Abstract
The present study contributes to the literature on labour reallocation and adaptation in response to weather anomalies. Existing literature on labour mobility and weather shocks primarily focus on migration to the neglect of worker commuting as a potential adaptation strategy. Utilizing individual-level panel data from the Village Dynamics in South Asia (VDSA) dataset for the year 2010–2014, the present study explores the impact of weather anomalies on migration and commuting as well as participation and earnings in the non-agricultural sector. The fixed effects regression results show that negative temperature shocks induce a flow of labour outside the village through labour out-migration and longer-distance commutes. Temperature stress also negatively impacts non-agricultural earnings. The effects of temperature shocks are heterogeneous across the baseline climate of the villages suggesting evidence of adaptation to weather shocks. The study emphasizes the crucial role of labour mobility and adaptation in coping with weather shocks. The paper concludes with some policy suggestions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material
Data would be available on request.
Code availability
Codes used in the generation of the results would be available on request.
Notes
I distinguish between low-tier and high-tier non-agricultural activity with the later being associated with high returns but requiring substantial start-up investments or upfront costs while the former requires little upfront investments but generates low returns. In a similar way, we can distinguish international migration from internal migration with the former akin to a high-return-high-investment sector while the later corresponds to the low-tier sector.
Following Auffhammer et al., (2013), I check the robustness of my results by considering two alternative re-analysis datasets: ERA5 and MERRA2. ERA5 is a re-analysis dataset, providing information on hourly temperature on a \({0.25}^{o}\times {0.25}^{o}\) latitude–longitude grid over the period 1979–2020 (Hersbach et al. 2020). Similarly, MERRA2 is a re-analysis dataset, providing information on hourly temperature on a \({0.5}^{o}\times {0.625}^{o}\) latitude–longitude grid over the period 1980–2020 (Gelaro et al. 2017).
I utilize village level maps from the India Village-Level Geospatial Socio-Economic Data Set, v1 (1991, 2001) to derive precise co-ordinates of each village in my dataset (Meiyappan et al. 2018). I further cross-check the co-ordinates using information on the location of the villages available in the VDSA website (ICRISAT 2020).
The study applies of a relative temperature threshold based on the long-term temperature distribution of the place rather than an absolute threshold temperature value, as has been used in the literature. This is justified based on the logic that different places adjust their agricultural operations based on their baseline climate and temperatures above a relative threshold would be better able to capture the shock of unusually high temperatures. Furthermore, I utilize temperature thresholds specific to a particular month over the growing season, so that I am able to capture the adverse impact of extreme temperature on plant growth for different stages of its life cycle. I show that my results are robust using absolute temperature thresholds.
Additional robustness checks test the sensitivity of the results to the inclusion of additional covariates.
Restricting my analysis to the sample of commuters, I find that an additional HDD is associated with a significant increase of 0.1 per cent in the distance travelled to the workplace (Table A2: Column 4).
Although a 100 unit rise in HDD seem unrealistically large, I find that more than half of the 30 villages in my study have an inter-temporal range of HDD of at least 100 units. As such, such large fluctuations in HDDs can be considered to be quite common in the context of my study.
The heat wave variable captures temperature anomaly is a different way as it only considers extreme temperatures experienced during a heat spell of at least 5 consecutive days during which the temperature consistently exceeds a threshold. As such, the effect of standalone days with extremely high temperatures is not taken into consideration. This is important as extreme temperatures may be especially harmful if experienced during a heat spell than otherwise.
The outcome variables are defined in Table A1.
