significantly negative at least at 0.10 level, with coefficients ranging from -0.005 to
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-0.026. These results provide additional support for H1 that firm scales decrease when reserve
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requirements increase under contractionary monetary policy. Overall, Table 3 provides consistent support for our H1 that both quantity and price based monetary policy instruments are
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effective in affecting firm scale in China. This conclusion holds after we control for firm level
5.2 Quantile Regression Results
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fixed effects and persistence of firm scale over time.
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As we argue earlier, results from mean regression models only reflect the average impact of
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monetary policy on firm size, but cannot tell whether firms of various sizes may react to monetary policy by adjusting their scales to a different degree. We thereby present quantile
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regression results in Table 4 to elaborate the asymmetric effect of monetary policy on firm size. Columns 1, 2, 3 report results of interest rates and columns 4, 5, 6 present results of reserve requirement ratios. ***Insert Table 4 about Here*** Table 4 suggests that both lending interest rates and reserve requirements have a significantly negative impact on firm scale at all three firm size quantile levels (25th, 50th, and
ACCEPTED MANUSCRIPT 75th) when examined separately. These results are consistent with results reported in the mean regression models, i.e. firms reduce their scales in response to tightening monetary policy. Importantly, we observe that the impact of monetary policy at different quantile points is not the same. Taken interest rates as an example, the coefficient is -0.098 at the 25th percentile, it
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decreases to -0.117 at the 50th percentile, and further reduces to -0.142 at the 75th percentile of
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firm size. These results suggest that larger firms respond more strongly to rising interest rates by
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decreasing their scales at a greater magnitude. The impact of reserve requirements shows the same pattern, with the negative coefficients of large firms being much larger in magnitude (β=-
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0.035) compared to those of median sized (β=-0.031) and smaller firms (β=-0.023). In general,
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these results support our H2 that larger firms reduce their scales to a greater degree in case of contractionary monetary policy.
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We next plot the size effects of interest rates and reserve requirements in Figure 2 to reveal
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the asymmetric monetary policy effect. For each dependent variable, we estimated nineteen separate quantile regressions for quantiles ranging from 0.05 to 0.95 at a 5% incremental step.
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The left side presents results of interest rates and the right side illustrates results of reserve
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requirement ratios. The horizontal x-axis in each figure shows the quantile scale and the vertical y-axis indicates coefficients of monetary policy effects on firm size. The solid line shows the
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estimate for each quantile τ and the shaded area represents the 90% confidence intervals from the regression estimates.
*** Insert Figure 2 about Here *** Figure 2 shows a clear decreasing trend between the size quantile and coefficients of monetary policy on firm size for both interest rates and deposit reserve ratios. We find that the effect of interest rates on firm scale is quantitatively smaller at lower percentiles than at higher
ACCEPTED MANUSCRIPT percentiles. Similarly, the effect of reserve requirements on firm size is also quantitatively smaller at lower percentiles than at higher ones. These results once again support H2 that larger firms react more strongly to monetary policy by adjusting their scales to a greater degree. 4.3 Inter-quantile Regression Results
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Our findings presented above suggest that monetary policy affects firm scale differently at
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different firm size levels. We next test whether these differences are statistically significant by
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performing inter-quantile regressions. Using the difference between the 75th quantile and the 50th quantile as an example, the estimates can be expressed as below:
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𝑄(75/50) = 𝑄0.75 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 ) − 𝑄0.50 (𝐿𝑜𝑔𝐴𝑠𝑠𝑒𝑡𝑖𝑡 )
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= (𝛼0.75 − 𝛼0.50 ) + (𝛽0.75 − 𝛽0.50 )𝑀𝑃𝑖𝑡 + (𝛾0.75 − 𝛾0 .50 )𝐶𝑜𝑛𝑡𝑟𝑜𝑙 ′ 𝑖𝑡 + 𝜀𝑖𝑡
(5)
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Here Q(75/50) is the difference- in-quantile estimate between the 75th quantile and the 50th quantile. Q0.75 (LogAsset) refers to the 75th quantile estimation, and Q0.50 (LogAsset) is the 50th
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quantile estimation. All other variables are defined as in Formula (4). We also report inter-
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quantile estimates for Q(50/25) and Q(75/25) in Table 5, with columns 1 to 3 on the impact of
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interest rates and columns 4 to 6 on the impact of reserve requirements. *** Insert Table 5 about Here ***
Table 5 suggests that compared to firms at the 25th percentile of size distribution, firms at the 75th percentile size distribution react much more strongly to interest rate changes by reducing their scales, with the coefficient being -0.044 and significant at 0.05 level. There are also statistically significant differences between firms at the 75th percentile size distribution and the 50th , as well as between firms at the 50th percentile size distribution and the 25th. In terms of
ACCEPTED MANUSCRIPT reserve requirement ratios, the difference between the 50th percentile and the 25th percentile Q(50/25) is statistically significant, which suggests that median-sized firms respond more strongly to increasing reserve requirements by decreasing their scales than smaller firms. However, no statistically significant difference is observed between other quantile pairs.
