Stabilizing growth has become a keyword in China's economy. Inflation and growth are the two primary macroeconomic objectives. According to classical macroeconomic theory, there is a trade-off between inflation and growth, at least in the short run, which is represented by the Phillips curve. The ability of central banks to control inflation depends on the strength of this trade-off. However, since the 1990s, there have been signs of a disconnect between inflation and growth in developed economies. One popular explanation for this disconnect is that the Phillips curves of these economies have flattened. This has led to an intense academic debate on whether and why the Phillips curves have flattened. Most studies show that the Phillips curves in developed economies have indeed flattened. However, a few economists disagree. Some studies explore whether China's Phillips curve has also flattened, with mixed results.
This study makes the following contributions.
First, most studies use partial equilibrium models to directly estimate various Phillips curves for China. However, this approach may suffer from varying degrees of misspecification and lack robustness. We indirectly analyze the trade-off between inflation and growth using demand shocks and a heteroskedastic monthly structural vector autoregressive model. The results show that inflation and output increase with a one-unit monetary policy shock. The inflation-relative response weakens substantially after 2010, with a significant decrease in the Phillips multiplier. In the robustness tests, the effect of economic policy uncertainty shocks on inflation likewise weakens substantially after 2010, and the primary findings hold in the factor model. Next, we exclude the possibility of a flat aggregate demand curve and find that the central bank's anti-inflationary awareness does not increase after 2010. Thus, the weakening inflation response can be interpreted as a flattening of the Phillips curve.
Second, considering that the period during which the Phillips curve flattens corresponds to a general slowdown in the Chinese economy, we argue that the two phenomena are compatible. We introduce knowledge capital accumulation and endogenous total factor productivity into the standard dynamic stochastic general equilibrium model. The results show that monetary policy and physical capital investment shocks are the main factors affecting inflation. The endogenous growth channel amplifies the effect of demand shocks on the output variables but weakens the effect on marginal costs and inflation. The endogenous growth channel negatively affects productivity, weakening the isokinetic relationship between inflation and output growth. The subsample estimation results show that the generalized Phillips curve flattens significantly after 2010 due to the combined effect of the endogenous growth channel and a weak marginal cost pass-through.
Our findings have broad implications for macroeconomic regulation and control in China. First, from a monetary policy perspective, the flattening of the Phillips curve eventually leads to a flattening of the aggregate supply curve, which implies that demand shocks play a prominent role and may dominate the economic cycle; therefore, it is beneficial for the central bank to take a Keynesian approach. If growth changes more than inflation, the central bank's objective function should empower growth and employment rather than strictly targeting inflation. A flat Phillips curve also implies a reduction in the central bank's ability to stabilize inflation, or a higher “sacrifice rate.” Ceteris paribus, greater changes in employment and output growth are needed to bring real inflation back on target. Second, from an endogenous growth perspective, more attention should be paid to “cross-cyclical adjustment.” To cope with the complex domestic and external economic environment, the government should make cross-cyclical adjustments and maintain policy continuity and stability. Due to the weakening relationship between inflation and output growth and the medium-to long-term nature of productivity evolution, policymakers should focus on the long-term dynamic equilibrium between growth stabilization and risk prevention, gradually moving away from smoothing out short-term fluctuations to providing medium-to long-term precautionary support. Third, from an inflationary perspective, although upside risks to global inflation remain, the central bank's Monetary Policy Implementation Report points out that China's inflation is largely manageable. Indeed, China's money supply has always matched its economic growth. Moreover, expanding the scope of demand-side policies may positively affect supply and productivity, thereby reducing the inflationary pressure on the economy.

The 2008 financial crisis caused a drastic decline in China's exports and slowed its economic growth. In response, China adopted stimulus plan to stimulate the economy. However, the resulting increase in money supply (M2) did not cause an increase in inflation, which is called the missing money puzzle. This puzzle was caused by the boom in the real estate sector and a sharp increase in housing prices. There were variations in land prices as well, as the prices of commercial and residential land rose sharply while the prices of industrial land remained relatively stable. This study explains the above phenomena in a unified framework.
We build a two-sector dynamic stochastic general equilibrium model comprising the real estate and non-real estate sectors to explain the missing money puzzle. Both sectors use labor, capital and land as material inputs of production. The entrepreneurs of both sectors face financial frictions, that is, they use land and capital as collateral to borrow from households. Land is provided by the local government. There is a land market division: an entrepreneur in the housing sector uses commercial and residential land to produce housing, while an entrepreneur in the non-housing sector uses industrial land to produce goods for consumption.