Bibliography
Adger WN, Lorenzoni I, O’Brien KL (2009) Adaptation now. In Adapting to Climate Change: Thresholds, Values, Governance (pp. 1–22). Cambridge, MA, USA: Cambridge University Press. https://doi.org/10.1017/cbo9780511596667.002
Angrist JD, Pischke J-S (2008) Mostly harmless econometrics: an empiricist’s companion. New Jersey, USA: Princeton University Press. https://doi.org/10.1017/CBO9781107415324.004
Auffhammer M, Schlenker W (2014) Empirical studies on agricultural impacts and adaptation. Energy Econ 46:555–561. https://doi.org/10.1016/j.eneco.2014.09.010
Auffhammer M, Hsiang SM, Schlenker W, Sobel A (2013) Using weather data and climate model output in economic analyses of climate change. Rev Environ Econ Policy 7(2):181–198. https://doi.org/10.1093/reep/ret016
Baez J, Caruso G, Mueller V, Niu C (2017) Droughts augment youth migration in Northern Latin America and the Caribbean. Clim Change 140(3–4):423–435. https://doi.org/10.1007/s10584-016-1863-2
Bardsley DK, Hugo GJ (2010) Migration and climate change: examining thresholds of change to guide effective adaptation decision-making. Popul Environ 32(2):238–262. https://doi.org/10.1007/s11111-010-0126-9
Barreca A, Clay K, Deschenes O, Greenstone M, Shapiro JS (2016) Adapting to climate change: the remarkable decline in the US temperature-mortality relationship over the Twentieth Century. J Polit Econ 124(1):105–159. https://doi.org/10.1086/684582
Bohra-Mishra P, Oppenheimer M, Cai R, Feng S, Licker R (2017) Climate variability and migration in the Philippines. Popul Environ 38(3):286–308. https://doi.org/10.1007/s11111-016-0263-x
Borderon M, Sakdapolrak P, Muttarak R, Kebede E, Pagogna R, Sporer E (2019) Migration influenced by environmental change in Africa: a systematic review of empirical evidence. Demogr Res 41(August):491–544. https://doi.org/10.4054/DemRes.2019.41.18
Butler EE, Huybers P (2013) Adaptation of US maize to temperature variations. Nat Clim Chang 3(1):68–72. https://doi.org/10.1038/nclimate1585
Cameron AC, Miller DL (2015) A practitioner’s guide to cluster-robust inference. J Hum Resour 50(2):317–372. https://doi.org/10.3368/jhr.50.2.317
Carleton TA, Hsiang SM (2016) Social and economic impacts of climate. Science, 353(6304). https://doi.org/10.1126/science.aad9837
Castells-Quintana D, del Lopez-Uribe MP, McDermott TKJ (2018) Adaptation to climate change: a review through a development economics lens. World Development, 104, 183–196. https://doi.org/10.1016/j.worlddev.2017.11.016
Cattaneo C, Peri G (2016) The migration response to increasing temperatures. J Dev Econ 122:127–146. https://doi.org/10.1016/j.jdeveco.2016.05.004
Chandrasekhar S (2011) Workers commuting between the rural and urban: estimates from NSSO data. Econ Pol Wkly 46(46):22–25
Chandrasekhar S, Mukhopadhyay A (2017) The changing nature of rurality: reframing the discourse on migration and commuting. In Rural Labour Mobility in Times of Structural Transformation: Dynamics and Perspectives from Asian Economies (pp. 183–207). Singapore: Palgrave Macmillan. https://doi.org/10.1007/978-981-10-5628-4
Chatterjee K, Chng S, Clark B, Davis A, De Vos J, Ettema D, … Reardon L (2020) Commuting and wellbeing: a critical overview of the literature with implications for policy and future research. Transp Rev, 40(1), 5–34. https://doi.org/10.1080/01441647.2019.1649317
Cunguara B, Langyintuo A, Darnhofer I (2011) The role of nonfarm income in coping with the effects of drought in southern Mozambique. Agric Econ 42(6):701–713. https://doi.org/10.1111/j.1574-0862.2011.00542.x
Dallmann I, Millock K (2017) Climate variability and inter-state migration in India. CESifo Econ Stud 63(4):560–594. https://doi.org/10.1093/cesifo/ifx014
Davis B, Winters P, Reardon T, Stamoulis K (2009) Rural nonfarm employment and farming: household-level linkages. Agric Econ 40(2):119–123
Davis B, Winters P, Carletto G, Covarrubias K, Quiñones EJ, Zezza A, … DiGiuseppe S (2010) A cross-country comparison of rural income generating activities. World Dev, 38(1), 48–63.
