# Monetary Policy, Fiscal Policy, and Capital Structure Dynamic Adjustment: Evidence from Chinese Listed Companies

Authors: Liangwei Wan
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Monetary and fiscal policies are important means of macroeconomic regulation by the Chinese government, and their combination is an important guarantee for the stable development of economy. We analyze the impact of monetary and fiscal policies on capital structure adjustment and empirically test the data of Chinese nonfinancial listed companies in A-share market from 2000 to 2020. The research shows that, under the loose monetary policy and expansionary fiscal policy, enterprises will adjust their capital structure upward, improve their asset-liability ratio, and speed up their capital structure adjustment. In addition, the speed of capital structure adjustment of enterprises with low financing constraints is faster than that of enterprises with high financing constraints.


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## Body

## 1. Introduction

Monetary and fiscal policies are important means for the Chinese government to carry out macroeconomic regulation and control. The combination of monetary and fiscal policies is an important guarantee for the stable development of economy in China. According to the instructions of the Central Economic Work Conference, the Chinese government has continued to implement a prudent monetary policy and a proactive fiscal policy since 2019.Judging from the actual operation of the People’s Bank of China (PBC), the Chinese central bank adopted a moderately loose monetary policy in 2019. In 2019, central banks around the world entered the easing mode and began to slash interest rates. From January 2018 to August 2019, the real interest rate of Chinese enterprises rose from 1.4% to 4.8% after the PPI adjustment, and the PBC lowered the required reserves ratio three times to release liquidity in the market. Monetary policy has been adjusted many times since 2004 in China. In 2004, China implemented a tight monetary policy to curb economic overheating. From 2005 to 2006, the Chinese government adopted a prudent monetary policy. In 2007, the PBC raised the required reserves ratio 10 times. What is more, China implemented a moderately loose monetary policy in response to the financial crisis in 2008 and maintained a prudent monetary policy from 2011 to 2013. In addition, in 2019, China continued to implement a proactive fiscal policy and began to cut taxes and fees on a large scale. China’s fiscal policy mainly goes through the following stages: first, the proactive fiscal policy aimed at expanding demand implemented in the early 21st century, second, the relatively prudent fiscal policy period from 2005 to 2007, which was aimed at preventing the economy from overheating, and third, the active fiscal policy since 2008, which mainly includes expanding government public investment, promoting tax and fee reform, expanding domestic demand and consumption, and optimizing the structure of fiscal expenditure.Adjustment of monetary policy and fiscal policy is closely related to the development of national economy. Monetary policy and fiscal policy will have a significant impact on the macroeconomic outlook, enterprise financing environment, financing constraints, and the cost of financing and then affect the behavior of enterprises, financial decision-making, and financing strategy and finally affect the enterprise’s capital structure. How will the monetary policy, fiscal policy, and other macroeconomic policies affect the adjustment of enterprise capital structure? What is the relationship between the adjustment of monetary policy and fiscal policy and the adjustment speed of enterprise capital structure? Are there differences in the impact of monetary policy and fiscal policy on the capital structure of enterprises with different financing constraints? All these problems are worth studying.To answer the above questions, we take the data of nonfinancial listed companies in China’s A-share market from 2000 to 2020 as samples, introduce macroeconomic policy factors such as monetary policy and fiscal policy into the partial adjustment model of capital structure, and systematically study the relationship between monetary policy, fiscal policy, and capital structure dynamic adjustment. We may have the following marginal contributions: First, it discusses the impact of China’s monetary and fiscal policies on the capital structure of enterprises from the perspective of the relationship between macroeconomic policies and enterprises’ microbehavior. Second, the heterogeneity analysis of monetary and fiscal policies on the speed of capital structure adjustment is studied from the perspective of financing constraints.


## 2. Literature Review

As Keynesian theory universally accepted by western governments, national economy is developing from the era of laissez-faire capitalism into the era of state capitalism. More and more governments take intervention to economy through macroeconomic policy. Chinese and other scholars have studied macroeconomic policies and the relationship of the capital structure adjustment. Huang and Jiang [1] found a correlation between product market competition and the speed of capital structure adjustment. Killi et al. [2]; Gu and Zhou [3]; and Li and Pei [4] found that economic policy uncertainty is correlated with dynamic adjustment of capital structure. Liu et al. [5] systematically investigated the influence of macroeconomic environment on corporate leverage ratio. Levy and Hennessy [6]; Su and Zeng [7]; Jiang and Huang [8]; Liu et al. [9] verified the significant impact of macroeconomic environment on capital structure adjustment through empirical research.Many Scholars pay attention to the research on the relationship between monetary policy and capital structure. Gertle and Gilchris [10] found that monetary policy would have an impact on the financing constraints of enterprises and then affect the financing decisions of enterprises and lead to changes in capital structure. Oliner and Rudebusch [11] studied the classification of enterprise credit grades and found that monetary policies have different influences on the financing decisions of enterprises with different credit grades. When monetary policies were tightened, enterprises with high credit grades tended to borrow money by issuing commercial paper while those enterprises with low credit grades tended to borrow money from banks. After the 21st century, the dynamic model of capital structure was established, and empirical analysis became a research hotspot. Banerjee et al. [12] established the dynamic adjustment model of capital structure for the first time and empirically analyzed the relationship between currency supply and the dynamic adjustment speed of domestic enterprises’ capital structure in the United States and the United Kingdom. The empirical results show that the capital structure adjustment speed of companies in American is faster than those in British. Cooley and Quadrini [13] studied the impact of monetary policy shocks on corporate financing decisions and found that different enterprise sizes would have different impacts in the face of monetary policy shocks, and the impact of monetary policy shocks on small companies was more significant. Cook and Tang [14] conducted an empirical study on the relationship between macroeconomic policies and the speed of capital structure adjustment and found that the speed of capital structure adjustment was faster in economic prosperity than in economic depression. After 2010, more and more Chinese scholars began to pay attention to the relationship between the changes of China’s monetary policy and the capital structure of enterprises. Ma and Hu [15] realized the connection between monetary policy and credit channel; the study found that monetary policy directly affects the supply of credit and the financing constraint degree was closely related to the supply of credit, which also proved that the capital structure of enterprises with high degree of financing constraint is more significantly affected by monetary policy. Nie and Luo [16] found that increasing currency supply and lowering real interest rate would speed up capital structure adjustment. Wu et al. [17] took monetary policy tools as a starting point and found that different monetary policy tools had different impacts on the speed of capital structure adjustment. Song ’et al. [18] found that monetary policies have different effects on the speed of capital structure adjustment due to different growth characteristics of enterprises. Yuan and Guo [19] found that easy monetary policy would accelerate the speed of capital structure adjustment, while monetary tightening policy would slow it down. Most scholars in the study of relationship between monetary policy and capital structure are given priority to theoretical analysis, analyzing the difference of the degree of financing constraints which caused by the microcharacteristics of companies such as enterprise scale, enterprise growth, and credit rating.On the relationship between fiscal policy and capital structure, many scholars have also carried out many researches. Choe et al. [20] and Boyer [21] discussed the influence of fiscal expenditure on macroeconomic aggregate demand, consumption, and enterprise investment. He [22] investigated the impact of fiscal policies on the capital structure of Chinese listed companies for the first time in China and found that the looser the national fiscal expenditure, the lower the target capital structure of enterprises, and the tighter the national financial expenditure, the higher the target capital structure of enterprises. Luo and Nie [16] believed that expansionary fiscal policy would speed up the adjustment of capital structure. Wang et al. [23] found that local fiscal expenditure had a positive impact on the target leverage ratio of enterprises, while central fiscal expenditure had a negative impact on the target leverage ratio. The papers directly researching relationship between the fiscal policy and the capital structure are not universal, and the Chinese scholars began to study the relationship between fiscal policy and capital structure in 2010, while the fiscal policy measured by dummy variables instead of quantitative indicators was lack of continuity.Gulen and Ion [24] point out that there are various forms of external environment uncertainty, and economic policy uncertainty is an important uncertainty faced by enterprises in developing economies. Policy uncertainty mainly consists of policy expectation, implementation, and policy stance change. Many scholars at home and abroad have shown that the uncertainty of economic policies will have an important impact on enterprise financing decisions and then affect the change of enterprise capital structure, which is mainly reflected in the following two aspects: On the one hand, at the level of capital demand, enterprise management will adjust its expectation of the government’s policy implementation according to changes in the uncertainty of economic policy. Rao et al. [25] and Fabian [26] found that the uncertainty of product demand is positively correlated with the uncertainty of economic policy. If the uncertainty of economic policy increases, Gulen and Ion [24] believe that corporate managers are risk-averse, and the increase of economic policy uncertainty will weaken their investment willingness, while the decrease of investment scale will reduce financing demand and thus promote downward adjustment of capital structure. In addition, Wang et al. 27 found that as the degree of economic uncertainty increases, the uncertainty of corporate cash flow will also increase, and the risk of corporate bankruptcy will also increase accordingly. Enterprises will reduce debt financing to reduce corporate leverage ratio. Julio and Yook [28] adopted the year of political election as a proxy variable of policy uncertainty and found that corporate investment decreased by 4.8% on average in presidential election years compared with nonelection years. On the other hand, the increase of economic policy uncertainty will have a negative impact on the external financing environment of enterprises. Cao [29] found that the increase of the uncertainty of economic policies would increase the market’s expectation of the uncertainty of monetary or credit policies and increase the risk of financial market. Pastor and Veronesi [30] pointed out that the increase in the uncertainty of economic policies would lead to drastic fluctuations in corporate cash flow and increase the information asymmetry between enterprises and creditors. Creditors’ estimation of the net present value of enterprise projects would also be more inaccurate, and the increase in the probability of default risk would lead to the reduction of capital supply by banks and other creditors. Francis et al. [31] showed that increased economic policy uncertainty would increase the nonperforming ratio of bank debt and lead to tighter credit policies. With the increase of financing costs and loan requirements of enterprises, banks would reduce capital supply in a disguised way. Finally, the change of economic policy uncertainty will have a significant impact on corporate leverage and capital structure. Gong Rukai et al. [32] found that economic policy uncertainty has a significant negative impact on corporate leverage, and this negative effect is more obvious in short-term debt ratio, private, small-scale, and manufacturing enterprises. Wang et al. [33] found that the uncertainty of economic policies hindered the dynamic adjustment of capital structure through the channel of uncertainty avoidance, and the difference in sensitivity of policy changes in different industries affected the capital structure adjustment of enterprises in the industry. Gu and Zhou [3] found that, with the increase of policy uncertainty, capital structure decisions tend to be more and more conservative, and the value of financial flexibility plays an important role in this process. The increase of policy uncertainty leads to the increase of financial flexibility value, thus accelerating the speed of capital structure adjustment of enterprises.Harrod [34] proved that capital structure is always in the process of dynamic adjustment. The macroeconomic policy is only one of the many factors that affect the adjustment of enterprise capital structure. Fischer et al. [35] pointed out that enterprises generally consider the adjusted income and adjustment cost when deciding whether to adjust capital structure, and enterprises will adjust only when the adjusted income is greater than the adjustment cost. Tong [36] verified the relationship between the speed and extent of capital structure adjustment and the adjustment cost. The adjustment cost is related to the adjustment speed; the higher the adjustment cost, the slower the capital structure adjustment speed; on the contrary, the lower the adjustment cost, the faster the capital structure adjustment speed. An et al. [37] found that bankruptcy risk and information disclosure are closely related to capital structure adjustment.


