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Factors Contributing to Income Inequalities among Agricultural Households in India

Seema Bathla ( teaches at the Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi. Anjani Kumar ( is a researcher at the International Food Policy Research Institute, New Delhi.

Inequality in agricultural households in 20 major states is estimated and its factors analysed. In most states, farming and livestock contribute over half the total income. Income inequalities, irrespective of farm size, are large, though these have not widened much over time; major sources are non-farm income, land, and farm assets. The relationship between growth in household income and land size is positive; it does not augur well for the government’s professed objective of promoting inclusive development. To bridge income gaps, mechanisms need to be developed to ensure the viability of increasingly small and fragmented landholdings.

(Annex Tables A1 to A5 accompanying this article are available on the EPW website.)

An earlier version of the paper was presented in a National Seminar on “Challenges of Growing Inequalities in India,” organised by the Council for Social Development, New Delhi during 14–15 July 2016 as part of the preparation for the Social Development Report 2018. We are grateful to T Haque and other participants in the seminar for their valuable suggestions.

Kuznets (1955) hypothesised the relationship between income inequality and average income as an inverted U-curve, which suggests that income inequality rises with higher economic growth in the initial stages of development and shrinks subsequently over time. Kuznets justified this argument based on the migration of workers from the low-wage, low-inequality rural sector to the high-wage, high-inequality urban sector. In the context of emerging economies such as India and China, Borooah et al (2015) argues that income inequality may also stem from the removal of economic regulatory controls; the resulting growth may have reduced absolute levels of poverty but, nonetheless, it increased inequality. Although many developing countries have experienced an increase in income inequality, studies have challenged this hypothesis on the grounds of significant differences in the sociocultural, historical, religious, and caste factors that shape household income levels and asset possession. It is also important to distinguish between inequality of outcomes—such as income and assets—and inequality of opportunity, measured through education, access to health and other
services, and gender and caste disparities (Kanbur et al 2014). Economic inequalities often are interlinked with social and cultural inequalities that result from differences in caste, religion, and gender.

Notwithstanding these facts, many have expressed interest in the concept of an equal distribution of income, whether in the context of economic growth, poverty–inequality trade-off, or globalisation (Basu 2006; Piketty 2014). Dev (2017) reported that global inequality is high, ranging between 0.55 and 0.70, and it has increased substantially in most advanced countries because of the growing income share of the top 10%. Also, income inequalities are becoming more pronounced in many developing economies, such as Indonesia, a situation largely explained by gaps in wealth and education (Wicaksono et al 2017). Efforts towards a more equal distribution of income constitute one of the key components of the United Nations Sustainable Development Goals, which all countries aim to achieve by 2030.

In India, inter- and intra-state income inequalities have been major policy issues. Studies have often focused on measuring regional inequalities in economic and social development indicators such as per capita income and consumption, infrastructural development, access to education, and the health of people in different castes and social groups. Bhattacharya and Sakhthivel (2004) probed regional disparity by analysing growth rates of aggregate and sectoral income of major states in the pre- and post-reform decades. The results indicate that although the growth rate of India’s gross domestic product (GDP)—that is, its income—improved only marginally following economic reforms, the regional disparity in gross state domestic product (GSDP) widened much more significantly. Industrial states grew much faster than agriculturally dominant states; there is little evidence that growth rates are converging. One disturbing aspect was an inverse relationship between population growth and GSDP growth, a phenomenon that has serious implications for India’s poverty and employment rates, and the country’s political economy.

An accentuation in regional disparities is a serious concern. States where the per capita income is below the national average are home to more than 60% of the population, most of whom belong to the poorer strata and disadvantaged groups (Planning Commission 2012). Recently, the per capita income has increased significantly in several states, such as Bihar, Madhya Pradesh, Chhattisgarh, and Odisha, but there is little decline in the income ratio of the richest state (Maharashtra) and the poorest state (Bihar). The gap between the richest and poorest states is much higher in the case of non-agricultural income; it was 6.7 from 1980–81 to 2013–14, and lately dropped to 4.6 in 2016–17. The agricultural income gap between Haryana and Bihar continues to be 3.4.1 From 1980–81 to 2011–12, only seven of the 24 Indian states showed a tendency towards convergence in agricultural income (Kaur and Dhillon 2017). Panagariya et al (2014) maintain that inequality is lower in rural incomes than in urban incomes—broadly representing agriculture and non-agriculture incomes—and has remained stable from 2004–05; they attribute the widening inter-state income inequality to increasing non-agricultural incomes.2

