In this paper, we discuss how environmental damage and emission reduction policies affect the conduct of monetary policy in a two-sector (clean and dirty) dynamic stochastic general equilibrium model. In particular, we examine the optimal response of the interest rate to changes in sectoral inflation due to standard supply shocks, conditional on a given environmental policy. We then compare the performance of a nonstandard monetary rule with sectoral inflation targets to that of a standard Taylor rule. Our main results are as follows: first, the optimal monetary policy is affected by the existence of environmental policy (carbon taxation), as this introduces a distortion in the relative price level between the clean and dirty sectors. Second, compared with a standard Taylor rule targeting aggregate inflation, a monetary policy rule with asymmetric responses to sector-specific inflation allows for reduced volatility in the inflation gap, output gap, and emissions. Third, a nonstandard monetary policy rule allows for a higher level of welfare, so the two goals of welfare maximization and emission minimization can be aligned.

This paper investigates the impact of manager-investor interaction quality on stock returns by utilizing an online IPO roadshow dataset and leveraging a word-embedding model. We find that such interactions are positively valued, as reflected in initial returns. The effect is particularly pronounced for firms characterized by higher levels of information asymmetry, greater investor attention, increased question uncertainty, or discussions on topics not covered in prospectus. Additionally, our research reveals that effective management communication leads to increased first-day turnover rates and thus higher returns. These heightened returns persist up to 180 days following the IPO, without displaying a significant long-term reversal associated with interaction quality. These findings underscore the meaningful impact of the quality of manager-investor interactions on firm valuation.

This paper investigates how state ownership affects financial reporting practices in China. Using several measures of state (government) ownership, we show that a one-standard-deviation increase in state ownership decreases financial statement comparability by 36.61%, and the impact is more pronounced when the central authority has majority control of the company. Moreover, lower earnings quality and lower levels of accounting conservatism among state-owned enterprises (SOEs) may explain the lower accounting comparability between SOEs and non-SOEs (NSOEs). Additionally, similar (different) managerial objectives converge (diverge) financial statement comparability between SOEs and NSOEs. Last, the geographical locations of firms also contribute to financial statement comparability. We employ a difference-in-differences design, changes regression and entropy balancing to mitigate potential endogeneity bias.

The recent advances in automation technology, robotics in particular, have sparked a heated debate over the future of labor and human society at large. The ongoing process of robotization may engender profound impacts on various segments of the labor market. Given the far-reaching implications of robots, it is thus very important to understand the scale and scope of robot use and characteristics of robot users. However, the main challenge is the limited availability of robot data at the microeconomic level (Raj and Seamans, 2018). Due to the data constraint, the bulk of the existing literature relies on cross-country industry-level data from the International Federation of Robotics (IFR). The lack of micro-level robot data makes it difficult to paint a comprehensive picture of robotization in industrial settings, and perhaps more importantly, to assess how within-industry firm level heterogeneity manifests itself in robot use and adoption.