## 3. Theoretical Analysis and Hypothesis

Monetary policy refers to the control and adjustment of the currency supply in circulation by the central bank through monetary tools such as interest rate, exchange rate, open-market operations, and required reserve ratio. First of all, the interest rate is not only the most important index of capital cost, but also the direct determinant of corporate debt financing cost. The interest rate will have an important impact on corporate capital structure. Under normal circumstances, when the interest rate is high, the PBC will carry out tight monetary policy. At this time, the cost of direct debt financing of enterprises increases, and the cost of capital structure adjustment of enterprises also increases correspondingly. Then the capital structure will be adjusted downward, leading to the decreasing of the speed of capital structure adjustment. On the contrary, when the interest rate is low, the PBC will carry out loose monetary policy, which would decrease the cost of enterprise capital structure adjustment. Then the capital structure will be adjusted upward and the speed of capital structure adjustment will be accelerated. Secondly, considering the credit transmission mechanism, the changes of monetary policy will affect bank credit scale through the credit transmission mechanism. In general, when the tight monetary policy is executed by the PBC, the banks will quickly reduce controllable credit scale through the credit transmission mechanism. It will be more difficult for the companies to get external financing, leading to the rise of the cost of capital structure adjustment. Thus, the capital structure will be adjusted downward and the speed of capital structure adjustment will be lowered. On the contrary, when the central bank implements loose monetary policy, the controllable credit scale of banks will increase. It will be easier for the companies to get external financing, leading to the decrease of the cost of capital structure adjustment. Thus, the capital structure will be adjusted upward and the speed of capital structure adjustment will be accelerated. Finally, considering the relationship between the monetary policy and financial ecological environment, Xie et al. [38] pointed out that the loose monetary policy will optimize the financial ecological environment, alleviate the financing pressure, and reduce the degree of financing constraints of the enterprise. To a certain extent, these will reduce the cost of capital structure adjustment and help to adjust capital structure upward and speed up the capital structure adjustment. In summary, the following hypotheses are proposed:H1: Tight monetary policy promotes the downward adjustment of enterprises’ capital structure, while loose monetary policy promotes the upward adjustment of enterprises’ capital structure.H2: Tight monetary policy will slow down the speed of enterprises’ capital structure adjustment; loose monetary policy will speed up the enterprises’ capital structure adjustment.Fiscal policy means that the government adjusts the total social demand through the allocation of fiscal expenditure and the adjustment of tax policy. Chen and Yang [39] pointed out that the government increased investment and reduced tax rate, which indicated that the government carried out the fiscal policy of expansion. With the macroeconomic situation getting better and better, enterprises could obtain more investing opportunities and have more growing space. First of all, from the perspectives of macroeconomic situation, the authorities carry out expansionary fiscal policy, referring to the positive macroeconomic situation, the improving of enterprises growth, and the optimizing of enterprise management. With the improvement of enterprise profitability and the expansion of production, the fixed assets are also increasing and the degree of financing constraints is decreasing. With the increasing of credit scale obtained from banks, the capital structure will be adjusted upward. At the same time, the financing barriers of enterprises are reduced and the financing cost is reduced, so the cost of capital structure adjustment is reduced and the speed of capital structure adjustment is accelerated. Second, according to the western finance theory, treasury bonds influence the price of capital in the market through the nature of the benchmark interest rates. When suffering the economic depression, the authorities tend to implement expansionary fiscal policy to stimulate the economy by reducing national bond rate to promote lower interest rates in financial markets. So the enterprise external financing cost is reduced; the capital structure is adjusted upward. The decline of financial market interest rate directly reduces the cost of capital structure adjustment and accelerates the speed of capital structure adjustment. Finally, considering the perspective of industrial support, the financial support to enterprises from Chinese government is a characteristic feature. In order to support industrial development, the authorities will provide financial capital to relevant enterprises by direct investment and financial subsidies. Wu et al. [40] pointed out that government supports including direct intervention means such as financial support, technology regulation, and market access, as well as indirect guidance means such as financial subsidies, tax incentives, and financial policies. When the government implements the fiscal policy of expansion, the direct financial support for enterprises will increase. Meanwhile, the increase of government expenditure will also indirectly improve the business environment and expand the capital source of enterprises. What is more, the government support policies for relevant industries will guide the investment of bank credit funds and adjust the capital structure upward. The decrease of capital structure adjustment cost accelerates the speed of capital structure adjustment. In summary, the following hypotheses are proposed:H3: Expanding fiscal policy promotes the capital structure adjusted upward, while tightening fiscal policy promotes the capital structure adjusted downward.H4: Tightening fiscal policy will slow down the speed of capital structure adjustment, while expanding fiscal policy will increase the speed of capital structure adjustment.


## 4. Methods and Variables

### 4.1. Model Construction

In order to study the relationship between corporate capital structure and monetary and fiscal policies, based on the practice of Faulkender et al. [41], we select a series of corporate characteristic variables to construct equation (1) to fit the target capital structure and deduce the capital structure adjustment speed of the enterprise:(1)debratei,t=γ+βXi,t−1+ηMt,debratei,t means the enterprise capital structure; the vector Xi,t−1 means the microcontrol variables influencing the enterprise capital structure, the vector Xi,t‐1 refers to a series of (t-1) year characteristic variables of enterprises, including company size, uniqueness, nondebt tax shields, profitability, business growth, liquidity, and degree of financing constraints. The β means the coefficient of characteristic variables of enterprises. The vector Mt means the macroeconomic factors affecting the capital structure. The vector Mt refers to the macroeconomic variables in t year, including economic growth, monetary policy, fiscal policy, and financial structure.In order to study the relationship between capital structure adjustment speed and monetary policy and fiscal policy, we constructed the following model by referring to Huang and Jiang [1]:(2)debtratei,t∗=γ+βXi,t−1+ηMt,debtratei,t∗ means the optimal capital structure of enterprises i in the year of t. Other variables have the same meanings as described above. We use the partial adjustment model to estimate and calculate the capital structure adjustment speed of enterprises. The standard partial adjustment model is established as follows:(3)debtratei,t−debtratei,t−1=θdebtratei,t∗−debtratei,t−1,debtratei,t represents the capital structure of enterprise i at the end of year t, debtratei,t−1 represents the capital structure of enterprise i at the beginning of year t, the capital structure is represented by the asset-liability ratio, and θ represents the speed of capital structure adjustment1. θ = 0 means the capital structure of enterprises has not been adjusted, and the asset-liability ratio remains unchanged; θ<0 represents that the capital structure adjustment of the enterprise deviates from the target capital structure; 0<θ<1 means the direction of enterprise adjustment is consistent with the target capital structure and has been partially adjusted; θ ＞1 means the enterprise has excess adjustment on the target capital structure.In order to study the influence of macroeconomic policy on the speed of capital structure adjustment, we regardθ as a linear function of monetary policy and fiscal policy as shown in (4)θ=α0+α1Mact,where Mact is the monetary policy (fiscal policy) in t year. Substitute equation (2) and equation (4) into equation (3), and get equation (5) after sorting.(5)debtratei,t=α0γ+1−α0debtratei,t−1−α1debtratei,t−1×Mact+α0βXi,t−1+α0ηMt+α1βXi,t−1×Mact+α0ηMt×Mact+μi+λt+εi,t.So the impact of monetary policy (fiscal policy) on the speed of capital structure adjustment can be examined throughα0 and α1. In formula (4), the second part on the right side of the equation is the first-order lag term of asset-liability ratio, the third part is the cross term of monetary policy or fiscal policy and the first-order lag term of asset-liability ratio, the fourth part is the microfactors controlled, the fifth part is the macroeconomic factors controlled (excluding monetary policy and fiscal policy), the sixth part is the cross term of monetary policy (fiscal policy) and microcontrol variables, and the seventh part is the cross term of monetary policy (fiscal policy) and macrocontrol variables. μi and λt are time and individual effects, respectively, and εi,t are random interference terms. We mainly focus on (1-α0) and -α1, which need to be converted into α0 and α1 to measure the impact of monetary policy (fiscal policy) on the speed of capital structure adjustment in equation (4).


