Socio Demographic, Health Related and Social Predictors of Psychological Well-Being of Working Women among Different Geographical Locations in Varanasi City ()
1. Introduction
Well-being occupies a central position in gender research and in interventions targeting the many factors that shape life outcomes. Ed Diener et al. (1999) describe well-being as individuals’ evaluations of their lives in both cognitive and affective terms. It emerges from a complex interplay of psychological, biological, socio-cultural, and spiritual influences. Definitions offered in sources such as the American Heritage Dictionary link well-being closely to mental health, happiness, vitality, the realization of one’s potential, prosperity, and physical health. Despite differences in emphasis, a common thread across perspectives is the idea of feeling good and functioning well. This broad view incorporates subjective experience while also inviting comparison between personal life circumstances and prevailing social norms and values.
Well-being is widely recognized as multidimensional, encompassing physical, mental, emotional, spiritual, financial, and occupational domains (Jacob et al., 2019; Jaiswal & Arun, 2020). These interrelated dimensions shape how individuals perceive and evaluate their quality of life in everyday contexts. Consequently, well-being is crucial not only for individuals but also for organizations and society at large (Ng, 2015; Kowalski & Loretto, 2017). A person’s sense of well-being is closely intertwined with the workplace, as employment often becomes central to self-evaluation and the formation of social identity. Owing to the mutually reinforcing nature of different well-being dimensions, experiences at work can substantially influence overall well-being (Sia & Duari, 2018).
Levi (1987) conceptualized psychological well-being as a dynamic state reflecting a reasonable balance between an individual’s capabilities, needs, and aspirations and the opportunities and demands posed by the environment. From this perspective, well-being involves engaging with existential concerns, such as developing a sense of purpose and achieving personal growth.
A related position was advanced by Ed Diener (2000), who emphasized individuals’ evaluations of their lives through both cognitive and affective processes. The affective component represents a hedonic appraisal based on emotions and mood states, including experiences of pleasant or unpleasant feelings. In contrast, the cognitive component refers to reflective, information-based judgments about life satisfaction. Recent developments in positive psychology have stimulated growing interdisciplinary interest in psychological well-being (e.g., Henn et al., 2016; Hides et al., 2016). This field has largely concentrated on understanding well-being as a broad state of positive functioning and subjective positivity.
Given that individuals devote a substantial portion of their lives to work, organizations increasingly recognize that prioritizing employee well-being can enhance performance while strengthening institutional reputation. Consequently, well-being has become a cornerstone of positive management practices (Salas et al., 2017). Scholars in human geography argue that mental health outcomes are shaped not only by psychological processes but also by spatial and locational dynamics (Coddington & Micieli-Voutsinas, 2017). Spatial context plays a crucial role in shaping access to resources, autonomy, and everyday experiences of support. Variations in neighbourhood infrastructure, transportation, safety, employment opportunities, and community networks can influence how women manage the competing demands of paid work and domestic responsibilities. Differences across urban locations may therefore translate into unequal exposure to stressors as well as unequal availability of protective factors. Examining psychological well-being through a zonal lens allows for a more grounded understanding of how place-based conditions interact with demographic, occupational, and health characteristics. In this way, geographical divisions within the city become analytically important for identifying patterns of advantage, vulnerability, and resilience among working women. As noted by Rachel Pain (2021), environments may sustain trauma, contribute to re-traumatization, or alternatively support recovery and the rebuilding of agency after adverse experiences. Understanding how surroundings can either buffer individuals from harm or intensify distress is therefore essential.
Within the Indian context, Shireen Jejeebhoy and Zeba Sathar (2001), in their comparison of Uttar Pradesh and Tamil Nadu, demonstrated that region exerts a persistent influence on women’s autonomy. Additional evidence indicates that psychological vulnerability, financial strain, social isolation, and demographic characteristics can heighten the risk of adverse emotional states and mental health problems, particularly among young people and women (Rossell et al., 2021).
Weissman and Klerman (1985) argued that women experience depression at higher rates than men and that this difference cannot be explained by reporting bias or patterns of health-care use. Critiquing the weak theoretical base of subjective well-being research, Carol Ryff (1989) proposed the model of psychological well-being, comprising six dimensions: environmental mastery, autonomy, purpose in life, positive relations with others, personal growth, and self-acceptance (Ryff, 1989; WHO, 2007). Working women are widely recognized as central to a nation’s socio-economic development (Alias et al., 2022). Their psychological well-being therefore has important implications for productivity and job performance (Wright & Cropanzano, 2012). Researchers have long sought to identify who remains happy and which factors contribute to psychological well-being. Evidence points to a broad constellation of influences, including personality traits, socio-demographic characteristics, economic conditions, family and health circumstances, as well as individual choices and aspirations. In light of this background, the present study undertook a systematic investigation in Varanasi to examine socio-demographic, health-related, and social predictors of psychological well-being.
2. Literature Review
In recent years, scholars have devoted increasing attention to psychological well-being and its socio-economic correlates among women employed across diverse occupational sectors. A substantial body of research has examined associations between well-being and factors such as socio-demographic characteristics, physical health, and elements of the social network.
Against this backdrop, the present study investigates the influence of socio-demographic, health-related, and social predictors on the psychological well-being of working women across different geographical locations within Varanasi.
