Extreme climate events are taking place more often and for longer, jeopardising the economic stability of agricultural households. This article shows that coping strategies adopted by households in response to climate shocks – such as occupational diversification and sale of livestock – yield lower returns relative to normal circumstances. A larger proportion of the educated agricultural workforce takes up casual labour, while livestock is sold at distress prices.
In the recently published the Indian Meteorological Department presented the enormity and urgency with which India needs to tackle climate change. According to the Atlas, 87% of the country’s districts and 93% of the population are moderate to very highly vulnerable to drought (the figures are 46% and 44%, respectively, in the case of extreme rainfall). Agriculture is one of the sectors in which effects of climate change are direct and prominent. Mild to extreme droughts, floods and cyclones have a significant effect on agriculture. According to information tabled before the Lok Sabha (the lower house of parliament), during 2019-2023, 23.2 million hectares of cropped area has been affected by hydrometeorological calamities (Government of India, 2022, 2023). Pandey (2022) reports that 35 million hectares of cropped area was damaged (that is, crop loss of over 33%) due to drought between 2016 and 2022.
Agriculture continues to employ a large section of India’s rural workforce. According to the latest Periodic Labour Force Survey, 59% of the labour force is engaged in agriculture, and farming is the main source of income for 47% of households (Ministry of Statistics and Progam Implementation (MoSPI), 2023). Therefore, climate variability and consequent agricultural losses will affect the livelihoods and welfare of a vast majority of rural India.
Crop failure and loss of earnings caused by climate variability force households to adopt coping strategies such as taking out a loan, reducing consumption, migrating in search of work, occupational diversification or sale of productive assets. Weather-related risks combined with the absence of credit and insurance markets force the farming households to adapt through precautionary and reactive actions – in order to protect their welfare but it comes at the cost of lower returns (Skoufias et al. 2017). In new research, we explore two coping strategies – occupational diversification and sale of productive assets.
We use data from the 77th round (Situation Assessment of Agricultural Households and Land and Holdings of Households in Rural India) of the National Sample Survey (NSS). The same households are surveyed in two time periods – July to December 2018 (visit 1) and January to June 2019 (visit 2). We examine the choices made by the households with respect to employment and sale of productive assets in visit 2 following the success or failure of the crop in the Kharif season1 that is, visit 1.
Extent of crop loss
In the Kharif season of 2018, 43% of the households reported crop loss, of which 37% was due to drought, flood and other natural factors (MoSPI, 2021). Figure 1 shows the extent of crop loss (that is, percentage of households that reported crop loss) for 14 major crops. Of the households reporting crop loss, 48% are small and marginal landowners, 29% are semi-medium and medium, and the rest have large land holdings.
Figure 1. Extent of crop loss of major crops during Kharif season, 2018
Occupational diversification
Occupational diversification is one of the primary coping strategies agriculture households adopt in response to shocks (Ito and Kurosaki 2009). In order to deal with income dips or during agricultural lean seasons, rural households engage in casual labour.
In general, agricultural households devote lesser workforce during Rabi season2 (January to June).According to the estimates of 77th round of the NSS, 12% of the workforce diversified out of agriculture between Kharif and Rabi seasons. However, the degree of diversification depends on the success of the crop in the previous season. We observe that as more households in a state declare crop loss during Kharif season, the percentage of the workforce that move out of agriculture in the following season increases. Among other reasons, the success of the previous agriculture season determines the occupational choice household members make in the short term. Figure 2 presents the relation between crop loss and occupational diversification in a few major Indian states.
Figure 2. Crop loss and occupational diversification away from agriculture
About 44 % of the workforce engaged in agriculture has upper primary and higher levels of education. When this workforce moves out of agriculture for a short period following crop loss, 71% engage in casual wage labour as compared to 56% in a scenario of no crop loss. However, what is particularly disturbing is to note that 62% of the workforce with tertiary education (graduation and above) also settle for casual labour post crop loss, whereas the figure is only 37% in case of no crop loss. Alternatively, under normal circumstances, 22% of the educated workforce in agriculture tends to take up regular salaried/wage employment when they move out of agriculture in the short term – while this figure is only 9% during difficult times.
