This paper addresses how various types of underemployment (time-related, income-related and qualification-related) are associated with the socio-economic well-being of executives in Punjab's agro-based industrial sector, providing evidence-based insights to frame organizational and policy-level interventions.
Data were collected from 325 middle-level and lower-level executives working in medium- and large-scale agro-based industrial units. The study used linear regression analysis to investigate how different types of underemployment are associated with several aspects of socio-economic well-being, such as economic strain and interpersonal connections.
The study found that underemployment is associated with lower socio-economic well-being, though the strength and nature of this relationship differ by type. Income-related underemployment has the most significant negative impact, followed by qualification-related and time-related underemployment.
By disaggregating underemployment into distinct types and examining their individual socio-economic consequences in a specific regional and sectoral context, the study adds a nuanced understanding to the existing literature. It provides insights for policymakers and groups attempting to address the detrimental effects of underemployment in an industrial setting.
1. Introduction
The COVID-19 pandemic has profoundly impacted global labor markets, not only by increasing unemployment but also intensifying underemployment – a condition in which people work fewer hours, earn less money or have lower skill levels (Cucinotta and Vanelli, 2020; McKee-Ryan and Harvey, 2011). While unemployment frequently attracts significant policy and scholarly attention, underemployment is more widespread yet understudied, particularly in emerging economies such as India (Maynard and Feldman, 2011). The quality of employment is becoming increasingly important in modern labor studies, as poor employment conditions are linked to negative psychological, economic and social outcomes (Allan et al., 2020).
Traditionally, employment status has been viewed in binary terms: employed or unemployed (Dooley, 2003). However, as labor markets have become more complex, attention has shifted to various forms of inadequate employment, such as underemployment, in which workers are officially employed but in roles that do not match their abilities or requirements.
Underemployment is broadly recognized as a prevalent form of non-standard employment, particularly among younger populations entering the labor market (Churchill and Khan, 2021). It is related to the inadequacy in work opportunities that can be in terms of hours, income, rank, or expertise. As such, underemployment depicts a conceptual space between employment and unemployment, representing an ambiguous employment status that often escapes traditional labor statistics (Gibbons, 2016). McKee-Ryan and Harvey (2011) defined underemployment as a situation in which individuals are unable to find adequate employment condition in comparison to some standard. It correlates with an alternative employment situation in which they are willing and available to engage but are not able to get it (ILO, 1998). In contrast to unemployment, underemployment is understood to be paid employment that is inherently suboptimal or of lower quality (Allan et al., 2017).
Underemployment manifests in multiple forms. The 16th International Conference on Labour Statisticians (ICLIS) Resolution divided it into two principal categories, i.e. time-related underemployment and inadequate employment situations. Time-related underemployment occurs when people, during some short reference period (usually one week), were willing to work for additional hours, they were available to do work during those additional hours and had worked for fewer hours than the predetermined hours. Inadequate employment situation occurs when people during that short reference period want to change their present work situations because that work limits their capacities and well-being, and they were available to do so (ILO, 1998).
Underemployment may be categorized as either visible or invisible. Visible underemployment includes time-related situations in whichemployees work for fewer hours than the normal hours of work determined for the event. Invisible underemployment, on the other hand, is a situation that prevents workers' full potential from being realized, i.e. insufficient use of their skills and experience (Senkrua, 2018).
In India, this issue has assumed particular urgency. Despite steady GDP growth, a large proportion of the labor force remains either informally employed or underemployed, especially in semi-formal industrial sectors such as textiles, food processing and agro-based manufacturing industries (Sahu and Behera, 2025). Evidence from Punjab and neighboring states also indicates persistent income volatility and weak skill utilization in agro-processing units (Gowtham and Manivel, 2025). More than one-third of employed youth in India are engaged in work that does not match their skill level or offers inadequate income stability. This mismatch directly impacts household welfare and quality of life (Roy, 2023). Furthermore, research suggests that job–skill mismatch affects organizational commitment, while income underemployment more strongly predicts household financial stress (Bischof, 2021; Allan et al., 2020).
Although numerous studies have examined unemployment, the different types of underemployment–time-related, income-related and qualification-related–remain underexplored in the Indian context. Previous research has largely focused on Western economies, emphasizing overqualification and job dissatisfaction, turnover, psychological well-being of employees, or treating underemployment as a single, homogeneous phenomenon (Maynard and Feldman, 2011; Allan et al., 2017). In developing economies like India, where structural disparities and informal labor markets are widespread, this approach overlooks crucial differences in how various forms of underemployment affect workers. Although limited empirical work has compared these socio-economic implications, this gap is significant because each type of underemployment has distinct consequences for individuals' financial security, family relationships and social well-being (Churchill and Khan, 2021). These contextual findings support the need to examine different forms of underemployment separately and justify the study's disaggregated methodology and sector-specific research questions.
