How do state taxes respond to changes in oil prices? For the United States, the answer remains inconclusive. We use a consumer optimization model to derive an optimal tax function, whose parameters are estimated from a structural model and a reduced-form model. Firstly, our study differs from the existing literature in the sense that it does not assume or predict a linear effect of oil prices on state taxes. Instead, we find a nonlinear concave relationship between state taxes and oil prices, suggesting that higher oil prices lead to diminishing marginal returns in state taxes. Secondly, our findings corroborate the notable hypothesis of Kilian's (2008) study, which highlights the importance of the source of the oil shock in understanding the oil-pass through. We find a negative average response of state taxes to changes in oil prices for periods led by negative oil supply shocks (for example, the Arab Spring). Alternatively, we find the opposite effect for periods led by positive oil demand shocks and positive oil supply shocks (for example, the oil price boom of the early 2000s and the 2014 oil glut). Lastly, we find strong distributional effects of oil price shocks on state taxes: oil-dependent states (Alaska, Oklahoma, Wyoming, Texas, New Mexico, North Dakota, and Louisiana) face a more elastic and volatile response of state taxes to changes in oil prices compared to other states. For tax policy implications, our model shows that motor fuel consumption is overtaxed relative to its importance in the consumer price index.

Minnesota experienced 23 bank failures during the Great Recession. However, the internal causes of these failures are not well addressed in the empirical literature: we contribute to the literature by addressing this issue. This study relies on survival analysis to model the risk of bank failure in Minnesota during the Great Recession. We explore the econometric gains of incorporating several parametric distributions in modeling the baseline hazard function. For the Great Recession, we show the importance of the lognormal distribution in modeling the baseline hazard rate. We find that the key bank-specific factors that inflate the instantaneous rate of bank failure include higher exposure to nonperforming real estate loans, moral hazard in bank lending, poor earning capacity to cover loan defaults, and inefficiency in managing interest expenses on deposits. For macroprudential implications, we find some weak evidence of contagion in the banking sector, and we highlight the importance of regulatory capital in explaining bank survival in Minnesota during the Great Recession.

Given the sharp increase in hate crimes reported in the United States during the Great Recession and the coronavirus pandemic, a few studies attempted to examine the relationship between unemployment and hate crimes. Particularly, because of the strong covariance between unemployment and hate crimes observed during these latter periods. However, this study finds that those current findings remain inconclusive due to several econometric issues that are likely to cause biased estimates of the effect of unemployment on hate crimes. Given the limitations of the hate crime dataset (for example, underreporting and data gaps), we contribute to the literature by addressing econometric issues and by providing new findings concerning linkages between unemployment and hate crimes. For the period considered here, using the two-step system GMM estimator, this paper finds a nonlinear convex relationship between the hate crime rate and the unemployment rate. Additionally, we highlight the role of inertia in understanding the long-run effect of unemployment on hate crimes. Although this study is likely to suffer from precision issues due to data limitations, using fixed effects and a large sample size allow us to obtain consistent parameter estimates.

This paper assesses the significance of the asset price channel of monetary policy in Vietnam. We estimate a New Keynesian (DSGE) model using Bayesian techniques and successfully match the relevant empirical results with a large-scale factor-augmented vector autoregression model (FAVAR). We find robust empirical evidence of a significant asset price channel of monetary policy in Vietnam: impulse responses of stock returns to monetary policy shocks (both positive and negative) are significant and consistent with standard macroeconomic theory. This is the first study in literature to provide empirical evidence of the impact of adverse and expansionary monetary policy shocks on different sectors of the Hanoi and Ho Chi Minh Stock exchanges by relying on an FAVAR model. More importantly, the results derived here highlight the relative importance of incorporating a rich-data environment in identifying monetary policy shocks. Here, we demonstrate that the FAVAR model provides consistent and more meaningful impulse responses in contrast to the widely used small-scale recursive VARs.