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Generally speaking, our inter-quantile regression results support our prediction in H2 that larger
of interest rate changes. 6. Additional Analyses
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6.1 Ownership Structure and Monetary Policy Effect
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firms react more strongly to monetary policy than their smaller counterparts, particularly in case
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A unique Chinese context is that a significant proportion of listed firms are state-owned enterprises (SOEs), in which the state retains sufficient shares and control. Unlike their private
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counterparts, maximizing shareholder value may not be the ultimate goal of SOEs who often
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pursue other social and political objectives such as providing employment and maintaining social stability (Chang and Wong, 2009). Moreover, SOEs are often sheltered from the market for
PT
corporate control as a result of “soft budget constraints” (Kornari et al., 2003). Consequently,
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they are often bailed out by the government through subsidies, additional bank loans, and other financial support in the event of financial distress instead of undergoing bankruptcy or takeover
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(Conyon and He, 2011). SOEs may also enjoy exclusive protection in some industries where government regulations create high entry barriers for private competitors. The combination of soft budget constraints and limited product market competition provides few incentives for SOE managers to make investment decision and adjust firm scales based on credit availability and cost of capital in the financial market. Wang et al. (2015) thus suggest that monetary policy instruments are less effective in influencing SOEs’ expansion or contraction decisions due to the
ACCEPTED MANUSCRIPT lack of market mechanisms to discipline SOEs. Similarly, we expect that the effect of Chinese monetary policy on firm scale may be less salient among SOEs than in non-SOEs. To test the moderating role of ownership structure on the influence of monetary policy a nd the policy’s differential impact on SOEs and non-SOEs at different size distribution, we add
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interaction variables of SOE with monetary policy to Equation (4) and report our quantile
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regression results in Table 6. A firm is classified as a SOE when the largest equity owner is the
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state and vice versa. Columns 1, 2, 3 of Table 6 report results of benchmark interest rates by adding SOE×Interest Rate-L and columns 4, 5, and 6 report results of reserve requirements by
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adding SOE×Reserve Ratio. Using interaction variables to test the moderating role of SOE is
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based on the assumption that relationships between control variables and the dependent variable are the same between the subgroups of SOEs and non-SOEs, while this assumption may not
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always hold. Therefore, we also perform split-sample analysis to allow other covariates to vary
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between the subgroups of SOEs and non-SOEs. These results are reported in Table 7, with columns 1 to 6 on interest rates and columns 7 to 9 on reserve ratios. We utilize quantile
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policy on firm size.
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regression methods in all these models to take into account the differential impact o f monetary
*** Insert Tables 6 and 7 about Here ***
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Table 6 shows that both interest rates and reserve requirements have significant negative effects on firm scale for all size quantiles of SOEs and non-SOEs. These results again confirm our prediction in H1 that both price- and quantity-based monetary policy instruments are effective in China. We also observe the asymmetric size effect, with absolute values of coefficients at lower quantiles being generally smaller than those at higher size quantiles. Consistent with H2, we find that larger firms respond to contractionary policy by reducing their
ACCEPTED MANUSCRIPT scales to a greater degree than smaller firms for both SOEs and non-SOEs. Importantly, Table 6 indicates that the coefficients of SOE×Interest Rate-L and SOE×Reserve Ratio are positively significant in all three quantiles. These results suggest that the impact of monetary policy on firms scale is less salient in SOEs than in non-SOEs across all quantiles of firm size distribution,
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thus are consistent with our prediction.
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Our split sample results in Table 7 reveal the same pattern. We find that increasing interest
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rates and reserve requirements result in a reduction of firm scale for all size quantiles of SOEs and non-SOEs as predicted by H1. Table 7 also reveals that larger firms respond more strongly
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to both types of monetary policy instruments than their smaller counterparts. In addition, the
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absolute values of coefficients are consistently smaller in SOEs than in non-SOEs for each pair of the size quantile. These results indicate that SOEs are less responsive to changes in monetary
M
policy instruments than private firms in all size distribution.
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To better uncover the distinct responses of SOEs and non-SOEs to monetary policy, we plot the impact of both policy instruments on firm size for these two types of firms in Figure 3. The
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left side presents the impact of interest rates and the right s ide depicts the influence of reserve
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requirements. We estimate separate quantile regressions for firm size quantiles ranging from 0.10 to 0.90, and present the firm size effect for 10% incremental steps. The horizontal x-axis in each
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figure shows the quantile scale and the vertical y-axis indicates coefficients of monetary policy effects on firm size. Figure 3 shows that both SOEs and non-SOEs respond to raising interest rates and reserve requirements by decreasing their sizes across all quantiles of size distribution. However, the coefficient lines of SOEs are much smoother than those of non-SOEs for both policy instruments, which confirms our prediction that SOEs make smaller adjustment to their scales than non-SOEs in case of monetary policy changes.
