Male Labour Migration and Economic Well-Being Among Rural Left-Behind Wives of India

Male Labour Migration and Economic Well-Being Among Rural Left-Behind Wives of 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 Male Labour Migration and Economic Well-Being Among Rural Left-Behind Wives of India SONEL SOM, Mahadeb Das, Piyal Basu Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8952197/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract This study investigates the impact of rural male labour migration on the economic well-being of left-behind wives in the Cooch Behar District of India, a less developed region. Utilizing a Multiple Linear Regression (MLR) model, the research identifies significant socio-economic factors influencing the economic well-being of these women. Key findings indicate that the number of days males are employed at their destination (Beta = 5.863) and the number of migrated husbands from a household (Beta = 0.904) are strong positive predictors of economic well-being, reflecting increased remittance. Conversely, factors such as the interval of husbands' return (Beta = -1.566), the number of dependents (Beta = -0.676), and the age of migrated husbands (Beta = -0.035) negatively affect economic well-being. The study highlights the dual role of migration, where remittances enhance economic stability, yet long absences and high dependency ratios pose challenges. It underscores the importance of remittance flow in improving economic conditions and suggests that increasing local job opportunities could mitigate migration, fostering regional development and better economic well-being for left-behind wives. Male labour Migration Left Behind women Economic well Being Remittance Figures Figure 1 Figure 2 Figure 3 1. Introduction Human population migration is an eternal integral element of mankind history. Since, pre-historic era humankind travelled far wide, spanning the continents for better opportunities of fundamental requirements .The universal phenomena of movement of people from one place to another for settling down is commonly known as ‘migration’ (Roy, 2001 ). The flow of migration largely depends on regional disparity whether physical or economic. Economic opportunities and benefits motivate an individual to relocate from one place to another (Zelinksky, 1971). This mechanism is recognised as ‘Labour Migration’ (Sanyal & Maity, 2018 ). Labour migration is indeed a gendered phenomenon eventually dominated by males and could be redefined as “Male Labour Migration” (Mendola & Carletto, 2012 ). It can be classified as external and internal, depending on male labourer’s place of origin. Internal male labour migration is an extensively prominent incident worldwide and not exception to India. A large share of male internal labour migrants (35.02 million) in total 41.43 million (Census, 2011) therefore, confirms the notion. Simultaneously, in Cooch Behar district of the country, every 17th rural male labourer is a migrated labourer and every 24th rural women are convinced to be left behind rural women (Office of District Magistrate, 2020). Rural male labour migration contributes an intricate multi-dimensional impact on physical, psychological, social and economic well-being of left-behind wives. Well-being varies from mothers to wives and children, from young’s to middle aged and olds, from less literates to more literates and so on (Desai and Banerjee, 2008; Sevoyan and Agadjanian ,2010; Gartuala et al, 2012; Sinha et al, 2012 ; Fakir and Abedin ,2020). Perhaps, husband’s migration encourage confidence, independence and empowerment of rural left-behind wives, but at the same time it also invite anxiety, stress and disappointment in their lives (Ullah, 2017 ). Although, the elementary objective of labour migration is to guarantee benefit in their income amount and proportionate hike in remittance amount. This result to economic prosperity, welfare and economic well-being of left-behind wives (Tumbe, 2011 ; Mahapatro et al. 2017 ). Hence, it is often hindered by several socio-economic elements like, extended family structure, large numbers of dependent, older age of the migrated husband, lower education level of the wives and longtime interval in return of the migrants (Sabur & Mahmud, 2008 ; Ghimire et al.2021). Therefore, potentiality and trend of economic well-being depend response of these socio-economic factors. 2. Male labour migration, Left-behind Wives and Economic well-being Rural Indian economy as a whole is regulated by monsoon dependent agricultural activity (Chakraborty & Shukla, 2020 ). Seasonal occurrence of Indian monsoonal rainfall creates fluctuation in agricultural productivity. Monsoonal season is highly productive season and non-monsoonal season is less productive season. Therefore, demand-supply of rural laborers in agricultural activities varies correspondingly with agricultural production (Fink et al., 2020 ). As, non-monsoonal season causes constrains in demand - supply of male agricultural laborers, a large quantity of them in the particular season are suddenly converted to unemployed laborers (Ali, 1993 ). They start suffering from misery of poverty, hunger, starvation, family feuds and financial crisis (Sarkar & Mishra, 2021 ). To resolve the present circumstance, they tend to migrate away from their native villages and joined in alternate livelihoods at different urban centres (Keshri, & Bhagat, 2012 ). Usually, they get transformed in secondary sector workers to assure their continuous income all year round (Rogaly & Coppard, 2003 ). Their uninterrupted earnings establish a counter behavioural link in form of continuous financial flow between migrant and economic welfare of the left-behind families (Stark & Fan, 2007 ; Parida & Madheswaran, 2011 ; Torres & Carte, 2016 ). As a consequence, income, consumption and investment strategies of these families are controlled by their received remittance (Wang et al., 2021 ). Economic well-being is a kind of objective well-being that does not only determine quantity of economic prosperity but also the quality of such prosperity in terms of choice of employment, control over income, maintain standard of life and maintaining consumption of food, clothing, housing, stability in income and above all investment for future (OECD, 2020 ). In fact, male labour migration definitely support economic well-being by performing income hike for left-behind families (Semyonov & Gorodzeisky, 2008 ). Consequently, planned remittance consumption as well as investment by rural left-behind wives in family welfare does exhibit economic well-being (Connell & Brown, 1995 ; Yabiku, 2010; Hunter & Hunter, 2018 ). Again, in absence of male counter-parts, they became primary recipient of remittance and are inspired to be household managers, autonomous economic decision makers and financial supervisors of their families (Archambault, 2010 ; Haan & Rooij, 2010 ; Singh et al. 2011 ). Their active participation in agriculture workforce, access to farm production, household income and resources, financial management, budget, food security and supply also initiate feminization of household economy (De Haan & Rogaly, 2002 ; Maharjan et al., 2012 ; De Brauw et al. 2013 ). The paradigm shift in rural household economics and its control together portrays their economic well-being (Nguyen et al., 2006 ). So far, several literatures tried to analyse the advantage of rural left behind wives in their social position, social contact, work participation rate, economic independence, decision making, family management and empowerment (Hadi, 2001 ; Rajan, 2004 ; Menjivar & Agadjanian, 2007; Mahapatro, 2018; Choithani, 2019, Fernandez-Sanchez et al, 2021) but somehow, their well-being remained ignored and unexplored. Also, there is dearth of scientific studies on economic well-being of rural left behind wives, putting husband’s migration and its nature at the central role. Analysis of level and trend of economic well-being is a complex procedure. The present study is distinctive in this sense as it not only examines rural left behind wives level of economic well-being but also recognize the definite factors those influence their economic well-being. 