The demand for housing, as a durable good, is more sensitive to changes in interest rates because of its near-constant shadow value and almost infinite elasticity of substitution. Therefore, when the interest rate declines, the demand for housing increases more than that of non-housing goods, and housing prices rise sharply. Furthermore, the increase in housing demand encourages real estate entrepreneurs to increase production and the material inputs of production, including land. As land of different types or in different markets cannot be transformed, commercial land prices rise significantly because of the fixed land supply. High land prices increase the collateral value of land and relax the borrowing constraints of real estate entrepreneurs, enabling them to gain more capital to increase production, which leads to a further expansion in the real estate sector. However, non-housing goods are less sensitive to changes in interest rates, so the demand for non-housing goods and the price of industrial land increase slowly, thereby limiting the borrowing capacity of entrepreneurs in the non-real estate sector. This leads to a modest rise in the non-real estate sector output and the Consumer Price Index.
We also compute the optimal Ramsey policy and compare it with the benchmark model. The monetary policy should be stable and limit the increase in the housing sector to achieve optimal social welfare and balanced development in the housing and non-housing sectors.
Based on the study findings, we make the following policy recommendations. Monetary policy tools should play the dual functions of aggregation and structure as houses are assets for living in, not for speculation. The monetary policy should maintain stability, and structural monetary policy instruments should be improved. It is also necessary to increase construction for residential purposes and affordable housing and develop the long-term rental market. Moreover, a prudent management system for real estate should be implemented to achieve balanced development of the housing and non-housing sectors.

Since the start of its economic reform and opening-up in 1978, China has witnessed rapid economic growth. However, this development has been unbalanced, and in the financial market, this imbalance is evidenced by inadequate and expensive financing for small and medium-sized firms, in contrast to the easily obtainable, low-cost funding available to large firms. The literature proposes that small banks can more effectively meet the financing needs of small and medium-sized firms than big banks. China's banking sector is dominated by four state-owned big banks, and entry into the industry is strictly regulated by the government. What drove the Chinese government to restrict the entry of new banks into the industry？ Considering China's rapid and sustained economic growth and the lack of a systemic financial crisis in the past four decades, is it economically rational for the government to restrict bank entry and allow the banking system to be monopolized by big banks in the early stage of China's economic reform？
This study argues that the deposits of big banks would be partially transferred to newly established small banks such that the total loanable funds available from big banks would sharply decline if the entry barriers to the banking sector were lowered, as capital and total fund supply were scarce in China in the early stages of reform and opening-up. Consequently, the financing costs of state-owned enterprises (SOEs) increase as credit availability decreases because SOEs have enormous capital needs, while newly established banks have a small asset scale and cannot provide large loans to diversify risks effectively. In this scenario, the government's intervention in the banking sector, or the entry barriers to new banks, becomes a sub-optimal arrangement.
From a transaction cost perspective, this study discusses how the catch-up strategy determines the banking structure in a country using a static game that includes firms and banks of different sizes. The theoretical analysis in this study focuses on two types of transaction costs when banks lend to firms—contracting and information costs. Contracting costs refer to the real costs incurred when finalizing loan agreements. One key feature of contracting costs is that they nearly change with loan size and can be approximated as fixed costs. The larger the amount of a single loan, the greater the economies of scale, and the lower the contracting cost per unit of financing. Information costs refer to the costs resulting from information asymmetry, such as the labor costs of screening firms and banks' losses from loan defaults.
This study shows that considering the contracting and information costs, big banks are in a better position to fund large firms because big banks have the economies of scale to lend to large firms with large capital demands, thereby lowering the transaction costs of financing. In contrast, if one or a few small banks provide large loans to a large firm, they take on a high risk of bankruptcy in the event of a loan default. If small banks are to diversify their investments or limit the sizes of single loans to reduce their risk of bankruptcy, large firms would have to borrow from multiple small banks simultaneously, which would sharply increase the transaction costs of financing.
This study provides policy references for further reform and development of China's banking industry. China's industrial landscape is characterized by the presence of a large number of labor-intensive small firms. Hence, this study proposes that local small banks should be developed further and interest rates should be liberalized further to meet the financing needs of small firms.