2.1. Impact of Socio-Demographic Variables on Psychological Well-Being among Working Women
A number of studies have explored the influence of socio-demographic factors—including age, education, gender, marital status, employment conditions, family type, and socio-economic position—on psychological well-being (e.g., O’Rourke 2009; Ed Diener et al., 2010; Brush et al., 1987; Joshi et al., 2011; Ng & Feldman, 2010). For instance, Sasaki et al. (2020) specifically examined age, education, gender, marital status, and employment as determinants of well-being. The socio-demographic variables selected for the present investigation were guided by this body of literature.
Research consistently identifies socio-economic status as a crucial determinant of psychological well-being. Lower status—often reflected in limited income and education—is associated with elevated mental distress. A growing body of work has also explored the complex relationship between geographical context and mental health (Fone et al., 2014; Graif et al., 2016; Hudson, 2012; Mair et al., 2008). However, these approaches are sometimes criticized for insufficiently theorizing the mechanisms through which place exerts its influence (Arcaya et al., 2012). As Pickett and Pearl (2001) argue, spatial processes shaping mental health should not be restricted to a single scale. Advances in multilevel methods have since enabled examination across multiple spatial levels (Fong et al., 2019; Propper et al., 2007).
Empirical studies further suggest that age is often positively related to job satisfaction (Herzog & Rodgers, 1981; Wunder & Wiencierz, 2013). Other research points to lower well-being among minority employees (Greenhaus et al., 2001), while gender differences in job satisfaction have also been documented (Lange, 2008).
De-Juanas, Romero, and Going (2020) reported that older participants tend to obtain higher scores on psychological well-being and its dimensions. Similarly, Ed Diener et al. (1999) suggested that with age individuals often cope better with adversity, and the gap between aspirations and reality narrows, leading to greater satisfaction. Efforts that promote personal growth are therefore likely to enhance psychological, emotional, and social well-being. Education has also been positively associated with well-being, particularly in relation to personal growth and purpose in life (Butler-Barnes et al., 2017; Henn et al., 2016). Work by Robitschek and Keyes (2009) links such development with lower psychological and emotional distress, a relationship also observed in earlier research (Robitschek & Kashubeck, 1999). Moreover, Carol Ryff and Corey Keyes (1995) emphasized self-acceptance as central to mental health. Gender differences have likewise been noted; for example, Matud et al. (2019) found higher self-acceptance and autonomy among men, whereas women reported stronger personal growth and more positive relationships. Additionally, the presence of job control and social support has been identified as moderating key elements influencing the connection between job demands and psychological well-being among teachers (Ibrahim et al. 2021). Singhal and Sud (2018) highlighted the importance of examining how gender shapes job satisfaction and psychological well-being among private-sector employees in North India. Comparing public and private employment, Bansal, Gulati, and Pathak (2021) found that government workers reported greater job satisfaction, lower perceived stress, and stronger positive relations with others—an essential component of well-being. Similarly, Guleria (2023) observed higher stress among private-sector employees, whereas government workers showed greater life satisfaction. Although not confined to women, these findings reinforce the broader view that the relative stability and structure of public-sector employment may foster better psychological well-being.
Findings on the relationship between family structure and mental health remain mixed. Ed Diener (2000) noted a positive association between age and life satisfaction, yet women in joint families may experience reduced well-being because of heightened work-family conflict. Supporting this view, Geldenhuys and Henn (2017) showed that age, marital status, and work-family pressures significantly influence women’s well-being. Likewise, Greenhaus et al. (2001) reported strong links between family involvement and work-life conflict.
Conversely, evidence from Kumari and Vishvakarma (2025) suggests that women in nuclear families display higher self-confidence, an important element of psychological well-being. Greater marital adjustment and autonomy have also been associated with nuclear arrangements (Sabre, 2016). Marital status further shapes outcomes. In a study of working women in Malaysia, Alias, Hashim, and Yahaya (2022) found that married women reported higher overall psychological well-being, particularly in self-acceptance and purpose in life, than single women.
Large cross-sectional research among older adults indicates that widowed women are more likely to report psychological distress and poorer self-rated health than married women, even after adjusting for socio-economic and demographic factors (Perkins et al., 2016). More broadly, the literature consistently shows that women face a higher risk of mental health problems than men. However, systematic examinations of socio-demographic and health predictors of psychological well-being among Indian working women across geographical contexts remain limited.
Income represents another important determinant. Ed Diener (2000) and subsequent studies (e.g., Kahneman & Deaton, 2010; Akkiraju & Rao, 2025) generally demonstrate that, despite debates about diminishing returns at very high levels, higher income tends to be associated with greater life satisfaction, happiness, and positive affect. For example, Cai et al. (2025), in a community study of urban residents, found that higher-income groups reported greater life satisfaction and positive affect and lower negative affect, with psychological security mediating this relationship. Similarly, Diener et al. (2010) concluded that income is more consistently related to evaluative well-being than to momentary emotional states.
Although income is generally linked to higher subjective well-being, consensus has not been reached (Cai et al., 2025). Some studies even report negative or limited associations. For instance, Suzuki et al. (2025), in a large study of working women in Japan, observed that among high-income married women the pressures of demanding jobs and family responsibilities can offset the benefits of earnings. Similarly, Kahneman and Deaton (2010) showed that while income improves life evaluation, gains in emotional well-being tend to level off beyond a certain point.
Overall, prior evidence indicates that socio-demographic characteristics are important predictors of well-being. The present study therefore investigates how selected demographic factors shape psychological well-being among Indian working women.