Punjab's agro-based industries, such as textiles and food processing, offer a relevant case for this investigation. These industries employ a significant share of the rural and semi-urban labor force and contribute substantially to regional economic output, yet they face structural challenges such as seasonal fluctuations, low wages and limited upward mobility (Niir, 2025). Understanding how various forms of underemployment influence workers' well-being in this context provides actionable insights for policymakers.
Accordingly, this study examines the associations between different forms of underemployment (time-, income- and qualification-related) and socio-economic well-being (economic strain and strained family, workplace and social relationships) among workers in Punjab's agro-based industries.
2. Literature review
Underemployment has remained a perennial problem in both developed and developing economies, but its forms and repercussions have become worse in recent years, particularly in the aftermath of global crises such as the COVID-19 pandemic (ILO, 2022). Contemporary research splits underemployment into three types: time-related, income-related and qualification-related underemployment. Time-related underemployment occurs when people work fewer hours than they would like, despite being available and willing to work more (ILO, 1998). Income-related underemployment occurs when employees earn much less than their talents or previous job roles warrant. Qualification-related underemployment, also known as skill mismatch or overqualification, happens when an individual's education or training surpasses the requirements of their current work (Maynard and Feldman, 2011; Harari et al., 2017).
2.1 Recent empirical findings and contextual relevance
Recent research has investigated the psychological, economic and social consequences of underemployment.
According to Allan et al. (2020), underemployment has a negative impact on individuals' feelings of meaningful work, particularly when they are forced into tasks that are below their talents. Churchill et al. (2025) found that underemployment among young adults in Australia is substantially associated with increased stress, financial hardship and declining mental health–effects that were more severe than in some situations of unemployment. It can affect the relationship of employees with their spouses, children, friends, relatives and peers, and hence affect family and friendship networks as well. It can lead to delayed marriages or greater marital discord (Maynard and Feldman, 2011).
Underemployment can reduce the earnings of individuals, which can lead to financial distress among them (McKee-Ryan and Harvey, 2011). Underemployment can also lead to less savings, which is a signal of labor underutilization (Li et al., 2015). It may generate feelings of social isolation and withdrawal among people. It can create the feeling of being left out of the loop among people because they may be missing major interactions (Gray and Laidlaw, 2002).
Kamerade and Richardson (2018) found that women in part-time or overqualified roles commonly experience long-term job dissatisfaction due to gender and occupational segregation during underemployment. Similarly, Harari et al. (2017) found that overqualification is associated with low job satisfaction, higher turnover intentions and emotions of psychological contract violation.
Moortel et al. (2020) investigated post-pandemic shifts and discovered that involuntary part-time workers, particularly in rural industries, experienced increased financial and emotional stress as a result of unpredictable work schedules and little social support. Meanwhile, Chambel et al. (2021) have shown that perceived overqualification is associated with increased emotional tiredness, particularly among younger workers in entry-level jobs.
In the Indian context, Ghatak et al. (2024) underlined that the country's concentration on job creation has frequently overlooked job quality, resulting in an invisible crisis of underused potential and wage repression. With a considerable proportion of India's labor population working in informal sectors, particularly agriculture-related industries, underemployment frequently manifests as disguised unemployment and chronic income insufficiency (Abraham and Kesar, 2025). Singh (2022) also emphasized that structural rigidities in India's regional labor markets prevent qualified workers from being placed in roles that match their skills, aggravating both underemployment and migration pressures.
2.2 Identified gaps in literature
Previous studies have demonstrated that various types of underemployment, such as skill mismatch, involuntary part-time work and income inadequacy, result in distinct psychological, social and economic consequences (Allan et al., 2020; Churchill et al., 2025). However, several conceptual and empirical gaps persist. First, many studies treat underemployment as a homogeneous condition or focus disproportionately on overqualification, overlooking income-related and time-related underemployment (Harari et al., 2017; Allan et al., 2020). Treating underemployment as a single variable risks masking how distinct mechanisms produce different socio-economic outcomes: income shortfalls directly affect household consumption and social participation, while qualification mismatch erodes professional identity and workplace attachment. Without disaggregation, policy recommendations become blunt and ineffective.
Second, there are methodological inconsistencies across studies. Many influential papers use cross-sectional designs and convenience or convenience-derived samples from Western contexts (Maynard and Feldman, 2011; Allan et al., 2017), undermining causal claims and cross-national generalizability.
Third, sectoral and regional contexts are underexamined. Most large-sample studies draw on national surveys from developed economies or focus on youth labor markets in urban settings (Churchill and Khan, 2021). There is limited evidence about manufacturing or agro-based sectors in developing-country regions, where labor markets are often semi-formal, wages are suppressed, and skill-utilization dynamics differ (Abraham and Kesar, 2025).
Finally, few studies analyze interpersonal and household-level consequences simultaneously. While psychological and organizational outcomes are reasonably well-covered (e.g. job satisfaction, organizational commitment), fewer studies link underemployment to family processes (child wellbeing, marital conflict), peer networks and material consumption simultaneously. Where family outcomes are studied, they are often qualitative and not integrated with quantitative measures of economic strain, limiting the ability to model joint pathways (Pendula and Newman, 2011).
2.3 Contribution of the present study
This study addresses these gaps by:
Dividing underemployment into three distinct types: time-related, income-related and qualification-related.
Examining the socio-economic implications of each type independently, including both economic strain and social well-being.
Focusing on a sector- and region-specific context–Punjab's agro-based industries–which has not been adequately explored in prior literature.
As a result, the study provides insights that can guide government initiatives, human resource strategies and labor market changes targeted at enhancing not only job availability but also employment quality and alignment with worker potential.
3. Research framework
3.1 Conceptual framework
Based on the review of the literature discussed, a conceptual model was developed. The model incorporated the key variables to examine the effect of different types of underemployment (time, income and qualification) on the socio-economic well-being of employees, which include variables such as a strained relationship with partner and children, economic strain, a strained relationship with peers and organization and a strained relationship with relatives and friends. The conceptual framework of this study can be visualized in Figure 1 below.
The flowchart shows a central, rounded rectangular box on the left labeled “UNDEREMPLOYMENT” with three bullet points listed vertically inside it: “Time-related underemployment” at the top, “Income-related underemployment” in the middle, and “Qualification-related underemployment” at the bottom. From the right side of this central box, four straight arrows diverge outward and point rightward to four separate rectangular boxes arranged in a vertical stack from top to bottom. The top arrow connects to a box labeled “Strained relationship with partner and children”. The second arrow from the top connects to a box labeled “Economic strain”. The third arrow connects to a box labeled “Strained relationship with peers and organisation”. The bottom arrow connects to a box labeled “Strained relationship with relatives and friends”.Conceptual framework for socio-economic consequences of underemployment (developed by the researcher)
The flowchart shows a central, rounded rectangular box on the left labeled “UNDEREMPLOYMENT” with three bullet points listed vertically inside it: “Time-related underemployment” at the top, “Income-related underemployment” in the middle, and “Qualification-related underemployment” at the bottom. From the right side of this central box, four straight arrows diverge outward and point rightward to four separate rectangular boxes arranged in a vertical stack from top to bottom. The top arrow connects to a box labeled “Strained relationship with partner and children”. The second arrow from the top connects to a box labeled “Economic strain”. The third arrow connects to a box labeled “Strained relationship with peers and organisation”. The bottom arrow connects to a box labeled “Strained relationship with relatives and friends”.Conceptual framework for socio-economic consequences of underemployment (developed by the researcher)
3.2 Research hypotheses
Drawing from established theories – equity theory (Adams, 1965), person–job fit theory (Kristof, 1996) and the conservation of resources (COR) theory (Hobfoll, 1989) — this study develops the following hypotheses.
From the perspective of COR theory, individuals strive to conserve resources (e.g. time, energy, income). When their resources are underutilized, as in time-related underemployment, psychological tension may arise. This mismatch can lead to financial insecurity and feelings of underutilization, which have a negative impact on family interactions and professional participation (ILO, 1998; Churchill et al., 2025).
There is a significant association between time-related underemployment and the socio-economic well-being of employees.
According to Equity Theory, individuals evaluate fairness in employment by comparing their inputs (skills, effort) to the outcomes they receive (income, recognition). When income or working conditions are perceived as unfair, individuals experience psychological distress and economic strain. Prior studies confirm that income-related underemployment increases financial stress and reduces job satisfaction (Allan et al., 2020).
There is a significant association between income-related underemployment and the socio-economic well-being of employees.
Drawing from Person–Job Fit Theory, qualification-related underemployment occurs when employees' skills or educational qualifications exceed job requirements, leading to frustration, reduced motivation, and workplace tension (Maynard and Feldman, 2011). In semi-formal sectors, such a mismatch may also limit career advancement and self-esteem, straining interpersonal relationships at work. Overqualified employees often report low job satisfaction, boredom and alienation, which may spill over into their social and professional relationships (Harari et al., 2017; Allan et al., 2020).
There is a significant association between qualification-related underemployment and the socio-economic well-being of employees.
4. Research methodology
The study focused on medium-scale and large-scale agro-based industries in Punjab, a state in north-west India. A multistage stratified random selection technique was employed to guarantee broad representation across varied agro-based industrial units in Punjab. Initially, two prominent industries, namely the textile industry and the food and beverage industry, were selected based on the highest number of medium- and large-scale units, as per the MSME directory (2016).
Textile industry: For the study, the textile industry is defined as the manufacturing sector involved in the production and processing of fibers, yarn, textiles and finished textile goods predominantly from natural raw materials such as cotton, wool, jute and silk.
Food and beverage industry: For the study, the food and beverage industry is defined as the sector that processes (including processing units for grains, dairy and packaged foods) and transforms agricultural and animal-based raw materials into consumable food products, as well as manufactures non-alcoholic beverages.
