Summary:Over the past decade, China's macroeconomic operation has shown a more obvious trend. The growth of major macroeconomic indicators has slowed down. The average growth rate of real GDP declined from 13% to 6.6% before the crisis; the growth rate of total retail sales of consumer goods and investment in fixed assets dropped rapidly from over 20% in 2008 to about 6% and 9%, respectively; and the growth rate of M2 declined from 16% before the crisis to a historic low of 8%. Although theoretical and policy discussions proposed many different explanations for this phenomenon, there is still a lack of adequate quantitative analysis. In general, when inflation is basically stable, the long-term downward trend in economic growth usually results from the change of potential output growth rate. What is the current trend of potential output in China？ What factors influence the trend change？ Will this trend sustain for a long time？ The answers to these questions are not only the focus of theoretical research, but also key to the decision-making of future macro-policies.
Measuring potential output and output gap is particularly important for central banks, as they not only are the core objectives of monetary policy, but also enter the monetary policy decision-making rules directly, which affect the operation of the whole policy. Unfortunately, both potential output and total factor productivity (TFP) are unobservable state variables and can only be estimated from various methods or models. Therefore, it is of great theoretical and practical significance to effectively estimate China's potential output, to analyze the impact of different factors on potential output, and to carry out medium-and long-term trend prediction.
In this paper, we first give a brief overview of the current mainstream methods for estimating potential output and compare their characteristics and applicability. Second, we estimate the potential output of China from 1993 to 2018 though four methods, including production function method, state space model, macro-econometric model and DSGE model from the perspective of monetary policy decision-making. Next, we analyze and forecast the causes and trends of potential output changes. Finally, the main conclusions and corresponding policy implications are obtained.
The main conclusions are as follows. (1) The average growth rate of China's potential output from 1993 to 2018 is 9.4%, slightly lower than that of real GDP at 9.5%. The average growth rates of TFP trend, capital input and labor input are 3.6%, 11.7% and 0.6%, respectively, while their average contribution rates to potential output growth are 38.3%, 58.3% and 3.4%, respectively. Overall, the trend of potential output growth rate in China shows a clear periodic characteristic. (2) The slowing down trend of potential output growth in recent years is mainly due to the decline of the growth rate of effective capital input which leads to the continuous decline of the pull of capital input on potential output. The slow progress of investment-specific technology, the rising cost of investment adjustment and the rapid decline of capital formation efficiency are the deep-seated reasons for the change of the growth rate of effective capital investment. (3) The low contribution rate of labor growth leads to the underutilization and effective exertion of China's population advantage, and further aggravates the slowdown of potential output growth in recent years. (4) In the next 5-10 years, the average growth rate of China's potential output will continue to decline slowly and stabilize in the range of 4.8% to 5.1%. However, compared with developed economies, China still has considerable room for improvement in potential output growth, especially from the area of labor input.
This paper proposes the following suggestions. (1) In balancing short-term demand and medium-term and long-term reform objectives, we should stabilize investment growth and pay more attention to optimizing the investment structure and improving investment quality, capital formation efficiency and the level of investment-specific technology progress. (2) We should further improve the labor market, reduce the searching and matching cost of employment, improve labor quality and the matching degree of demand, and reduce ineffective supply to increase the contribution rate of labor input. (3) Institutional mechanism construction should be strengthened to further promote structural reform. While deepening the implementation of innovation-driven strategy and increasing R&D investment, we should take various measures to promote the overall improvement of TFP. (4) Faced with the trend change of potential output, we should pay attention to the identification of macroeconomic trend change and cyclical fluctuation in making monetary policies, strengthen policy coordination, grasp the implementation of policies, and improve the accuracy of policy response.

How monetary policy affects output and inflation is a fundamental question in macroeconomics. Consumption and investment are the two main components of the gross domestic product (GDP). Although a large body of literature has examined the inflation of consumption goods, few studies have focused on the inflation of investment goods. The inflation of these two sectors in China moved in a highly correlated manner before 2011, and then diverged after that year. Therefore, the relative price gap has increased since 2011, which suggests a different mechanism of inflation dynamics. This raises the question of how the monetary policy on output and inflation has affected these two sectors, how the monetary transmission mechanism differs between the two sectors, and what factors determine the inflation dynamics of the two sectors. By studying these problems, we can better understand the different behaviors of consumption and investment in relation to macroeconomic fluctuations. We also examine the impact of monetary policy on the economy and provide helpful suggestions for policy making in business cycles.
To provide empirical evidence, we use Bayesian VAR to evaluate the effects of monetary shocks on the output and inflation of the consumption and investment sectors. Following Litterman (1986), we estimate the BVAR and obtain the impulse response functions. Our impulse response analysis shows that when monetary policy is expansive, the output and inflation of both sectors increase. However, the increase in the output and inflation of the investment sector is greater than that in the consumption sector.