With a new round of technological revolution and industrial reform, the global manufacturing industry is changing from a “single production” mode to a “production+service” mode. Modern industrial development focuses on integrating the manufacturing and service industries. Therefore, it is important to understand how to accelerate the process of integrating the two industries to enhance the core competitiveness of China's manufacturing industry, build a modern industrial system and achieve high-quality economic development. Industrial robots are being increasingly used in production. Undoubtedly, the introduction of industrial robots will have a profound impact on the process of integrating the two industries in China. However, not many studies have examined the impact of industrial robots on integrating the two industries in China; this study seeks to fill this gap.
First, this study uses data from the China Industrial Enterprise and China Customs Trade databases and the Inter-country Input-Output Tables published by the Organisation for Economic Cooperation and Development to measure the service value added of Chinese manufacturing enterprises. The rate of service value added at the enterprise level is divided into the domestic service value added rate and foreign service value added rate, according to the enterprise using the service elements in the source country, to measure the input level of service or manufacturing enterprises and further international federation of robot docking nation-year-industry aspects of statistical data of industrial robots. On this basis, this study investigates the effect of industrial robots on integration of the two industries. The results show that industrial robots significantly increase the service value added rate of Chinese manufacturing enterprises, but this increase is mainly achieved by promoting the use of foreign services and inhibiting the use of domestic services. The results of the heterogeneity test show that the effect of industrial robots on integration of the two industries is mainly concentrated in processing trade enterprises; enterprises in the Eastern region; foreign investments; enterprises in Hong Kong, Macao and Taiwan; and labor-intensive and technology-intensive enterprises. The effect of industrial robots on integration of the two industries is mainly achieved by increasing technological innovation and production efficiency and extending the production chain, while human capital investment has a negative substitution effect. Finally, this study also discusses the influence of industrial robots on the factor substitution and industry diffusion effects in the integration of the two industries.
This study makes the following contributions. First, it uses enterprise data in China and systematically tests the influence of industrial robots on integration of the two industries by constructing Batik instrumental variables and adopting the propensity score matching method after overcoming potential endogeneity. Second, it explores trade added value accounting and the double advantage of micro measurement based on the combination of macro to micro measurement.
Effective utilization of large amounts of information on industry departments and industrial enterprises can be used to build a measure for companies on the basis of the characteristics and source of a service, dual trade structure and other factors. This overcomes the deficiency of neglecting the individual characteristics of enterprises that manufacture inputs. Third, the internal mechanism and the differentiation effect of robots on integrating the two industries are analyzed deeply, and the characteristics of the effects of industrial robots on the service-oriented transformation of manufacturing enterprises are discussed. Fourth, based on the conditions in China, this study finds that industrial robots in processing trade enterprises have significant elements of domestic service rather than foreign service, and the application of industrial robots significantly promotes the integration and development of two-industry manufacturing processes through industry spread and diffusion effects between industries.

Under the vision of carbon peaking and carbon neutrality, industrial enterprises are increasingly subject to carbon emission reduction constraints. Therefore, the credit rating that incorporates carbon element-related risks aims to reasonably distinguish the credit risk levels of different enterprises, and provides a reference for commercial banks to effectively warn enterprises of low-carbon transition risks and make green credit decisions. Based on the perspective of financial stability, this paper proposes a set of credit rating methods for carbon emission reduction performance of industrial enterprises, that is, while evaluating the performance of enterprises' carbon emission reduction, taking into account the solvency of corporate funds, and achieving a balance between ecological and economic benefits.
The credit rating is divided into three steps, including the calculation of the credit levels of industrial enterprises, the credit rating of systemically important industrial enterprises, and the credit rating of all industrial enterprises. The above three parts are progressive, and constitutes the three rating steps. The first step is the calculation of credit levels for industrial enterprises. In this paper, the five financial indicators in the core credit indicator combination and the three low-carbon indicators, which are corporate carbon emission reduction potential, corporate carbon emission reduction capability and corporate environmental information disclosure level, are substituted into Structural Equation Modeling (SEM) to calculate the credit levels for industrial enterprises. The credit levels are the basis for credit rating, that is, the higher the credit level, the higher the credit rating; and vice versa. The second step is the credit rating of systemically important industrial enterprises. This step intends to construct a programming model (with the objective function of “the highest degree of matching between the credit rating of an enterprise and its anti-risk capability”, and to block each inter-enterprise credit risk contagion path as a constraint) to solve the optimal credit rating of enterprises. Therefore, all industrial enterprises are divided into different credit grade intervals to reduce the difficulty of classification, and the financial stability objective is integrated into the rating model from the perspective of credit risk contagion. The third step is the credit rating of all industrial enterprises. This step divides the remaining industrial enterprises into different credit grade intervals based on the credit levels of the enterprises, thereby simplifying the nine-class division problem into a binary division problem; at the same time, based on the “small range traversal +Sequential Forward Selection (SFS)”, the optimal critical samples between grades are searched to achieve credit ratings for all industrial enterprises. The main innovations are as follows.
