Advertisement

Climatic Change

, Volume 156, Issue 1–2, pp 209–229 | Cite as

Temperature and production efficiency growth: empirical evidence

  • Surender KumarEmail author
  • Madhu Khanna
Article

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.

Keywords

Temperature Production efficiency growth Stochastic frontier analysis (SFA) Non-linear effects 

JEL classification

E23 O13 Q54 Q56 

Notes

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.

Supplementary material

10584_2019_2515_MOESM1_ESM.docx (10.4 mb)
ESM 1 (DOCX 10901 kb)

References

  1. Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6(1):21–37CrossRefGoogle Scholar
  2. Athey, S, Bayati, M, Imbens, G, Qu, Z, 2019. Ensemble methods for causal effects in panel data settings. AEA Papers and Proceedings, 109: 65-70CrossRefGoogle Scholar
  3. Barreca AI (2012) Climate change, humidity, and mortality in the United States. J Environ Econ Manag 63(1):19–34CrossRefGoogle Scholar
  4. Bera AK, Sharma SC (1999) Estimating production uncertainty in stochastic frontier production function models. J Prod Anal 12(2):187–210CrossRefGoogle Scholar
  5. Burke M, Hsiang SM, Miguel E (2015) Global non-linear effect of temperature on economic production. Nature 527:235–239CrossRefGoogle Scholar
  6. Carleton TA, Hsiang SM (2016) Social and economic impacts of climate. Science 353:6304CrossRefGoogle Scholar
  7. Chen Y-Y, Schmidt P, Wang H-J (2014) Consistent estimation of the fixed effects stochastic frontier model. J Econ 181:65–76CrossRefGoogle Scholar
  8. Dell, Melissa, Jones, Benjamin F, Olken, Benjamin A, 2012. Temperature shocks and economic growth: evidence from the last half century. Am Econ J Macroecon, 4(3):66–95Google Scholar
  9. Dell M, Jones BF, Olken BA (2014) What do we learn from the weather? The new climate-economy literature. J Econ Lit 52:740–798CrossRefGoogle Scholar
  10. Deschênes, Olivier, Greenstone, Michael, 2007. The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev, 97(1):354–385CrossRefGoogle Scholar
  11. Dietz S, Stern N (2015) Endogenous growth, convexity of damage and climate risk: how Nordhaus’ framework supports deep cuts in carbon emissions. Economic Journal 125(583):574–620CrossRefGoogle Scholar
  12. Forrest K, Tarroja B, Chiang F, AghaKouchak A, Samuelsen S (2018) Assessing climate change impacts on California hydropower generation and ancillary services provision. Clim Chang 151(3–4):395–412CrossRefGoogle Scholar
  13. Graff-Zivin J, Neidell M (2014) Temperature and the allocation of time: implications for climate change. J Labor Econ 32:1–26CrossRefGoogle Scholar
  14. Greene WH (2005) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J Econ 126:269–303CrossRefGoogle Scholar
  15. Harrris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations–the CRU TS3.10 dataset. Int J Climatol 34:623–642CrossRefGoogle Scholar
  16. Heal, Geoffrey, Park, Jisung, 2013. Feeling the heat: temperature, physiology & the wealth of nations, NBER Working Paper 19725, National Bureau of Economic Research, IncGoogle Scholar
  17. Hsiang SM, Burke M, Miguel E (2013) Quantifying the influence of climate on human conflict. Science 341:1235367–1231-14CrossRefGoogle Scholar
  18. Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74CrossRefGoogle Scholar
  19. Letta M, Tol RSJ (2018) Weather, climate and total factor productivity. Environ Resour Econ.  https://doi.org/10.1007/s10640-018-0262-8 CrossRefGoogle Scholar
  20. Maddala G, Wu S (1999) A comparative study of unit root tests with panel data and a simple new test. Oxf Bull Econ Stat 61:631–652CrossRefGoogle Scholar
  21. Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 18(2):435–444CrossRefGoogle Scholar