2.2. Impact of Social Network on Psychological Well-Being among
Working Women
The results pertaining to the association between the social network and psychological well-being established some significant relationship between emotional support, tangible support and psychological well-being of working women. However, the magnitude of correlation coefficient for emotional support is slighter higher than tangible support. The findings of the study are also supported by Inagaki and Orehek (2017), who established that those who received emotional support have lower stress and promote satisfaction with relationships, builds social connection, and enhances well-being. Similar observations by Yamada et al. (2005) who analyzed emotional support from husbands, family, and colleagues as crucial for balancing career and family responsibilities. Furthermore, both types of support contribute to psychological well-being, with emotional support linked to positive affect and life satisfaction, while tangible support reduces negative affect (Friedman & King, 1994; Chant & Pedwell, 2008). On the contrary, family members may offer less emotional support and encouragement for healing compared to friends (Savage & Russell, 2005).
According to Uberoi (2003), the fundamental mechanism for the subordination of women includes the patriarchal ideology ordering the inner domain of home and while men are entrenched in public domain. Some researchers have also illustrated that for enhancing mental health, emotional support is associated with a reduction in psychological distress and anxiety (Velden et al., 2020; Fitzgerald et al., 2020). Similarly, Tangible or instrumental support involves providing practical help to individuals, such as financial aid (Rackow et al., 2017). When delivered effectively, this type of support can assist people in maintaining overall health and can also facilitate recovery from illness (House et al., 1998). In one classic study, with older women Yang and Jiang (2020) found that emotional support was more strongly related to positive affect and life satisfaction, while tangible support was mainly linked to reducing negative emotions rather than increasing positive well-being. This suggests emotional support may be more important for overall psychological health. Likewise, Friedman & King (1994) reported that greater emotional support was related to greater positive affect and satisfaction with life. Greater tangible support was related to less negative affect.
In this streamline Cahill and Sias (1997) highlighted about women perceiving emotional support more important than men, and have larger and more satisfying emotional support networks. Similarly, family support including spouse and social support is important for working women to balance personal and work life (Indra, 2014). Overall, to enhance working women’s social networks, strategies may include changing traditional gender roles and increasing community services (Yamada et al., 2005). Multiple roles and work-family conflicts can lead to stress among working women, although social support may mitigate these effects (Sinha, 2017). According to Keyes (2009) who utilized physical and emotional health data into multivariate models and concluded that it is found mostly among older adults, educated, and higher incomes. Further, purpose and personal growth have a tendency to decline with age while relationship with others, and one’s self-acceptance remains the same. A self-evaluation of personal goal standards can be attributed to improvement in life satisfaction and help in accommodative coping mechanisms (Diener et al., 1999).
2.3. Impact of Physical Health Variables on Psychological
Well-Being among Working Women
Physical health indicators such as body weight, chronic illness, and day-to-day bodily symptoms are powerful correlates of psychological well-being in employed women. Because many working women juggle occupational demands with caregiving and household roles, limitations in health often carry emotional as well as functional consequences. Research across populations shows a consistent association between higher BMI and poorer psychological outcomes, including lower quality of life, increased depression and anxiety, and elevated stress levels. Meta-analyses and reviews indicate that people with overweight or obesity are more prone to mental health difficulties compared to those with normal weight (Segal & Gunturu, 2024). Body Mass Index (BMI), while a simple physical measure, is strongly linked with psychological well-being through its relationship with body image and self-perceptions. Research shows that higher BMI is often correlated with lower body-related self-esteem, and this in turn is associated with higher levels of depressive and anxious symptoms. For example, studies indicate that individuals with elevated body weight are more likely to report dissatisfaction with their body shape, which co-occurs with lower self-esteem and greater depression and anxiety compared to those with healthier body perceptions (de Jong, Sportel, de Hullu, & Nauta, 2012). One literature review finds that weight stigma and negative social perceptions associated with obesity contribute significantly to psychological distress, reduced self-esteem, anxiety, and depression—factors that would especially affect working women due to social compared stigma pressures in professional environments (Wu & Berry, 2018). According to Wu and Berry, (2018), weight stigma is associated with adverse physiological and psychological outcomes and there a need to increase public and professional awareness about the issue of weight stigma and the importance of the further development of assessment and prevention strategies of weight stigma. In one study, Bookwala and Boyar (2008) found that higher-than-normal BMI strongly impacts women’s mental well-being, specifically, women in all higher-than-normal BMI groups (overweight/obese) reported lower psychological well-being compared to normal-weight women. Women in the most severe obese category (Obese III) had significantly lower well-being than those just in the overweight category. In another study, Vieira et al. (2012) examined BMI-based obesity categories and risk for poor health-related quality of life (HRQOL) and psychological well-being (PWB). The authors found that for weight-related HRQOL and body image, worse psychosocial scores were observed linearly with higher obesity levels. Self-esteem was lower in overweight and obese women in comparison with normal-weight women, with no difference between class I and class II obesity.
An expanding body of evidence indicates that psychological well-being tends to deteriorate as the number of diagnosed medical conditions rises. Large population studies demonstrate that people with multiple chronic diseases show substantially poorer subjective well-being compared with those who have none or only one condition. In one study, Liao et al. (2022) found that mental distress was positively associated with chronic diseases. Age and sex are crucial in this relationship. Studies of multimorbidity show that women with co-existing chronic conditions tend to report lower health-related quality of life (HRQoL)—a measure that includes psychological and emotional functioning—compared to women with fewer or no chronic conditions.