First, during the evaluation process, not only the vertical comparison of the corporate credit levels, but also the contagious effects of credit risk among enterprises, between enterprises and the banking system are considered to prevent the occurrence of systemic risks, thus incorporating financial stability objectives into the rating model. Specifically, as far as the relationship between enterprises and the banking system is concerned, this paper is based on the principle of “the higher credit rating of an enterprise, the stronger its ability to withstand the risk impact of the banking system”. The degree of matching between credit rating and the anti-risk ability is measured by the “inconsistency”, which is the objective function of the programming model. As far as the inter-enterprise association is concerned, based on the network analysis method, this paper constructs an association network among systemically important industrial enterprises, and then clarifies the contagion paths of credit risk among enterprises. On this basis, this paper blocks the paths for preventing systemic risks, which on the way of “dividing enterprisei and jinto different credit ratings”. And each path is a constraint of the programming model.
Second, dividing all industrial enterprises into four credit grade intervals based on the credit ratings of systemically important industrial enterprises, and then convert the nine-class division problem into a binary division problem to avoid the “combinatorial explosion”. The division of credit rating of industrial enterprises can be regarded as an unconstrained combinatorial optimization problem. For the solution of this problem, the traversal method is the most reliable, that is, on the basis of considering all possible combinations of credit ratings of enterprises, the optimal solution that makes the evaluation function perform the best is obtained. However, the traversal method is limited to the case where the number of enterprisesn is small, and whenn is large, the method will lead to “combinatorial explosion” in the solution process of the optimization problem. Through the determination of the four credit rating interval structures, the problem of “dividing all industrial enterprises into 9 grades” is transformed into a binary division problem for the samples which come from the credit grade interval. This greatly reduces the difficulty of classification, and cleverly avoids the “ combinatorial explosion ” in the process of credit rating for all industrial enterprises.
Third, searching for the optimal critical samples among different credit grades within the grade interval for binary division based on the “ small range traversal +Sequential Forward Selection”, so as to realize the credit ratings for all industrial enterprises. This idea not only avoids the risk of corporate default losses caused by inflated credit ratings to a certain extent, but also does not hinder the green and low-carbon transformation of industrial enterprises due to excessively stringent rating requirements.

The global financial crisis highlighted excessive indebtedness of the household sector as an important source of systemic risk and demonstrated that the health of balance sheets in the household sector plays a key role in the economy. A healthy balance sheet in the household sector provides a solid foundation for rapid economic recovery in the post-pandemic era, whereas a rapid and excessive accumulation of household sector debt may lead to financial risks in the economy. Preventing and controlling financial risks is a core objective of China's economic and financial system. In recent years, against the background of a loose monetary policy and a rapid rise in real estate prices, Chinese residents have begun to buy houses with a large amount of debt, leading to a rapid increase in the leverage ratio (Debt/GDP) of the Chinese household sector. According to the Bank for International Settlements, from 2011 to 2021, the leverage ratio of China's household sector rose by 33.8 percentage points, ranking first in the world. The rising leverage ratio in China is quite similar to the conditions before the outbreak of economic crises in America and Japan, and therefore warrants vigilance. The underlying danger of systemic risk caused by an increasing leverage ratio and insufficient repayment ability has attracted increasing attention. Many studies have assessed the solvency and liquidity risks of banks and other financial institutions, but there remains a lack of measurement of financial fragility caused by the growing debt risk of the household sector. The financial risk and solvency of this sector needs to be addressed.
Using China Household Finance Survey data from 2011 to 2017, this study studies the solvency of Chinese households. Drawing on the most common measures of household debt burden, this study selects an indicator to comprehensively measure the financial fragility of households, which is used as an early warning signal to measure the default risk of indebted households, and adds liquid assets to consider household solvency. First, we measure household solvency using the financial margin (FM), then identify financially vulnerable households according to whether the household's liquid assets can cover the negative financial margin in a given period, and calibrate this according to the non-performing loan rate at the macro- level. Then, through the exogenous variable of expected housing returns, we further analyze the influencing factors and channels of solvency of the Chinese household sector.