Research on interstate inequalities is extensive, but few studies analyse intra-state and interpersonal income growth and disparities. District-level data limitations are cited to be one of the important reasons for the lack of research at the disaggregate level. Nonetheless, the comparative study at the disaggregate level is important, as most Indian states have large populations and are heterogeneous in terms of ownership of land, agroclimatic conditions, economic activities and sectoral performance, access to social amenities, and level of urbanisation and infrastructure. The dimensions of such economic and social differences have widened across the districts and within and across states (Ghosh and Das Gupta 2017; Planning Commission 2012). Dubey (2017) estimates intra-state disparities in Gujarat, Haryana, Kerala, Odisha, and Punjab based on the National Sample Survey Office (NSSo) consumption expenditure data for two quinquennial rounds undertaken during the 2000s. The three indicators chosen for the analysis—consumption, inequality (Gini coefficient) and the incidence of poverty—showed an increase in disparities, with the greatest increase noted in the headcount ratio. These rising intra-state disparities observed since 1993–94 have coincided with the per capita income growth surge.

A few studies analyse and compare rural and urban inter-household income and poverty dispersion and provide interesting insights. Rawal (2013) identifies high inequality in rural incomes, at 0.76, based on the NSSo 70th (2012–13) round, and attributes this finding to skewed ownership of both physical and financial assets. Using the two NSSo rounds, the 59th (2002–03) and the 70th (2012–13), Chakravorty et al (2016) estimate changes in the income of agricultural households from various sources, and confirm a high Gini ratio (around 0.60) in income in most states during 2002–03 and a slight decrease during the subsequent year. They ascribe about half the inter-household income inequality to income from cultivation, which in turn depended primarily on variance in landownership. Based on a primary survey in tribal-dominated areas in Jharkhand, Meena et al (2017) find higher and pervasive income inequality among labouring households, albeit without a consistent relationship to farm size. Education, adoption of high-yielding varieties, and access to non-farm income opportunities were significant factors influencing income in the study region.

Borooah et al (2015) focus on caste-based discrimination while measuring inter-household inequalities in rural areas. The study used the Indian Human Development Survey 2004–05 to explain variations in consumption expenditure across social groups by ownership of land; livestock; productivity-enhancing assets such as tractors, tube wells, and diesel pump sets; education; and location. These factors varied significantly across caste groups.

Intra- and Interstate Income

What explains these growing inter- and intra-state income inequalities in India? The literature attributes inequalities to a bias in sectoral policies; reductions in public investment; differing levels of infrastructure development, economic reforms and trade liberalisation, and foreign direct investment; and weak institutions and governance (Kundu and Varghese 2010; Pal and Ghosh 2007; Rao 2017). The World Bank (2008) ascribes India’s regional inequalities to lower levels of urbanisation, a larger share of tribal population, poor roads and market infrastructure, and little private investment in the lowest-income states and districts. Additional factors affecting the agricultural sector include weak initial conditions, persistent gaps in economic and social amenities, unfavourable climate and production conditions, market failures, and increasingly fragmented landholdings.

To address these issues, agricultural policies may need to be redesigned, public investments stepped up, and efforts made to target social benefits and appropriate institutions to enhance land productivity (Kanbur et al 2014). Bathla et al (2018) find large disparities in public expenditure across states with low, middle, and high per capita income from 1981–82 to 2013–14. The share of public investment in agriculture and irrigation in total investment has been decreasing steadily in almost all states, although investment has improved in some low-income states like Bihar, Madhya Pradesh, and Uttar Pradesh. Of the various types of public investment, investment in health and nutrition and in education have had greater effects on reducing the impact of interstate income inequality than investment in roads and transport, agricultural research and development, and rural energy. However, the marginal returns in terms of income equality from additional investments in the latter categories are higher in low-income, agriculture-dependent states.