For instance, Lukkala, Honkanen, Rauma, Williams, Quirk, Kröger, et al. (2016) report that multimorbidity is strongly related to lower life satisfaction and greater psychological distress. This trend indicates that as working women experience a greater number of medical conditions (such as diabetes, hypertension, or arthritis), their psychological well-being often declines. This deterioration is likely related to increased pain, functional limitations, worry about future health, and reduced ability to fulfil both work and family roles. Thus, it can be apparent from the above-mentioned discussion that people who live with multimorbidity commonly experience stronger worries about declining health, greater psychological distress, and a reduced feeling of control over everyday activities. Having multiple chronic conditions is also linked to fears about being able to maintain work performance, together with increased fatigue and burnout. Moreover, the simultaneous management of several illnesses can constrain participation in work, family, and social responsibilities, which in turn may lower overall satisfaction with life and general well-being.
These findings highlight the complex interplay between BMI, employment, and psychological well-being among working women. The psychological well-being of working women is influenced by a set of factors beyond just employment, including health issues and work family balance. Lack of support reduces one’s capacity to cope successfully, increase the risk of diseases and enhance the possibility of poor psychological well-being. Thus, poor BMI and increased number diseases led to the poorer health conditions of working women. In general, the experience of ability to create an environment and lifestyle oneself according to their liking and feeling if they are good fit to the community led to the manifestation of improved experience of working women over years. Women, including those in midlife and working age, are more likely to experience multimorbidity than men, due in part to biological, reproductive, and social role factors. In an Indian research, Puri, Sinha, Mahapatra & Pati (2022) have reported that chronic conditions such as hypertension, musculoskeletal disorders, and gastrointestinal issues are common in middle-aged women, and those with multiple conditions report worse quality of life indicators, including reduced physical functioning and emotional well-being.
3. Rationale of the Study
1) India is marked by profound socio-economic and cultural diversity, and the position of women has undergone substantial transformation in recent decades. Although well-being has gained prominence within geographical scholarship through welfare approaches, systematic research on how geographical context and socio-demographic factors shape women’s social and psychological well-being remains limited in India. Spatial analyses consistently demonstrate that where women live influences access to resources, exposure to constraints, and everyday opportunities. Furthermore, gender norms mediate women’s interaction with their environments in ways that differ from men. Much of the existing literature concentrates on rural regions, major metropolitan centres, or national aggregates, often overlooking the realities of other urban settings. Despite extensive international evidence on the socio-demographic and economic correlates of well-being, little is known about how these relationships operate among working women within specific urban contexts in India. In particular, the interaction between geography, employment conditions, and access to support systems remains insufficiently examined. Varanasi, one of India’s most historically rooted cities, is undergoing rapid urban transformation. Such change can widen opportunities for women’s education, employment, and mobility, yet it may simultaneously disrupt established social arrangements and introduce new forms of strain (Anderson, 2000).
2) Across contexts, employed women frequently remain primary caregivers and household managers while having limited access to services that could ease these responsibilities. Although employment is often assumed to enhance women’s social well-being (Sinha, 2017), workplace competition and time pressures may also erode social relationships. Many women shoulder a “triple burden” of paid work, domestic labour, and care for children and elders, often at the expense of their own rest and leisure. Gendered norms within households can further prioritize the needs of male members, reinforcing inequalities in the distribution of resources and attention. In this setting, it becomes essential to examine how changing divisions of labour shape women’s psychological well-being. Experiences are likely to vary according to workplace demands, identity derived from employment, and the physiological and emotional strain of balancing work and family. Investigating these dynamics is especially important at a time when women’s economic contributions are expanding but remain insufficiently recognized. The present study therefore seeks to clarify how psycho-social and demographic factors influence the well-being of employed women.
3) Although the women are working and have financial independence, they lack financial expertise and lower economic well-being. Dual burden of household and workplace enhances their responsibility resulting in poor psychological well-being. In India, a systematic investigation into the comprehensive health related and socio-demographic variables affecting the psychological well-being of working women across different job categories is still lacking and a handful of empirical research indicated inconsistent findings, Further, there is also too much reliance on secondary data with all its attendant problems. So, the study attempts to delve into the well-being of women and factors affecting it through primary and secondary data as well. Against this backdrop, a primary goal of the current research is to investigate and compare the relative impact of chosen demographic factors on the prediction of and psychological well-being of working women in Varanasi City.
4. Methodology
4.1. Geographical Dispersion of Study Area
The study area i.e., Varanasi City has been divided into three geographical zones on morphological basis. From each geographical zones, samples have been taken in proportion to female work participation in the zone. Therefore, a total of 130 respondents from core, 150 from middle and 120 from outer have been surveyed for the study.
The city is divided into three separate geographical areas distinguished by variations in street patterns, land usage, settlement configurations, and the morphology of the city (Figure 1).
A. Core Zone
The core zone is bounded by Lanka-Cantt Road in the west, the Allahabad-Mughalsarai railway line in the north, and the Assi Nala in the south. It includes the heritage stretch along the Ganges from Rajghat to Assi, the old city located on higher ground above the flood level, and adjacent low-lying areas. Owing to early settlement and economic activity, this area developed first. Singh (1955) noted that the core was earlier limited to the Chauk-Rajghat road and the Dasaswamedh-Assi Road.
B. Middle Zone
The middle zone forms a semicircle from Rajghat in the north to Assi in the south, extending westward to Lanka. It is bounded by the Panchkroshi Road to the north, the DLW-Lahartara-Cantt Road to the west, and the Lanka-DLW road together with the Assi Nala to the south. Muslim traders and settlers traditionally lived along the main roads, and while newer colonies have emerged along the outer edge, the interior still reflects a blend of Hindu and Muslim architecture and culture. Much of the housing developed after the 1980s, with highway corridors gradually transforming into commercial areas.
