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Temperature and production efficiency growth: empirical evidence

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Abstract

This paper examines the marginal effects of temperature on the growth rate and variability in growth rate of total factor productivity (TFP) of a country, as measured by its production efficiency relative to a stochastic frontier. Using panel data for 168 countries for the period 1950–2014 to estimate a one-step stochastic frontier function, we find that temperature has a concave relationship with the growth rate of production efficiency and with the variability in this growth rate. We observe that hotter than the average temperature is not only detrimental to production efficiency growth but also makes the growth less stable than otherwise, and these effects are larger in very hot countries with average annual temperature greater than 25 °C. More importantly, we observe that the detrimental marginal effects of higher temperature depend on the level of economic development of a country; they are larger for poor countries relative to rich countries. Our findings have implications for the specification of climate damage functions in integrated assessment models and estimates of country-specific social cost of carbon.

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Notes

  1. For comprehensive review on the impact of climate change on various economic activities, see Dell et al. (2014) and Carleton and Hsiang (2016)

  2. By a non-linear effect, we mean that temperature can have both positive and negative effects on efficiency growth and its variability, within a sample depending on the values of temperature.

  3. Burke et al. (2015) find that productivity peaks at 13 °C and declines at higher temperatures. Therefore, the fall-off in productivity concerning hotter and colder limits implies an optimal temperature zone for economic activities.

  4. In the microeconomic literature, a single peaked relationship between productivity and temperature (e.g., Graff-Zivin and Neidell 2014; Schlenker and Roberts 2009) has been observed.

  5. Our application captures climate-driven changes in the “fatness” of efficiency growth distribution tails; the importance of which was stressed in Weitzman (2011).

  6. A number of seminal papers starting with Aigner et al. (1977) and Meeusen and van den Broeck (1977) have contributed in this area.

  7. Formulas of estimating the marginal effects are given in the Appendix.

  8. http://sdwebx.worldbank.org/climateportal/index.cfm?page=country_historical_climate

  9. For details on data for climatic factors, please see Burke et al. (2015).

  10. For details on the climatic variable dataset provided by the UEA, please refer to Harrris et al. (2014)

  11. Relevance of population-weighted temperature data over area-weighted temperature data for measuring economic impacts is sector-specific. For example, if the objective is to measure the impacts on labor productivity, then, population-weighted temperature data may be well suited, but if we are measuring the impacts for agriculture, then, this might not be the case. We are thankful to one of the reviewers for pointing out this concern.

  12. Choice of countries has been restricted by availability of data. For the countries included in the study, see Appendix Table A.

  13. Burke et al. (2015) have considered a country to be poor if its purchasing power parity (PPP) adjusted per capita income was below the global median in 1980. An alternative way is to include yearly per capita income as many countries have progressed and have better capacity to adapt to climatic changes since then. But allowing the classification to vary over time could make it an endogenous variable since the unobservable variables that affect current per capita income could also affect TFP.

  14. We assume that the one-sided error term has a truncated normal distribution.

  15. Detailed country level panel results of production efficiency are available from the authors.

  16. 5-bin classification has been done following Heal and Park (2013), and the classification of countries based on these bins is given in Appendix Table A.

  17. Since ∂E(∆μ)/∂T = − ∂E(∆ln y)/∂T, the magnitude of marginal effect of 0.1 percentage points translates into a decrease in output growth by 0.1 percentage points.

  18. Note that we observe a small cluster of points about the optimal zone above the fitted regression line (Fig. 2, panel A1 and panel B1).These points belong to Bhutan, which is a poor country. It reflects that it is not only the location of a country but also level of development that determines climatic effects on production efficiency growth and its variability.

  19. Average annual marginal effects of temperature on mean and variance of production efficiency growth at the country level are provided in Appendix Table A6, and Fig. A1 maps the effects based on temperature (WB).

  20. Projected marginal effects of temperature on the mean and variance of production efficiency growth at country level for short run and long run are provided in Appendix Table A7, and Fig. A3 maps the projected short-run values.

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Acknowledgements

We would like to thank Saumya Verma for helping us in drawing the figures. Madhu Khanna gratefully acknowledges support from NIFA, USDA for this research.

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Authors and Affiliations

  1. Department of Economics, Delhi School of Economics, University of Delhi, Delhi, 110007, India

    Surender Kumar

  2. Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, 1301, W. Gregory Drive, Urbana, IL, 61801, USA

    Madhu Khanna

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  1. Surender Kumar
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Correspondence to Surender Kumar.

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Kumar, S., Khanna, M. Temperature and production efficiency growth: empirical evidence. Climatic Change 156, 209–229 (2019). https://doi.org/10.1007/s10584-019-02515-5

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