### 4.2. Description of Variables

Monetary policy [18] used 1-year loan interest rate (calculated by monthly weighted average method) and annual year-on-year growth rate of broad monetary aggregates M2, respectively, to measure the degree of monetary policy tightness. If the interest rate is higher, the authorities will carry out tight monetary policy. If the interest rate is lower, the authorities will carry out loose monetary policy. If the growth rate of monetary supply is fast, it indicates that the authorities carry out relatively loose monetary policy. And a slower pace of monetary supply growth indicates a tightening of monetary policy.Fiscal policy [16] used the annual growth rate of fiscal expenditure to measure the tightness of fiscal policy. The high growth rate of fiscal expenditure indicates that the government is pursuing an expansionary fiscal policy. The low growth rate of fiscal spending indicates tightening fiscal policy.Asset-liability ratio [42] is widely used to measure capital structure, that is, the ratio of total liabilities to total assets of an enterprise.Since the enterprise’s own characteristics and the macroeconomic factors will affect the capital structure, we draw on the experience of most scholars to selected microcharacteristic factors such as the company size, nondebt tax shields, profitability, business growth, tangible assets, and macrofactors such as economic growth and financial structure as control variables. The specific definitions are shown in Table1.(1)
Firm size [43] is measured by the natural logarithm of total assets. In general, the larger the size of the company, the stronger the operation capacity of the company. And the probability of financial distress is smaller with the higher asset-liability ratio.(2)
Nondebt tax shield [14] is measured by the proportion of accumulated depreciation, deferred expenses, and deferred assets in total assets. The larger the scale of the company, the more significant the tax shield effect. Increasing the credit ratio will further enhance the value of the enterprise.(3)
Profitability [44] is measured by the ratio of net profit to total assets. The higher profitability indicates the more stable cash flow, the less bankruptcy risk, and the stronger antirisk ability. So the enterprise operators tend to increase the asset-liability ratio.(4)
Growth [45] is measured by the growth rate of sales revenue. The better the growth of an enterprise is, the more inclined it is to increase financial leverage to expand its scale to increase market value.(5)
Tangible assets [46] are measured by the ratio of fixed assets to total assets. Generally, the more fixed assets an enterprise has, the greater the probability of obtaining mortgage loans from financial institutions, and the company operators are more willing to increase the asset-liability ratio.(6)
Economic growth rate [14] is measured by GDP growth rate. The higher the economic growth rate is, the better the macroeconomic environment is, and the enterprises will increase the asset-liability ratio.(7)
Financial structure [6] is measured by the ratio of total stock market value to the total loans from financial institutions. When the market financial structure is dominated by capital market, enterprises are more willing to increase the asset-liability ratio. When the financial structure is dominated by banks, enterprises will decrease the asset-liability ratio.Table 1
Variable declarations.
TypeNameCodeExplanationExplained variableCapital structuredebtrateTotal liability/ Total assetExplaining variablesMonetary policyMon1Mon21-year loan rate year-on-year growth rate of M2Fiscal policyFinGrowth rate of fiscal expenditureControl variablesFirm sizelnassetThe natural log of total assetsNondebt tax shieldnontax(Accumulated depreciation + Deferred expenses + Deferred assets)/Total assetProfitabilityprofitNet profit/Total assetGrowthgrowSales growth rateTangible assetstangFixed assets/Total assetsEconomic growth rateGDPgrowGDP growth rateFinancial structurestruTotal stock market value/ Total loans from financial institutions


## 4.1. Model Construction

In order to study the relationship between corporate capital structure and monetary and fiscal policies, based on the practice of Faulkender et al. [41], we select a series of corporate characteristic variables to construct equation (1) to fit the target capital structure and deduce the capital structure adjustment speed of the enterprise:(1)debratei,t=γ+βXi,t−1+ηMt,debratei,t means the enterprise capital structure; the vector Xi,t−1 means the microcontrol variables influencing the enterprise capital structure, the vector Xi,t‐1 refers to a series of (t-1) year characteristic variables of enterprises, including company size, uniqueness, nondebt tax shields, profitability, business growth, liquidity, and degree of financing constraints. The β means the coefficient of characteristic variables of enterprises. The vector Mt means the macroeconomic factors affecting the capital structure. The vector Mt refers to the macroeconomic variables in t year, including economic growth, monetary policy, fiscal policy, and financial structure.In order to study the relationship between capital structure adjustment speed and monetary policy and fiscal policy, we constructed the following model by referring to Huang and Jiang [1]:(2)debtratei,t∗=γ+βXi,t−1+ηMt,debtratei,t∗ means the optimal capital structure of enterprises i in the year of t. Other variables have the same meanings as described above. We use the partial adjustment model to estimate and calculate the capital structure adjustment speed of enterprises. The standard partial adjustment model is established as follows:(3)debtratei,t−debtratei,t−1=θdebtratei,t∗−debtratei,t−1,debtratei,t represents the capital structure of enterprise i at the end of year t, debtratei,t−1 represents the capital structure of enterprise i at the beginning of year t, the capital structure is represented by the asset-liability ratio, and θ represents the speed of capital structure adjustment1. θ = 0 means the capital structure of enterprises has not been adjusted, and the asset-liability ratio remains unchanged; θ<0 represents that the capital structure adjustment of the enterprise deviates from the target capital structure; 0<θ<1 means the direction of enterprise adjustment is consistent with the target capital structure and has been partially adjusted; θ ＞1 means the enterprise has excess adjustment on the target capital structure.In order to study the influence of macroeconomic policy on the speed of capital structure adjustment, we regardθ as a linear function of monetary policy and fiscal policy as shown in (4)θ=α0+α1Mact,where Mact is the monetary policy (fiscal policy) in t year. Substitute equation (2) and equation (4) into equation (3), and get equation (5) after sorting.(5)debtratei,t=α0γ+1−α0debtratei,t−1−α1debtratei,t−1×Mact+α0βXi,t−1+α0ηMt+α1βXi,t−1×Mact+α0ηMt×Mact+μi+λt+εi,t.So the impact of monetary policy (fiscal policy) on the speed of capital structure adjustment can be examined throughα0 and α1. In formula (4), the second part on the right side of the equation is the first-order lag term of asset-liability ratio, the third part is the cross term of monetary policy or fiscal policy and the first-order lag term of asset-liability ratio, the fourth part is the microfactors controlled, the fifth part is the macroeconomic factors controlled (excluding monetary policy and fiscal policy), the sixth part is the cross term of monetary policy (fiscal policy) and microcontrol variables, and the seventh part is the cross term of monetary policy (fiscal policy) and macrocontrol variables. μi and λt are time and individual effects, respectively, and εi,t are random interference terms. We mainly focus on (1-α0) and -α1, which need to be converted into α0 and α1 to measure the impact of monetary policy (fiscal policy) on the speed of capital structure adjustment in equation (4).


## 4.2. Description of Variables

Monetary policy [18] used 1-year loan interest rate (calculated by monthly weighted average method) and annual year-on-year growth rate of broad monetary aggregates M2, respectively, to measure the degree of monetary policy tightness. If the interest rate is higher, the authorities will carry out tight monetary policy. If the interest rate is lower, the authorities will carry out loose monetary policy. If the growth rate of monetary supply is fast, it indicates that the authorities carry out relatively loose monetary policy. And a slower pace of monetary supply growth indicates a tightening of monetary policy.Fiscal policy [16] used the annual growth rate of fiscal expenditure to measure the tightness of fiscal policy. The high growth rate of fiscal expenditure indicates that the government is pursuing an expansionary fiscal policy. The low growth rate of fiscal spending indicates tightening fiscal policy.Asset-liability ratio [42] is widely used to measure capital structure, that is, the ratio of total liabilities to total assets of an enterprise.Since the enterprise’s own characteristics and the macroeconomic factors will affect the capital structure, we draw on the experience of most scholars to selected microcharacteristic factors such as the company size, nondebt tax shields, profitability, business growth, tangible assets, and macrofactors such as economic growth and financial structure as control variables. The specific definitions are shown in Table1.(1)
Firm size [43] is measured by the natural logarithm of total assets. In general, the larger the size of the company, the stronger the operation capacity of the company. And the probability of financial distress is smaller with the higher asset-liability ratio.(2)
Nondebt tax shield [14] is measured by the proportion of accumulated depreciation, deferred expenses, and deferred assets in total assets. The larger the scale of the company, the more significant the tax shield effect. Increasing the credit ratio will further enhance the value of the enterprise.(3)
Profitability [44] is measured by the ratio of net profit to total assets. The higher profitability indicates the more stable cash flow, the less bankruptcy risk, and the stronger antirisk ability. So the enterprise operators tend to increase the asset-liability ratio.(4)
Growth [45] is measured by the growth rate of sales revenue. The better the growth of an enterprise is, the more inclined it is to increase financial leverage to expand its scale to increase market value.(5)
Tangible assets [46] are measured by the ratio of fixed assets to total assets. Generally, the more fixed assets an enterprise has, the greater the probability of obtaining mortgage loans from financial institutions, and the company operators are more willing to increase the asset-liability ratio.(6)
Economic growth rate [14] is measured by GDP growth rate. The higher the economic growth rate is, the better the macroeconomic environment is, and the enterprises will increase the asset-liability ratio.(7)
Financial structure [6] is measured by the ratio of total stock market value to the total loans from financial institutions. When the market financial structure is dominated by capital market, enterprises are more willing to increase the asset-liability ratio. When the financial structure is dominated by banks, enterprises will decrease the asset-liability ratio.Table 1
Variable declarations.
TypeNameCodeExplanationExplained variableCapital structuredebtrateTotal liability/ Total assetExplaining variablesMonetary policyMon1Mon21-year loan rate year-on-year growth rate of M2Fiscal policyFinGrowth rate of fiscal expenditureControl variablesFirm sizelnassetThe natural log of total assetsNondebt tax shieldnontax(Accumulated depreciation + Deferred expenses + Deferred assets)/Total assetProfitabilityprofitNet profit/Total assetGrowthgrowSales growth rateTangible assetstangFixed assets/Total assetsEconomic growth rateGDPgrowGDP growth rateFinancial structurestruTotal stock market value/ Total loans from financial institutions


## 5. Data and Results

### 5.1. Data and Statistic

The data comes from Wind database. We select the annual data of nonfinancial enterprises listed in China’s A-share market from 2000 to 2020 as the research object and perform the following screening procedures: (1) excluding the samples of financial listed companies; (2) excluding samples of B shares or H shares issued at the same time; (3) excluding the samples treated with special treatment (ST or PT); (4) eliminating samples with negative owner’s equity; (5) deleting missing data. Finally, a total of 41569 data samples were obtained. In order to eliminate the possible influence of outliers on the robustness of regression results, Winsorize the upper and lower 1% of all continuous variables.Figure1 reflects the trend of monetary and fiscal policies in recent years in China. The monetary policy gradually showed a trend of easing from 1998 and reached its peak around 2008. When the financial crisis broke out in 2008, the PBC released RMB 300 billion liquidity to rescue the market and the degree of monetary policy easing reached its peak. Then the PBC began to tighten the monetary policy. Since 1998, fiscal policy began to show a trend of gradual expansion, and the expansionary fiscal policy in China reached its peak around 2009. Subsequently, Chinese government implemented a prudent fiscal policy, and the degree of fiscal expansion gradually decreased and reached its lowest point in 2018.Figure 1
Trends of monetary policy (M2 growth rate) and fiscal policy (fiscal expenditure growth rate) in China.Table2 presents descriptive statistical results for the main variables. The average asset-liability ratio is 45%, which is at a medium level worldwide, so there is considerable room for adjustment. Macrofactors: (1) average 1-year loan interest rate is 4.64%, average M2 year-on-year growth rate (monetary policy) is 14.6%, and maximum is 26.5%, indicating that monetary policy is relatively loose; (2) average year-on-year growth rate of fiscal expenditure (fiscal policy) is 14.3%, indicating that fiscal policy is also in an expansionary state; (3) the average financial structure is 68.5% and the type is still bank-dominated in China, meaning indirect financing of bank loans occupies the mainstream of social financing. Microfactors: (1) the profitability is not outstanding with the average level being 3.14%, (2) the average proportion of tangible assets (fixed assets) to total assets is 23.9%; the lower ratio indicates that financing difficulties still exist to some extent since bank loans need to be secured by fixed assets.Table 2
Descriptive statistical results for the main variables.
VariableObsMeanStd.Dev.MinMaxdebtrate41,5690.4500.2050.08210.873lnasset41,56921.551.40512.3128.52nontax41,5690.03310.013420.001830.09451profit41,5690.03140.0684−0.3670.219grow41,569−0.2933.345−23.507.750tang41,5690.2390.17400.971Mon141,5690.04640.02160.04960.0743Mon241,5690.1460.04380.08280.265Fin41,5690.1430.05920.06300.257stru41,5690.6850.2120.2231.421GDPgrow41,5690.08530.01980.06600.142