Figure 1. Various zones of Varanasi city.
C. Outer Zone
The northern and southern fringes of the city constitute the outside zone, which is discontinuous. Its southern limit lies beyond the Lanka-DLW Road, while the northern boundary extends past the Panchkoshi Road. Rapid urban expansion after 1990 encouraged middle- and upper-class families to purchase land here and move outward in search of larger homes. Agricultural land has increasingly been converted into residential colonies, many of them unlicensed. Open tracts remain along highways in areas such as Pahariya, Shivpur, and Sarnath. This zone also contains the Sarnath area, recognized for its conservation and heritage value. The outer margins are marked by newly built housing and strong (Table 1).
Table 1. Respondents from various zones of the city.
Type of Occupation |
Core |
Middle |
Outer |
|
No. |
% |
No. |
% |
No. |
% |
Total |
Banking |
21 |
35.0 |
24 |
40.0 |
15 |
25.0 |
60 |
Engineering |
18 |
30.0 |
23 |
38.3 |
19 |
31.7 |
60 |
Entertainment and Media |
14 |
35.0 |
13 |
32.5 |
13 |
32.5 |
40 |
Medical |
17 |
28.3 |
27 |
45.0 |
16 |
26.71 |
60 |
Police |
27 |
45.0 |
19 |
31.7 |
14 |
23.3 |
60 |
Transportation |
19 |
31.7 |
18 |
30.0 |
23 |
38.3 |
60 |
Teaching |
14 |
23.3 |
26 |
43.3 |
20 |
30.0 |
60 |
Total |
130 |
32.5 |
150 |
37.5 |
120 |
30.0 |
400 |
Source: Table by Authors.
Participants were classified into: core (32.5%), middle (37.5%), and outer (30%).
4.2. Sample
To select the sample and categorize working women, standard occupational frameworks—including the National Industrial Classification (2008), the Census of India (2011) classification, and the Periodic Labour Force Survey—were consulted to identify relevant employment sectors. The study concentrated on the service sector, incorporating respondents from banking, engineering, medical services, policing, transportation, media and entertainment, and the teaching profession. The service sector was prioritized because it represents one of the fastest-growing sources of employment for women and encompasses a wide range of skill levels, institutional settings, and work cultures. It includes both public and private organizations, formal and semi-formal arrangements, and diverse schedules and responsibilities. Such heterogeneity makes the sector particularly suitable for examining how socio-demographic background, health status, and support systems interact to shape psychological well-being. Focusing on this sector therefore enables a comprehensive understanding of the opportunities and constraints experienced by contemporary working women.
The required sample size was determined using the Yamane formula, presented below.
n = N/1 + N(e)2
n = Required Sample size.
N = Total Population size.
e = level of precision.
n = 77197/(1 + 77197 × 0.052)
=77197/192.99 = 400.005 or 400
Thus, the final sample size of 400 is taken from three zones of the city. We have clubbed the participants because no significant differences were observed among them related to the three zones of the city.
Participants were drawn from seven occupational sectors: banking, engineering, entertainment and media, medical, police, transportation, and teaching. Sixty respondents were recruited from each sector, except entertainment and media, from which 40 participants were included. The final sample comprised 400 working women from Varanasi city, categorized into government (254; 63.5%) and private employment (146; 36.5%).
Socio-demographic predictors considered in the study were age, education, marital status, occupation, designation, nature of work, working hours, job tenure, commuting distance, and income (see Table 2). The mean age of participants was 35.11 years (SD = 9.01), and the average job tenure was 7.20 years (SD = 7.57). Detailed sample characteristics are presented in Table 2.
Data were collected using a questionnaire accompanied by a cover letter explaining the objectives of the study. Psychological well-being was measured using Ryff’s Psychological Well-Being Scale (PWBS). The scale consists of 42 items representing six dimensions: environmental mastery, autonomy, positive relations with others, personal growth, purpose in life, and self-acceptance. The sum of these dimensions yielded the overall psychological well-being score. Social network support was assessed through six items developed by the researcher, grouped into two subscales: emotional support and tangible support, with three items each. The reliability of both subscales was satisfactory (Cronbach’s alpha > 0.80). Physical health predictors included body mass index (BMI) and the number of symptoms or diseases experienced during the previous six months.
A total of 460 questionnaires were distributed, of which 400 (86.95%) were complete and included in the final analysis. Participation followed established ethical guidelines for research involving human subjects, and completed questionnaires were kept confidential and accessed only by the researcher.
Table 2. Demographic characteristics of the sample.