3. Methodology To fulfil the objective of the study following methodology has been adopted: 3.1. Sampling Technique and sample size: 400 respondents (95% Confidence Interval, 4% Margin of error) (Cochran,1977; Moore and McCabe, 1989 ) from rural left-behind wives of Coochbehar District were selected by snowball sampling technique from 120 villages during field visit in 2019–2022. The cohort of the respondents belong to 18–70 age group and both from small and large families. Responses are collected with help of a self-structured questionnaire. All respondents participate spontaneously with their self-consent in the interview procedure. 3.2. Economic Well-being Index: Economic Well-Being Index was formulated with the help 19 items (amount of remittance received, average monthly income of the family, involvement of the respondent in any paid job, expenditure in basic needs, education, healthcare, others; investment in agriculture, house construction and several savings scheme, interval of receiving remittance, dependency on remittance, achieving monetary solvency through remittance, possession of agricultural land, bank savings, house type, banking operation, decision and control over remittance expense and budget, management of remittance in financial crisis) (ABS, 2015; OECD, 2020 ; Hasan & Jebin, 2020). All the items are quantified in a five-point Likert Scale (never = 1 and always = 5). The internal consistency of the items was measured performing reliability test and Cronbach’s Alpha value has been found acceptable (0.621) (Cronbach, 1951 ). The item scores are then sum-up to develop the Economic Well-being Index Score (EWBI). ECONOMIC WELL-BEING INDEX (EWBI) = \(\sum_{p=1}^{p}{m}_{p}\) $$={m}_{1}+{m}_{2}+\dots\dots\dots.+{m}_{\text{p}}$$ ( \({m}_{1}\) ………………. \({m}_{\text{p}}\) are the items.) 3.3. Statistical Method: In order to test the hypothesis, Multiple Linear Regression (MLR) was used. MLR, one of the mostly used statistical techniques, helps to predict an outcome variable by using several exploratory variables (Field, 2013 ). MLR model can be much more realistic than the uni-factorial regression model which use only one exploratory variable to predict the outcome variable (Turóczy & Marian 2012 ). In this work MLR was implemented to predict the dependent variable Y by five no of predictors in SPSS (Statistical Package for Social Sciences, 20). 3.3.1. Dependent Variable: Economic Well-Being Index Score has been considered as outcome variable. 3.3.2. Independent Variables: Five variables of migration have been considered as predictors in the MLR model. The Variables are a) No. of migrated husband from a house hold . b) No. of days employed at destination c) No. of days interval of migrants return d) No. of dependents in a household . e) Age of the migrant : The migrants aged above 18 years has been considered (Trager,1984; Thamas & Adhikari, 2012 ; Bhattachrjee,2020; Arokkiaraj et.al,2021; Sarkar &Mishra, 2021 ). The equation of MLR is: y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3+ β 4 X 4 + β 5 X 5 + e where y is the Economic Well-Being Index Score, β 0 is the intercept value, X 1 is No. of migrated husband from a house hold, X 2 is No. of days employed at destination, X 3 is No. of days interval of migrants return, X 4 is No. of dependents in a household and X 5 is Age of the migrant, and the β 1 to β 5 are the estimated regression co-efficient of respective independent variables and e is the model error i.e., the variation of estimate of y to the real value. In order to generate the MLR model the following assumptions were verified: No multicollinearity among the independent variables, verified through (a) correlation coefficients between 0.3 to 0.8 (Table no. 1) (b) tolerances >0.2 (Table no.2) (?) and (c) variance inflation factors (VIFs)0.001) (Tabachnick, & Fidell, 2013). The independence of residuals (or errors) by the Durbin-Watson value, 1.548 (Table No. 3), signifies the independence of the residuals; the acceptable range is1.5 to 2.5 (Durbin & Watson, 1971). The value of Cook’s distance found 0.093 (Table No.3)i.e. less than the threshold value 1 , proved that there were no outliers in the data set that may negatively affect the estimate of the coefficients. The normal P-P plot of Regression standardized Residual with Expected Normal Value in Y axis and Observed value in X axis shows location of points closure to the diagonal line prove the normality of the residual .The histogram with a superimposed normal curve also demonstrates the normal distribution of the residuals (Fig no. 1(a,b)). The homogeneity of residual variance (homoscedasticity) was verified by scatter plot of standardized residuals" against the "standardized predicted value (Fig No.2). The linear relationship between (a) dependent variable and each of the independent variable and (b) the dependent variable and independent variable collectively were checked through partial regression plot (Fig No.3). To measure the goodness of fit of MLR model the coefficient of determination i.e., R 2 (0 ≥ R 2 ≥ 1) is calculated that explain the fraction of variance of y predicted by x regressor included in the model. When the value of the R 2 is tend to 1 the regressor produce good predictions of the outcome variable and when it tends to 0 its vice-versa. The level of significant α is equal to 0.05 to test the null hypothesis. Table No 1: Pearson Correlation Between Independent Variables No. of days interval of migrants return No. of migrated husband from a house hold No. of Dependent in a house hold Age of the Migrant No. of days employed at destination No. of days interval of migrants return No. of migrated husband from a house hold No. of Dependent in a house hold Age of the Migrant No. of days employed at destination 1 − .650** .472** .506** − .418** 1 − .570** − .640** .613** 1 .531** − .476** 1 − .539** 1 **. Correlation is significant at the 0.01 level (2-tailed) Table no. 2: Collinearity Statistics Input Variable Tolerance VIF No. of migrated husband from a house hold No. of days employed at destination No of Dependent in a house hold Age of the migrant No. of days interval of migrants return .554 .378 .495 .609 .547 1.806 2.644 2.022 1.642 1.827 Table No. 3: Model Summary, Fisher’s exact test and Residual statistics Model R R 2 Adjusted R 2 Std. Error of Estimate Durbin- Watson Cook’s Distance Sum of square Df Mean Square F p-value Min Max Regression Residual Total .904 .816 .814 1.139 1.548 0.000 0.093 2250.435 506.221 2756.656 5 390 395 450.087 1.298 364.754 0.000 4. Results After verification of all the assumptions, the MLR model was applied by using Enter method and the summary of the model is showed in Table no. 3. The MLR model for predicting the Economic Well-Being Index was statistically significant, F (5,390) =346.754, p<0.001, and accounted 81.6% of variance of Economic Well-Being Index (R 2 =0.816, Adjusted R 2 =0.814) which indicates a strong relationship between (Cohen,1988). The raw and standardized regression coefficient of the predictors together with their correlation with Economic Well-Being Index, their Std. Error, t value, sig value, semi partial correlation and structure coefficient are shown in Table No.4.The analysis show, that all the predictors had significantly predict the Economic Well-Being Index (No. of days interval of migrants return Beta = -1.566, t (395) = -4.834, p<0.001, No. of migrated husband from a house hold Beta = 0.904, t(395)=9.439, p<0.001, Age of the Migrant Beta=-0.035, t(395)=-5.356, p<0.001, No. of Dependent in a Household Beta = -0.676, t (395)=-7.889, p<0.001, No. of days employed at destination Beta =5.863, t(395)=8.736,p<0.001). No. of Migrated husband from a household received the strongest weight in the model followed by No. of days employed at destination, No. of dependents in a household, Age of the migrant and No. of days interval of migrants return. With the sizable correlation between the predictors, the unique variance explained by the each of the variable index by squared semi partial correlation was quite low. Table no. 4: Regression Coefficient of the MLR analysis Model Unstandardized Coefficient Standardized Coefficient t Sig