ACCEPTED MANUSCRIPT *** Insert Figure 3 about Here *** 6.2 Regulatory Reform and Monetary Policy Effect During our study period, the PBC has initiated some major monetary policy reforms. Before July 2013, the PBC not only issued the benchmark lending interest rates, but also imposed a band for
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financial institutions to set their retail lending rates (Fernald et al., 2014). This range restriction is
IP
removed after July 2013 when financial institutions have greater leeway in determining their
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retail loan interest rates based on the published benchmark lending rates. The enforcement of a rate band in early years seriously constrain variations in commercial banks’ retail lending rates,
US
and consequently affect incentives of banks to engage in more or less active lending. As a result,
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we expect that the effect of the monetary transmission mechanism on firm investment will be limited in this period. In contrast, the removal of the band allows retail interest rates to fluctuate
M
to a greater degree, thus enables commercial banks to better adjust their lending rates and scales
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based on firm risks and other characteristics. Consequently, the impact of monetary policy instruments on firm investment and other outcomes shall also be larger during this period. Taken
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together, we expect that monetary policy mechanisms will be more effective in affecting firm
CE
scale after the removal of the floating restriction. We next apply both the interaction variable approach and the split sample approach to test
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the moderating role of this regulatory reform. We first create a dummy variable Reform, which is equal to 1 if the period is after the second quarter of 2013 and 0 if it is before this cutoff point. We report results of our interaction models in Table 8 with columns 1, 2, and 3 adding Reform×Interest Rate-L to test the influence of lending interest rate changes a nd columns 4, 5, and 6 adding Reform×Reserve Ratio to examine the impact of reserve requirements. We also report our split-sample results for the pre-reform period and the post-reform period in Table 9,
ACCEPTED MANUSCRIPT with columns 1 to 6 showing results of interest rates, and columns 7 to 9 reporting results of reserve requirements. We again apply quantile regression methods in all models to investigate the asymmetric effect of firm size. *** Insert Tables 8 and 9 about Here ***
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Table 8 indicates that the impact of interest rates on firm scale is more salient for firms at the
IP
75% quantile during the post-reform period compared to the earlier years, while no significant
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difference between the pre-reform and the post-reform periods is identified for the other two size quantiles. These results suggest that the impact of interest rates on firm scale is more salient for
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larger firms during the post-reform period than in the pre-reform period. We also observe that
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raising reserve ratios has no significant influence on firm scale before the regulatory reform, but results in significant reduction in firm scale in the post reform period for all three firm size
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quantiles. These results suggest that reserve requirement is more effective in controlling firm
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scale after the regulatory reform. Our split sample results in Table 9 reveal a similar but clearer trend on the influence of regulatory reform on monetary policy effect. In the post-reform period,
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when lending interest rates or reserve requirement rates increase, firms at all three size quantiles
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reduce their scales. Such a negative relationship however is not consistent in the pre-reform period. These findings indicate that both price- and quantity-based monetary policy instruments
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are more likely to achieve their intended goals during the post-reform period when the government allows commercial banks more leeway in setting their lending rates based on market conditions. 7. Discussion and Conclusion This paper investigates the asymmetric effect of monetary policy on firm scale at different firm size levels. Using panel data of C hinese listed firms between 2011 and 2016, we document that
ACCEPTED MANUSCRIPT Chinese firms respond to raising interest rates and reserve requirement ratios by decreasing their scales. Importantly, such a response is stronger for larger firms compared to smaller firms in case of both monetary policy instruments. Our results also indicate that SOEs react more mildly to monetary policy instruments than privately controlled firms do. In addition, monetary policy
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commercial banks having greater leeway in setting their lending rates.
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tools are more effective in influencing firm scales during the post-reform period when
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Our paper reveals that monetary transmission mechanisms do seem to work in China to influence firm expansion and contraction decisions at least for our sample companies during our
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sample period. This result echoes findings of prior research that has adopted macroeconomic
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data or bank data to study monetary transmission mechanisms in China (e.g., e.g., Chen et al., 2017; He and Wang, 2012; Ouyang and Shi, 2010; Ouyang and Wang, 2009; Sun, 2013). We
M
also highlight heterogeneity in the effectiveness of monetary policy instruments. Contrary to
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evidence in western economies that smaller firms are more heavily influenced by monetary policy (Gertler and Gilchrist, 1994; Ehrmann, 2000), we instead find that larger firms in China
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respond more strongly to monetary policy by adjusting their scales to a greater degree than
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smaller firms do. These results could probably be attributed to the fact that smaller Chinese firms tend to rely less on formal financial institutions for investment needs and are thus less sensitive
AC
to governmental policy changes. We also observe that monetary policy has a smaller influence on SOEs than on privately controlled firms. This is likely a result of SOEs’ soft budget constraints that relax their liquidity constrains and consequently mitigate the influence of monetary policy instruments. In addition, our results indicate that the effect of monetary policy on firm scale is stronger in the post-reform period when financial institutions have more leeway in setting their lending rates.
ACCEPTED MANUSCRIPT From the methodological point of view, our study is the first to apply the quantile regression method to examine the differential impact of monetary policy on firm scale at different firm size distributions. In contrast to conditional mean regression models that predict the conditional average of the outcome variable, quantile regression generates estimates from a complete range
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of conditional quantile functions to characterize the whole distribution of the response variable
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(Li, 2015). Pertinent to our paper, the utilization of quantile regression method allows us to
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describe the relationship between monetary policy indicators and firm scales for firms at different size distribution. It thus offers a more complete picture of the size impact of monetary
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policy than prior studies applying standard conditional mean regressions. From this aspect, our
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study also augments prior empirical studies utilizing the quantile regression method to further highlight merits and applicability of this method in economic and finance literature, which we
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hope may stimulate more research along this line (e.g., Conyon and He, 2017; Klomp and Haan,
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2012; Lee and Li, 2012).
Our study also possesses significant practical implications. Our results reveal that Chinese
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firms’ investment decisions are still heavily influenced by the grabbing hand of the government
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instead of the invisible hand of the market. Chen et al (2017) observe that Chinese bank loans do not respond to monetary policy shocks in the way as banks at developed countries. Specifically,
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they point out that the growth rate of bank loans in China is somehow detached from conditions on the interbank market set by the central bank. While our empirical results indicate that monetary policy tools are generally more effective in the post-reform period when the government relaxes its control on commercial banks’ retail lending rates, the liberalization of the Chinese financial market still has a long way to go for the market to play a more central role in influencing business behavior and economic outcomes.
ACCEPTED MANUSCRIPT Our research is also subject to a number of limitations that warrant further investigations. First, the outcome variable in this study is firm scale measured using firm total assets. Monetary policy instruments can influence many other firm decisions and outcomes, such as long-term investment, R&D expenditure, and capital structure. Future research could further explore
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additional process and outcome variables to help untangle the impact of monetary policy on
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micro firm behavior. Second, our study uses data from China. The extant literature suggests that
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there may exist significant cross-country differences in the monetary policy effect. Therefore, it may also be valuable for future research to investigate the effect of monetary policy in a broader
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multi-country setting to explore how country heterogeneity and firm heterogeneity may
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intertwine with each other to influence the functioning of monetary transmission mechanisms. To sum up, our paper provides the first micro- level evidence on the asymmetric effects of
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monetary policy on firm scales at different firm size levels by utilizing the quantile regression
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method. We suggest that quantile regression approach has important merits in overcoming the limitations of the conditional mean regression to provide a more thorough investigation of the
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influence of firm heterogeneity on monetary policy effect. We hope that our findings will
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stimulate more research on the asymmetric effect of monetary policy instruments on firm
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behavior and economic outcomes in China and beyond.