Variables |
Codes |
Number |
Percentage |
Age Group (in years) |
|
No. |
|
<30 |
1 |
128 |
32% |
30 - 40 |
2 |
176 |
44.0% |
>40 |
3 |
96 |
24.0% |
Education |
|
|
|
Intermediate |
1 |
26 |
6.5% |
Graduation |
2 |
239 |
59.7% |
Post Graduation |
3 |
108 |
27% |
Ph.D. or Higher degree |
4 |
26 |
6.5% |
Type of Family |
|
|
|
Nuclear |
1 |
239 |
59.7% |
Joint |
2 |
161 |
40.3% |
Household Size |
|
|
|
≤5 |
1 |
253 |
63.2% |
>5 |
2 |
147 |
36.8% |
Marital status |
|
|
|
Married |
1 |
281 |
70.3% |
Unmarried |
2 |
104 |
26.0% |
Widow |
3 |
15 |
3.8% |
Nature of Job |
|
|
|
Government |
1 |
254 |
63.5% |
Private |
2 |
146 |
36.5% |
Years Served |
|
|
|
<5 yeas |
1 |
226 |
56.5% |
5 - 10 years |
2 |
71 |
17.8% |
>10 years |
3 |
103 |
25.8% |
Distance travelled from home to job (km) |
|
|
|
<3 |
1 |
214 |
53.5% |
3 - 6 |
2 |
92 |
23.0% |
6 - 9 |
3 |
52 |
13.0% |
>9 |
4 |
42 |
10.5% |
Income (Rs.) |
|
|
|
<45,000 |
1 |
165 |
41.3% |
45,000 - 90,000 |
2 |
162 |
40.5% |
>90,000 |
3 |
73 |
18.3% |
Source: Table by Authors.
4.3. Data Analysis
Data were analyzed using IBM SPSS Statistics. Descriptive statistics were computed initially, followed by stepwise multiple regression to determine the relative influence of several independent variables on psychological well-being.
In the stepwise method, predictors are entered sequentially based on their statistical contribution to the explained variance in the dependent variable. This procedure produces a parsimonious model by retaining only variables that significantly improve prediction while excluding redundant factors. The incremental change in R2 at each step was examined to estimate the unique contribution of each predictor.
Age, education, family type, household size, marital status, nature of job, years of service, commuting distance, and income were entered as categorical predictors, whereas overall psychological well-being was treated as a continuous criterion variable. Proportionate representation of participants from the three urban zones of the city was ensured to reduce sampling bias. The results showed that geographical location had no statistically significant effect on the study variables, suggesting broadly similar socio-economic and occupational conditions across zones. Consequently, respondents from all zones were pooled for subsequent analyses.
5. Results
It is quite apparent from the results that for psychological well-being (PWB), the factors that emerged contributing to variance in PWB were Age which makes the highest 29.2% (F1, 398 = 164.138, p < 0.01) contribution and followed by Nature of Job 6.3% (F1, 397 = 38.768, p < 0.01), type of family 3.3% (F1, 396 = 21.386, p < 0.01), Income 1.7% (F1, 395 = 11.369, p < 0.01), Marital status 1.5% (F1, 394 = 10.033, p < 0.01). All these five predictors jointly explained 42.0% (F5, 394 = 57.043, p < 0.01) variance in prediction of psychological Well-being of Working women.
The results of Stepwise Multiple Regression Analysis (MRA) with selected demographic variables and Overall Psychological Well-Being are predicted in Table 3.
Table 3. Results of Stepwise Multiple Regression Analysis (MRA) with selected demographic variables and Overall Psychological Well-Being:
Demographic Variables |
R |
R2 |
R2 Change |
F |
F Change |
df |
β |
t |
Age |
0.540 |
0.292 |
0.292 |
164.138 |
164.138** |
1, 398 |
0.557 |
11.906** |
Nature of Job |
0.596 |
0.355 |
0.063 |
109.241 |
38.768** |
1, 397 |
−0.287 |
−7.207** |
Type of Family |
0.623 |
0.388 |
0.033 |
83.696 |
21.386** |
1, 396 |
−0.169 |
−4.353** |
Income |
0.637 |
0.405 |
0.017 |
67.258 |
11.369** |
1, 395 |
−0.175 |
−3.791** |
Marital Status |
0.648 |
0.420 |
0.015 |
57.043 |
10.033** |
1, 394 |
−0.124 |
−3.167** |
*p < 0.05, **p < 0.01 Codes: Age: 1 (<30), 2 (30 - 40), 3 (>40); Nature of job: 1 (Government), 2 (Private); Type of Family: (Nuclear) 2 (Joint); Income: 1 (<45,000) 2 (45,000 - 90,000) 3 (>90,000); Marital Status: 1 (Married) 2 (Unmarried).
It is further indicated by beta values that whereas Age (β = 0.557) has a positive or enhancing impact on Psychological Well-Being while Nature of Job, (β = −0.287), Type of family (β = −0.169), Income (β = −0.175), and marital status (β = −0.124), have a negative or reducing impact on Psychological Well-Being of working women.
Table 4. Results of Stepwise Multiple Regression Analysis (MRA) with Social Network as predictor variable and Psychological Well-Being as criterion variable.
Demographic Variables |
R |
R2 |
R2 Change |
F |
F Change |
df |
β |
t |
Emotional Support |
0.262 |
0.069 |
0.069 |
29.436 |
29.436** |
1, 398 |
0.285 |
5.829** |
Tangible support |
0.285 |
0.081 |
0.012 |
17.307 |
5.263** |
1, 397 |
0.113 |
2.294* |
*p < 0.05, **p < 0.01.
The results of Stepwise Multiple Regression Analysis (MRA) with social network as predictor variable and Psychological Well-Being as criterion variable are predicted in Table 4. The obtained results clearly depicted that emotional support explained 6.9% (F1, 398 = 29.436) variance in the prediction of social network among working women. Further, tangible support contributed for 1.2% (F1, 397 = 5.263) variance in the prediction of Psychological Well-being. The beta values also indicate that Emotional support (β = 0.285) and Tangible support (β = 0.113) both have positive impact on Psychological Well-being.
Table 5. Results of Stepwise Multiple Regression Analysis (MRA) with selected physical health variables as predictor variable and Psychological Well-Being as criterion variable.
