For generations, rural life in Assam, one of India’s northeastern states known for its rich biodiversity and varied landscapes, has revolved around agriculture. Paddy cultivation, tea plantation, fishing, and allied activities have long formed the backbone of household incomes across the state.Footnote 1 Yet in recent decades, this foundation has become increasingly fragile. Shrinking landholdings, unstable crop yields, frequent flooding, and growing climate uncertainty have made farming alone an unreliable means of survival. As a result, rural households are being forced to rethink how they earn a living.Footnote 2
Across Assam’s villages, one response has become increasingly common: livelihood diversification. By combining farming with wage labor, small businesses, services, or self-employment, households spread risk, stabilize income, and respond more flexibly to environmental and economic shocks. Recognizing this reality, the Ministry of Rural Development, Government of India launched the National Rural Livelihoods Mission (NRLM) in 2010, a flagship program considered as the world’s largest poverty-alleviation program. The program rests on a simple, but ambitious premise: that rural poverty can be reduced not only by raising incomes, but also by expanding the range of ways households earn them.Footnote 3
However, a basic question remains unanswered: does NRLM actually help rural households diversify their livelihoods, or does it simply repackage old vulnerabilities in new institutional forms? Drawing on household-level evidence from rural Assam, this article examines how participation in NRLM shapes livelihood choices and what this means for rural resilience and public policy.
1. Why livelihood diversification matters in Assam
Assam is one of India’s most agriculture-dependent states, with over 70 percent of its population living in rural areas. Despite its fertile river valleys and rich biodiversity, the state faces persistent livelihood challenges. Annual floods disrupt agricultural cycles, damage infrastructure, and erode assets. Productivity remains low, nonfarm employment opportunities are limited, and access to markets and services is uneven across districts.
In such conditions, reliance on a single source of income particularly agriculture can be risky. Livelihood diversification offers a pathway to greater stability.Footnote 4 A household that combines farming with other nonfarm livelihood activities is better positioned to withstand environmental uncertainties, crop failure, or seasonal income gaps. Diversification is not merely about income growth; it is about economic resilience.Footnote 5
NRLM was designed with this understanding in mind. Rather than focusing only on income transfers, the mission emphasizes collective organization through Self-Help Groups (SHGs), access to credit, training, and gradual expansion into multiple livelihood activities. Assam’s implementation of NRLM, through the Assam State Rural Livelihoods Mission Society (ASRLMS), has sought to promote these goals across diverse social and geographic contexts.Footnote 6
Yet systematic evidence on whether NRLM actually translates into more diversified livelihoods especially in northeastern India has remained limited. This study seeks to fill that gap.
2. Studying NRLM on the ground
To explore this question, the article draws on a household survey conducted in three districts of Assam—Jorhat, Morigaon, and Dhubri—representing upper, middle, and lower Assam. The study compares NRLM participants and nonparticipants, focusing not on income alone but on the mix of livelihood activities that households pursue. The goal is straightforward: to understand whether NRLM participation is associated with more diversified and resilient ways of earning a living.
3. What the evidence shows
3.1. NRLM households diversify more
Households participating in NRLM consistently pursue a wider range of livelihood activities than those outside the program. Rather than relying solely on agriculture, beneficiary households are more likely to combine farming with nonfarm work such as wage labor, small trade, and services. The primary data on the principal occupation which is the main source of household income shows clear differences between beneficiaries and nonbeneficiaries. About 55 percent of beneficiaries depend on farm sector, compared to 66 percent of the nonbeneficiaries, indicating lower agricultural dependence among beneficiaries. Beneficiaries are also more engaged in nonfarm activities, with 45 percent relying on nonfarm income versus 34 percent among nonbeneficiaries as shown in Table 1.
Principal occupations of the sample households
Table 1 Long description
The table has five columns: Sources, Beneficiary percentage of households, Beneficiary mean income per month in rupees, Nonbeneficiary percentage of households, and Nonbeneficiary mean income per month in rupees. The first row lists farm livelihoods with 54.75 percent of beneficiary households earning a mean income of 4818.56 rupees, and 66.30 percent of nonbeneficiary households earning 4065.19 rupees. The second row lists nonfarm livelihoods with 45.25 percent of beneficiary households earning a mean income of 7063.50 rupees, and 33.69 percent of nonbeneficiary households earning 5580 rupees.
Source: Primary Data.
The beneficiaries also earn higher average incomes from both farm and nonfarm sources, with notable advantages in salaried work and nonagricultural labor. Overall, NRLM participation is associated with reduced reliance on agriculture, higher earnings, and greater involvement in nonfarm livelihoods.
In addition to examining income sources, the study also assesses the number of livelihood activities pursued by households to better understand the nature of diversification. As shown in Figure 1, beneficiary households are more likely to engage in multiple livelihood activities.
Percentage of sample households according to number of activities. Source: Primary Data.
From Figure 1, it can be seen that among beneficiaries, households are more likely to engage in multiple livelihood activities, with a noticeable concentration in three or more sources of income. In contrast, nonbeneficiaries tend to be concentrated in fewer activities, typically relying on one or two sources. Overall, beneficiary households display a higher degree of diversification, while only a small proportion of households in either group engage in a very large number of activities, with such patterns slightly more common among beneficiaries.