ACCEPTED MANUSCRIPT
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ACCEPTED MANUSCRIPT Table 1. Sample Distribution by Years
Year Firm # Observation #
2011 996 3,849
2012 1,046 3,921
2013 1,064 4,157
2014 1,063 3,949
2015 1,069 4,200
2016 1,070 4,239
PT CE AC
CR
Max 6.560 21.500 27.104 27.320 0.337 2.885 38.000 31.806 74.350 84.998 36.018 10.407 139.615
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Mean 5.521 18.882 22.177 19.754 0.513 0.003 19.344 0.017 38.729 30.920 9.140 7.777 116.362
M
Median 5.600 19.000 22.095 19.998 0.506 0.007 19.000 0.011 36.350 32.252 8.679 8.210 116.379
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Variable Interest_L (%) Reserve Ratio (%) Log Assets Log Fix Assets Leverage Subsidy Firm Age ROA Largest % State % GDP Growth (%) Marketization Index Real Estate Index
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Table 2. Descriptive Statistics of Key Variables
Min 4.350 16.500 17.441 5.594 0.091 0.000 11.000 -28.756 9.440 0.000 -10.823 2.500 104.273
SD 0.766 1.613 1.390 2.361 0.281 0.205 4.202 4.811 16.116 24.102 11.943 1.942 9.790
ACCEPTED MANUSCRIPT Table 3. The Impact of Monetary Policy on Firm Scale: Conditional Mean Regression Models
Interest Rate_L Reserve Ratio
Log Assets Fix Effects (3) (4) -0.030*** (0.010) -0.005* (0.003)
Log Fix Assets Largest % State % GDP Growth Marketization Index
AC
R2 Observations
CE
Real Estate Index
-0.000 (0.000) 0.000* (0.000) 0.112*** (0.004) 0.007*** (0.001) 0.193*** (0.002) 0.002*** (0.000) 0.104*** (0.025) -0.003*** (0.000) -0.042*** (0.010) 0.002*** (0.001) Yes 16.333*** (0.131) 0.528 24,281
US
-0.000 (0.000) 0.000* (0.000) 0.104*** (0.005) 0.007*** (0.001) 0.193*** (0.002) 0.002*** (0.000) 0.107*** (0.025) -0.002*** (0.000) -0.043*** (0.010) 0.002*** (0.001) Yes 16.584*** (0.163) 0.528 24,281
AN
ROA
M
Firm Age
0.006*** (0.000) 0.004*** (0.000) 0.002 (0.002) 0.038*** (0.001) 0.360*** (0.003) 0.009*** (0.001) -0.017 (0.035) -0.000 (0.001) 0.086*** (0.004) 0.012*** (0.001) Yes 12.794*** (0.223) 0.471 24,281
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Subsidy
0.006*** (0.000) 0.004*** (0.000) 0.001 (0.002) 0.038*** (0.001) 0.358*** (0.003) 0.009*** (0.001) -0.009 (0.035) 0.001* (0.001) 0.086*** (0.004) 0.007*** (0.001) Yes 13.591*** (0.233) 0.472 24,281
PT
Leverage
CR
L2.LogAssets
Time Dummy Constant
-0.026*** (0.008)
0.078*** (0.021) -0.005 (0.009) 0.005*** (0.002) 0.000 (0.000) 0.067*** (0.005) 0.005*** (0.001) 0.157*** (0.010) 0.000 (0.001) 0.000 (0.000) -0.002*** (0.000) 0.000 (0.016) 0.001*** (0.000)
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L1.LogAssets
Log Assets Dynamic Panel Data (5) (6)
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Variables
Log Assets OLS (1) (2) -0.129*** (0.016) -0.026*** (0.006)
-0.010* (0.006) 0.091*** (0.021) -0.002 (0.009) 0.005*** (0.002) 0.000 (0.000) 0.065*** (0.008) 0.005*** (0.001) 0.158*** (0.010) -0.000 (0.001) 0.000 (0.000) -0.002*** (0.000) -0.000 (0.016) 0.001*** (0.000)
Yes
Yes
15.874*** (0.495)
15.601*** (0.483)
/ 20,331
/ 20,331
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors in
parentheses.