Pearson r sr 2 Structure Coefficient B Std. Error Beta Constant 10.602 0.745 14.225 0.000 No. of days interval of migrants return -1.566 0.324 -0.141 -4.834 0.000 -0.652** 0.011 -0.721 No. of migrated husband from a house hold 0.904 0.096 0.333 9.439 0.000 0.812** 0.042 0.898 Age of the Migrant -0.035 0.007 -0.165 -5.356 0.000 -0.718** 0.013 -0.794 No. of Dependent in a Household -0.676 0.086 -0.219 -7.889 0.000 -0.685** 0.029 -0.758 No. of days employed at destination 5.863 0.671 0.256 8.736 0.000 0.722** 0.036 0.799 ** Pearson Correlation ( r ) is significant at the 0.01 level (2-tailed) 5. Discussion The mechanism of rural male labour migration is directly associated with economic development and well-being of left-behind wives within their household and society (Antman, 2013 ; Murard, 2019 ). Further economic well-being, a continuous phenomenon, in reality reflects importance of remittance (Jacka, 2012 ). It is governed by several socio-economic indicators of regional development. In the context of Cooch Behar District, a less developed region of India, the nature of rural male labour migration is characterised by some distinguished socio-economic factors. The MLR model of the present study, significantly establishes those factors as independent variables to determine level and trend of left-behind wive’s economic well-being. Since, MLR model highlights that, no. of days migrated males are employed in their destination place (Beta = 5.863) is the strongest predictor and no. of migrated husband from a household receives highest weight (sr 2 = .042) that explains highest unique variance among the predictors. Both the variables, numbers of days employed at destination place and numbers of migrated husbands come across the quantitative character of economic well-being. They manifested a strong positive relation with economic well-being (Démurger, 2015 ). An increase of economic well-being by 5 units and .9 units with every one unit increase in numbers of days of employment and numbers of husbands migrated from a household respectively illustrates their positive relationship. Hopefully, both the variables together accomplished primary objective of rural male labour migration i.e. hike and continuity in their income level (Osberg & Sharpe, 2001 ; Datta & Mishra, 2011 ) that can provide proportionately adequate amount of remittance to left-behind wives to satisfy their economic well-being. Apart from this point of view, other nature of male labour migration describe qualitative aspect of economic well-being through imparting negative relation with them. The MLR model highlights that with one unit change in the variables viz. Numbers of days interval of husband’s return (Beta = -1.566), numbers of dependents in their households (Beta = -0.676) and age of the migrated husband (Beta=-0.035) there will be 1.6 units, .68 units and .04 units drop in economic well-being respectively. Long duration absence of husbands from their families and large numbers of dependents in a household is a burden upon received remittance (Paris et al, 2005 ; Sunman, 2014; Green et al., 2019 ). This inculcate hindrance in their subsistence living by flow of insufficient remittance (Sultana, 2014 ). Aging of migrated husbands causes decline in their employment opportunity too. Therefore, left-behind wives became disappointed about their economic stability, security, household resources and above all their economic well-being. Among the five predictors, numbers of migrated husbands from a household (sr 2 = .042) and number of days they are employed at destination (sr 2 = .036) received strong weights and could explain maximum variance of economic well-being (Maity et. al, 2018 ). As, these two predictors together bring enhancement in amount of remittance it can be accepted that maximum economic well-being of rural left-behind wives is possible by increasing amount of remittance. The other predictors those make lesser difference to reach ultimate economic well-being could be eliminated by adopting alternative approaches. Economic well-being of left-behind wives is a dynamic phenomenon, assured by sufficiency continuity, management and control over remittance. Again, it is determined by underlying gendered socio-economic experiences of migration (Pedraza, 1991 ). Apparently, Long term duration of spousal absence helps wives to gain control over household economy and financial decision making power (Tong et.al 2019 ). Whereas, in reality husbands tries to retain their primary control over household resources (Rammohan et al., 2022). Although, family structure of left-behind wives demonstrates, smaller numbers of dependents in a household could provide them more economic satisfaction (Glytsos, 1997 ; Deschênes et al., 2020 ; West et al., 2021 ). They are capable of transforming their subsistence remittance to productive household investment (Koc & Onan, 2004 ) and could satisfy optimum level of economic well-being in their lives. 6. Conclusion The study examines the consequences of male labour migration on economic well-being of left behind wives in Cooch Behar. Economic well-being is a complex phenomenon and cannot be stigmatized by only its objective nature. It is not merely an incident but a stage of modification in the lives of left-behind wives. They could recognize and take savour of the stage by conquering all the economic worries, fulfilling all the economic responsibilities. But, living in a traditional patriarchal society, dominated by patrilineality and virilocality, achieving optimum economic well-being is an extreme challenge to left-behind wives. Their well-being is truly justified by their social, cultural and demographic attributes. According to Development Economics and Welfare approach, the fundamental aim of well-being is to bring equality in the society and eliminate the disparities. Migration plays dual role upon left-behind wives economic well-being, positive and negative. Numbers of migration and no. of days they get employment play positive role on economic well-being, whereas interval of their return, age and numbers of dependent in their families play negative roles. Rural male labourers migration occurred due to regional economic disparity and the present research tries to build a connectivity between economic well-being of left-behind wives at the present and with regional development in the future by emphasizing on the importance of remittance flow in their lives. Remittance is a kind of income for migration source regions that fulfils needs and desires of left-behind families over there. With the outset of maximising job opportunity in the rural areas that generates, increases and insures income to the migrating labourers could restrict flow of migration to some extent. Henceforth, increasing income opportunity can also transform an economically less developed region to an economically strong and autonomous well-being region. Declarations Competing Interests The authors declare that there is no competing interest regarding the publication of this article. Ethical Approval The study involved human participants and was conducted in accordance with established ethical standards for social science research. The research was carried out in accordance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by Ethics Committee of Cooch Behar College. Informed Consent Informed consent was obtained from all participants, and participation was entirely voluntary. It will be provided if required. Human Ethics and Consent to Participate Human Ethics and Consent to Participate declarations: applicable and addressed as above. Clinical Trial Number Not applicable. Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution All authors whose names appear on the submission made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data.drafted the work or revised it critically for important intellectual content.approved the version to be published.agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Acknowledgement The authors would like to express their sincere gratitude to all individuals that contributed to the completion of this study. We thank the participants for their time and cooperation, and the academic and administrative staff who provided valuable support during data collection and analysis. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Ali, A. M. S. (1993). 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Paris, T., Singh, A., Luis, J., & Hossain, M. (2005). Labour outmigration, livelihood of rice farming households and women left behind: a case study in Eastern Uttar Pradesh. Economic and political weekly , 2522-2529. Pedraza, S. (1991). Women and Migration: The Social Consequences of Gender. Annual Review of Sociology , 17 , 303–325. http://www.jstor.org/stable/2083345 Rajan, S. I. (2004). From Kerala to the Gulf: Impacts of labor migration. Asian and Pacific Migration Journal , 13 (4), 497-509. Ram Mohan, R., Puskur, R., & Valera, H. G. A. (2022). Do gender dynamics in intra-household decision making shift with male migration? Evidence from rice-farming households in Eastern India. Gender, Technology and Development , 1-27. Rogaly, B., & Coppard, D. (2003). ‘They used to go to eat, now they go to earn’: The changing meanings of seasonal migration from Puruliya District in West Bengal, India. Journal of agrarian change , 3 (3), 395-433. Roy,A.K. (2001). Distress Migration and ‘Left behind’ Women. Rawat. Sabur, M. A., & Mahmud, H. (2008). Political impacts of remittances: A micro-level study of migrants’ remittances in a village in Bangladesh. Asian Social Science , 4 (12), 128-134. Sanyal, T., & Maity, K. (2018). On labour migration in India: Trends, causes and impacts. Economic Affairs , 63 (1), 57-69. Sarkar, S., & Mishra, D. K. (2021). Circular labour migration from rural India: A study of out-migration of male labour from West Bengal. Journal of Asian and African Studies , 56 (6), 1403-1418. Sarkar, S., & Mishra, D. K. (2021). Circular labour migration from rural India: A study of out-migration of male labour from West Bengal. Journal of Asian and African Studies , 56 (6), 1403-1418. Semyonov, M., & Gorodzeisky, A. (2008). Labor migration, remittances and economic well-being of households in the Philippines. Population Research and policy review , 27 , 619-637. Sevoyan, A., & Agadjanian, V. (2010). Male migration, women left behind, and sexually transmitted diseases in Armenia. International Migration Review , 44 (2), 354-375. Singh, N. P., Singh, R. P., Kumar, R., Padaria, R. N., Singh, A., & Varghese, N. (2011). Labour migration in Indo-Gangetic plains: Determinants and impacts on socio-economic welfare. Agricultural Economics Research Review , 24 (347-2016-16978), 449-458. Sinha, B., Jha, S., & Negi, N. S. (2012). Migration and empowerment: the experience of women in households in India where migration of a husband has occurred. Journal of Gender Studies , 21 (1), 61-76. Stark, O., & Fan, C. S. (2007). The analytics of seasonal migration. Economics Letters , 94 (2), 304-312. Sultana, A. (2014). Visiting husbands: Issues and challenges of women left behind. Pakistan Journal of Women's Studies= Alam-e-Niswan= Alam-i Nisvan , 21 (1), 57. Sunam, R. (2014). Marginalised dalits in international labour migration: reconfiguring economic and social relations in Nepal. Journal of Ethnic and Migration Studies , 40 (12), 2030-2048. Tabachnick, Barbara G., Linda S. Fidell, and Jodie B. Ullman. Using multivariate statistics . Vol. 6. Boston, MA: pearson, 2013. Thamas, B., & Adhikari, S. (2012). Male migration: Dynamics, issues and difficulties of left behind families. Asia Pac J Soc Sci , 4 , 109-30. Tong, Y., Chen, F., & Shu, B. (2019). Spousal migration and married adults’ psychological distress in rural China: The roles of intimacy, autonomy and responsibility. Social science research , 83 , 102312. Torres, R. M., & Carte, L. (2016). Migration and development? The gendered costs of migration on Mexico's rural “left behind”. Geographical Review , 106 (3), 399-420. Trager, L. (1984). Migration and remittances: urban income and rural households in the Philippines. The Journal of Developing Areas , 18 (3), 317-340. Tumbe, C. (2011). Remittances in India: facts & issues. IIM Bangalore Research Paper , (331). Turóczy, Z., & Marian, L. (2012). Multiple regression analysis of performance indicators in the ceramic industry. Procedia Economics and Finance , 3 , 509-514. Ullah, A. A. (2017). Male migration and ‘left–behind’women: Bane or boon?. Environment and Urbanization ASIA , 8 (1), 59-73. Wang, D., Hagedorn, A., & Chi, G. (2021). Remittances and household spending strategies: evidence from the Life in Kyrgyzstan Study, 2011–2013. Journal of ethnic and migration studies , 47 (13), 3015-3036. West, H. S., Robbins, M. E., Moucheraud, C., Razzaque, A., & Kuhn, R. (2021). Effects of spousal migration on access to healthcare for women left behind: A cross-sectional follow-up study. Plos one , 16 (12), e0260219. Yabiku, S. T., Agadjanian, V., & Sevoyan, A. (2010). Husbands' labour migration and wives' autonomy, Mozambique 2000–2006. Population studies , 64 (3), 293-306. Zelinsky, W. (1971). The hypothesis of the mobility transition. Geographical review , 219-249. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eHuman population migration is an eternal integral element of mankind history. Since, pre-historic era humankind travelled far wide, spanning the continents for better opportunities of fundamental requirements .The universal phenomena of movement of people from one place to another for settling down is commonly known as \u0026lsquo;migration\u0026rsquo; (Roy, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The flow of migration largely depends on regional disparity whether physical or economic. Economic opportunities and benefits motivate an individual to relocate from one place to another (Zelinksky, 1971). This mechanism is recognised as \u0026lsquo;Labour Migration\u0026rsquo; (Sanyal \u0026amp; Maity, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Labour migration is indeed a gendered phenomenon eventually dominated by males and could be redefined as \u0026ldquo;Male Labour Migration\u0026rdquo; (Mendola \u0026amp; Carletto, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It can be classified as external and internal, depending on male labourer\u0026rsquo;s place of origin. Internal male labour migration is an extensively prominent incident worldwide and not exception to India. A large share of male internal labour migrants (35.02\u0026nbsp;million) in total 41.43\u0026nbsp;million (Census, 2011) therefore, confirms the notion. Simultaneously, in Cooch Behar district of the country, every 17th rural male labourer is a migrated labourer and every 24th rural women are convinced to be left behind rural women (Office of District Magistrate, 2020). Rural male labour migration contributes an intricate multi-dimensional impact on physical, psychological, social and economic well-being of left-behind wives. Well-being varies from mothers to wives and children, from young\u0026rsquo;s to middle aged and olds, from less literates to more literates and so on (Desai and Banerjee, 2008; Sevoyan and Agadjanian ,2010; Gartuala et al, 2012; Sinha et al, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fakir and Abedin ,2020). Perhaps, husband\u0026rsquo;s migration encourage confidence, independence and empowerment of rural left-behind wives, but at the same time it also invite anxiety, stress and disappointment in their lives (Ullah, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although, the elementary objective of labour migration is to guarantee benefit in their income amount and proportionate hike in remittance amount. This result to economic prosperity, welfare and economic well-being of left-behind wives (Tumbe, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mahapatro et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, it is often hindered by several socio-economic elements like, extended family structure, large numbers of dependent, older age of the migrated husband, lower education level of the wives and longtime interval in return of the migrants (Sabur \u0026amp; Mahmud, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ghimire et al.2021). Therefore, potentiality and trend of economic well-being depend response of these socio-economic factors.