To capture this extent of diversification technically, the study uses the Simpson Index of Diversity (SID), which ranges from 0 (complete dependence on a single income source) to values closer to 1, indicating a more diversified and balanced mix of multiple income sources.Footnote 7
The results shown in Figure 2 highlight a clear difference. NRLM households exhibit higher levels of diversification compared to nonbeneficiaries, indicating a broader and more balanced livelihood portfolio. In districts such as Jorhat and Morigaon, the difference between beneficiaries and nonbeneficiaries is especially pronounced. Here, NRLM households are significantly more engaged in activities such as wage employment, small trade, and service work. In Dhubri, where market access and infrastructure are more limited, the gap is smaller but still evident.
Extent of livelihood diversification among the different sample districts. Source: Field Survey Data.
Diversification under NRLM is strongly shaped by collective processes. Participation in SHGs, particularly among women, provides access to credit, skills, and shared learning. These group-based mechanisms encourage households to experiment with new income activities and reduce dependence on agriculture. Overall, the evidence suggests that NRLM is enabling rural households to move beyond single-source livelihoods by expanding both opportunities and institutional support.
4. What shapes livelihood diversification?
Education and skills emerge as one of the strongest factors associated with livelihood diversification. Better-educated households are more likely to move into nonfarm activities, as education improves access to information and employment opportunities.Footnote 8 Similarly, skill training under NRLM expands livelihood options by equipping households with capabilities for small enterprises and services. Diversification, therefore, depends not just on opportunity, but on human capital built through education and collective platforms like SHGs.
Geography continues to shape economic opportunity.Footnote 9 Households located closer to urban centers or market towns are more likely to diversify their livelihoods. Proximity reduces transaction costs, increases access to wage employment and services, and allows households to respond more quickly to market demand.
Nature of occupation further shapes diversification.Footnote 10 Households engaged in nonfarm work tend to diversify more, as relatively stable incomes allow them to explore additional activities. For farming households, diversification is often constrained by seasonal risks and limited resources.
One of the less visible but most consequential effects of NRLM lies in its impact on rural women. Participation in SHGs has expanded women’s economic roles beyond unpaid farm labor and household work. In many cases, women’s involvement in savings groups, micro-enterprises, and skill training has enabled households to diversify livelihoods that were previously inaccessible. This shift not only contributes to income stability but also alters intra-household decision-making, with women playing a greater role in financial planning and risk management. Livelihood diversification, therefore, emerges as both an economic and a gendered process.
5. What matters less than expected
Interestingly, some factors commonly assumed to influence diversification appear less decisive in this context. Landholding size, access to credit, age of the household head, and dependency ratio, which were also considered for the present study do not show a strong independent association with diversification once other factors are taken into account.
This does not mean these factors are unimportant, but rather that their effects may operate indirectly through education, training, or occupation rather than shaping livelihood choices on their own.
6. What this means for public policy
What emerges from this evidence is not simply an evaluation of a government program, but a broader lesson about rural development. NRLM appears to matter most where it strengthens institutions that help households adapt through skills, social networks, and collective confidence rather than where it functions as a narrow income intervention. Collective organization through SHGs, especially among women, enhances access to information, credit, and confidence factors that conventional livelihood programs often overlook.
Second, NRLM is making a difference, particularly in encouraging households to diversify beyond agriculture. This reinforces the value of institutional approaches that focus on collective organization, capacity building, and gradual livelihood transformation rather than short-term income support.
Third, education and skill development deserve greater emphasis. Expanding access to training especially for women and youth can significantly enhance the effectiveness of livelihood programs. Skill-based nonfarm employment holds particular promise in flood-prone and land-scarce regions like Assam.
Fourth, place matters. Without improvements in rural connectivity, market access, and local infrastructure, the benefits of livelihood programs will remain uneven. Investments in roads, transport, and digital access can amplify the impact of NRLM by expanding the range of feasible livelihood options.
Finally, livelihood diversification should be understood not merely as an economic outcome, but as a form of resilience. In regions facing climate stress and environmental uncertainty, diversified livelihoods provide households with the flexibility needed to adapt and survive.
7. Limits and looking ahead
This study focuses on three districts of Assam and draws on cross-sectional survey data. While the findings offer valuable insights, they cannot capture changes over time or establish causal relationships with complete certainty. Livelihood choices are shaped by social norms, gender roles, and local institutions factors that merit deeper qualitative exploration.
Future research combining longitudinal data with ethnographic insights could further illuminate how rural households navigate economic uncertainty and how programs like NRLM can better support them.
8. Conclusion
As rural Assam confronts deepening economic and environmental pressures, livelihood diversification has become less a strategy of choice than a strategy of survival. The experience of NRLM suggests that diversification works best when it is supported not only by markets and skills but also by institutions that expand agency and collective capacity. Building rural resilience, in other words, is not just about helping people earn more. It is about helping them imagine and sustain more than one way to live.
Yet diversification is not a simple or uniform process. It depends on education, geography, infrastructure, and sustained institutional support. Strengthening NRLM’s training components, improving rural connectivity, and tailoring interventions to local contexts can further enhance its impact.
The experience of Assam offers an important reminder: building rural resilience means enabling people not just to earn more, but to earn differently and to do so through institutions that strengthen social capital, agency, and collective capacity.
Acknowledgements
The authors gratefully acknowledge the cooperation of all the surveyed households for sharing their time and experiences, without which this study would not have been possible. We also thank local officials and field facilitators associated with the Assam State Rural Livelihoods Mission (ASRLMS) for their support during data collection. The authors are grateful to colleagues and anonymous reviewers for their constructive comments and suggestions, which helped improve the clarity and quality of the manuscript. Any remaining errors are the sole responsibility of the authors.
Author contribution
B.D. conceptualized the study, designed the research framework, conducted the field survey, performed the data analysis, and prepared the first draft of the manuscript.
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