The literature on both inter- and intra-state investigations pays little attention to sectoral analysis. The highlights specific to the agricultural sector include the following points. Income inequality over the period has been high, but stable. Social factors, such as religion and caste, play a dominant role in rural areas, which in turn explain differences in access to finance and ownership of land, livestock, and assets. Differences in income generation patterns are significant, but farming and livestock continue to be the main income-generating activities, and explain the high disparity compared to income from non-farm activities. For appropriate policy interventions, it is crucial to understand the measurement and dimensions of inequalities across geographical domains.

Against this backdrop, this study estimates income inequality in agricultural households in the major Indian states and assesses its factors. Agriculture is India’s largest economic sector, supporting more than 70% of the country’s population and 48% of the workforce. Agriculture has diverse patterns of growth, because of varied climatic and production conditions, skewed land distribution, and high poverty rates. A subnational analysis can help to evaluate how public policies may lessen income gaps within each state. The analysis looks at 20 major states and a countrywide assessment, based on representative NSSo data for the 59th (2002–03) and 70th (2012–13) rounds of the Situation Assessment Survey of agricultural households.

Data and Methodology

The study utilises farm-level data from nationally representative surveys conducted by the NSSO, identified as the 59th and 70th Situation Assessment Surveys. The main purpose of each survey is to assess the status of farmers and farming in India. The survey was spread over 4,000 villages across the country and collected information from 51,770 households during 2002–03 and 35,200 households during 2012–13 on a wide array of characteristics (NSSo 2006, 2014).

Officials of the NSSO visited the same households twice, first between January and July and again between August and December; at each visit, they collected information on expenses and cultivation receipts for the periods in question. Different reference periods were used for other information such as income, consumption, household characteristics, and income from sources other than farming. For instance, information on land possession and indebtedness was “as on the date of survey;” farming of animals was collected as in “last 30 days;” and non-farm business, consumer expenditures and principal source of income were collected on a “last 365 days” basis.

The two NSSo rounds are not directly comparable because of intervening changes in the survey definitions. In the 59th round, an essential condition for defining a person as a farmer (agricultural household) was possession of land; however, this condition was relaxed in the 70th round. Thus, as defined in the 70th round, agricultural households may or may not have possessed land. Moreover, the only households included in the survey were those that had at least one member self-employed in agriculture, either as principal or subsidiary employment, and had a total value of produce exceeding ₹ 3,000 over the past 365 days. This condition was set to exclude households that engaged only in small farming activities (such as kitchen gardens), which had been included in the 59th round. This study addressed such differences by dropping households that did not possess land in the 70th round, and estimating that ₹ 3,000 in 2012–13 was equivalent to ₹ 1,400 at 2003 prices.3 Households from the 59th round were included that had an annual agricultural income of at least ₹ 1,400 and had at least one member engaged in agriculture during the past 365 days.4

The 20 states chosen for analysis were Andhra Pradesh (undivided), Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Odisha, Tamil Nadu, Uttarakhand, Uttar Pradesh, and West Bengal. These states cover more than 90% of the population (1.32 billion) and net sown area (141 million hectares [ha]) in India. Farm size was classified in four intervals: up to 1 ha (marginal), 1.01 to 2 ha (small), 2.01 to 10 ha (medium), and more than 10 ha (large). The net income (value of output minus value of input), expenditure on farm assets, and other variables given in nominal prices were converted into 2011–12 real prices. The GDP deflator given in the National Accounts Statistics, Central Statistics Office, Government of India, was used.

