Stock Markets, Financial Depth, and Economic Growth in China: Evidence from ARDL Model

1 Introduction and Literature Review

China’s financial system plays a significant role in mobilizing capital in society. With a huge banking sector, a very active stock market, a growing bond market, and an increasingly complex shadow banking sector, China’s financial system is large but still underdeveloped (Azimi, 2022). In addition, the Chinese financial system has undergone several reforms over the past 40 years, marked by exceptional economic growth.

Researchers (Destek et al., 2020; Hassan et al., 2020; Azimi, 2022) and policymakers recognize the importance of the financial system for economic growth. Empirical research has highlighted the link between financial development and economic growth. Model analyses indicate that developed financial markets contribute to a more efficient allocation of resources, resulting in faster growth in the long term. In a sample of 35 countries, Goldsmith (1969) was the first to show a positive relationship between financial development and growth. However, Robinson (1979) argues that financial development has no impact on economic growth. Financial demand increases as economic growth increases, and financial markets only respond passively to this increase.

In the context of the Chinese economy, Hasan et al. (2009) found that strong economic growth results from financial market growth. However, Liang and Teng (2006) demonstrated that it is economic growth that enables financial development.

However, the exploration of empirical literature reveals major gaps. Firstly, given the complexity of the financial sector, whose growth cannot be captured by a single indicator, various economists have undertaken to study the correlation between a segment of the financial system and growth in the real economy. Indeed, the identification of the relationship between the development of the financial system and economic growth has been done by referring its majority (Pan and Mishra, 2018) to financial market indicators (Levine and Zervos, 1998; Levine, 2005; Pan and Mishra, 2018) or by limiting themselves to indicators of the depth of financial institutions (Hasan et al., 2009; Bhattarai, 2015; Zhao et al., 2018; Koh et al., 2020). Thus, there are hardly any studies that investigate the overall impact of the financial system (markets and banks) on economic growth, especially in the case of China. Secondly, there is no consensus on the relationship between the development of the financial system and economic growth in China, highlighting the need to explore alternative research avenues. Thirdly, although there is a great deal of empirical work on the link between financial development and economic growth in developed countries (Levine et al., 2000; Coccorese and Silipo, 2015; Swamy and Dharani, 2020; Shobande and Ogbeifun, 2021), the empirical studies for China remain relatively limited, demonstrating the need for further empirical evidence. Finally, most recent empirical studies for China assuming that the relationship between financial development and growth is symmetric have used linear methods such as vector autoregressive (VAR) (Shan and Qi, 2006; Wang et al., 2019), impulse response function (IRF) (Shan and Jianhong, 2006), principal component analysis (PCA) (Peng et al., 2014), or dynamic general equilibrium model (DEGM) (Peng, 2019). Other studies have used panel regression or panel cointegration (Kandil et al., 2017), whose conclusions have been widely criticized because of the lack of variables or heterogeneity bias. Only a few studies have used an autoregressive distributed lag (ARDL) model (Jalil et al., 2010; Pan and Mishra, 2018; Wu et al., 2020; Laajoul and Oulhaj, 2021), which is particularly useful for analyzing non-stationary time series.

In this sense, aiming to overcome these gaps, this study highlights its contribution to empirical research. Specifically, this article contributes to the literature in three ways. First, it aims to provide some answers to the question of whether and how the different sub-sectors of the financial system contribute to China’s economic growth. Indeed, this study aims to identify this relationship through both capital market (stock and money markets) and banking intermediation indicators to account for the relative importance of one financing channel compared to the other. To our knowledge, this is the first attempt in this literature. This article therefore contributes to the literature and fills the bibliographic gap left by recent studies. Second, it is worth mentioning that the choice of China was motivated by two main reasons. On the one hand, China, the world’s second-largest economy, has experienced remarkable economic growth and development in recent decades, accompanied by reforms and progress in its financial system. On the other hand, the role of the financial sector in China’s economic development is still a controversial issue. Although some argue that accumulation and productivity improvement are the main drivers of China’s economic growth, the role of finance is not clear yet. Third, this study aims to complement existing research on the link between the financial system and economic growth in China based on time series analysis at the aggregate level. To overcome the problems inherent in small time series analysis, an autoregressive distributed lag (ARDL) model is used to study the long-run cointegration relationship between financial development and economic growth between 1980 and 2020. There are several reasons for choosing the ARDL technique over other cointegration methods. Firstly, this method allows different orders of integration to be considered. In addition, unlike other cointegration methods, the ARDL approach is suitable for small sample sizes. In addition, the ARDL method has the advantage of allowing the appropriate selection of lags, which helps to improve the quality of the estimates obtained. Finally, it should be noted that the ARDL approach also incorporates an analysis of the dynamic error correction model (ECM).