Variables |
R |
R2 |
R2 Change |
F |
F Change |
df |
β |
t |
BMI |
0.276 |
0.076 |
0.076 |
32.899 |
32.899** |
1, 398 |
−0.225 |
4.550** |
No. of Diseases |
0.327 |
0.107 |
0.031 |
23.804 |
13.662** |
1, 397 |
−0.183 |
3.696** |
*p < 0.05, **p < 0.01.
The results of Stepwise Multiple Regression Analysis (MRA) with physical health as predictor variable and Psychological Well-Being as criterion variable are predicted in Table 5. It is evident from Table 5 that for Psychological Well-Being, the factors that emerged contributing to variance in Psychological Well-Being was BMI which contributed 7.6% (F1, 398 = 32.899, p < 0.01) and no. of diseases 10.7% (F1, 397 = 13.662, p < 0.01). Both predictors jointly explained 10.7% (F2, 397 = 23.804, p < 0.01) variance in prediction of Psychological Well-Being of working women. The beta values further indicate that both BMI (β = −0.225) and lesser no. of diseases (β = −0.183), has a positive or enhancing impact on Psychological Well-Being of working women.
6. Discussions
This investigation compared the relative contribution of demographic, social support, and health-related factors in predicting psychological well-being. Stepwise multiple regression identified age, nature of job, type of family, income, and marital status as the most important predictors. These factors likely matter because they structure daily demands, resource availability, role expectations, and access to support, thereby shaping stress exposure and coping capacity.
A positive association between age and psychological well-being indicates that well-being tends to improve as women grow older. Age is closely related to life stage, career development, and coping resources (De-Juanus, Romero, & Going, 2020). With increasing age, women often develop stronger emotional regulation, reduced reactivity to routine stressors, and more effective problem-solving abilities, which together enhance psychological well-being.
Life and work experience enable women to develop more realistic expectations, a clearer identity, and stronger confidence in their abilities. With seniority often comes greater job security and autonomy, and perceived control is consistently associated with higher psychological well-being. Over time, priorities tend to shift toward balance and emotional meaning rather than competition, thereby supporting better mental health. These results align with Noor (2008), who reported a positive association between age and life satisfaction. Similarly, Diener et al. (1999) observed that older adults cope more effectively with adversity and experience a smaller gap between expectations and reality, leading to greater contentment—consistent with the present findings.
The second important predictor of psychological well-being was nature of job (government vs private). The results indicated that women working in government job experience better psychological well-being in comparison to women working in private job. This finding supports the studies of Bansal et al. (2021) who also found that Indian government workers had higher job satisfaction and lower perceived stress, and also showed stronger positive relationships with others, which is a key dimension of psychological well-being. Likewise, in a recent study, Guleria (2023) also reported private sector workers tend to be more stressed, while those in government roles experience relatively higher life satisfaction and lower stress. The reason may be because public sector work often provides conditions that reduce uncertainty and chronic strain. Women in government jobs often report better psychological well-being than those in private employment because in India public sector work typically offers greater job security, predictable career paths, and clear rules, which reduce uncertainty about the future. More regulated hours, generous leave, and family-friendly benefits make balancing work and home responsibilities easier. Structured pay, pensions, and healthcare and mental health facility provide financial stability, while performance pressures may be less intense than in profit-driven private settings. Overall, the combination of security, predictability, and manageable demands tends to support higher well-being.
The advantage may stem from the fact that public sector employment often provides conditions that minimize uncertainty and chronic strain. Women working in government positions frequently report better psychological well-being than those in private organizations, as public employment in India typically ensures stronger job security, predictable career progression, and clearly defined rules regarding roles and promotions. Regulated working hours, relatively generous leave provisions, and family-friendly benefits can make it easier to manage both occupational and domestic responsibilities. In addition, structured salary systems, pensions, and access to healthcare services contribute to financial stability, while performance pressures may be less intense than in profit-oriented environments. Taken together, this combination of security, predictability, and comparatively manageable demands is likely to foster higher levels of well-being.
The findings further indicated that working women in nuclear families reported better psychological well-being, possibly because such arrangements enhance autonomy, role clarity, and decision-making freedom while reducing interpersonal strain. Supporting this view, Kumari and Vishvakarma (2025) observed that women in nuclear families demonstrated significantly higher self-confidence, an important component of well-being. Similarly, Sabre (2016) linked nuclear family structures with better marital adjustment and greater personal autonomy, both theoretically associated with improved psychological functioning.
With regard to marital status, married working women showed higher psychological well-being than single or widowed women. Marriage may provide emotional, social, and economic resources, along with a sense of security and shared responsibility, which can ease the gendered pressures of balancing work and family demands. At the same time, other research suggests that marriage can intensify women’s role overload, particularly in contexts where expectations regarding household and caregiving duties remain. Therefore, the positive influence of marriage on well-being is neither automatic nor uniform but depends greatly on the quality of spousal support and the distribution of domestic labour.
Another notable finding was that higher income was associated with lower psychological well-being among working women. Although this may appear surprising, it becomes understandable when considering the greater pressures that often accompany higher earnings. This result aligns with Suzuki et al. (2025) and Kahneman and Deaton (2010), but contrasts with studies such as Diener et al. (2010) and Akkiraju & Rao (2025), which reported a positive association between income and well-being. As argued by Kahneman and Deaton (2010), higher income is commonly linked to demanding roles characterized by heavier workloads, longer hours, tighter deadlines, and greater responsibility for performance. Such chronic strain can offset the emotional benefits of additional income.
