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Weather, Climate and Total Factor Productivity

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Recently it has been hypothesized that climate change will affect total factor productivity growth. Given the importance of TFP for long-run economic growth, if true this would entail a substantial upward revision of current impact estimates. Using macro TFP data from a recently developed dataset in the Penn World Table, we test this hypothesis by directly examining the nature of the relationship between annual temperature shocks and TFP growth rates in the period 1960–2006. The results show a negative relationship only exists in poor countries, where a 1 °C annual increase in temperature decreases TFP growth rates by about 1.1–1.8 percentage points, whereas the impact is indistinguishable from zero in rich countries. Extrapolating from weather to climate, the possibility of dynamic effects of climate change in poor countries increases concerns over the distributional issues of future impacts and, from a policy perspective, restates the case for complementarity between climate policy and poverty reduction.

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  1. Further changes to the DICE framework they undertake are allowing for convexity of the damage function (Weitzman 2010) and for high values of the climate sensitivity parameter (Weitzman 2009, 2011).

  2. DICE (Nordhaus 2008), FUND (Anthoff et al. 2009) and PAGE (Hope 2006).

  3. Also, they notice how impacts on growth would contribute to settle the debate on the discount rate sparked after the publication of the Stern Review (Stern et al. 2006). See also Nordhaus (2007), Stern (2013) and Tol et al. (2006).

  4. As they explain: “panel data exploit the exogeneity of cross-time weather variation, allowing for causative identification”.

  5. The damage function is usually calibrated ad hoc on the basis of impact studies of climate change. The quadratic form has been criticized because it does not allow for a steep increase of damages at higher temperatures (Stern 2013; Weitzman 2010).

  6. Note that this series has only recently become available. Previous studies of the impact of climate change on economic growth, reviewed above, therefore did not have access to these data.

  7. For the panel unit root tests for annual TFP growth, temperature change and precipitation change, see Web Appendix (2), Tables A.1–A.6.

  8. The choice of the countries has been made on the basis of data availability. For the list of countries, see Web Appendix (4).

  9. For the appropriateness of the FE approach compared to a random effects (RE) specification, see Web Appendix (2), Table A.7.

  10. For the list of regions, see Web Appendix (5).

  11. See, e.g., Colmer (2017) on the link between weather changes and labour reallocation across sectors.

  12. The cut-off point for GDP per capita is approximately 2684.33 international Geary–Khamis dollars (1990 benchmark year).

  13. Incidentally, it is also worth remarking how precipitation change has a negative and significant effect, but this control variable has proved to be very sensitive to specifications throughout the entire empirical analysis and its results should therefore be interpreted with caution and are no further discussed here.

  14. In this case the cut-off point for GDP per capita is approximately 4417.1 international Geary–Khamis dollars (1990 benchmark year).

  15. See Web Appendix (3).

  16. Supplementary Information, page 20.

  17. In natural logarithm: 10.447 (SE = 1.234).

  18. In natural logarithm: 9.609 (SE = 0.351).

  19. In natural logarithm: 10.150 (SE = 0.283).

  20. Cf. Table A.24 in Web Appendix (3) for the alternative sample.

  21. Given that the standard deviation for annual temperature change is 0.56 °C (cf. Table 1), interannual variability is quite large relative to the projected trend, so while this extrapolation should be interpreted with the usual caution, its implications should not be a priori dismissed.

  22. The data (for both temperature and precipitation change) were downloaded from:

  23. In the DICE model, temperature in 2005 is already 0.83 °C above pre-industrial.


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Correspondence to Marco Letta.

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We thank Melissa Dell, Ivan Faiella, Robert Inklaar, Pierluigi Montalbano, L. Alan Winters, seminar participants at University of Sussex, Sapienza University of Rome, the 2016 Annual Conference of the Royal Economic Society and the Bank of Italy. We are grateful to six anonymous referees for excellent comments on earlier drafts. The Erasmus Exchange Programme between Sapienza U and U Sussex enabled this collaboration. All errors and opinions are ours.

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Letta, M., Tol, R.S.J. Weather, Climate and Total Factor Productivity. Environ Resource Econ 73, 283–305 (2019).

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