Dell M, Jones BF, Olken BA (2014) What do we learn from the weather? The new climate-economy literature. J Econ Lit 52(3):740–798. https://doi.org/10.1257/jel.52.3.740
Desbureaux S, Rodella A (2019) Drought in the city: the economic impact of water scarcity in Latin American metropolitan areas. World Dev 114:13–27. https://doi.org/10.1016/j.worlddev.2018.09.026
Deschenes O (2014) Temperature, human health, and adaptation: a review of the empirical literature. Energy Econ 46:606–619. https://doi.org/10.1016/j.eneco.2013.10.013
Emerick K (2018) Agricultural productivity and the sectoral reallocation of labor in rural India. J Dev Econ 135(August):488–503. https://doi.org/10.1016/j.jdeveco.2018.08.013
Fishman R (2018) Groundwater depletion limits the scope for adaptation to increased rainfall variability in India. Clim Change 147(1–2):195–209. https://doi.org/10.1007/s10584-018-2146-x
French MT, Popovici I, Timming AR (2020) Analysing the effect of commuting time on earnings among young adults. Appl Econ 52(48):5282–5297. https://doi.org/10.1080/00036846.2020.1761537
Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, … Zhao B (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
Haggblade S, Hazell P, Reardon T (2010) The rural non-farm economy: prospects for growth and poverty reduction. World Dev 38(10):1429–1441. https://doi.org/10.1016/j.worlddev.2009.06.008
Haggblade S, Hazell P, Reardon T (2007) Transforming the rural nonfarm economy. Johns Hopkins University Press. Baltimore: The Johns Hopkins University Press
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, … Thépaut JN (2020) The ERA5 global reanalysis. QJR Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/qj.3803
Hsiang SM (2010) Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. Proc Natl Acad Sci 107(35):15367–15372. https://doi.org/10.1073/pnas.1009510107
Hsiang S (2016) Climate econometrics. Annu Rev Resour Econ 8(1):43–75. https://doi.org/10.1146/annurev-resource-100815-095343
Hsiang SM, Narita D (2012) Adaptation to cyclone risk: evidence from the global cross-section. Clim Change Econ, 3(2). https://doi.org/10.1142/S201000781250011X
Hunter LM, Luna JK, Norton RM (2015) Environmental dimensions of migration. Ann Rev Sociol 41:377–397. https://doi.org/10.1146/annurev-soc-073014-112223
ICRISAT (2020) Village Dynamics in South Asia. Retrieved from http://vdsa.icrisat.ac.in/vdsa-vprofile.aspx. Accessed 20 Dec 2020
Jennings JA, Gray CL (2015) Climate variability and human migration in the Netherlands, 1865–1937. Popul Environ 36(3):255–278. https://doi.org/10.1007/s11111-014-0218-z
Jessoe K, Manning DT, Taylor JE (2018) Climate change and labour allocation in rural Mexico: evidence from annual fluctuations in weather. Econ J 128(608):230–261. https://doi.org/10.1111/ecoj.12448
Kahn ME (2005) The death toll from natural disasters: the role of income, geography and institutions. Rev Econ Stat 87(2):271–284
Kijima Y, Matsumoto T, Yamano T (2006) Nonfarm employment, agricultural shocks, and poverty dynamics: evidence from rural Uganda. Agric Econ 35(SUPPL. 3):459–467. https://doi.org/10.1111/j.1574-0862.2006.00191.x
Klaiber HA (2014) Migration and household adaptation to climate: a review of empirical research. Energy Econ 46:539–547. https://doi.org/10.1016/j.eneco.2014.04.001
Kochar A (1999) Smoothing consumption by smoothing income: hours-of-work responses to idiosyncratic agricultural shocks in Rural India. Rev Econ Stat 81(1):50–61. https://doi.org/10.1162/003465399767923818
Kousky C (2014) Informing climate adaptation: A review of the economic costs of natural disasters. Energy Econ 46:576–592. https://doi.org/10.1016/j.eneco.2013.09.029
Kunn-Nelen A (2016) Does commuting affect health? Health Econ 25(8):984–1004. https://doi.org/10.1002/hec
Lanjouw J, Lanjouw P (2001) The rural non-farm sector: issues and evidence from developing countries. Agric Econ 26:1–23
Lobell DB, Bänziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Clim Chang 1(1):42–45. https://doi.org/10.1038/nclimate1043
Lobell DB, Sibley A, Ivan Ortiz-Monasterio J (2012) Extreme heat effects on wheat senescence in India. Nat Clim Chang 2(3):186–189. https://doi.org/10.1038/nclimate1356
Ma J, Maystadt JF (2017) The impact of weather variations on maize yields and household income: income diversification as adaptation in rural China. Glob Environ Chang 42:93–106. https://doi.org/10.1016/j.gloenvcha.2016.12.006