This study yields several key findings. First, the proportion of financially vulnerable households in China increased from 2011 to 2017, with obvious population and regional heterogeneities observed in the changes of financially vulnerable households.The risk posed by the household sector to the stability of the financial system are generally contained in China. We should continue to pay attention to the debt repayment risks of young households, rural households and households in the Eastern region. Household financial risks are significantly related to household characteristics. Attention should be paid to the debt repayment risks of low-income households and to strengthen social security. Second, a rise in expected returns leads to excessive debt behavior in households, which may be an important factor for the deterioration of the household financial fragility, and the financial fragility of households continues to increase mainly through the channels of real estate purchase and risk preference. With increasing economic uncetrtainty, we should focus on guiding real estate market expectations, improving financial literacy nationwide and helping households make reasonable investment and consumption decisions.
The study makes three main contributions. First,using household data to directly and effectively measure the solvency of China's household sector, and accuracy is improved further by the calibration of the non-performing loan ratio at the macro-level. Second, the study comprehensively addresses the financial risk of the household sector and identifies the dynamic changes and structural distribution of China's household financial fragility using panel data from 2011 to 2017. Accordingly, this study resolves the identification errors by using cross-sectional data, which is crucial for the monitoring, assessment and early warning of financial risk in the household sector. Third, this study explores the factors that cause households to fall into financial distress through the channel of expected housing returns, thus providing a new perspective on the forces driving household financial fragility. This perspective can help to evaluate the debt risk in China's household sector in the post-pandemic era and provides support for the implementation of macro policies.

Financial products, especially financial technology, have become increasingly sophisticated and complicated with the growth of the digital economy. Financial products are deeply embedded in the investment and consumption behavior of individuals. The popularization and spread of financial literacy can help to improve asset allocation efficiency and prevent debt risks, which are important for the prosperity of all people. Since the 2008 global financial crisis, financial education has become an important topic of discussion, and governments around the world are incorporating it into their national strategy and creating conditions to enable it through legislation and institutional development. In the process of building a modern financial system, the financial market may break the “rigid payment” and the financial market may “break the net”, and the shortcomings of households lacking financial literacy may be amplified more quickly. Hence, targeted intervention measures are urgently needed for a scientific evaluation of the effect of financial education.
This study systematically reviews the cognitive differences between different academic groups on the effectiveness of financial education using data from the 2012 survey of Chinese urban households by the China Center for Financial Studies of Tsinghua University and the 2015 China Household Finance Survey. Using the propensity score matching (PSM) and instrumental variable (IV) methods, this study empirically investigates the effect of financial education on the financial investment behavior of Chinese households and the moderating role of social interaction. The results show that financially literate households have a more diversified asset portfolio and higher Sharpe ratio than financially illiterate households. Financially literate households are also more likely to engage in financial planning over a longer planning horizon, and to ask for help when their equity suffers. Furthermore, increased social interaction increases the optimization effect of financial education. This study provides positive evidence that financial education helps optimize household investment behavior. Although the work in this study is a preliminary exploration compared with the complex evaluation framework used by studies conducted abroad, it has important practical and policy implications.
First, there should be a greater focus on the spread of financial literacy, and the process should be expedited through legislation and the establishment of dedicated institutions. The strategic importance of financial education should be examined from the perspective of improving the financial well-being of people. Second, diverse financial education programs should be implemented by category to strengthen the overall effect of financial education. As people become more financially literate, the program should guide them to increase their social interactions simultaneously so as to enhance the effect of financial education. Finally, there should be active exploration of an evaluation framework for effective innovative financial education using an optimal combination of policy tools. Various alternatives should be identified for different objectives, and a policy evaluation system of “objectives-solutions (alternatives)-cost benefit evaluation” must be established; this will help to evaluate the effectiveness of financial education, clarify its feasibility, and determine the optimal mode and level of financial education.
This study makes the following three contributions. First, this study considers household portfolio diversification, portfolio effectiveness and financial planning, and consumer protection as indicators of the effectiveness of financial education, thus extending the measurement category of the domestic literature on financial education intervention. Second, we comprehensively use the PSM and IV methods to scientifically deal with possible endogeneity, such as self-selection, and use different questionnaires for empirical analysis. The results of this study are more reliable than those of similar studies in China. Third, this study deeply discusses the heterogeneous effect of social interaction on the effect of financial education, especially the key role played by social interaction in less financially literate families, which enriches the research dimensions of the domestic literature on financial education effect evaluation. Although there are some limitations in the evaluation methods used by this study compared with studies conducted abroad, it makes an important contribution to the construction of a conceptual framework, selection of evaluation indicators and technical means, and expansion of research perspectives to evaluate the effectiveness of domestic financial education.

