Men's labor migration and attitudes towards women's sexual autonomy: Evidence from the National Family Health Survey 2019-21

Men's labor migration and attitudes towards women's sexual autonomy: Evidence from the National Family Health Survey 2019-21 | 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 Men's labor migration and attitudes towards women's sexual autonomy: Evidence from the National Family Health Survey 2019-21 Vikesh Kumar, Ankit Gupta, Priti ., Phanidhar Upadhyaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9239808/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 Background Male labour migration results in prolonged spousal separation and shifts in household decision-making structures. In many low- and middle-income settings, the temporary or long-term migration of men creates shifts in household responsibilities, decision-making power, and the regulation of sexual and reproductive behaviour within families. Migration not only affects economic conditions but also transforms household power relations, gender roles, and attitudes toward women’s autonomy. Sexual autonomy, which refers to a person's capacity to negotiate safer sexual practices. Data & Methods The present study utilises data from NFHS-5. We used a total of 101,839 samples in which 19241 samples has been deleted as they were not working. There are women aged 15–49 and men aged 15–54. Considering that he is working and has been away from home for more than one month, he must be a migrant in India. So, this study excludes non-working respondents to reduce error in the migrant sample. In our study, we used a binary logistic regression model to examine the association between dependent and explanatory variables Results The results show how migration is linked to men's gender perceptions and significant sociodemographic variations in migration trends. This study revealed that approximately 15.3% of men were temporary migrants, including 8.9% short-term migrants and 6.4% long-term migrants, indicating that temporary migration remains a significant livelihood strategy among men in India. In the unadjusted model, I show that men who were migrants (both STM and LTM) and who used substances were significantly more likely to have a negative attitude towards sexual autonomy. Logistic estimates (Model II) further show that men belonging to the middle-aged group (45–54 years), scheduled castes, and having higher education were significantly associated with lower odds of a negative attitude. Conclusion This research shows that men's perceptions of women's sexual autonomy in India are significantly influenced by their immigration status. The findings show that both temporary and permanent migrants are more likely to have positive attitudes than non-migrants. In order to encourage gender-equitable views among women, the study emphasizes the necessity of focused interventions that address behavioural and social issues. Labour migration Sexual autonomy LMICs Gender Immigration NFHS-5 Figures Figure 1 Figure 2 Introduction Migration has long been recognized as a powerful social and economic force that shapes family structures, gender relations, and community norms. In many low- and middle-income countries (LMICs), the temporary or long-term migration of men creates shifts in household responsibilities, decision-making power, and the regulation of sexual and reproductive behaviour within families (Torosyan et al., 2016 ). Due to gender norms in Asian countries, men are considered breadwinners and are expected to arrange livelihoods, whereas women are expected to take care of the household, and such norms often lead to male migration alone, leaving their families behind at home, especially in economically backwards regions (Forste & Fox, n.d.; Hughes et al., 2020 ; Wang, n.d.). According to the Census of India ( 2011 ), There were 41 million labour migrants; however, it was estimated that there would be approximately 100 million migrant workers, and most of them were (approximately 85%) male migrants (Deshingkar & Akter, n.d.). In the Indian context, internal migration is predominantly male dominated and closely linked to labour market dynamics. This form of migration not only affects economic conditions but also transforms household power relations, gender roles, and attitudes toward women’s autonomy (Imran Khan et al., 2023 ; Ram Mohan et al., 2023 ). Male labour migration, particularly in low- and middle-income countries, often results in prolonged spousal separation and shifts in household decision-making structures (Desai & Banerji, 2008a ). The long-term absence of migrants with their spouses can reduce closeness and intimacy in marital relationship. (Menjívar & Agadjanian, 2007a ). In addition to the increased burden and obligations, the migration of husbands also gives left-behind wives more autonomy and decision-making authority. In India, women are more likely to have a say in daily cooking decisions, household expenses for expensive goods, and the marriage and health care of children when their husbands are away (Desai & Banerji, 2008b ). After their spouses have left the house, women in Mozambique now have greater freedom to visit friends and family outdoors, look for work, to visit the city or district capital, and get tested for HIV (Yabiku et al., 2010a ). Men's migration increases their spouses' autonomy, independence, and decision-making power (Yabiku et al., 2010b ). Historically, women have had less personal autonomy in patriarchal countries (Bloom et al., 2001 ). Compared to men, they have less access to resources and less control over decision-making processes (Daniel Ikuomola, n.d.; Osezua, 2013 ; Singh, 2024 ). Despite extensive research on migration and women’s economic and decision-making autonomy, limited attention has been given to understanding how men’s labour migration shapes men’s own attitudes towards women’s sexual autonomy. Sexual autonomy, which refers to a person's capacity to negotiate safer sexual practices, decrease unwanted sexual relations, make informed and voluntary decisions about sexual activity, and exercise control over their own body and reproductive choices, is a crucial component of this larger concept (Aboagye et al., 2022 ; Memiah et al., 2019 ; Willie et al., 2023 ). Many people agree that women's sexual autonomy is a basic human right and a vital aspect of their sexual and reproductive health and well-being (Heidari, 2015 ). Various studies suggest that women who have more sexual autonomy are more likely to negotiate condom use, utilize contraception, and steer clear negative reproductive health outcomes, such as STIs and unwanted pregnancies (Abada & Tenkorang, 2012 ; Crissman et al., 2012 ). The roles and power dynamics in sexual interactions are shaped by conventional normative conceptions of masculinity, femininity, and broader gender inequality (Amaro, 1995 ). Migration adds an additional layer of complexity: while some studies suggest that male out-migration may increase women’s household autonomy, this does not necessarily translate into increased sexual autonomy due to persistent patriarchal norms, social surveillance, and economic dependency (Agadjanian & Hayford, 2018 ; Matz & Mbaye, 2023 ; Yabiku et al., 2010c ). Labour migration may influence men’s attitudes towards women’s sexual autonomy through multiple interrelated pathways. First, economic mobility and remittance flows may strengthen men’s provider role, reinforcing traditional patriarchal authority within households (Desai & Banerji, 2008a ). Second, prolonged spousal separation may alter emotional intimacy and marital communication, potentially increasing suspicion, control, or insecurity regarding wives’ sexuality (Menjívar & Agadjanian, 2007b ). Third, exposure to urban or destination contexts may introduce alternative gender norms, including more egalitarian attitudes toward women’s decision-making and sexual rights (Levitt, 1998a ; Yabiku et al., 2010c ). Conversely, harsh migration experiences, including poor working conditions and social isolation, may increase stress, alcohol consumption, and risk-taking behaviours, which have been associated with more controlling and coercive sexual attitudes (Jewkes et al., 2015 ; Narushima et al., 2016 ). These competing pathways suggest that migration may either liberalize or intensify patriarchal attitudes, making empirical investigation essential. Drawing from theories of gender transformation and hegemonic masculinity, this study conceptualises labour migration as a structural experience that reshapes men’s economic roles, social exposure, and psychological stress, which in turn influences attitudes toward women’s sexual autonomy (Levitt, 1998b ). Rather than assuming that migration uniformly empowers women, this study shifts the analytical focus to men and examines whether migration produces attitudinal liberalisation through exposure and social learning or whether it reinforces patriarchal control through economic dominance and masculine insecurity. By distinguishing between short-term and long-term migration, the study further explores whether the duration and intensity of migration differentially shape these attitudinal outcomes. Although a large body of research has examined the impact of male labour migration on women's economic autonomy and household decision-making, far less attention has been given to how migration experiences shape men’s own gender attitudes, particularly attitudes towards women’s sexual autonomy. Existing studies have primarily focused on the consequences of migration for women who remain at home, whereas the attitudinal changes among migrant men themselves remain understudied. Understanding men’s attitudes toward women's sexual autonomy is critical because such attitudes directly influence sexual negotiation, reproductive health outcomes, and gender power relations during marriage. Labour migration may reshape men’s attitudes towards women’s sexual autonomy through several pathways. Migration often involves prolonged spousal separation, exposure to new social environments, and economic changes that can alter gender relations. On the one hand, exposure to urban settings and diverse social norms may promote more egalitarian views regarding women’s rights and autonomy. On the other hand, migration-related stress, social isolation, and masculine expectations of economic provision may reinforce patriarchal authority and suspicion toward women’s sexuality. These contrasting mechanisms suggest that migration may either liberalise or intensify patriarchal gender attitudes, making empirical investigation essential. In a country such as India, where patriarchal norms strongly regulate women's sexuality and where male labour migration remains a common livelihood strategy, understanding this relationship is particularly important. Therefore, this study examines the association between men’s labour migration and attitudes towards women’s sexual autonomy in India using nationally representative data from the National Family Health Survey (NFHS-5). By distinguishing between short-term and long-term migration and examining the role of substance use and socio-demographic factors, this study contributes to the literature on migration and gender relations by shifting the analytical focus from women’s outcomes to men’s gender attitudes. Methodology Data Sources This study draws on data from the fifth round of the National Family Health Survey (NFHS-5), carried out between 2019 and 2021. NFHS-5 is a nationally representative, large-scale survey that provides comprehensive information on population characteristics, family planning, maternal and child health, nutrition, morbidity, and socio-economic and demographic conditions across all states and Union Territories of India. The survey employed a two-stage sampling design in rural areas and a three-stage design in urban areas. Conducted in two phases, it covered 636,699 households and included 724,115 women aged 15–49 and 101,839 men aged 15–54. Considering that he is working and has been away from home for more than one month, he must be a migrant in India. So, this study excludes non-working respondents to reduce error in the migrant sample. Detailed information on the survey methodology is available in the NFHS-5 India report (International Institute for Population Sciences (IIPS) and ICF, 2021). Outcome Variable For this study, the main outcome variable is men’s attitudes towards the sexual autonomy of women. According to the NFHS-5 data, men been asked if their wives refuse to have sex; the husband has the right to (a) get angry, (b) refuse financial support, (c) use force for sex, and (d) have sex with another woman. The outcome variable was generated by combining the responses of these questions, and men gave a positive response to at least one of the four questions; they were considered to have a negative attitude and were coded as ‘1’; otherwise, they were coded as ‘0’. (Scale reliability coefficient: alpha = 0.84). Explanatory variables: Migration status There is no direct question on migration status in the NFHS-5; however, some indirect questions have been asked. The respondents were asked (i) In the last 12 