### 5.2. Correlation Analysis

In order to test whether multicollinearity exists between explanatory variables and control variables, correlation test is conducted before regression analysis. The results are shown in Table3. The correlations between monetary policy fiscal policy and economic growth rate are 0.644 and 0.750, that between monetary policy and fiscal policy also reaches 0.684, and the correlation between loan interest rate and M2 growth rate is -0.723. Correlations between other variables are weak. In addition, it is understandable that monetary policies and fiscal policies are strongly correlated with GDP growth rate. Macroeconomic policies are adjusted according to macroeconomic development, and fiscal policies and monetary policies are often used together to regulate the macroeconomy. Therefore, multicollinearity of the model can be ruled out on the whole.Table 3
Correlation coefficient matrix of variables.
DebtrateLnassetNontaxProfitGrowTangMon1Mon2FinStruGDPgrowDebtrate1lnasset0.2791Nontax0.06040.02161Profit−0.291−0.147−0.01961Epsgrow−0.1180.0110−0.05730.3331Tang0.1120.07330.0778−0.0440−0.05761Mon1−0.1150.02460.10250.0139−0.00140.10721Mon20.121−0.2280.1010.06920.02790.117−0.723∗1Fin0.130−0.2270.1010.0520−0.004900.1210.08210.684∗∗1Stru−0.04410.0721−0.05220.07080.0491−0.09660.0776−0.08810.1431GDPgrow0.155−0.2170.1250.03080.01880.155−0.4260.644∗0.750∗−0.05671∗∗∗, ∗∗, and ∗ represent significance at 1%, 5%, and 10% levels, respectively.


### 5.3. Regression Results

#### 5.3.1. Monetary Policy, Fiscal Policy, and Capital Structure

In order to verify the relationship between monetary policy, fiscal policy, and capital structure, linear regression is performed on equation (1), and the actual capital structure of an enterprise is used to replace the optimal capital structure to discuss the correlation between capital structure, fiscal policy, and monetary policy. The regression results are shown in Table 4.Table 4
Regression results of capital structure, monetary policy, and fiscal policy.
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtrateDebtratetDebtrateDebtrateMon1−0.072∗−0.061∗∗−0.067∗(0.0413)(0.0298)(0.0385)Mon20.185∗∗∗0.358∗∗∗0.230∗∗∗(0.0120)(0.0197)(0.0209)Fin0.271∗∗∗0.410∗∗∗0.403∗∗∗0.337∗∗∗(0.0159)(0.0179)(0.0224)(0.0191)Stru0.0172∗∗∗0.0137∗∗∗0.0405∗∗∗0.0417∗∗∗0.0361∗∗∗(0.00298)(0.00302)(0.00324)(0.00585)(0.00326)GDPgrow0.658∗∗∗0.739∗∗∗0.363∗∗∗0.271∗∗∗0.282∗∗∗(0.0375)(0.0459)(0.0521)(0.0514)(0.0525)Inasset_lag0.0410∗∗∗0.0307∗∗∗0.0316∗∗∗0.0417∗∗∗0.0343∗∗∗(0.00245)(0.00105)(0.00104)(0.00103)(0.00106)Nontax_lag−0.744∗∗∗−0.766∗∗∗−0.719∗∗∗−0.689∗∗∗−0.728∗∗∗(0.0872)(0.0957)(0.0954)(0.1025)(0.0953)Profit_lag−0.512∗∗∗−0.457∗∗∗−0.472∗∗∗−0.394∗∗∗−0.482∗∗∗(0.0142)(0.0128)(0.0128)(0.0174)(0.0128)Grow_lag−0.0017∗∗∗−0.00163∗∗∗−0.00178∗∗∗−0.00157∗∗∗−0.00165∗∗∗(0.00021)(0.000204)(0.000203)(0.000194)(0.000203)Tang_lag−0.0172∗∗−0.0191∗∗−0.0193∗∗−0.0199∗∗−0.0201∗∗(0.00924)(0.00851)(0.00849)(0.00960)(0.00847)Constant0.416∗∗∗0.425∗∗∗0.412∗∗∗−0.319∗∗∗−0.204∗∗∗−0.176∗∗∗−0.249∗∗∗−0.252∗∗∗(0.00204)(0.00183)(0.00241)(0.0278)(0.0252)(0.0244)(0.0266)(0.0253)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0060.0060.0080.1820.1890.1940.1890.197∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table4 shows the results of the fixed effects regression. The columns (1), (2), and (3) are the regression results of monetary policy, fiscal policy, and enterprise capital structure without control variables, showing that Mon1 is significantly negative at 10% level and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at 1% level. Columns (4), (5), and (6), respectively, represent the regression results with control variables. Mon1 is significantly negative at the level of 5%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. Columns (7) and (8) include fiscal policy, monetary policy, and control variables in the regression model. The regression results show that Mon1 is significantly negative at the level of 10%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. To sum up, loose monetary policy and expansionary fiscal policy will promote the upward adjustment of capital structure of enterprises and increase the asset-liability ratio of enterprises. When monetary and fiscal policies are tightened, companies will adjust capital structure downward and reduce their debt to asset ratios. Hypothesis H1 and hypothesis H3 are verified.


#### 5.3.2. Monetary Policy, Fiscal Policy, and the Speed of Capital Structure Adjustment

In order to verify the relationship between monetary policies, fiscal policies, and the speed of capital structure adjustment, linear regression is performed on equation (5). Firstly, regression is made on the relationship between monetary policy and the speed of capital structure adjustment. And the whole sample is divided into subsamples for discussion according to the degree of financing constraint. Here, tangible assets are taken as the measurement index of financing constraint degree. Generally, if tangible assets (fixed assets) account for a large proportion in enterprises, the probability of obtaining financial support from banks and other external financial institutions is relatively high, and the degree of external financing constraint is relatively low. So the tangible assets can be used as an index to measure the degree of financing constraint of enterprises. The higher the proportion of tangible assets in total assets, the lower the degree of financing constraints. The results are shown in Table 5.Table 5
Regression results of speed of capital structure adjustment and monetary policy.
VariablesTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsDebtrate_lag0.472∗∗∗0.519∗∗∗0.565∗∗∗0.560∗∗∗0.463∗∗∗0.445∗∗∗(0.0157)(0.0129)(0.0197)(0.0206)(0.0182)(0.0174)Debtrate_lag × Mon10.371∗0.428∗0.527∗(0.205)(0.252)(0.314)Debtrate_lag × Mon2−0.775∗∗∗−0.441∗∗∗−0.947∗∗∗(0.0814)(0.127)(0.112)Constant−0.286∗∗∗−0.247∗∗∗0.104∗∗∗−0.169∗∗∗0.117∗∗∗−0.226∗∗∗(0.0223)(0.0206)(0.0214)(0.0341)(0.0305)(0.0288)Control variablesControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,808R-squared0.4670.4960.4480.5210.4310.420∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table5 shows the results of fixed-effect regression on equation (5). Under the total samples (1) and (2), the speed of capital structure adjustment can be deduced: θ  = 0.528-0.371∗Mon1 and θ  = 0.481 + 0.775∗Mon2, indicating that if the Mon1 increases by 1%, the speed of capital adjustment will decrease by 0.37%; if the M2 growth rate (Mon2) increases by 1%, the speed of capital adjustment will increase by 0.775%. The above analysis shows that loose monetary policy will accelerate the speed of capital structure adjustment, while tight monetary policy will slow down the speed of capital structure adjustment. In addition, we divide the total sample into two subsamples with high degree and low degree according to the mean value of financing constraint. It can be found that, under the impetus of loose monetary policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree, which verifies hypothesis H2 to some extent.Then, regression is made on the relationship between fiscal policy and the speed of capital structure adjustment. The whole sample is also divided into subsamples for discussion according to the degree of financing constraint. The results are shown in Table6.Table 6
Regression results of speed of capital structure adjustment and fiscal policy.
VariablesTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.571∗∗∗0.642∗∗∗0.512∗∗∗(0.00896)(0.0161)(0.0128)Debtrate_lag × Fin−0.474∗∗∗−0.0722−0.435∗∗∗(0.0584)(0.0895)(0.0965)Constant−0.314∗∗∗−0.185∗∗∗−0.157∗∗∗(0.0228)(0.0298)(0.0321)Control variablesControlControlControlObservations37,77415,96621,808R-squared0.4850.5070.422∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table6 shows the results of fixed-effect regression on equation (5); under the total sample column, we can get θ = 0.429 + 0.474∗Fin, indicating that if fiscal expenditure increases by 1%, the speed of capital structure adjustment will increase by 0.474%. The expansionary fiscal policy will accelerate the speed of capital structure adjustment. In addition, we also divide the total sample into two subsamples with high degree and low degree of financing constraint according to the mean value. It can be found that, under the impetus of expansionary fiscal policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree. The hypothesis H4 is verified.