In this paper, we examine the link between economic growth and finance by reviewing the relevant theoretical and empirical literature (section 2). We then describe the data and variables related to the different financing channels and the econometric techniques used (section 3). The main results of the study are then presented (section 4), followed by discussions (section 5). Finally, we conclude and propose a set of policy measures based on the results (section 6).

2 Literature Background

The debate on the relationship between economic growth and financial system development has raised questions and controversies. However, there is no simple and clear solution to explain this relationship, either theoretically or empirically.

Since the introduction of the endogenous growth model, empirical work has been done to examine this relationship and better understand how the two economic variables interact. To address the issue raised in this paper and to propose our research hypotheses, empirical studies will be presented by distinguishing those studying the relationship concerning capital markets from those focusing on the contribution of the banking system to economic growth, with a particular interest in the case of China.

The literature on China’s stock markets is far more extensive than that on its financial market reform. They are typically drawn from the traditional financial literature. We have chosen them not only for their importance but also for their close link to the real economy. Empirical research by Levine and Zervos (1998) has shown a positive relationship between the stock market and economic growth.

In their study on China, Peng et al. (2014) find significant positive effects of liberalization on growth in the short and long run. Kandil et al. (2017), in their study on China and India, conclude that, in the long term, financial development promotes economic growth. The Chinese economy is driven by two main forces: labor input and financial development, which cause a two-way causality between stock market development and economic growth. Their results support the hypothesis that finance can be a catalyst for growth.

In the same vein, Azimi (2022), in his study on the impact of financial and money markets on economic growth in China, shows that the development of capital markets enables sound financial projects to materialize, resulting in long-term sustainable economic growth. His results indicate that market capitalization and the money market interest rate have a relatively strong impact on economic growth. It should be noted that this study is one of the few to show an asymmetrical relationship between financial development and economic growth in China in the short and long term.

However, Pan and Mishra (2018), using unit root testing in the presence of structural breaks and the autoregressive distributed lag (ARDL) model, show that China’s stock market is proliferating but does not have a significant impact on the real economy in the short term. On the contrary, state monopolies have the greatest effect on China’s economic performance, stimulating economic growth in the short run. Their study shows that the Shanghai A-share market (quoted in Chinese yuan and mainly intended for Chinese investors and qualified foreign investors) has a negative impact on economic growth. This negative impact, albeit small, can be explained by the Chinese government’s decision to use the stock market to promote economic transition by using citizens’ bank deposits to finance the development of SMEs. This phenomenon could therefore be described as irrational prosperity, which is having a negative impact on the Shanghai A-share market. As a result, the development of China’s A-share markets does not correspond to economic growth.

In addition to capital markets, financial intermediaries, particularly banks, can provide benefits such as identifying viable projects, mobilizing savings, pooling risk, and reducing transaction costs. Recall that according to Schumpeter (1911), the banking system is a key determinant of economic growth, as it allows savings to be allocated, innovation to be stimulated, and productive investments to be financed. However, in transition countries, the role of banks is complicated by the legacy of the planned economy era. Gorton and Winton (1998) found that established banks in transition economies tend to face excess bad loans and implicit subsidies that often encourage continued lending to inefficient state-owned enterprises, thereby limiting growth. Therefore, the contribution of the banking sector to economic growth during the transition process is not clear.

Samargandi and Kutan (2016), using a Global Vector Autoregressive (GVAR) model, show that domestic credit to the private sector has a positive impact on China’s economic growth. Their result is in line with the endogenous growth model (Greenwood and Jovanovic, 1990; Roubini and Sala-i-Martin, 1992). In the same vein, Wang et al. (2019), analyzing the impact of financial development on economic growth in the Beijing-Tianjin-Hebei (BTH) region, found a positive impact of credit. According to Jalil et al. (2010), domestic credit to the private sector has a positive impact on China’s economic growth in the long term. These studies are consistent with numerous prior empirical studies (King and Levine, 1993; Levine and Zervos, 1998; Levine, 2005).