months, have you been away from home for one month or more at a time, and those who gave a positive response; furthermore, they were asked (ii) In the last 12 months, have you been away from home for one month or more at a time? Respondents who have never been away from home for at least one month were considered non-migrant, those who have been away for at least one month but who had lived for less than six months were considered short-term or temporary migrants, and those who have been away for six months or more were considered long-term or semi-permanent migrants (Coffey et al., n.d.; Keshri & Bhagat, 2013 ; NSSO, 2010 ). Other explanatory variables Other explanatory variables are respondents’ age (15–29, 30–44 and 45–54), education (no education, primary, secondary and higher), marital status (never married, married, others), occupation (professional, clerical, sales, services, agriculture, production worker, and other), residence (rural, urban), religion (Hindu, Muslim, Christian, and others), social group (Scheduled Caste, Scheduled Tribe, Other Backwards Classes, and Others), wealth index (poorer, poor, middle class, rich and richest), media exposure, smoking behaviour and use of alcohol (no, yes). Data source NFHS-5 Methods Descriptive statistics were used to present the distribution of outcome and explanatory variables. The analysis included a total sample of 82,598 men aged 15–54 years, comprising 70,039 non-migrants, 7,253 short-term migrants, and 5,306 long-term migrants. The outcome variable of the study is men’s attitudes towards women’s sexual autonomy (MASAW), constructed using four survey questions that asked whether a husband is justified in reacting negatively if his wife refuses sexual relations: (a) becoming angry, (b) refusing financial support, (c) using force for sex, and (d) having sex with another woman. Statistical Methods Methods Descriptive statistics were used to present the distribution of outcome and explanatory variables. The analysis included a total sample of 82,598 men aged 15–54 years, comprising 70,039 non-migrants, 7,253 short-term migrants, and 5,306 long-term migrants. The outcome variable of the study is men’s attitudes towards women’s sexual autonomy (MASAW), constructed using four survey questions that asked whether a husband is justified in reacting negatively if his wife refuses sexual relations: (a) becoming angry, (b) refusing financial support, (c) using force for sex, and (d) having sex with another woman. Statistical Methods (a) Data source and tools: - We have accessed the data for analysis, the person file (PR) and records from the Demographic Health Survey (DHS). The Program is internationally responsible for collecting and disseminating accurate data on health and nutrition in low- and middle-income countries (LMICs). The initiative, which has been running since 1984, is mostly funded by the United States Agency for International Development (USAID) and provides representative data on population and health in developing nations. The United States Agency for International Development (USAID) provided the majority of the funding for the project, which is carried out by ICF International. Other contributors were UNICEF, UNFPA, WHO, and UNAIDS. It provides high-quality data for researchers and policymakers. National Family Health Survey: -The National Family Health Survey (NFHS) is a multi-round, extensive survey. The Ministry of Health and Family Welfare (MoHFW), Government of India, is in charge of conducting this version of the Demographic and Health Survey (DHS). International organisations like USAID, UNICEF, UNFPA, the Bill & Melinda Gates Foundation, and WHO sponsor the study, which is conducted by the International Institute for Population Sciences (IIPS), Mumbai, in cooperation with other field agencies. The NFHS has been crucial in delivering trustworthy and nationally representative data since its establishment in 1992–1993. For analysing the NFHS data, such as recording and cross-tabulation, we used STATA (64-bit). (b) Regression Analysis: - In our study, we used a binary logistic regression model to examine the association between dependent and explanatory variables (a) become angry, (b) refuse financial support, (c) use force for sex. Find the unadjusted association between key explanatory variables and MASAW, reporting Unadjusted Odds Ratios (UOR) with 95% Confidence Intervals (CI), controls for socio-demographic covariates, and reports Adjusted Odds Ratios (AOR) with 95% CI. The Logistic Regression Model. logit(πi) =log(πi/1−πi) =β0+β1Xi+εi β0= (Intercept) baseline log-odds when X=0 β1= (Coefficient) effect of independent variable Xi= (Predictor) Migration status εi= (Error term) Results Demographic Characteristics of the Sample According to the defined temporary migration status, among the 82,598 male samples, 8.9% were short-term migrants (STMs) and 6.4% were long-term migrants (LTMs) (Table 1 ). Approximately 49% of men were in the age group 15–34 years , whereas long-term migrants (approximately 61.5% ) were younger than non-migrants (approximately 47.6% ). In terms of educational qualifications (such as high school or above), long-term migrants ( 18.2% ) were more qualified than short-term migrants ( 15.5% ) and non-migrants ( 15.7% ). Approximately 39.8% of men were engaged in agricultural activities, while smaller shares were engaged in other occupations. Compared with migrants, non-migrants had greater shares in agriculture ( 40.9% vs 35.1% vs 31.1% ) and sales ( 10.5% vs 8.4% vs 7.7% ) than migrants did (STM and LTM). Most men, approximately 78% LTMs, 79% STMs , and 74% non-migrants , were from rural areas. Men from the Hindu religion had a slightly lower share of short-term migration and a higher share of long-term migration than Muslims and Christians. Scheduled Castes and Scheduled Tribes were more engaged in short-term migration than long-term migration, whereas men from Other Backward Classes were more engaged in long-term migration. Short-term migrants had a lower economic status ( 26.9% in the poorest category ) than non-migrants ( 19.7% ) and long-term migrants ( 23.9% ). Men from the richest wealth quantile were less likely to migrate. Compared with short-term migrants and non-migrants, long-term migrants had greater media exposure ( 77.8% vs 74.1% vs 73.6% ). Migrants (STM and LTM) were more prone to smoking and consuming alcohol. Table 1 Percentage of men (age 15–54) according to temporary migration status and their background characteristics Characteristics Migration Status Total NM STM LTM % Number Total Sample (%) 70039 (84.80) 7253 (8.78) 5,306 (6.42) 100 82,598 Age 15–24 17.3 24.3 25.1 18.4 15,198 25–34 30.3 34.0 36.4 31.0 25,628 35–44 28.6 25.0 22.9 28.0 23,085 45–54 23.8 16.8 15.6 22.6 18,687 Years of Schooling No education 14.1 13.3 13.2 13.9 11,507 5 years 13.5 14.9 12.5 13.5 11,179 6–9 years 56.7 56.4 56.1 56.7 46,800 10 years or above 15.7 15.5 18.2 15.9 13,112 Marital Status Never married 22.8 27.6 31.9 23.8 19,674 Currently marrid 75.4 70.7 66.5 74.4 31,447 Others 1.8 1.8 1.7 1.8 1,477 Occupation agriculture 40.93 35.1 31.12 39.79 32,864 Professional 6.23 6.25 8.48 6.38 5,266 clerical 1.9 1.81 2.3 1.92 1,587 sales 10.45 8.4 7.65 10.09 8,334 services 7.33 8.53 11.1 7.68 6,341 Production worker 28.21 34.14 31.98 28.97 23,930 Others 4.95 5.78 7.37 5.18 4,276 Residence Rural 73.7 79.2 78.1 74.5 61,537 Urban 26.3 20.8 21.9 25.5 21,061 Religion Hindu 76.1 73.9 76.3 76.0 62,738 Muslim 11.7 13.4 12.4 11.9 9,810 Christian 7.0 8.1 6.2 7.0 5,818 Others 5.2 4.6 5.1 5.1 4,232 Caste SC 18.8 20.5 20.2 19.1 15,739 ST 19.2 22.0 19.1 19.4 16,057 OBC 38.7 35.4 40.7 38.6 31,847 Others 23.3 22.1 20.0 23.0 18,955 Wealth Index (MPCE) Poorest 19.7 26.9 23.9 20.6 17,033 Poor 22.1 25.1 23.4 22.4 18,524 Middle 21.3 20.4 21.7 21.4 17,529 Rich 19.9 15.6 18.4 19.4 16,044 Richest 17.0 12.1 12.6 16.3 13,468 Media Exposure No 26.4 26.0 22.2 26.1 21,524 Yes 73.6 74.1 77.8 73.9 61,074 Smoking No 85.0 79.3 78.4 84.0 69,405 Yes 15.1 20.8 21.6 16.0 13,193 Substance Use No 70.8 65.4 66.0 70.0 57,832 Yes 29.2 34.6 34.0 30.0 24,766 Figure 2 shows the percentage of men’s attitudes towards the sexual autonomy of women based on four questions. The questions are: if the wife refuses to have sex, the husband has the right to do the following: (i) get angry, (ii) refuse financial support, (iii) use force for sex, and (iv) have sex with another woman, where positive responses are considered as a negative attitude. Short-term migrants had a higher negative attitude towards the first question compared to non-migrants but slightly lower than long-term migrants (19.29% vs 21.05% vs 21.64%). However, for the third question, short-term migrants had a lower negative attitude (12.35% vs 11.44% vs 15.6%) than long-term migrants and non-migrants. Overall, long-term migrants had a higher negative attitude towards most of the questions compared to the other groups. Table 2 shows that long-term migrants (33.6%) and short-term migrants (30.2%) had a more negative attitude towards the sexual autonomy of women than non-migrants (27.8%). Men who used substances had a more negative attitude (32.2%) than those who did not (27.0%). Men who were more qualified and belonged to higher wealth quantiles had a lower negative attitude than their counterparts. Men who were engaged in agricultural activities (30.0%) and the service sector (29.4%) had a higher negative attitude compared with several other occupational groups. Similarly, men from the Muslim religion and the Scheduled Castes had a higher negative attitude than their counterparts. Men who smoked had a higher negative attitude (33.6%) than those who did not smoke (27.4%). Table 2 Prevalence of men’s attitudes towards the sexual autonomy of women (MASAW) by their background characteristics Characteristics % (W) P Value Positive Attitude Negative Attitude MASAW 71.7 28.3 Migration status Non-migrants 72.2 27.8 < 0.001 Short-term migrants 69.8 30.2 Long-term migrants 66.4 33.6 Age 15–24 71.6 28.4 < 0.001 25–34 72.0 28.0 35–44 71.2 28.8 45–54 71.8 28.2 Education 0.028 No education 69.7 30.3 Primary 69.3 30.7 Secondary 71.6 28.4 Higher 75.3 24.7 < 0.001 Marital Status Never married 71.6 28.5 Currently married 71.9 28.1 Others 72.7 27.33 Occupation Agriculture 70.0 30.03 < 0.001 Professonal 74.6 25.4 Clercial 78.8 21.2 Sales 73.5 26.5 Servises 70.6 29.4 Production worker 73.2 26.8 Others 64.3 35.7 Residence Rural 73.0 27.0 0.907 Urban 73.2 26.8 Religion Hindu 72.6 27.4 < 0.001 Muslim 69.1 30.9 Christian 70.6 29.4 Others 58.7 41.3 Social Group SCs 70.2 29.8 < 0.001 STs 74.1 25.9 OBCs 71.4 28.6 Others 72.3 27.7 MPCE Poorest 70.5 29.5 < 0.001 Poor 71.6 28.4 Middle 71.3 28.8 Rich 71.4 28.6 Richest 73.6 26.4 Media Exposure No 72.3 27.7 0.001 Yes 71.5 28.5 Smoking No 72.6 27.4 0.001 Yes 66.39 33.6 Substance use No 73.0 27.0 < 0.001 Yes 67.8 32.2 Note: SC: Scheduled Caste; ST: Scheduled Tribes; OBC: Other Backwards Classes; a: Includes technical, administrative, and managerial occupations; b: Includes skilled and unskilled manual occupations Associations between temporary migration status, substance use, and men’s attitudes towards the sexual autonomy of wives . Table 3 shows the estimates of logistic regression to examine the association between temporary migration status and men’s attitudes towards the sexual autonomy of women. Approximately 28.3% of men exhibited negative attitudes towards women’s sexual autonomy. In the unadjusted model, the results show that men who were migrants (both short-term and long-term) were significantly more likely to have negative attitudes towards sexual autonomy. After adjusting for the covariates, Model II shows that short-term migrants (AOR: 1.13; CI: 1.07–1.19) and long-term migrants (AOR: 1.41; CI: 1.32–1.49) were more likely to have negative attitudes than non-migrants. Similarly, men who used substances were 1.16 times more likely (AOR: 1.16; CI: 1.12–1.20) to have negative attitudes. Logistic estimates in Model II further show that men belonging to the older age group (45–54 years), those with higher education, and those belonging to Scheduled Tribes and Other Backward Classes had significantly lower odds of negative attitudes. On the other hand, men belonging to the Muslim religion and those in higher wealth categories had significantly higher odds of having negative attitudes towards the sexual autonomy of women. Compared with those engaged in agriculture (reference category), men working in professional, clerical, sales, service, and production occupations had lower odds of negative attitudes. Table 3 Binary logistic estimates showing the associations between temporary migration status and men’s attitudes towards the sexual autonomy of women Characteristics Model 1 Model 2 UOR CI (95%) AOR CI (95%) Migration status Non-migrants® 1 [1.00,1.00] 1 [1.00,1.00] Short-term migrants 1.12*** [1.06,1.18] 1.13*** [1.07,1.19] Long term migrants 1.40*** [1.32,1.49] 1.41*** [1.32,1.49] Age® 15–24 1 [1.00,1.00] 25–34 0.95** [0.90,1.00] 35–44 0.93** [0.87,0.98] 45–54 0.89*** [0.83,0.95] Education® No education 1 [1.00,1.00] Primary 0.92*** [0.86,0.97] Secondary 0.82*** [0.78,0.86] Higher 0.69*** [0.64,0.73] Marital