#### 5.3.3. Robustness Test

Different robustness test methods were used to test the relevant results. (1) Replace variables. Because the measurements of monetary policy and fiscal policy measure are not unified, different macroeconomic policy measures may lead to the different research conclusion. So we use M3 year-on-year growth rate (Mon3) and the ratio of fiscal expenditure to fiscal revenue (Fin2) to measure the monetary policy and fiscal policy. The higher M3 year-on-year growth rate indicates the loose monetary policy. The larger ratio of fiscal expenditure to fiscal revenue indicates that the government carries out the expansionary fiscal policy. The estimated results are consistent with the above. (2) Different models are used to estimate the target capital structure. The fixed-effect model (FE) (Anet al., 2015) is used to estimate equation (5) as showed in previous part. GMM model is also used to estimate equation (5) here, and the conclusions are consistent. The specific results are shown in the following tables. Tables 7 and 8 use M3 year-on-year growth rate and ratio of fiscal expenditure to fiscal revenue to measure the changes and adjustments of monetary and fiscal policies. Tables 9 and 10 use GMM model to estimate equation (5) and get consistent results (∗∗∗, ∗∗, and ∗ in the tables represent significance at 1%, 5%, and 10% levels, respectively, and values in brackets represent standard deviations).Table 7
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (substitution variables).
Variables(1)(2)(3)(4)(5)DebtrateDebtrateDebtratetDebtratetDebtratetMon30.154∗∗∗0.277∗∗∗0.231∗∗∗(0.0132)(0.0193)(0.0207)Fin20.187∗∗∗0.353∗∗∗0.342∗∗∗(0.0168)(0.0215)(0.0147)Constant0.465∗∗∗0.359∗∗∗−0.216∗∗∗−0.157∗∗∗−0.198∗∗∗(0.00186)(0.00172)(0.0175)(0.0242)(0.0354)Control variablesNot controlNot controlControlControlControlObservations41,56941,56937,77437,77437,774R-squared0.0080.0100.1520.1710.182Table 8
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (substitution variables).
VariablesMonetary policyFiscal policyTotal sampleHigh Financing ConstraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.428∗∗∗0.567∗∗∗0.426∗∗∗0.497∗∗∗0.624∗∗∗0.523∗∗∗(0.0129)(0.0374)(0.0208)(0.01024)(0.0342)(0.0185)Debtrate_lag × Mon3−0.672∗∗∗−0.467∗∗∗−0.935∗∗∗(0.0937)(0.117)(0.114)Debtrate_lag × Fin2−0.565∗∗∗−0.0778−0.624∗∗∗(0.0734)(0.0850)(0.0935)Constant−0.445∗∗∗−0.184∗∗∗−0.227∗∗∗−0.275∗∗∗−0.247∗∗∗−0.175∗∗∗(0.0375)(0.0307)(0.0214)(0.0475)(0.0354)(0.0317)Control variablesControlControlControlControlControlControlObservations37,77415,96621,80837,77415,96621,808R-squared0.4210.5040.4630.4970.5120.424Table 9
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (GMM model).
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtratetDebtratetDebtrateDebtratetMon1−0.069∗−0.075∗∗−0.072∗(0.0411)(0.0312)(0.0399)Mon20.176∗∗∗0.360∗∗∗0.216∗∗∗(0.0142)(0.0199)(0.0218)Fin0.282∗∗∗0.431∗∗∗0.417∗∗∗0.342∗∗∗(0.0175)(0.0185)(0.0231)(0.0196)Control variablesNot controlNot controlNot controlControlControlControlControlControlConstant0.422∗∗∗0.421∗∗∗0.418∗∗∗−0.325∗∗∗−0.234∗∗∗−0.175∗∗∗−0.252∗∗∗−0.249∗∗∗(0.00218)(0.00175)(0.00255)(0.0296)(0.0356)(0.0243)(0.0275)(0.0287)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0070.0070.0080.1790.1720.1910.1860.192Table 10
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (GMM model).
VariablesMonetary policyFiscal policyTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.475∗∗∗0.502∗∗∗0.593∗∗∗0.561∗∗∗0.467∗∗∗0.468∗∗∗0.583∗∗∗0.671∗∗∗0.441∗∗∗(0.0165)(0.0134)(0.0259)(0.0207)(0.0196)(0.0198)(0.0125)(0.0254)(0.0147)Debtrate_lag × Mon10.381∗0.431∗0.529∗(0.216)(0.256)(0.321)Debtrate_lag × Mon2−0.763∗∗∗−0.442∗∗∗−0.987∗∗∗(0.0925)(0.123)(0.156)Debtrate_lag × Fin−0.463∗∗∗−0.0773−0.576∗∗∗(0.06741)(0.0944)(0.0752)Constant−0.279∗∗∗−0.258∗∗∗0.113∗∗∗−0.165∗∗∗0.120∗∗∗−0.234∗∗∗−0.245∗∗∗−0.125∗∗∗−0.221∗∗∗(0.0235)(0.0213)(0.0285)(0.0371)(0.0317)(0.0321)(0.02324)(0.0332)(0.0476)Control variablesControlControlControlControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,80837,77415,96621,808R-squared0.4520.4420.4750.5230.4420.4210.4720.5010.439


## 5.1. Data and Statistic

The data comes from Wind database. We select the annual data of nonfinancial enterprises listed in China’s A-share market from 2000 to 2020 as the research object and perform the following screening procedures: (1) excluding the samples of financial listed companies; (2) excluding samples of B shares or H shares issued at the same time; (3) excluding the samples treated with special treatment (ST or PT); (4) eliminating samples with negative owner’s equity; (5) deleting missing data. Finally, a total of 41569 data samples were obtained. In order to eliminate the possible influence of outliers on the robustness of regression results, Winsorize the upper and lower 1% of all continuous variables.Figure1 reflects the trend of monetary and fiscal policies in recent years in China. The monetary policy gradually showed a trend of easing from 1998 and reached its peak around 2008. When the financial crisis broke out in 2008, the PBC released RMB 300 billion liquidity to rescue the market and the degree of monetary policy easing reached its peak. Then the PBC began to tighten the monetary policy. Since 1998, fiscal policy began to show a trend of gradual expansion, and the expansionary fiscal policy in China reached its peak around 2009. Subsequently, Chinese government implemented a prudent fiscal policy, and the degree of fiscal expansion gradually decreased and reached its lowest point in 2018.Figure 1
Trends of monetary policy (M2 growth rate) and fiscal policy (fiscal expenditure growth rate) in China.Table2 presents descriptive statistical results for the main variables. The average asset-liability ratio is 45%, which is at a medium level worldwide, so there is considerable room for adjustment. Macrofactors: (1) average 1-year loan interest rate is 4.64%, average M2 year-on-year growth rate (monetary policy) is 14.6%, and maximum is 26.5%, indicating that monetary policy is relatively loose; (2) average year-on-year growth rate of fiscal expenditure (fiscal policy) is 14.3%, indicating that fiscal policy is also in an expansionary state; (3) the average financial structure is 68.5% and the type is still bank-dominated in China, meaning indirect financing of bank loans occupies the mainstream of social financing. Microfactors: (1) the profitability is not outstanding with the average level being 3.14%, (2) the average proportion of tangible assets (fixed assets) to total assets is 23.9%; the lower ratio indicates that financing difficulties still exist to some extent since bank loans need to be secured by fixed assets.Table 2
Descriptive statistical results for the main variables.
VariableObsMeanStd.Dev.MinMaxdebtrate41,5690.4500.2050.08210.873lnasset41,56921.551.40512.3128.52nontax41,5690.03310.013420.001830.09451profit41,5690.03140.0684−0.3670.219grow41,569−0.2933.345−23.507.750tang41,5690.2390.17400.971Mon141,5690.04640.02160.04960.0743Mon241,5690.1460.04380.08280.265Fin41,5690.1430.05920.06300.257stru41,5690.6850.2120.2231.421GDPgrow41,5690.08530.01980.06600.142


## 5.2. Correlation Analysis

In order to test whether multicollinearity exists between explanatory variables and control variables, correlation test is conducted before regression analysis. The results are shown in Table3. The correlations between monetary policy fiscal policy and economic growth rate are 0.644 and 0.750, that between monetary policy and fiscal policy also reaches 0.684, and the correlation between loan interest rate and M2 growth rate is -0.723. Correlations between other variables are weak. In addition, it is understandable that monetary policies and fiscal policies are strongly correlated with GDP growth rate. Macroeconomic policies are adjusted according to macroeconomic development, and fiscal policies and monetary policies are often used together to regulate the macroeconomy. Therefore, multicollinearity of the model can be ruled out on the whole.Table 3
Correlation coefficient matrix of variables.
DebtrateLnassetNontaxProfitGrowTangMon1Mon2FinStruGDPgrowDebtrate1lnasset0.2791Nontax0.06040.02161Profit−0.291−0.147−0.01961Epsgrow−0.1180.0110−0.05730.3331Tang0.1120.07330.0778−0.0440−0.05761Mon1−0.1150.02460.10250.0139−0.00140.10721Mon20.121−0.2280.1010.06920.02790.117−0.723∗1Fin0.130−0.2270.1010.0520−0.004900.1210.08210.684∗∗1Stru−0.04410.0721−0.05220.07080.0491−0.09660.0776−0.08810.1431GDPgrow0.155−0.2170.1250.03080.01880.155−0.4260.644∗0.750∗−0.05671∗∗∗, ∗∗, and ∗ represent significance at 1%, 5%, and 10% levels, respectively.