However, Ouyang and Li (2018), using two indicators to assess the depth of financial institutions, the domestic credit and broad money (M2) ratio to nominal GDP, find a negative impact on China’s economic growth. Additionally, this study shows that economic growth reacts negatively to financial development, as measured by most indicators. The authors argue that any imprudent expansion of the financial sector in China should be avoided to ensure that economic growth is maintained.

Along the same lines, Laajoul and Oulhaj (2021) find that in the short term, the impact of credit to the private sector on economic growth in China is negative. In the long term, however, the impact is positive. It should be noted that this study also considered market capitalization to determine the impact of the stock market. The results showed a positive impact in both the short and long term. However, despite considering bank intermediation and the financial market in their analysis, they only considered two variables (credit to the private sector and market capitalization), which are not enough to reflect the Chinese financial system.

At the outcome of this exploration, it is possible to note the absence of consensus on the impact of stock markets and financial intermediaries on economic growth in China, which proves the usefulness of our study despite the abundance of subsequent research. While using a similar econometric approach to previous studies by Ouyang and Li (2018), Pan and Mishra (2018), and Laajoul and Oulhaj (2021), this study differs in that it captures this relationship through both bank intermediation indicators and capital market indicators (stock and money markets).

3 Empirical Methodology

4 Empirical Analysis

4.1 Univariate Treatment of Variables

Recall that this study focuses on the interaction between financial development and economic growth in China during the period 1980–2020. In the following, the different variables are presented statistically to describe their behavior in the next section. In the first step, Table 2 presents the main descriptive statistics of the different variables. In the second step, the graphs present the evolution of each variable.

Table 2

Descriptive Statistics of Variables

Designation	GDPG	Activity	GFCF	MS	MMR	CAP	PC	INFL
Mean	9.267	75.400	35.473	126.358	2.068	39.094	106.431	5.320
Median	9.236	76.820	34.440	135.580	2.6381	29.044	105.763	3.175
Maximum	15.191	44.518	44.518	211.387	7.3564	126.153	182.432	24.256
Minimum	2.347	23.988	23.988	36.426	–7.989	17.5791	64.769	–1.401
Standard Deviation	2.919	3.475	6.264	55.216	3.258	22.190	31.762	5.740
Skewness	–0.041	–0.382	–0.010	–0.127	–0.707	2.000	0.489	1.678
Kurtosis	–0.048	–1.580	–1.340	–1.302	0.990	4.776	–0.543	2.738
Jaque-Bera (JB) test	0.015	5.268	3.069	3.010	5.092	66.332	2.144	32.057
Probability JB	0.992	0.071	0.215	0.221	0.078	0.000	0.342	0.000
Ljung-Box (LB) (p=1) test	19.834	211.169	157.307	210.206	16.306	42.180	185.929	28.646
Probability LB	0.001	0.000	0.000	0.000	0.006	0.000	0.000	0.000
Observations	41	41	41	41	41	41	41	41

1. Note: “p” refers to the number of delays in the Ljung-Box test.

Overall, all variables have a serial autocorrelation problem where all probabilities of the Ljung-Box test statistic are less than 5%.

Figure 1 presents the evolution of the GDP growth rate (GDPG) between 1980 and 2020. During this period, we observe a non-stable evolution in terms of trend and the presence of several readable breaks since the 1980s, characterizing the structural changes that China has experienced during the study period. More precisely, the variable displays an overall average of 9.267 with a standard deviation of 2.919, showing the low homogeneity of the observations. All 41 years are ranked between 2.34 and 15.19. The sample distribution is slightly left-skewed (Skewness = –0.041<0) and platykurtic (Kurtosis = –0.048<0). In total, this distribution rejects the normality hypothesis since the probability of the Jarque-Bera statistic is less than 5%.

Figure 1

Trend Evolution & Histogram of GDP Growth Rate

Concerning the money supply (MS), it is characterized by an upward trend with the presence of several readable breaks between 1990 and 2000. Concerning Figure 2 and the results in Table 2, our series presents an overall average of 126.358 with a standard deviation of 55.216, showing the low homogeneity of the observations. The observations ranged between 36.426 and 211.387, where the sample distribution is left-skewed (Skewness = –0.126<0) and platykurtic (Kurtosis = –1.302<0). Overall, this distribution accepts the normality assumption since the probability of the Jarque-Bera statistic is greater than 5%.

