An Empirical Assessment of National Rural Livelihoods Mission’s Efficacy in Promoting Livelihood Diversification in Rural Assam, India

An Empirical Assessment of National Rural Livelihoods Mission’s Efficacy in Promoting Livelihood Diversification in Rural Assam, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Empirical Assessment of National Rural Livelihoods Mission’s Efficacy in Promoting Livelihood Diversification in Rural Assam, India Bhargab Das, Suranjan Sarma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7585604/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Livelihood diversification is increasingly recognized as a crucial strategy for rural households in underdeveloped economies to reduce risks linked to agricultural dependence and climate variability. This study assesses the effectiveness of India’s National Rural Livelihoods Mission (NRLM) in promoting livelihood diversification, based on primary data from 405 rural households across Jorhat, Morigaon, and Dhubri districts of Assam, a northeastern state of India. The Simpson Index of Diversity (SID) was used to measure the extent of diversification, while logistic regression analysis identified key socio-economic and institutional determinants. The findings indicate that NRLM beneficiary households have significantly more diversified livelihoods than non-beneficiaries. Factors such as educational attainment, household size, NRLM membership, participation in training programs, and proximity to urban centers were found to significantly influence diversification decisions. The study confirms NRLM’s role in expanding rural livelihood opportunities beyond traditional agriculture. To enhance economic resilience and reduce rural vulnerability, policy efforts should focus on strengthening skill development, improving access to education, and expanding NRLM’s institutional support across rural Assam. Livelihood Diversification National Rural Livelihood Mission Rural Assam Simpson Index of Diversification Logistic Regression Impact Assessment 1. Introduction In the dynamic context of rural economies, livelihood diversification has emerged as a critical strategy for poverty alleviation and economic resilience. Defined as the process by which rural households develop a diverse portfolio of income-generating activities (Ellis, 1998 ), diversification enables communities to mitigate risks associated with agricultural dependence and environmental uncertainties. It is the process of combining both agricultural and non-agricultural activities to survive and improve the standard of living (Ellis, 1998 ; Niehof, 2004 ; Martin & Lorenzen, 2016 ). Globally, and particularly in developing regions such as South and Southeast Asia, the diversification of rural livelihoods is increasingly recognized as a pathway to economic stability and improved living standards (FAO/World Bank, 2001; Loison, 2019 ). In India, where the rural population heavily depends on agriculture, the relative importance of crop cultivation has decreased in most recent times. The agricultural sector in the country is characterized by decreasing farm sizes, low levels of output per farm, fluctuating productivity, vulnerability to climatic shocks, a high degree of subsistence farming, with increases in production being driven mainly by area and not yields growth which have driven households to seek non-farm income sources (Jirström et al. 2011 ). Assam is one of the 35 states and union territories of India, situated in the north-eastern part, is a land of varied landscapes, rich biodiversity, and distinct geographical features. The state is predominantly agrarian, with more than 70% of its population reliant on agriculture and allied activities (Government of Assam, 2016 ). Assam’s economy traditionally revolves around paddy cultivation, tea plantations, and fishing. However, frequent floods, fluctuating agricultural yields, and limited non-farm employment opportunities have made rural livelihoods highly vulnerable. This situation has pushed rural communities to increasingly explore non-agricultural activities such as petty trade, services, and rural non-farm labor as supplementary sources of income (Das & Bora, 2020 ). Recognizing this shift, the Government of India launched the National Rural Livelihoods Mission (NRLM) in the year 2010 which is a flagship programme aimed at fostering sustainable livelihoods through diversification of livelihoods, skill development, and institutional support. From the inception, the Mission encourages the poor households to have multiple livelihoods in order to cope with vulnerabilities. It looks at the entire portfolio of livelihoods of each poor household, and work towards stabilizing and enhancing the existing livelihoods and subsequently diversifying their livelihoods (Government of India, 2012). The Assam State Rural Livelihood Mission Society (ASRLMS), as the NRLM's implementing body in Assam, works to encourage livelihood diversification by facilitating access to credit, providing training, and promoting the formation of Self-Help Groups (SHGs). However, despite NRLM’s significant policy focus and implementation efforts, empirical evidence assessing its actual impact on fostering livelihood diversification remains limited, particularly in the North-Eastern context. Existing studies in India have largely focused on income enhancement or poverty reduction outcomes of NRLM, with limited analysis of how the programme contributes to livelihood diversification specifically. Moreover, most research has concentrated on southern or central Indian states, leaving northeastern states like Assam underexplored (Misra, 2018). With this backdrop, the present study seeks to: Assess the current extent of livelihood diversification among NRLM beneficiary households in rural Assam and compare it with that of non-beneficiary households. Identify the key socio-economic, demographic, and institutional factors influencing livelihood diversification decisions of rural households. 2. Review of Literature The literature on livelihood diversification has expanded significantly since Ellis ( 1998 ) established its central role in rural development. It is seen as both a survival strategy for risk-prone households and an accumulation strategy for those seeking upward mobility (Barrett, Reardon, & Webb, 2001). In Sub-Saharan Africa, studies highlight how diversification into non-farm activities reduces vulnerability to climate shocks and enhances resilience (Reardon, 1997 ; Ayana et al., 2021 ). In Asia, rural households often combine farming with services, wage labor, and small businesses to stabilize incomes (Loison, 2019 ). Recent work stresses the importance of proximity to markets, access to credit, access to infrastructure, primary occupation of the households and education in shaping diversification outcomes (Rahut & Scharf, 2012; Dinku, 2018 ; Habib et al., 2023 ). A strand of research also explores the gendered dimensions of diversification. Loison ( 2019 ) and Martin & Lorenzen ( 2016 ) demonstrate that women’s participation in non-farm activities contributes not only to household income but also to empowerment, though structural constraints often limit access. Similarly, Iorio & Corsale ( 2010 ) and Swain & Batabyal ( 2016 ) highlight tourism and community-based enterprises as alternative pathways to rural livelihood diversification. The agricultural sector in India is characterized by decreasing farm sizes, low levels of output per farm, low productivity, a high degree of subsistence farming, with increases in production being driven mainly by area and not yield growth (Jirström et al., 2011 ). In the Indian context, diversification has been a key response to agricultural stagnation and rural poverty. Studies across different states show that education, landholding size, access to credit, and infrastructure strongly determine diversification levels (Khatun & Roy, 2012 ; Roy, Khatun & Roy, 2018 ). Non-farm employment now constitutes a growing share of rural incomes, driven by migration, rural industries, and services (Reardon, 1997 ; Shan & Ahmed, 2020 ). However, regional disparities remain significant, with eastern and northeastern states lagging behind in creating sustainable non-farm opportunities. Policy programmes have attempted to address these challenges. The earlier Swarnjayanti Gram Swarozgar Yojana (SGSY) and later NRLM were designed to enhance rural incomes through the strategy of livelihood diversification. However, empirical studies have shown mixed outcomes: while some report improvements in income and women’s empowerment (Mishra, 2018 ), others find limited evidence of structural livelihood transformation. Recent studies in Karnataka, Bihar, and West Bengal suggest NRLM has helped households reduce dependence on subsistence farming but that outcomes vary by region (Roy & Basu, 2020 ; Swargiary & Mahanta, 2020 ). From the review, two key gaps emerge. Firstly, while NRLM has been widely studied in southern and central India, there is a paucity of research in Assam and the broader northeast. Again, existing studies emphasize income enhancement but rarely evaluate diversification as a distinct outcome. Therefore, this study addresses these gaps by empirically assessing NRLM’s role in livelihood diversification in rural Assam, thereby situating the findings within both the rural development and regional studies literature. 3. Methodology Data Sources The core work of the present study is based on primary data. The required primary data on extent and determinants of livelihood diversification have been collected from the sample households with the help of appropriately constructed interview schedule. Multistage sampling technique has been employed to select the sample households for the present study. In the first stage, three sample districts have been selected purposively to represent the all three regional division of Brahmaputra valley of Assam. Thus from upper Assam Jorhat district, from middle Assam Marigaon district and from lower Assam Dhubri district have been selected purposively as sample districts in order to conduct the present study. In the second stage, development blocks within each selected district were chosen randomly. From Jorhat district, which comprises six development blocks, Kaliapani and Titabor blocks were selected. In Dhubri district, out of 11 development blocks, Bilasipara and Gauripur blocks were chosen as sample blocks. Similarly, from Marigaon district, Kapili and Moirabari blocks were selected randomly. Thus, a total of six development blocks were identified for the study. In the next step from every selected block, two sample villages have selected for the purpose of the study. One village where livelihood intervention of NRLM has started and one village where livelihood intervention has not started yet have been selected. Thus from Kaliapani Block Boria Gaon and Kotikuchia Gaon and from Titabor Block Bosa Gaon and Bongal Gaon have been selected. Again from Bilasipara Block Baghmari and Kaimari and from Gauripur Block Bagulamari and Bhelakoba villages have been selected as sample villages. Garmai and Kalmoubari from Kapili Block and Haldhibari and Borbori villages from Moirabari block have been selected as the sample village to conduct the present study. After the selection of the sample villages 10 percent households have been selected from both types of sample villages i.e. where livelihood intervention of NRLM has started and where livelihood intervention of NRLN has not started yet. Thus a total of 405 households have been selected as sample households out of which 221 are beneficiary households and 184 are non beneficiary households (See Table 1 for the sampling design of the study). Table 1 Sampling Design: Sample Districts Sample Blocks Sample Villages Total Household Sample Households Jorhat Kaliapani Boria Gaon 335 34 Kotikuchia Gaon 372 37 Titabor Bongal Gaon 313 31 Bosa Gaon 311 31 Dhubri Bilasipara Baghmari 463 47 Kaimari 281 28 Gauripur Bagulamari 447 45 Bhelakoba 160 16 Morigaon Kapili Garmari 224 23 Kalmoubari 574 58 Moirabari Haldhibari 135 14 Borbori 415 42 Total Sample Households 405 Methods : Simpson Index of Diversity : In the present study, the Simpson Index of Diversity (SID) is used to measure the extent or level of livelihood diversification among the sample households. The SID takes into account both the number of income sources and how evenly the distributions of income are made between the various sources. This justifies the use of the SID to measure the level of livelihood diversification in this study over other diversification measures, such as Herfindahl, Shannon, and others (Roy, B. C., Khatun, D., & Roy, A. 2018; Alemu, 2023). It is defined as SID = 1- \(\:{\sum\:}_{\text{i}=1}^{\text{n}}{\text{p}}_{\text{i}}^{2}\) Where, Pi as the proportion of income coming from source i. The value of SID always falls between 0 and 1. If there is just one source of income, Pi = 1, so SID = 0. As the number of sources increases, the shares (Pi) decline, as does the sum of the squared shares, so that SID approaches to 1. If there are k sources of income, then SID falls between zero and 1–1/k. Accordingly, households with most diversified incomes will have the largest SID, and the less diversified incomes are associated with the smallest SID. For least diversified households SID takes on its minimum value of 0. Logistic Regression Model : To the key socio-economic, demographic, and institutional factors influencing livelihood diversification decisions of rural households the logistic regression model is used by taking household-level Simpson Index of Diversification (SID) as the dependent. Since the dependent variable i.e., the Simpson Index of Diversification is an index whose value is confined between the range 0 to1. In this case, use of Ordinary Linear Regression model may predict values for dependent variable which may not be bounded to the range of 0 to1 (Gujarati, 2003 ; Roy, B.C., et.al. 2018 ). To address this, a logistic transformation is employed to map the bounded dependent variable into the entire real line. The logistic function ensures that the predicted values remain within the (0, 1) interval, thereby preserving the theoretical and logical bounds of the index (Papke & Wooldridge, 1996 ). The Logistic regression model has been formulated as follows: Y = \(\:\frac{1}{1+\:{e}^{-z}}\) Where Y is the dependent variable (Simpson Index of Diversification) and z represents the explanatory variables. Despite the non-linear nature of the original model, estimation can be carried out using linear regression by following the steps of the logistic regression model, which uses Z as the dependent variable. Z values can now be created by applying the following transformation formula: Zt = \(\:\text{l}\text{n}\left(\frac{{\text{Y}}_{\text{t}}}{1-\:{\text{Y}}_{\text{t}}}\right)\) It can be seen from the above formula that Zt is not confined between 0 and 1. However, it adheres rigorously to a relationship with the original index values of the dependent variable. When Y tends to 1, Z tends to ∞ and when Y tends to 0, Z tends to -∞. Therefore, Ordinary Least Square regression model may be applied for estimating the parameters of the below equation. Zt = \(\:\text{l}\text{n}\left(\frac{{\text{Y}}_{\text{t}}}{1-\:{\text{Y}}_{\text{t}}}\right)\) = β0 + β1​X1t​ + β2​X2​t +…………+ βk​Xkt + Ut Zt = Log-odds of the outcome (e.g., diversification) Xt = Explanatory Variables β = Parameters Ut = Error Terms 4. Results and Discussions 4.1 Demographic and Socio Economic Profile of the Sample Households: The socio-economic characteristics of rural households are of critical importance in understanding livelihood strategies and the degree of livelihood diversification in a region (Braveman PA, et al., 2005 ). In Assam, as in other parts of rural India, factors such as education, household size, family type, marital status, education, income, access to institutional credit, proximity to urban centers etc. play pivotal roles in shaping livelihood patterns. The present study presents a detailed socio-economic and demographic profile of the sample households, distinguishing between NRLM beneficiaries and non-beneficiaries across three selected districts of Assam. Key variables such as religion, caste, family size, family type, marital status, education, income level, and engagement in livelihood activities have been considered. The detailed demographic and socio-economic profile is presented in Table 2 . These parameters help to elucidate how socio-economic background influences the households’ access to livelihood opportunities and participation in diversification activities. Table 2 Demographic and Socio Economic Profile of the Sample Households Distribution of Sample Households of the basis of their Religion Religion Beneficiary Non-Beneficiary Hindu 57.91% (128) 65.21% (120) Muslim 42.09% (93) 33.15% (61) Christian 0% (0) 1.63% (3) Distribution of Sample Households of the basis of Family Type Family Type Beneficiary Non-Beneficiary Joint 23.53% (52) 22.28% (41) Nuclear 76.47% (169) 77.71% (143) Distribution of Sample Households of the basis of their Social Groups Social Groups Beneficiary Non-Beneficiary General 47.05% (104) 52.17% (96) OBC 23.07% (51) 19.56% (36) SC 16.29% (36) 16.84% (31) ST 13.56% (30) 11.41% (21) Distribution of Sample Households of the basis of their Number of Family Members Family Members Beneficiary Non-Beneficiary Up to 3 19.90% (44) 17.93% (33) 4–6 43.43% (96) 40.76% (75) 7–8 21.26% (47) 28.80% (53) Above 8 15.38% (34) 12.5% (23) Distribution of Sample Households of the basis of their Marital Status Marital Status Beneficiary Non-Beneficiary Married 83.25% (184) 89.13% (164) Unmarried 4.97% (11) 1.08% (2) Widow 11.76% (26) 9.78% (18) Distribution of Sample Households of the basis of their Level of Education Level of Education Beneficiary Non-Beneficiary Illiterate 20.36% (45) 26.08% (48) Primary 31.22% (69) 27.17% (50) Middle 25.33% (56) 25% (46) High School 19.45% (43) 19.02% (35) Higher Secondary 2.71% (6) 2.71% (5) Graduate and Above 0.90% (2) 0% (0) Distribution of Sample Households of the basis of their Monthly Households Income Monthly Income Beneficiary Non-Beneficiary 0 to 5000 6.79% (15) 32.97% (60) 5000 to 10000 29.41% (65) 58.24% (106) 10000 to 15000 38.46% (85) 7.14% (13) 15000 to 20000 9.95% (22) 1.65% (3) 20000 to 30000 8.60% (19) 0.00% (0) More than 30000 6.79% (15) 0.00% (0) Distribution of Sample Households of the basis of the Number of Livelihood Activities Pursued Number of Activities Pursued Beneficiary Non-Beneficiary 1 18.55% (41) 23.08% (42) 2 26.24% (58) 37.91% (69) 3 33.48% (74) 19.78% (36) 4 16.29% (36) 12.64% (23) 5 4.07% (9) 4.40% (8) 6 1.36% (3) 2.20% (4) Source: Primary Data The socio-economic profile of the sampled households reveals distinct patterns between NRLM beneficiaries and non-beneficiaries. In terms of religion, Hindus constitute the majority in both groups, though slightly higher among non-beneficiaries (65.21%) compared to beneficiaries (57.91%), while the proportion of Muslims is notably higher among beneficiaries (42.09%) than non-beneficiaries (33.15%). Regarding family structure, nuclear families dominate in both categories, accounting for over around 77% of beneficiary households and nearly 78% of non-beneficiary households. In terms of social grouping, both categories are predominantly from the General and OBC categories, although beneficiaries show a marginally higher representation from SC and ST communities, suggesting NRLM’s outreach among marginalized groups. Household size distribution shows that most households in both categories consist of 4 to 6 members, yet non-beneficiaries tend to have slightly larger households, as seen in the higher percentage (28.80%) falling within the 7–8 member category. Education and income profiles highlight further contrasts. A higher proportion of beneficiaries have attained primary and middle-level education, whereas the non-beneficiary group shows a larger share of illiterate members (26.08% compared to 20.36% among beneficiaries). Notably, higher education levels are limited across both groups. Income distribution reveals a significant disparity: while nearly 60% of non-beneficiary households fall within the lower income brackets (below ₹10,000 per month), a substantial portion of beneficiary households report monthly incomes between ₹10,000 and ₹30,000, indicating the possible positive economic impact of NRLM interventions. Livelihood diversification data further supports this, as 33.48% of beneficiary households are engaged in three livelihood activities, compared to just 19.78% among non-beneficiaries. This suggests that NRLM beneficiaries pursue more diverse livelihood strategies, likely contributing to their higher income levels and greater economic resilience. 4.2 Extent of Livelihood Diversification among the Sample Households: The traditional agricultural sector in Assam faces persistent challenges such as declining land holdings, fluctuating yields, climatic vulnerabilities, and frequent flooding, all of which constrain household income and stability (Bora et. al, 2021 ). In this context, rural households are compelled to supplement their agricultural activities with non-farm income sources including petty trade, wage labor, handloom and handicraft and services. The Assam State Rural Livelihood Mission Society (ASRLMS), responsible for implementing NRLM in the state, actively promotes livelihood diversification through formation of Self-Help Groups (SHGs), training, and access to institutional credit. Measuring the extent of livelihood diversification among rural households is critical for understanding the effectiveness of these interventions. In this study, the Simpson Index of Diversification (SID) has been employed to quantify diversification levels across NRLM beneficiaries and non-beneficiaries. This is shown in the Table 3 . Table 3 Extent of Livelihood Diversification among the Different Sample District Sample Districts Beneficiaries Non-Beneficiaries Value of Simpson Index Value of Simpson Index Jorhat 0.426 0.359 Morigaon 0.377 0.243 Dhubri 0.398 0.311 Source: Primary Data The Table 3 highlights the variation in livelihood diversification between beneficiaries and non-beneficiaries across different districts. In Jorhat district, beneficiaries have a Simpson Index of 0.426, which is notably higher than the non-beneficiaries' index of 0.359. In Morigaon, there is a more significant disparity between beneficiaries and non-beneficiaries, with beneficiaries showing an index value of 0.377 compared to 0.243 for non-beneficiaries. This substantial difference suggests that the NRLM's intervention in Morigaon and Jorhat have been particularly effective in promoting livelihood diversification. Non-beneficiaries, however, have much lower index values, indicating a more concentrated reliance on fewer income sources, likely dominated by agriculture. In Dhubri district, both beneficiaries and non-beneficiaries have relatively close Simpson Index values, with beneficiaries at 0.398 and non-beneficiaries at 0.311. While beneficiaries still exhibit more diversified livelihoods, the difference is smaller than in the other districts. The overall extent of livelihood diversification among the beneficiaries and non beneficiaries are shown in Table 4 . Table 4 Overall Extent of Livelihood Diversification among the Beneficiaries and Non Beneficiaries Groups Value of Simpson Index “t” value Beneficiaries 0.401 2.89 P < 0.00 Non-Beneficiaries 0.302 Source: Primary Data The data on Table 4 reveals a significant difference in extent of livelihood diversification between beneficiaries and non-beneficiaries, with beneficiaries exhibiting a higher value of the Simpson Index (0.401) compared to non-beneficiaries (0.302). This indicates that NRLM beneficiaries have more diversified sources of income, reflecting the programme’s effectiveness in promoting both farm and non-farm livelihoods. The “t” value of 2.89 with a p-value of less than 0.00 confirms that the difference is statistically significant. For further analysis of degree of extent of livelihood diversification, the sample households have been classified into six categories as per their extent of livelihood diversification. The extent of household’s livelihood diversification have been classified into no diversification, very low diversification, low diversification, average diversification, high diversification and full diversification groups. Table 5 Distribution of Households by the Extent of Livelihood Diversification Classification Value of SID Extent of diversification Beneficiaries Non-Beneficiaries Nos. Percentage Nos. Percentage Minimum 0 No diversification 8 3.62 16 8.70 \(\:{0>to\:}_{\text{X}}^{-}-\:{\sigma\:}\) to 0.11 Very low diversification 33 14.93 32 17.39 \(\:{}_{\text{X}}{}^{-}-\:{\sigma\:}\:\text{t}\text{o}\:{}_{\text{X}}{}^{-}\:\) 0.12 to 0.33 Low diversification 67 30.32 77 41.85 \(\:{}_{\text{X}}{}^{-}\:\:\text{t}\text{o}\:{}_{\text{X}}{}^{-}\:+\:{\sigma\:}\) 0.34 to 0.54 Average diversification 87 39.37 48 26.09 \(\:{}_{\text{X}}{}^{-}+\:{\sigma\:}\:\text{t}\text{o}<\:1\) 0.55 to < 1 High diversification 26 11.76 11 5.98 Maximum Value 1 Full diversification 0 0.00 0 0.00 Total no of household: 221 100.00 184 100.00 x̅= 0.33, σ = 0.22 Source: Primary Data The distribution of households by the extent of livelihood diversification shows clear differences between NRLM beneficiaries and non-beneficiaries. The data on Table 5 reveals that beneficiaries are more likely to have higher levels of livelihood diversification compared to non-beneficiaries. For beneficiaries, the majority (39.37%) fall into the "Average diversification" category (SID between 0.34 and 0.54), followed by 30.32% in the "Low diversification" category (SID between 0.12 and 0.33). Only 11.76% of beneficiaries exhibit "High diversification" (SID between 0.55 and 1). A small proportion (3.62%) has no diversification at all, while 14.93% show very low diversification. These figures indicate that NRLM beneficiaries generally experience moderate to low levels of diversification, with a notable portion engaged in both agricultural and non-agricultural activities, though they are still in the process of increasing their diversification. On the other hand, non-beneficiaries show a different pattern. A larger portion (41.85%) of non-beneficiaries fall under the "Low diversification" category, and 26.09% fall under "Average diversification." Notably, only 5.98% of non-beneficiaries reach the "High diversification" category, much lower than the 11.76% of beneficiaries. Moreover, 8.70% of non-beneficiaries have no diversification at all, and 17.39% show very low diversification. These figures highlight that non-beneficiaries are more likely to have concentrated income sources, particularly in agriculture, compared to beneficiaries. In summary, the data indicates that NRLM beneficiaries, on average, enjoy greater livelihood diversification, as reflected by their higher presence in the "Average" and "High diversification" categories, whereas non-beneficiaries remain more reliant on limited income sources, showing lower overall diversification. 4.3 Determinants of Livelihood Diversification: Different studies on livelihood diversification have revealed that the nature and the extent of livelihood diversification are influenced by different socio-economic, demographic, environmental and cultural factors. Ayantoye et al., ( 2017 ) identified that gender, marital status, poverty status, primary occupation and membership of association are the most important factors that influence the households’ level of livelihood diversification. Similar study carried out by Ng’ang’a et al. ( 2011 ) reported that educational level had a positive correlation on level of diversified livelihood. Ibrahim et al., ( 2009 ) reported that distance to market directly influence livelihood diversification while study by Birhanu and Getachew ( 2016 ) revealed that access to credit had a direct relationship with livelihood diversification. Kelechi ( 2014 ) also reported that age, primary occupation, household size, farm income, access to credit, experience and membership to cooperative society were the significant determinants of farmer’s decision to embark on non-farm enterprise. It is also found that distance to market and access (Saha and Bahal, 2015 ), location and distance to markets (Dinku, 2018 ), growth in infrastructures (Rahut and Scharf, 2012), access to public assets, social factors like networks and association (Ellis, 1998 ) and developmental factors like tourism (Iorio and Corsale, 2010 ; Swain and Batabyal, 2016 ) had also enhanced livelihood diversification in some areas. It is, thus, observed that various factors influenced the extent of livelihood diversification of a household to great extent. Therefore, this portion of this study identifies the different factors determining the diversity of livelihoods among the sample households and quantitatively assesses their relative influence. Description of the Variables : The selection of these variables is informed by a range of theoretical and empirical studies conducted across India and other regions, including works by Roy, A., & Basu, S. ( 2020 ); Roy, B. C., Khatun, D., & Roy, A. (2018); Kassegn, A., & Abdinasir, U. ( 2023 ); Swargiary et al., (2020); Khatun and Roy, ( 2012 ); Leary and Kulkarni (2007) etc. A total of ten key explanatory variables have been identified to assess their impact on the level of livelihood diversification. The details of these variables are outlined below. See Table 6 for the description of variables and their expected signs used in the logistic regression model. Education Education is considered as a significant determinant of livelihood diversification (Habib, N., Rankin, P., Alauddin, M., & Cramb, R., 2023 ). Persons with higher levels of education are generally skilled enough to engage themselves in a variety of income-generating activities. In other words, education gives people the ability to identify and pursue a variety of income sources outside of traditional occupations (Roy, A., & Basu, S. 2020 ). We took into account the level of education of the respondent to quantify the impact of education on the level of livelihood diversification. It is assumed that there is a positive correlation between education and level of livelihood diversification. Household size One significant factor influencing diversification of livelihood is the size of the family. Total number of working hands available in the household plays a crucial role in determining the extent of livelihood diversification of a household (Roy, B. C., Khatun, D., & Roy, A. 2018). According to Reardon ( 1997 ), a household's capacity to provide labor for the farm is influenced by the size of its household. While some members of a big family may be able to participate in conventional farming activities, others may be able to pursue non-farm pursuits which will lessen the chance of livelihood failure. In the present study a positive association is assumed between livelihood diversification and household size. Dependency Ratio While choosing whether or not to diversify, the dependency ratio is a crucial consideration. The ability of a household to meet subsistence needs decreases as the dependency ratio rises, and the household is forced to diversify their sources of income. Put another way, it can be said that as the dependency ratio rises, households' capacity to provide