This paper establishes a two-sector new Keynesian DSGE model of consumption and investment goods production, and incorporates a financial accelerator to study the effects of a monetary shock on consumption and investment inflation. By incorporating the financial accelerator, our model can better characterize a firm's investment behavior. China's quarterly macroeconomic data are used to estimate the model using a Bayesian approach. The estimation results show that the degree of the nominal price rigidities of the consumption and investment sectors are high and very close. However, the external finance premium has different effects on firms' investment behavior, with the financial accelerator effect being stronger in the investment sector. The impulse response of the model under a monetary shock is consistent with the empirical evidence based on the BVAR analysis.
We further show that the demand structure heterogeneity of the two sectors is the key to explaining the effects of a monetary shock. Although firms are on the demand side of investment goods, they are also subject to financial friction when borrowing from financial intermediaries. The financial accelerator thus amplifies a firm's investment demand when the monetary policy is expansive. However, the demand for consumption goods has a smaller response to monetary shock, because households prefer consumption smoothing. Therefore, the heterogeneous demand effects result in different output and inflation dynamics. Our numerical simulation shows that the financial accelerator is the main factor influencing the effects of monetary shocks on investment output and inflation, and that it has minor effects on the consumption sector. A variance decomposition shows that aggregate technology, investment marginal efficiency, and monetary shocks are important determinants of business cycles.
This paper contributes to the literature by constructing a complete two-sector model characterizing the demand and supply sides of the consumption and investment sectors. Using a Bayesian estimation, we show that demand structure heterogeneity rather than nominal price rigidity is the key factor explaining the different responses of the two sectors to monetary shocks. Although our analysis of the investment sector is more generalized than in previous studies, our estimation results show that investment shocks are the main driving force of business cycles.
Overall, our results suggest that the central bank should take the structure and characteristics of different sectors into consideration when implementing monetary policy. The central bank should also pay attention to the financial condition of firms because it can change the transmission mechanism of monetary policy in different sectors.

From 2012 to 2016, China's CPI and PPI presented a long-term trend of deviation, clearly characterizing overall structural deflation. The long-term divergence distorted the price signal, and thus made it difficult to accurately judge the price levels and economic situation. In addition to presenting a new research problem, the divergence caused confusion in macroeconomic policy making. The traditional theories propose that there are three representative causes of deflation: a shortage of aggregate demand (Keynes, 1936), “debt-deflation” (Fisher, 1933), and monetary tightening (Friedman, 1971). However, few studies have examined the cyclical and structural factors underlying the deviation in the CPI and PPI between 2012 and 2016. In this paper, we use a combination of theoretical and empirical approaches to identify the cyclical and structural factors underlying the long-term divergence of the CPI and PPI since 2012, and analyze the causes of structural deflation in China. Based on our findings, we also provide a number of recommendations for researchers and policymakers.
Following Wu and Cao (2014), we introduce the Balassa-Samuelson effect into the aggregate demand-aggregate supply model (AD-AS model), and considering the actual situation of China, further introduce external shocks, overcapacity, and other factors to better explain the mechanism of structural deflation in China. In the modeling process, we use the “general to specific method” (Hendry, 2001; Ericsson, 2007; Wu, 2014) and conduct diagnostic tests on various combinations of factors. The CPI/PPI, as an indicator of structural inflation, is used as a substitute variable for the relative price of non-tradable goods to tradable goods. Variables such as the commodity prices, economic growth, imports and exports, fixed asset investment, household income, and money supply are included as cyclical factors. Variables such as overcapacity, industrial structure, the relative labor productivity of tradable goods against non-tradable goods, corporate debt-to-asset ratio, and terms of trade are included as structural factors. All of the data are from the WIND database and the study period is from the first quarter of 2002 to the fourth quarter of 2016.
The findings show that the structural deflation reflects the Chinese government's attempts to simultaneously deal with the slowdown in economic growth, make difficult structural adjustments, and absorb the effects of previous economic stimulus policies, and the risks and challenges that the economy may face. First, cyclical factors are an important cause of structural deflation in China. The weak internal and external demand and the downturn in international commodity prices had a negative shock on the CPI and PPI, although the impact was relatively short. As the domestic and foreign economies recovered and commodity prices began to rise, this negative impact was reduced or disappeared, and even had a positive impact on the CPI and PPI. Second, various structural and institutional factors are the underlying causes of structural deflation. In recent years, multiple factors, including supply-side shocks, such as the slowdown of China's labor productivity growth, overcapacity caused by the large-scale stimulus plans, insufficient domestic demand resulting from insufficient institutional reform of the housing, pension, education, and medical sectors, and the existence of the Barcelona effect, have led to an inadequate supply of high-end products and services, and a serious excess of medium and low-end industrial capacity, which have had structural and long-term impacts on deflation.
