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Maria Elena Bontempi

Publications and source records attributed to Maria Elena Bontempi.

2 recordsLinked to original sources

Shocking concerns: public perception about climate change and the macroeconomy

Public perceptions of climate change arguably contribute to shaping private adaptation and support for policy intervention. In this paper, we propose a novel Climate Concern Index (CCI), based on disaggregated web-search volumes related to climate change topics, to gauge the intensity and dynamic evolution of collective climate perceptions, and evaluate its impacts on the business cycle. Using data from the United States over the 2004:2024 span, we capture widespread shifts in perceived climate-related risks, particularly those consistent with the postcognitive interpretation of affective responses to extreme climate events. To assess the aggregate implications of evolving public concerns about the climate, we estimate a proxy-SVAR model and find that exogenous variation in the CCI entails a statistically significant drop in both employment and private consumption and a persistent surge in stock market volatility, while core inflation remains largely unaffected. These results suggest that, even in the absence of direct physical risks, heightened concerns for climate-related phenomena can trigger behavioral adaptation with nontrivial consequences for the macroeconomy, thereby demanding attention from institutional players in the macro-financial field.

econ.GN↗

GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications

We introduce a new estimator, CRE-GMM, which exploits the correlated random effects (CRE) approach within the generalised method of moments (GMM), specifically applied to level equations, GMM-lev. It has the advantage of estimating the effect of measurable time-invariant covariates using all available information. This is not possible with GMM-dif, applied to the equations of each period transformed into first differences, while GMM-sys uses little information as it adds the equation in levels for only one period. The GMM-lev, by implying a two-component error term containing individual heterogeneity and shock, exposes the explanatory variables to possible double endogeneity. For example, the estimation of actual persistence could suffer from bias if instruments were correlated with the unit-specific error component. The CRE-GMM deals with double endogeneity, captures initial conditions and enhance inference. Monte Carlo simulations for different panel types and under different double endogeneity assumptions show the advantage of our approach. The empirical applications on production and R&D contribute to clarify the advantages of using CRE-GMM.

econ.EM↗