Temperature shocks and welfare costs

Abstract

This paper examines the welfare implications of rising temperatures. Using a standard VAR, we empirically show that a temperature shock has a sizable, negative and statistically significant impact on TFP, output, and labor productivity. We rationalize these findings within a production economy featuring long-run temperature risk. In the model, macro-aggregates drop in response to a temperature shock, consistent with the novel evidence in the data. Such adverse effects are long-lasting. Over a 50-year horizon, a one-standard deviation temperature shock lowers both cumulative output and labor productivity growth by 1.4 percentage points. Based on the model, we also show that temperature risk is associated with non-negligible welfare costs which amount to 18.4% of the agent’s lifetime utility and grow exponentially with the size of the impact of temperature on TFP. Finally, we show that faster adaptation to temperature shocks results in lower welfare costs. These welfare benefits become substantially higher in the presence of permanent improvements in the speed of adaptation.

Introduction

Long-term global changes in temperature and precipitation indicate that our entire planet is undergoing a climate change. Despite a decades-spanning debate, climatologists and economists alike have not reached a consensus about the long-term economic effects of this dramatic development (see Pindyck, 2013). In this paper, we quantify the effect of temperature shifts on aggregate productivity, labor, consumption, and asset prices. More specifically, we integrate time-varying temperature dynamics into a production-based model featuring recursive preferences, long-run risk, and investment adjustment costs. This setup provides us with the opportunity to expand the scope of the analysis considerably beyond what is possible in an endowment-based model, e.g., when it comes to the dynamics of investment and labor.

Temperature statistics suggest that the average temperature level has been increasing over the last century both globally and among major advanced economies. Using a bivariate vector autoregression (VAR) analysis and data on U.S. temperature, we observe a statistically significant and long-lasting negative impact of temperature on total factor productivity (TFP). Quantitatively, a one-standard deviation temperature shock leads to a drop in one-year future aggregate U.S. TFP growth by around 0.2 percentage points (pp). When including other macroeconomic variables, the effect on TFP growth becomes (statistically) weaker. However, we are still able to observe an overall negative impact on the economy as indicated by a decrease in future consumption growth ($-$0.3pp), output growth ($-$0.5pp), investment growth ($-$1pp) and labor productivity growth ($-$0.5pp), consistent with recent empirical evidence (see Colacito et al., 2016). By accounting for the dynamics of asset prices in the VAR estimation, the negative effect of temperature on future TFP growth remains qualitatively unaffected.

We explain our empirical findings in a production-based model featuring temperature dynamics. By calibrating the model to data on the evolution of temperature in the U.S., we are also able to estimate the welfare losses associated with temperature shocks. Our findings show that, in the long-run, rising temperature has strong adverse effects on key macroeconomic aggregates, productivity, and asset valuations. Further, our model provides a theoretical equilibrium explanation for the negative effect of temperature increases on labor productivity found in empirical studies (see Deryugina, Hsiang, 2014, Park, 2016).

Greenhouse gas (GHG) emissions due to human activities are the most important cause of the climatic developments that followed the Industrial Revolution in 1750 (Hartmann et al., 2013). Greenhouse gases affect atmospheric composition, leading to a rise in surface temperature on earth which, in turn, increases the probability of certain types of extreme weather events, such as heavy rainfalls, floods, hurricanes, or droughts (see, e.g., Villarini et al., 2013). One of the most significant greenhouse gases is carbon dioxide which is released into the atmosphere due to fossil energy usage. There is an ongoing debate on how to contain CO2 concentration most effectively, and a popular approach is to estimate the overall welfare costs of CO2 emissions in order to impose a fossil fuel taxation that ensures a balance between economic growth and GHG emission (see Golosov et al., 2014).

A growing number of studies investigates the empirical linkage between economic performance and weather events. Hsiang (2010), for instance, documents that industries such as agriculture and tourism, where relocation is either completely impossible or at least very expensive, are affected most by higher temperatures and increasing rainfall. Schlenker and Roberts (2009) instead observe that higher temperatures have non-linear effects on crop yields, i.e., above a certain threshold higher temperatures no longer increase yields but are extremely detrimental. Other recent empirical findings suggest also that extreme whether events may lead to an increase in mortality (Deschênes and Moretti, 2009), a reduction in labor supply (Zivin and Neidell, 2014), and a general drop in firms’ productivity (Cachon et al., 2012).