This phenomenon could also be tracked by looking at the composition of sectors in which the educated workforce is engaged. When not affected by crop loss, 9% of the workforce diversifies into manufacturing and 8% into the service sector between the two visits of the survey. On the other hand, under duress, little over 5% of the workforce diversifies into the service sector, and none are engaged in the manufacturing sector.
Notwithstanding a network of socioeconomic factors including local policies, sectoral growth, labour market dynamics, migration, and cropping patterns that determine occupational choices, these observations hint at distress employment among agricultural households in response to climate shocks. Clearly, this is reflected in the wages earned (see section III in Table 1).
Table 1. Sectoral composition, employment status and wages of educated workforce in response to climate shock
I. Sectoral composition (%) i |
No shock |
Shock |
||
Construction |
51.48 |
68.11 |
||
Manufacturing |
9.07 |
- |
||
Wholesale and Retail trade |
6.74 |
9.38 |
||
Transport and Storage |
5.89 |
3.4 |
||
Education |
4.43 |
1.76 |
||
Forestry and logging |
2.65 |
3.09 |
||
Electricity, gas, steam and air conditioning supply |
2.26 |
- |
||
Accommodation and Food service activities |
1.94 |
1.88 |
||
Other personal service activities |
1.45 |
1.58 |
||
Human health and social work activities |
1.1 |
- |
||
II. Employment Status (%) |
Upper primary to higher secondary |
graduate and above |
Upper primary to higher secondary |
graduate and above |
Own account worker |
16.59 |
19.48 |
15.1 |
18.19 |
Employer |
0.45 |
0.1 |
0.67 |
1.47 |
Worked as helper in household enterprise (unpaid family worker) |
4.12 |
6.28 |
3.3 |
2.12 |
Worked as regular salaried/wage employee |
20.29 |
37.1 |
8.42 |
15.58 |
Worked as casual wage labour |
58.55 |
37.04 |
72.51 |
62.64 |
III. Median Wages (Rs. per capita)ii,iii |
|
|||
Primary to higher secondary |
22,000 |
17,600 |
||
Graduate and above |
36,000 |
22,000 |
Notes: i) 90% of the total educated workforce that diverted from agriculture between the two seasons are employed in these 10 sectors. ii) Aggregated for six months. iii) Statistical testing suggests that 57-63 out of 100 times, the wages earned through distress employment is significantly lesser than wages earned under normal circumstances.
This unusual labour market dynamics of the educated rural workforce in the context of climate variability is worth deeper investigation. Nevertheless, if these observations are any indication, as climate shocks occur more frequently and for longer, excess labour will become concentrated in unskilled, low wage sectors.
Sale of livestock
Another strategy often adopted by agricultural households in case of shock is sale of assets, primarily livestock. Usually, in case of relatively small income shocks, the households rely on stocks of grains and preserve livestock (Acosta et al. 2021). However, when the losses are larger, households tend to sell livestock. Fodder shortage and water scarcity during drought is another compelling reason to sell livestock.
Figure 3 presents the percentage of households against a range of receipts the households obtain on the sale of livestock. We examine these receipts across land size classes – marginal and small, semi-medium and medium, and large3. We observe that, at the lowest end of receipts (far left on the horizontal axis), percentage of households that experienced crop loss is more than the percentage of households that did not. As the receipts value increases (towards the right of the horizontal axis), percentage of households that did not experience shock exceeds the percentage of households that did. This suggests that a greater number of households that faced shocks (than those that did not) are compelled to sell their livestock at lower prices. The median receipt value (per hectare of land) against sale of livestock post shock is Rs. 3,337 vis-à-vis Rs. 5,000 when livestock is sold under normal circumstances.
Figure 3. Receipt of sale of livestock across land holding sizes
Source: 77th round of the NSS (MoSPI, 2021).
