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We identify the effectiveness of social distancing policies in reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policies, where we control time-varying, observed and unobserved, state-level heterogeneities. Using panel data on infection and deaths in all US states from February 20 to April 20, 2020, we find that stay-at-home orders and other types of social distancing policies significantly reduced the growth rate of infection and deaths. We show that the effects are time-varying and range from the weakest at the beginning of policy intervention to the strongest by the end of our sample period. We also found that social distancing policies were more effective in states with higher income, better education, more white people, more democratic voters, and higher CNN viewership.

This paper proposes a time-zone vector autoregression (VAR) model to investigate comovements in the global financial market. Analyzing daily data from 36 national equity markets, we explore the subprime and European debt crises using static analysis and the COVID-19 crisis through a rolling window method. Our study of comovements using VAR coefficients reveals a resonance effect in the global system. Findings on densities and assortativities suggest the existence of the transmission mechanism in all periods and abnormal structural changes during the crises. Strength analysis uncovers the information transmission mechanism across continents over normal and turmoil periods and emphasizes specific stock markets' unique roles. We examine dynamic continent strengths to demonstrate the contagion mechanism in the global equity market over an extended period. Incorporating the time-zone effect significantly enhances the VAR model's interpretability. Signed networks provide more information on global equity markets and better identifies critical contagion patterns than unsigned networks.

The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the LASSO method to construct the stock network and further use the Map Equation method to analyze the evolution of modules in the SSE A-shares market. Empirical results show: (1) The connected effect is more significant in bear markets than bull markets; (2) A system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) Some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies.

The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the maximum strongly connected subgraph, and $3\sigma$ principle to iteratively determine the threshold value for building a dynamic correlation network of SSE A-shares. Analyses are carried out based on the networking structure, intra-sector connectivity, and node status, identifying several contributions. First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015. Second, the finance, energy and utilities sectors have a stronger intra-industry connectivity than other sectors. Third, HUB nodes drive the growth of the SSE A-shares market during bull periods, while stocks have a think-tail degree distribution in bear periods and show distinct characteristics in terms of market value and finance. Granger linear and non-linear causality networks are also considered for the comparison purpose. Studies on the evolution of inter-cycle connectivity in the SSE A-share market may help investors improve portfolios and develop more robust risk management policies.

In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making. How would this impact the distributional outcomes across social groups? Here we investigate the gender and racial biases of OpenAIs GPT, a widely used LLM, in a high stakes decision making setting, specifically assessing entry level job candidates from diverse social groups. Instructing GPT to score approximately 361000 resumes with randomized social identities, we find that the LLM awards higher assessment scores for female candidates with similar work experience, education, and skills, while lower scores for black male candidates with comparable qualifications. These biases may result in a 1 or 2 percentage point difference in hiring probabilities for otherwise similar candidates at a certain threshold and are consistent across various job positions and subsamples. Meanwhile, we also find stronger pro female and weaker anti black male patterns in democratic states. Our results demonstrate that this LLM based AI system has the potential to mitigate the gender bias, but it may not necessarily cure the racial bias. Further research is needed to comprehend the root causes of these outcomes and develop strategies to minimize the remaining biases in AI systems. As AI based decision making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups.

We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. We also provide an empirical analysis on the effect of maternal smoking on babies' birth weight and the effect of job training program on future earnings.