Status® Never married 1 [1.00,1.00] Currently married 0.95** [0.90,1.00] Others 0.98 [0.86,1.11] Occupation® agriculture 1 [1.00,1.00] Professional 0.79*** [0.73,0.86] clerical 0.72*** [0.64,0.81] sales 0.84*** [0.79,0.89] services 0.87*** [0.82,0.93] Production worker 0.78*** [0.75,0.81] Others 1.16*** [1.08,1.25] Residence® Rural 1 [1.00,1.00] Urban 1 [0.96,1.04] Religion® Hindu 1 [1.00,1.00] Muslim 1.22*** [1.16,1.28] Christian 1.14*** [1.07,1.22] Others 2.53*** [2.37,2.70] Social Group® SCs 1 [1.00,1.00] STs 0.73*** [0.69,0.77] OBCs 0.85*** [0.82,0.89] Others 0.86*** [0.82,0.91] MPCE® Poorest 1 [1.00,1.00] Poor 1 [0.95,1.05] Middle 1.11*** [1.05,1.17] Rich 1.17*** [1.10,1.24] Richest 1.29*** [1.21,1.38] Media Exposure® No 1 [1.00,1.00] Yes 1.08*** [1.03,1.12] Smoking® No 1 [1.00,1.00] Yes 1.15*** [1.10,1.21] Substance use® No 1 [1.00,1.00] Yes 1.16*** [1.12,1.20] Discussion This research reveals the associations between temporary migration and men’s attitudes toward the sexual autonomy of women in India (MASAW). Results: The results show how migration is linked to men's gender perceptions and reveal significant sociodemographic variations in migration trends. Our study revealed that approximately 14% of men were temporary migrants, including 7.7% short-term migrants and 6.2% long-term migrants, indicating that temporary migration remains a significant livelihood strategy among men in India. Migrants were younger, more educated, and more likely to work in non-agricultural jobs than non-migrants were, which is consistent with earlier migration studies. Existing studies have also shown that migration processes can influence social norms, behaviours, and family dynamics, particularly in traditional gender hierarchal societies where gender relations are strongly structured by cultural norms(Desai & Andrist, 2011; Agadjanian et al., 2011 ) Long-term migrants, in particular, had higher levels of education and media exposure than both short-term migrants and while non-migrants did not, whereas short-term migrants were more concentrated in lower wealth categories, indicating that economic vulnerability is still a significant factor in temporary migration. To account for the impact of individual, family, and community characteristics on migration decisions and movement forms, migration behaviour involves the use of factors at several levels (Guilmoto, 1998 ). The results also indicate that migrant men were more likely to smoke and use substances, which is consistent with previous studies indicating that stress connected to migration, social isolation, and new urban social contexts may promote risky behaviours among migrants (Weine & Kashuba, 2012 ). Peer pressure and the lack of family supervision during migration may also be factors contributing to substance abuse among migrants (Ahmadi, 2003 ). The study also identifies troubling trends in men's perceptions of women's sexual autonomy. Approximately 28% of males overall indicated negative sentiments, suggesting that men continue to defend against dominating behaviours when their spouses decline to engage in sexual activity (Sabarwal et al., 2014 ).These beliefs are more common among migrants, especially long-term migrants (33%), than among short-term migrants (30%) and non-migrants (27%). This implies that attitudes and gender norms may be influenced by migratory experiences. These gender-sex disparities, together with other research on gender-sex-specific migratory trends, were reported in this study (Joseph, Wang, Chellaraj, Luis, et al., 2022 ). Negative gender attitudes were also found to be significantly correlated with substance use. Compared non-users, men who reported using drugs were 15% more likely to have negative opinions on women's sexual autonomy. This finding is consistent with the literature linking substance use with aggressive behaviour, reduced impulse control, and higher acceptance of gender-based dominance (Pradhan & De, 2025 ). Similarly, smoking behaviour was associated with higher odds of negative attitudes, suggesting that risky health behaviours often co-exist with regressive gender norms. It seemed that education had a protective effect. Education may encourage gender-equitable standards and an understanding of women's rights, as seen by the much-reduced likelihood of negative sentiments among men with higher levels of education (Sabarwal et al., 2014 ). In a similar vein, older age groups were less likely than younger men were to have unfavourable sentiments, indicating that gender perceptions may be influenced by maturity and life experience. These discrepancies could be the result of regional contexts, economic circumstances, and sociocultural norms that influence gender perspectives. Our study emphasises the importance of addressing migration-related vulnerabilities and substance use behaviours while promoting gender-equitable attitudes among men. Substance use prevention techniques, behavioural change communication, and gender sensitisation programmes should all be included in policies and interventions targeted at immigrant communities. Media campaigns, community interventions, and workplace-based awareness programs may all contribute to the reduction of gender-inequitable attitudes and the promotion of respect for women's autonomy. This study has several limitations, such as the use of a sizable nationally representative sample. Establishing causal links between migration, substance use, and gender attitudes is restricted by the cross-sectional methodology used. Furthermore, self-reported answers were used to evaluate attitudes about sexual autonomy, which could be skewed by social desirability. Longitudinal data could help future studies better understand how migration experiences affect gender perceptions over time. Conclusion Our study highlights that migration status is a significant determinant of men’s attitudes towards the sexual autonomy of women in India. The results indicate that, in comparison to non-migrants, both temporary and permanent migrants are more likely to have positive sentiments. Such sentiments are also positively correlated with substance usage, suggesting a complex behavioural relationship. These sentiments are further influenced by sociodemographic variables like wealth, occupation, education, and religion. In order to encourage gender-equitable views among women, the study emphasises the necessity of focused interventions that address behavioural and social issues. Declarations Ethics approval and consent to participate: This study is based entirely on secondary data from the publicly available National Family Health Survey (NFHS-5, 2019–21). NFHS-5 data were collected under the supervision of the Ministry of Health and Family Welfare (MoHFW), Government of India, and ethical approvals for the survey were obtained from the Institutional Review Boards (IRB) of the International Institute for Population Sciences (IIPS), Mumbai, and other collaborating institutions. Written informed consent was obtained from all participants by the survey teams before data collection. No separate ethical approval was required for this analysis, as it uses anonymized, publicly available data. Consent for publication: As NFHS data is a publicly available data on DHS program website and IIPS website too. Anyone who wants to explore or wants to do research they can access data freely, analyse, research and publish it. So, there is no need for the consent of publication. Availability of data and materials : The data used in this study were obtained from the National Family Health Survey (NFHS-5), conducted by the Ministry of Health and Family Welfare, Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Mumbai. The dataset is publicly available and can be accessed from the DHS Program website or the https://www.iipsdata.ac.in/datacatalog_detail/1 . Competing interests: The authors declare that they have no competing interests. Funding : There is no available funding for doing this research. Researchers have done it by self as it is based on the secondary publicly available dataset. Authors' contributions: 1 Vikesh Kumar: Conceptualization of the research idea; formal analysis; data curation and data analysis; preparation of tables; writing the data, methods, and results sections; project administration; provision of resources; and overall supervision. 2 Ankit Gupta: Conceptual support in manuscript preparation; drafting the introduction; formatting and visualization of results; identification of potential funding sources; information gathering for publication; and finalization of the manuscript draft for submission. 3 Priti: Writing the discussion section; drafting and revising the manuscript; methodological application; validation of analytical results; and preparation of the original draft. 4 Phanidhar Upadhyaya : Investigation; manuscript review and editorial support; providing recommendations for improving manuscript quality; ethical oversight; and methodological guidance. Acknowledgements: Ministry of Health and Family Welfare, Government of India, and the International Institute for Population Sciences (IIPS), Mumbai, and Dr. Kunal Keshri, Assistant Professor, Department of Urbanization and Migration Studies for their constant guidance. References Abada, T., & Tenkorang, E. Y. (2012). Women’s autonomy and unintended pregnancies in the Philippines. Journal of Biosocial Science , 44 (6), 703–718. https://doi.org/10.1017/S0021932012000120 Aboagye, R. G., Dadzie, L. K., Arthur-Holmes, F., Okyere, J., Agbaglo, E., Ahinkorah, B. O., & Seidu, A.-A. (2022). Intimate partner violence against married and cohabiting women in sub-Saharan Africa: does sexual autonomy matter? Reproductive Health , 19 (1), 79. https://doi.org/10.1186/s12978-022-01382-1 Agadjanian, V., & Hayford, S. R. (2018). Men’s Migration, Women’s Autonomy, and Union Dissolution in Rural Mozambique. Journal of Family Issues , 39 (5), 1236–1257. https://doi.org/10.1177/0192513X17698184 Agadjanian, V., Yabiku, S. T., & Cau, B. (2011). Men’s Migration and Women’s Fertility in Rural Mozambique. Demography , 48 (3), 1029–1048. https://doi.org/10.1007/s13524-011-0039-y Ahmadi, N. (2003). Migration challenges views on sexuality. Ethnic and Racial Studies , 26 (4), 684–706. https://doi.org/10.1080/0141987032000087361 Amaro, H. (1995). Love, sex, and power. Considering women’s realities in HIV prevention. The American Psychologist , 50 (6), 437–447. https://doi.org/10.1037//0003-066x.50.6.437 Bloom, S. S., Wypij, D., & Das Gupta, M. (2001). Dimensions of women’s autonomy and the influence on maternal health care utilization in a North Indian city. Demography , 38 (1), 67–78. https://doi.org/10.1353/dem.2001.0001 Census of India. (2011). Office of the Registrar General & Census Commissioner, India. https://censusindia.gov.in/census.website/data/census-tables Coffey, D., Papp, J., & Spears, D. (n.d.). Short-Term Labor Migration from Rural North India: Evidence from New Survey Data . https://doi.org/10.1007/s11113-014-9349 Crissman, H. P., Adanu, R. M., & Harlow, S. D. (2012). Women’s sexual empowerment and contraceptive use in Ghana. Studies in Family Planning , 43 (3), 201–212. https://doi.org/10.1111/j.1728-4465.2012.00318.x Daniel Ikuomola, A. (n.d.). An Exploration of Socioc Socio-c ultural Challenges of Left behind Wives in Edo State, Nigeria . Desai, S., & Andrist, L. (2010). Gender scripts and age at marriage in India. Demography , 47 (3), 667–687. https://doi.org/10.1353/dem.0.0118 Desai, S., & Banerji, M. (2008a). Negotiated identities: Male migration and left-behind wives in India. Journal of Population Research , 25 (3), 337–355. https://doi.org/10.1007/BF03033894 Desai, S., & Banerji, M. (2008b). Negotiated identities: Male migration and left-behind wives in India. Journal of Population Research , 25 (3), 337–355. https://doi.org/10.1007/BF03033894 Deshingkar, P., & Akter, S. (n.d.). Human Development Research Paper 2009/13 Migration and Human Development in India . Forste, R., & Fox, K. (n.d.). Household Labor, Gender Roles, and Family Satisfaction: A Cross-Household Labor, Gender Roles, and Family Satisfaction: A Cross-National Comparison National Comparison Household Labor, Gender Roles, and Family Satisfaction: A Cross-National Comparison . Retrieved https://scholarsarchive.byu.edu/facpub Guilmoto, C. Z. (1998). Institutions and migrations. Short-term versus long-term moves in rural West Africa. Population Studies , 52 (1), 85–103. https://doi.org/10.1080/0032472031000150196 Heidari, S. (2015). Sexual rights and bodily integrity as human rights. Reproductive Health Matters , 23 (46), 1–6. https://doi.org/10.1016/j.rhm.2015.12.001 Hughes, C., Bhandari, P., Young-DeMarco, L., Swindle, J., Thornton, A., & Williams, N. (2020). Family Obligation Attitudes, Gender, and Migration. International Journal of Sociology , 50 (4), 237–264. https://doi.org/10.1080/00207659.2020.1726109 Imran Khan, M., Alharthi, M., Haque, A., & Illiyan, A. (2023). Statistical analysis of push and pull factors of migration: A case study of India. Journal of King Saud University - Science , 35 (8). https://doi.org/10.1016/j.jksus.2023.102859 Jewkes, R., Flood, M., & Lang, J. (2015). From work with men