ACCEPTED MANUSCRIPT Table 4. The Impact of Monetary Policy on Firm Scale: Quantile Regression Models Q75 (3) -0.142*** (0.028)
Reserve Ratio
EP T
Log Assets Q50 (5)
Q75 (6)
-0.023*** (0.006) 0.004*** (0.001) 0.003*** (0.001) -0.006*** (0.001) 0.036*** (0.002) 0.514*** (0.006) 0.005*** (0.000) 0.006 (0.033) 0.001 (0.000) 0.080*** (0.003) 0.007*** (0.001) Yes 9.906*** (0.252) 0.334 24,281
-0.031*** (0.006) 0.011*** (0.001) 0.005*** (0.001) 0.011*** (0.002) 0.047*** (0.002) 0.385*** (0.007) 0.009*** (0.000) -0.046 (0.042) 0.001** (0.001) 0.080*** (0.003) 0.009*** (0.001) Yes 12.193*** (0.255) 0.294 24,281
-0.035*** (0.009) 0.018*** (0.001) 0.005*** (0.001) 0.015*** (0.003) 0.055*** (0.002) 0.299*** (0.007) 0.014*** (0.001) -0.211*** (0.052) 0.002*** (0.001) 0.077*** (0.005) 0.010*** (0.001) Yes 13.938*** (0.315) 0.279 24,281
SC
0.018*** (0.001) 0.005*** (0.001) 0.014*** (0.002) 0.056*** (0.002) 0.296*** (0.007) 0.014*** (0.001) -0.204*** (0.040) 0.003*** (0.001) 0.078*** (0.005) 0.005** (0.002) Yes 14.651*** (0.486) 0.279 24,281
NU
0.011*** (0.000) 0.005*** (0.001) 0.009*** (0.002) 0.047*** (0.002) 0.383*** (0.005) 0.009*** (0.000) -0.031* (0.018) 0.002*** (0.000) 0.081*** (0.003) 0.006*** (0.001) Yes 12.664*** (0.293) 0.294 24,281
MA
0.004*** (0.000) Subsidy 0.004*** (0.001) Firm Age -0.007*** (0.002) ROA 0.037*** (0.001) Log Fix Assets 0.513*** (0.007) Largest % 0.005*** (0.000) State % 0.000 (0.027) GDP Growth 0.002*** (0.000) Marketization Index 0.080*** (0.003) Real Estate Index 0.004*** (0.001) Time Dummy Yes Constant 10.431*** (0.307) Pseudo R2 0.334 Observations 24,281
ED
Leverage
Q25 (4)
PT
Log Assets Q50 (2) -0.117*** (0.016)
RI
Interest Rate_L
Q25 (1) -0.098*** (0.015)
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
AC C
in parentheses.
ACCEPTED MANUSCRIPT Table 5. Monetary Policy and Firm Scale: Inter-quantile Consistency Tests
Reserve Ratio
Largest % State % GDP Growth Marketization Index Real Estate Index Time Dummy Constant Observations
Log Assets Q (75/50) Q (50/25) (5) (6)
-0.012 (0.012) 0.015*** (0.001) 0.002*** (0.001) 0.021*** (0.003) 0.019*** (0.003) -0.215*** (0.006) 0.009*** (0.001) -0.217*** (0.047) 0.002* (0.001) -0.003 (0.006) 0.002 (0.002) Yes 4.032*** (0.432) 24,281
-0.004 (0.009) 0.007*** (0.000) 0.001 (0.001) 0.004* (0.002) 0.008*** (0.002) -0.086*** (0.003) 0.005*** (0.001) -0.165*** (0.043) 0.001 (0.001) -0.002 (0.005) 0.001 (0.002) Yes 1.745*** (0.345) 24,281
SC
Log Fix Assets
NU
ROA
0.007*** (0.000) 0.001** (0.000) 0.017*** (0.002) 0.011*** (0.002) -0.130*** (0.005) 0.003*** (0.001) -0.031 (0.040) 0.001 (0.001) 0.001 (0.003) 0.002** (0.001) Yes 2.233*** (0.162) 24,281
MA
Firm Age
0.007*** (0.000) 0.001 (0.001) 0.005** (0.002) 0.008*** (0.002) -0.087*** (0.006) 0.005*** (0.001) -0.173*** (0.034) 0.001 (0.001) -0.003 (0.006) -0.001 (0.001) Yes 1.987*** (0.250) 24,281
ED
Subsidy
0.014*** (0.000) 0.002*** (0.001) 0.021*** (0.003) 0.019*** (0.002) -0.217*** (0.006) 0.009*** (0.001) -0.204*** (0.054) 0.002** (0.001) -0.002 (0.006) 0.001 (0.001) Yes 4.220*** (0.284) 24,281
EP T
Leverage
Q (75/25) (4)
PT
Log Assets Q (75/50) Q (50/25) (2) (3) -0.025* -0.018* (0.013) (0.011)
RI
Interest_L
Q (75/25) (1) -0.044** (0.018)
-0.008** (0.004) 0.007*** (0.000) 0.001** (0.000) 0.017*** (0.001) 0.011*** (0.002) -0.129*** (0.006) 0.004*** (0.000) -0.052 (0.035) 0.001* (0.000) -0.000 (0.004) 0.002** (0.001) Yes 2.287*** (0.166) 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
AC C
in parentheses.
ACCEPTED MANUSCRIPT Table 6. The Influence of SOE on Monetary Policy Effect: Interaction Models Q75 (3)
-0.159*** (0.021) 0.091*** (0.019)
-0.158*** (0.025) 0.070*** (0.017)
-0.190*** (0.038) 0.079*** (0.027)
Reserve Ratio
ROA Log Fix Assets Largest % State % GDP Growth Marketization Index Real Estate Index
0.336 24,281
0.296 24,281
AC C
Time Dummy Constant Pseudo R2 Observations
Log Assets Q50 (5)
Q75 (6)
-0.045*** (0.010) 0.034*** (0.009) -0.527*** (0.176) 0.003*** (0.000) 0.004*** (0.001) -0.007*** (0.002) 0.037*** (0.001) 0.511*** (0.005) 0.005*** (0.001) -0.001*** (0.001) 0.001 (0.001) 0.080*** (0.003) 0.007*** (0.001) Yes 10.402*** (0.336)
-0.053*** (0.010) 0.039*** (0.009) -0.564*** (0.168) 0.011*** (0.001) 0.005*** (0.001) 0.008*** (0.002) 0.046*** (0.002) 0.383*** (0.006) 0.009*** (0.001) -0.003*** (0.001) 0.002*** (0.000) 0.082*** (0.004) 0.009*** (0.001) Yes 12.715*** (0.291)
-0.057*** (0.012) 0.033*** (0.012) -0.380* (0.227) 0.018*** (0.000) 0.005*** (0.001) 0.013*** (0.002) 0.053*** (0.002) 0.297*** (0.005) 0.014*** (0.001) -0.005*** (0.000) 0.002** (0.001) 0.076*** (0.005) 0.009*** (0.002) Yes 14.424*** (0.392)
0.335 24,281
0.296 24,281
0.282 24,281
SC
Firm Age
-0.195 (0.162) 0.018*** (0.000) 0.005*** (0.001) 0.012*** (0.002) 0.054*** (0.003) 0.294*** (0.006) 0.014*** (0.001) -0.004*** (0.001) 0.003*** (0.001) 0.078*** (0.005) 0.006*** (0.002) Yes 14.770*** (0.308)
NU
Subsidy
-0.220** (0.095) 0.011*** (0.000) 0.005*** (0.001) 0.007*** (0.002) 0.047*** (0.002) 0.380*** (0.006) 0.009*** (0.000) -0.003*** (0.001) 0.002*** (0.001) 0.083*** (0.003) 0.005*** (0.001) Yes 12.947*** (0.152)
MA
Leverage
-0.389*** (0.098) 0.004*** (0.001) 0.004*** (0.001) -0.008*** (0.002) 0.037*** (0.002) 0.510*** (0.006) 0.005*** (0.001) -0.001*** (0.000) 0.001** (0.001) 0.079*** (0.003) 0.004*** (0.001) Yes 10.547*** (0.207)
ED
SOE
EP T
SOE× Reserve Ratio
Q25 (4)
PT
SOE×InterestRate_L
Log Assets Q50 (2)
RI
Interest Rate_L
Q25 (1)
0.283 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
in parentheses.