To effectively resolve structural deflation, China should introduce different policy tools and accelerate the pace of the supply-side structural reforms. International experience suggests that the central bank's ability to implement expansionary and tightening monetary policies is asymmetric. In the case of structural deflation, the efficiency of monetary policy is often greatly reduced due to the risk control of commercial banks and the changing expectations of the currency holders. Under these circumstances, an excessively loose monetary policy will not benefit capacity clearing, and may lead to a new round of asset price bubbles. In the new normal of the economy, the regulatory effects of aggregate demand and supply appear to be asymmetric. In the future, while maintaining a prudent and neutral aggregate policy stance and managing aggregate demand, China should pay more attention to supply-side reforms by introducing policies to optimize the market structure, improve the supply side environment and mechanism, stimulate the vitality of micro subjects, comprehensively improve the total factor productivity, and realize the market clearing of overcapacity and the optimal adjustment of the economy. It is also necessary to continuously track the factors related to structural deflation and the evolution trends at home and abroad. This will require new innovative research methods and further theoretical and policy related research.

Since the global financial crisis of 2008, a major event in China's financial system has been the ascent of shadow banking. Consisting of bank wealth management business, inter-bank business, trust loans, entrusted loans, and various asset management products, so-called shadow banking is rapidly expanding in scale, has an increasingly complex transaction structure, and involves a wide variety of market entities. According to our estimation, shadow banking in China amounted to RMB 51.1 trillion yuan at the end of 2017, which is 7.7 greater that at the end of 2008. Its average annual growth rate is 25.5%, the highest being 80%. At the end of 2018, shadow banking stocks were estimated at 48 trillion yuan.
China's shadow banking has exerted broad influence on monetary policy regulation and financial stability. When expanding, it provides the real economy with additional financing other than traditional loans and bonds. When shrinking, it makes credit decrease faster, and increases the downward economic pressure. Against this background, it has important theoretical and timely practical significance for understanding the business structure of shadow banking and accurately estimating its aggregate quantity change, thus helping us to grasp the business of shadow banking, strengthen our understanding of the financial sector in economic development, and conduct high-quality theoretical modelling and empirical research.
The main work and conclusions of this study are as follows.
First, this paper gives a definition of shadow banking based on functionality and analyzes different shadow banking businesses by type. Shadow banking is defined as financial businesses that rely on the banking system and conduct banking business but without strict banking regulations. Specifically, it includes all financial businesses that are beyond the scope of on-balance sheet loans and bond investment of banks, the essential functionality being credit, maturity, and liquidity transformation, the essential result being the creation of “money,” and the principal entity being banks that endorse the development of shadow banking. Shadow banking in line with this definition is more closely correlated with monetary policy regulation and financial stability. This study provides a structural map of shadow banking focused on both the source and application of funding, and providing greater clarity compared with other maps in the current literature. Shadow banking business is classified into undiscounted bankers' acceptances, quasi-loans, and financial nesting. Quasi-loans include the repos of bankers' acceptances, inter-bank entrusted payments, and repos of beneficial rights to trust products. From the perspective of the funding source, financial nesting includes on-balance sheet bank funding and off-balance sheet wealth management business, while from the perspective of the application of funds, it includes trust products, wealth management products, entrusted loans, bond investments, etc. This study summarizes the transaction structure, business entity, business substance, source of funding, legal basis, and balance sheet expression of all business types.
Second, this study accurately estimates the monthly data of shadow banking aggregates and balance sheet structure from 2002 till now. The seven principles for estimation are reliability, replicability, no double counting, conservative estimation, high frequency and continuity, comparability, and the matching of stock values with increments. We start from the liability side of the balance sheet, which fully covers all shadow banking businesses and produces its aggregate quantity, while avoiding double counting. The estimation results of this study are accurate. Further, after adequate statistical adjustment, the data reaches the goal of matching stock values and increments, making it convenient for calculating year-on-year and month-on-month indicators. This study presents detailed data sources and estimation methods for each major item in the shadow banking balance sheet, with the liability side including undiscounted bankers' acceptances (on both asset and liability sides), inter-bank quasi-loans, financial nesting (bank funding), and financial nesting (bank wealth management business), while the asset side includes entrusted loans, trust loans, bond investments, and other assets. The results are monthly time series data for each major item from 2002 until now.Estimation methods and data in this paper could be tested and improved by future researchers. The detailed and accurate data in this paper also lays solid foundation for future research on shadow banking.

