Authors: Shriyanshi Yadav, Radhika Tandon, Dabita Biswas, Swachhatoya

Abstract

Engineering education in India has expanded rapidly over the past two decades, yet the labour market has struggled to absorb the growing number of graduates into suitable employment, giving rise to widespread underemployment. This study examines the prevalence of underemployment among engineering and technical graduates aged 18–30 using nationally representative data from the Periodic Labour Force Survey (PLFS) 2023-24. Employing a quantitative, descriptive, and cross-sectional design, the analysis draws on an unweighted sample of 5,767 individuals across 34 Indian states and union territories who reported at least one technical qualification. Underemployment is operationalised as unemployment or engagement in employment categorised as “Other” despite holding a technical qualification — a conservative, composite indicator of the absence of suitable paid work rather than a direct measure of skill mismatch or over-education. Findings show that 25.30% of technical graduates were underemployed, with only a small difference by gender (25.75% for men versus 24.55% for women) that should not be read as a substantive gender disparity in the absence of formal significance testing. Larger differences were observed by place of residence, with rural graduates recording a higher underemployment rate (27.55%) than urban graduates (24.01%), and by qualification type, where certificate-course holders (40.00%, n = 15), ITI/vocational trainees (30.00%, n = 10), agriculture technical degree holders (32.26%, n = 31), and engineering diploma holders (30.76%, n = 712) recorded higher rates than engineering/technology degree holders (25.80%, n = 1,508) or medical graduates (18.78%, n = 245); the first three of these subgroups are small and their estimates should be treated as unstable. Considerable state-level variation was also observed, with Nagaland, Jammu & Kashmir, Himachal Pradesh, Rajasthan, and Kerala recording rates well above the national average, though several of these estimates also rest on small state-level samples. These descriptive patterns are consistent with underemployment being associated more strongly with residence, qualification tier, and regional context than with gender, though the cross-sectional, unweighted design does not permit causal conclusions or population-level inference. The study concludes that curriculum reform, stronger industry–academia collaboration, expanded internships and apprenticeships, and balanced regional development are plausible directions for improving graduate employability, and that future work should incorporate survey weights, formal inferential tests, and direct measures of skill mismatch.

Keywords: underemployment; skill mismatch; engineering graduates; employability; technical education; Periodic Labour Force Survey (PLFS); India

Introduction

Higher education is widely regarded as central to a nation’s human capital formation and long-term economic development, and engineering education in particular has received sustained policy attention in India for its contribution to industrial growth, technological innovation, and infrastructure development. Until the 1990s, technical education was concentrated largely in publicly funded institutions such as the IITs and NITs. The subsequent information technology boom and rising demand for software professionals triggered an unprecedented expansion of engineering colleges, encouraged further by economic liberalisation and government initiatives to widen access to higher education. India has consequently become one of the world’s largest producers of engineering graduates, with more than 1.5 million students completing engineering programmes annually (All India Survey on Higher Education [AISHE], 2023; Government of India, 2024; Tilak & Choudhury, 2021).

Despite this expansion, the labour market has struggled to absorb the growing number of graduates into suitable employment. An engineering degree has traditionally been associated with stable employment and attractive career prospects, yet the employment landscape has changed considerably in recent years. Many graduates experience prolonged job searches, while others accept positions that neither utilise their technical knowledge nor match their qualifications. This phenomenon, commonly termed underemployment, describes a situation in which individuals are employed below their level of education, skills, or training, or are compelled to accept low-paying, temporary, or unrelated work because suitable opportunities are unavailable. Such outcomes reduce individual career prospects and reflect inefficient utilisation of skilled human resources, meaning that educational investment fails to generate its expected returns for either individuals or the economy (McGuinness, 2006; Leuven & Oosterbeek, 2011; Quintini, 2011). The Centre for Monitoring Indian Economy (CMIE, 2023) reported youth unemployment of 23.4% in 2023, with professional-degree holders experiencing particularly high underemployment (Lakshmi, 2025).

This issue has become increasingly salient in India because engineering education has expanded far more rapidly than the availability of high-quality employment. Recent labour market evidence suggests that improvements in educational attainment have not consistently translated into better employment outcomes for young graduates. Although India’s youthful population offers a demographic advantage, the labour market continues to face challenges related to skill mismatch, limited employment opportunities, and uneven regional development (International Labour Organization [ILO] & Institute for Human Development [IHD], 2024). Even the aggregate Labour Force Participation Rate, which the Periodic Labour Force Survey (PLFS) 2023-24 shows has improved steadily, masks considerable unevenness: rural participation consistently exceeds urban participation, and male participation remains markedly higher than female participation across categories.