The study used the Gini index to investigate the extent of income inequality among agricultural households (total and as per farm size) in each state. To identify factors that contribute to inequality, it used the regression-based inequality decomposition method based on Shorrocks (1982) and Fields and Yoo (2000).5 The approach rests on the neoclassical framework through the specification of a Cobb–Douglas production function. At first, the household income (y) is explained by key variables (xi)—family size, operated land; capital (represented by expenditure on farm assets); other sources of household income (represented by number of family members involved in non-farm activities); access to institutional credit; number of years of education of the household head; household caste; and age. The variables education and age are taken to represent skill/knowledge and productivity, and can be highly correlated with income. The logarithmic form of the income equation is given as:

y = a + ∑ki=1 xi + ε(1)

The error term ε represents stochastic shocks to income and is assumed to be unrelated to other variables. The next step is to decompose the variance of y to estimate the inequality coefficient and quantify the contribution of each variable to inequality as follows:

σ2(y) = ki=1 βicov(y, xi)+ σ2(ε) (2)

where, σ2(y) is the variance of y and cov(y,) represents covariance of y with other variables (xi). Taking y in the logarithmic form, sigma (y) is the standard inequality measure, called logarithmic variance. Studies posit that the covariance term on the right-hand side of equation (2) can be considered as factor component contributions to total inequality. The production function was estimated using ordinary least squares based on unit-level data for 2012–13. District dummies were specified to see the disaggregated location-specific effect in each state. Caste dummies were also included. The variable access to credit was highly correlated with expenditure on assets and therefore was dropped from the equation.

Agricultural Household Income

India’s rural economy has undergone significant changes over the period covered by this study. From 1993–94 to 2011–12, the share of agriculture in rural income declined from 57% to 39%, and its share in rural employment decreased from 78% to 64%. In the rural economy, however, this transition is not uniform; it differs widely across the sectors and states (Chand et al 2017). Even within the agricultural sector, different household categories show economic variations.

Income Levels and Growth

Annex Table A1 provides general characteristics of agricultural households during 2002–03 and 2012–13 based on farm size. The size of operated area under each category hardly changed during this period; the national average was 7.54 ha in 2003 and 6.62 ha in 2013. Nonetheless, the differences in household dependence on agriculture and livestock activities are evident. Marginal and small landholders (less than 2 ha) constituted more than 60% of households and depended more on livestock and wages as alternate sources of livelihood. At the national level, the share of non-farm sector (NFS) income earned through wages and non-farm activities of agricultural households income declined from 49.0% in 2003 to 40.1% in 2013. An increasing income share of crop cultivation is attributed to the revival of agriculture from the mid-2000s. During 2004–05 to 2011–12, agriculture grew at an impressive average annual growth rate, close to 4%. Further, the share of NFS income depicted a negative relationship with land size—as one moves from marginal to large farmers, the share of NFS income in total income declines. Household education levels were abysmally low: 54.85% were not literate, and only 8.64% had attained at least a higher secondary degree.

Table 1 shows the pattern of change in how agricultural and NFS income contribute to the economy of agricultural households in different states. The contribution of NFS income was similar across the states, except in Jammu and Kashmir, Bihar, and West Bengal, where it increased as part of total income between 2003 and 2013. The real per capita income of agricultural households grew at an annual rate of 3.9% during 2003 and 2013. In absolute terms, per capita annual real income increased from ₹ 9,806 to ₹ 13,572 during this period. Again, the robust growth observed at the national level is not uniform. Income levels and growth in agricultural households differ considerably between states. Bihar, Chhattisgarh, Jharkhand, Odisha, Uttarakhand, and Uttar Pradesh continue to be at the lower end of income distribution, while Punjab, Haryana, Kerala, and Jammu and Kashmir remain among the richer states. The gap between income of agricultural households in laggard and rich states remains.

The income of agricultural households declined in Bihar (−0.20%) and Uttarakhand (−2.24%), due perhaps to recurrent floods and other unfavourable climatic conditions, along with neglect of public investment in agriculture and irrigation. Low income is linked largely to small landholding size. Smaller landholders earn more from sources other than agriculture, such as animal husbandry and horticulture; policies to support these sources should likewise be strengthened. Income registered more impressive growth, greater than 5%, in Odisha (8.59%), Rajasthan (7.80%), Haryana (7.39%), Andhra Pradesh (6.82%), Madhya Pradesh (6.25%), Chhattisgarh (5.26%), Tamil Nadu (5.25%), and Karnataka (5.23%). Growth remained between 3% and 5% in Maharashtra (4.29%), Punjab (3.97%), Kerala (3.96%), Gujarat (3.88%), Himachal Pradesh (3.51%), and Uttar Pradesh (3.28%).

