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In many small developing countries, the benefits of capital market development are not realized, as banks dominate and stock markets struggle to establish a firm footing in the economy, remaining relatively illiquid and volatile. This paper examines the determinants of stock market volatility in such conditions, specifically investigating the role that banks play in engendering volatility. Although significant amounts of research have been conducted on the determinants of stock market volatility in large developed country and emerging market exchanges, the stock exchanges in small developing countries have largely been ignored, as has been the relationship between banking operations and stock market volatility. Using a Generalized Autoregressive specification, this paper investigates the conditions under which banks in conducting their core functions impact stock market volatility in a small, bank-dominated developing country. The results show that factors which affect banks' profitability, such as inefficiency, ill-advised financial transactions and overly stringent or inconsistently applied regulations, can increase stock market volatility. They also indicate that in an economy wherein most listed real-sector firms survive through a combination of equity and credit financing, the effectiveness of financial intermediation impacts stock market volatility by affecting the profitability of such firms. We show that in small bank-dominated economies, profitable, well-functioning banks are needed if capital markets are to develop. Suggestions are provided as to how banks, regulators and policymakers can aid in the reduction of stock market volatility. JEL Classifications: O16, G21

Background We analyze a sample of 2,816 Medicare-certified acute care hospitals across all US states, using January to December 2019 CMS Hospital Compare datasets merged with county-level socio-demographic data. These data allow us to identify how features of the community in which a hospital serves differentially relate to its patients’ experiences based on the quality of that hospital. Methods A Finite Mixture Model (FMM) is used to uncover a mix of latent groups of hospitals that differ in quality. In the FMM, a multinomial logistic equation relates hospital-level factors to the odds of being in a particular group. And a multiple linear regression relates the characteristics of communities served by hospitals to the patients’ expected ratings of their experiences at hospitals in each group. Thus, this association potentially varies with hospital quality. We conducted the analysis via Stata. Results We provide evidence that relatively low-quality hospitals have much more variability in patient experience ratings than relatively high-quality ones. Moreover, the experiences at low-quality hospitals are more sensitive to county demographic factors, which means exogenous shocks, like COVID-19, will likely affect these hospitals differently, as such shocks are known to disproportionately affect their communities. Conclusion Our results imply that low-quality hospitals with more variability in their HCAHPS responses are more likely to face adverse patient experiences due to COVID-19 than relatively high-quality hospitals. Pandemics like COVID-19 create conditions that intensify the already high demands placed on hospitals and make it even more challenging to deliver quality care.

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The Short‐Time Compensation (STC) program enables US firms to reduce work hours via pro‐rated Unemployment Insurance (UI) benefits, rather than relying on layoffs as a cost‐cutting tool. Despite the program's potential to preclude skill loss and rehiring/retraining costs, firms' participation rates are still very low in response to economic downturns. Using firm‐level UI administrative data and semi‐parametric methods, we show why by illustrating which type of firms benefit from the program and which do not. A key finding is that cyclically sensitive firms have about 14% lower layoff rates when they use STC, but we find no difference for more cyclically stable firms.