Figure 2

Trend Evolution & Histogram of Money Supply

Regarding Table 2 and Figure 3, MMR represents an overall mean of 2.068 with a mean standard deviation of 3.258, which may influence its stability over time. The set of 54 observations is ranked between 17,579 and 126,153, with a high concentration of around 3.97, where the sample distribution is right-skewed (Skewness = –0.707<0) and leptokurtic (Kurtosis = 0.990>0). Based on the probability of the Jarque-Bera statistic being greater than 5%, we accept the assumption for normality of this distribution.

Figure 3

Trend Evolution & Histogram of Money Market Interest Rate

As mentioned earlier, market capitalization (CAP) is an important variable. Figure 4 of this variable is characterized by the existence of breaks during the study period. Indeed, the results of Table 2 show us an overall average of 39.09 with a standard deviation of 22.19. The 41 observations are bounded between 17.57 and 126.15, where the sample distribution is slightly right-skewed (Skewness = 2.000>0) and leptokurtic (Kurtosis = 4.776>0). According to the probability of the Jarque-Bera statistic, which is greater than 5%, we accept the assumption of normality for this distribution.

Figure 4

Trend Evolution & Histogram of Market Capitalization

Figure 5 illustrates the evolution of the PC variable which is characterized by an upward trend. Indeed, according to Table 2, the descriptive analysis of the PC variable, shows us an overall average of 106.431 with a low standard deviation of 31.762, also showing the strong homogeneity of the observations. The set of values is bounded between 64.768 and 182.43, where the sample distribution is left-skewed (Skewness = 0.489>0) and platykurtic (Kurtosis = –0.543<0). However, according to the probability of the Jarque-Bera statistic, which is greater than 5%, this distribution accepts the null hypothesis of normality.

Figure 5

Trend Evolution & Histogram of Domestic Credit to the Private Sector

In 1997, Perron stated that the variable admits a unit root without changing the break in the null hypothesis. However, the alternative hypothesis is consistent with the stationarity of the variable with an “endogenously” added break relating to an ignored date. According to Table 3, the results of Perron (1997) show that all the series are non-stationary in level with the presence of significant breaks relative to the different shocks, including those of the 1983 crisis, the financial crises of 1986 and 1993, and the subprime crisis in 2007 and even in 2018. However, the first-difference unit root test in Table 3 proves that all the variables are stationary. Thus, we can consider them to be integrated into order 1 (I[1]).

Table 3

Results of the Unit Root Test with a Break

	Model A		Model B		Model C		Decision
Variables	t-Statistic	Break	t-Statistic	Break	t-Statistic	Break
	In level
GDPG	–4.730	2013	–5.015	2001	–4.850	2011	NS
ACTIVITY	–4.077	2000	–3.509	1999	–4.161	1990	NS
GFCF	–4.772	2001	–4.314	2005	–4.223	2014	NS
MS	–3.928	2015	–5.169	2000	–3.908	2004	NS
MMR	–4.884	1993	–6.365	2008	–4.343	1987	NS
CAP	–27.326	2007	–34.659	2004	–6.157	1998	NS
PC	–3.902	2018	–5.087	2005	-p4.733	2016	NS
INFL	–6.355	1996	–9.428	2007	–4.569	1983	NS
	In first difference
GDPG	–6.907	1983	–6.456	1988	–6.104	1982	S
ACTIVITY	–9.330	2009	–9.303	2008	–5.117	1997	S
GFCF	–5.771	1992	–5.753	2007	–5.296	2005	S
MS	–6.522	2007	–6.966	2007	–4.953	1990	S
MMR	–7.209	1993	–6.911	1998	–4.938	1983	S
CAP	–24.796	2007	–39.548	2004	–7.028	2019	S
PC	–6.092	2007	–5.407	1999	–4.116	2011	S
INFL	–7.178	1993	–6.589	2001	–6.203	1997	S

1. Notes: S: Stationary; NS: Non-stationary. Stat. Perron’s statistic. Critical value at 5%: A (–5.23), B (–5.59) and C (–4.83). Break: Break Date.

4.2 Short- and Long-Term ARDL Estimates

Table 4 presents the short-run estimates, the recall strength of the ECM model, and the set of validity diagnostics of the first growth model.