Policy reforms initiated during the mid-2000s under various flagship programmes—such as the National Food Security Mission, the National Horticulture Mission, the Rashtriya Krishi Vikas Yojana, and the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS)—along with robust growth in industry and services sectors may have contributed towards the higher rate of income growth in some of these states (Bathla et al 2017). Income inequalities decreased among agricultural households in all states except Jammu and Kashmir, Punjab, Bihar, Karnataka, and Kerala, where income inequalities
increased for marginal and small farm households (Annex Table A2). On average, a farm household in India earned ₹ 49,038 per annum during 2003, which increased to ₹ 69,223 by 2013. Household annual income varied widely across states. The highest was in Punjab (₹ 1,94,913), Haryana (₹ 1,56,211), Jammu and Kashmir (₹ 1,37,568), and Kerala (₹ 1,28,896). The lowest income was in Bihar (₹ 38,801), West Bengal (₹ 43,113), Uttarakhand (₹ 50,510), and Uttar Pradesh, Jharkhand, and Odisha each at nearly ₹ 52,818. Despite an increase in NFS
activities, agriculture remained a predominant source of income in most states (Annex Table A3).

Inequalities in Income of Agricultural Households

Inequality in per capita income of agricultural households across India, as measured by the Gini index, is high; it was 0.67 in 2003, and dropped slightly to 0.63 in 2013. This drop is attributed partially to changes in three states—Madhya Pradesh, Chhattisgarh, and Rajasthan—where maximum reductions were observed (Table 2). In these three states, per capita income of agricultural households grew 5% or more. Income inequality among agricultural households widened further in several poorer states like Bihar, Kerala, and West Bengal.

As expected, the level of income of agricultural households depicted a positive relationship with land size (Table 3). The share of NFS income is inversely proportional to land size, as larger farmers depend more on agricultural income. However, a positive relationship between growth in household income and land size does not augur well for the avowed objective of promoting inclusive development. The difference realised in the growth of household income has widened the gap between marginal and large farmers. This finding suggests a strong need to take corrective measures to narrow this gap. The inequality in income of agricultural households may have declined in each category, but the positive relationship between land size and income inequality has remained intact.

Sources of Income Inequalities

The analysis begins with an estimation of factors determining household income based on age, family size, education of household head, NFS income, land, and capital (Equation 1). Table 4 provides elasticity estimates with levels of significance for each explanatory variable. Elasticity was much higher for NFS income, age, and years of education of the household head than for other variables. The coefficients of operated land and assets are positive and low in almost every state. The negative sign for land in Odisha, Andhra Pradesh, Maharashtra, and Tamil Nadu indicates that land is a constraining factor in these areas. Moreover, land as a factor of production is limited in each state, which is empirically verified by taking its square in a non-linear regression equation. The result is similar with the square of assets, which also bears a negative sign.

Based on the estimated elasticities, Table 5 (p 60) shows various factors affecting income inequality. Among the factors evaluated to explain inequalities, NFS income, land, and farm assets contributed the maximum—respectively, 28.6%, 25.8%, and 14.3%—across India, with considerable differences in their share in each state. Land as a source of inequality was prominent in the northern states and in Bihar, Chhattisgarh, and Kerala, whereas NFS income was significant in the southern states and in Haryana, Jharkhand, West Bengal, and Odisha. Between 2002 and 2013, the percentage of family members engaged in NFS activities increased from 9% to 16% (Annex Table A4).

The upturn in NFS income noted in the low-income states can be associated with agrarian distress and government employment programmes. The inequality attributed to livestock and other farm assets is identified largely in Uttarakhand, Uttar Pradesh, Chhattisgarh, Madhya Pradesh, and Maharashtra. On average, in 2013, households spent ₹ 2,964 on assets, of which livestock, tractors and tube wells held major shares (Annex Table A5). Equal access to land can enhance households’ capital base, which can have significant effects on productivity and growth and reduce income gaps among households. However, investment in assets is highly correlated with household access to credit.