for their families declines and their likelihood of diversifying their sources of income to non-farm pursuits rises (Kassegn, A., & Abdinasir, U. 2023 ). Therefore, we postulated a positive correlation between the dependency ratio and livelihood diversification. Age Since younger people are more active and have a higher propensity to participate in non-farm activities, age plays a significant role in determining the degree of diversification. Younger people typically possess the skills which are required for urban-centered non-farm sector work (Roy, A., & Basu, S. 2020 ). Therefore, the age of the respondents are considered as an independent variable for determining extent of livelihood diversification. It is hypothesized that a younger respondent will be more inclined to engage in a variety of non-farm pursuits, which will increase the diversification of their sources of income (Roy, B. C., Khatun, D., & Roy, A. 2018). Table 6 Descriptions of Variables and their Expected Sign used in the Logistic Regression Model Variable Definition Measurement Variable Type Expected Sign Dependent Variable Simpson Index of Diversification Value of Simpson Index of Livelihood Diversification of the family Index Value (0–1) Independent variables Education Education level of the respondent Number of Years Continuous + Household size No. of family members in a household No. of family members Continuous + Dependency Ratio Percentage of household members below 18 years old and above 60 years old Percentage Continuous + Age Age of the respondent No. of years Continuous - Land holding Size of landholding of the respondent Bigha Continuous + NRLM membership Whether or not any household member is a beneficiary of NRLM 1 = yes, 0 = no Dummy + Training whether or not any household member received any formal training on livelihood skill development 1 = yes, 0 = no Dummy + Access to credit Whether the respondent has access to credit in the preceding five years 1 = yes, 0 = no Dummy + Distance Distance of the nearest urban center from the household Kilometers Continuous - Principal Occupation Principal occupation of the households 1 = Non Farm, 0 = Farm Dummy +/- Land holding Ownership of land of the households is considered to have a significant effect on the increments of their livelihood diversification (Admasu, T. T., Damtie, Y. A., & Taye, M. A. 2022). In rural regions of counties like India, land ownership is crucial for both farmers and non-farmers. Land holding enables the farmers to diversify towards high value crops and non farmers towards non-farm sector livelihood activities self employment, petty business or even leasing out. Therefore, land ownership by the rural households are expected to increase the households’ extent of livelihood diversifications (Roy, B. C., Khatun, D., & Roy, A. 2018). NRLM membership Encouraging poor households to have multiple livelihoods in order to cope with vulnerabilities is one of the most important objectives of NRLM (Government of India, 2012). NRLM would look at the entire portfolio of livelihoods of each poor household, and work towards stabilizing and enhancing the existing livelihoods and subsequently diversifying their livelihoods. Membership to a formal social organization like SHG/cooperative/village committee, etc. is an important social capital in determining the extent of livelihood diversification. Memberships of such organization empower the person which increases the scope to engage in multiple livelihood activities (Roy, B. C., Khatun, D., & Roy, A. 2018). Therefore, we postulate that NRLM membership and livelihood diversification are positively correlated. Training Training is considered to have a positive relationship with livelihood diversification since it raises the skill and competency level of individuals (Kumar, M. A., & Umesh, K. B. (2020). Training and skill development aid in the acquisition of new employment opportunities in the non-farm sectors, as the majority of activities in this sector are skill-based. Diversification of livelihood, especially from the farm to non-farm sectors, is hampered by the lack of programmes and initiatives for training and skill development (Swargiary et al., 2020). Certain self-employment jobs, such as tailoring, machine repairing, small-scale agricultural processing, etc., also call for training. Therefore, a positive correlation is expected between training and livelihood diversification. Access to credit The majority of the have people living in rural areas are marginal and small farmers who have poor financial conditions (Khatun, D., & Roy, B. C. 2012). They lack the funds necessary for high value crops. Furthermore, a significant amount of capital investment is needed for non-farm economic activity. Therefore, the accessibility of institutional credit at a low interest rate would enable rural residents to engage in multiple economic endeavors (Swargiary et al., 2020). Access to institutional credit is therefore considered a determinant of livelihood diversification. It is assumed a positive association between institutional credit and livelihood diversification for the present study (Khatun and Roy, 2012 ). Distance Diversification of livelihoods is also significantly influenced by geographic factors. The prospects of non-farm employment opportunities rise with market or urban center proximity (Kumar, M. A., & Umesh, K. B. 2020). Households that are closest to the urban centers are expected to engage in non-farm activities. When looking for jobs, especially during the off-season, the workers can easily communicate. As a result, households in villages near metropolitan centers are more likely to participate in non-farm activities. Therefore, it is anticipated that the there will be a negative correlation between the distance to the city center and level of livelihood diversification (Roy, B. C., Khatun, D., & Roy, A. (2018). Principal Occupation Principal occupation of the household is also considered as one of the important determinants of livelihood diversification. A household's income sources can be divided into two categories, such as farm and non-farm. Families where farming is the primary source of income might want to diversify their economic activity to non-farm sector because of the risk and uncertainty associated with the farm sector. People view diversification as a risk-reduction tactic that allows them to choose multiple lifestyle options at the same time (Swargiary et al., 2020). On the other hand, people engaged in the non-farm sector are more likely to meet the essential requirements of the family and are more likely to have a more stable livelihood (Leary and Kulkarni 2007). However, because there are many other economic opportunities in the non-farm sector, those whose primary source of employment is not farming may also diversify their sources of income. Descriptive Statistics of the Explanatory and Dependent Variables of the Sample Households Table 7 presents the descriptive statistics of the variables used in the Logistic regression model. The descriptive statistics of the explanatory variables used in the model reveal key insights into livelihood diversification. The Simpson Index of Diversification, ranging from 0 to 0.83 with a mean of 0.356, suggests moderate diversification among respondents. Education levels vary widely (0–15 years), averaging 6.12 years, indicating a generally literate population with significant variation. Household size, with a mean of 4.77 and a range of 2–11, shows average family units. However, the dependency ratio, averaging 37.17 but with a maximum of 78, highlights a high burden of non-working dependents in many households. Age distribution (18–70 years, mean 39.22) suggests a group of respondents largely in middle age, likely influencing employment choices and income diversification. Table 7 Descriptive Statistics of the Explanatory Variables used in the Logistic Regression Model Variable N Minimum Maximum Mean Standard Deviation Simpson Index of Diversification 405 0 0.83 0.356 0.24 Education 405 0 15 6.12 3.79 Household size 405 2 11 4.77 1.09 Dependency Ratio 405 0 78 37.17 16.99 Age 405 18 70 39.22 11.57 Land holding 405 0.5 10 1.82 2.28 NRLM membership 405 0 1 0.545 0.49 Training 405 0 1 0.473 0.502 Access to credit 405 0 1 0.481 0.501 Distance 405 1 9 3.76 1.49 Principal Occupation 405 0 1 .60 0.49 Source : Author’s estimation based on field survey Landholding size varies significantly (0.5–10 Bighas, mean 1.82, SD 2.28), reflecting disparities in resource access. Institutional factors such as NRLM membership (mean 0.545), training (0.473), and access to credit (0.481) indicate that nearly half of respondents have access to livelihood-supporting programs. Distance to nearest urban centers, averaging 3.76 (range 1–9), suggests moderate accessibility challenges. Principal occupation (mean 0.60, SD 0.49) shows that 60% of respondents rely on a farm sector, while others are engaged in non-farm sector. Diagnostic Tests : When the independent variables have a perfect linear connection or are highly correlated, then this is known as multicollinearity (Wooldridge, 2010). The variance inflation factor (VIF) is used to identify the existence of multicollinearity in the model. Maddala ( 2001 ) asserts that multicollinearity is an issue if the VIF for any regressor is equal to or higher than 5. According to the findings in Table 8 , the VIF for each variable is less than 5. Therefore, multicollinearity is not an issue in the model. Again, robust standard error is estimated to address the issue of heteroscedasticity. Table 8 Variance Inflation Factor for Explanatory Variables: Multicollinearity Test Variables VIF 1/VIF Education 1.21 0.83 Household size 1.11 0.90 Dependency Ratio 1.09 0.92 Age 1.96 0.51 Land holding 1.20 0.83 NRLM membership 3.36 0.30 Training 2.15 0.47 Access to credit 1.42 0.70 Distance 1.05 0.95 Principal Occupation 1.07 0.93 Mean VIF 1.56 Source : Author’s estimation based on field survey Results of Logistic Regression : Table 9 presents the logistic regression results examining the factors that determine livelihood diversification among rural households. Based on 405 observations, the model demonstrates strong explanatory power, with an R² value of 0.545 and an Adjusted R² of 0.501, indicating that approximately 50 percent of the variation in livelihood diversification can be explained by the set of independent variables included. Out of the ten explanatory variables included in the model six are found statistically significant to influence the level of livelihood diversification. Explanatory variables like education, household size, NRLM Membership, training, distance and principal occupation of the household have significant influence on the level of livelihood diversification. On the other hand, dependency ratio, age, land holding and access to credit are not found to be statistically significant to influence the level of livelihood diversification of the sample households. Table 9 Factors Determining Livelihood Diversification: Results of Logistic Regression Variable Coefficient Standard Error t value p value Education 0.252 0.079 2.969 0.004*** Household size 0.270 0.233 3.027 0.003*** Dependency Ratio 0.015 0.014 0.224 0.823 Age 0.088 0.025 1.103 0.273 Land holding -0.069 0.116 -0.954 0.342 NRLM membership 0.174 0.081 2.148 0.033** Training 0.016 0.007 2.286 0.024** Access to credit -0.005 0.533 -0.073 0.942 Distance -0.279 0.148 -3.745 0.000*** Principal Occupation 0.113 0.055 2.050 0.041** Constant -5.712 1.796 -3.181 0.002 R2 0.545 Adjusted R2 0.501 Note: *** p < 0.01, ** p < 0.05, * p < 0.10 Source : Author’s estimation based on field survey Among the variables, education (coefficient = 0.252, p = 0.004) shows a positive and statistically significant influence on livelihood diversification at the 1% level. This implies that, holding other factors constant, a one-unit increase in educational attainment increases the log-odds of engaging in diversified livelihoods by 0.252 units. Like other parts of India, in Assam too lack of education is a significant barrier to entry in to the non farm sector, especially the salaried and small businesses. While illiterate and low educated people engage themselves in wage earning, highly educated people choose to diversify their sources of income by choosing self-employment, salaried jobs, and other ventures. Consequently, expanding access to higher education will assist rural people in obtaining alternate sources of income. Raising educational attainment will raise the likelihood of non-farm activity participation and livelihood diversification. This finding is supported by other studies such as Mbewana & Kaseeram ( 2024 ), Habib et al. ( 2023 ), Shan & Ahmed ( 2020 ), Roy et al. (2012). According to these studies, the extent of livelihood diversification increases with the level of education. In line with the expectation the result of the Logistic regression analysis reveals that household size (coefficient = 0.270, p = 0.003) is positively associated with livelihood diversification. Each additional household member increases the log-odds of diversification by 0.270 units, supporting the idea that larger families offer greater labor capacity, necessitating the pursuit of multiple livelihood options. Larger family sizes result in a greater number of available working members, which simultaneously increases the necessity to seek alternative livelihoods beyond farming. This result coincides with the findings Roy et al ( 2018 ), Kelechi ( 2014 ), Kumar et al (2020). These studies proved that households with more family members are more likely participate in non-farm livelihood activities due to the positive correlation between larger family size and household labor as well as corresponding demand for food. As expected NRLM membership is also positively associated with livelihood diversification, with a coefficient of 0.174 (p = 0.033). This indicates that participation in NRLM increases the log-odds of diversification by 0.174 units, reflecting the role of institutional support in facilitating access to training, finance, capacity building and livelihood opportunities. NRLM members benefit from collective action, self-help group engagement, and targeted interventions that empower them economically and socially. Likewise, access to training is a significant factor, with a coefficient of 0.016 (p = 0.024), suggesting that participation in training programmes increases the log-odds of diversification by 0.016 units. Though modest in magnitude, the result highlights the importance of skill development in enabling rural households to participate in varied economic activities. This finding aligns with the observations of Roy et al. ( 2018 ) and Khatun et al. (2012), who emphasize the pivotal role of targeted capacity-building efforts in enabling rural households to transition into non-farm and income-generating ventures. Another critical factor is distance to the urban center, which has a significant negative coefficient of -0.279 (p = 0.000). This implies that for every additional unit increase in distance (e.g., kilometers from urban services), the log-odds of livelihood diversification decrease by 0.279 units. The extent of diversity of one's source of income increases with closer proximity to the urban center (reduced distance). This is because living close to towns or cities offers better job opportunities in non-farm sector. Findings of the studies of Ayana et al. ( 2021 ), Ibrahim et al. ( 2009 ), Roy et al. ( 2018 ) also confirm this argument. The principal occupation of the household also plays a significant role. With a coefficient of 0.113 (p = 0.041), it implies that households primarily engaged in non-farm occupations experience a 0.113 unit increase in the log-odds of diversification. This suggests that engagement in non-agricultural work—such as salaried employment, small businesses, or skilled trades—provides both the financial stability and exposure to additional opportunities that encourage further livelihood diversification. Unlike farming, which is often subject to seasonal and climatic risks, the non-farm sector tends to offer more stable income streams, enabling households to invest in multiple economic ventures. This diversification not only serves as a strategy for income enhancement but also for risk mitigation, improving the household's overall economic resilience (Leary and Kulkarni 2007). On the other hand, variables such as dependency ratio (p = 0.823), age of the household head (p = 0.273), landholding size (p = 0.342), and access to credit (p = 0.942) do not have statistically significant impacts on livelihood diversification in this model. This suggests that these factors may not have a direct impact on the dependent variable within this sample, or their effects might be mediated through other variables not captured in the model. Overall, the model highlights the importance of education, household size, NRLM membership, training, distance to the urban center and nature of primary occupation of the household while other factors like landholding, age, dependency ratio and credit access appear less influential in this context. 