Climate change and the nonlinear impact of precipitation anomalies on income inequality.

Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.

Unconventional monetary policies in an agent-based model with mark-to-market standards.

We employ an agent-based model to shed light on the macroeconomic effects of accounting principles, unconventional monetary policies, and of their possible interactions. If mark-to-market accounting standards may entail positive feedbacks which amplify economic or financial shocks, unconventional policies may introduce negative feedbacks that might dampen instabilities in financial and real markets. For these reasons, we jointly study these two sets of policies by employing a modified version of the Schumpeter meeting Keynes (K+S) macroeconomic agent-based model. Our results confirm that, due to its pro-cyclical nature, the mark-to-market accounting standard amplifies credit cycles, generating more instability with respect to a simulated economy wherein the historical accounting principle is employed. In contrast, unconventional monetary policy is counter-cyclical and it improves macroeconomic indicators. Finally, we study a scenario wherein mark-to-market accounting and unconventional monetary policy interact. We find that unconventional monetary policy can counterbalance the negative effects brought about by the application of mark-to-market accounting. Our results suggest that unconventional monetary policy instruments should not be considered as temporary interventions to be employed only during crisis periods. They should be part of the toolbox of central banks also in normal times.

Evidence for sharp increase in the economic damages of extreme natural disasters.

Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to capture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tail-fattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically significant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme damages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative.