I propose a new conceptual framework to disentangle the impacts of weather and climate on economic activity and growth: A stochastic frontier model with climate in the production frontier and weather shocks as a source of inefficiency. I test it on a sample of 160 countries over the period 1950-2014. Temperature and rainfall determine production possibilities in both rich and poor countries; positively in cold countries and negatively in hot ones. Weather anomalies reduce inefficiency in rich countries but increase inefficiency in poor and hot countries; and more so in countries with low weather variability. The climate effect is larger that the weather effect.
From the introduction.
Weather affects economic activity, and so the measurement of the impact of climate on economic activity. Weather can be seen as noise, but that noise may well be correlated with climate, the right-hand-side variable of interest. I therefore propose a new way to simultaneously model the impact of climate and weather, to show that both matter and that previous work is misspecified.
The empirical strategy rests on the following assumptions. Climate affects production possibilities. This is obvious for agriculture: Holstein cows do well in Denmark but jasmine rice does not; the reverse is true in Thailand. Climate also affects energy and transport, and thus all other sectors of the economy. Weather affects the realization of the production potential. Hot weather may slow down workers, frost may damage crops, floods may disrupt
transport and manufacturing. Conceptualized thus, climate affects the production frontier, and weather the distance from that frontier. The econometric specification is therefore a stochastic frontier analysis with weather variables in inefficiency and climate variables in the frontier.4 Climate affects potential output, weather the output gap.
Here is the discussion and conclusion section.
I use stochastic frontier analysis to jointly model the impacts of weather and climate on economic activity in most countries over 65 years. I distinguish production potential, affected by climate, and the realisation of economic output, affected by weather. Weather shocks thus have a transient effect, climate change a permanent impact. Warming affects production potential, positively in cold, negatively in hot countries; and more so in rich, wet countries. Changes in precipitation also affect the frontier. The impacts are heterogeneous without an obvious pattern. Climate change also affects inefficiency, particularly in countries with little climate variability, reducing the output gap in rich countries but increasing it in poor and hot countries. The weather effect is small compared to the climate effect. These results are qualitatively and quantitatively robust to alternative specifications, controls, and estimators.
Dell et al. (2012) find that poor countries are particularly vulnerable to weather shocks, Burke et al. (2015) find that hot countries are. In the Burke (Dell) specification, countries would grow more (less) vulnerable to unusual weather in a hotter and richer future. I find that both are true, and that the impact of heat is about as strong as the impact of poverty. Reduced outdoor work and manual labour, decreased relative importance agriculture in output and work force, and greater diffusion of adaptive capital such as air conditioning would help poorer countries to dampen the negative effects of weather shocks—but only to a degree, as the effort needed to alleviate the heat rises with the temperature.
The impact of weather shocks found here cannot directly be compared to previous studies. Letta and Tol (2018) model economic growth as a function of the change in temperature, Dell et al. (2012), Burke et al. (2015), Pretis et al. (2018) and Kalkuhl and Wenz (2020) as a function of the temperature level. Kahn et al. (2019) come closest to my specification, but they use (asymmetric) weather anomalies rather than standardized weather. Another key difference with those papers is that, here, the impact of a weather shock is transitory. Unusual weather increases inefficiency, but the economy bounces back the next year, registering higher growth. If my specification is right, then previous studies that excluded lagged temperature effects are wrong.19
Previous studies, Barrios et al. (2010) and Generoso et al. (2020) excepted, did not find a significant impact of precipitation. This is a puzzling result, as droughts and floods are more devastating than heat and cold. The same result is found here, in the frontier, unless I interact precipitation with temperature and poverty. Net water—rainfall minus evaporation—matters rather than gross water—rainfall—and more so in countries that depend more on agriculture. Precipitation also has a significant effect on inefficiency, one that varies strongly with its variability. Previous studies did not standardize weather variables.
The impact on the frontier is larger than in previous studies of the impact of climate change (Tol, 2018). Compared to some previous empirical studies (Easterly and Levine, 2003, Rodrik et al., 2004), climate has a significant effect, also when controlling for institutional quality, perhaps because I used more data (as did Nordhaus, 2006, Dell et al., 2009, Henderson et al., 2018, Kalkuhl and Wenz, 2020), perhaps because I modelled heteroskedasticity. Previous studies did not do this and therefore their estimators would be inefficient and, if weather-related heteroskedasticity correlates with climate, may be biased.
Higher income, more capital nor better institutions fully insulate countries from the influence of their climate. This contradicts earlier studies (Acemoglu et al., 2001, 2002, Alsan, 2015).
Besides the methodological advance and the new insights, the model proposed here also provides a way forward for stochastic integrated assessment models, some of which (e.g. Cai and Lontzek, 2019, Hambel et al., 2021) combine a deterministic climate change impact function with stochastic weather realisations.20 The framework in this paper separates the deterministic from the stochastic.
I do not include all impacts of climate change. I omit direct impacts on human welfare, such as biodiversity and health. The model does not capture the range of events which could be triggered by climate change but lie outside the current range of historical experience, such as thawing permafrost(Wirths et al., 2018), a thermohaline circulation shutdown (Anthoff et al., 2016) or unprecedented sea level rise (Nordhaus, 2019). Because of data availability, I use democracy as a proxy for high-quality government. I limit the attention to aggregate economic activity. Adaptation and expectations are implicit in the model, as are production risks and risk preferences. The projections with respect to climate change are static, not dynamic.
The econometrics also need improvement. While cointegration does not seem to be an issue, the stationarity tests used here were not designed for the error structure assumed. I ignored heterogeneity, time-varying parameters, cross-sectional dependence, and spatial spillovers.
The numerical results are therefore far from final. The methodological advancement in this work is more important: the joint, simultaneous estimation of the impact of two different, but often confused, phenomena: weather and climate. I defer to future research the task of refining the theoretical and empirical framework proposed here, and applying it to other macro contexts and, crucially, household and firm data.