In the second stage, the Micro, Small and Medium Enterprises (MSME) directory was used to compile a list of medium- and large-scale units from each district for both industries, and three districts were chosen based on unit concentration: Ludhiana, Patiala and Sangrur for textiles; Ludhiana, Patiala and SAS Nagar for food and beverage. In the third stage, five units from each district were randomly selected, for a total of 30 units (15units of each industry), but only 13 units from the textile industry and 12 from the food and beverage industry returned entirely completed questionnaires, for a final sample of 25 units. In the fourth stage, 20 middle- and lower-level executives from each unit were chosen based on their willingness to participate, making the total sample size of 500 respondents (260 from Textile and 240 from Food and Beverage).
4.1 Profile of sample
The study analyzed data from 325 total respondents who were found to be underemployed. The majority of the respondents were male (77.85%), married (74.46%), aged 31–40 years old (4.00%), held a graduation degree (46.46%), were employed at middle level (78.15%) and were earning Rs 20,000–50,000 per month (51.38%). Detailed profiles of respondents can be found in Table 1.
Demographic profile of the respondents (n = 325)
| Demographic variables | Textile industry | Food and beverage industry | Total | |
|---|---|---|---|---|
| Gender | Male | 132 (83.50) | 121 (72.50) | 253 (77.85) |
| Female | 26 (16.50) | 46 (27.50) | 72 (22.15) | |
| Marital Status | Unmarried | 31 (19.60) | 52 (31.10) | 83 (25.54) |
| Married | 127 (80.40) | 115 (68.90) | 242 (74.46) | |
| Age | 21–30 | 35 (22.15) | 43 (25.75) | 78 (24.00) |
| 31–40 | 62 (39.24) | 68 (40.72) | 130 (40.00) | |
| 41–50 | 39 (24.68) | 38 (22.75) | 77 (23.69) | |
| 51–60 | 17 (10.76) | 18 (10.78) | 35 (10.77) | |
| 61–70 | 5 (3.17) | 0 (0.00) | 5 (1.54) | |
| Qualification | Diploma | 17 (10.80) | 14 (8.40) | 31 (9.54) |
| Graduation | 81 (51.30) | 70 (41.90) | 151 (46.46) | |
| Post Graduation | 60 (38.00) | 83 (49.70) | 143 (44.00) | |
| Designation | Middle level | 123 (77.80) | 131 (78.40) | 254 (78.15) |
| Lower level | 35 (22.20) | 36 (21.60) | 71 (21.85) | |
| Monthly Income | >20,000 | 32 (20.30) | 39 (23.40) | 71 (21.85) |
| 20,000–50,000 | 83 (52.50) | 84 (50.30) | 167 (51.38) | |
| 50,000–80,000 | 19 (12.00) | 24 (14.40) | 43 (13.24) | |
| 80,000–1 lakh | 9 (5.70) | 14 (8.40) | 23 (7.08) | |
| >1 lakh | 15 (9.50) | 6 (3.60) | 21 (6.45) | |
Note(s): Figures in parentheses indicate percentages
4.2 Measures
Respondents were classified into three types of underemployment: time-underemployed, income-underemployed and qualification-underemployed. According to the Labour Utilization Framework, different kinds of underemployment situations are considered to be mutually exclusive (Sullivan, 1978); therefore, they are organized in a hierarchy that starts with time-related underemployment, followed by income-related underemployment and ends with qualification-related underemployment. Consequently, individuals falling into lower levels of underemployment situations will not be included in other higher levels of underemployment situations (i.e. individuals who are classified as time-underemployed cannot be classified as income-underemployed).
4.2.1 Time-related underemployment
It was computed through the “Version A” of the working time module from the ILO Labour Force Surveys Pilot Studies (Benes and Walsh, 2018). Version A contains the set of questions that need to be asked from the employees in order to classify them as time-related underemployed.
4.2.2 Income-related underemployment
To measure income-related underemployment, the survey used 14 income-related underemployment items, 7 items were adopted from the study “Construction and validation of the subjective underemployment scales” (Allan et al., 2017) and the other 7 items were developed for the study. Each item was answered on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The scale reported good estimated internal consistency (α = 0.78). Respondents who were classified as time-related underemployed were not included in this category.
4.2.3 Qualification-related underemployment
To measure perceived overqualification, the survey used 18 perceived overqualification items, some were adopted from the different scales used by researchers and some were developed for the purpose of the study. A total of 9 items were adopted from the scale developed by Maynard and colleagues, i.e. Scale of Perceived Over qualification (Maynard et al., 2006), 6 items were adopted from the study “The antecedents and consequences of underemployment among expatriates” (Bolino and Feldman, 2000) and 3 items were developed for the study. Each item was answered on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The scale reported good estimated internal consistency (α = 0.76). Respondents who were classified as time-related or income-related underemployed were not included in this category.