Since the Tax-sharing Reform of 1994, fiscal relations between China's central and local governments have shown the characteristics of “centralization of fiscal revenue and decentralization of fiscal expenditure and responsibilities.” Accordingly, the central government provides a large number of intergovernmental transfer payments to cope with the mismatch between financial resources and responsibilities. Studies have shown that intergovernmental transfer payments can reduce the financial gap and income inequality between regions (Yin and Zhu, 2009; Mao et al., 2011; Su and Xie, 2015; An and Wu, 2016), promote the equalization of basic public services (Guo and Jia, 2008; Fan and Zhang, 2013), increase the economic growth rate (Guo et al., 2009; Ma et al., 2016), and improve the quality of the ecological environment (Fu and Miao, 2015). At the same time, intergovernmental transfer payments also bring negative effects such as the flypaper effect. The flypaper effect refers to the phenomenon whereby intergovernmental transfer payments lead to greater fiscal expenditure by local governments than if the equivalent fiscal revenue was raised by local governments themselves (Gramlich, 1969; Inman, 2008; Dahlberg et al., 2008; Lundqvist, 2015; Gennari and Messina, 2014; Leduc and Wilson, 2017). The flypaper effect indicates that although intergovernmental transfer payments only redistribute financial resources across different regions, they affect the scale of fiscal expenditure by changing the revenue structure of local governments.
As shown by many empirical studies, the size of the flypaper effect varies between countries. The estimated size of the flypaper effect is 0.25–1.06 in the U.S. (Hines and Thaler, 1995; Brennan and Pincus, 1996; Knight, 2002; Gordon, 2004; Lutz, 2010; Leduc and Wilson, 2017) and about 1 in Europe (Dahlberg et al., 2008; Lundqvist, 2015; Gennari and Messina, 2014). Astonishingly, based on a limited number of studies, the estimated size of the flypaper effect in China is considerably larger than in Western countries. For example, Liu and Ma et al.(2015) find that a 1-percent increase in general and special transfer payments leads to a 1.5 and 3-percent increase, respectively, in the county government's fiscal expenditure. The county-level estimation in Mao et al. (2015) shows that the flypaper effect of general transfers is as high as 2.2–2.5. As a result, it is necessary to explain China's high flypaper effect in light of China's particular budgetary and intergovernmental transfer systems.
Using China's province-level panel data from 1994 to 2015, we find that a 1 RMB increase in general and special transfer payments is associated with a 1.61 and 2.12 RMB increase, respectively, in fiscal expenditure, revealing a large flypaper effect. We also find that the flypaper effect of general transfer payments reduced after 2010 when the central government began to release the transfer payment quota to subnational governments in advance. Further investigation using monthly data indicates that special transfer payments augment the flypaper effect through a well-known mechanism: the year-end crash expenditure. Our findings add to the impression that the uncertainty of merited transfer payments to subnational governments, delays in the distribution and appropriation process of transfer payments, and the rigidity of China's budgetary system jointly contribute to the larger flypaper effect in China.
This paper adds to the literature in the following ways. First, based on the interaction between China's special fiscal budget management system and the intergovernmental transfer payment system, this paper explains why China's flypaper effect as found in the empirical literature is so large. This paper is also the first to use provincial fiscal data to calculate the monthly contribution to the annual flypaper effect. This decomposition helps us to observe details concealed by the annual data. Our study indicates that the central government should regulate the intergovernmental transfer payment system, expand the scope of intergovernmental transfer targets, expedite the progress of funding, build a cross-annual budget balance mechanism, and prevent the moral hazard problem of intergovernmental transfer payments. Subnational governments should in turn improve their implementation of budget management and strengthen their adherence to governmental budgets.

China's local economic development and large-scale urban construction rely heavily on the urban land market. Land is used as a form of leverage to instigate the financing of urban construction, which also triggers the risk of excessive local debt. An important entity in local debt is the financing platform. The financing platforms are local government-backed investment companies, which are used to seek financing channels to bypass the legal constraints. Most researchers focus on municipal investment bonds, while less attention is paid to project loans from banks, when in fact bank loans account for a larger share of local debt. Among bank loans, land mortgages play an important role.
However, due to data limitations, research on the land mortgage behavior of local financing platforms rarely includes rigorous empirical analysis based on microdata. This study is the first to analyze micro-level land mortgage data, and it finds that financing platforms have higher mortgage rates and larger amounts in land mortgages. We also discuss the strong motivation and ability of local governments to intervene, leading to a mismatch in credit resources that is inefficient and contains a large debt risk.