\u003c/p\u003e"},{"header":"2. Male labour migration, Left-behind Wives and Economic well-being","content":"\u003cp\u003eRural Indian economy as a whole is regulated by monsoon dependent agricultural activity (Chakraborty \u0026amp; Shukla, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Seasonal occurrence of Indian monsoonal rainfall creates fluctuation in agricultural productivity. Monsoonal season is highly productive season and non-monsoonal season is less productive season. Therefore, demand-supply of rural laborers in agricultural activities varies correspondingly with agricultural production (Fink et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As, non-monsoonal season causes constrains in demand - supply of male agricultural laborers, a large quantity of them in the particular season are suddenly converted to unemployed laborers (Ali, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). They start suffering from misery of poverty, hunger, starvation, family feuds and financial crisis (Sarkar \u0026amp; Mishra, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To resolve the present circumstance, they tend to migrate away from their native villages and joined in alternate livelihoods at different urban centres (Keshri, \u0026amp; Bhagat, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Usually, they get transformed in secondary sector workers to assure their continuous income all year round (Rogaly \u0026amp; Coppard, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Their uninterrupted earnings establish a counter behavioural link in form of continuous financial flow between migrant and economic welfare of the left-behind families (Stark \u0026amp; Fan, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Parida \u0026amp; Madheswaran, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Torres \u0026amp; Carte, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a consequence, income, consumption and investment strategies of these families are controlled by their received remittance (Wang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Economic well-being is a kind of objective well-being that does not only determine quantity of economic prosperity but also the quality of such prosperity in terms of choice of employment, control over income, maintain standard of life and maintaining consumption of food, clothing, housing, stability in income and above all investment for future (OECD, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In fact, male labour migration definitely support economic well-being by performing income hike for left-behind families (Semyonov \u0026amp; Gorodzeisky, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Consequently, planned remittance consumption as well as investment by rural left-behind wives in family welfare does exhibit economic well-being (Connell \u0026amp; Brown, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Yabiku, 2010; Hunter \u0026amp; Hunter, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Again, in absence of male counter-parts, they became primary recipient of remittance and are inspired to be household managers, autonomous economic decision makers and financial supervisors of their families (Archambault, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Haan \u0026amp; Rooij, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Their active participation in agriculture workforce, access to farm production, household income and resources, financial management, budget, food security and supply also initiate feminization of household economy (De Haan \u0026amp; Rogaly, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Maharjan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; De Brauw et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The paradigm shift in rural household economics and its control together portrays their economic well-being (Nguyen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSo far, several literatures tried to analyse the advantage of rural left behind wives in their social position, social contact, work participation rate, economic independence, decision making, family management and empowerment (Hadi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rajan, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Menjivar \u0026amp; Agadjanian, 2007; Mahapatro, 2018; Choithani, 2019, Fernandez-Sanchez et al, 2021) but somehow, their well-being remained ignored and unexplored. Also, there is dearth of scientific studies on economic well-being of rural left behind wives, putting husband\u0026rsquo;s migration and its nature at the central role. Analysis of level and trend of economic well-being is a complex procedure. The present study is distinctive in this sense as it not only examines rural left behind wives level of economic well-being but also recognize the definite factors those influence their economic well-being.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eTo fulfil the objective of the study following methodology has been adopted:\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Sampling Technique and sample size:\u003c/h2\u003e\n \u003cp\u003e400 respondents (95% Confidence Interval, 4% Margin of error) (Cochran,1977; Moore and McCabe, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e) from rural left-behind wives of Coochbehar District were selected by snowball sampling technique from 120 villages during field visit in 2019\u0026ndash;2022. The cohort of the respondents belong to 18\u0026ndash;70 age group and both from small and large families. Responses are collected with help of a self-structured questionnaire. All respondents participate spontaneously with their self-consent in the interview procedure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Economic Well-being Index:\u003c/h2\u003e\n \u003cp\u003eEconomic Well-Being Index was formulated with the help 19 items (amount of remittance received, average monthly income of the family, involvement of the respondent in any paid job, expenditure in basic needs, education, healthcare, others; investment in agriculture, house construction and several savings scheme, interval of receiving remittance, dependency on remittance, achieving monetary solvency through remittance, possession of agricultural land, bank savings, house type, banking operation, decision and control over remittance expense and budget, management of remittance in financial crisis) (ABS, 2015; OECD, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Hasan \u0026amp; Jebin, 2020). All the items are quantified in a five-point Likert Scale (never\u0026thinsp;=\u0026thinsp;1 and always\u0026thinsp;=\u0026thinsp;5). The internal consistency of the items was measured performing reliability test and Cronbach\u0026rsquo;s Alpha value has been found acceptable (0.621) (Cronbach, \u003cspan class=\"CitationRef\"\u003e1951\u003c/span\u003e). The item scores are then sum-up to develop the Economic Well-being Index Score (EWBI).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eECONOMIC WELL-BEING INDEX (EWBI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum_{p=1}^{p}{m}_{p}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$={m}_{1}+{m}_{2}+\\dots\\dots\\dots.+{m}_{\\text{p}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{\\text{p}}\\)\u003c/span\u003e\u003c/span\u003e are the items.)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Statistical Method:\u003c/h2\u003e\n \u003cp\u003eIn order to test the hypothesis, Multiple Linear Regression (MLR) was used. MLR, one of the mostly used statistical techniques, helps to predict an outcome variable by using several exploratory variables (Field, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). MLR model can be much more realistic than the uni-factorial regression model which use only one exploratory variable to predict the outcome variable (Tur\u0026oacute;czy \u0026amp; Marian \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this work MLR was implemented to predict the dependent variable Y by five no of predictors in SPSS (Statistical Package for Social Sciences, 20).\u003c/p\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1. Dependent Variable:\u003c/h2\u003e\n \u003cp\u003eEconomic Well-Being Index Score has been considered as outcome variable.