Mastrorillo M, Licker R, Bohra-Mishra P, Fagiolo G, D. Estes, L., & Oppenheimer, M. (2016). The influence of climate variability on internal migration flows in South Africa. Glob Environ Chang, 39, 155–169. https://doi.org/10.1016/j.gloenvcha.2016.04.014
Mathenge MK, Tschirley DL (2015) Off-farm labor market decisions and agricultural shocks among rural households in Kenya. Agric Econ (U. K.) 46(5):603–616. https://doi.org/10.1111/agec.12157
McLeman R (2018) Thresholds in climate migration. Popul Environ 39(4):319–338. https://doi.org/10.1007/s11111-017-0290-2
Meiyappan P, Roy PS, Soliman A, Li T, Mondal P, Wang S, Jain AK (2018) India village-level geospatial socio-economic data set: 1991, 2001. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
Millock K (2015) Migration and environment. Annu Rev Resour Econ 7(1):35–60. https://doi.org/10.1146/annurev-resource-100814-125031
Mueller VA, Osgood DE (2009) Long-term impacts of droughts on labour markets in developing countries: evidence from Brazil. J Dev Stud 45(10):1651–1662. https://doi.org/10.1080/00220380902935865
Mueller V, Gray C, Kosec K (2014) Heat stress increases long-term human migration in rural Pakistan. Nat Clim Chang 4(3):182–185. https://doi.org/10.1038/nclimate2103
Neog BJ, Sahoo BK (2020) Rural non-farm diversification, agricultural feminisation and women’s autonomy in the farm: evidence from India. Aust J Agric Resour Econ 64(3):940–959. https://doi.org/10.1111/1467-8489.12374
NOAA/OAR/ESRL PSL (2020) CPC Global Temperature data. Boulder, Colorado, USA
Noy I (2009) The macroeconomic consequences of disasters. J Dev Econ 88(2):221–231. https://doi.org/10.1016/j.jdeveco.2008.02.005
Robalino J, Jimenez J, Chacón A (2015) The effect of hydro-meteorological emergencies on internal migration. World Dev 67:438–448. https://doi.org/10.1016/j.worlddev.2014.10.031
Rose E (2001) Ex ante and ex post labor supply response to risk in a low-income area. J Dev Econ 64(2):371–388. https://doi.org/10.1016/S0304-3878(00)00142-5
Sedova B, Kalkuhl M (2020) Who are the climate migrants and where do they go Evidence? from rural India. World Development 129:104848. https://doi.org/10.1016/j.worlddev.2019.104848
Sharma A, Chandrasekhar S (2016) Impact of commuting by workers on household dietary diversity in rural India. Food Policy 59:34–43. https://doi.org/10.1016/j.foodpol.2015.11.005
Skoufias E, Bandyopadhyay S, Olivieri S (2016) Occupational diversification as an adaptation to rainfall variability in rural India. Agric Econ (U. K.) 48(1):77–89. https://doi.org/10.1111/agec.12296
Taraz V (2018) Can farmers adapt to higher temperatures? Evidence from India. World Dev 112:205–219. https://doi.org/10.1016/j.worlddev.2018.08.006
Thiede B, Gray C, Mueller V (2016) Climate variability and inter-provincial migration in South America, 1970–2011. Glob Environ Chang 41:228–240. https://doi.org/10.1016/j.gloenvcha.2016.10.005
Toya H, Skidmore M (2007) Economic development and the impacts of natural disasters. Econ Lett 94(1):20–25. https://doi.org/10.1016/j.econlet.2006.06.020
Willmott CJ, Matsuura K (2001) Terrestrial air temperature and precipitation: monthly and annual time series (1950 - 1999). Colorado, USA
Winters P, Davis B, Carletto G, Covarrubias K, Quiñones EJ, Zezza A, … Stamoulis K (2009) Assets, activities and rural income generation: evidence from a multicountry analysis. World Dev, 37(9), 1435–1452. https://doi.org/10.1016/j.worlddev.2009.01.010
Zaveri E, Lobell DB (2019) The role of irrigation in changing wheat yields and heat sensitivity in India. Nat Commun 10(1):1–7. https://doi.org/10.1038/s41467-019-12183-9
Acknowledgements
I would like to thank the editor, Dr. Detlef van Vuuren, and two anonymous referees for their valuable comments and suggestions. I also thank Prof. Arup Mitra, Prof. E. Somanathan and Prof. S. Chandrasekhar, as well as participants at the 16th Annual Conference on Economic Growth and Development, ISI Delhi, New Delhi, India (2021) and 8th Pan IIM World Management Conference, IIM Kozhikode, Kozhikode, India (2021) for fruitful discussions. The usual disclaimers apply.
Author information
Authors and Affiliations
Contributions
Dr. Bhaskar Jyoti Neog, the sole author of the manuscript, declares that he is the sole contributor in the manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The author declares no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Neog, B.J. Temperature shocks and rural labour markets: evidence from India. Climatic Change 171, 16 (2022). https://doi.org/10.1007/s10584-022-03334-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10584-022-03334-x