In the present study, certain additional statements were incorporated to ensure comprehensive coverage of the constructs under investigation. This modification was undertaken to enhance the content validity of the instrument by including items that reflect the specific context of the textile and food and beverage industries under study. Such inclusion also aimed to improve the reliability of the instrument by providing a broader range of indicators measuring the intended dimensions.
Out of the 500 respondents surveyed, 325 were identified as underemployed based on self-reported indicators and validated classification criteria. These were further categorized into time-related (n = 20), income-related (n = 282) and qualification-related underemployment (n = 23) related underemployment. The remaining 175 respondents either did not report underemployment or did not meet the threshold for any specific underemployment type and were thus classified as adequately employed. These respondents were excluded from the regression analysis.
5. Results
5.1 Factor analysis: constructs for socio-economic consequences of underemployment
A total of 30 different statements regarding the socio-economic consequences of underemployment were formed on the basis of a literature review. With regard to pre-analysis testing for the suitability of the entire sample for factor analysis, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was found to be 0.839. Thus, it indicates that the sample taken was suitable for factor analysis procedures. It was found that there were a total of 4 factors that explained the majority of the variance in the study (see Table 2), as initially 30 variables were taken into consideration and these variables may be clubbed into only 4 factors. Cronbach's alpha was calculated to check the reliability of the scale and it was found to be 0.87, ensuring the reliability of the used scale.
Factors with the percentage of variance explained
| Factors | Eigen values | Variance explained (%) | Cumulative variance (%) |
|---|---|---|---|
| Strained Relationship with partner and children | 7.975 | 22.337 | 22.337 |
| Economic strain | 6.591 | 15.406 | 37.743 |
| Strained relationship with peers and organization | 3.938 | 14.945 | 52.688 |
| Strained Relationship with relatives and friends | 1.633 | 14.436 | 67.124 |
We finally found that the variables X7, X12, X14, X18, X20, X22, X25, X27 and X29 were loaded on factor 1 (Strained relationship with partner and children), the variables X2, X5, X6, X19, X21, X23, X28 and X30 were loaded on factor 2 (Economic strain), the variables X1, X4, X9, X10, X15 and X17, were loaded on factor 3 (Strained relationship with peers and organization) and the variables X3, X8, X11, X13, X16, X24 and X26 were loaded on factor 4 (Strained relationship with relatives and friends) (see Table 3).
Factor labels
| Factor | Loadings | Statements included in the factor |
|---|---|---|
| Strained Relationship with partner and children (α = 0.85) | 0.867 | My children have started losing faith in the value of higher education. (X7) |
| 0.766 | I often worry because I am not able to send my children to good school. (X12) | |
| 0.858 | Me and my children spend less time together. (X14) | |
| 0.876 | Me and my children usually have unpleasant arguments with each another. (X18) | |
| 0.849 | Me and my partner spend less time together. (X20) | |
| 0.871 | I do not have a warm and comfortable relationship with my partner. (X22) | |
| 0.845 | Me and my partner are not planning children. (X25) | |
| 0.898 | Me and my partner usually engage into conflicts. (X27) | |
| 0.887 | My marriage has been delayed. (X29) | |
| Economic strain (α = 0.81) | 0.727 | I often depend on my parents for financial help. (X2) |
| 0.721 | I often put off family activities because of expenses. (X5) | |
| 0.649 | I set aside money for savings. (X6) | |
| 0.678 | There is no regular plan for savings. (X19) | |
| 0.758 | I have to decline invitations to join social activities with my friends because I cannot financially afford it. (X21) | |
| 0.683 | I am not able to save money for future. (X23) | |
| 0.699 | My income usually allows me to do the things I want. (X28) | |
| 0.762 | I do not feel secure with my present financial position. (X28) | |
| Strained relationship with peers and organization (α = 0.70) | 0.870 | I would be very happy to spend my rest of career with this organization. (X1) |
| 0.851 | I like spending work hours with my peers. (X4) | |
| 0.835 | I consider myself inferior among my peers. (X9) | |
| 0.860 | I think that I could easily become as attached to another organization as I am to this one. (X10) | |
| 0.820 | I have good relationship with my peers who are getting better salary than me. (X15) | |
| 0.847 | I do not feel like “part of the family” at my organization. (X17) | |
| Strained Relationship with relatives and friends (α = 0.89) | 0.733 | I get the emotional support that I need from my family and friends. (X3) |
| 0.686 | Financial problem interferes with my relationship with other people. (X8) | |
| 0.728 | I feel that I am losing a sense of social connectedness. (X11) | |
| 0.734 | I feel left out socially. (X13) | |
| 0.777 | My friends and family members are always there to help me. (X16) | |
| 0.706 | My family often criticises me. (X24) | |
| 0.729 | My social relationships are superficial. (X26) |
The derived factors represent the socio-economic consequences of underemployment. Referring to Table 2, the first factor represents the strained relationship with partner and children. The second factor represents economic strain, followed by strained relationships with peers and organization and the fourth factor deals with strained relationships with relatives and friends of employees.