Overall, the findings suggest that socio-demographic variables—particularly age, nature of job, type of family, marital status, and income—significantly predict the psychological well-being of working women. With regard to social networks, emotional support emerged as a stronger predictor of well-being than tangible support. This result is in line with the work of Inagaki and Orehek (2017) and Yamada et al. (2005), who argue that emotional support fosters feelings of belonging and reassurance, especially during periods of strain or isolation, thereby enhancing psychological health. At the same time, tangible support remains important because it enables working women to manage responsibilities at home and at the workplace more effectively. By easing practical burdens, such assistance can enhance satisfaction and indirectly contribute to better psychological well-being. The findings concerning physical health indicate that working women with lower BMI and fewer illnesses report better psychological well-being. Better physical condition likely supports higher energy, productivity, and a more manageable balance between work and family roles. These results are consistent with Bookwala and Boyar (2008) and Vieira et al. (2012), who observed that higher BMI is linked to poorer psychological well-being among women. The present study also aligns with Segal and Gunturu (2024), who connected increased BMI with greater depression and anxiety and reduced quality of life. Similarly, de Jong et al. (2012) associated higher BMI with lower body-related self-esteem and more emotional problems, while Wu and Berry (2018) highlighted the role of weight stigma in intensifying psychological distress, particularly in workplace contexts.
In addition, Ostry et al. (2006) reported that long working hours and physically demanding conditions are related to BMI among employed men and women. Beyond body weight, the accumulation of chronic diseases also plays a crucial role: psychological well-being tends to decline as the number of medical conditions increases. These findings are consistent with Liao et al. (2022), who reported a positive association between chronic disease and mental distress, and with research linking multimorbidity to lower life satisfaction and heightened psychological strain. Thus, the psychological well-being of working women is shaped by factors beyond employment, including health conditions and the demands of balancing work and family. Chronic illnesses contribute to increased emotional burden and reduced role functioning, highlighting the complex interplay between BMI, health, and occupational responsibilities.
The results can be understood in terms of the cumulative burden faced by working women: managing multiple health problems diminishes energy and stamina, increases pain and discomfort, and complicates the balance between work and domestic responsibilities. Physical limitations combined with role pressures restrict women’s full participation in daily life, thereby undermining well-being. Inadequate social support further weakens coping capacity, increasing vulnerability to illness and psychological distress. Conversely, the ability to shape one’s environment and lifestyle according to personal preferences, coupled with a sense of belonging within the community, appears to enhance experiences and foster better well-being over time.
These findings suggest that managing multiple health problems can reduce energy and stamina, increase pain and discomfort, and make it more difficult for working women to balance job responsibilities with household duties. The cumulative burden of illness may therefore disproportionately affect their well-being, as physical limitations and associated stressors restrict full participation in daily life. Additionally, inadequate social support diminishes coping capacity, heightens vulnerability to illness, and increases the likelihood of poorer psychological outcomes. Consequently, higher BMI and the presence of chronic diseases contribute to lower overall health and well-being among working women. Conversely, the ability to shape one’s environment and lifestyle according to personal preferences, coupled with a sense of belonging within the community, appears to foster more positive experiences and improved well-being over time.
7. Contributions, Limitations and Future Research
Work-related challenges faced by women vary across locations and therefore require spatially grounded understanding. Much of the existing research is situated at national or global scales, often overlooking the everyday realities of women in smaller or specific urban centres. Examining working women at the city level can thus generate a more nuanced appreciation of their lived difficulties. The present study contributes to the literature by comparatively assessing the influence of multiple demographic variables in predicting the psychological well-being of working women. An additional contribution lies in its focus on the service sector, one of the largest sources of employment for women in India. However, the study is not without limitations.
A key limitation of the study is its reliance on self-reported data, which restricts the ability to draw causal inferences or examine relationships over extended periods. Nevertheless, subjective assessments of health, status, and well-being remain valuable for understanding personal experiences and perceptions. The study is also limited by its focus on a restricted set of occupational sectors. Future research should employ adequately powered samples across a wider range of occupations to obtain a more comprehensive picture of women’s psychological well-being and related dimensions. Exploratory investigations involving diverse forms of employment would help clarify occupation-specific challenges. In addition, the findings warrant replication among women working in the unorganized sector.
Support systems available to working women in India remain limited. There is therefore a need to identify groups that are particularly vulnerable and to determine which forms of assistance can most effectively enhance psychological well-being across different occupational structures. Such efforts would help tailor support to women’s specific circumstances rather than assuming uniform needs. If future investigations corroborate these findings, women professionals and organizational stakeholders across employment sectors could use this knowledge to strengthen decision-making and foster more meaningful collaboration across roles. A differentiated approach would allow the development of occupation-specific supports instead of relying on a one-size-fits-all model. Ultimately, this may optimize the design of interventions and improve the psychological well-being of employed women. Because the present study focused exclusively on working women, future research could profitably undertake comparative analyses involving both male and female employees. Such work would clarify how the identified factors operate across gender and test the robustness of demographic predictors in different samples.
The emphasis here was on variations within women rather than gender differences per se; however, examining contrasts between men and women may further illuminate which determinants exert the strongest influence on mental health outcomes. Accordingly, subsequent investigations should explore patterns across multiple dimensions of psychological well-being for male and female workers. Future studies may also incorporate broader social variables amenable to intervention, such as community resources and organizational support systems.