and boys to changes of social norms and reduction of inequities in gender relations: a conceptual shift in prevention of violence against women and girls. The Lancet , 385 (9977), 1580–1589. https://doi.org/10.1016/S0140-6736(14)61683-4 Joseph, G., Wang, Q., Chellaraj, G., Luis, E. O. T., Syed, A., Javaid, U., & Rajan, I. (2022). Beyond Money: Does Migration Experience Transfer Gender Norms? Empirical Evidence from Kerala, India . http://www.worldbank.org/prwp. Keshri, K., & Bhagat, R. B. (2013). SOCIOECONOMIC DETERMINANTS OF TEMPORARY LABOUR MIGRATION IN INDIA: A regional analysis. Asian Population Studies , 9 (2), 175–195. https://doi.org/10.1080/17441730.2013.797294 Levitt, P. (1998a). Social Remittances: Migration Driven Local-Level Forms of Cultural Diffusion. In Source: The International Migration Review (Vol. 32, Number 4). Winter. Levitt, P. (1998b). Social Remittances: Migration Driven Local-Level Forms of Cultural Diffusion. International Migration Review , 32 (4), 926–948. https://doi.org/10.1177/019791839803200404 Matz, J. A., & Mbaye, L. M. (2023). Migration and the Autonomy of Women Left Behind. European Journal of Development Research , 35 (5), 1059–1079. https://doi.org/10.1057/s41287-022-00559-5 Memiah, P., Opanga, Y., Bond, T., Cook, C., Mwangi, M., Fried, J., Joseph, M. A., Owuor, K., Mochache, V., & Machira, Y. W. (2019). Is sexual autonomy a protective factor for neonatal, child, and infant mortality? A mult i i- country analysis. PloS One , 14 (2), e0212413. https://doi.org/10.1371/journal.pone.0212413 Menjívar, C., & Agadjanian, V. (2007a). Men’s Migration and Women’s Lives: Views from Rural Armenia and Guatemala * . Social Science Quarterly , 88 (5), 1243–1262. https://doi.org/10.1111/j.1540-6237.2007.00501.x Menjívar, C., & Agadjanian, V. (2007b). Men’s Migration and Women’s Lives: Views from Rural Armenia and Guatemala * . Social Science Quarterly , 88 (5), 1243–1262. https://doi.org/10.1111/j.1540-6237.2007.00501.x Narushima, M., McLaughlin, J., & Barrett-Greene, J. (2016). Needs, Risks, and Context in Sexual Health Among Temporary Foreign Migrant Farmworkers in Canada: A Pilot Study with Mexican and Caribbean Workers. Journal of Immigrant and Minority Health , 18 (2), 374–381. https://doi.org/10.1007/s10903-015-0189-x NSSO. (2010). Migration in India . Osezua, C. A. (2013). Changing Status of Women and the Phenomenon Trafficking of Women for Transactional Sex in Nigeria: A Qualitative Analysis. In Journal of International Women’s Studies (Vol. 14, Number 3). Pradhan, M. R., & De, P. (2025). Men’s gender role and attitude toward sexual autonomy of women in India. PLoS ONE , 20 (1). https://doi.org/10.1371/journal.pone.0317301 Ram Mohan, R., Puskur, R., Chandrasekar, D., & Valera, H. G. A. (2023). Do gender dynamics in intrah intra-h ousehold decision making shift with male migration? Evidence from rice-farming households in Eastern India. Gender, Technology and Development , 27 (2), 157–183. https://doi.org/10.1080/09718524.2022.2140381 Sabarwal, S., Santhya, K. G., & Jejeebhoy, S. J. (2014). Women’s autonomy and experience of physical violence within marriage in rural India: Evidence from a prospective study. Journal of Interpersonal Violence , 29 (2), 332–347. https://doi.org/10.1177/0886260513505144 Singh, R. (2024). The Rural ‒ - Urban Gap in Domestic Violence and Women’s Economic Empowerment. Social Science and Humanities Journal , 8 (03), 34755–34758. https://doi.org/10.18535/sshj.v8i03.994 Torosyan, K., Gerber, T. P., & Goñalons-Pons, P. (2016). Migration, Household Tasks, and Gender: Evidence from the Republic of Georgia. The International Migration Review , 50 (2), 445–474. https://doi.org/10.1111/imre.12147 Wang, S. (n.d.). A Study on the Impact of Parental Migration on the Lives of Children Left Behind in Northeast China . Weine, S. M., & Kashuba, A. B. (2012). Labor migration and HIV risk: A systematic review of the literature. In AIDS and Behavior (Vol. 16, Number 6, pp. 1605–1621). https://doi.org/10.1007/s10461-012-0183-4 Willie, T. C., Callands, T., Alexander, K. A., & Kershaw, T. (2023). Measuring women’s sexual autonomy: Development and preliminary validation of the women’s sexual autonomy scale. Women’s Health (London, England) , 19 , 17455057231183836. https://doi.org/10.1177/17455057231183837 Yabiku, S. T., Agadjanian, V., & Sevoyan, A. (2010a). Husbands’ labour migration and wives’ autonomy, Mozambique 2000–2006. Population Studies , 64 (3), 293–306. https://doi.org/10.1080/00324728.2010.510200 Yabiku, S. T., Agadjanian, V., & Sevoyan, A. (2010b). Husbands’ labour migration and wives’ autonomy, Mozambique 2000–2006. Population Studies , 64 (3), 293–306. https://doi.org/10.1080/00324728.2010.510200 Yabiku, S. T., Agadjanian, V., & Sevoyan, A. (2010c). Husbands’ labour migration and wives’ autonomy, Mozambique 2000-2006. Population Studies , 64 (3), 293–306. https://doi.org/10.1080/00324728.2010.510200 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 26 Mar, 2026 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-9239808","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624562934,"identity":"92db4494-677a-467c-ae65-8793d9f00ee1","order_by":0,"name":"Vikesh Kumar","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vikesh","middleName":"","lastName":"Kumar","suffix":""},{"id":624562935,"identity":"14387072-1b16-4f4e-b98b-c4e862099a95","order_by":1,"name":"Ankit Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBADAzYG5mPMEDZjAyHVYBUGbGxsaSRqYWDjMWMmykHyEbnHH/youWPMJ9/z7XFhG4M8fwNz2wN8Wgxv5CU29hx7ZsbGxrvdeGYbg+GMA4ztBni1zMgxbOBhO2wD1LJNmreNgXEDA2ObBCEtjX/+gbTwPANpsSeoRV4ix7CZt+0w0GE8bCAtiQS1GPC8S5wt23fYGBjG5sY85ySSZxwmZEt77oGPb74dNpzffPjZY54yG9v+9vZn+G05wIPCByomFDvyDTwEVIyCUTAKRsEoAACKX0HUDa8eTgAAAABJRU5ErkJggg==","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Gupta","suffix":""},{"id":624562936,"identity":"f70c508b-7792-40dd-9c6d-0da1b044e19d","order_by":2,"name":"Priti .","email":"","orcid":"","institution":"Institute of Infrastructure Technology Research and Management","correspondingAuthor":false,"prefix":"","firstName":"Priti","middleName":"","lastName":".","suffix":""},{"id":624562937,"identity":"99ab7b19-157d-40ba-996a-18353288684e","order_by":3,"name":"Phanidhar Upadhyaya","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Phanidhar","middleName":"","lastName":"Upadhyaya","suffix":""}],"badges":[],"createdAt":"2026-03-27 04:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9239808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9239808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107138373,"identity":"933b0185-a02a-47f0-b1a6-469467655987","added_by":"auto","created_at":"2026-04-17 08:29:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42689,"visible":true,"origin":"","legend":"\u003cp\u003epresents the distribution of the study sample according to migration status, including non-migrants, short-term migrants, and long-term migrants.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9239808/v1/20a0df42c4dd2d8c4640a496.png"},{"id":107138374,"identity":"2d6a7d93-2d0f-40f2-a76e-4d84a6ba2139","added_by":"auto","created_at":"2026-04-17 08:29:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48649,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Men's attitudes towards the sexual autonomy of women by Migration status\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9239808/v1/73df6782d52ef7c6687dcd6f.png"},{"id":107481277,"identity":"1b01526d-e53a-4bd6-9966-20af2b44607f","added_by":"auto","created_at":"2026-04-22 02:16:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1387180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9239808/v1/14b980f7-c067-442e-923f-0b200365eb38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Men's labor migration and attitudes towards women's sexual autonomy: Evidence from the National Family Health Survey 2019-21","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMigration has long been recognized as a powerful social and economic force that shapes family structures, gender relations, and community norms. In many low- and middle-income countries (LMICs), the temporary or long-term migration of men creates shifts in household responsibilities, decision-making power, and the regulation of sexual and reproductive behaviour within families (Torosyan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to gender norms in Asian countries, men are considered breadwinners and are expected to arrange livelihoods, whereas women are expected to take care of the household, and such norms often lead to male migration alone, leaving their families behind at home, especially in economically backwards regions (Forste \u0026amp; Fox, n.d.; Hughes et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang, n.d.). According to the Census of India (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), There were 41\u0026nbsp;million labour migrants; however, it was estimated that there would be approximately 100\u0026nbsp;million migrant workers, and most of them were (approximately 85%) male migrants (Deshingkar \u0026amp; Akter, n.d.). In the Indian context, internal migration is predominantly male dominated and closely linked to labour market dynamics. This form of migration not only affects economic conditions but also transforms household power relations, gender roles, and attitudes toward women\u0026rsquo;s autonomy (Imran Khan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ram Mohan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMale labour migration, particularly in low- and middle-income countries, often results in prolonged spousal separation and shifts in household decision-making structures (Desai \u0026amp; Banerji, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). The long-term absence of migrants with their spouses can reduce closeness and intimacy in marital relationship. (Menj\u0026iacute;var \u0026amp; Agadjanian, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). In addition to the increased burden and obligations, the migration of husbands also gives left-behind wives more autonomy and decision-making authority. In India, women are more likely to have a say in daily cooking decisions, household expenses for expensive goods, and the marriage and health care of children when their husbands are away (Desai \u0026amp; Banerji, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). After their spouses have left the house, women in Mozambique now have greater freedom to visit friends and family outdoors, look for work, to visit the city or district capital, and get tested for HIV (Yabiku et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). Men's migration increases their spouses' autonomy, independence, and decision-making power (Yabiku et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). Historically, women have had less personal autonomy in patriarchal countries (Bloom et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Compared to men, they have less access to resources and less control over decision-making processes (Daniel Ikuomola, n.d.; Osezua, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Singh, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite extensive research on migration and women\u0026rsquo;s economic and decision-making autonomy, limited attention has been given to understanding how men\u0026rsquo;s labour migration shapes men\u0026rsquo;s own attitudes towards women\u0026rsquo;s sexual autonomy.\u003c/p\u003e \u003cp\u003eSexual autonomy, which refers to a person's capacity to negotiate safer sexual practices, decrease unwanted sexual relations, make informed and voluntary decisions about sexual activity, and exercise control over their own body and reproductive choices, is a crucial component of this larger concept (Aboagye et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Memiah et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Willie et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many people agree that women's sexual autonomy is a basic human right and a vital aspect of their sexual and reproductive health and well-being (Heidari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Various studies suggest that women who have more sexual autonomy are more likely to negotiate condom use, utilize contraception, and steer clear negative reproductive health outcomes, such as STIs and unwanted pregnancies (Abada \u0026amp; Tenkorang, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Crissman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe roles and power dynamics in sexual interactions are shaped by conventional normative conceptions of masculinity, femininity, and broader gender inequality (Amaro, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Migration adds an additional layer of complexity: while some studies suggest that male out-migration may increase women\u0026rsquo;s household autonomy, this does not necessarily translate into increased sexual autonomy due to persistent patriarchal norms, social surveillance, and economic dependency (Agadjanian \u0026amp; Hayford, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Matz \u0026amp; Mbaye, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yabiku et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010c\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLabour migration may influence men\u0026rsquo;s attitudes towards women\u0026rsquo;s sexual autonomy through multiple interrelated pathways. First, economic mobility and remittance flows may strengthen men\u0026rsquo;s provider role, reinforcing traditional patriarchal authority within households (Desai \u0026amp; Banerji, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). Second, prolonged spousal separation may alter emotional intimacy and marital communication, potentially increasing suspicion, control, or insecurity regarding wives\u0026rsquo; sexuality (Menj\u0026iacute;var \u0026amp; Agadjanian, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e). Third, exposure to urban or destination contexts may introduce alternative gender norms, including more egalitarian attitudes toward women\u0026rsquo;s decision-making and sexual rights (Levitt, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998a\u003c/span\u003e; Yabiku et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010c\u003c/span\u003e). Conversely, harsh migration experiences, including poor working conditions and social isolation, may increase stress, alcohol consumption, and risk-taking behaviours, which have been associated with more controlling and coercive sexual attitudes (Jewkes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Narushima et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These competing pathways suggest that migration may either liberalize or intensify patriarchal attitudes, making empirical investigation essential.