Table 4

Short-Term ARDL Results

Endogenous variable: ΔGDPGt	ARDL (2,0,1,0,0,1,2,2)		Maximum number of lags: 2
	Coefficient	Standard Deviation	T-Statistic	P-value
Constant	–539.739	105.051	–5.138	0.000
GDPGt-1	–3.098	0.453	–6.836	0.000
ACTIVITYt-1	6.071	1.175	5.169	0.000
GFCFt-1	3.543	0.630	5.624	0.000
MSt-1	–0.104	0.031	–3.377	0.005
MMRt-1	–0.795	0.239	–3.325	0.005
CAPt-1	0.211	0.044	4.800	0.000
PCt-1	0.034	0.012	2.892	0.007
INFLt-1	–0.486	0.149	–3.257	0.006
ΔGDPGt-1	1.444	0.315	4.583	0.000
ΔGDPGt-2	0.706	0.178	3.967	0.001
ΔACTIVITYt	1.733	1.172	1.479	0.161
ΔGFCFt	1.307	0.194	6.733	0.000
ΔGFCFt-1	–1.331	0.343	–3.885	0.002
ΔMSt	–0.015	0.062	–0.245	0.810
ΔMMRt	–0.516	0.195	–2.643	0.019
ΔCAPt	0.074	0.014	5.291	0.000
ΔCAPt-1	–0.057	0.019	–2.946	0.011
ΔPCt	–0.159	0.079	–2.024	0.062
ΔPCt-1	–0.082	0.075	–1.091	0.294
ΔPCt-2	–0.243	0.064	–3.777	0.002
ΔINFLt	–0.469	0.136	–3.460	0.004
ΔINFLt-1	–0.218	0.079	–2.768	0.015
ΔINFLt-2	–0.311	0.088	–3.515	0.003
θ (ECTt-1)	–0.188	0.062	–3.014	0.006
R2	0.797
F-statistic bounds test	7.895	CV at 5% (k=7)	4.379	0.000
LM test (p=5)	14.035			0.121
ARCH test (p=1)	6.018			0.214
Jarque-Bera test	1.451			0.484
Ljung-Box test	–0.103			0.611
RESET (10)	1.769			0.274

1. Notes: The LM test is the Lagrange Multiplier test (Breusch-Godfrey serial correlation). ARCH is the Autoregressive Conditional Heteroskedasticity Test. RESET is the Ramsey Regression Equation Specifi cation Error Test. θ (ECT) is the error correction term that shows the speed of adjustment towards the long-run equilibrium (this term must be signifi cantly negative to ensure the existence of the long-run relationship). k is the number of explanatory variables. CV represents the critical value of the bound test from Narayan (2005).

The optimal number of lags is determined for a trendless 2-lag ARDL model where the maximum trendless Fisher value is equal to 4.626, higher than the critical value according to Narayan (2005) at 5%, which is 4.38 for k = 7, representing the number of explanatory variables. This leads us to reject the null hypothesis of the absence of a long-term cointegration relationship. More precisely, the optimal growth model is verified by an ARDL (2,0,1,0,0,1,2,2) where the F-statistic of the bounds test displays a large value of 7.895, which is well above the 5% critical value of 4.38.

Alternatively, the functional form of our specifications is correct. Furthermore, the CUSUM and CUSUM squared tests show that the estimated parameters are stable in mean and variance over the study period (see Figure 6).

Figure 6

Evolution of CUSUM and CUSUMSQ Statistics

In addition, we find that the coefficient of the force of recall towards equilibrium, ECT_t-1 = –0.188, is negative and significantly different from 0 at the 1% threshold. This implies that the results support the existence of a long-term relationship between the variables. In other words, this coefficient associated with the recall force makes it possible to conclude that the shocks on economic growth in China are corrected to 18.8% by the supposedly significant “feedback” effect. This suggests a very good speed of adjustment in the relationship process following a shock last year. Thus, we can identify an average lag equal to |1 /0.188| ≈ 5.319. This means that a shock to Chinese economic growth is completely absorbed after 5 years and almost 4 months. This result is consistent with the literature (Pan and Mishra, 2018; Laajoul and Oulhaj, 2021).

In the long term, the results (see Table 5) show that all variables have a significant effect on the evolution of the economic growth rate.