## 5.3. Regression Results

### 5.3.1. Monetary Policy, Fiscal Policy, and Capital Structure

In order to verify the relationship between monetary policy, fiscal policy, and capital structure, linear regression is performed on equation (1), and the actual capital structure of an enterprise is used to replace the optimal capital structure to discuss the correlation between capital structure, fiscal policy, and monetary policy. The regression results are shown in Table 4.Table 4
Regression results of capital structure, monetary policy, and fiscal policy.
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtrateDebtratetDebtrateDebtrateMon1−0.072∗−0.061∗∗−0.067∗(0.0413)(0.0298)(0.0385)Mon20.185∗∗∗0.358∗∗∗0.230∗∗∗(0.0120)(0.0197)(0.0209)Fin0.271∗∗∗0.410∗∗∗0.403∗∗∗0.337∗∗∗(0.0159)(0.0179)(0.0224)(0.0191)Stru0.0172∗∗∗0.0137∗∗∗0.0405∗∗∗0.0417∗∗∗0.0361∗∗∗(0.00298)(0.00302)(0.00324)(0.00585)(0.00326)GDPgrow0.658∗∗∗0.739∗∗∗0.363∗∗∗0.271∗∗∗0.282∗∗∗(0.0375)(0.0459)(0.0521)(0.0514)(0.0525)Inasset_lag0.0410∗∗∗0.0307∗∗∗0.0316∗∗∗0.0417∗∗∗0.0343∗∗∗(0.00245)(0.00105)(0.00104)(0.00103)(0.00106)Nontax_lag−0.744∗∗∗−0.766∗∗∗−0.719∗∗∗−0.689∗∗∗−0.728∗∗∗(0.0872)(0.0957)(0.0954)(0.1025)(0.0953)Profit_lag−0.512∗∗∗−0.457∗∗∗−0.472∗∗∗−0.394∗∗∗−0.482∗∗∗(0.0142)(0.0128)(0.0128)(0.0174)(0.0128)Grow_lag−0.0017∗∗∗−0.00163∗∗∗−0.00178∗∗∗−0.00157∗∗∗−0.00165∗∗∗(0.00021)(0.000204)(0.000203)(0.000194)(0.000203)Tang_lag−0.0172∗∗−0.0191∗∗−0.0193∗∗−0.0199∗∗−0.0201∗∗(0.00924)(0.00851)(0.00849)(0.00960)(0.00847)Constant0.416∗∗∗0.425∗∗∗0.412∗∗∗−0.319∗∗∗−0.204∗∗∗−0.176∗∗∗−0.249∗∗∗−0.252∗∗∗(0.00204)(0.00183)(0.00241)(0.0278)(0.0252)(0.0244)(0.0266)(0.0253)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0060.0060.0080.1820.1890.1940.1890.197∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table4 shows the results of the fixed effects regression. The columns (1), (2), and (3) are the regression results of monetary policy, fiscal policy, and enterprise capital structure without control variables, showing that Mon1 is significantly negative at 10% level and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at 1% level. Columns (4), (5), and (6), respectively, represent the regression results with control variables. Mon1 is significantly negative at the level of 5%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. Columns (7) and (8) include fiscal policy, monetary policy, and control variables in the regression model. The regression results show that Mon1 is significantly negative at the level of 10%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. To sum up, loose monetary policy and expansionary fiscal policy will promote the upward adjustment of capital structure of enterprises and increase the asset-liability ratio of enterprises. When monetary and fiscal policies are tightened, companies will adjust capital structure downward and reduce their debt to asset ratios. Hypothesis H1 and hypothesis H3 are verified.


### 5.3.2. Monetary Policy, Fiscal Policy, and the Speed of Capital Structure Adjustment

In order to verify the relationship between monetary policies, fiscal policies, and the speed of capital structure adjustment, linear regression is performed on equation (5). Firstly, regression is made on the relationship between monetary policy and the speed of capital structure adjustment. And the whole sample is divided into subsamples for discussion according to the degree of financing constraint. Here, tangible assets are taken as the measurement index of financing constraint degree. Generally, if tangible assets (fixed assets) account for a large proportion in enterprises, the probability of obtaining financial support from banks and other external financial institutions is relatively high, and the degree of external financing constraint is relatively low. So the tangible assets can be used as an index to measure the degree of financing constraint of enterprises. The higher the proportion of tangible assets in total assets, the lower the degree of financing constraints. The results are shown in Table 5.Table 5
Regression results of speed of capital structure adjustment and monetary policy.
VariablesTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsDebtrate_lag0.472∗∗∗0.519∗∗∗0.565∗∗∗0.560∗∗∗0.463∗∗∗0.445∗∗∗(0.0157)(0.0129)(0.0197)(0.0206)(0.0182)(0.0174)Debtrate_lag × Mon10.371∗0.428∗0.527∗(0.205)(0.252)(0.314)Debtrate_lag × Mon2−0.775∗∗∗−0.441∗∗∗−0.947∗∗∗(0.0814)(0.127)(0.112)Constant−0.286∗∗∗−0.247∗∗∗0.104∗∗∗−0.169∗∗∗0.117∗∗∗−0.226∗∗∗(0.0223)(0.0206)(0.0214)(0.0341)(0.0305)(0.0288)Control variablesControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,808R-squared0.4670.4960.4480.5210.4310.420∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table5 shows the results of fixed-effect regression on equation (5). Under the total samples (1) and (2), the speed of capital structure adjustment can be deduced: θ  = 0.528-0.371∗Mon1 and θ  = 0.481 + 0.775∗Mon2, indicating that if the Mon1 increases by 1%, the speed of capital adjustment will decrease by 0.37%; if the M2 growth rate (Mon2) increases by 1%, the speed of capital adjustment will increase by 0.775%. The above analysis shows that loose monetary policy will accelerate the speed of capital structure adjustment, while tight monetary policy will slow down the speed of capital structure adjustment. In addition, we divide the total sample into two subsamples with high degree and low degree according to the mean value of financing constraint. It can be found that, under the impetus of loose monetary policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree, which verifies hypothesis H2 to some extent.Then, regression is made on the relationship between fiscal policy and the speed of capital structure adjustment. The whole sample is also divided into subsamples for discussion according to the degree of financing constraint. The results are shown in Table6.Table 6
Regression results of speed of capital structure adjustment and fiscal policy.
VariablesTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.571∗∗∗0.642∗∗∗0.512∗∗∗(0.00896)(0.0161)(0.0128)Debtrate_lag × Fin−0.474∗∗∗−0.0722−0.435∗∗∗(0.0584)(0.0895)(0.0965)Constant−0.314∗∗∗−0.185∗∗∗−0.157∗∗∗(0.0228)(0.0298)(0.0321)Control variablesControlControlControlObservations37,77415,96621,808R-squared0.4850.5070.422∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table6 shows the results of fixed-effect regression on equation (5); under the total sample column, we can get θ = 0.429 + 0.474∗Fin, indicating that if fiscal expenditure increases by 1%, the speed of capital structure adjustment will increase by 0.474%. The expansionary fiscal policy will accelerate the speed of capital structure adjustment. In addition, we also divide the total sample into two subsamples with high degree and low degree of financing constraint according to the mean value. It can be found that, under the impetus of expansionary fiscal policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree. The hypothesis H4 is verified.


### 5.3.3. Robustness Test

Different robustness test methods were used to test the relevant results. (1) Replace variables. Because the measurements of monetary policy and fiscal policy measure are not unified, different macroeconomic policy measures may lead to the different research conclusion. So we use M3 year-on-year growth rate (Mon3) and the ratio of fiscal expenditure to fiscal revenue (Fin2) to measure the monetary policy and fiscal policy. The higher M3 year-on-year growth rate indicates the loose monetary policy. The larger ratio of fiscal expenditure to fiscal revenue indicates that the government carries out the expansionary fiscal policy. The estimated results are consistent with the above. (2) Different models are used to estimate the target capital structure. The fixed-effect model (FE) (Anet al., 2015) is used to estimate equation (5) as showed in previous part. GMM model is also used to estimate equation (5) here, and the conclusions are consistent. The specific results are shown in the following tables. Tables 7 and 8 use M3 year-on-year growth rate and ratio of fiscal expenditure to fiscal revenue to measure the changes and adjustments of monetary and fiscal policies. Tables 9 and 10 use GMM model to estimate equation (5) and get consistent results (∗∗∗, ∗∗, and ∗ in the tables represent significance at 1%, 5%, and 10% levels, respectively, and values in brackets represent standard deviations).Table 7
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (substitution variables).
Variables(1)(2)(3)(4)(5)DebtrateDebtrateDebtratetDebtratetDebtratetMon30.154∗∗∗0.277∗∗∗0.231∗∗∗(0.0132)(0.0193)(0.0207)Fin20.187∗∗∗0.353∗∗∗0.342∗∗∗(0.0168)(0.0215)(0.0147)Constant0.465∗∗∗0.359∗∗∗−0.216∗∗∗−0.157∗∗∗−0.198∗∗∗(0.00186)(0.00172)(0.0175)(0.0242)(0.0354)Control variablesNot controlNot controlControlControlControlObservations41,56941,56937,77437,77437,774R-squared0.0080.0100.1520.1710.182Table 8
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (substitution variables).
VariablesMonetary policyFiscal policyTotal sampleHigh Financing ConstraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.428∗∗∗0.567∗∗∗0.426∗∗∗0.497∗∗∗0.624∗∗∗0.523∗∗∗(0.0129)(0.0374)(0.0208)(0.01024)(0.0342)(0.0185)Debtrate_lag × Mon3−0.672∗∗∗−0.467∗∗∗−0.935∗∗∗(0.0937)(0.117)(0.114)Debtrate_lag × Fin2−0.565∗∗∗−0.0778−0.624∗∗∗(0.0734)(0.0850)(0.0935)Constant−0.445∗∗∗−0.184∗∗∗−0.227∗∗∗−0.275∗∗∗−0.247∗∗∗−0.175∗∗∗(0.0375)(0.0307)(0.0214)(0.0475)(0.0354)(0.0317)Control variablesControlControlControlControlControlControlObservations37,77415,96621,80837,77415,96621,808R-squared0.4210.5040.4630.4970.5120.424Table 9
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (GMM model).
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtratetDebtratetDebtrateDebtratetMon1−0.069∗−0.075∗∗−0.072∗(0.0411)(0.0312)(0.0399)Mon20.176∗∗∗0.360∗∗∗0.216∗∗∗(0.0142)(0.0199)(0.0218)Fin0.282∗∗∗0.431∗∗∗0.417∗∗∗0.342∗∗∗(0.0175)(0.0185)(0.0231)(0.0196)Control variablesNot controlNot controlNot controlControlControlControlControlControlConstant0.422∗∗∗0.421∗∗∗0.418∗∗∗−0.325∗∗∗−0.234∗∗∗−0.175∗∗∗−0.252∗∗∗−0.249∗∗∗(0.00218)(0.00175)(0.00255)(0.0296)(0.0356)(0.0243)(0.0275)(0.0287)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0070.0070.0080.1790.1720.1910.1860.192Table 10
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (GMM model).
VariablesMonetary policyFiscal policyTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.475∗∗∗0.502∗∗∗0.593∗∗∗0.561∗∗∗0.467∗∗∗0.468∗∗∗0.583∗∗∗0.671∗∗∗0.441∗∗∗(0.0165)(0.0134)(0.0259)(0.0207)(0.0196)(0.0198)(0.0125)(0.0254)(0.0147)Debtrate_lag × Mon10.381∗0.431∗0.529∗(0.216)(0.256)(0.321)Debtrate_lag × Mon2−0.763∗∗∗−0.442∗∗∗−0.987∗∗∗(0.0925)(0.123)(0.156)Debtrate_lag × Fin−0.463∗∗∗−0.0773−0.576∗∗∗(0.06741)(0.0944)(0.0752)Constant−0.279∗∗∗−0.258∗∗∗0.113∗∗∗−0.165∗∗∗0.120∗∗∗−0.234∗∗∗−0.245∗∗∗−0.125∗∗∗−0.221∗∗∗(0.0235)(0.0213)(0.0285)(0.0371)(0.0317)(0.0321)(0.02324)(0.0332)(0.0476)Control variablesControlControlControlControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,80837,77415,96621,808R-squared0.4520.4420.4750.5230.4420.4210.4720.5010.439


