Computing Macro-Effects and Welfare Costs of Temperature Volatility: A Structural Approach
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Computing Macro‑Effects and Welfare Costs of Temperature Volatility: A Structural Approach Michael Donadelli1 · Marcus Jüppner2,3 · Antonio Paradiso4 · Christian Schlag5 Accepted: 19 July 2020 © The Author(s) 2020
Abstract We produce novel empirical evidence on the relevance of temperature volatility shocks for the dynamics of productivity, macroeconomic aggregates and asset prices. Using two centuries of UK temperature data, we document that the relationship between temperature volatility and the macroeconomy varies over time. First, the sign of the causality from temperature volatility to TFP growth is negative in the post-war period (i.e., 1950–2015) and positive before (i.e., 1800–1950). Second, over the pre-1950 (post-1950) period temperature volatility shocks positively (negatively) affect TFP growth. In the post-1950 period, temperature volatility shocks are also found to undermine equity valuations and other main macroeconomic aggregates. More importantly, temperature volatility shocks are priced in the cross section of returns and command a positive premium. We rationalize these findings within a production economy featuring long-run productivity and temperature volatility risk. In the model temperature volatility shocks generate non-negligible welfare costs. Such costs decrease (increase) when coupled with immediate technology adaptation (capital depreciation). Keywords Temperature volatility · Productivity · Asset prices · Welfare costs JEL Classification E30 · G12 · Q0
1 Introduction There is near unanimous scientific consensus that climate change affects human health, behavior, and activity (Patz et al. 2005; Deschênes and Moretti 2009; Zivin and Neidell 2014; Cattaneo and Peri 2016) and has a negative impact on economic development (Stern 2007; Hsiang and Meng 2015). Over the past decades, the * Antonio Paradiso [email protected] Extended author information available on the last page of the article
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economic risk of climate change has been quantified by means of the so-called Integrated Assessment Models (IAMs). In this class of models climate change effects (costs and benefits) are captured via damage functions. IAMs easily allow to relate climate variables (e.g., temperature, sea-level rise, rainfall, CO2 concentration) to economic welfare. However, even if widely used, IAMs have been subject to severe criticism. Above all, IAMs have been questioned to have no empirical supports (Pindyck 2013; Diaz and Moore 2017). Moreover, Pindyck (2013) argues that the use of IAMs as a climate change policy tool faces a major problem: “the modeler has a great deal of freedom in choosing functional forms, parameter values, and other inputs, and different choices can give wildly different estimates of the social cost of carbon and the optimal amount of abatement”. In other words, he points out that IAMs can deliver any result one desires. In the end the crucial flaws of IAMs make them “close to useless as tools for policy analysis” Pindyck (2013) (pag. 86
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