This study makes the following contributions. (1) It studies urban land in China, whereas most researchers study land transfers by local governments or debt financing by financing platforms. This paper is the first to investigate financing platforms for land mortgages and conduct an empirical analysis based on micro-level data. (2) It contributes to the study of China's local debt risk by verifying the impact of credit discrimination on land mortgages, providing evidence that local government intervention leads to higher debt and inefficient allocation of credit resources, and revealing the potential risk of excessive land mortgage loans. (3) It provides empirical support to solve the problem of high leverage in China. We demonstrate that local governments have an inescapable responsibility for the high economic leverage.
In the empirical section, based on the Chinese Land Market website (www.land-china.com), we collect the details of land mortgages from January 1, 2006 to February 28, 2016. The regional economic data is taken from the Regional Economic Statistical Yearbook, the China Urban Statistical Yearbook, and the Wind database. The list of financing platforms is taken from the China Banking Regulatory Commission. The data on officials are collected from the resumes on People's Daily Online, the Xinhua website, and the Baidu Encyclopedia.
The results show that the mortgage amounts and rates are significantly higher with financing platforms. Although mortgages by financing platforms are restricted through policies and regulations, the overall over-borrowing is noteworthy. Given the systematic differences between financing platforms and non-platform companies, we use the propensity score matching (PSM) method for robustness testing. We then further explore the political and economic factors behind the phenomenon. First, we examine the regional heterogeneity and find that mortgage loans and mortgage rates were significantly higher in the central and western region. Second, we investigate the impact of municipal investment bonds, and find a positive relationship between the stock of municipal investment bonds and land mortgages. Third, we discuss government goals and official incentives, and find that the increase in land mortgages is related to financing needs under the pressure of expansion. Finally, we explore the existence of government intervention through the differences in coefficient of the different government levels and land uses.
A series of empirical results shows that a financing platform can indeed obtain more land mortgage loans and higher mortgage rates. The reason behind this is that local governments have strong motives and intervention capabilities, resulting in inefficient allocation of credit resources. This means that even such a low-risk financing channel still carries local debt risk and crowds out the financing space of other entities. In future, they should be regulated through a reform in the budgetary and financing system, or the development of government financing channels.
Further research should include an empirical analysis of the impact of the financing platform's own performance on land mortgages. Specifically, based on the findings of this paper, we would further control the characteristics of local financing platforms, including financial data and other information related to solvency.

Despite the common market practice of predicting policy rate movements from inflation and adjusting portfolios correspondingly, few studies have formally explored the problem of portfolio selection under any given monetary policy paradigm. From a theoretical point of view, although the foundations of the Dynamic New Keynesian (DNK) model for macroeconomic policymaking and the stochastic dynamic models for portfolio selection both stem from the classical growth model, their different focuses have led to divergent formalisms and solution techniques. This paper explores a portfolio selection model under a continuous-time sticky-price general equilibrium and studies the implications of asset allocation under endogenous macroeconomic dynamics and monetary policy rules.
It is well known that the inflation-targeting paradigm that many central banks currently adopt is supported by a DNK framework in which the inefficiency of the economy is caused by inertia in price adjustments. An inflation-targeting monetary policy in this economy coincides with the optimal policy of a benevolent central bank whose optimization target is the intertemporal utility of a representative agent. Moreover, a rule-based monetary policy such as the Taylor rule avoids the inconsistency of discretionary policies. However, intuitively, investors can take advantage of a rule-based central bank and improve their portfolio performance.
More specifically, by constructing a continuous-time model of portfolio selection under a benchmark DNK economy and discussing its numerical solution techniques, this paper shows that, under optimal allocation strategies, the inverse of the Arrow–Pratt relative risk aversion function of investors decreases monotonically with a rising risk-free nominal interest rate, and exhibits a U shape with respect to inflation. This means that under the premise of an inflation-targeting monetary policy rule, the relative inclination of investors toward risky assets grows when inflation deviates from its steady state in expectation of a countervailing nominal policy rate. The results show that prior to the subprime crisis, the proposed model outperforms a traditional model that does not take monetary policy into consideration, but falls behind thereafter. A possible explanation is that before the crisis, the Fed's monetary policy can clearly be approximated by a Taylor rule that meets the prerequisites for the proposed allocation strategy, whereas after the introduction of quantitative easing, not only can the risk-free rate no longer be approximated by the Taylor rule, but the monetary policy also begins to take financial markets and institutions into account.
However, because an investor can now use risky assets for intertemporal resource allocation and its utility involves additional gains/losses from holding risky assets, the optimality of a monetary policy that targets the general price level needs to be reexamined. This paper uses the aforementioned model to discuss the macro-prudential problem of the feedback effects of investor profit-maximizing behavior on the economy. Preliminary results show that the existence of risky assets in the economy can have an effect similar to that of the financial accelerator. This gives another possible explanation for the wedge effect and differentiated risk appetite introduced in previous works on the credit channel of monetary policy transmission.