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Independent Variables:\u003c/h2\u003e\n \u003cp\u003eFive variables of migration have been considered as predictors in the MLR model. The Variables are\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003ea) No. of migrated husband from a house hold\u003c/strong\u003e.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eb) No. of days employed at destination\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003ec) No. of days interval of migrants return\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003ed) No. of dependents in a household\u003c/strong\u003e.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003ee) Age of the migrant\u003c/strong\u003e: The migrants aged above 18 years has been considered (Trager,1984; Thamas \u0026amp; Adhikari, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bhattachrjee,2020; Arokkiaraj et.al,2021; Sarkar \u0026amp;Mishra, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe equation of MLR is:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cstrong\u003ey\u0026thinsp;=\u0026thinsp;\u0026beta;\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003e0\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3+\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;e\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cstrong\u003ey\u003c/strong\u003e is the Economic Well-Being Index Score, \u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/sub\u003e is the intercept value, \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e is No. of migrated husband from a house hold, \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e is No. of days employed at destination, \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e is No. of days interval of migrants return, \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e is No. of dependents in a household and \u003cstrong\u003eX\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/sub\u003e is Age of the migrant, and the \u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e to \u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/sub\u003e are the estimated regression co-efficient of respective independent variables and \u003cstrong\u003ee\u003c/strong\u003e is the model error i.e., the variation of estimate of \u003cstrong\u003ey\u003c/strong\u003e to the real value. In order to generate the MLR model the following assumptions were verified:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003eNo multicollinearity among the independent variables, verified through (a) correlation coefficients between 0.3 to 0.8 (Table no. 1) (b) tolerances \u0026gt;0.2 (Table no.2) (?) and (c) variance inflation factors (VIFs)\u0026lt;10 (Table no.2).\u003c/li\u003e\n \u003cli\u003eFour multivariate outliers were found and removed\u0026nbsp;using a Mahalanobis\u0026rsquo;s Distance Test (p= \u0026gt;0.001) (Tabachnick, \u0026amp; Fidell, 2013).\u003c/li\u003e\n \u003cli\u003eThe independence of residuals (or errors) by the Durbin-Watson value, 1.548 (Table No. 3), signifies the independence of the residuals; the acceptable range is1.5 to 2.5 (Durbin \u0026amp; Watson, 1971).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe value of Cook\u0026rsquo;s distance found 0.093 (Table No.3)i.e. less than the threshold value 1 , proved that there were no outliers in the data set that may negatively affect the estimate of the coefficients.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe normal P-P plot\u0026nbsp;of Regression standardized Residual with Expected Normal Value in Y axis and Observed value in X axis\u0026nbsp;shows\u0026nbsp;location of points closure to the diagonal line prove the normality of the residual .The\u0026nbsp;histogram with a superimposed normal curve also demonstrates the normal distribution of the residuals (Fig no. 1(a,b)).\u003c/li\u003e\n \u003cli\u003eThe homogeneity of residual variance (homoscedasticity) was verified by scatter plot of standardized residuals\u0026quot; against the \u0026quot;standardized predicted value (Fig No.2).\u003c/li\u003e\n \u003cli\u003eThe linear relationship between (a) dependent variable and each of the independent variable and (b) the dependent variable and independent variable collectively were checked through partial regression plot (Fig No.3).\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eTo measure the goodness of fit of MLR model the coefficient of determination i.e., R\u003csup\u003e2\u003c/sup\u003e (0 \u0026ge; R\u003csup\u003e2\u003c/sup\u003e \u0026ge; 1) is calculated that explain the fraction of variance of \u003cstrong\u003ey\u0026nbsp;\u003c/strong\u003epredicted by \u003cstrong\u003ex\u003c/strong\u003e regressor included in the model. When the value of the R\u003csup\u003e2\u003c/sup\u003e is tend to 1 the regressor produce good predictions of the outcome variable and when it tends to 0 its vice-versa. The level of significant \u0026alpha; is equal to 0.05 to test the null hypothesis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"\" class=\"colspec\"\u003e\u003cstrong\u003eTable No 1: Pearson Correlation Between Independent Variables\u003c/strong\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of days interval of migrants return\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of migrated husband\u003c/p\u003e\n \u003cp\u003efrom a house hold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of Dependent in a house hold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of the Migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of days employed at destination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo. of days interval of migrants return\u003c/p\u003e\n \u003cp\u003eNo. of migrated husband\u003c/p\u003e\n \u003cp\u003efrom a house hold\u003c/p\u003e\n \u003cp\u003eNo. of Dependent in a house hold\u003c/p\u003e\n \u003cp\u003eAge of the Migrant\u003c/p\u003e\n \u003cp\u003eNo. of days employed at destination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.650**\u003c/p\u003e\n \u003cp\u003e.472**\u003c/p\u003e\n \u003cp\u003e.506**\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.418**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.570**\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.640**\u003c/p\u003e\n \u003cp\u003e.613**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e.531**\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.476**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.539**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"\" class=\"colspec\"\u003e\u003cstrong\u003eTable no. 2: Collinearity Statistics\u003c/strong\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInput Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of migrated husband\u003c/p\u003e\n \u003cp\u003efrom a house hold\u003c/p\u003e\n \u003cp\u003eNo. of days employed at destination\u003c/p\u003e\n \u003cp\u003eNo of Dependent in a house hold\u003c/p\u003e\n \u003cp\u003eAge of the migrant\u003c/p\u003e\n \u003cp\u003eNo. of days interval of migrants return\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.554\u003c/p\u003e\n \u003cp\u003e.378\u003c/p\u003e\n \u003cp\u003e.495\u003c/p\u003e\n \u003cp\u003e.609\u003c/p\u003e\n \u003cp\u003e.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.806\u003c/p\u003e\n \u003cp\u003e2.644\u003c/p\u003e\n \u003cp\u003e2.022\u003c/p\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003cp\u003e1.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv align=\"\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"\" class=\"colspec\"\u003e\u003cstrong\u003eTable No. 3: Model Summary, Fisher\u0026rsquo;s exact test and Residual statistics\u003c/strong\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStd. Error of Estimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDurbin- Watson\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCook\u0026rsquo;s Distance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSum of square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250.435\u003c/p\u003e\n \u003cp\u003e506.221\u003c/p\u003e\n \u003cp\u003e2756.