## 5.3.1. Monetary Policy, Fiscal Policy, and Capital Structure

In order to verify the relationship between monetary policy, fiscal policy, and capital structure, linear regression is performed on equation (1), and the actual capital structure of an enterprise is used to replace the optimal capital structure to discuss the correlation between capital structure, fiscal policy, and monetary policy. The regression results are shown in Table 4.Table 4
Regression results of capital structure, monetary policy, and fiscal policy.
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtrateDebtratetDebtrateDebtrateMon1−0.072∗−0.061∗∗−0.067∗(0.0413)(0.0298)(0.0385)Mon20.185∗∗∗0.358∗∗∗0.230∗∗∗(0.0120)(0.0197)(0.0209)Fin0.271∗∗∗0.410∗∗∗0.403∗∗∗0.337∗∗∗(0.0159)(0.0179)(0.0224)(0.0191)Stru0.0172∗∗∗0.0137∗∗∗0.0405∗∗∗0.0417∗∗∗0.0361∗∗∗(0.00298)(0.00302)(0.00324)(0.00585)(0.00326)GDPgrow0.658∗∗∗0.739∗∗∗0.363∗∗∗0.271∗∗∗0.282∗∗∗(0.0375)(0.0459)(0.0521)(0.0514)(0.0525)Inasset_lag0.0410∗∗∗0.0307∗∗∗0.0316∗∗∗0.0417∗∗∗0.0343∗∗∗(0.00245)(0.00105)(0.00104)(0.00103)(0.00106)Nontax_lag−0.744∗∗∗−0.766∗∗∗−0.719∗∗∗−0.689∗∗∗−0.728∗∗∗(0.0872)(0.0957)(0.0954)(0.1025)(0.0953)Profit_lag−0.512∗∗∗−0.457∗∗∗−0.472∗∗∗−0.394∗∗∗−0.482∗∗∗(0.0142)(0.0128)(0.0128)(0.0174)(0.0128)Grow_lag−0.0017∗∗∗−0.00163∗∗∗−0.00178∗∗∗−0.00157∗∗∗−0.00165∗∗∗(0.00021)(0.000204)(0.000203)(0.000194)(0.000203)Tang_lag−0.0172∗∗−0.0191∗∗−0.0193∗∗−0.0199∗∗−0.0201∗∗(0.00924)(0.00851)(0.00849)(0.00960)(0.00847)Constant0.416∗∗∗0.425∗∗∗0.412∗∗∗−0.319∗∗∗−0.204∗∗∗−0.176∗∗∗−0.249∗∗∗−0.252∗∗∗(0.00204)(0.00183)(0.00241)(0.0278)(0.0252)(0.0244)(0.0266)(0.0253)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0060.0060.0080.1820.1890.1940.1890.197∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table4 shows the results of the fixed effects regression. The columns (1), (2), and (3) are the regression results of monetary policy, fiscal policy, and enterprise capital structure without control variables, showing that Mon1 is significantly negative at 10% level and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at 1% level. Columns (4), (5), and (6), respectively, represent the regression results with control variables. Mon1 is significantly negative at the level of 5%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. Columns (7) and (8) include fiscal policy, monetary policy, and control variables in the regression model. The regression results show that Mon1 is significantly negative at the level of 10%, and the M2 growth rate (Mon2) and fiscal expenditure growth rate (Fin) are significantly positive at the level of 1%. To sum up, loose monetary policy and expansionary fiscal policy will promote the upward adjustment of capital structure of enterprises and increase the asset-liability ratio of enterprises. When monetary and fiscal policies are tightened, companies will adjust capital structure downward and reduce their debt to asset ratios. Hypothesis H1 and hypothesis H3 are verified.


## 5.3.2. Monetary Policy, Fiscal Policy, and the Speed of Capital Structure Adjustment

In order to verify the relationship between monetary policies, fiscal policies, and the speed of capital structure adjustment, linear regression is performed on equation (5). Firstly, regression is made on the relationship between monetary policy and the speed of capital structure adjustment. And the whole sample is divided into subsamples for discussion according to the degree of financing constraint. Here, tangible assets are taken as the measurement index of financing constraint degree. Generally, if tangible assets (fixed assets) account for a large proportion in enterprises, the probability of obtaining financial support from banks and other external financial institutions is relatively high, and the degree of external financing constraint is relatively low. So the tangible assets can be used as an index to measure the degree of financing constraint of enterprises. The higher the proportion of tangible assets in total assets, the lower the degree of financing constraints. The results are shown in Table 5.Table 5
Regression results of speed of capital structure adjustment and monetary policy.
VariablesTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsDebtrate_lag0.472∗∗∗0.519∗∗∗0.565∗∗∗0.560∗∗∗0.463∗∗∗0.445∗∗∗(0.0157)(0.0129)(0.0197)(0.0206)(0.0182)(0.0174)Debtrate_lag × Mon10.371∗0.428∗0.527∗(0.205)(0.252)(0.314)Debtrate_lag × Mon2−0.775∗∗∗−0.441∗∗∗−0.947∗∗∗(0.0814)(0.127)(0.112)Constant−0.286∗∗∗−0.247∗∗∗0.104∗∗∗−0.169∗∗∗0.117∗∗∗−0.226∗∗∗(0.0223)(0.0206)(0.0214)(0.0341)(0.0305)(0.0288)Control variablesControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,808R-squared0.4670.4960.4480.5210.4310.420∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table5 shows the results of fixed-effect regression on equation (5). Under the total samples (1) and (2), the speed of capital structure adjustment can be deduced: θ  = 0.528-0.371∗Mon1 and θ  = 0.481 + 0.775∗Mon2, indicating that if the Mon1 increases by 1%, the speed of capital adjustment will decrease by 0.37%; if the M2 growth rate (Mon2) increases by 1%, the speed of capital adjustment will increase by 0.775%. The above analysis shows that loose monetary policy will accelerate the speed of capital structure adjustment, while tight monetary policy will slow down the speed of capital structure adjustment. In addition, we divide the total sample into two subsamples with high degree and low degree according to the mean value of financing constraint. It can be found that, under the impetus of loose monetary policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree, which verifies hypothesis H2 to some extent.Then, regression is made on the relationship between fiscal policy and the speed of capital structure adjustment. The whole sample is also divided into subsamples for discussion according to the degree of financing constraint. The results are shown in Table6.Table 6
Regression results of speed of capital structure adjustment and fiscal policy.
VariablesTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.571∗∗∗0.642∗∗∗0.512∗∗∗(0.00896)(0.0161)(0.0128)Debtrate_lag × Fin−0.474∗∗∗−0.0722−0.435∗∗∗(0.0584)(0.0895)(0.0965)Constant−0.314∗∗∗−0.185∗∗∗−0.157∗∗∗(0.0228)(0.0298)(0.0321)Control variablesControlControlControlObservations37,77415,96621,808R-squared0.4850.5070.422∗∗∗, ∗∗, and ∗ in the table represent significance at 1%, 5%, and 10% levels, respectively, and the values in brackets represent standard deviations.Table6 shows the results of fixed-effect regression on equation (5); under the total sample column, we can get θ = 0.429 + 0.474∗Fin, indicating that if fiscal expenditure increases by 1%, the speed of capital structure adjustment will increase by 0.474%. The expansionary fiscal policy will accelerate the speed of capital structure adjustment. In addition, we also divide the total sample into two subsamples with high degree and low degree of financing constraint according to the mean value. It can be found that, under the impetus of expansionary fiscal policy, enterprises with low degree of financing constraint adjust their capital structure faster than those with high degree. The hypothesis H4 is verified.