Notes: (i) Figures a, b, and c represents the frequency distribution of livestock receipts of small and marginal, semi-medium and medium, and large households, respectively. Figure d is for all agriculture households. (ii) The yellow dotted circles highlights that at the lowest end of receipts, the percentage of households that experienced crop loss (red line) is more than the percentage of households that did not (green line). Green dotted line shows that as the sale value increases, households that faced crop loss fall behind. (iii) Statistical test shows that 57 out of 100 times, the average receipts on sale of livestock (per hectare of land) are significantly lower when sold in distress compared to the sales that are made in normal circumstances.
Building safety nets: Crop insurance, and alternative work opportunities
India’s agriculture sector is under great stress due to increasing climate variability. Already struggling with low farm incomes, rural households are exposed to more uncertainty with every passing agriculture season. Short run, ex-post coping strategies are a rather desperate bid to counteract climate shock-led income loss. The disadvantageous outcomes of these strategies highlight the vulnerability of these households and absence of systems that can cushion the blow.
In the face of a climate shock, our research finds that a high percentage of educated rural workforce takes up casual labour under duress, earning significantly less wages than they would in regular wage employment. Sale of livestock at distress prices also indicates the desperation of the households to stay afloat. While the empirical evidence of vulnerability presented in this article are preliminary observations and more rigorous analysis is warranted, this highlights the need for making not just agriculture, but the rural economy as a whole, resilient to climate variability.
One crucial step towards building safety nets is to strengthen India’s crop insurance schemes. According to the latest farmer assessment survey, only 10% of the total farm households report to have insured their crops (MoSPI, 2021). Across the land classes, more than 50% of the households without crop insurance are either not aware of crop insurance or about the availability of the facility.
Secondly, creating alternative employment opportunities is key to making the rural economy resilient to climate shocks. Training rural educated youth in skills that are compatible with local growth and developmental requirements and expanding rural employment guarantee schemes to accommodate this group is one way to achieve it.
The current debate on climate change and employment is focused on emission reduction in the energy sector and the jobs lost or created in the transition from coal to renewable energy. With escalating climate variability and as farming becomes an unreliable source of income, short- and long-term movement of labour out of agriculture will be increasingly common. The resulting consequences and measures to tackle it ought to garner similar policy and academic attention.
The views expressed are of the authors alone and do not necessarily reflect those of their respective organisations.
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Notes:
- The sowing for Kharif crops takes place during the monsoons and these are harvested in the autumn season.
- These crops are sown in the winter and harvested during spring.
- See (MoSPI, 2021) for the definitions of land size class.
Further Reading
- Acosta, Alejandro, Francesco Nicolli and Panagiotis Karfakis (2021), "Coping with climate shocks: The complex role of livestock portfolios", World Development, 146, 105546.
- Government of India (2022), 'Losses Suffered due to Natural Calamities', Unstarred Lok Sabha Question No. 1609, Ministry of Home Affairs.
- Government of India (2023), 'Assessment of crop loss', Unstarred Lok Sabha Question No. 1903, Ministry of Home Affairs.
- Hazard Atlas of India (2023), 'Climate Hazards and Vulnerability Atlas of India', Indian Meteorological Department, Pune.
- Ito, Takahiro and Takashi Kurosaki (2009), "Weather Risk, Wages in Kind, and the Off-Farm Labor Supply of Agricultural Households in a Developing Country", American Journal of Agricultural Economics, 91(3), 697-710.
- Ministry of Statistics and Programme Implementation (2021), 'Situation Assessment of Rural Households and Land and Holdings of Households in Rural India, 2019', 77th Round, National Statistical Office.
- Ministry of Statistics and Programme Implementation (2023), 'Periodic Labour Force Survey (2021-2022)', National Statistical Office.
- Pandey, K (2022), 'A year of extreme weather events has weighed heavy on India’s agricultural sector' Mongabay-India.
- Skoufias, Emmanuel, Sushenjit Bandyopadhyay and Sergio Olivieri (2017), "Occupational diversification as an adaptation to rainfall variability in rural India", Agricultural Economics, 48(1), 77–89.
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