5. Conclusions This study provides empirical evidence on the role of the National Rural Livelihoods Mission (NRLM) in promoting livelihood diversification among rural households in Assam. Findings reveal that NRLM beneficiaries exhibit significantly higher levels of livelihood diversification compared to non-beneficiaries, reflecting the effectiveness of the mission’s interventions in encouraging rural households to pursue multiple income-generating activities. The analysis further identifies key socio-economic and institutional factors—such as education, household size, NRLM membership, training, proximity to urban centers, and primary occupation—that significantly influence diversification decisions. From a policy perspective, there is a clear need to strengthen rural education and skill development initiatives to broaden livelihood options, particularly in the non-farm sector. Expanding NRLM’s reach, deepening training interventions, and improving access to markets and infrastructure are essential to enhance non-farm employment opportunities. Special focus should be given to women and marginalized groups through targeted capacity-building and financial inclusion strategies. Policies should also encourage the development of market-oriented enterprises and localized interventions based on district-specific needs, ultimately promoting sustainable livelihoods and greater economic resilience in rural Assam. One key limitation of this study is its reliance on cross-sectional primary data, which restricts the ability to capture changes in livelihood patterns over time or establish causality between NRLM interventions and diversification outcomes. The study is also geographically confined to three districts of Assam, which, while representing regional diversity, may limit the generalizability of the findings to other parts of the state or country. Additionally, the study primarily uses quantitative methods and may not fully capture the nuanced social, cultural, and behavioral factors that influence livelihood decisions. Future research incorporating longitudinal data and qualitative insights could offer a more comprehensive understanding of the dynamics of livelihood diversification. Declarations Ethics Declaration: Not applicable. Clinical trial number Not Applicable. Funding Declaration This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Bhargab Das (First Author, Corresponding Author) was responsible for the conceptualization and design of the study. He conducted extensive fieldwork across Jorhat, Morigaon, and Dhubri districts of Assam, collecting and compiling primary data. He developed the methodology, including the application of the Simpson Index of Diversification and logistic regression analysis. Bhargab Das carried out the statistical analysis, interpreted the results, and drafted the initial manuscript, including the preparation of all tables and figures. He was also responsible for incorporating reviewer feedback and revising the manuscript for final submission.Dr. Suranjan Sarma (Second Author) provided overall academic supervision and guidance throughout the study. He contributed to refining the research objectives, framing the methodology, and validating the analytical approaches. Dr. Sarma critically reviewed the initial and subsequent drafts of the manuscript, offering valuable insights that improved the theoretical and analytical depth of the paper. His role also included final manuscript editing, quality assurance, and approval for publication.Both authors have reviewed and approved the final version of the manuscript prior to submission. Acknowledgement The authors express their sincere gratitude to all the rural households in Jorhat, Morigaon, and Dhubri districts of Assam who generously participated in the field survey, sharing their valuable time and insights. We are also thankful to the officials of the Assam State Rural Livelihoods Mission Society (ASRLMS) for their cooperation and support during data collection.Special thanks are extended to the Department of Economics, Gauhati University, for providing necessary academic guidance and research facilities. The insightful suggestions from our colleagues and academic mentors have greatly enriched this study. References Admasu, T. T., Damtie, Y. A., & Taye, M. A. (2022). Determinants of Livelihood Diversification among Households in the Sub‐Saharan Town of Merawi, Ethiopia. Advances in Agriculture, 2022(1), 6600178. Alemu, T., Pertoldi, C., Hundera, K., & Ambelu, A. (2023). Spatial patterns of riparian vegetation community composition and diversity along human‐affected East African highland streams. Ecohydrology, 16(4), e2524. Ayana, G. F., Megento, T. L., & Kussa, F. G. (2021). 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Livelihood diversification in rural Laos. World development, 83, 231-243. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7585604","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516408925,"identity":"5bca53ec-982b-4414-9823-7d6bdaeca83d","order_by":0,"name":"Bhargab Das","email":"data:image/png;base64,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","orcid":"","institution":"Gauhati University","correspondingAuthor":true,"prefix":"","firstName":"Bhargab","middleName":"","lastName":"Das","suffix":""},{"id":516408926,"identity":"fe7a1a02-fa39-4e20-a0d3-6855d4556d9e","order_by":1,"name":"Suranjan Sarma","email":"","orcid":"","institution":"Gauhati University","correspondingAuthor":false,"prefix":"","firstName":"Suranjan","middleName":"","lastName":"Sarma","suffix":""}],"badges":[],"createdAt":"2025-09-10 18:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7585604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7585604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92145664,"identity":"18dc5fdf-ac8d-4711-b63c-2645310771b8","added_by":"auto","created_at":"2025-09-25 07:05:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62817,"visible":true,"origin":"","legend":"","description":"","filename":"AnEmpiricalAssessmentofNRLMsEfficacy12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/a344b9740a1f08eaff86726c.docx"},{"id":92146618,"identity":"517dadde-a496-413e-926d-62b65891097e","added_by":"auto","created_at":"2025-09-25 07:13:00","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5683,"visible":true,"origin":"","legend":"","description":"","filename":"2ef1998760ec4d199b7df98c4b771ea0.json","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/51cd69f8b4220ff54dead7b0.json"},{"id":92145668,"identity":"e8f66b6f-ea39-4ca6-8a3e-822f3c83cccb","added_by":"auto","created_at":"2025-09-25 07:05:00","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148686,"visible":true,"origin":"","legend":"","description":"","filename":"2ef1998760ec4d199b7df98c4b771ea01enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/54660c7a9773e6852d492b28.xml"},{"id":92145667,"identity":"2a4b11b2-2b6c-451f-9935-cf9d0477571d","added_by":"auto","created_at":"2025-09-25 07:05:00","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147085,"visible":true,"origin":"","legend":"","description":"","filename":"2ef1998760ec4d199b7df98c4b771ea01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/31c639ceddbdc51c54a83806.xml"},{"id":92145666,"identity":"a4609c68-1049-4580-9f64-9cdb9fa8e708","added_by":"auto","created_at":"2025-09-25 07:05:00","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154667,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/5d3a45b9224fec81c07aa54a.html"},{"id":104404418,"identity":"78cbcc9b-01d9-4253-b871-e9b1ccfedf02","added_by":"auto","created_at":"2026-03-11 12:20:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1731739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7585604/v1/f3c1ecb6-a0bd-4ae7-8448-43caf014072b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Empirical Assessment of National Rural Livelihoods Mission’s Efficacy in Promoting Livelihood Diversification in Rural Assam, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the dynamic context of rural economies, livelihood diversification has emerged as a critical strategy for poverty alleviation and economic resilience. Defined as the process by which rural households develop a diverse portfolio of income-generating activities (Ellis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), diversification enables communities to mitigate risks associated with agricultural dependence and environmental uncertainties. It is the process of combining both agricultural and non-agricultural activities to survive and improve the standard of living (Ellis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Niehof, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Martin \u0026amp; Lorenzen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Globally, and particularly in developing regions such as South and Southeast Asia, the diversification of rural livelihoods is increasingly recognized as a pathway to economic stability and improved living standards (FAO/World Bank, 2001; Loison, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn India, where the rural population heavily depends on agriculture, the relative importance of crop cultivation has decreased in most recent times. The agricultural sector in the country is characterized by decreasing farm sizes, low levels of output per farm, fluctuating productivity, vulnerability to climatic shocks, a high degree of subsistence farming, with increases in production being driven mainly by area and not yields growth which have driven households to seek non-farm income sources (Jirström et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Assam is one of the 35 states and union territories of India, situated in the north-eastern part, is a land of varied landscapes, rich biodiversity, and distinct geographical features. The state is predominantly agrarian, with more than 70% of its population reliant on agriculture and allied activities (Government of Assam, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Assam’s economy traditionally revolves around paddy cultivation, tea plantations, and fishing. However, frequent floods, fluctuating agricultural yields, and limited non-farm employment opportunities have made rural livelihoods highly vulnerable. This situation has pushed rural communities to increasingly explore non-agricultural activities such as petty trade, services, and rural non-farm labor as supplementary sources of income (Das \u0026amp; Bora, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecognizing this shift, the Government of India launched the National Rural Livelihoods Mission (NRLM) in the year 2010 which is a flagship programme aimed at fostering sustainable livelihoods through diversification of livelihoods, skill development, and institutional support. From the inception, the Mission encourages the poor households to have multiple livelihoods in order to cope with vulnerabilities. It looks at the entire portfolio of livelihoods of each poor household, and work towards stabilizing and enhancing the existing livelihoods and subsequently diversifying their livelihoods (Government of India, 2012). The Assam State Rural Livelihood Mission Society (ASRLMS), as the NRLM's implementing body in Assam, works to encourage livelihood diversification by facilitating access to credit, providing training, and promoting the formation of Self-Help Groups (SHGs). However, despite NRLM’s significant policy focus and implementation efforts, empirical evidence assessing its actual impact on fostering livelihood diversification remains limited, particularly in the North-Eastern context. Existing studies in India have largely focused on income enhancement or poverty reduction outcomes of NRLM, with limited analysis of how the programme contributes to livelihood diversification specifically. Moreover, most research has concentrated on southern or central Indian states, leaving northeastern states like Assam underexplored (Misra, 2018).\u003c/p\u003e\u003cp\u003eWith this backdrop, the present study seeks to:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAssess the current extent of livelihood diversification among NRLM beneficiary households in rural Assam and compare it with that of non-beneficiary households.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIdentify the key socio-economic, demographic, and institutional factors influencing livelihood diversification decisions of rural households.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Review of Literature","content":"\u003cp\u003eThe literature on livelihood diversification has expanded significantly since Ellis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) established its central role in rural development. It is seen as both a survival strategy for risk-prone households and an accumulation strategy for those seeking upward mobility (Barrett, Reardon, \u0026amp; Webb, 2001). In Sub-Saharan Africa, studies highlight how diversification into non-farm activities reduces vulnerability to climate shocks and enhances resilience (Reardon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Ayana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Asia, rural households often combine farming with services, wage labor, and small businesses to stabilize incomes (Loison, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent work stresses the importance of proximity to markets, access to credit, access to infrastructure, primary occupation of the households and education in shaping diversification outcomes (Rahut \u0026amp; Scharf, 2012; Dinku, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Habib et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A strand of research also explores the gendered dimensions of diversification. Loison (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Martin \u0026amp; Lorenzen (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrate that women’s participation in non-farm activities contributes not only to household income but also to empowerment, though structural constraints often limit access. Similarly, Iorio \u0026amp; Corsale (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Swain \u0026amp; Batabyal (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlight tourism and community-based enterprises as alternative pathways to rural livelihood diversification.