The assumptions that lead to the above results may need further justification. For instance, the price processes of risky assets are given exogenously in this paper; moreover, the assumption that the monetary policy follows the Taylor rule may not comply with the post-crisis practice of monetary authorities. However, endogenizing risky asset dynamics and the optimal behavior of central banks will render the Bellman equation too large to be solvable, given that the complexity of the numerical method utilized in this paper grows exponentially with the number of state variables. Therefore, a more efficient solution algorithm is a prerequisite for further extensions. From the viewpoint of monetary policymaking, although rule-based policies facilitate the management of expectations and avoid the inconsistency of discretion, they may also bring about arbitrage opportunities. Therefore, future research on the design of an incentive-compatible macro-prudential regulation framework is needed to prevent investors from taking excessive risks.

The concept of financial inclusion was formally proposed during the “International Year of Microcredit” in 2005, and focuses on broad inclusiveness and a commitment to provide reasonably priced, convenient, and efficient financial services to all social strata. In theory, financial inclusion can alleviate the financial constraints of the poor rural population and promote investment in production, operations, and human capital through providing savings, credit, and insurance, which are helpful in alleviating poverty. Can financial inclusion therefore improve the current situation of uneven income distribution and reduce the incidence of poverty？ Is there a significant difference between traditional finance and information-based financial inclusion？ This study addresses this question.
This focus of this study is the impact of financial inclusion on income distribution and poverty alleviation. Specifically, a theoretical model is first used to explain the connotation of financial inclusion and construct a financial index system for financial inclusion. The study then examines the impact of financial inclusion on income distribution and poverty alleviation at the country level, and also on regional heterogeneity in the concentrated and non-concentrated destitute areas, the western and eastern regions. Second, it examines why financial inclusion has failed to reduce the incidence of poverty via two mechanisms: capture of the rural credit market by the elite and lack of financial knowledge. Third, it uses a difference-in-differences quasi-natural experiment to empirically analyze the influence of financial inclusion on income distribution and poverty alleviation. Then, using the two dimensions of banking and insurance, it examines the role of the financial inclusion framework design in reducing the incidence of poverty, taking broad coverage, specific matching, and sustainability into account. Finally, it explores the positive role of information-based financial inclusion compared with the traditional financial system.
Based on the county level data of 2009 and 2015, this study empirically examines the impact of financial inclusion on urban-rural income distribution and the per-capita disposable income of rural residents in concentrated and non-concentrated areas. In addition, we select data for 31 provinces, municipalities, and autonomous regions from 2002 to 2015 to empirically analyze the policy effects of financial inclusion. The county-level data are derived from “The Yearbook of Socio-economic Statistics of County (City) in China from 2000 to 2015,” the “China Statistical Yearbook for Regional Economy,” the CEInet Statistics Database, and manual collection. The provincial-level data are derived from the 2015 “Report on China's Regional Financial Operation,” the “China Statistical Yearbook,” the website of the People's Bank of China, and the Wind database.
The results show that in the initial stage, financial inclusion can narrow the rural-urban income gap, but this effect is only significant in concentrated destitute areas and the western region. Elite capture of the rural credit market and lack of financial knowledge are the reasons that financial inclusion has not alleviated poverty. A policy framework needs to take account of extensive inclusivity, specific ratios, and commercial sustainability to realize the dual goals of efficiency and fairness. Information-based financial inclusion can overcome the high thresholds, high service costs, and adverse selection problems of formal financial institutions, becoming the optimal framework for the selection of Pareto efficiency to promote fair income distribution and alleviate poverty.
The contribution of this paper lies mainly in the following aspects. First, it overcomes the overlapping problem of financial inclusion and the Financial Exclusion Index, improving the design of the Inclusive Financial Index system. Second, it examines the effect of financial inclusion on income distribution and poverty reduction at the county and provincial level, answering the question of whether financial inclusion can improve the situation of uneven distribution and poverty, and further verifying the differential impact of the institutional environment on the economic consequences of financial inclusion. Third, it compares and analyzes the problems and advantages of traditional financial institutions and information-based finance in the development of financial inclusion, providing theoretical support and practical guidance for the selection of a policy framework, and helping to improve the role of financial inclusion in improving income distribution and alleviating poverty.

According to the precautionary savings or liquidity constraints theory, households with liquidity constraints will use savings to cope with risk. The emergence of credit cards has reduced liquidity constraints and consequently improved household consumption. Based on the household choice to consume, save, or invest, the development of credit cards is likely to affect investment decisions. However, few studies have explored this effect.