ACCEPTED MANUSCRIPT Table 7. The Influence of SOE on Monetary Policy Effect: Split Sample Models Log Assets
Interest Rate_L
Q25 (1) -0.079*** (0.016)
SOEs Q50 (2) -0.095*** (0.020)
Q75 (3) -0.100*** (0.019)
Log Assets
Q25 (4) -0.141*** (0.028)
Non-SOEs Q50 (5) -0.181*** (0.032)
Q75 (6) -0.194*** (0.038)
Q25 (7)
SOEs Q50 (8)
T P
I R
Reserve Ratio
Q75 (9)
-0.016** -0.018** -0.023* (0.007) (0.008) (0.012) Leverage 0.006*** 0.012*** 0.019*** 0.001 0.009*** 0.016*** 0.006*** 0.012*** 0.018*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) (0.001) Subsidy 0.006*** 0.006*** 0.008*** 0.002*** 0.002*** 0.002** 0.006*** 0.006*** 0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) Firm Age 0.004* 0.009*** 0.014*** -0.030*** -0.007 -0.005 0.004** 0.010*** 0.016*** (0.002) (0.003) (0.003) (0.004) (0.004) (0.005) (0.002) (0.002) (0.003) ROA 0.046*** 0.056*** 0.057*** 0.026*** 0.037*** 0.047*** 0.046*** 0.055*** 0.056*** (0.002) (0.003) (0.003) (0.004) (0.003) (0.005) (0.002) (0.003) (0.003) Log Fix 0.510*** 0.398*** 0.327*** 0.479*** 0.352*** 0.224*** 0.511*** 0.400*** 0.330*** (0.007) (0.006) (0.007) (0.012) (0.011) (0.008) (0.006) (0.008) (0.007) Largest % 0.007*** 0.010*** 0.014*** 0.004*** 0.007*** 0.009*** 0.007*** 0.010*** 0.014*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) (0.001) State % -0.111* -0.311*** -0.436*** -0.248*** -0.456*** -0.586*** -0.103 -0.318*** -0.423*** (0.063) (0.059) (0.058) (0.075) (0.092) (0.094) (0.073) (0.068) (0.065) GDP Growth 0.002*** 0.002*** 0.003*** 0.001 0.002 0.003** 0.001* 0.002** 0.003*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Market. Index 0.076*** 0.093*** 0.102*** 0.092*** 0.084*** 0.022** 0.076*** 0.092*** 0.102*** (0.004) (0.005) (0.005) (0.008) (0.007) (0.010) (0.004) (0.005) (0.007) Real Estate Index 0.002* 0.004*** 0.004*** 0.007*** 0.006** 0.009*** 0.005*** 0.007*** 0.007*** (0.001) (0.001) (0.001) (0.002) (0.003) (0.003) (0.001) (0.002) (0.002) Time Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 10.262*** 12.396*** 13.915*** 11.526*** 14.065*** 16.996*** 9.790*** 11.781*** 13.358*** (0.245) (0.299) (0.290) (0.470) (0.482) (0.535) (0.262) (0.298) (0.513) Pseudo R2 0.316 0.306 0.302 0.317 0.261 0.231 0.316 0.306 0.302 Observations 16,046 16,046 16,046 8,235 8,235 8,235 16,046 16,046 16,046 Note: ***,** and * represent significance at levels of 1%, 5% and 10% respectively. Robust standard errors in parentheses.