\u003c/p\u003e\u003cp\u003eThe agricultural sector in India is characterized by decreasing farm sizes, low levels of output per farm, low productivity, a high degree of subsistence farming, with increases in production being driven mainly by area and not yield growth (Jirström et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the Indian context, diversification has been a key response to agricultural stagnation and rural poverty. Studies across different states show that education, landholding size, access to credit, and infrastructure strongly determine diversification levels (Khatun \u0026amp; Roy, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Roy, Khatun \u0026amp; Roy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Non-farm employment now constitutes a growing share of rural incomes, driven by migration, rural industries, and services (Reardon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Shan \u0026amp; Ahmed, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, regional disparities remain significant, with eastern and northeastern states lagging behind in creating sustainable non-farm opportunities.\u003c/p\u003e\u003cp\u003ePolicy programmes have attempted to address these challenges. The earlier Swarnjayanti Gram Swarozgar Yojana (SGSY) and later NRLM were designed to enhance rural incomes through the strategy of livelihood diversification. However, empirical studies have shown mixed outcomes: while some report improvements in income and women’s empowerment (Mishra, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), others find limited evidence of structural livelihood transformation. Recent studies in Karnataka, Bihar, and West Bengal suggest NRLM has helped households reduce dependence on subsistence farming but that outcomes vary by region (Roy \u0026amp; Basu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Swargiary \u0026amp; Mahanta, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom the review, two key gaps emerge. Firstly, while NRLM has been widely studied in southern and central India, there is a paucity of research in Assam and the broader northeast. Again, existing studies emphasize income enhancement but rarely evaluate diversification as a distinct outcome. Therefore, this study addresses these gaps by empirically assessing NRLM’s role in livelihood diversification in rural Assam, thereby situating the findings within both the rural development and regional studies literature.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e\u003cstrong\u003eData Sources\u003c/strong\u003e\u003cp\u003eThe core work of the present study is based on primary data. The required primary data on extent and determinants of livelihood diversification have been collected from the sample households with the help of appropriately constructed interview schedule. Multistage sampling technique has been employed to select the sample households for the present study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn the first stage, three sample districts have been selected purposively to represent the all three regional division of Brahmaputra valley of Assam. Thus from upper Assam Jorhat district, from middle Assam Marigaon district and from lower Assam Dhubri district have been selected purposively as sample districts in order to conduct the present study. In the second stage, development blocks within each selected district were chosen randomly. From Jorhat district, which comprises six development blocks, Kaliapani and Titabor blocks were selected. In Dhubri district, out of 11 development blocks, Bilasipara and Gauripur blocks were chosen as sample blocks. Similarly, from Marigaon district, Kapili and Moirabari blocks were selected randomly. Thus, a total of six development blocks were identified for the study. In the next step from every selected block, two sample villages have selected for the purpose of the study. One village where livelihood intervention of NRLM has started and one village where livelihood intervention has not started yet have been selected. Thus from Kaliapani Block Boria Gaon and Kotikuchia Gaon and from Titabor Block Bosa Gaon and Bongal Gaon have been selected. Again from Bilasipara Block Baghmari and Kaimari and from Gauripur Block Bagulamari and Bhelakoba villages have been selected as sample villages. Garmai and Kalmoubari from Kapili Block and Haldhibari and Borbori villages from Moirabari block have been selected as the sample village to conduct the present study. After the selection of the sample villages 10 percent households have been selected from both types of sample villages i.e. where livelihood intervention of NRLM has started and where livelihood intervention of NRLN has not started yet. Thus a total of 405 households have been selected as sample households out of which 221 are beneficiary households and 184 are non beneficiary households (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the sampling design of the study).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSampling Design:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Districts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Blocks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample Villages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Household\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSample Households\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eJorhat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKaliapani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBoria Gaon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKotikuchia Gaon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTitabor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBongal Gaon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBosa Gaon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDhubri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBilasipara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaghmari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKaimari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGauripur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBagulamari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBhelakoba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMorigaon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKapili\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGarmari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKalmoubari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMoirabari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHaldhibari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBorbori\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Sample Households\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e405\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cb\u003eSimpson Index of Diversity\u003c/b\u003e: In the present study, the Simpson Index of Diversity (SID) is used to measure the extent or level of livelihood diversification among the sample households. The SID takes into account both the number of income sources and how evenly the distributions of income are made between the various sources. This justifies the use of the SID to measure the level of livelihood diversification in this study over other diversification measures, such as Herfindahl, Shannon, and others (Roy, B. C., Khatun, D., \u0026amp; Roy, A. 2018; Alemu, 2023). It is defined as\u003c/p\u003e\u003cp\u003eSID\u0026thinsp;=\u0026thinsp;1- \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{\\text{i}=1}^{\\text{n}}{\\text{p}}_{\\text{i}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere, Pi as the proportion of income coming from source i. The value of SID always falls between 0 and 1. If there is just one source of income, Pi\u0026thinsp;=\u0026thinsp;1, so SID\u0026thinsp;=\u0026thinsp;0. As the number of sources increases, the shares (Pi) decline, as does the sum of the squared shares, so that SID approaches to 1. If there are k sources of income, then SID falls between zero and 1\u0026ndash;1/k. Accordingly, households with most diversified incomes will have the largest SID, and the less diversified incomes are associated with the smallest SID. For least diversified households SID takes on its minimum value of 0.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLogistic Regression Model\u003c/b\u003e: To the key socio-economic, demographic, and institutional factors influencing livelihood diversification decisions of rural households the logistic regression model is used by taking household-level Simpson Index of Diversification (SID) as the dependent. Since the dependent variable i.e., the Simpson Index of Diversification is an index whose value is confined between the range 0 to1. In this case, use of Ordinary Linear Regression model may predict values for dependent variable which may not be bounded to the range of 0 to1 (Gujarati, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Roy, B.C., et.al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To address this, a logistic transformation is employed to map the bounded dependent variable into the entire real line. The logistic function ensures that the predicted values remain within the (0, 1) interval, thereby preserving the theoretical and logical bounds of the index (Papke \u0026amp; Wooldridge, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The Logistic regression model has been formulated as follows:\u003c/p\u003e\u003cp\u003eY = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{1+\\:{e}^{-z}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere Y is the dependent variable (Simpson Index of Diversification) and z represents the explanatory variables.\u003c/p\u003e\u003cp\u003eDespite the non-linear nature of the original model, estimation can be carried out using linear regression by following the steps of the logistic regression model, which uses Z as the dependent variable.\u003c/p\u003e\u003cp\u003eZ values can now be created by applying the following transformation formula:\u003c/p\u003e\u003cp\u003eZt = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\left(\\frac{{\\text{Y}}_{\\text{t}}}{1-\\:{\\text{Y}}_{\\text{t}}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eIt can be seen from the above formula that Zt is not confined between 0 and 1. However, it adheres rigorously to a relationship with the original index values of the dependent variable. When Y tends to 1, Z tends to \u0026infin;\u0026thinsp;and when Y tends to 0, Z tends to -\u0026infin;. Therefore, Ordinary Least Square regression model may be applied for estimating the parameters of the below equation.\u003c/p\u003e\u003cp\u003eZt = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\left(\\frac{{\\text{Y}}_{\\text{t}}}{1-\\:{\\text{Y}}_{\\text{t}}}\\right)\\)\u003c/span\u003e\u003c/span\u003e = β0\u0026thinsp;+\u0026thinsp;β1​X1t​ + β2​X2​t +\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;+ βk​Xkt + Ut\u003c/p\u003e\u003cp\u003eZt\u0026thinsp;=\u0026thinsp;Log-odds of the outcome (e.g., diversification)\u003c/p\u003e\u003cp\u003eXt\u0026thinsp;=\u0026thinsp;Explanatory Variables\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;Parameters\u003c/p\u003e\u003cp\u003eUt\u0026thinsp;=\u0026thinsp;Error Terms\u003c/p\u003e"},{"header":"4. Results and Discussions","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Demographic and Socio Economic Profile of the Sample Households:\u003c/h2\u003e\u003cp\u003eThe socio-economic characteristics of rural households are of critical importance in understanding livelihood strategies and the degree of livelihood diversification in a region (Braveman PA, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In Assam, as in other parts of rural India, factors such as education, household size, family type, marital status, education, income, access to institutional credit, proximity to urban centers etc. play pivotal roles in shaping livelihood patterns. The present study presents a detailed socio-economic and demographic profile of the sample households, distinguishing between NRLM beneficiaries and non-beneficiaries across three selected districts of Assam. Key variables such as religion, caste, family size, family type, marital status, education, income level, and engagement in livelihood activities have been considered. The detailed demographic and socio-economic profile is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These parameters help to elucidate how socio-economic background influences the households’ access to livelihood opportunities and participation in diversification activities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Socio Economic Profile of the Sample Households\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDistribution of Sample Households of the basis of their Religion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeneficiary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Beneficiary\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.91% (128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.21% (120)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.09% (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.15% (61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0% (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63% (3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of Family Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.53% (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.28% (41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNuclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.47% (169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.71% (143)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of their Social Groups\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial Groups\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.05% (104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.17% (96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.07% (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.56% (36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.29% (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.84% (31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.56% (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.41% (21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of their Number of Family Members\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily Members\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUp to 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.90% (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.93% (33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4–6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.43% (96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.76% (75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7–8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.26% (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.80% (53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.38% (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5% (23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of their Marital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.25% (184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.13% (164)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.97% (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08% (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.76% (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.78% (18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of their Level of Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.36% (45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.08% (48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.22% (69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.17% (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.33% (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25% (46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.45% (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.02% (35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.71% (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71% (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduate and Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90% (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0% (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of their Monthly Households Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 to 5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.79% (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.97% (60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5000 to 10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.41% (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.24% (106)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10000 to 15000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.46% (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.14% (13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15000 to 20000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.95% (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65% (3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20000 to 30000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.60% (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00% (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 30000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.79% (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00% (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution of Sample Households of the basis of the Number of Livelihood Activities Pursued\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Activities Pursued\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBeneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.55% (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.08% (42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.24% (58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.91% (69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.48% (74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.78% (36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.29% (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.64% (23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.07% (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.40% (8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36% (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.20% (4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Primary Data\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe socio-economic profile of the sampled households reveals distinct patterns between NRLM beneficiaries and non-beneficiaries. In terms of religion, Hindus constitute the majority in both groups, though slightly higher among non-beneficiaries (65.21%) compared to beneficiaries (57.91%), while the proportion of Muslims is notably higher among beneficiaries (42.09%) than non-beneficiaries (33.15%). Regarding family structure, nuclear families dominate in both categories, accounting for over around 77% of beneficiary households and nearly 78% of non-beneficiary households. In terms of social grouping, both categories are predominantly from the General and OBC categories, although beneficiaries show a marginally higher representation from SC and ST communities, suggesting NRLM’s outreach among marginalized groups. Household size distribution shows that most households in both categories consist of 4 to 6 members, yet non-beneficiaries tend to have slightly larger households, as seen in the higher percentage (28.80%) falling within the 7–8 member category.\u003c/p\u003e\u003cp\u003eEducation and income profiles highlight further contrasts. A higher proportion of beneficiaries have attained primary and middle-level education, whereas the non-beneficiary group shows a larger share of illiterate members (26.08% compared to 20.36% among beneficiaries). Notably, higher education levels are limited across both groups. Income distribution reveals a significant disparity: while nearly 60% of non-beneficiary households fall within the lower income brackets (below ₹10,000 per month), a substantial portion of beneficiary households report monthly incomes between ₹10,000 and ₹30,000, indicating the possible positive economic impact of NRLM interventions. Livelihood diversification data further supports this, as 33.48% of beneficiary households are engaged in three livelihood activities, compared to just 19.78% among non-beneficiaries. This suggests that NRLM beneficiaries pursue more diverse livelihood strategies, likely contributing to their higher income levels and greater economic resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Extent of Livelihood Diversification among the Sample Households:\u003c/h2\u003e\u003cp\u003eThe traditional agricultural sector in Assam faces persistent challenges such as declining land holdings, fluctuating yields, climatic vulnerabilities, and frequent flooding, all of which constrain household income and stability (Bora et. al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, rural households are compelled to supplement their agricultural activities with non-farm income sources including petty trade, wage labor, handloom and handicraft and services. The Assam State Rural Livelihood Mission Society (ASRLMS), responsible for implementing NRLM in the state, actively promotes livelihood diversification through formation of Self-Help Groups (SHGs), training, and access to institutional credit. Measuring the extent of livelihood diversification among rural households is critical for understanding the effectiveness of these interventions. In this study, the Simpson Index of Diversification (SID) has been employed to quantify diversification levels across NRLM beneficiaries and non-beneficiaries. This is shown in the Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExtent of Livelihood Diversification among the Different Sample District\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample Districts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeneficiaries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Beneficiaries\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue of Simpson Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue of Simpson Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJorhat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMorigaon\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDhubri\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Primary Data\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e highlights the variation in livelihood diversification between beneficiaries and non-beneficiaries across different districts. In Jorhat district, beneficiaries have a Simpson Index of 0.426, which is notably higher than the non-beneficiaries' index of 0.359. In Morigaon, there is a more significant disparity between beneficiaries and non-beneficiaries, with beneficiaries showing an index value of 0.377 compared to 0.243 for non-beneficiaries. This substantial difference suggests that the NRLM's intervention in Morigaon and Jorhat have been particularly effective in promoting livelihood diversification. Non-beneficiaries, however, have much lower index values, indicating a more concentrated reliance on fewer income sources, likely dominated by agriculture.\u003c/p\u003e\u003cp\u003eIn Dhubri district, both beneficiaries and non-beneficiaries have relatively close Simpson Index values, with beneficiaries at 0.398 and non-beneficiaries at 0.311. While beneficiaries still exhibit more diversified livelihoods, the difference is smaller than in the other districts.\u003c/p\u003e\u003cp\u003eThe overall extent of livelihood diversification among the beneficiaries and non beneficiaries are shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall Extent of Livelihood Diversification among the Beneficiaries and Non Beneficiaries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue of Simpson Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e“t” value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBeneficiaries\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003cp\u003eP \u0026lt; 0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Beneficiaries\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Primary Data\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe data on Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals a significant difference in extent of livelihood diversification between beneficiaries and non-beneficiaries, with beneficiaries exhibiting a higher value of the Simpson Index (0.401) compared to non-beneficiaries (0.302). This indicates that NRLM beneficiaries have more diversified sources of income, reflecting the programme’s effectiveness in promoting both farm and non-farm livelihoods. The “t” value of 2.89 with a p-value of less than 0.00 confirms that the difference is statistically significant.\u003c/p\u003e\u003cp\u003eFor further analysis of degree of extent of livelihood diversification, the sample households have been classified into six categories as per their extent of livelihood diversification. The extent of household’s livelihood diversification have been classified into no diversification, very low diversification, low diversification, average diversification, high diversification and full diversification groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Households by the Extent of Livelihood Diversification\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eValue of SID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExtent of diversification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBeneficiaries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eNon-Beneficiaries\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNos.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNos.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{0\u0026gt;to\\:}_{\\text{X}}^{-}-\\:{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eto 0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}_{\\text{X}}{}^{-}-\\:{\\sigma\\:}\\:\\text{t}\\text{o}\\:{}_{\\text{X}}{}^{-}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 to 0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}_{\\text{X}}{}^{-}\\:\\:\\text{t}\\text{o}\\:{}_{\\text{X}}{}^{-}\\:+\\:{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34 to 0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}_{\\text{X}}{}^{-}+\\:{\\sigma\\:}\\:\\text{t}\\text{o}\u0026lt;\\:1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55 to \u0026lt; 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFull diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTotal no of household:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003ex̅= 0.33, σ = 0.22 Source: Primary Data\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe distribution of households by the extent of livelihood diversification shows clear differences between NRLM beneficiaries and non-beneficiaries. The data on Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals that beneficiaries are more likely to have higher levels of livelihood diversification compared to non-beneficiaries.\u003c/p\u003e\u003cp\u003eFor beneficiaries, the majority (39.37%) fall into the \"Average diversification\" category (SID between 0.34 and 0.54), followed by 30.32% in the \"Low diversification\" category (SID between 0.12 and 0.33). Only 11.76% of beneficiaries exhibit \"High diversification\" (SID between 0.55 and 1). A small proportion (3.62%) has no diversification at all, while 14.93% show very low diversification. These figures indicate that NRLM beneficiaries generally experience moderate to low levels of diversification, with a notable portion engaged in both agricultural and non-agricultural activities, though they are still in the process of increasing their diversification. On the other hand, non-beneficiaries show a different pattern. A larger portion (41.85%) of non-beneficiaries fall under the \"Low diversification\" category, and 26.09% fall under \"Average diversification.\" Notably, only 5.98% of non-beneficiaries reach the \"High diversification\" category, much lower than the 11.76% of beneficiaries. Moreover, 8.70% of non-beneficiaries have no diversification at all, and 17.39% show very low diversification. These figures highlight that non-beneficiaries are more likely to have concentrated income sources, particularly in agriculture, compared to beneficiaries.\u003c/p\u003e\u003cp\u003eIn summary, the data indicates that NRLM beneficiaries, on average, enjoy greater livelihood diversification, as reflected by their higher presence in the \"Average\" and \"High diversification\" categories, whereas non-beneficiaries remain more reliant on limited income sources, showing lower overall diversification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Determinants of Livelihood Diversification:\u003c/h2\u003e\u003cp\u003eDifferent studies on livelihood diversification have revealed that the nature and the extent of livelihood diversification are influenced by different socio-economic, demographic, environmental and cultural factors. Ayantoye et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified that gender, marital status, poverty status, primary occupation and membership of association are the most important factors that influence the households’ level of livelihood diversification. Similar study carried out by Ng’ang’a et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that educational level had a positive correlation on level of diversified livelihood. Ibrahim et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) reported that distance to market directly influence livelihood diversification while study by Birhanu and Getachew (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) revealed that access to credit had a direct relationship with livelihood diversification. Kelechi (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also reported that age, primary occupation, household size, farm income, access to credit, experience and membership to cooperative society were the significant determinants of farmer’s decision to embark on non-farm enterprise. It is also found that distance to market and access (Saha and Bahal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), location and distance to markets (Dinku, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), growth in infrastructures (Rahut and Scharf, 2012), access to public assets, social factors like networks and association (Ellis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and developmental factors like tourism (Iorio and Corsale, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Swain and Batabyal, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) had also enhanced livelihood diversification in some areas.\u003c/p\u003e\u003cp\u003eIt is, thus, observed that various factors influenced the extent of livelihood diversification of a household to great extent. Therefore, this portion of this study identifies the different factors determining the diversity of livelihoods among the sample households and quantitatively assesses their relative influence.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescription of the Variables\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe selection of these variables is informed by a range of theoretical and empirical studies conducted across India and other regions, including works by Roy, A., \u0026amp; Basu, S. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Roy, B. C., Khatun, D., \u0026amp; Roy, A. (2018); Kassegn, A., \u0026amp; Abdinasir, U. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Swargiary et al., (2020); Khatun and Roy, (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Leary and Kulkarni (2007) etc. A total of ten key explanatory variables have been identified to assess their impact on the level of livelihood diversification. The details of these variables are outlined below. See Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for the description of variables and their expected signs used in the logistic regression model.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eEducation is considered as a significant determinant of livelihood diversification (Habib, N., Rankin, P., Alauddin, M., \u0026amp; Cramb, R., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Persons with higher levels of education are generally skilled enough to engage themselves in a variety of income-generating activities. In other words, education gives people the ability to identify and pursue a variety of income sources outside of traditional occupations (Roy, A., \u0026amp; Basu, S. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We took into account the level of education of the respondent to quantify the impact of education on the level of livelihood diversification. It is assumed that there is a positive correlation between education and level of livelihood diversification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHousehold size\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOne significant factor influencing diversification of livelihood is the size of the family. Total number of working hands available in the household plays a crucial role in determining the extent of livelihood diversification of a household (Roy, B. C., Khatun, D., \u0026amp; Roy, A. 2018). According to Reardon (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), a household's capacity to provide labor for the farm is influenced by the size of its household. While some members of a big family may be able to participate in conventional farming activities, others may be able to pursue non-farm pursuits which will lessen the chance of livelihood failure. In the present study a positive association is assumed between livelihood diversification and household size.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDependency Ratio\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWhile choosing whether or not to diversify, the dependency ratio is a crucial consideration. The ability of a household to meet subsistence needs decreases as the dependency ratio rises, and the household is forced to diversify their sources of income. Put another way, it can be said that as the dependency ratio rises, households' capacity to provide for their families declines and their likelihood of diversifying their sources of income to non-farm pursuits rises (Kassegn, A., \u0026amp; Abdinasir, U. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, we postulated a positive correlation between the dependency ratio and livelihood diversification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSince younger people are more active and have a higher propensity to participate in non-farm activities, age plays a significant role in determining the degree of diversification. Younger people typically possess the skills which are required for urban-centered non-farm sector work (Roy, A., \u0026amp; Basu, S. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the age of the respondents are considered as an independent variable for determining extent of livelihood diversification. It is hypothesized that a younger respondent will be more inclined to engage in a variety of non-farm pursuits, which will increase the diversification of their sources of income (Roy, B. C., Khatun, D., \u0026amp; Roy, A. 2018).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptions of Variables and their Expected Sign used in the Logistic Regression Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpected Sign\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDependent Variable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson Index of Diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue of Simpson Index of Livelihood Diversification of the family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex Value (0–1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation level of the respondent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of family members in a household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of family members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependency Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of household members below 18 years old and above 60 years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge of the respondent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand holding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize of landholding of the respondent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBigha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRLM membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhether or not any household member is a beneficiary of NRLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 = yes, 0 = no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewhether or not any household member received any formal training on livelihood skill development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 = yes, 0 = no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhether the respondent has access to credit in the preceding five years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 = yes, 0 = no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance of the nearest urban center from the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKilometers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrincipal Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrincipal occupation of the households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 = Non Farm, 0 = Farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+/-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLand holding\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOwnership of land of the households is considered to have a significant effect on the increments of their livelihood diversification (Admasu, T. T., Damtie, Y. A., \u0026amp; Taye, M. A. 2022). In rural regions of counties like India, land ownership is crucial for both farmers and non-farmers. Land holding enables the farmers to diversify towards high value crops and non farmers towards non-farm sector livelihood activities self employment, petty business or even leasing out. Therefore, land ownership by the rural households are expected to increase the households’ extent of livelihood diversifications (Roy, B. C., Khatun, D., \u0026amp; Roy, A. 2018).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNRLM membership\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eEncouraging poor households to have multiple livelihoods in order to cope with vulnerabilities is one of the most important objectives of NRLM (Government of India, 2012). NRLM would look at the entire portfolio of livelihoods of each poor household, and work towards stabilizing and enhancing the existing livelihoods and subsequently diversifying their livelihoods. Membership to a formal social organization like SHG/cooperative/village committee, etc. is an important social capital in determining the extent of livelihood diversification. Memberships of such organization empower the person which increases the scope to engage in multiple livelihood activities (Roy, B. C., Khatun, D., \u0026amp; Roy, A. 2018). Therefore, we postulate that NRLM membership and livelihood diversification are positively correlated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTraining\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTraining is considered to have a positive relationship with livelihood diversification since it raises the skill and competency level of individuals (Kumar, M. A., \u0026amp; Umesh, K. B. (2020). Training and skill development aid in the acquisition of new employment opportunities in the non-farm sectors, as the majority of activities in this sector are skill-based. Diversification of livelihood, especially from the farm to non-farm sectors, is hampered by the lack of programmes and initiatives for training and skill development (Swargiary et al., 2020). Certain self-employment jobs, such as tailoring, machine repairing, small-scale agricultural processing, etc., also call for training. Therefore, a positive correlation is expected between training and livelihood diversification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAccess to credit\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe majority of the have people living in rural areas are marginal and small farmers who have poor financial conditions (Khatun, D., \u0026amp; Roy, B. C. 2012). They lack the funds necessary for high value crops. Furthermore, a significant amount of capital investment is needed for non-farm economic activity. Therefore, the accessibility of institutional credit at a low interest rate would enable rural residents to engage in multiple economic endeavors (Swargiary et al., 2020). Access to institutional credit is therefore considered a determinant of livelihood diversification. It is assumed a positive association between institutional credit and livelihood diversification for the present study (Khatun and Roy, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDistance\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDiversification of livelihoods is also significantly influenced by geographic factors. The prospects of non-farm employment opportunities rise with market or urban center proximity (Kumar, M. A., \u0026amp; Umesh, K. B. 2020). Households that are closest to the urban centers are expected to engage in non-farm activities. When looking for jobs, especially during the off-season, the workers can easily communicate. As a result, households in villages near metropolitan centers are more likely to participate in non-farm activities. Therefore, it is anticipated that the there will be a negative correlation between the distance to the city center and level of livelihood diversification (Roy, B. C., Khatun, D., \u0026amp; Roy, A. (2018).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePrincipal Occupation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrincipal occupation of the household is also considered as one of the important determinants of livelihood diversification. A household's income sources can be divided into two categories, such as farm and non-farm. Families where farming is the primary source of income might want to diversify their economic activity to non-farm sector because of the risk and uncertainty associated with the farm sector. People view diversification as a risk-reduction tactic that allows them to choose multiple lifestyle options at the same time (Swargiary et al., 2020). On the other hand, people engaged in the non-farm sector are more likely to meet the essential requirements of the family and are more likely to have a more stable livelihood (Leary and Kulkarni 2007). However, because there are many other economic opportunities in the non-farm sector, those whose primary source of employment is not farming may also diversify their sources of income.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDescriptive Statistics of the Explanatory and Dependent Variables of the Sample Households\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the descriptive statistics of the variables used in the Logistic regression model. The descriptive statistics of the explanatory variables used in the model reveal key insights into livelihood diversification. The Simpson Index of Diversification, ranging from 0 to 0.83 with a mean of 0.356, suggests moderate diversification among respondents. Education levels vary widely (0–15 years), averaging 6.12 years, indicating a generally literate population with significant variation. Household size, with a mean of 4.77 and a range of 2–11, shows average family units. However, the dependency ratio, averaging 37.17 but with a maximum of 78, highlights a high burden of non-working dependents in many households. Age distribution (18–70 years, mean 39.22) suggests a group of respondents largely in middle age, likely influencing employment choices and income diversification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of the Explanatory Variables used in the Logistic Regression Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson Index of Diversification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependency Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand holding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRLM membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrincipal Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author’s estimation based on field survey\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLandholding size varies significantly (0.5–10 Bighas, mean 1.82, SD 2.28), reflecting disparities in resource access. Institutional factors such as NRLM membership (mean 0.545), training (0.473), and access to credit (0.481) indicate that nearly half of respondents have access to livelihood-supporting programs. Distance to nearest urban centers, averaging 3.76 (range 1–9), suggests moderate accessibility challenges. Principal occupation (mean 0.60, SD 0.49) shows that 60% of respondents rely on a farm sector, while others are engaged in non-farm sector.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Tests\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eWhen the independent variables have a perfect linear connection or are highly correlated, then this is known as multicollinearity (Wooldridge, 2010). The variance inflation factor (VIF) is used to identify the existence of multicollinearity in the model. Maddala (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) asserts that multicollinearity is an issue if the VIF for any regressor is equal to or higher than 5. According to the findings in Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the VIF for each variable is less than 5. Therefore, multicollinearity is not an issue in the model. Again, robust standard error is estimated to address the issue of heteroscedasticity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariance Inflation Factor for Explanatory Variables: Multicollinearity Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/VIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependency Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand holding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRLM membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrincipal Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean VIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author’s estimation based on field survey\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults of Logistic Regression\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the logistic regression results examining the factors that determine livelihood diversification among rural households. Based on 405 observations, the model demonstrates strong explanatory power, with an R² value of 0.545 and an Adjusted R² of 0.501, indicating that approximately 50 percent of the variation in livelihood diversification can be explained by the set of independent variables included. Out of the ten explanatory variables included in the model six are found statistically significant to influence the level of livelihood diversification. Explanatory variables like education, household size, NRLM Membership, training, distance and principal occupation of the household have significant influence on the level of livelihood diversification. On the other hand, dependency ratio, age, land holding and access to credit are not found to be statistically significant to influence the level of livelihood diversification of the sample households.