Insurance is the basic instrument for managing risk in a market economy. It is the bridge that links long-term savings with long-term investment and plays an important role in economic compensation, financial integration, and social management. This study enriches and expands theories on household financial decision-making and provides policy implications for deepening financial market reform and increasing the value-added capacity of commercial insurance and household welfare.
Optimism has complex implications for household commercial insurance purchases. With the development of the financial markets, commercial insurance is increasingly moving from “insurance only for protection” to “insurance for both protection and investment” (such as life insurance, universal insurance). Insurance for protection and investment provides investment attributes in terms of returns and dividends and the protection attribute of risk hedging. Optimism from the protection attribute causes individuals to underestimate the probability of unfavorable events and increases their risk appetite, thereby reducing the demand for financial assets with risk protection functions, such as commercial insurance. However, optimism from the investment attribute leads individuals to have more positive expectations about future uncertainty, thereby increasing the demand for financial assets with higher investment returns, such as commercial insurance and stocks.
However, the literature does not pay much attention to the intrinsic relationship between optimism and commercial insurance market participation and has not reached a definite conclusion. A few empirical studies mainly focus on the protection attribute of commercial insurance. Most of them find that optimism reduces the willingness of individuals to purchase commercial insurance for protection, while some point out that this impact may be limited. As the related literature ignores the potential impact of optimism when commercial insurance reflects the investment attribute of household asset allocation, the conclusions are likely to be one-sided and have limited practical and instructive value. Therefore, it is important to understand how optimism can affect household commercial insurance demand by taking into consideration the protection attribute and investment attribute. As China has a high savings rate, extensive government guarantees, and a collectivist culture, would the impact of optimism on commercial insurance purchase differ depending on the ability of households to protect against risks？ An investigation of this issue can contribute to understanding the mystery of limited participation in the commercial insurance market from a new perspective of subjective beliefs, expand the literature on household financial asset allocation, help to optimize the structure of household assets, and promote the insurance industry.
Using data from the China Family Panel Survey and China Household Finance Survey, this study empirically analyzes the effect of optimism on the purchase of household commercial insurance. The empirical results show that optimism has a significant and positive effect on the purchase of household commercial insurance. That is, the more optimistic the householder, the more likely they are to purchase commercial insurance of a higher amount, which seems inconsistent with the findings of studies conducted abroad. After differentiating between protection-type commercial insurance and investment-type commercial insurance, we find that the positive effect of optimistic expectations on the purchase of commercial insurance mainly results from an increased demand for investment-type commercial insurance, whereas optimistic expectations have no significant effect on the purchase of protection-type commercial insurance. This result shows that optimism has heterogenous effects on the purchase of commercial insurance across households with different protection capabilities. Specifically, optimism has a greater positive effect on households with more savings, social security coverage, and social capital than on households with less savings, social security coverage, and social capital.
The contributions of this study are as follows. First, we examine the effect of optimism on the purchase of household commercial insurance using a new perspective that combines the protection and investment attributes of commercial insurance, thereby extending the research on the determinants of commercial insurance. Second, we empirically explain why optimism promotes the purchase of household commercial insurance by conducting further analyses using China-specific characteristics, such as a high savings rate, emphasis on government guarantees, and a collectivist culture.

On April 10, 2022, the Central Committee of the Communist Party of China and the State Council jointly released the Opinions on Accelerating the Construction of the National Unified Market. This document outlines the construction of a national unified market to break down market segmentation, improve market efficiency, and ultimately boost economy growth. How we can build a nationwide integrated market is an important research question. In this paper, we study whether the transmission of price information facilitates market integration. Specifically, we exploit the staggered listing of commodity futures as shocks to the transmission of price information and use a difference-in-differences (DID) research design to examine whether enhanced information transmission leads to a higher degree of market integration.
Our sample covers 45 commodity types, and the sample period spans from 2003 to 2018. During the sample period, several commodity futures were listed and became tradable in the Zhengzhou Commodity Exchange, Dalian Commodity Exchange, or Shanghai Futures Exchange. These tradable commodities include, among others, cotton, white sugar, soybean meal, corn, steel rebar, steel wire rod, and hot-rolled coil steel. To measure market integration, we rely on high-frequency price information in the spot market of each commodity. We obtain province-level spot price data from the Wind database. For each type of commodity, we follow the literature and measure a province's market integration as the extent of price comovement between the province and its adjacent provinces. After dropping observations with missing data for the key variables, the final sample for our DID analyses consists of 7968 commodity-province-year observations.
