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High Temperatures Effects Agricultural Income and Human Capital

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Mukta Kaushik

NEW DELHI : Studies and researches have established that rise in temperature directly effects working capability of human beings. Besides, it also adversely effects crop yields. Being a tropical country, situation in India is particularly grave because of long summers. As more than 60% of Indian population lives in rural areas whose major occupation is agriculture, they are more at the receiving end.

It has been established that there is a direct relation between heat stress and crop growth (Schlenker and Roberts 2009, Schlenker and Lobell 2010). Schlenker and Roberts found out that yields increase with temperature up to 29° C for corn, 30° C for soybeans, and 32° C for cotton but temperatures above these thresholds are very harmful for crops. The slope of the decline above the optimum is significantly steeper than the incline below it.

In already liquidity constrained agricultural household (Rosenzweig and Stark 1989, Rosenzweig and Wolpin 1993, Paxson 1993, Townsend 1994, Deaton 1997, Dercon and Krishnan 2000, Dercon 2005, Cole et al. 2013) it could have direct consequences for investment on children’s human capital. India currently experiences about 50 days per year with an average temperature above 29 degree Celsius, this number is expected to increase by the end of 21 st century. Thus, understanding the effects of high temperatures is of immediate importance to policymakers so as to make informed decisions.

A recent research by Garg, Jagnani, and Taraz, 2018 present evidence that higher-than-normal temperatures over a longer-run, measured as a number of hot days in the calendar year prior to the year of study, reduce agricultural incomes and have large negative impacts on children’s subsequent human capital outcomes.

They used math and reading test scores of more than 4.5 million children in primary and secondary school to examine the effects of higher temperatures on human capital production in India. They used data of Annual Status of Education Report (ASER). From data, they deduced that temperatures affect both math and reading scores. Ten extra days with average daily temperature above 29 degree Celsius reduced performance by 0.03and 0.02 standard deviations respectively. Here they further divided data into growing and non-growing seasons in India. It is found that 10 extra hot days in previous year growing season effectively wipes out gains made from a median educational intervention (Mc Ewan and Patrick 2015).

Differences between effects of higher temperature during growing and non-growing seasons further reflect on agricultural income mechanism. Effects of temperature on test scores were more pronounced in districts where dominant crops were not heat resistant whereas, no economically meaningful effect of temperature on tests scores can be seen in districts that grow heat-resistant crops. Thus, strongly suggesting that mechanism underlying temperature-test score relationship in India is agricultural income.

They also found out that more hot days in the previous year’s reduces current-period school attendance and children’s Body Mass Index (BMI). Other channels through which high-temperature influences children’s human capital in India is the incidence of disease that thrives in hot and wet conditions.

1. Schlenker, Wolfram and Michael J Roberts (2009), “Nonlinear temperature effects indicate severe damages to US crop yields under climate change”, Proceedings of the National Academy of Sciences, 106(37):15594-15598. http://www.pnas.org/content/106/37/15594

2. Schlenker, Wolfram and David B Lobell (2010), “Robust negative impacts of climate change on African agriculture”, Environmental Research Letters, 5(1). http://iopscience.iop.org/article/10.1088/1748-9326/5/1/014010/pdf

3. Rosenzweig, Mark R and Oded Stark (1989), “Consumption smoothing, migration, and
marriage: Evidence from rural India”, Journal of Political Economy, 97(4):905-926.

4. Rosenzweig, Mark R and Kenneth I Wolpin (1993), “Credit market constraints, consumption smoothing, and the accumulation of durable production assets in low-income countries: Investments in bullocks in India”, Journal of Political Economy, 101(2):223-244. http://www.academicroom.com/article/credit-market-constraints-consumption-smoothing- and-accumulation-durable-production-assets-low-income-countries-investme

5. Paxson, Christina H (1993), “Consumption and income seasonality in Thailand”, Journal ofPolitical Economy, 101(1):39-72. https://www.journals.uchicago.edu/doi/10.1086/261865

6. Townsend, Robert M (1994), “Risk and insurance in village India”, Econometrica, 62(3):539-591. https://www.jstor.org/stable/2951659?seq=1#page_scan_tab_contents

7. Deaton, Angus (1997), ‘The analysis of household surveys: a microeconometric approach to development policy’, World Bank Publications. http://documents.worldbank.org/curated/en/593871468777303124/The-analysis-of- household-surveys-a-microeconometric-approach-to-development-policy

8. Dercon, Stefan and Pramila Krishnan (2000), “In sickness and in health: Risk sharing within households in rural Ethiopia”, Journal of Political Economy, 108(4):688-727. https://www.journals.uchicago.edu/doi/10.1086/316098

9. Dercon, S (2005), Insurance against poverty, Oxford University Press. http://www.oxfordscholarship.com/view/10.1093/0199276838.001.0001/acprof-

10. Cole, Shawn, Xavier Gine, Jeremy Tobacman, Petia Topalova, Robert Townsend and James Vickery (2013), “Barriers to household risk management: Evidence from India”, American Economic Journal: Applied Economics, 5(1):104-135. https://www.aeaweb.org/articles?id=10.1257/app.5.1.104

11. Garg, T, M Jagnani and V Taraz (2018), ‘Temperature and Human Capital in India’, Mimeo. http://www.ideasforindia.in/topics/environment/temperature-and-human-capital-in- india.html

12. McEwan, Patrick J (2015), “Improving learning in primary schools of developing countries: A meta-analysis of randomized experiments”, Review of Educational Research,85(3):353-394. http://journals.sagepub.com/doi/abs/10.3102/0034654314553127

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