C S
U N
D E
T P
C A
E C
A M
Q25 (10)
Non-SOEs Q50 (11)
Q75 (12)
-0.033** (0.014) 0.000 (0.001) 0.002*** (0.000) -0.029*** (0.004) 0.027*** (0.004) 0.478*** (0.009) 0.004*** (0.001) -0.300*** (0.071) -0.000 (0.001) 0.092*** (0.005) 0.012*** (0.002) Yes 10.847*** (0.465) 0.316 8,235
-0.047*** (0.013) 0.009*** (0.001) 0.002*** (0.001) -0.002 (0.004) 0.036*** (0.003) 0.360*** (0.010) 0.007*** (0.001) -0.478*** (0.075) 0.001 (0.001) 0.078*** (0.006) 0.011*** (0.002) Yes 13.255*** (0.508) 0.260 8,235
-0.045*** (0.016) 0.016*** (0.001) 0.003*** (0.001) -0.004 (0.005) 0.046*** (0.003) 0.227*** (0.007) 0.009*** (0.002) -0.651*** (0.085) 0.001 (0.001) 0.022*** (0.008) 0.015*** (0.003) Yes 16.075*** (0.638) 0.229 8,235
ACCEPTED MANUSCRIPT Table 8. The Influence of Regulatory Reform on Monetary Policy Effect: Interaction Models Q75 (3)
-0.029 (0.048) -0.062 (0.048)
-0.065* (0.034) -0.040 (0.036)
-0.004 (0.069) -0.113* (0.068)
Reserve Ratio Reform×Reserve Ratio
ROA Log Fix Assets Largest % State % GDP Growth Marketization Index Real Estate Index
AC C
Time Dummy Constant Pseudo R2 Observations
0.335 24,281
0.295 24,281
Log Assets Q50 (5)
0.008 0.003 (0.008) (0.010) -0.059*** -0.059*** (0.011) (0.013) 1.287*** 1.287*** (0.230) (0.265) 0.004*** 0.011*** (0.000) (0.000) 0.004*** 0.005*** (0.000) (0.000) -0.008*** 0.010*** (0.002) (0.002) 0.037*** 0.047*** (0.001) (0.002) 0.512*** 0.384*** (0.008) (0.007) 0.005*** 0.009*** (0.001) (0.000) -0.000 -0.000 (0.000) (0.000) 0.002*** 0.003*** (0.001) (0.001) 0.082*** 0.081*** (0.003) (0.003) 0.001 0.003*** (0.001) (0.001) Yes Yes 9.978*** 12.115*** (0.192) (0.188)
SC
NU
Firm Age
0.817* (0.421) 0.018*** (0.001) 0.005*** (0.000) 0.013*** (0.003) 0.055*** (0.003) 0.297*** (0.005) 0.014*** (0.001) -0.002*** (0.000) 0.004*** (0.001) 0.078*** (0.006) 0.003** (0.001) Yes 14.025*** (0.499)
MA
Subsidy
0.330 (0.221) 0.011*** (0.000) 0.005*** (0.000) 0.009*** (0.001) 0.048*** (0.001) 0.383*** (0.006) 0.008*** (0.000) -0.000 (0.000) 0.003*** (0.000) 0.082*** (0.003) 0.004*** (0.001) Yes 12.554*** (0.288)
ED
Debt to Asset
0.462 (0.301) 0.004*** (0.001) 0.004*** (0.001) -0.008*** (0.002) 0.037*** (0.002) 0.512*** (0.005) 0.006*** (0.000) -0.000 (0.000) 0.002*** (0.000) 0.081*** (0.004) 0.002* (0.001) Yes 10.222*** (0.342)
EP T
Reform
Q25 (4)
PT
Reform×InterestRate_L
Log Assets Q50 (2)
RI
Interest Rate_L
Q25 (1)
0.280 24,281
0.335 24,281
0.295 24,281
Q75 (6)
0.016 (0.014) -0.081*** (0.015) 1.759*** (0.313) 0.018*** (0.000) 0.006*** (0.001) 0.013*** (0.003) 0.055*** (0.002) 0.297*** (0.007) 0.014*** (0.001) -0.002*** (0.000) 0.005*** (0.001) 0.079*** (0.005) 0.002 (0.002) Yes 13.804*** (0.306)
0.280 24,281
Note: ***,** and * represent significance at levels of 0.01, 0.05, and 0.10 respectively. Robust standard errors
in parentheses.
ACCEPTED MANUSCRIPT Table 9. The Influence of Regulatory Reform on Monetary Policy Effect: Split Sample Models Log Assets
Before Reform Interest Rate_L
Q25 (1) -0.082* (0.049)
Q50 (2) -0.062 (0.057)
Q75 (3) -0.089 (0.072)
Log Assets
After Reform Q25 (4) -0.094*** (0.011)
Q50 (5) -0.100*** (0.020)
Q75 (6) -0.105*** (0.024)
Before Reform Q25 (7)
Q50 (8)
T P
I R
Reserve Ratio
Q75 (9)
After Reform Q25 (10)
Q50 (11)
Q75 (12)
0.001 0.001 0.002 -0.051*** -0.056*** -0.060*** (0.007) (0.010) (0.014) (0.010) (0.008) (0.012) Leverage -0.000 0.004*** 0.010*** 0.008*** 0.016*** 0.022*** -0.000 0.004*** 0.010*** 0.008*** 0.016*** 0.023*** (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) Subsidy 0.003*** 0.003*** 0.005*** 0.004*** 0.005*** 0.005*** 0.003*** 0.004*** 0.005*** 0.004*** 0.004*** 0.005*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Firm Age -0.008*** 0.007** 0.016*** -0.005** 0.010*** 0.012*** -0.008*** 0.007** 0.017*** -0.005** 0.010*** 0.011*** (0.002) (0.003) (0.006) (0.002) (0.003) (0.004) (0.003) (0.003) (0.006) (0.002) (0.002) (0.003) ROA 0.033*** 0.041*** 0.045*** 0.041*** 0.050*** 0.058*** 0.032*** 0.041*** 0.045*** 0.041*** 0.050*** 0.057*** (0.002) (0.004) (0.005) (0.003) (0.003) (0.003) (0.002) (0.002) (0.004) (0.003) (0.002) (0.003) Log Fix 0.461*** 0.359*** 0.273*** 0.527*** 0.399*** 0.316*** 0.462*** 0.360*** 0.274*** 0.527*** 0.398*** 0.315*** (0.008) (0.008) (0.011) (0.009) (0.007) (0.007) (0.010) (0.008) (0.008) (0.009) (0.005) (0.008) Largest % 0.003*** 0.007*** 0.014*** 0.009*** 0.011*** 0.015*** 0.003*** 0.007*** 0.014*** 0.009*** 0.011*** 0.015*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) State % 0.234*** 0.101 -0.279*** -0.223*** -0.108** -0.309*** 0.239*** 0.088 -0.278*** -0.222*** -0.109*** -0.312*** (0.047) (0.072) (0.090) (0.055) (0.043) (0.051) (0.057) (0.057) (0.048) (0.038) (0.041) (0.046) GDP Growth -0.000 0.001 0.001 0.003*** 0.004*** 0.005*** 0.000 0.001 0.001 0.003*** 0.003*** 