Empirical evidence from our staggered DID analyses shows that the degree of market integration increases significantly after the corresponding commodity futures are listed. To check the parallel trend assumption that is critical to a DID design, we show that there is no significant difference in market integration between the treatment and control groups in the pre-listing period. In contrast, we document a significantly higher degree of market integration for the treatment group immediately after the listing year. These differences persist in the post-listing period. We also conduct a series of robustness tests to show that our main results are robust to alternative measures of market integration, alternative fixed effects, and an alternative sample. Our main results are consistent with our hypothesis that enhanced information transmission facilitates market integration. Commodity futures listing enhances information transmission because the trading price on the commodity/futures exchange is publicly available and informative for spot market participants.
To shed light on the information transmission channel through which commodity futures listing can affect market integration, we conduct several additional tests. First, we show that the positive effect of commodity futures listing on market integration is more pronounced for provinces where information can be more easily transmitted. Specifically, we find that the market integration enhancement effect of commodity futures listing is stronger for provinces with higher Internet penetration and for those with a higher degree of marketization. Second, we provide direct evidence that commodity futures listing leads to higher spot price synchronicity, lower price delay, and lower transaction costs. These findings suggest that commodity futures listing enhances the transmission of price information. Finally, we focus on the information quality of the trading price on the commodity/futures exchange and conduct a test on a subsample of traded commodities. We find that price informativeness and trading liquidity are positively associated with market integration, suggesting that price information does indeed matter. Overall, these tests provide corroborating evidence that the transmission of price information from the futures market to the spot market is a plausible channel for improving market integration.
Our paper adds to the literature by showing information spillover effects from the financial market to the commodity market. To the best of our knowledge, we are the first to use commodity futures listing as a setting to provide causal evidence of the effects of the transmission of price information on market integration. Our finding that enhanced information transmission facilitates market integration could have policy implications on how to accelerate the construction of the national unified market. In addition, our results based on the emerging China market could be generalized to other developing countries.

The stock markets have played a key role in China's economic development. In 2021, the total operating income of the companies listed on the Shanghai and Shenzhen Stock Exchanges was more than half the value of China's GDP. An increase in the number of listed companies is an important sign of stock market development and an indicator of economic development. Corporate innovation is an important part of national innovation and drives economic development. The unlisted companies in China are small and medium-sized enterprises, which contribute more than 70% of the country's technological innovation and are an important source of employment and social stability. It is important to examine the effect of the number of listed companies on the innovation of unlisted companies to understand the determinants of corporate innovation in China.
There are two channels through which an increasing number of listed companies affect the innovation of unlisted companies. First, the information disclosed by listed companies reduces the information asymmetry between external investors, creditors (including banks) and related unlisted companies. Hence, unlisted companies in the same industry or region find it easier to raise funds to support their innovation activities. Second, the information disclosed by listed companies helps unlisted companies in the same industry or region to clarify the direction of their innovation activities and regional policy support, thereby reducing the uncertainty in innovation activities and promoting innovation by unlisted companies.
Using data from the Industrial Enterprise Database, we investigate the effect of the number of listed companies on the innovation of unlisted companies and find that unlisted companies increase their innovation as the number of listed companies increases. This positive relationship is stronger for unlisted companies with more serious financial constraints than for those with fewer financial constraints. The innovation activities of listed companies enhance this effect through knowledge dissemination. Furthermore, analysts' following and high-quality auditing improve the quality of information disclosure of listed companies and enhance the effect even more. Further evidence shows that college graduates strengthen the positive relationship by providing more regional labor. Regional marketization promotes regional R&D spillover effects, and financial marketization helps unlisted companies obtain more external financing. Therefore, a higher degree of regional and financial marketization strengthen the positive relationship. These results are robust to different specifications.
This study contributes to the literature in the following aspects. First, this study enriches research on the spillover effect of the number of listed companies on unlisted companies. The literature has investigated the impact of the number of listed companies on macroeconomic growth and on the investment and entrepreneurship of unlisted companies at the microeconomic level. Our study further investigates the effect of the number of listed companies on the innovation of unlisted companies. Although Matray (2021) studied the impact of the innovation of listed companies on the innovation of unlisted companies, this study addresses the effects of both the innovation activities of listed companies and, in particular, the number of listed companies on the innovation of unlisted companies by reducing financing constraints. Therefore, this study extends the research on the impact of listed companies on the innovation of unlisted companies. Second, this study adds to the literature on the impact of market forces on the innovation of unlisted companies. Studies have focused on the impact of government policies, such as tax policies, government procurement policies, industrial supporting policies and government subsidies, on the innovation of unlisted companies. However, this study examines the influence of market forces (stock market development) on the innovation of unlisted companies. The literature explores market forces, such as market concentration, market competition and industrial agglomeration, that influence the innovation of unlisted companies. Our study focuses on the influence of stock market development (i.e., increase in the number of listed companies) on the innovation of unlisted companies to enrich our understanding of these effects of market forces.