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors Determining Livelihood Diversification: Results of Logistic Regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependency Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand holding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRLM membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrincipal Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.041**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: *** p \u0026lt; 0.01, ** p \u0026lt; 0.05, * p \u0026lt; 0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author’s estimation based on field survey\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the variables, education (coefficient = 0.252, p = 0.004) shows a positive and statistically significant influence on livelihood diversification at the 1% level. This implies that, holding other factors constant, a one-unit increase in educational attainment increases the log-odds of engaging in diversified livelihoods by 0.252 units. Like other parts of India, in Assam too lack of education is a significant barrier to entry in to the non farm sector, especially the salaried and small businesses. While illiterate and low educated people engage themselves in wage earning, highly educated people choose to diversify their sources of income by choosing self-employment, salaried jobs, and other ventures. Consequently, expanding access to higher education will assist rural people in obtaining alternate sources of income. Raising educational attainment will raise the likelihood of non-farm activity participation and livelihood diversification. This finding is supported by other studies such as Mbewana \u0026amp; Kaseeram (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Habib et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Shan \u0026amp; Ahmed (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Roy et al. (2012). According to these studies, the extent of livelihood diversification increases with the level of education.\u003c/p\u003e\u003cp\u003eIn line with the expectation the result of the Logistic regression analysis reveals that household size (coefficient = 0.270, p = 0.003) is positively associated with livelihood diversification. Each additional household member increases the log-odds of diversification by 0.270 units, supporting the idea that larger families offer greater labor capacity, necessitating the pursuit of multiple livelihood options. Larger family sizes result in a greater number of available working members, which simultaneously increases the necessity to seek alternative livelihoods beyond farming. This result coincides with the findings Roy et al (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Kelechi (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Kumar et al (2020). These studies proved that households with more family members are more likely participate in non-farm livelihood activities due to the positive correlation between larger family size and household labor as well as corresponding demand for food.\u003c/p\u003e\u003cp\u003eAs expected NRLM membership is also positively associated with livelihood diversification, with a coefficient of 0.174 (p = 0.033). This indicates that participation in NRLM increases the log-odds of diversification by 0.174 units, reflecting the role of institutional support in facilitating access to training, finance, capacity building and livelihood opportunities. NRLM members benefit from collective action, self-help group engagement, and targeted interventions that empower them economically and socially. Likewise, access to training is a significant factor, with a coefficient of 0.016 (p = 0.024), suggesting that participation in training programmes increases the log-odds of diversification by 0.016 units. Though modest in magnitude, the result highlights the importance of skill development in enabling rural households to participate in varied economic activities. This finding aligns with the observations of Roy et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Khatun et al. (2012), who emphasize the pivotal role of targeted capacity-building efforts in enabling rural households to transition into non-farm and income-generating ventures.\u003c/p\u003e\u003cp\u003eAnother critical factor is distance to the urban center, which has a significant negative coefficient of -0.279 (p = 0.000). This implies that for every additional unit increase in distance (e.g., kilometers from urban services), the log-odds of livelihood diversification decrease by 0.279 units. The extent of diversity of one's source of income increases with closer proximity to the urban center (reduced distance). This is because living close to towns or cities offers better job opportunities in non-farm sector. Findings of the studies of Ayana et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Ibrahim et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Roy et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also confirm this argument.\u003c/p\u003e\u003cp\u003eThe principal occupation of the household also plays a significant role. With a coefficient of 0.113 (p = 0.041), it implies that households primarily engaged in non-farm occupations experience a 0.113 unit increase in the log-odds of diversification. This suggests that engagement in non-agricultural work—such as salaried employment, small businesses, or skilled trades—provides both the financial stability and exposure to additional opportunities that encourage further livelihood diversification. Unlike farming, which is often subject to seasonal and climatic risks, the non-farm sector tends to offer more stable income streams, enabling households to invest in multiple economic ventures. This diversification not only serves as a strategy for income enhancement but also for risk mitigation, improving the household's overall economic resilience (Leary and Kulkarni 2007).\u003c/p\u003e\u003cp\u003eOn the other hand, variables such as dependency ratio (p = 0.823), age of the household head (p = 0.273), landholding size (p = 0.342), and access to credit (p = 0.942) do not have statistically significant impacts on livelihood diversification in this model. This suggests that these factors may not have a direct impact on the dependent variable within this sample, or their effects might be mediated through other variables not captured in the model. Overall, the model highlights the importance of education, household size, NRLM membership, training, distance to the urban center and nature of primary occupation of the household while other factors like landholding, age, dependency ratio and credit access appear less influential in this context.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e This study provides empirical evidence on the role of the National Rural Livelihoods Mission (NRLM) in promoting livelihood diversification among rural households in Assam. Findings reveal that NRLM beneficiaries exhibit significantly higher levels of livelihood diversification compared to non-beneficiaries, reflecting the effectiveness of the mission’s interventions in encouraging rural households to pursue multiple income-generating activities. The analysis further identifies key socio-economic and institutional factors—such as education, household size, NRLM membership, training, proximity to urban centers, and primary occupation—that significantly influence diversification decisions.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, there is a clear need to strengthen rural education and skill development initiatives to broaden livelihood options, particularly in the non-farm sector. Expanding NRLM’s reach, deepening training interventions, and improving access to markets and infrastructure are essential to enhance non-farm employment opportunities. Special focus should be given to women and marginalized groups through targeted capacity-building and financial inclusion strategies. Policies should also encourage the development of market-oriented enterprises and localized interventions based on district-specific needs, ultimately promoting sustainable livelihoods and greater economic resilience in rural Assam.\u003c/p\u003e\u003cp\u003eOne key limitation of this study is its reliance on cross-sectional primary data, which restricts the ability to capture changes in livelihood patterns over time or establish causality between NRLM interventions and diversification outcomes. The study is also geographically confined to three districts of Assam, which, while representing regional diversity, may limit the generalizability of the findings to other parts of the state or country. Additionally, the study primarily uses quantitative methods and may not fully capture the nuanced social, cultural, and behavioral factors that influence livelihood decisions. Future research incorporating longitudinal data and qualitative insights could offer a more comprehensive understanding of the dynamics of livelihood diversification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics Declaration:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Declaration\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBhargab Das (First Author, Corresponding Author) was responsible for the conceptualization and design of the study. He conducted extensive fieldwork across Jorhat, Morigaon, and Dhubri districts of Assam, collecting and compiling primary data. He developed the methodology, including the application of the Simpson Index of Diversification and logistic regression analysis. Bhargab Das carried out the statistical analysis, interpreted the results, and drafted the initial manuscript, including the preparation of all tables and figures. He was also responsible for incorporating reviewer feedback and revising the manuscript for final submission.Dr. Suranjan Sarma (Second Author) provided overall academic supervision and guidance throughout the study. He contributed to refining the research objectives, framing the methodology, and validating the analytical approaches. Dr. Sarma critically reviewed the initial and subsequent drafts of the manuscript, offering valuable insights that improved the theoretical and analytical depth of the paper. His role also included final manuscript editing, quality assurance, and approval for publication.Both authors have reviewed and approved the final version of the manuscript prior to submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their sincere gratitude to all the rural households in Jorhat, Morigaon, and Dhubri districts of Assam who generously participated in the field survey, sharing their valuable time and insights. We are also thankful to the officials of the Assam State Rural Livelihoods Mission Society (ASRLMS) for their cooperation and support during data collection.Special thanks are extended to the Department of Economics, Gauhati University, for providing necessary academic guidance and research facilities. The insightful suggestions from our colleagues and academic mentors have greatly enriched this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdmasu, T. T., Damtie, Y. A., \u0026amp; Taye, M. A. (2022). Determinants of Livelihood Diversification among Households in the Sub‐Saharan Town of Merawi, Ethiopia. Advances in Agriculture, 2022(1), 6600178.\u003c/li\u003e\n\u003cli\u003eAlemu, T., Pertoldi, C., Hundera, K., \u0026amp; Ambelu, A. (2023). Spatial patterns of riparian vegetation community composition and diversity along human‐affected East African highland streams. Ecohydrology, 16(4), e2524.\u003c/li\u003e\n\u003cli\u003eAyana, G. F., Megento, T. L., \u0026amp; Kussa, F. G. (2021). The extent of livelihood diversification on the determinants of livelihood diversification in Assosa Wereda, Western Ethiopia. GeoJournal, 1-25.\u003c/li\u003e\n\u003cli\u003eAyantoye, K.I, Amao, J. O. and Fanifosi, G. E.(2017).Determinants of livelihood diversification among rural households in Kwara State, Nigeria. International Journal of Advanced Agricultural Research, 5, 82-88\u003c/li\u003e\n\u003cli\u003eBirhanu, N.D. and Getachew, D.D. (2016). Livelihood Diversification: Strategies, Determinants and Challenges for Pastoral and Agro-Pastoral Communities of Bale Zone, Ethiopia. International Review of Social Sciences and Humanities, 11(2), 37-51.\u003c/li\u003e\n\u003cli\u003eBora, D., Das, B., \u0026amp; Bhuyan, R. (2021). KAMRUP (R) DISTRICT IN ASSAM. Journal of Rural Development, 40(4), 515-529.\u003c/li\u003e\n\u003cli\u003eBraveman, P. A., Cubbin, C., Egerter, S., Chideya, S., Marchi, K. S., Metzler, M., \u0026amp; Posner, S. (2005). Socioeconomic status in health research: one size does not fit all. Jama, 294(22), 2879-2888.\u003c/li\u003e\n\u003cli\u003eDas, B., \u0026amp; Bora, D. (2020). Determinants of farm productivity in flood prone area: A study in Dhemaji District of Assam. 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World development, 83, 231-243.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Livelihood Diversification, National Rural Livelihood Mission, Rural Assam, Simpson Index of Diversification, Logistic Regression, Impact Assessment","lastPublishedDoi":"10.21203/rs.3.rs-7585604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7585604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLivelihood diversification is increasingly recognized as a crucial strategy for rural households in underdeveloped economies to reduce risks linked to agricultural dependence and climate variability. This study assesses the effectiveness of India\u0026rsquo;s National Rural Livelihoods Mission (NRLM) in promoting livelihood diversification, based on primary data from 405 rural households across Jorhat, Morigaon, and Dhubri districts of Assam, a northeastern state of India. The Simpson Index of Diversity (SID) was used to measure the extent of diversification, while logistic regression analysis identified key socio-economic and institutional determinants. The findings indicate that NRLM beneficiary households have significantly more diversified livelihoods than non-beneficiaries. Factors such as educational attainment, household size, NRLM membership, participation in training programs, and proximity to urban centers were found to significantly influence diversification decisions. The study confirms NRLM\u0026rsquo;s role in expanding rural livelihood opportunities beyond traditional agriculture. To enhance economic resilience and reduce rural vulnerability, policy efforts should focus on strengthening skill development, improving access to education, and expanding NRLM\u0026rsquo;s institutional support across rural Assam.\u003c/p\u003e","manuscriptTitle":"An Empirical Assessment of National Rural Livelihoods Mission’s Efficacy in Promoting Livelihood Diversification in Rural Assam, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 07:04:55","doi":"10.21203/rs.3.rs-7585604/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd3e2993-db61-44bf-802c-452fef2f57fe","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T13:57:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 07:04:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7585604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7585604","identity":"rs-7585604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}