0.005*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Market. index 0.085*** 0.083*** 0.049*** 0.074*** 0.079*** 0.080*** 0.085*** 0.084*** 0.050*** 0.074*** 0.080*** 0.082*** (0.004) (0.005) (0.009) (0.003) (0.004) (0.005) (0.006) (0.005) (0.007) (0.005) (0.005) (0.006) Real Estate Index 0.005 0.007 0.007 0.002 0.004*** 0.003 0.007* 0.008 0.009 0.001 0.002* 0.002 (0.004) (0.005) (0.007) (0.001) (0.001) (0.002) (0.004) (0.006) (0.006) (0.002) (0.001) (0.002) Time Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 11.469*** 13.156*** 15.279*** 10.158*** 12.261*** 14.177*** 10.686*** 12.577*** 14.349*** 10.706*** 12.930*** 14.937*** (0.655) (0.678) (1.009) (0.281) (0.262) (0.410) (0.429) (0.585) (0.630) (0.351) (0.312) (0.469) 2 Pseudo R 0.287 0.233 0.223 0.356 0.322 0.301 0.287 0.233 0.223 0.356 0.322 0.306 Observations 9,819 9,819 9,819 14,462 14,462 14,462 9,819 9,819 9,819 14,462 14,462 14,462 Note: ***,** and * represent significance at levels of 1%, 5% and 10% respectively. Robust standard errors in parentheses.
C S
U N
D E
T P
C A
E C
A M
ACCEPTED MANUSCRIPT
Figure 1. Movements of Monetary Policy Instruments
25
6
20
T
15
IP
4
3
10
2
CR
Interest rate
5
Deposit reserve ratio
7
1
0
Interest_L
AN
Interest_D
US
201103 201106 201109 201112 201203 201206 201209 201212 201303 201306 201309 201312 201403 201406 201409 201412 201503 201506 201509 201512 201603 201606 201609 201612
0
5
Deposit reserve ratio
M
Notes: X axis indicates periods ranging from 03/ 2011 to 12/ 2016. The Y axis represents interest rates or reserve requirement ratios. The solid line depicts lending interest rates, the dotted line represents reserve requirement ratios, and the dashed lines indicates deposit interest rates.
.05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 .75 .8 .85 .9 .95 Quantile
0.00 -0.02 -0.04 -0.06
Reserve_Ratio
CE AC
-0.10 -0.20
-0.15
Interest_L
-0.05
PT
0.00
ED
Figure 2. The Impact of Monetary Policy on Firm Scale
.05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 .75 .8 .85 .9 .95 Quantile
Notes: The left figure is the coefficient estimates of lending interest rates, and the right figure is coefficient estimates of reserve requirement ratios. The solid line indicates estimates for each quantile and the shaded area represents the 90% point-wise confidence interval from the regression estimates.
ACCEPTED MANUSCRIPT
PT
ED
M
AN
US
CR
IP
T
Figure 3. The Impact of Monetary Policy on Firm Scale: SOEs vs. Non-SOEs
AC
CE
Notes: The left figure is the coefficient estimates of lending interest rates, and the right figure is coefficient estimates of reserve requirement ratios. The solid line indicates estimates for each quantile for the subsample of SOEs and the dotted line represents estimates for each quantile for the subsample of non-SOEs.
ACCEPTED MANUSCRIPT Appendix 1. Variable Definition
= =
AC
CE
PT
ED
M
=
T
GDP Growth % Marketization Index Real Estate Index
IP
= =
CR
State % Largest %
Benchmark lending interest rate % by quarter. Benchmark deposit interest rate% by quarter. Required reserve ratio by quarter Logarithm of the end of the quarter total assets Quarterly debt to asset ratio quarterly amount of government subsidies divided by total sales Log value of net fixed assets. Quarterly return on assets ratio The proportion of independent directors on the board. Firm age measured as current year minus the firm founding year plus one. The proportion of state-owned shares in total shares outstanding The proportion of largest shareholder equity ownership in total shares outstanding Quarterly real GDP growth rate NERI market development index, an annual composite index created for each of the 31 Chinese provinces and four municipalities. Inflation adjusted average real estate price using quarterly real estate price data of the 100 most representative Chinese cities.
US
= = = = = = = = = =
AN
Interest Rate-L Interest Rate-D Reserve Ratio Log Assets Leverage Subsidy Log Fixed ROA Largest % Age
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN
US
CR
IP
T
Highlights: Explore asymmetric effects of monetary policy on firm scale at different firm size levels. Chinese firms respond to raising benchmark lending interest rates and deposit reserve requirements by decreasing their scales. Larger firms respond more strongly to both policy instruments by adjusting their scales to a greater degree than smaller firms do. SOEs react less strongly to monetary policy changes than privately controlled listed firms at all firm size distribution. The impact of monetary policy on firm scale is stronger after the policy reform allowing commercial banks more leeway in setting their interest rates. Firm heterogeneity and regulatory environment both affect the effectiveness of monetary policy in influencing firm behavior.