## 5.3.3. Robustness Test

Different robustness test methods were used to test the relevant results. (1) Replace variables. Because the measurements of monetary policy and fiscal policy measure are not unified, different macroeconomic policy measures may lead to the different research conclusion. So we use M3 year-on-year growth rate (Mon3) and the ratio of fiscal expenditure to fiscal revenue (Fin2) to measure the monetary policy and fiscal policy. The higher M3 year-on-year growth rate indicates the loose monetary policy. The larger ratio of fiscal expenditure to fiscal revenue indicates that the government carries out the expansionary fiscal policy. The estimated results are consistent with the above. (2) Different models are used to estimate the target capital structure. The fixed-effect model (FE) (Anet al., 2015) is used to estimate equation (5) as showed in previous part. GMM model is also used to estimate equation (5) here, and the conclusions are consistent. The specific results are shown in the following tables. Tables 7 and 8 use M3 year-on-year growth rate and ratio of fiscal expenditure to fiscal revenue to measure the changes and adjustments of monetary and fiscal policies. Tables 9 and 10 use GMM model to estimate equation (5) and get consistent results (∗∗∗, ∗∗, and ∗ in the tables represent significance at 1%, 5%, and 10% levels, respectively, and values in brackets represent standard deviations).Table 7
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (substitution variables).
Variables(1)(2)(3)(4)(5)DebtrateDebtrateDebtratetDebtratetDebtratetMon30.154∗∗∗0.277∗∗∗0.231∗∗∗(0.0132)(0.0193)(0.0207)Fin20.187∗∗∗0.353∗∗∗0.342∗∗∗(0.0168)(0.0215)(0.0147)Constant0.465∗∗∗0.359∗∗∗−0.216∗∗∗−0.157∗∗∗−0.198∗∗∗(0.00186)(0.00172)(0.0175)(0.0242)(0.0354)Control variablesNot controlNot controlControlControlControlObservations41,56941,56937,77437,77437,774R-squared0.0080.0100.1520.1710.182Table 8
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (substitution variables).
VariablesMonetary policyFiscal policyTotal sampleHigh Financing ConstraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.428∗∗∗0.567∗∗∗0.426∗∗∗0.497∗∗∗0.624∗∗∗0.523∗∗∗(0.0129)(0.0374)(0.0208)(0.01024)(0.0342)(0.0185)Debtrate_lag × Mon3−0.672∗∗∗−0.467∗∗∗−0.935∗∗∗(0.0937)(0.117)(0.114)Debtrate_lag × Fin2−0.565∗∗∗−0.0778−0.624∗∗∗(0.0734)(0.0850)(0.0935)Constant−0.445∗∗∗−0.184∗∗∗−0.227∗∗∗−0.275∗∗∗−0.247∗∗∗−0.175∗∗∗(0.0375)(0.0307)(0.0214)(0.0475)(0.0354)(0.0317)Control variablesControlControlControlControlControlControlObservations37,77415,96621,80837,77415,96621,808R-squared0.4210.5040.4630.4970.5120.424Table 9
Robustness test of the relationship between monetary policy, fiscal policy, and capital structure (GMM model).
Variables(1)(2)(3)(4)(5)(6)(7)(8)DebtrateDebtrateDebtrateDebtrateDebtratetDebtratetDebtrateDebtratetMon1−0.069∗−0.075∗∗−0.072∗(0.0411)(0.0312)(0.0399)Mon20.176∗∗∗0.360∗∗∗0.216∗∗∗(0.0142)(0.0199)(0.0218)Fin0.282∗∗∗0.431∗∗∗0.417∗∗∗0.342∗∗∗(0.0175)(0.0185)(0.0231)(0.0196)Control variablesNot controlNot controlNot controlControlControlControlControlControlConstant0.422∗∗∗0.421∗∗∗0.418∗∗∗−0.325∗∗∗−0.234∗∗∗−0.175∗∗∗−0.252∗∗∗−0.249∗∗∗(0.00218)(0.00175)(0.00255)(0.0296)(0.0356)(0.0243)(0.0275)(0.0287)Observations41,56941,56941,56937,77437,77437,77437,77437,774R-squared0.0070.0070.0080.1790.1720.1910.1860.192Table 10
Robustness test of the relationship between monetary policy, fiscal policy, and the speed of capital structure adjustment (GMM model).
VariablesMonetary policyFiscal policyTotal sampleTotal sampleHigh financing constraintsHigh financing constraintsLow financing constraintsLow financing constraintsTotal sampleHigh financing constraintsLow financing constraintsDebtrate_lag0.475∗∗∗0.502∗∗∗0.593∗∗∗0.561∗∗∗0.467∗∗∗0.468∗∗∗0.583∗∗∗0.671∗∗∗0.441∗∗∗(0.0165)(0.0134)(0.0259)(0.0207)(0.0196)(0.0198)(0.0125)(0.0254)(0.0147)Debtrate_lag × Mon10.381∗0.431∗0.529∗(0.216)(0.256)(0.321)Debtrate_lag × Mon2−0.763∗∗∗−0.442∗∗∗−0.987∗∗∗(0.0925)(0.123)(0.156)Debtrate_lag × Fin−0.463∗∗∗−0.0773−0.576∗∗∗(0.06741)(0.0944)(0.0752)Constant−0.279∗∗∗−0.258∗∗∗0.113∗∗∗−0.165∗∗∗0.120∗∗∗−0.234∗∗∗−0.245∗∗∗−0.125∗∗∗−0.221∗∗∗(0.0235)(0.0213)(0.0285)(0.0371)(0.0317)(0.0321)(0.02324)(0.0332)(0.0476)Control variablesControlControlControlControlControlControlControlControlControlObservations37,77437,77415,96615,96621,80821,80837,77415,96621,808R-squared0.4520.4420.4750.5230.4420.4210.4720.5010.439


## 6. Conclusion and Suggestions

### 6.1. Conclusion

We analyze the impact of monetary policy and fiscal policy on capital structure and the speed of capital structure adjustment and empirically test the data of nonfinancial listed companies in A-share market from 2000 to 2020 in China. After empirical research, the following conclusions are drawn: (1) Under the loose monetary policy, enterprises will adjust the capital structure upward, improve the asset-liability ratio, and accelerate the speed of capital structure adjustment. In addition, the speed of capital structure adjustment of enterprises with low financing constraints is faster than that with high financing constraints. (2) Under the expansionary fiscal policy, enterprises will also adjust their capital structure upward, improve their asset-liability ratio, and accelerate the speed of capital structure adjustment. During the adjustment process, enterprises with low financing constraints adjust their capital structure faster than those with high financing constraints.


### 6.2. Suggestions

Based on the conclusions drawn from the research, we put forward corresponding policy suggestions from both macro- and microaspects.Macroaspects. (1) In the implementation of monetary policy and fiscal policy, the government should pay sufficient attention to the enterprise’s capital structure change and take some fine-tuning of policy according to the macroeconomic environment. When the macroeconomic is in downturn, expansionary fiscal policy and loose monetary policy will reduce the enterprise’s financing cost and speed up the enterprise to the optimal capital structure adjustment. When the economy is overheated, the reverse macropolicy operation will curb the excessive adjustment of the capital structure of enterprises. (2) Monetary policies and fiscal policies should be used in a correct and scientific way. The market’s expectation of macropolicies will have an impact on the entire financial environment, which is closely related to the financing cost and operating conditions of enterprises. (3) The government should establish an enterprise credit management system and collect enterprise credit files to strengthen the relationship between financial institutions and enterprises and further improve the capital market and financing environment. Establishing a platform to help small-scale enterprises raise external funds and giving more policy support to small-scale enterprises deeply constrained by financing are effective measures to relieve the pressure of financing constraints faced by small-scale enterprises, but also enable them to broaden financing channels and obtain external funds they need. (4) The adjustment of monetary policy and fiscal policy has different impact on enterprises with different degree of financing constraint, and the capital structure adjustment speed of enterprises with different degree of financing constraint has different sensitivity to the same type of economic policy adjustment. Therefore, the microtransmission mechanism of macroeconomic policy should be fully considered. Differentiated monetary and fiscal policies and corresponding credit policies should be formulated to provide favorable conditions for optimizing the capital structure of enterprises. (5) Government departments should disclose information about changes in fiscal and monetary policies through various channels, improve the transparency of government policies, and help improve marketization to promote the capital structure adjustment of enterprises.Microaspects. (1) Different degrees of financing constraints cause differences in the impact of macroeconomic policies on capital structure of enterprises. Enterprises should expand internal financing and improve the operation to reduce their dependence on bank credit capital. (2) Enterprises should make a correct judgement of macroeconomic policies and adjust their financing decisions according to the changes of policies. (3) Enterprises should adjust their capital structure in time according to the changes of policies and make timely measures to deal with various risk factors brought by policy changes to enterprise financing. In this way, enterprises can effectively respond to the impact of policy adjustment on their capital structure. At the same time, enterprises should expand the breadth and depth of information disclosure and improve the quality of information disclosure to reduce the degree of information asymmetry with financial institutions, which help reduce the degree of financing constraints. Therefore, we can optimize the capital structure of enterprises, promote the speed of capital structure adjustment, and promote the sustainable and healthy development of enterprises. (4) Enterprises should establish a policy analysis department which help establish a regular policy analysis mechanism, so that it can timely grasp the changes of monetary and fiscal policies and then provide an important basis for enterprise capital structure adjustment and change decisions.


## 6.1. Conclusion

We analyze the impact of monetary policy and fiscal policy on capital structure and the speed of capital structure adjustment and empirically test the data of nonfinancial listed companies in A-share market from 2000 to 2020 in China. After empirical research, the following conclusions are drawn: (1) Under the loose monetary policy, enterprises will adjust the capital structure upward, improve the asset-liability ratio, and accelerate the speed of capital structure adjustment. In addition, the speed of capital structure adjustment of enterprises with low financing constraints is faster than that with high financing constraints. (2) Under the expansionary fiscal policy, enterprises will also adjust their capital structure upward, improve their asset-liability ratio, and accelerate the speed of capital structure adjustment. During the adjustment process, enterprises with low financing constraints adjust their capital structure faster than those with high financing constraints.


## 6.2. Suggestions

Based on the conclusions drawn from the research, we put forward corresponding policy suggestions from both macro- and microaspects.Macroaspects. (1) In the implementation of monetary policy and fiscal policy, the government should pay sufficient attention to the enterprise’s capital structure change and take some fine-tuning of policy according to the macroeconomic environment. When the macroeconomic is in downturn, expansionary fiscal policy and loose monetary policy will reduce the enterprise’s financing cost and speed up the enterprise to the optimal capital structure adjustment. When the economy is overheated, the reverse macropolicy operation will curb the excessive adjustment of the capital structure of enterprises. (2) Monetary policies and fiscal policies should be used in a correct and scientific way. The market’s expectation of macropolicies will have an impact on the entire financial environment, which is closely related to the financing cost and operating conditions of enterprises. (3) The government should establish an enterprise credit management system and collect enterprise credit files to strengthen the relationship between financial institutions and enterprises and further improve the capital market and financing environment. Establishing a platform to help small-scale enterprises raise external funds and giving more policy support to small-scale enterprises deeply constrained by financing are effective measures to relieve the pressure of financing constraints faced by small-scale enterprises, but also enable them to broaden financing channels and obtain external funds they need. (4) The adjustment of monetary policy and fiscal policy has different impact on enterprises with different degree of financing constraint, and the capital structure adjustment speed of enterprises with different degree of financing constraint has different sensitivity to the same type of economic policy adjustment. Therefore, the microtransmission mechanism of macroeconomic policy should be fully considered. Differentiated monetary and fiscal policies and corresponding credit policies should be formulated to provide favorable conditions for optimizing the capital structure of enterprises. (5) Government departments should disclose information about changes in fiscal and monetary policies through various channels, improve the transparency of government policies, and help improve marketization to promote the capital structure adjustment of enterprises.Microaspects. (1) Different degrees of financing constraints cause differences in the impact of macroeconomic policies on capital structure of enterprises. Enterprises should expand internal financing and improve the operation to reduce their dependence on bank credit capital. (2) Enterprises should make a correct judgement of macroeconomic policies and adjust their financing decisions according to the changes of policies. (3) Enterprises should adjust their capital structure in time according to the changes of policies and make timely measures to deal with various risk factors brought by policy changes to enterprise financing. In this way, enterprises can effectively respond to the impact of policy adjustment on their capital structure. At the same time, enterprises should expand the breadth and depth of information disclosure and improve the quality of information disclosure to reduce the degree of information asymmetry with financial institutions, which help reduce the degree of financing constraints. Therefore, we can optimize the capital structure of enterprises, promote the speed of capital structure adjustment, and promote the sustainable and healthy development of enterprises. (4) Enterprises should establish a policy analysis department which help establish a regular policy analysis mechanism, so that it can timely grasp the changes of monetary and fiscal policies and then provide an important basis for enterprise capital structure adjustment and change decisions.



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*Source: 8040641-2022-08-08.xml*