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 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.

Using proprietary data from AngelList Talent, we study how startup job seekers’ search and application behavior changed during the COVID-19 downturn. We find that workers shifted their searches and applications away from less-established startups and toward more-established ones, even within the same individual over time. At the firm level, this shift was not offset by an influx of new job seekers. Less-established startups experienced a relative decline in the quantity and quality of applications, ultimately affecting their hiring. Our findings uncover a flight-to-safety channel in the labor market that may amplify the procyclical nature of entrepreneurial activities.