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450.087\u003c/p\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eAfter verification of all the assumptions, the MLR model was applied by using Enter method and the summary of the model is showed in Table no. 3. The MLR model for predicting the Economic Well-Being Index was statistically significant, F (5,390) =346.754, p\u0026lt;0.001, and accounted 81.6% of variance of Economic Well-Being Index (R\u003csup\u003e2\u003c/sup\u003e=0.816, Adjusted R\u003csup\u003e2\u003c/sup\u003e=0.814) which indicates a strong relationship between (Cohen,1988). The raw and standardized regression coefficient of the predictors together with their correlation with Economic Well-Being Index, their Std. Error, t value, sig value, semi partial correlation and structure coefficient are shown in Table No.4.The analysis show, that all the predictors had significantly predict the Economic Well-Being Index (No. of days interval of migrants return\u0026nbsp;Beta = -1.566, t (395) = -4.834, p\u0026lt;0.001,\u0026nbsp;No. of migrated husband from a house hold\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBeta = 0.904, t(395)=9.439, p\u0026lt;0.001, Age of the Migrant Beta=-0.035, t(395)=-5.356, p\u0026lt;0.001,\u0026nbsp;No. of Dependent in a Household Beta = -0.676, t (395)=-7.889, p\u0026lt;0.001, No. of days employed at destination\u0026nbsp;Beta =5.863, t(395)=8.736,p\u0026lt;0.001). No. of Migrated husband from a household received the strongest weight in the model followed by\u0026nbsp;No. of days employed at destination, No. of dependents in a household, Age of the migrant and\u0026nbsp;No. of days interval of migrants return.\u0026nbsp;With the sizable correlation between the predictors, the unique variance explained by the each of the variable index by squared semi partial correlation was quite low.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable no. 4: Regression Coefficient of the MLR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"720\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson \u003cem\u003er\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esr\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of days interval of migrants return\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.652**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of migrated husband\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003efrom a house hold\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.812**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of the Migrant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-5.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.718**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Dependent in a Household\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-7.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.685**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of days employed at destination\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.722**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e** Pearson Correlation (\u003cem\u003er\u003c/em\u003e) is significant at the 0.01 level (2-tailed)\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe mechanism of rural male labour migration is directly associated with economic development and well-being of left-behind wives within their household and society (Antman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Murard, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further economic well-being, a continuous phenomenon, in reality reflects importance of remittance (Jacka, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is governed by several socio-economic indicators of regional development. In the context of Cooch Behar District, a less developed region of India, the nature of rural male labour migration is characterised by some distinguished socio-economic factors. The MLR model of the present study, significantly establishes those factors as independent variables to determine level and trend of left-behind wive\u0026rsquo;s economic well-being. Since, MLR model highlights that, no. of days migrated males are employed in their destination place (Beta\u0026thinsp;=\u0026thinsp;5.863) is the strongest predictor and no. of migrated husband from a household receives highest weight (sr\u003csup\u003e2\u003c/sup\u003e = .042) that explains highest unique variance among the predictors.\u003c/p\u003e \u003cp\u003eBoth the variables, numbers of days employed at destination place and numbers of migrated husbands come across the quantitative character of economic well-being. They manifested a strong positive relation with economic well-being (D\u0026eacute;murger, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An increase of economic well-being by 5 units and .9 units with every one unit increase in numbers of days of employment and numbers of husbands migrated from a household respectively illustrates their positive relationship. Hopefully, both the variables together accomplished primary objective of rural male labour migration i.e. hike and continuity in their income level (Osberg \u0026amp; Sharpe, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e ; Datta \u0026amp; Mishra, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) that can provide proportionately adequate amount of remittance to left-behind wives to satisfy their economic well-being.\u003c/p\u003e \u003cp\u003eApart from this point of view, other nature of male labour migration describe qualitative aspect of economic well-being through imparting negative relation with them. The MLR model highlights that with one unit change in the variables viz. Numbers of days interval of husband\u0026rsquo;s return (Beta = -1.566), numbers of dependents in their households (Beta = -0.676) and age of the migrated husband (Beta=-0.035) there will be 1.6 units, .68 units and .04 units drop in economic well-being respectively. Long duration absence of husbands from their families and large numbers of dependents in a household is a burden upon received remittance (Paris et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sunman, 2014; Green et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This inculcate hindrance in their subsistence living by flow of insufficient remittance (Sultana, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Aging of migrated husbands causes decline in their employment opportunity too. Therefore, left-behind wives became disappointed about their economic stability, security, household resources and above all their economic well-being.\u003c/p\u003e \u003cp\u003eAmong the five predictors, numbers of migrated husbands from a household (sr\u003csup\u003e2\u003c/sup\u003e = .042) and number of days they are employed at destination (sr\u003csup\u003e2\u003c/sup\u003e = .036) received strong weights and could explain maximum variance of economic well-being (Maity et. al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As, these two predictors together bring enhancement in amount of remittance it can be accepted that maximum economic well-being of rural left-behind wives is possible by increasing amount of remittance. The other predictors those make lesser difference to reach ultimate economic well-being could be eliminated by adopting alternative approaches.\u003c/p\u003e \u003cp\u003eEconomic well-being of left-behind wives is a dynamic phenomenon, assured by sufficiency continuity, management and control over remittance. Again, it is determined by underlying gendered socio-economic experiences of migration (Pedraza, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Apparently, Long term duration of spousal absence helps wives to gain control over household economy and financial decision making power (Tong et.al \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas, in reality husbands tries to retain their primary control over household resources (Rammohan et al., 2022). Although, family structure of left-behind wives demonstrates, smaller numbers of dependents in a household could provide them more economic satisfaction (Glytsos, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Desch\u0026ecirc;nes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; West et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They are capable of transforming their subsistence remittance to productive household investment (Koc \u0026amp; Onan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and could satisfy optimum level of economic well-being in their lives.