Table 5

Long-Term ARDL Results

Endogenous variable: GDPG	Coefficient	Standard Error	t-Statistic	p-value
Constant	–174.214	14.268	–12.209	0.000
ACTIVITYt	1.959	0.155	12.582	0.000
GFCFt	1.143	0.069	16.462	0.000
MSt	–0.033	0.008	–3.801	0.001
MMRt	–0.256	0.056	–4.570	0.000
CAPt	0.067	0.008	8.162	0.000
PCt	0.011	0.012	2.899	0.007
INFLt	–0.156	0.0359	–4.366	0.000

In this context, we note that the money supply negatively and significantly stimulates economic growth over the entire study period of 1980–2020. Since increased savings promote income and therefore technical progress, a 1% increase in the money supply share leads to a 0.033% decrease in GDP. Furthermore, the results of our estimations confirm the positive impact of the activity rate and gross fixed capital formation. On the other hand, inflation and the money market interest rate have negative and significant impacts, while domestic credit to the private sector has a significant and positive impact. These results are consistent with the literature previously cited (Hwang and Wu, 2011; Cai and Lu, 2013).

The analysis of the long-term results of the autoregressive distributed lag procedure (ARDL) is subjected to a robustness check using the Dynamic Ordinary Least Squares Estimator (DOLS) of Saikkonen (1991). The results obtained with DOLS, presented in Table 6, are consistent with the fundamental estimates of ARDL as reported in Table 5.

Table 6

DOLS Estimates (Robustness Test)

Endogenous variable: GDPG	Coefficient	Standard Error	t-Statistic	p-value
Constant	–108.386	30.218	–3.590	0.001
ACTIVITYt	1.340	0.330	4.060	0.000
GFCFt	0.689	0.168	4.090	0.000
MSt	–0.034	0.016	–2.110	0.043
MMRt	–0.340	0.110	–3.080	0.004
CAPt	0.043	0.010	4.090	0.000
PCt	0.113	0.028	4.070	0.000
INFLt	–0.193	0.067	–2.870	0.007
Dickey-Fuller unit root test for residuals	–4.769	Critical value at 5%: –1.950
Engle-Granger test for cointegration	–5.969	Critical value at 5%: –5.817
Durbin-Watson	2.272

In addition, the diagnostic tests for the residuals of the DOLS model, namely the Dickey-Fuller unit root test for residuals, the Engle-Granger cointegration test, and the Durbin-Watson test, are in favor of the stationarity of the residuals, the presence of a cointegration relationship, and the absence of a first-order serial autocorrelation problem.

5 Discussions

Based on the main objective of this study, the results are analyzed on two levels. The first is the impact of capital market development on economic growth, while the second is the contribution of the banking system to China’s economic growth.

Regarding the contribution of capital markets to economic growth, the results show that the two markets have opposite effects on both in the short and long run. Indeed, while financial market development is conducive to economic growth, money market development has a negative impact on economic growth. The negative impact of the money market interest rate can be explained by the restrictive monetary policy adopted by China (especially from 2009 to 2015) (Chen et al., 2018) to control money creation and inflation, whose impact on economic growth is negative (Hwang and Wu, 2011). In addition, policies such as issuing additional shares, reducing shareholdings, and launching an open-ended fund introduced by the Chinese government are leading to a tightening of the money supply and a weakening of the relationship between China’s real sector and its financial sector (Pan and Mishra, 2018). Market capitalization, which measures the contribution of the stock market, has a positive impact on economic growth in China. These results can be explained by the nature of the Chinese economy, which is driven by two main forces: labor input and financial development, which cause a two-way causality between stock market development and economic growth. These results support the hypothesis that finance can be a catalyst for growth and are consistent with the literature (Peng et al., 2014; Kandil et al., 2017; Pan and Mishra, 2018) supporting the existence of a positive impact of the stock market on economic growth in the long term.