\u003c/p\u003e \u003cp\u003eDrawing from theories of gender transformation and hegemonic masculinity, this study conceptualises labour migration as a structural experience that reshapes men\u0026rsquo;s economic roles, social exposure, and psychological stress, which in turn influences attitudes toward women\u0026rsquo;s sexual autonomy (Levitt, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998b\u003c/span\u003e). Rather than assuming that migration uniformly empowers women, this study shifts the analytical focus to men and examines whether migration produces attitudinal liberalisation through exposure and social learning or whether it reinforces patriarchal control through economic dominance and masculine insecurity. By distinguishing between short-term and long-term migration, the study further explores whether the duration and intensity of migration differentially shape these attitudinal outcomes.\u003c/p\u003e \u003cp\u003eAlthough a large body of research has examined the impact of male labour migration on women's economic autonomy and household decision-making, far less attention has been given to how migration experiences shape men\u0026rsquo;s own gender attitudes, particularly attitudes towards women\u0026rsquo;s sexual autonomy. Existing studies have primarily focused on the consequences of migration for women who remain at home, whereas the attitudinal changes among migrant men themselves remain understudied. Understanding men\u0026rsquo;s attitudes toward women's sexual autonomy is critical because such attitudes directly influence sexual negotiation, reproductive health outcomes, and gender power relations during marriage.\u003c/p\u003e \u003cp\u003eLabour migration may reshape men\u0026rsquo;s attitudes towards women\u0026rsquo;s sexual autonomy through several pathways. Migration often involves prolonged spousal separation, exposure to new social environments, and economic changes that can alter gender relations. On the one hand, exposure to urban settings and diverse social norms may promote more egalitarian views regarding women\u0026rsquo;s rights and autonomy. On the other hand, migration-related stress, social isolation, and masculine expectations of economic provision may reinforce patriarchal authority and suspicion toward women\u0026rsquo;s sexuality. These contrasting mechanisms suggest that migration may either liberalise or intensify patriarchal gender attitudes, making empirical investigation essential.\u003c/p\u003e \u003cp\u003eIn a country such as India, where patriarchal norms strongly regulate women's sexuality and where male labour migration remains a common livelihood strategy, understanding this relationship is particularly important. Therefore, this study examines the association between men\u0026rsquo;s labour migration and attitudes towards women\u0026rsquo;s sexual autonomy in India using nationally representative data from the National Family Health Survey (NFHS-5). By distinguishing between short-term and long-term migration and examining the role of substance use and socio-demographic factors, this study contributes to the literature on migration and gender relations by shifting the analytical focus from women\u0026rsquo;s outcomes to men\u0026rsquo;s gender attitudes.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData Sources\u003c/h2\u003e\n \u003cp\u003eThis study draws on data from the fifth round of the National Family Health Survey (NFHS-5), carried out between 2019 and 2021. NFHS-5 is a nationally representative, large-scale survey that provides comprehensive information on population characteristics, family planning, maternal and child health, nutrition, morbidity, and socio-economic and demographic conditions across all states and Union Territories of India. The survey employed a two-stage sampling design in rural areas and a three-stage design in urban areas. Conducted in two phases, it covered 636,699 households and included 724,115 women aged 15\u0026ndash;49 and 101,839 men aged 15\u0026ndash;54. Considering that he is working and has been away from home for more than one month, he must be a migrant in India. So, this study excludes non-working respondents to reduce error in the migrant sample.\u003c/p\u003e\n \u003cp\u003eDetailed information on the survey methodology is available in the NFHS-5 India report (International Institute for Population Sciences (IIPS) and ICF, 2021).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eOutcome Variable\u003c/h3\u003e\n\u003cp\u003eFor this study, the main outcome variable is men\u0026rsquo;s attitudes towards the sexual autonomy of women. According to the NFHS-5 data, men been asked if their wives refuse to have sex; the husband has the right to (a) get angry, (b) refuse financial support, (c) use force for sex, and (d) have sex with another woman. The outcome variable was generated by combining the responses of these questions, and men gave a positive response to at least one of the four questions; they were considered to have a negative attitude and were coded as \u0026lsquo;1\u0026rsquo;; otherwise, they were coded as \u0026lsquo;0\u0026rsquo;. (Scale reliability coefficient: alpha\u0026thinsp;=\u0026thinsp;0.84).\u003c/p\u003e\n\u003ch3\u003eExplanatory variables: Migration status\u003c/h3\u003e\n\u003cp\u003eThere is no direct question on migration status in the NFHS-5; however, some indirect questions have been asked. The respondents were asked (i) In the last 12 months, have you been away from home for one month or more at a time, and those who gave a positive response; furthermore, they were asked (ii) In the last 12 months, have you been away from home for one month or more at a time? Respondents who have never been away from home for at least one month were considered non-migrant, those who have been away for at least one month but who had lived for less than six months were considered short-term or temporary migrants, and those who have been away for six months or more were considered long-term or semi-permanent migrants (Coffey et al., n.d.; Keshri \u0026amp; Bhagat, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; NSSO, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eOther explanatory variables\u003c/h3\u003e\n\u003cp\u003eOther explanatory variables are respondents\u0026rsquo; age (15\u0026ndash;29, 30\u0026ndash;44 and 45\u0026ndash;54), education (no education, primary, secondary and higher), marital status (never married, married, others), occupation (professional, clerical, sales, services, agriculture, production worker, and other), residence (rural, urban), religion (Hindu, Muslim, Christian, and others), social group (Scheduled Caste, Scheduled Tribe, Other Backwards Classes, and Others), wealth index (poorer, poor, middle class, rich and richest), media exposure, smoking behaviour and use of alcohol (no, yes).\u003c/p\u003e\n\u003ch3\u003eData source NFHS-5\u003c/h3\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMethods\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics were used to present the distribution of outcome and explanatory variables. The analysis included a total sample of 82,598 men aged 15\u0026ndash;54 years, comprising 70,039 non-migrants, 7,253 short-term migrants, and 5,306 long-term migrants. The outcome variable of the study is men\u0026rsquo;s attitudes towards women\u0026rsquo;s sexual autonomy (MASAW), constructed using four survey questions that asked whether a husband is justified in reacting negatively if his wife refuses sexual relations: (a) becoming angry, (b) refusing financial support, (c) using force for sex, and (d) having sex with another woman.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e"},{"header":"Methods","content":"\u003cp\u003eDescriptive statistics were used to present the distribution of outcome and explanatory variables. The analysis included a total sample of 82,598 men aged 15\u0026ndash;54 years, comprising 70,039 non-migrants, 7,253 short-term migrants, and 5,306 long-term migrants. The outcome variable of the study is men\u0026rsquo;s attitudes towards women\u0026rsquo;s sexual autonomy (MASAW), constructed using four survey questions that asked whether a husband is justified in reacting negatively if his wife refuses sexual relations: (a) becoming angry, (b) refusing financial support, (c) using force for sex, and (d) having sex with another woman.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Data source and tools: - We have accessed the data for analysis, the person file (PR) and records from the Demographic Health Survey (DHS). The Program is internationally responsible for collecting and disseminating accurate data on health and nutrition in low- and middle-income countries (LMICs). The initiative, which has been running since 1984, is mostly funded by the United States Agency for International Development (USAID) and provides representative data on population and health in developing nations. The United States Agency for International Development (USAID) provided the majority of the funding for the project, which is carried out by ICF International. Other contributors were UNICEF, UNFPA, WHO, and UNAIDS. It provides high-quality data for researchers and policymakers.\u003c/p\u003e\n\u003cp\u003eNational Family Health Survey: -The National Family Health Survey (NFHS) is a multi-round, extensive survey. The Ministry of Health and Family Welfare (MoHFW), Government of India, is in charge of conducting this version of the Demographic and Health Survey (DHS). International organisations like USAID, UNICEF, UNFPA, the Bill \u0026amp; Melinda Gates Foundation, and WHO sponsor the study, which is conducted by the International Institute for Population Sciences (IIPS), Mumbai, in cooperation with other field agencies. The NFHS has been crucial in delivering trustworthy and nationally representative data since its establishment in 1992\u0026ndash;1993. For analysing the NFHS data, such as recording and cross-tabulation, we used STATA (64-bit).\u003c/p\u003e\n\u003cp\u003e(b) Regression Analysis: - In our study, we used a binary logistic regression model to examine the association between dependent and explanatory variables (a) become angry, (b) refuse financial support, (c) use force for sex. Find the unadjusted association between key explanatory variables and MASAW, reporting \u003cstrong\u003eUnadjusted Odds Ratios (UOR)\u003c/strong\u003e with 95% Confidence Intervals (CI), controls for socio-demographic covariates, and reports Adjusted Odds Ratios (AOR) with 95% CI.\u003c/p\u003e\n\u003cp\u003eThe Logistic Regression Model.