5.1.1 Strained relationship with partner and children (F1)
A perusal of Table 2 revealed that it is the most significant factor with 22.337% of the total variance explained. A Total of nine variables have been loaded on this factor. Factor analysis identified a component in which items related to strained relationships with partner and children loaded strongly together. This suggests that these items form a distinct underlying dimension of socio-economic well-being.
5.1.2 Economic strain (F2)
Examination of Table 2 revealed that it is the second most important factor with 15.406% of variance explained. A total of eight variables have been loaded on this factor. Factor analysis identified a component in which items related to economic strain loaded strongly together. This suggests that these items form a distinct underlying dimension of socio-economic well-being.
5.1.3 Strained relationship with peers and organization (F3)
A perusal of Table 2 revealed that it is the third significant factor with 14.945% of the total variance explained. A total of six variables have been loaded on this factor. Factor analysis identified a component in which items related to strained relationships with peers and organization loaded strongly together. This suggests that these items form a distinct underlying dimension of socio-economic well-being.
5.1.4 Strained relationship with relatives and friends (F4)
Analysis of Table 2 showed that it is the fourth most important factor with 14.436% of variance explained. A total of seven variables have been loaded on this factor. Factor analysis identified a component in which items related to strained relationships with relatives and friends loaded strongly together. This suggests that these items form a distinct underlying dimension of socio-economic well-being.
5.2 Mean comparison between textile industry and food and beverage industry with respect to socio-economic consequences of underemployment
An independent sample t-test was conducted at the 0.05 level of significance to examine whether there were statistically significant differences in the perceptions of underemployment-related socio-economic well-being between employees in the textile and food & beverage industries (see Table 4).
Mean Comparison between textile industry and food and beverage industry with respect to socio-economic consequences of underemployment
| S.No. | Statements | Textile industry | Food and beverage industry | Difference of means t-value | p-value | ||
|---|---|---|---|---|---|---|---|
| Mean | S.D. | Mean | S.D. | ||||
| 1 | Strained relationship with partner and children | 2.31 | 0.64 | 2.60 | 0.80 | 2.90 | 0.01 |
| 2 | Economic strain | 4.17 | 0.46 | 3.95 | 0.51 | 4.08 | 0.01 |
| 3 | Strained relationship with peers and organization | 4.08 | 0.49 | 3.80 | 0.46 | 5.40 | 0.01 |
| 4 | Strained relationship with relatives and friends | 0.51 | 4.30 | 0.54 | 1.72 | 0.09 | |
The results revealed that: strained relationship with partner and children: A statistically significant difference was observed, with executives in the food and beverage industry showing higher relational strain (t = 2.90, p < 0.05). Economic strain: executives in the textile industry reported significantly higher levels of economic strain (M = 4.17, SD = 0.46) than those in the food and beverage industry (M = 3.95, SD = 0.51), t = 4.08, p < 0.05). Strained relationship with peers and organization: A statistically significant difference was observed, with executives in the textile industry showing higher relational strain (t = 5.40, p < 0.05). Strained relationship with friends and relatives: No statistically significant difference was found (t = 1.72, p > 0.05).
The observed inter-industry differences can be explained by the structural and operational distinctions between the two sectors. While Textile industry employees face continuous work but low and rigid pay structures, leading to greater economic strain and workplace tension (Belete et al., 2020) Food and Beverage employees experience seasonal employment and income volatility, resulting in higher family-related relational strain (Cleveland, 2007).
5.3 Regression model of socio-economic consequences of underemployment
A linear regression model was applied to determine the association of different types of underemployment with strained relationships with partner and children, economic strain, strained relationships with peers and organizations and strained relationships with relatives and friends.
5.3.1 Linear regression model 1
1.1. (DV = Strained Relationship with partner and children; IDV = Time-related underemployment)
1.2. (DV = Economic strain; IDV = Time-related underemployment)
1.3. (DV = Strained relationship with peers and organization; IDV = Time-related underemployment)
1.4. (DV = Strained relationship with relatives and friends; IDV = Time-related underemployment)
Time-related underemployment was found to be significantly related to economic strain and strained relationships with peers and organization, p < 0.05 (see Table 5). These findings support the hypothesis that the more time-related underemployment is, the more executives will experience economic strain and strained relationships with peers and organization. Moreover, the value of R2 = 0.64 depicts that 64.00% of the variation in economic strain and R2 = 0.28 depicts that 28.00% of the variation in strained relationships with peers and organization is explained by time-related underemployment. There was no significant association of time-related underemployment with a strained relationship with partner and children and a strained relationship with relatives and friends, p > 0.05 (see Table 5).
