Distributional Impacts of Weather and Climate in Rural India

Abstract

Climate-related costs and benefits may not be evenly distributed across the population. We study distributional implications of seasonal weather and climate on within-country inequality in rural India. Utilizing a first difference approach, we find that the poor are more sensitive to weather variations than the non-poor. The poor respond more strongly to (seasonal) temperature changes: negatively in the (warm) spring season, more positively in the (cold) rabi season. Less precipitation is harmful to the poor in the monsoon kharif season and beneficial in the winter and spring seasons. We show that adverse weather aggravates inequality by reducing consumption of the poor farming households. Future global warming predicted under RCP8.5 is likely to exacerbate these effects, reducing consumption of poor farming households by one third until the year 2100. We also find inequality in consumption across seasons with higher consumption during the harvest and lower consumption during the sowing seasons.

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Notes

  1. 1.

    This publication Contains modified Copernicus Climate Change Service Information [2019]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains.

  2. 2.

    Observations with missing values relevant for the analysis were dropped from the sample. Observations, where rural/urban categorization changes between the two IHDS rounds were dropped, likewise.

  3. 3.

    The poverty line is a nation-wide set poverty line that is adjusted for rural/urban and state-specific purchasing power.

  4. 4.

    During IHDS-I households were interviewed either in 2004 or 2005 and in IHDS-II either in 2011 or 2012 in one of the 12 months.

  5. 5.

    As mentioned in “Household Data”, IHDS collected data on consumption using information from the last 30 days and from the last 365 days.

  6. 6.

    These regression results are available form the authors upon the request.

  7. 7.

    We also conducted a correlation analysis between the binary variable Poor and the district-specific climates. The absolute values of all correlation coefficients are lower than 0.15, which signalizes that the distribution of rural poor is only partially conditioned by climate.

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Correspondence to Barbora Sedova.

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Appendices

Appendix A: Cross-Correlations

Table 7 Cross-correlation table: changes in weather and historical climate (ERA5 data)
Table 8 Cross-correlation table: controls (IHDS-I data)

Appendix B: Consumption Types

Table 9 Effects of seasonal weather on households’ food consumption, by wealth group
Table 10 Effects of seasonal weather on households’ non-food consumption, by wealth group

Appendix C: Seasonal Effects

Table 11 Effects of seasonal weather on households’ consumption by wealth group. Coefficients of the remaining controls from the main analysis (Table 4)
Table 12 Effects of seasonal weather on households’ food consumption by wealth group. Coefficients of the remaining controls from the main analysis (Table 9)
Table 13 Effects of seasonal weather on households’ non-food consumption by wealth group. Coefficients of the remaining controls from the main analysis (Table 10)

Appendix D: Sensitivity Analyses

Table 14 Effects of seasonal weather on households’ assets, by wealth group
Table 15 Effects of seasonal temperature on households’ consumption, by wealth group
Table 16 Effects of seasonal weather on households’ consumption, by wealth group

Appendix E: Vulnerabilities

Table 17 T-test of differences in historical climate, by wealth group (ERA5 data and IHDS-I data)
Table 18 Effects of seasonal weather on households’ consumption, by wealth group (results from equation 3)

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Sedova, B., Kalkuhl, M. & Mendelsohn, R. Distributional Impacts of Weather and Climate in Rural India. EconDisCliCha 4, 5–44 (2020). https://doi.org/10.1007/s41885-019-00051-1

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Keywords

  • Climate change
  • Weather
  • Inequality
  • Household analysis
  • India
  • Econometrics