According to the Chinese Household Finance Survey (CHFS) data for 2013, the percentage of urban households in China with credit cards surged from 0.5 percent in 2000 to 24 percent in 2012. Meanwhile, the percentage of households participating in the stock market rose from less than 1 percent in 2000 to 11.5 percent in 2012, showing the same upward trend as the credit card market. Theoretically, there are two possible channels: first, credit cards can reduce liquidity constraints, and second, they can be considered as financial tools for coping with risk. This study uses Chinese household microdata to investigate the relationship between credit cards and household investment, including the mechanisms behind this effect. It contributes to our understanding about the effect of credit card markets development in China on household investment decisions, and it reveals empirical evidence for the hypothesis of liquidity constraints and limited stock participation.
The Chinese Household Finance Survey (CHFS) was conducted by Southwestern University of Finance and Economics in China. It provides representative financial panel data at the household and individual level, including financial and non-financial assets, liabilities, credit cards, credit constraints, income, consumption, demographic characteristics, and payment habits. This comprehensive and systematic household panel survey focused on the Chinese financial system and covered 8,438 households in 2011 and 28,143 during the second wave in 2013. Among these samples, 6,847 households could be followed up, comprising 66.7 percent of urban households.
This study conducts an empirical analysis using not only cross-sectional pool data but also panel data that could be followed up. The findings are as follows. First, credit card holders in urban China are more likely than non-holders to participate in the stock market. The total investment value also increases with the enhancement of the credit line. Second, the credit card is considered to be a financial tool for coping with short-term income risk. By keeping consumption level constant, households can allocate more to stock assets instead of holding cash and fixed savings deposits. This study also finds that the more credit cards households hold, the more they participate in the stock market, and the investment value in the stock market depends mainly on the credit line.
To avoid measurement errors generated by reverse causality and omitted variables, we apply the instrumental variable method of estimation using the 2SLS-Probilt model. Specifically, the instrumental variable of whether the household holds a credit card is measured by whether there are promotions in the household's community. To exclude the impact of omitted variables, we perform numerous robustness checks, including applying the fixed effect model using panel data that could be followed from 2011 to 2013. Furthermore, we use relatively exogenous proxy variables such as the number of credit cards and the credit line to estimate the effect of credit cards on stock market investment. In addition to proxy variables, we consider the sequence of credit card holding and stock holding to avoid reverse causality problems. The results of these variant methods all support our conclusion.
Although there is international empirical evidence that credit cards can boost consumption, consistent with the prediction of precautionary savings theory, the development of the Chinese credit card market has not reduced the household savings rate or increased consumption as previously concluded. On the contrary, Chinese households prefer to use credit cards as financing tools to respond to short-term income risks, and increase risky investments by cutting back non-risk assets (cash or fixed deposits) with little reduction in savings on average. To sum up, further research is needed to establish whether the development of credit cards enhances consumption or investment.

China's achievements in high-speed rail have attracted worldwide attention. The development of the high-speed rail network has effectively broken the spatial barriers between cities and facilitated the mobility of resources and production factors, especially people and information. As information intermediaries in the financial market, securities analysts actively collect important information on listed companies through various channels, such as company announcements, field investigations, and strategy meetings. They then use this information to make earnings forecasts and guide their investment decisions in the capital market. For security analysts, the high-speed rail network has effectively shortened the space-time distance between cities and reduced the cost of communicating information. The network has also facilitated more field investigations of listed companies and private communications with management, and thus reduced the original information restrictions and helped improve the accuracy of analysts' earnings forecasts.
For our empirical analysis, we collected data on the high-speed rail stations in 201 Chinese prefecture-level cities from 2006 to 2016, and then matched these data with the panel data of 1,244 listed companies in the A share market. Using the operation of high-speed rail as a quasi-natural experiment and using the difference in difference (DID) model, we studied the impact of high-speed rail on the earnings forecasts of security analysts. We found that after the high-speed railway entered service, the accuracy of the analyst earnings forecasts for companies located along the network was significantly improved, and the forecast divergence and optimism of the analysts' earnings were reduced. Further analysis of the internal mechanism showed that the introduction of high-speed rail prompted analysts to conduct more field investigations of the listed companies along the network. Compared with firms not located along the network, the number of analysts participating in investigations and the number of investigations per capita increased for the companies on the network after high-speed rail was introduced. Moreover, we found that the high-speed rail had heterogeneous effects on analysts' earnings forecasts. For enterprises with lower information processing costs, better corporate governance, and lower fund shareholding, the accuracy of the analysts' earnings forecasts clearly improved, and the divergence and optimism of the earnings forecasts were effectively reduced. In addition, from a dynamic point of view, the impact is mainly detected two years after the high-speed rail network begins operating, which indicates that it takes some time for high-speed rail to affect the mobility of the information in the capital market by increasing the research frequency and private information of analysts.