Regression coefficients predicting underemployment and socio-economic consequences
| Variable | Strained relationship with partner and children (1.1) | Economic strain (1.2) | Strained relationship with peers and organization (1.3) | Strained relationship with relatives and friends (1.4) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | |
| Constant | 0.36 | 2.01 | – | −0.38 | 0.81 | – | 1.17 | 1.15 | – | 1.61 | 2.03 | – |
| Time-related underemployment | 0.51 | 0.45 | 0.35 | 1.02 | 0.18 | 0.81 | 0.67 | 0.26 | 0.53 | 0.55 | 0.45 | 0.28 |
| R2 | 0.12 | 0.64 | 0.28 | 0.08 | ||||||||
| F | 1.25 (NS) | 32.27** | 6.85* | 1.50 (NS) | ||||||||
Note(s): N = 20, *p < 0.05 and **p < 0.01
5.3.2 Linear regression model 2
2.1. (DV = Strained Relationship with partner and children; IDV = Income-related underemployment)
2.2. (DV = Economic strain; IDV = Income-related underemployment)
2.3. (DV = Strained relationship with peers and organization; IDV = Income-related underemployment)
2.4. (DV = Strained relationship with relatives and friends; IDV = Income-related underemployment)
Income-related underemployment was found to be significantly related to a strained relationship with partner and children, economic strain, strained relationship with peers and organization and strained relationship with relatives and friends, p < 0.01 (see Table 6). These findings support the hypothesis that the more income-related underemployment is, the more executives will experience the strained relationship with their partner and children, economic strain, strained relationships with peers and organizations and strained relationships with relatives and friends. Moreover, the value of R2 = 0.05 depicts that 5.00% of the variation in strained relationship with partner and children, R2 = 0.46 depicts that 46.00% of the variation in economic strain, R2 = 0.19 depicts that 19.00% of variation in relationship with peers and organization and R2 = 0.38 depicts that 38.00% of variation in strained relationship with relatives and friends is explained by income-related underemployment (see Table 6).
Regression coefficients predicting underemployment and socio-economic consequences
| Variable | Strained relationship with partner and children (2.1) | Economic strain (2.2) | Strained relationship with peers and organization (2.3) | Strained relationship with relatives and friends (2.4) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | |
| Constant | 0.98 | 0.43 | – | 1.00 | 0.20 | – | 1.91 | 0.25 | – | 1.54 | 0.21 | – |
| Income-related underemployment | 0.32 | 0.10 | 0.21 | 0.71 | 0.05 | 0.68 | 0.47 | 0.06 | 0.44 | 0.64 | 0.05 | 0.62 |
| R2 | 0.05 | 0.46 | 0.19 | 0.38 | ||||||||
| F | 10.24** | 238.72** | 65.89** | 171.62** | ||||||||
Note(s): N = 282, *p < 0.05 and **p < 0.01
5.3.3 Linear regression model 3
3.1. (DV = Strained Relationship with partner and children; IDV = Qualification-related underemployment).
3.2. (DV = Economic strain; IDV = Qualification-related underemployment)
3.3. (DV = Strained relationship with peers and organization; IDV = Qualification-related underemployment)
3.4. (DV = Strained relationship with relatives and friends; IDV = Qualification-related underemployment)
Qualification-related underemployment was found to be significantly related to economic strain, strained relationship with peers and organization and strained relationship with relatives and friends, p < 0.05 (see Table 7). These findings support the hypothesis that the more qualification-related underemployment is, the more executives will experience the economic strain, strained relationships with peers and organizations and strained relationships with relatives and friends. Moreover, the value of R2 = 0.21 depicts that 21.00% of the variation in economic strain, R2 = 0.44 depicts that 44.00% of the variation in strained relationships with peers and organizations, and R2 = 0.35 depicts that 35.00% of variation in strained relationships with relatives and friends is explained by qualification-related underemployment. There was no significant association of qualification-related underemployment with a strained relationship with partner and children, p > 0.05 (see Table 7).
Regression coefficients predicting underemployment and socio-economic consequences
| Variable | Strained relationship with partner and children (3.1) | Economic strain (3.2) | Strained relationship with peers and organization (3.3) | Strained relationship with relatives and friends (3.4) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | Coefficient | SE | β | |
| Constant | 2.54 | 1.65 | – | −0.65 | 1.81 | – | −1.97 | 1.43 | – | −2.64 | 1.88 | – |
| Qualification-related underemployment | 0.26 | 0.44 | 0.17 | 1.13 | 0.48 | 0.46 | 1.52 | 0.38 | 0.66 | 1.65 | 0.50 | 0.59 |
| R2 | 0.03 | 0.21 | 0.44 | 0.35 | ||||||||
| F | 0.35 (NS) | 5.55* | 16.24** | 11.05** | ||||||||
Note(s): N = 23, *p < 0.05 and **p < 0.01
6. Discussion and conclusion
The present study explored the associations between different types of underemployment–time-related, income-related and qualification-related–and four dimensions of socio-economic well-being among executives in agro-based industries in Punjab, India. Using validated constructs and regression analysis, the findings revealed that different types of underemployment were differentially associated with economic strain and strained interpersonal relationships.
