The impact of weather on economic growth and its production factors


We investigate the influence of weather on countries’ GDP and their main components of production, namely total factor productivity, capital stock, and employment. Our panel dataset includes annual observations on 103 countries for the period 1961–2010. We find that the main impacts of weather occur through temperature and drive the growth in GDP. Our results show that, for higher levels of temperature, the poor countries are much more strongly impacted than the rich countries. We also find that weather impacts per capita GDP growth through all its factors of production, with the largest impacts on total factor productivity. Again it is the poor countries for which these impacts are the strongest. The findings provide empirical evidence for negative impacts of temperature on economic growth and its factors of production and furthermore point towards climate change as an important driver of international inequality.

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  1. 1.

    If we use a similar cutoff point as Burke et al. (2015) in order to distinguish between rich and poor, then we also do not find a difference between rich and poor countries.

  2. 2.

    Another factor of production is human capital. However, there is no annual data on human capital, but instead, it is only available at 5-year intervals and then gets interpolated to the annual level. Thus, this variable would not be useful for our purposes.

  3. 3.

    We also run robustness of this cutoff point.

  4. 4.

    Also, in this kind of approach, a system of equations faces many problems. Firstly, issues result from the convergence due to the large amount of dummies and trend variables. Secondly, a system of equations that uses the same independent variables would yield the same empirical results as running each regression separately. Thus, in order to make a system of equations meaningful, one would need to use restrictions. Suitable restrictions can only be obtained from a deep model. This is an entirely different, although viable and complementary, approach.

  5. 5.

    In general, total factor productivity represents our “measure of ignorance,” and it comprises as diverse factors such as technological progress or structural changes.

  6. 6.

    The quantitative differences in the results might be caused by different datasets used. Burke et al. (2015) used data from the World Development Indicators and from the Penn World Tables as robustness. Our results do not fully correspond to theirs from the Penn World Tables as they used a previous version of this dataset.

  7. 7.

    Using log differences as Burke et al. (2015) instead of the actual growth rate increases the turning point to 11 °C.

  8. 8.

    We are cautious to interpret the seemingly negative relationship between temperature and the growth of capital stock for very low levels of temperature levels, since this finding results from few observations.

  9. 9.

    The only exception being a marginally significant temperature lag at t − 2, while the t − 1 lag is not significant.

  10. 10.

    Anomalies are calculated as \( {x}_{it}^a=\left({x}_{it}-\overline{x_i}\right)/ sd{(x)}_i \), where \( {x}_{it}^a \) is the anomaly of the climatic variable x in country i at t, xit is the country-time specific observation \( \overline{x_i} \) is the country-specific average of xit over the time horizon, and sd(x)i is the country-specific standard deviation of the variable during the sample.

  11. 11.

    How precisely climate policy then should address the various impacts is more a microeconomic question that a macroeconomic study such as ours cannot answer.


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We thank Marshall Burke, Michael Oppenheimer, and Gary Yohe for comments on a previous version, as well as three anonymous referees and the editor for their valuable suggestions that helped improve the article.

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Correspondence to Ingmar Schumacher.

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Henseler, M., Schumacher, I. The impact of weather on economic growth and its production factors. Climatic Change 154, 417–433 (2019).

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JEL classification

  • Q54