Kumar et al (2017) and Bathla and Kumari (2017) find that 63% of households invest by using credit from institutional sources, but access to credit is highly uneven, as large farmers (with more than 10 ha of land) have the maximum share in institutional loans—smaller and marginal farmers should have this share. Compared to these factors, education was not as important a contributor to income inequality in this study except in Jammu and Kashmir, Odisha, Bihar, Madhya Pradesh, Andhra Pradesh, and Tamil Nadu. Age and family size also ­explain income inequality. Their share across India was modest at, respectively, 6.72% and 11.08%; slightly higher in Madhya Pradesh; and nearly 10% each in Rajasthan, Odisha, and Gujarat.


Significant differences exist in the sources of income generation of agricultural households in each of the 20 major Indian states. Farming and livestock contribute more than 50% in total income in most states. The annual rate of growth in real net income per household varied from nearly 7% in Haryana and Rajasthan to as low as 1% in Jammu and Kashmir, Assam, and Jharkhand; it was negative in Uttarakhand and Bihar. Inter-state income inequality decreased marginally from 0.67 in 2003 to 0.63 in 2013. Statewise analysis reveals that inequality has risen only in Jammu and Kashmir, Bihar, Andhra Pradesh, Karnataka, and Kerala. Nonetheless, each state shows a high Gini ratio (more than 0.50) irrespective of farm size, indicating the persistence of large income inequalities in India.

Among the factors examined to explain income inequalities, NFS income, land, and farm assets contributed the maximum, to the tune of 28.6%, 25.8%, and 14.3% respectively across India, with considerable differences in their share in each state. Land as a source of inequality is prominent in the northern states and in Bihar, Chhattisgarh, and Kerala, whereas NFS income is significant in the southern states and in Haryana, Jharkhand, West Bengal, and Odisha. Inequality owing to differing asset levels was identified largely in Uttarakhand, Uttar Pradesh, Chhattisgarh, Madhya Pradesh, and Maharashtra. Education as a source of inequality was identified in Jammu and Kashmir, Odisha, Bihar, Madhya Pradesh, Andhra Pradesh, and Tamil Nadu.

The real challenge in mitigating income gaps is to find ways to improve the viability of increasingly fragmented landholdings. A positive relationship between growth in household income and land size does not bode well for the national government’s purported objective of promoting inclusive development. State governments should initiate measures to provide marginal and small farmers with increased access to land, credit, technology, and irrigation to accelerate investments and productivity. Concerted efforts also should be made to enhance education offerings as part of the country’s aspiration to achieve its long-desired equality of opportunity in the agricultural sector.


1 Income inequality based on per capita consumption expenditure also shows an increase in the ratio of urban to rural expenditure from 1993–94 to 2004–05 (Anand et al 2014; Sen and Himanshu 2005).

2 Agricultural income shows less divergence across states, possibly because it grew faster in poorer states than in richer states during the 2000s.

3 The state-level Monthly Consumer Price Index–Agricultural Labour (CPI–AL) with the 1986–87 base has been used to deflate the 2013 prices. The All India CPI–AL was calculated by taking the weighted average of the state CPI. The CPI–AL was 322 for the period from July 2002 to June 2003; it was 691 for the period from July 2012 to June 2013. If the base is changed to 2003, the CPI–AL becomes 100 for the period from July 2002 to June 2003 and 214 for the period from July 2012 to June 2013. Therefore, the deflation factor for 2013 prices was 100/214=0.4667, and ₹ 3,000 in 2013 is equivalent to ₹ 1,400 in 2003.

4 The definition mentions “at least one member self-employed in agriculture either in principal status or subsidiary status,” but because these data were unavailable in the 59th round, the sample includes households having at least one member engaged in agriculture during the past 365 days.

5 In this context, the regression-based approach is preferable to the other decomposition methods of inequality, as it provides an efficient, flexible way to quantify the conditional roles of variables (Heshmati 2004).


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Updated On : 24th May, 2019


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