6.1 Theoretical contributions
The association between income-related underemployment and economic strain aligns with earlier studies such as Allan et al. (2020) and Mavromaras et al. (2013), which reported that income inadequacy due to underemployment contributes significantly to financial insecurity and mental distress. Our findings confirm this relationship in the context of Indian agro-based industries, adding empirical support from a non-Western setting. However, the observed moderate relationship between qualification-related underemployment and strained workplace relationships diverges from Maynard and Feldman (2011), who found stronger associations between overqualification and overall job dissatisfaction. This suggests that in the Indian industrial context, social factors at work may be moderated by other variables such as job security or limited employment alternatives. As predicted, financial instability can lead to conflict within families and social relationships and cause feelings of guilt and failure among people (Cuervo and Chesters, 2019) and can also affect the children's social and psychological well-being and behavior (Premji, 2018).
The absence of a significant association between time-related and qualification-related underemployment with strained family and social relationships (p > 0.05) can be explained by the socio-cultural and economic context of Punjab's agro-industrial workforce. Time-related underemployment in agro-based industries is often seasonal and expected, and workers typically compensate through secondary income activities, preventing sustained household strain (ILO, 2022). Similarly, qualification-related underemployment does not disrupt family harmony because employment continuity and income security are valued more than job–skill. Overqualification is often viewed as socially acceptable if it ensures financial stability (Deng, 2023). Moreover, employees–especially men–tend to internalize work frustration, avoiding spillover into personal relationships. Overall, these findings highlight that cultural resilience and economic pragmatism mitigate the relational consequences of non-income forms of underemployment in the Indian context.
This study offers several new insights. First, it disaggregates underemployment into its three distinct forms and shows that each type is uniquely associated with specific socio-economic outcomes. This adds to the growing body of research that calls for a multidimensional understanding of underemployment. Second, it applies these constructs in the context of Indian agro-based industries–a sector that is often underrepresented in underemployment research. By doing so, the study contributes context-specific knowledge to the literature on labor market inadequacy in developing countries. Moreover, the finding that income-related underemployment is most prevalent and most strongly associated with economic strain and family stress highlights the urgent need to address wage and compensation practices in semi-formal industries. This emphasis on income underemployment adds depth to a literature that has often focused disproportionately on qualification mismatch.
This study contributes to the theoretical literature by validating the distinct association of different underemployment types with various facets of socio-economic well-being. This strengthens the argument that underemployment is not a homogeneous condition and that it should be studied in disaggregated terms.
6.2 Policy implications
The findings of this study indicate several actionable policy directions for improving job quality and worker well-being in Punjab's agro-based industries. First, the strong effect of income-related underemployment on economic strain highlights the need for stricter enforcement of minimum wage laws, predictable wage schedules and sector-specific pay standards to reduce income volatility in semi-formal manufacturing settings.
Second, the evidence related to qualification-related underemployment suggests the importance of better skill–job matching. Policymakers and industry associations should strengthen vocational counseling, promote targeted upskilling programs and align training curricula with actual industry requirements to ensure more effective utilization of workers' qualifications.
Third, the largely non-significant relational effects of time-related underemployment underscore its seasonal nature in agro-processing. Introducing seasonal work guarantees, minimum hour protections, or short-term unemployment support could help reduce uncertainty during production downtime.
Finally, the buffering role of family and community support observed in the findings points to the need for complementary social protection measures–such as affordable health insurance, childcare support and financial literacy programs–to reinforce household resilience.
Overall, the study demonstrates that addressing underemployment requires a coordinated policy approach combining wage regulation, skills alignment, social security and industry-level workforce planning.
6.3 Future research directions
Future research could benefit from examining overlapping underemployment experiences, as many workers are likely to face more than one type simultaneously. Structural equation modeling could be employed to understand mediating variables such as job satisfaction or psychological resilience. Longitudinal studies are also necessary to assess the long-term socio-economic consequences of underemployment. Further, replicating this study in other industrial sectors or regions of India would help assess the generalizability of the findings.
6.4 Limitations
There are several limitations to this study. Firstly, the 325 middle-level and lower-level executives from the agro-based industry who made up the study's sample size may not be representative of the broader workforce or other industries. Secondly, it is possible that some important factors that could have influenced the association between underemployment and socioeconomic well-being were overlooked in the study. Thirdly, the small sample size in time-related (n = 20) and qualification-related (n = 23) categories limits the generalizability of findings for these groups. Finally, focusing exclusively on the textile industry and food and beverage industry may limit the generalizability of the findings to other sectors with different employment dynamics and socio-economic characteristics.
6.5 Summary
Overall, this study adds empirical depth and contextual relevance to the literature on underemployment by uncovering differentiated patterns of association between underemployment types and socio-economic well-being. It emphasizes the need for nuanced labor policies and sector-specific strategies in addressing the underemployment crisis in India.