The daily price limit rule with a range of 10%, which aims to dampen abnormal price fluctuations and mitigate price bubbles, has been strictly imposed in Chinese stock markets since 1996. The launch of the Sci-Tech Innovation Board on the Shanghai Stock Exchange on July 22, 2019, increased the price limit range from 10% to 20%. Following the gradually advanced market-oriented reform of trading mechanisms, such price limit relaxation was introduced to the ChiNext board on the Shenzhen Stock Exchange on August 24, 2020. Until now, few researchers have investigated whether price limit relaxation can enhance market efficiency without exacerbating market stability. Academic researchers document both benefits and costs to imposing a price limit. Supporters argue that a price limit helps to reduce overreaction by panicked investors, moderate price volatility, and increase market efficiency (e.g., Greenwald and Stein, 1991; Kim et al., 2013). Opponents, however, criticize that a price limit may result in trading interference, which delays the instant price discovery process and squeezes liquidity (e.g., De Long et al., 1990; Kim and Rhee, 1997; Chen et al., 2017). The above studies are either based on the change in the price limit policy in the 1990s or compare Chinese stock markets to other markets without price limit constraints, which signifies a lack of time effectiveness as Chinese stock markets have become more mature after 20 years of development.
This paper uses the relaxation of the price limit on the ChiNext board as an exogenous shock and investigates the effect of price limit relaxation on market efficiency. Because the policy is imposed on all stocks traded on the ChiNext board without considering the pre-policy conditions of firm characteristics or market status, the shock to the price limit is purely exogenous, so that our setting is immune to most endogeneity issues in the literature. Specifically, we divide the full sample period into the pre-policy regime period, consisting of the 62 trading days before the policy effective date, and the post-policy regime period, consisting of the 64 days after the policy effective date (August 24, 2020). For our sample, we obtain transaction data for 742 stocks for each regime. We define the beta coefficient and R² based on the market model as the proxy for market efficiency in each regime and compare their changes over the policy shock. The mean difference test suggests that the stock price is more sensitive to public market information and incorporates more idiosyncratic firm information after the policy change. Moreover, we define stocks on the ChiNext board as the treatment group and stocks on the PSM-matched SME board as the control group, and the difference-in-differences test shows that the stock price of the treatment group is more sensitive to public information and incorporate more firm-specific information. In summary, our evidence suggests that the relaxation of the price limit helps improve price efficiency at the market level.
Following the literature evaluating the effect of the price limit (e.g., Kim and Rhee, 1997), we conduct an event study to provide a mechanism analysis. We compare abnormal returns, intraday volatility, and turnover around the price-limit-hitting events before and after the policy shock. When the price limit relaxes from 10% to 20%, we observe a significant decrease in abnormal returns, intraday volatility, and turnover following the event window. These findings demonstrate that price limit relaxation helps mitigate trading interference and prevent delaying trading behavior so that information can be more quickly incorporated into the stock price, resulting in less volatility spillover and delayed price discovery. Thus, our firm-level evidence identifies the direct mechanism through which price limit relaxation improves price efficiency.
Moreover, we classify firms into low and high information transparency groups based on a comprehensive information transparency score and conduct a heterogeneity test based on the two subsamples. We find that firms with low information transparency benefit from more improvement in price efficiency at both market and firm levels. In addition, we exploit the high-frequency intraday data in 5-minute increments to estimate the magnet effect, volatility clustering, and spillover effect around the price-limit-hitting events before and after the policy shock. We show that price limit relaxation decreases the magnet effect, intraday volatility clustering, and volatility spillover, consistent with evidence from our market-and firm-level analyses.
In conclusion, this study highlights that price limit relaxation can improve market performance by speeding up price discovery and mitigating market fluctuations. Policymakers could refer to our first-hand evidence to evaluate the effectiveness of the gradually advanced market-oriented reform on trading mechanisms. Our findings suggest that regulators could try to relax the price limit on stocks that satisfy certain liquidity requirements, which could further improve market efficiency and competitiveness.