We develop a model economy with active financial markets in which a policymaker's adoption of a gradualistic approach constitutes a Bayesian Nash equilibrium. In our model, the ex ante policy proposal influences the supply side of the economy, while the ex post policy action affects the demand side and shapes market equilibrium. When choosing policies, the policymaker internalizes the impact of her decisions on the precision of the firm-value signal. Moreover, financial markets provide a price signal that informs the government. The policymaker learns about the productivity shocks not only from firm-value performance signals but also from financial market prices. Access to information through both channels creates strong incentives for the policymaker to adopt a gradualistic approach in a time-consistent manner. Smaller policy steps yield more precise information about the productivity shock. These results hold robustly for both exogenous and endogenous information models.

We leverage machine learning to introduce belief dispersion measures to distinguish different opinion (DO) and information asymmetry (IA). Our measures align with the human-based measure and relate to economic outcomes in a manner consistent with theoretical prediction: DO positively relates to trading volume and negatively linked to bid-ask spread, whereas IA shows the opposite effects. Moreover, IA negatively predicts the cross-section of stock returns, while DO positively predicts returns for underpriced stocks and negatively for overpriced ones. Our findings reconcile conflicting disagree-return relations in the literature and are consistent with Atmaz and Basak (2018)’s model. We also show that the return predictability of DO and IA stems from their unique economic rationales, underscoring that components of disagreement can influence market equilibrium via distinct mechanisms.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.

The e-CNY, with its information transparency and financial inclusion, activates an innovative solution to cure the financing obstacles among the small and medium enterprises in China. The research establishes a game model between enterprises and commercial banks embedded in information asymmetry, and incorporates the e-CNY payment choice within the framework to analyse the cure effect of e-CNY on enterprise financing obstacles. With equilibrium results calculated, it simulates the outcomes of changing parameters on the behaviours of enterprises and banks. The findings involve that, based on the incremental utility of e-CNY and subsidies attached, e-CNY is preferred in transaction, reducing the bad debt risk caused by misalignment when both achieving excess returns. The People’s Bank of China must strengthen a more transparent publicity of e-CNY and structure an inclusive system of financial regulation to well use digital currency and realise high-quality socio-economic development.

We develop a signaling game model of investment to explore the effects of ambiguity aversion on corporate equilibrium strategies, investment dynamics, and financing decisions in incomplete markets with asymmetric information. Our analysis shows that volatility ambiguity aversion has a similar but more pronounced effect than asymmetric information, leading to higher financing costs, lower investment probabilities, and a greater likelihood of non-participation in investment. Importantly, volatility ambiguity aversion exhibits an amplifier effect, magnifying financing costs, adverse selection costs, and distortion in investment choices under asymmetric information. This increased ambiguity aversion raises the chances of inefficient separating and pooling equilibria, resulting in notable welfare losses. These findings highlight the significant impact of ambiguity aversion on strategic decision-making and equilibrium outcomes in investment, particularly in settings marked by information asymmetry and incomplete markets.

This paper develops an SOE (small open economy) dynamic general equilibrium model to study the impact of climate change on the current account. By calibrating the model to Chinese economy, we find the following results. First, the current account-output ratio improves in the first decade following an increase in global temperature caused by climate change. It then deteriorates in the following next three decades. Second, the overall current account-output ratio dynamics in response to climate change is neither affected by the types and stringency of climate policies, nor by the levels and growth rates of temperature increases. Third, the impact of an increase in temperature from 1.28 ℃ to 1.5 ℃ relative to the pre-industrial periods (1850-1900) on the current account-output ratio is equivalent to that of an approximate 0.14% permanent decline in TFP. Finally, although the current account-output ratio is likely to deteriorate in the first year when temperature increases instantly, it might not be true if the coefficient of relative risk aversion, or interest rate premium is larger, or debt sensitivity to interest rate is smaller.

We examine the performance of actively managed Chinese mutual Funds over the period 2002-2020. Using the bootstrap-based false discovery technique, we find that 19.25% of Chinese actively managed mutual funds produce positive-alpha, which contrasts with existing studies documented by others in developed markets. Our findings survive a battery of robustness tests. Unlike in developed markets, equilibrium accounting may not hold in China as the Chinese stock market is dominated by retail investors instead of mutual funds, and thus the mutual funds in China can be more skilled at the expense of the retail investors. We find supportive evidence of the applicability of the bootstrap-based false discovery rate method by conducting simulations.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.

This study examines the effects of the robotic subsidy policy in China’s manufacturing sector. The demand-side subsidy policy aims at encouraging manufacturing firms to invest in robotics by lowering the cost of purchase. Our difference-in-difference analysis reveals distributional impacts of municipality-level robot subsidies on manufacturing firms of different scales. Although the subsidy brings a 14.2% increase in the application of robot patents, the facilitated access to robotics has not transformed into new firm entries. Strikingly, new firm entry decreases by 23.5% after the policy implementation. On the other hand, robot subsidies have increased the revenue, total asset, and employment of larger manufacturing firms by 9.8%, 6.9%, and 6.7%, respectively. To interpret the mechanism, we develop a simplified framework incorporating financial frictions into a task-based model. The model reveals that idiosyncratic borrowing costs lead to an inefficient equilibrium by generally depressing automation adoption and creating automation dispersion across firms. Such ex-ante distortion results in a uniform subsidy disproportionately benefiting firms with better capital access, thus creating a trade-off in terms of efficiency: while the subsidy can enhance overall automation, it simultaneously exacerbates automation dispersion. To quantify the efficiency implications, we embed this simplified model into a dynamic heterogeneous-agent framework, calibrated to the 2010 productivity distribution, financial frictions, and robot density in the industrial sector in China. Our dynamic model reveals that a 20% robot subsidy narrows the gap between mean and optimal automation level by 22% percentage points, while raises automation dispersion by 49%. This results in a 1.23% increase in aggregate output at the cost of a 2.40% decline in TFP. This dynamic model proposes a novel mechanism that automation exacerbates capital misallocation by enlarging asset accumulation dispersion between workers and entrepreneurs. Controlling for this dynamic feedback could enhance the subsidy-induced output gain by an additional 26%

Understanding the impacts of new technology and innovations on the banking sector is important and of growing interest. However, there is limited research on the detailed channels of the impacts, and consequently, the evaluations for the aggregate welfare impacts. We contribute both empirically and quantitatively. We construct a new data set for Chinese banks. We ffnd banking innovations can improve efficiency, and mostly reduce non-interest costs but not so much on deposit rates. We show the ffnding is quite robust under a battery of checks. In a new structural, quantitative model, banks have heterogeneous capital, decide innovation investment and also risky lending, face regulations on the capital requirement and have limited liability. When aggregate new technology improves, it can reduce financial intermediation costs and social deadweight loss; however, it will also change the bank’s risk consideration and increases moral hazard when the cost is largely reduced. We also find several other new implications for R&D investment credit policy and Capital Requirement policy (CAR).