\u003c/p\u003e\n\u003cp\u003elogit(\u0026pi;i) =log(\u0026pi;i/1\u0026minus;\u0026pi;i) =\u0026beta;0+\u0026beta;1Xi+\u0026epsilon;i \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026beta;0= (Intercept) baseline log-odds when X=0\u003c/p\u003e\n\u003cp\u003e\u0026beta;1= (Coefficient) effect of independent variable\u003c/p\u003e\n\u003cp\u003eXi= (Predictor) Migration status\u003c/p\u003e\n\u003cp\u003e\u0026epsilon;i= (Error term)\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics of the Sample\u003c/h2\u003e \u003cp\u003eAccording to the defined temporary migration status, among the \u003cb\u003e82,598\u003c/b\u003e male samples, \u003cb\u003e8.9% were short-term migrants (STMs)\u003c/b\u003e and \u003cb\u003e6.4% were long-term migrants (LTMs)\u003c/b\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Approximately \u003cb\u003e49% of men were in the age group 15\u0026ndash;34 years\u003c/b\u003e, whereas long-term migrants (approximately \u003cb\u003e61.5%\u003c/b\u003e) were younger than non-migrants (approximately \u003cb\u003e47.6%\u003c/b\u003e). In terms of educational qualifications (such as high school or above), long-term migrants (\u003cb\u003e18.2%\u003c/b\u003e) were more qualified than short-term migrants (\u003cb\u003e15.5%\u003c/b\u003e) and non-migrants (\u003cb\u003e15.7%\u003c/b\u003e). Approximately \u003cb\u003e39.8%\u003c/b\u003e of men were engaged in agricultural activities, while smaller shares were engaged in other occupations. Compared with migrants, non-migrants had greater shares in agriculture (\u003cb\u003e40.9% vs 35.1% vs 31.1%\u003c/b\u003e) and sales (\u003cb\u003e10.5% vs 8.4% vs 7.7%\u003c/b\u003e) than migrants did (STM and LTM). Most men, approximately \u003cb\u003e78% LTMs, 79% STMs\u003c/b\u003e, and \u003cb\u003e74% non-migrants\u003c/b\u003e, were from rural areas. Men from the Hindu religion had a slightly lower share of short-term migration and a higher share of long-term migration than Muslims and Christians. Scheduled Castes and Scheduled Tribes were more engaged in short-term migration than long-term migration, whereas men from Other Backward Classes were more engaged in long-term migration. Short-term migrants had a lower economic status (\u003cb\u003e26.9% in the poorest category\u003c/b\u003e) than non-migrants (\u003cb\u003e19.7%\u003c/b\u003e) and long-term migrants (\u003cb\u003e23.9%\u003c/b\u003e). Men from the richest wealth quantile were less likely to migrate. Compared with short-term migrants and non-migrants, long-term migrants had greater media exposure (\u003cb\u003e77.8% vs 74.1% vs 73.6%\u003c/b\u003e). Migrants (STM and LTM) were more prone to smoking and consuming alcohol.\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\u003ePercentage of men (age 15\u0026ndash;54) according to temporary migration status and their background characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMigration Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSTM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLTM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNumber\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sample (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e70039\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(84.80)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7253\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(8.78)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5,306\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(6.42)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e100\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e82,598\u003c/em\u003e\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\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25,628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23,085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of Schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 years or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,112\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently marrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31,447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32,864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclerical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eservices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23,930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61,537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21,061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62,738\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,810\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaste\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,739\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,057\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31,847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index (MPCE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,524\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21,524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61,074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69,405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubstance Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57,832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,766\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the percentage of men\u0026rsquo;s attitudes towards the sexual autonomy of women based on four questions. The questions are: if the wife refuses to have sex, the husband has the right to do the following: (i) get angry, (ii) refuse financial support, (iii) use force for sex, and (iv) have sex with another woman, where positive responses are considered as a negative attitude. Short-term migrants had a higher negative attitude towards the first question compared to non-migrants but slightly lower than long-term migrants (19.29% vs 21.05% vs 21.64%). However, for the third question, short-term migrants had a lower negative attitude (12.35% vs 11.44% vs 15.6%) than long-term migrants and non-migrants. Overall, long-term migrants had a higher negative attitude towards most of the questions compared to the other groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that long-term migrants (33.6%) and short-term migrants (30.2%) had a more negative attitude towards the sexual autonomy of women than non-migrants (27.8%). Men who used substances had a more negative attitude (32.2%) than those who did not (27.0%). Men who were more qualified and belonged to higher wealth quantiles had a lower negative attitude than their counterparts. Men who were engaged in agricultural activities (30.0%) and the service sector (29.4%) had a higher negative attitude compared with several other occupational groups. Similarly, men from the Muslim religion and the Scheduled Castes had a higher negative attitude than their counterparts. Men who smoked had a higher negative attitude (33.6%) than those who did not smoke (27.4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003ePrevalence of men\u0026rsquo;s attitudes towards the sexual autonomy of women (MASAW) by their background characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e% (W)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive Attitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNegative Attitude\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASAW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMigration status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-term migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.9\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e70.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMPCE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubstance use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: SC: Scheduled Caste; ST: Scheduled Tribes; OBC: Other Backwards Classes; a: Includes technical, administrative, and managerial occupations; b: Includes skilled and unskilled manual occupations\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between temporary migration status, substance use, and men\u0026rsquo;s attitudes towards the sexual autonomy of wives\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimates of logistic regression to examine the association between temporary migration status and men\u0026rsquo;s attitudes towards the sexual autonomy of women. Approximately 28.3% of men exhibited negative attitudes towards women\u0026rsquo;s sexual autonomy. In the unadjusted model, the results show that men who were migrants (both short-term and long-term) were significantly more likely to have negative attitudes towards sexual autonomy. After adjusting for the covariates, Model II shows that short-term migrants (AOR: 1.13; CI: 1.07\u0026ndash;1.19) and long-term migrants (AOR: 1.41; CI: 1.32\u0026ndash;1.49) were more likely to have negative attitudes than non-migrants. Similarly, men who used substances were 1.16 times more likely (AOR: 1.16; CI: 1.12\u0026ndash;1.20) to have negative attitudes. Logistic estimates in Model II further show that men belonging to the older age group (45\u0026ndash;54 years), those with higher education, and those belonging to Scheduled Tribes and Other Backward Classes had significantly lower odds of negative attitudes. On the other hand, men belonging to the Muslim religion and those in higher wealth categories had significantly higher odds of having negative attitudes towards the sexual autonomy of women. Compared with those engaged in agriculture (reference category), men working in professional, clerical, sales, service, and production occupations had lower odds of negative attitudes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eBinary logistic estimates showing the associations between temporary migration status and men\u0026rsquo;s attitudes towards the sexual autonomy of women\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigration status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-migrants\u0026reg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-term migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.06,1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.07,1.19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong term migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.32,1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.32,1.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.90,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.87,0.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.83,0.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.86,0.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.78,0.86]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.64,0.73]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.90,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.86,1.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eagriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.73,0.86]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclerical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.64,0.81]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.79,0.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eservices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.82,0.93]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.75,0.81]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.08,1.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.96,1.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.16,1.28]\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.07,1.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[2.37,2.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eSCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.69,0.77]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.82,0.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.82,0.91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMPCE\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.95,1.05]\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.05,1.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.10,1.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.21,1.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia Exposure\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.03,1.