Income Distribution and Inequalities among Rural Farmers of Nagaland, India

Authors: Sungjeminla Longkumer
Income Distribution and Inequalities among Rural Farmers of Nagaland, India
DIN
IJOEAR-SEP-2026-7
Abstract

The study attempts to analyze sources and distribution of rural households' income from farm and non-farm activities and examines the extent of income inequalities across farm sizes. The study is based on primary data collected from 200 farm households of Nagaland through a multi-stage simple random sampling technique. The results indicate that nearly half of the income is from non-farm sources (48.57 percent). Contrary to the popular belief that farming is the principal source of income for rural farm households, the study shows that households derive nearly half of their income from non-farm sources. Another significant finding from the analysis is that across farm size, farm size and farm income show a positive relationship, whereas farm size and non-farm income show an inverse relationship. The non-farm income is most unequally distributed among sources of income, followed by other farm income. The estimation of Gini ratio also reveals that income inequality is higher among marginal and small farmers.

Keywords
Farm income Non-farm income Participation Gini coefficient.
Introduction

The rural economy in India is predominantly dependent on agriculture and allied activities, on which roughly 54 percent of the rural households depend, while 46 percent depend on non-agricultural activities. However, for the past few years, the structure of Indian agriculture has been experiencing a decline in farm size, which has led to a rise in the marginalization of holdings (Meena et al., 2017). The distribution of area in percentage for marginal farmers increased from 29.8 percent to 34.5 percent between 2012–13 and 2018–19. In contrast, the percentage area for medium farms decreased from 23.1 percent in 2002–03 to 14.7 percent in 2018–19, and for large farmers, it decreased from 11.6 percent in 2002–03 to 5.8 percent in 2012–13 and then further declined to 3.9 percent (National Statistical Survey Report, 2019). As one of the developing economies, India still relies heavily on agriculture to generate income and employment in its rural areas, while it is widely acknowledged that this sector alone cannot keep up with the country's growing population.

It has long been assumed that rural households in developing countries are almost entirely focused on farming and neglect other non-farm activities in the countryside, according to the World Bank (2008). In order to improve rural well-being and lower poverty in the world's growing nations, a comprehensive approach that goes beyond agricultural development is therefore required. According to Chand et al. (2011), small landowners who relied solely on agriculture for their income were likely to have continued to live in poverty. Given the issue of income variability, rural farmers turned to non-farm income streams as an alternate source of income. Identifying the most effective ways to promote non-farm activities is vital, since non-farm income is essential for rural households to sustain and improve their standard of living and may even be a means of escaping poverty (FAO, 2002).

Since the beginning of the green revolution in the early 1960s, Indian agriculture has evolved towards modern agriculture, resulting in the adoption of improved technology and the development of infrastructure within the agricultural sector. While productivity grew, the experience of its benefits was uneven across regions and farm sizes, which has created economic inequality among agricultural households (Dhanagare, 1988; Freebairn, 1995). The green revolution promoted expensive technology investments for the richer farmers in those resource-rich areas, keeping away the small farmers in resource-scarce areas (Shiva, 1993). Because of scarce resources, farmers were more competitive, which increased the gaps between social classes and geographic areas and, ultimately, led to an uneven distribution of income. The adoption of economic reforms and liberalization in India, which has predominantly benefited the top 1 percent of the population, who held more than one-fifth of the entire national income in 2021, is another example of growing inequality. The bottom half, however, only has 13 percent of the income (World Inequality Lab, 2022). India is one of the most unequal countries as a result. Land ownership and agricultural revenue are strongly related. The very unequal distribution of land in India has resulted in significant economic inequalities within rural communities, as disclosed by Ranganathan et al. (2016).

Conclusion

In Nagaland, the study shows a positive relationship between rural farmers' average total income and farm sizes. The estimation of Gini ratio varies from 0.293 to 0.466 across farm sizes. The marginal farmers exhibited the highest inequality in terms of overall income (0.311) and crop production income (0.220) and for non-farm income the highest inequality is among small farmers (0.455). Hence, there is a need to reduce inequality particularly among marginal and small farmers.

The study suggests that it is imperative to intensify crop production process by prioritizing high-value crops with better infrastructure base and to maximize benefits from non-farm income, specific steps must be taken to educate impoverished households on how to create opportunities for earning income from non-farm sources. To address disparities in income distribution and promote more inclusive and equitable growth in the State, the government, agricultural organizations, and local administration must collaborate to ensure their effectiveness at the regional level and regularly evaluates their impact across farm size. This will be helpful in reducing inequalities between marginal and large farm households.

Limitations of the Study: The study is based on a single district (Peren) in Nagaland, which limits the generalizability of the findings to other districts with different socio-economic and agro-climatic conditions. The cross-sectional design captures income at a single point in time and does not capture seasonal or inter-annual variations. The study did not examine the determinants of income diversification and inequality, which could provide insights for policy interventions. Future research should extend the analysis to multiple districts, employ panel data to capture income dynamics, and examine the factors influencing income diversification and inequality.

Agriculture Journal IJOEAR Call for Papers

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