The study makes three main contributions to the literature. First, it extends the research on the economic effects of high-speed rail, and uses data at the micro-company level for the first time to demonstrate the impact of a high-speed rail network on the earnings forecasts of security analysts from the perspective of private information on the capital market. Second, by using the high-speed rail network as a quasi-natural experiment in which the ability of analysts to obtain private information is improved, we verify the impact of the relaxation of the information constraints on analysts' earnings forecasts and effectively alleviate the problems of self-selection and endogeneity in the samples. Third, we confirm the internal mechanism by which the introduction of high-speed rail improves the accuracy of analysts' earnings forecasts through the increased field research and information mobility of the analysts.
The findings of this study have two main implications for policymakers. First, our findings show that high-speed rail helps to accelerate the movement of people and information between adjacent cities, and hence enhances the information disclosure of listed companies and reduces the degree of information asymmetry in the capital market. This shows that the construction of the high-speed rail network produced positive externalities and provides empirical support for the construction of high-speed rail networks. Second, we also add to the literature on analysts' earnings forecasts. The capital market is itself a market for information. Thus, we examine the exogenous impact of high-speed rail on the cost of communicating information. We then study the impact of the acquisition of private information on analysts' forecasting behavior, and find that there is still a serious degree of information asymmetry between analysts and listed companies. Field investigations by analysts provide an effective channel for alleviating this information asymmetry and help to improve the pricing function and resource allocation efficiency of the capital market.

The question of why different assets deliver different returns is a fundamental problem in finance. In this regard, the literature has mainly focused on the relationship between the profitability and subsequent stock returns of firms. Profitability is also an important factor in the newly proposed asset pricing models. Furthermore, the empirical research on asset pricing has shown that a large number of firm characteristics can be used to forecast a cross-section of stock returns. However, because some of these factors have lost their predictability after being identified in academic papers or learned by the market, the question of how to extract the commonalty of the predictors and aggregate the effective information has become a key issue in empirical finance. Different from the literature, which explores the predictability of individual profitability-related proxies, in this paper, we aggregate a composite profitability measure of Chinese firms from a set of individual profitability-related indicators. We then investigate the relation between a firm's composite profitability and stock returns in the Chinese stock market.
Specifically, we use the partial least squares (PLS) and forecast combination (FC) methods to aggregate a composite profitability measure from 12 individual profitability related proxies. Composite profitability provides a comprehensive measure of a firm's profitability, and may provide the basis for a new asset pricing model. We obtain data from the China Stock Market and Accounting Research (CSMAR) database from 2000 to December 2017, including accounting data, monthly stock returns, Fama-French (1993, 2015) common factors, and the Chinese risk-free rate. We find that firms with high composite profitability always have high future stock returns. Using the single factor PLS method and taking the 12-month average slopes is the most efficient way to aggregate the composite profitability. The long-short portfolio generates 15% average annualized returns, with a Sharpe ratio of 0.75. In comparison, using the FC method to calculate the composite profitability generates lower subsequent stock returns. The main objective of PLS is to extract a common factor from a set of predictors that has the highest covariance with the predicted variable, which is a “disciplined” dimension reduction technique. The FC approach averages the univariate predictive regression values of firms' profitability equally, but it ignores the multivariate information structure and interaction between firms' profitability. Hence, the PLS approach is more effective in aggregating information for cross-sectional analyses, and makes more accurate future return predictions.
We use different asset pricing models to calculate the abnormal returns generated by the composite profitability, including the Fama-French five-factor model. The results show that when using the PLS single factor model, the abnormal returns of the monthly long-short portfolios are 1.27% (t=3.07), 1.50% (t=3.16), and 1.22% (t=2.94) based on the capital asset pricing model, and the Fama-French three-factor and five-factor models. After controlling for other firm characteristics and risks, such as firm size, book-to-market ratio, and reversal, the positive relation between composite profitability and stock returns is still significant and robust. We then investigate why firms with a high composite profitability have higher future stock returns. The results indicate that the composite profitability premium is stronger among firms with low investment friction, which is consistent with the implications of the investment-based q-theory asset pricing models. However, the premium is not stronger among firms with high mispricing, which contradicts the behavioral mispricing explanations.
Our results differ from the findings on the U.S. market, which suggests that investors in the Chinese market also have to focus on the rational expectation-based model. Our findings also indicate that the research on the international markets cannot adequately explain what happens in the Chinese market. Furthermore, reducing the investment friction helps the market to value its composite profitability more precisely. Future studies should focus on other aggregated information on firm characteristics, such as the investment and trading frictions. Moreover, the economic links and information structure of these factors should also be explored to understand the uniqueness of the Chinese stock market.