Temperature and economic activity: evidence from India

ABSTRACT

This paper investigates the impact of temperature on economic activity in India, using state-level data from 1980–2015. We estimate that a $1^{\circ }$C increase in contemporaneous temperature (relative to our sample mean) reduces the economic growth rate that year by 2.5 percentage points. The adverse impact of higher temperatures is more severe in poorer states and in the primary sector. Our analysis of lagged temperatures suggests that our effects are driven by the contemporaneous effect of temperature on output; we do not find evidence of a permanent impact of contemporaneous temperatures on future growth rates.

KEYWORDS:

• Climate change
• economic growth
• India
• panel data
• temperature

JEL CODES:

• O44
• Q54

Acknowledgment

We thank Julia Bouzaher and Ahana Raina for excellent research assistance. We thank Elizabeth Savoca, Susan Stratton Sayre, and Andrew Zimbalist for valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The GSDP data are available at https://niti.gov.in/state-statistics. These data are also used by other researchers, including Kumar and Managi (2012) and Bhattacharya (2019).

2 The ERA-Interim Archive is available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim.

3 We prefer conceptually to base our heterogeneity analysis on values from the beginning of the sample period, as they are more exogenous relative to shocks experienced during the sample period.

4 Appendix Table A2 presents a two-way frequency table for the ‘poor’ and ‘high agricultural’ variables. As can be seen from the table, the categorisation varies substantively across the two measures.

5 We also fail to find evidence of a change over time of the relationship, if we restrict the analysis to only the richer states, or only the high agriculture states. Results available from the authors upon request.

6 In Appendix Table A3, values are bold if GSDP per capita in a given year and state was below that year's median GSDP per capita, and similarly in Appendix Table A4 for agricultural shares.

7 The R-squared of the regression also falls dramatically, from 0.401 to 0.265.

8 The hilly states that we exclude are Arunachal Pradesh, Himachal Pradesh, Jammu and Kashmir, Sikkim, and Uttarakhand. These states are small and together represent less than 3% of India's GDP.