  22. Mendelsohn R, Nordhaus WD, Shaw D (1994) The impact of global warming on agriculture: a Ricardian analysis. Am Econ Rev 84:753–771Google Scholar
  23. Moore FC, Diaz DB (2015) Temperature impacts on economic growth warrant stringent mitigation policy. Nat Clim Chang 5:127–131CrossRefGoogle Scholar
  24. Moyer E, Woolley MM, Matteson NJ, Glotter MM, Weisbach D (2014) Drivers of uncertainty in the social cost of carbon. J Leg Stud 43:401–425CrossRefGoogle Scholar
  25. O’Donnell C (2016) Using information about technologies, markets and firm behaviour to decompose a proper productivity index. J Econ 190:328–340CrossRefGoogle Scholar
  26. Ortiz-Bobea A, Knippenberg E, Chambers RG (2018) Growing climatic sensitivity of U.S. agricultural linked to technological change and regional specialization. Sci Adv 4(12):1–9CrossRefGoogle Scholar
  27. Pretis F, Schwarz M, Tang K, Haustein K, Allen MR., 2018. Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Phil Trans R Soc A 376(2119): 1–19CrossRefGoogle Scholar
  28. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to us crop yields under climate change. Proceedings of National Academic Sciences 106(37):15594–15598CrossRefGoogle Scholar
  29. Shee A, Stefanou S (2015) Endogeneity corrected stochastic production frontier and technical efficiency. Am J Agric Econ 97:939–952CrossRefGoogle Scholar
  30. Syverson C (2011) What determines productivity? J Econ Lit 49(2):326–365CrossRefGoogle Scholar
  31. Tisgaris P, Wood J (2016) A simple climate-Solow model for introducing the economics of climate change to undergraduate students. International Review of Economics Education 23:65–81CrossRefGoogle Scholar
  32. Tol R (2017) Population and trends in the global mean temperature. Atmósfera 30:121–135CrossRefGoogle Scholar
  33. Urban DW, Roberts MJ, Schlenker W, Lobell DB (2012) Projected temperature changes indicate significant increase in inter-annual variability of US maize yields. Clim Chang 112(2):525–533CrossRefGoogle Scholar
  34. Van Biesebroeck J (2008) The sensitivity of productivity estimates: revisiting three important debates. J Bus Econ Stat 26(3):311–328CrossRefGoogle Scholar
  35. Wang H-J (2002) Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. J Prod Anal 18(3):241–253CrossRefGoogle Scholar
  36. Wang H-J (2003) A stochastic frontier analysis of financing constraints on investment: the case of financial liberalization in Taiwan. J Bus Econ Stat 21:406–419CrossRefGoogle Scholar
  37. Wang H-J, Schmidt P (2002) One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. J Prod Anal 18:129–144CrossRefGoogle Scholar
  38. Weitzman ML (2011) Fat-tailed uncertainty in the economics of catastrophic climate change. Review of Environmental Economics and Policy 5(2):275–292CrossRefGoogle Scholar
  39. Wenz A, Leverman A (2016) Enhanced economic connectivity to foster heat stress– related losses. Sci Adv 2:e1501026CrossRefGoogle Scholar
  40. Willner N, Otto C, Leverman A (2018) Global economic response to river floods. Nat Clim Chang 8:594–598CrossRefGoogle Scholar
  41. Yunfeng Y, Laike Y (2010) China’s foreign trade and climate change: a case study of CO2 emissions. Energy Policy 38(1):350–356CrossRefGoogle Scholar
  42. Zhang P, Deschenes O, Meng K, Zhang J (2018) Temperature effects on productivity and factor reallocation: evidence from a half million Chinese manufacturing plants. J Environ Econ Manag 88:1–17CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Economics, Delhi School of EconomicsUniversity of DelhiDelhiIndia
  2. 2.Department of Agricultural and Consumer EconomicsUniversity of IllinoisUrbanaUSA

Personalised recommendations