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe study examines the consequences of male labour migration on economic well-being of left behind wives in Cooch Behar. Economic well-being is a complex phenomenon and cannot be stigmatized by only its objective nature. It is not merely an incident but a stage of modification in the lives of left-behind wives. They could recognize and take savour of the stage by conquering all the economic worries, fulfilling all the economic responsibilities. But, living in a traditional patriarchal society, dominated by patrilineality and virilocality, achieving optimum economic well-being is an extreme challenge to left-behind wives. Their well-being is truly justified by their social, cultural and demographic attributes. According to Development Economics and Welfare approach, the fundamental aim of well-being is to bring equality in the society and eliminate the disparities. Migration plays dual role upon left-behind wives economic well-being, positive and negative. Numbers of migration and no. of days they get employment play positive role on economic well-being, whereas interval of their return, age and numbers of dependent in their families play negative roles. Rural male labourers migration occurred due to regional economic disparity and the present research tries to build a connectivity between economic well-being of left-behind wives at the present and with regional development in the future by emphasizing on the importance of remittance flow in their lives. Remittance is a kind of income for migration source regions that fulfils needs and desires of left-behind families over there. With the outset of maximising job opportunity in the rural areas that generates, increases and insures income to the migrating labourers could restrict flow of migration to some extent. Henceforth, increasing income opportunity can also transform an economically less developed region to an economically strong and autonomous well-being region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there is no competing interest regarding the publication of this article.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe study involved human participants and was conducted in accordance with established ethical standards for social science research. The research was carried out in accordance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by Ethics Committee of Cooch Behar College.\u003c/p\u003e\n\u003ch2\u003eInformed Consent\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all participants, and participation was entirely voluntary. It will be provided if required.\u003c/p\u003e\n\u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: applicable and addressed as above.\u003c/p\u003e\n\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding Statement\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors whose names appear on the submission made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data.drafted the work or revised it critically for important intellectual content.approved the version to be published.agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all individuals that contributed to the completion of this study. We thank the participants for their time and cooperation, and the academic and administrative staff who provided valuable support during data collection and analysis.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli, A. M. S. (1993). Unemployment in agriculture and opportunities for and contributions of off-farm employment to rural economy: a case study from southwestern Bangladesh. \u003cem\u003eHuman Ecology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 431-445.\u003c/li\u003e\n\u003cli\u003eAntman, F. M. (2013). The impact of migration on family left behind. In \u003cem\u003eInternational handbook on the economics of migration\u003c/em\u003e (pp. 293-308). Edward Elgar Publishing.\u003c/li\u003e\n\u003cli\u003eArchambault, C. S. (2010). 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The hypothesis of the mobility transition. \u003cem\u003eGeographical review\u003c/em\u003e, 219-249.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-global-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Global Society](https://www.springer.com/journal/44282)","snPcode":"44282","submissionUrl":"https://submission.nature.com/new-submission/44282/3","title":"Discover Global Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Male labour, Migration, Left Behind women, Economic well Being, Remittance","lastPublishedDoi":"10.21203/rs.3.rs-8952197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8952197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of rural male labour migration on the economic well-being of left-behind wives in the Cooch Behar District of India, a less developed region. Utilizing a Multiple Linear Regression (MLR) model, the research identifies significant socio-economic factors influencing the economic well-being of these women. Key findings indicate that the number of days males are employed at their destination (Beta = 5.863) and the number of migrated husbands from a household (Beta = 0.904) are strong positive predictors of economic well-being, reflecting increased remittance. Conversely, factors such as the interval of husbands' return (Beta = -1.566), the number of dependents (Beta = -0.676), and the age of migrated husbands (Beta = -0.035) negatively affect economic well-being. The study highlights the dual role of migration, where remittances enhance economic stability, yet long absences and high dependency ratios pose challenges. It underscores the importance of remittance flow in improving economic conditions and suggests that increasing local job opportunities could mitigate migration, fostering regional development and better economic well-being for left-behind wives.\u003c/p\u003e","manuscriptTitle":"Male Labour Migration and Economic Well-Being Among Rural Left-Behind Wives of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 16:01:25","doi":"10.21203/rs.3.rs-8952197/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T10:51:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T07:27:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T05:40:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T07:05:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177074783227463919359671403966091019160","date":"2026-03-23T05:16:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173533708021700060897861988924305701245","date":"2026-03-23T05:04:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T09:52:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148600065959459638424330311266101721584","date":"2026-03-04T11:59:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4646865343692649489682734890791420963","date":"2026-03-03T09:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T05:15:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-02T18:39:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-28T07:17:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T04:47:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Global Society","date":"2026-02-27T04:33:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-global-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Global Society](https://www.springer.com/journal/44282)","snPcode":"44282","submissionUrl":"https://submission.nature.com/new-submission/44282/3","title":"Discover Global Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fa2ebfe-f904-4710-a82c-fcdf0b43ba15","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T10:55:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 16:01:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8952197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8952197","identity":"rs-8952197","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}