Pricing the risks associated with climate change is essential for comparing the costs for different measures to contain the adverse climatic developments. A popular approach is to use so-called integrated assessment models (IAMs) (Nordhaus, 2008, Stern, 2007). However, the usefulness of these models in estimating the social cost of climate change and increasing carbon emissions is at the center of an ongoing debate. For example, Pindyck (2013) criticizes IAMs as having little theoretical or empirical foundation. He finds that the model inputs, such as parameter values and functional forms, are chosen arbitrarily, while the choice of the discount rate reaches an ethical dimension.¹ Furthermore, he stresses that the majority of economic studies on climate change imposes a loss function on the level instead on the growth rate of output. This assumption does not seem appropriate, since climate change is likely to have a permanent economic impact, e.g., through the destruction of ecosystems, deaths from weather extremes, or social disruption, and it also contradicts the empirical findings provided by Dell et al. (2012). According to Revesz et al. (2014), current models also omit adverse effects on labor productivity, productivity growth, and the value of the capital stock. Finally, one can as well criticize the deterministic nature of IAMs used for policy analyses, since uncertainty about economic and climate conditions is likely to affect people’s behavior. This latter point is addressed by Golosov et al. (2014) and Cai et al. (2015) who study climate change within a dynamic stochastic general equilibrium (DSGE) framework. However, as in the traditional IAMs, they model adverse effects of temperature by means of a damage function on the level of GDP.

Our DSGE model responds to the issues raised by these critics in a straightforward way. It builds on the production economy framework introduced by Croce (2014) who shows that long-run productivity risks coupled with preferences for early resolution of uncertainty have strong implications for macroeconomic quantities and asset prices.² We augment the model in Croce (2014) by temperature dynamics as suggested by Bansal and Ochoa (2011). Specifically, temperature shocks negatively impact the long-run productivity growth in the economy, and this assumption is strongly supported by empirical evidence.³ This link between temperature and TFP ensures that the impact of temperature is actually on the growth rate of macro-aggregates and not on their level. Furthermore, in our general equilibrium framework the agent chooses labor input optimally. This model feature allows us to investigate the potential effects of temperature changes on employment and labor productivity.

In the spirit of Bansal and Ochoa (2011), we parametrize our production-based asset pricing model using results from the bivariate VAR analysis for temperature and TFP growth and set the model parameters in order to match asset prices, macroeconomic quantities and U.S. temperature statistics. Since positive temperature shocks reduce TFP growth instantaneously, consumption, output, investment, and labor productivity growth decline both in the short-run and over a longer horizon, which leads to lower asset valuations as well. An important feature of our model is, thus, that it endogenously generates the negative effect of rising temperatures on labor productivity found in the data (see Deryugina, Hsiang, 2014, Park, 2016). When we express the economic costs of higher temperatures in terms of additional consumption needed to compensate the agent for temperature risk, we find that welfare costs are quite sensitive to the degree to which temperature changes impact TFP growth. Increasing the negative impact of temperature in absolute terms makes welfare costs rise exponentially, which provides further evidence for the dramatic impact that temperature-related climate change can have on the real economic activity. Specifically, welfare costs amount to 18.4% of composite consumption in our benchmark economy, but if we allow for higher adverse temperature effects, which are still in the range of empirical estimates, those costs amount to 36.8%. Moreover, in the model, a rise in temperature is found to have long-lasting negative effects on output and labor productivity growth. Over a 50-year horizon, a single one-standard deviation shock reduces both cumulative output and labor productivity growth by 1.4pp. Finally, we study the welfare implications of varying adaptation efforts by agents in response to temperature changes. A faster adaptation to positive temperature shocks results in lower welfare costs and vice versa. Policies aiming at increasing the speed of adaptation permanently bring substantial benefits in terms of welfare, while a permanently slower adaptation can have dramatic consequences with exponentially increasing welfare losses.

The remainder of this study is organized as follows. Section 2 provides empirical evidence on the effects of temperature shifts on macroeconomic aggregates and asset prices. Section 3 describes the model. The benchmark calibration and main quantitative results are presented in Section 4. Section 5 concludes.