Regarding the contribution of the banking system to China’s economic growth, the two variables measuring the depth of financial institutions showed opposite impacts in both the short and long term. The first variable used to capture the extent of intermediation in China (MS) (the ratio of M2 to GDP) shows a negative impact on economic growth. However, this negative, albeit surprising, impact can be explained by the specificities of the Chinese banking system, which has undergone several reforms since 1978. Indeed, on the one hand, this system remains under pressure because of the problems of bad debts that persist. On the other hand, the Chinese banking system is highly regulated and dominated by public banks, notably the “Big Five,”^[1] which may explain its inefficiency. Finally, another explanation could be found in the spectacular expansion of its parallel banking system, which is crowding out the formal Chinese banking system. These are all explanations for the negative impact of bank contributions on Chinese economic growth (He and Wei, 2023). The Chinese banking sector reduces the efficiency of capital allocation rather than increasing it through unlimited support for loss-making state-owned enterprises and the government’s poverty reduction policy (Hasan et al., 2009). However, on its own, this variable cannot reflect the ability of the financial system to redirect funds from depositors to investments, which is why this study included a second indicator. The second variable chosen is domestic credit to the private sector (PC), a commonly used indicator of the depth of financial institutions. The results show a positive impact of the financial resources provided to the private sector by financial corporations, through loans, purchases of non-equity securities, trade credits, and other debtors that claim repayment. It reflects the efficiency of resource allocation by excluding credit provided to the public sector to better reflect the extent of efficient resource allocation. This result is also consistent with our theoretical framework, which states that this financing channel has a positive impact on Chinese economic growth (King and Levine, 1993; Levine and Zervos, 1998; Levine, 2005; Jalil et al., 2010; Laajoul and Oulhaj, 2021).

6 Conclusions and Policy Implications

The lack of a definitive answer from economic theory regarding the impact of financial development on economic growth necessitates empirical analysis for better understanding. Our research contributes to the research movement by examining the relationship between the development of the financial system and economic growth in China over the period 1980–2020. This study has identified this relationship through both capital market (stock and money markets) and banking intermediation indicators.

This study has yielded the following conclusions: First, the negative impact of the money market interest rate on economic growth is observed, due to the restrictive monetary policy adopted by China (especially from 2009 to 2015) to control money creation and inflation, whose impact on economic growth is negative. In addition, policies such as issuing additional shares, reducing shareholdings, and launching an open-ended fund introduced by the Chinese government are leading to a tightening of the money supply and a weakening of the relationship between China’s real sector and its financial sector. Secondly, according to our results, the Chinese stock markets are acting in a positive and significant way on economic growth. This last result is consistent with the literature and shows the contribution of Chinese capital markets to economic growth. This contribution is attributable to the various reforms carried out since the 1990s, which have made Chinese stock markets a global benchmark. Thirdly, the first variable used to capture the extent of intermediation in China (MS) (the ratio of M2 to GDP) shows a negative impact on economic growth. However, this negative, albeit surprising, impact can be explained by the specificities of the Chinese banking system, which has undergone several reforms since 1978, reducing the efficiency of capital allocation rather than increasing it through unlimited support for loss-making state-owned enterprises and the government’s poverty reduction policy. However, results showed a positive impact of domestic credit on the private sector. The results reflect the efficiency of resource allocation by excluding credit provided to the public sector to better reflect the extent of efficient resource allocation. This result is also consistent with our theoretical framework, which states that this financing channel has a positive impact on Chinese economic growth. It is clear from these results that research on the impact of financial development on economic growth needs to consider the different components of the financial system and multiple variables. The impact of a single variable does not allow us to decide on the impact, as in the case of the impact of banking intermediation, the results are mixed.

The important policy implications of this study can be presented as follows:

First, the results of this study indicate that capital markets play a key role in stimulating China’s economic growth. Thus, to ensure the sustainable and sustained development of these markets, China needs to maintain an enabling environment, including sustained growth, market sovereignty, an efficient and fair legal system, as well as an effective and appropriate regulatory environment. Second, the contribution of bank-based financing, which is positive only through credit, should prompt Chinese regulators, as in developed countries, to better control the remarkable expansion of the informal banking system. The shadow banking sector poses a threat to financial stability and the real economy, which requires limiting its extreme risks. However, regulators must consider the possible consequences before acting. Indeed, the brutal crackdown on shadow financing in the capital market in 2015 led to liquidity shortages and unintended effects. In contrast, the gradual restrictions imposed at the end of 2017 have had positive results. In addition, policies such as the issuance of additional shares, the reduction of shareholdings, and the launch of an open-end fund introduced by the Chinese government need to be reviewed to reduce the tightening of the money supply and improve the relationship between China’s real and financial sectors. Finally, regulators need to review their policies to reduce imbalances in capital allocation and prevent potential systemic financial risks.

However, the ARDL estimation approach used in this study involves constant disturbance terms with a constant speed of adjustment. In this sense, as a perspective, it is possible to move towards investigating possible asymmetric effects through the non-linear ARDL (NARDL) approach (Shin et al., 2014) of the financial system on economic growth in China (Azimi, 2022) by taking into consideration the informal banking sector and non-banking financing channels that are increasingly important to support the Chinese real economy.