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.10,1.21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubstance use\u0026reg;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[1.12,1.20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research reveals the associations between temporary migration and men\u0026rsquo;s attitudes toward the sexual autonomy of women in India (MASAW). Results: The results show how migration is linked to men's gender perceptions and reveal significant sociodemographic variations in migration trends. Our study revealed that approximately 14% of men were temporary migrants, including 7.7% short-term migrants and 6.2% long-term migrants, indicating that temporary migration remains a significant livelihood strategy among men in India. Migrants were younger, more educated, and more likely to work in non-agricultural jobs than non-migrants were, which is consistent with earlier migration studies. Existing studies have also shown that migration processes can influence social norms, behaviours, and family dynamics, particularly in traditional gender hierarchal societies where gender relations are strongly structured by cultural norms(Desai \u0026amp; Andrist, 2011; Agadjanian et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eLong-term migrants, in particular, had higher levels of education and media exposure than both short-term migrants and while non-migrants did not, whereas short-term migrants were more concentrated in lower wealth categories, indicating that economic vulnerability is still a significant factor in temporary migration. To account for the impact of individual, family, and community characteristics on migration decisions and movement forms, migration behaviour involves the use of factors at several levels (Guilmoto, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results also indicate that migrant men were more likely to smoke and use substances, which is consistent with previous studies indicating that stress connected to migration, social isolation, and new urban social contexts may promote risky behaviours among migrants (Weine \u0026amp; Kashuba, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Peer pressure and the lack of family supervision during migration may also be factors contributing to substance abuse among migrants (Ahmadi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study also identifies troubling trends in men's perceptions of women's sexual autonomy. Approximately 28% of males overall indicated negative sentiments, suggesting that men continue to defend against dominating behaviours when their spouses decline to engage in sexual activity (Sabarwal et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).These beliefs are more common among migrants, especially long-term migrants (33%), than among short-term migrants (30%) and non-migrants (27%). This implies that attitudes and gender norms may be influenced by migratory experiences. These gender-sex disparities, together with other research on gender-sex-specific migratory trends, were reported in this study (Joseph, Wang, Chellaraj, Luis, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNegative gender attitudes were also found to be significantly correlated with substance use. Compared non-users, men who reported using drugs were 15% more likely to have negative opinions on women's sexual autonomy. This finding is consistent with the literature linking substance use with aggressive behaviour, reduced impulse control, and higher acceptance of gender-based dominance (Pradhan \u0026amp; De, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, smoking behaviour was associated with higher odds of negative attitudes, suggesting that risky health behaviours often co-exist with regressive gender norms. It seemed that education had a protective effect. Education may encourage gender-equitable standards and an understanding of women's rights, as seen by the much-reduced likelihood of negative sentiments among men with higher levels of education (Sabarwal et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In a similar vein, older age groups were less likely than younger men were to have unfavourable sentiments, indicating that gender perceptions may be influenced by maturity and life experience. These discrepancies could be the result of regional contexts, economic circumstances, and sociocultural norms that influence gender perspectives.\u003c/p\u003e \u003cp\u003eOur study emphasises the importance of addressing migration-related vulnerabilities and substance use behaviours while promoting gender-equitable attitudes among men. Substance use prevention techniques, behavioural change communication, and gender sensitisation programmes should all be included in policies and interventions targeted at immigrant communities. Media campaigns, community interventions, and workplace-based awareness programs may all contribute to the reduction of gender-inequitable attitudes and the promotion of respect for women's autonomy. This study has several limitations, such as the use of a sizable nationally representative sample. Establishing causal links between migration, substance use, and gender attitudes is restricted by the cross-sectional methodology used. Furthermore, self-reported answers were used to evaluate attitudes about sexual autonomy, which could be skewed by social desirability. Longitudinal data could help future studies better understand how migration experiences affect gender perceptions over time.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights that migration status is a significant determinant of men\u0026rsquo;s attitudes towards the sexual autonomy of women in India. The results indicate that, in comparison to non-migrants, both temporary and permanent migrants are more likely to have positive sentiments. Such sentiments are also positively correlated with substance usage, suggesting a complex behavioural relationship. These sentiments are further influenced by sociodemographic variables like wealth, occupation, education, and religion. In order to encourage gender-equitable views among women, the study emphasises the necessity of focused interventions that address behavioural and social issues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eThis study is based entirely on secondary data from the publicly available National Family Health Survey (NFHS-5, 2019\u0026ndash;21). NFHS-5 data were collected under the supervision of the Ministry of Health and Family Welfare (MoHFW), Government of India, and ethical approvals for the survey were obtained from the Institutional Review Boards (IRB) of the International Institute for Population Sciences (IIPS), Mumbai, and other collaborating institutions. Written informed consent was obtained from all participants by the survey teams before data collection. No separate ethical approval was required for this analysis, as it uses anonymized, publicly available data.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAs NFHS data is a publicly available data on DHS program website and IIPS website too. Anyone who wants to explore or wants to do research they can access data freely, analyse, research and publish it. So, there is no need for the consent of publication.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eThe data used in this study were obtained from the National Family Health Survey (NFHS-5), conducted by the Ministry of Health and Family Welfare, Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Mumbai. The dataset is publicly available and can be accessed from the DHS Program website or the\u0026nbsp;\u003c/em\u003e\u003cem\u003ehttps://www.iipsdata.ac.in/datacatalog_detail/1\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eThe authors declare that they have no competing interests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eThere is no available funding for doing this research. Researchers have done it by self as it is based on the secondary publicly available dataset.\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eVikesh Kumar:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eConceptualization of the research idea; formal analysis; data curation and data analysis; preparation of tables; writing the data, methods, and results sections; project administration; provision of resources; and overall supervision.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAnkit Gupta:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eConceptual support in manuscript preparation; drafting the introduction; formatting and visualization of results; identification of potential funding sources; information gathering for publication; and finalization of the manuscript draft for submission.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ePriti:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eWriting the discussion section; drafting and revising the manuscript; methodological application; validation of analytical results; and preparation of the original draft.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ePhanidhar Upadhyaya\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eInvestigation; manuscript review and editorial support; providing recommendations for improving manuscript quality; ethical oversight; and methodological guidance.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMinistry of Health and Family Welfare, Government of India, and the International Institute for Population Sciences (IIPS), Mumbai, and Dr. Kunal Keshri, Assistant Professor, Department of Urbanization and Migration Studies for their constant guidance.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbada, T., \u0026amp; Tenkorang, E. Y. (2012). Women\u0026rsquo;s autonomy and unintended pregnancies in the Philippines. \u003cem\u003eJournal of Biosocial Science\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(6), 703\u0026ndash;718. https://doi.org/10.1017/S0021932012000120\u003c/li\u003e\n\u003cli\u003eAboagye, R. G., Dadzie, L. K., Arthur-Holmes, F., Okyere, J., Agbaglo, E., Ahinkorah, B. O., \u0026amp; Seidu, A.-A. (2022). Intimate partner violence against married and cohabiting women in sub-Saharan Africa: does sexual autonomy matter? \u003cem\u003eReproductive Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 79. https://doi.org/10.1186/s12978-022-01382-1\u003c/li\u003e\n\u003cli\u003eAgadjanian, V., \u0026amp; Hayford, S. R. (2018). Men\u0026rsquo;s Migration, Women\u0026rsquo;s Autonomy, and Union Dissolution in Rural Mozambique. \u003cem\u003eJournal of Family Issues\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(5), 1236\u0026ndash;1257. https://doi.org/10.1177/0192513X17698184\u003c/li\u003e\n\u003cli\u003eAgadjanian, V., Yabiku, S. T., \u0026amp; Cau, B. (2011). Men\u0026rsquo;s Migration and Women\u0026rsquo;s Fertility in Rural Mozambique. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(3), 1029\u0026ndash;1048. https://doi.org/10.1007/s13524-011-0039-y\u003c/li\u003e\n\u003cli\u003eAhmadi, N. (2003). Migration challenges views on sexuality. \u003cem\u003eEthnic and Racial Studies\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 684\u0026ndash;706. https://doi.org/10.1080/0141987032000087361\u003c/li\u003e\n\u003cli\u003eAmaro, H. (1995). Love, sex, and power. Considering women\u0026rsquo;s realities in HIV prevention. \u003cem\u003eThe American Psychologist\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(6), 437\u0026ndash;447. https://doi.org/10.1037//0003-066x.50.6.437\u003c/li\u003e\n\u003cli\u003eBloom, S. S., Wypij, D., \u0026amp; Das Gupta, M. (2001). Dimensions of women\u0026rsquo;s autonomy and the influence on maternal health care utilization in a North Indian city. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 67\u0026ndash;78. https://doi.org/10.1353/dem.2001.0001\u003c/li\u003e\n\u003cli\u003eCensus of India. (2011). \u003cem\u003eOffice of the Registrar General \u0026amp; Census Commissioner, India.\u003c/em\u003e https://censusindia.gov.in/census.website/data/census-tables\u003c/li\u003e\n\u003cli\u003eCoffey, D., Papp, J., \u0026amp; Spears, D. (n.d.). \u003cem\u003eShort-Term Labor Migration from Rural North India: Evidence from New Survey Data\u003c/em\u003e. https://doi.org/10.1007/s11113-014-9349\u003c/li\u003e\n\u003cli\u003eCrissman, H. P., Adanu, R. M., \u0026amp; Harlow, S. D. (2012). Women\u0026rsquo;s sexual empowerment and contraceptive use in Ghana. \u003cem\u003eStudies in Family Planning\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(3), 201\u0026ndash;212. https://doi.org/10.1111/j.1728-4465.2012.00318.x\u003c/li\u003e\n\u003cli\u003eDaniel Ikuomola, A. (n.d.). \u003cem\u003eAn Exploration of \u003cins cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003eSocioc\u003c/ins\u003e\u003cdel cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003eSocio-c\u003c/del\u003eultural Challenges of Left behind Wives in Edo State, Nigeria\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDesai, S., \u0026amp; Andrist, L. (2010). Gender scripts and age at marriage in India. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(3), 667\u0026ndash;687. https://doi.org/10.1353/dem.0.0118\u003c/li\u003e\n\u003cli\u003eDesai, S., \u0026amp; Banerji, M. (2008a). Negotiated identities: Male migration and left-behind wives in India. \u003cem\u003eJournal of Population Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 337\u0026ndash;355. https://doi.org/10.1007/BF03033894\u003c/li\u003e\n\u003cli\u003eDesai, S., \u0026amp; Banerji, M. (2008b). Negotiated identities: Male migration and left-behind wives in India. \u003cem\u003eJournal of Population Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 337\u0026ndash;355. https://doi.org/10.1007/BF03033894\u003c/li\u003e\n\u003cli\u003eDeshingkar, P., \u0026amp; Akter, S. (n.d.). \u003cem\u003eHuman Development Research Paper 2009/13 Migration and Human Development in India\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eForste, R., \u0026amp; Fox, K. (n.d.). \u003cem\u003eHousehold Labor, Gender Roles, and Family Satisfaction: A Cross-Household Labor, Gender Roles, and Family Satisfaction: A Cross-National Comparison National Comparison Household Labor, Gender Roles, and Family Satisfaction: A Cross-National Comparison\u003c/em\u003e. Retrieved https://scholarsarchive.byu.edu/facpub\u003c/li\u003e\n\u003cli\u003eGuilmoto, C. Z. (1998). Institutions and migrations. Short-term versus long-term moves in rural West Africa. \u003cem\u003ePopulation Studies\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 85\u0026ndash;103. https://doi.org/10.1080/0032472031000150196\u003c/li\u003e\n\u003cli\u003eHeidari, S. (2015). Sexual rights and bodily integrity as human rights. \u003cem\u003eReproductive Health Matters\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(46), 1\u0026ndash;6. https://doi.org/10.1016/j.rhm.2015.12.001\u003c/li\u003e\n\u003cli\u003eHughes, C., Bhandari, P., Young-DeMarco, L., Swindle, J., Thornton, A., \u0026amp; Williams, N. (2020). Family Obligation Attitudes, Gender, and Migration. \u003cem\u003eInternational Journal of Sociology\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(4), 237\u0026ndash;264. https://doi.org/10.1080/00207659.2020.1726109\u003c/li\u003e\n\u003cli\u003eImran Khan, M., Alharthi, M., Haque, A., \u0026amp; Illiyan, A. (2023). Statistical analysis of push and pull factors of migration: A case study of India. \u003cem\u003eJournal of King Saud University - Science\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(8). https://doi.org/10.1016/j.jksus.2023.102859\u003c/li\u003e\n\u003cli\u003eJewkes, R., Flood, M., \u0026amp; Lang, J. (2015). From work with men and boys to changes of social norms and reduction of inequities in gender relations: a conceptual shift in prevention of violence against women and girls. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e385\u003c/em\u003e(9977), 1580\u0026ndash;1589. https://doi.org/10.1016/S0140-6736(14)61683-4\u003c/li\u003e\n\u003cli\u003eJoseph, G., Wang, Q., Chellaraj, G., Luis, E. O. T., Syed, A., Javaid, U., \u0026amp; Rajan, I. (2022). \u003cem\u003eBeyond Money: Does Migration Experience Transfer Gender Norms? Empirical Evidence from Kerala, India\u003c/em\u003e. http://www.worldbank.org/prwp.\u003c/li\u003e\n\u003cli\u003eKeshri, K., \u0026amp; Bhagat, R. B. (2013). SOCIOECONOMIC DETERMINANTS OF TEMPORARY LABOUR MIGRATION IN INDIA: A regional analysis. \u003cem\u003eAsian Population Studies\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 175\u0026ndash;195. https://doi.org/10.1080/17441730.2013.797294\u003c/li\u003e\n\u003cli\u003eLevitt, P. (1998a). Social Remittances: Migration Driven Local-Level Forms of Cultural Diffusion. In \u003cem\u003eSource: The International Migration Review\u003c/em\u003e (Vol. 32, Number 4). Winter.\u003c/li\u003e\n\u003cli\u003eLevitt, P. (1998b). Social Remittances: Migration Driven Local-Level Forms of Cultural Diffusion. \u003cem\u003eInternational Migration Review\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 926\u0026ndash;948. https://doi.org/10.1177/019791839803200404\u003c/li\u003e\n\u003cli\u003eMatz, J. A., \u0026amp; Mbaye, L. M. (2023). Migration and the Autonomy of Women Left Behind. \u003cem\u003eEuropean Journal of Development Research\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 1059\u0026ndash;1079. https://doi.org/10.1057/s41287-022-00559-5\u003c/li\u003e\n\u003cli\u003eMemiah, P., Opanga, Y., Bond, T., Cook, C., Mwangi, M., Fried, J., Joseph, M. A., Owuor, K., Mochache, V., \u0026amp; Machira, Y. W. (2019). Is sexual autonomy a protective factor for neonatal, child, and infant mortality? A mult\u003cins cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003ei\u003c/ins\u003e\u003cdel cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003ei-\u003c/del\u003ecountry analysis. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), e0212413. https://doi.org/10.1371/journal.pone.0212413\u003c/li\u003e\n\u003cli\u003eMenj\u0026iacute;var, C., \u0026amp; Agadjanian, V. (2007a). Men\u0026rsquo;s Migration and Women\u0026rsquo;s Lives: Views from Rural Armenia and Guatemala \u003csup\u003e*\u003c/sup\u003e. \u003cem\u003eSocial Science Quarterly\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(5), 1243\u0026ndash;1262. https://doi.org/10.1111/j.1540-6237.2007.00501.x\u003c/li\u003e\n\u003cli\u003eMenj\u0026iacute;var, C., \u0026amp; Agadjanian, V. (2007b). Men\u0026rsquo;s Migration and Women\u0026rsquo;s Lives: Views from Rural Armenia and Guatemala \u003csup\u003e*\u003c/sup\u003e. \u003cem\u003eSocial Science Quarterly\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(5), 1243\u0026ndash;1262. https://doi.org/10.1111/j.1540-6237.2007.00501.x\u003c/li\u003e\n\u003cli\u003eNarushima, M., McLaughlin, J., \u0026amp; Barrett-Greene, J. (2016). Needs, Risks, and Context in Sexual Health Among Temporary Foreign Migrant Farmworkers in Canada: A Pilot Study with Mexican and Caribbean Workers. \u003cem\u003eJournal of Immigrant and Minority Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 374\u0026ndash;381. https://doi.org/10.1007/s10903-015-0189-x\u003c/li\u003e\n\u003cli\u003eNSSO. (2010). \u003cem\u003eMigration in India\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOsezua, C. A. (2013). Changing Status of Women and the Phenomenon Trafficking of Women for Transactional Sex in Nigeria: A Qualitative Analysis. In \u003cem\u003eJournal of International Women\u0026rsquo;s Studies\u003c/em\u003e (Vol. 14, Number 3).\u003c/li\u003e\n\u003cli\u003ePradhan, M. R., \u0026amp; De, P. (2025). Men\u0026rsquo;s gender role and attitude toward sexual autonomy of women in India. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1). https://doi.org/10.1371/journal.pone.0317301\u003c/li\u003e\n\u003cli\u003eRam Mohan, R., Puskur, R., Chandrasekar, D., \u0026amp; Valera, H. G. A. (2023). Do gender dynamics in \u003cins cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003eintrah\u003c/ins\u003e\u003cdel cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003eintra-h\u003c/del\u003eousehold decision making shift with male migration? Evidence from rice-farming households in Eastern India. \u003cem\u003eGender, Technology and Development\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2), 157\u0026ndash;183. https://doi.org/10.1080/09718524.2022.2140381\u003c/li\u003e\n\u003cli\u003eSabarwal, S., Santhya, K. G., \u0026amp; Jejeebhoy, S. J. (2014). Women\u0026rsquo;s autonomy and experience of physical violence within marriage in rural India: Evidence from a prospective study. \u003cem\u003eJournal of Interpersonal Violence\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 332\u0026ndash;347. https://doi.org/10.1177/0886260513505144\u003c/li\u003e\n\u003cli\u003eSingh, R. (2024). The Rural\u003cins cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003e‒\u003c/ins\u003e\u003cdel cite=\"mailto:Editor\" datetime=\"2026-03-17T09:50\"\u003e-\u003c/del\u003eUrban Gap in Domestic Violence and Women\u0026rsquo;s Economic Empowerment. \u003cem\u003eSocial Science and Humanities Journal\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(03), 34755\u0026ndash;34758. https://doi.org/10.18535/sshj.v8i03.994\u003c/li\u003e\n\u003cli\u003eTorosyan, K., Gerber, T. P., \u0026amp; Go\u0026ntilde;alons-Pons, P. (2016). Migration, Household Tasks, and Gender: Evidence from the Republic of Georgia. \u003cem\u003eThe International Migration Review\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 445\u0026ndash;474. https://doi.org/10.1111/imre.12147\u003c/li\u003e\n\u003cli\u003eWang, S. (n.d.). \u003cem\u003eA Study on the Impact of Parental Migration on the Lives of Children Left Behind in Northeast China\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWeine, S. M., \u0026amp; Kashuba, A. B. (2012). Labor migration and HIV risk: A systematic review of the literature. In \u003cem\u003eAIDS and Behavior\u003c/em\u003e (Vol. 16, Number 6, pp. 1605\u0026ndash;1621). https://doi.org/10.1007/s10461-012-0183-4\u003c/li\u003e\n\u003cli\u003eWillie, T. C., Callands, T., Alexander, K. A., \u0026amp; Kershaw, T. (2023). Measuring women\u0026rsquo;s sexual autonomy: Development and preliminary validation of the women\u0026rsquo;s sexual autonomy scale. \u003cem\u003eWomen\u0026rsquo;s Health (London, England)\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 17455057231183836. https://doi.org/10.1177/17455057231183837\u003c/li\u003e\n\u003cli\u003eYabiku, S. T., Agadjanian, V., \u0026amp; Sevoyan, A. (2010a). Husbands\u0026rsquo; labour migration and wives\u0026rsquo; autonomy, Mozambique 2000\u0026ndash;2006. \u003cem\u003ePopulation Studies\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(3), 293\u0026ndash;306. https://doi.org/10.1080/00324728.2010.510200\u003c/li\u003e\n\u003cli\u003eYabiku, S. T., Agadjanian, V., \u0026amp; Sevoyan, A. (2010b). Husbands\u0026rsquo; labour migration and wives\u0026rsquo; autonomy, Mozambique 2000\u0026ndash;2006. \u003cem\u003ePopulation Studies\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(3), 293\u0026ndash;306. https://doi.org/10.1080/00324728.2010.510200\u003c/li\u003e\n\u003cli\u003eYabiku, S. T., Agadjanian, V., \u0026amp; Sevoyan, A. (2010c). Husbands\u0026rsquo; labour migration and wives\u0026rsquo; autonomy, Mozambique 2000-2006. \u003cem\u003ePopulation Studies\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(3), 293\u0026ndash;306. https://doi.org/10.1080/00324728.2010.510200\u003cstrong\u003e\u003c/strong\u003e\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Labour migration, Sexual autonomy, LMICs, Gender, Immigration, NFHS-5","lastPublishedDoi":"10.21203/rs.3.rs-9239808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9239808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMale labour migration results in prolonged spousal separation and shifts in household decision-making structures. In many low- and middle-income settings, the temporary or long-term migration of men creates shifts in household responsibilities, decision-making power, and the regulation of sexual and reproductive behaviour within families. Migration not only affects economic conditions but also transforms household power relations, gender roles, and attitudes toward women\u0026rsquo;s autonomy. Sexual autonomy, which refers to a person's capacity to negotiate safer sexual practices.\u003c/p\u003e\u003ch2\u003eData \u0026amp; Methods\u003c/h2\u003e \u003cp\u003eThe present study utilises data from NFHS-5. We used a total of 101,839 samples in which 19241 samples has been deleted as they were not working. There are women aged 15\u0026ndash;49 and men aged 15\u0026ndash;54. Considering that he is working and has been away from home for more than one month, he must be a migrant in India. So, this study excludes non-working respondents to reduce error in the migrant sample. In our study, we used a binary logistic regression model to examine the association between dependent and explanatory variables\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results show how migration is linked to men's gender perceptions and significant sociodemographic variations in migration trends. This study revealed that approximately 15.3% of men were temporary migrants, including 8.9% short-term migrants and 6.4% long-term migrants, indicating that temporary migration remains a significant livelihood strategy among men in India. In the unadjusted model, I show that men who were migrants (both STM and LTM) and who used substances were significantly more likely to have a negative attitude towards sexual autonomy. Logistic estimates (Model II) further show that men belonging to the middle-aged group (45\u0026ndash;54 years), scheduled castes, and having higher education were significantly associated with lower odds of a negative attitude.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis research shows that men's perceptions of women's sexual autonomy in India are significantly influenced by their immigration status. The findings show that both temporary and permanent migrants are more likely to have positive attitudes than non-migrants. In order to encourage gender-equitable views among women, the study emphasizes the necessity of focused interventions that address behavioural and social issues.\u003c/p\u003e","manuscriptTitle":"Men's labor migration and attitudes towards women's sexual autonomy: Evidence from the National Family Health Survey 2019-21","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 08:29:19","doi":"10.21203/rs.3.rs-9239808/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T08:15:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321911580960260281179375929607564728940","date":"2026-04-13T12:30:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317726295611322438659410026792817904667","date":"2026-04-13T08:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267590288166983218090954450593688130468","date":"2026-04-13T05:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131269994512532598455990107142160255523","date":"2026-04-11T17:24:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17371589913418850052336096437309222834","date":"2026-04-10T12:16:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246603230639953065480459788937936122962","date":"2026-04-10T04:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286380847104120428760623224533994298687","date":"2026-04-09T16:07:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231993239763967929021660849262636465752","date":"2026-04-09T15:05:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T14:35:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T10:13:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T14:19:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T14:19:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-27T03:54:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9afa8d0-bd13-47ae-b534-ea5cf80c1996","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T04:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 08:29:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9239808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9239808","identity":"rs-9239808","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}