Several factors contribute to underemployment among engineering graduates, including an oversupply of graduates relative to skilled positions, outdated curricula, inadequate practical training, limited industry-academia collaboration, regional disparities, and a persistent gap between the skills employers require and those graduates possess (World Bank, 2023). Rapid technological change has further raised demand for advanced digital and interdisciplinary competencies, putting graduates at a disadvantage relative to conventional training (Organisation for Economic Co-operation and Development [OECD], 2023). Employers increasingly expect graduates to combine technical knowledge with communication, teamwork, problem-solving, and adaptability; George and Baskar (2024) report that 72% of recruiters believe new graduates require substantial additional training before becoming productive, while internships and soft-skills training have been shown to narrow this gap (Gokuladas, 2010; Sinha et al., 2020; Pandey et al., 2026). Beyond individual skill gaps, structural and demographic inequities persist: caste and gender continue to shape placement outcomes, with marginalised students and women in male-dominated disciplines facing distinct disadvantages (Prakash & Yadav, 2024).

The consequences extend beyond economics. Graduates employed below their qualifications report reduced job satisfaction, lower productivity, and slower career progression, weakening the return on educational investment made by students, institutions, and government while limiting the country’s capacity for innovation (ILO, 2024). In response, the National Education Policy 2020 emphasises multidisciplinary learning, experiential education, internships, and industry partnerships, while the World Bank has stressed closer institution-employer ties so that graduates acquire labour-market-relevant skills (Government of India, 2020; Arnhold et al., 2022).

Although graduate employability and skill development have received considerable research attention in India, relatively few studies have examined underemployment among engineering graduates using recent, nationally representative data; most existing evidence relies on institutional surveys or single-college samples, limiting understanding of underemployment at the national level (Gokuladas, 2010; Sinha et al., 2020; Tilak & Choudhury, 2021; Lakshmi, 2025; Pandey et al., 2026). This study addresses that gap by analysing PLFS 2023-24 microdata, collected by the National Sample Survey Office, Ministry of Statistics and Programme Implementation, which provides nationally representative information on employment, qualifications, and demographic characteristics. Focusing on technical graduates aged 18-30, the study estimates the prevalence of underemployment and examines its variation by gender, place of residence, type of technical qualification, and geographic region, with the broader aim of informing curriculum reform, industry partnerships, and skill development strategies that better align engineering education with labour market demand.

2. Literature Review

2.1 Concept of Underemployment and Skill Mismatch

Underemployment is best understood not merely as a labour-market status but as an analytical lens on skill mismatch: the two concepts are closely linked, since it is often a mismatch between an individual’s qualifications and the requirements of the job that produces underemployment. Unlike unemployment, which refers to individuals actively seeking work but unable to find it, underemployment describes situations where individuals are employed in jobs that do not fully utilise their educational qualifications, skills, or experience, including part-time or temporary employment accepted for want of suitable alternatives. The problem therefore extends beyond the absence of employment and reflects the inefficient utilisation of human capital within an economy (McGuinness, 2006).

Given the unplanned growth of engineering education relative to labour demand, this mismatch manifests in two distinct forms. One is skill deficit, where a worker’s competence falls short of job requirements (Blom & Saeki, 2011). The second is skill underutilisation or over-education, which arises when a worker’s education and skill exceed job requirements, causing “bumping down” as higher-skilled workers displace lower-skilled workers from available positions. Allen and Van der Velden (2001) distinguish educational mismatch, which is more closely associated with wage differences, from skill mismatch, which more strongly affects job satisfaction, productivity, and turnover intention, a distinction that matters because graduates may hold appropriate credentials while simultaneously lacking the practical competencies employers require. The literature further distinguishes vertical mismatch, where workers hold higher or lower education than their occupation requires, from horizontal mismatch, or employment outside one’s field of study; engineering graduates in unrelated or lower-qualification jobs often exhibit both forms simultaneously, which the literature associates with reduced productivity, earnings, and career advancement (Leuven & Oosterbeek, 2011; Quintini, 2011). In developing countries, the ILO (2019) attributes persistent mismatch to rapid educational expansion, technological change, structural transformation, regional disparities, and limited industrial diversification all evident in the Indian labour market, where technical education has expanded without a proportionate rise in engineering-relevant employment (Lakshmi, 2025; Tilak & Choudhury, 2021).

The concept of skill mismatch, as used by the OECD, further ties these distinctions to observable labour-market outcomes: it describes any situation in which workers’ qualifications or competencies differ from those required in the job actually held, whether because of technological change, rapid expansion of higher education, shifting industrial demand, or limitations within the education system itself. Workers whose qualifications exceed job requirements typically report lower job satisfaction, reduced productivity, and slower career progression, while employers may simultaneously report shortages of appropriately skilled labour, an apparent paradox that is, in fact, consistent with mismatch operating at both ends of the skill distribution at once.

2.2 Theoretical Perspectives

Three theories, read together, can be understood as describing successive stages of the same employment process: the decision to invest in education, the employer’s screening of that investment at the point of hiring, and the structural sorting of workers once they enter the labour market rather than as competing, freestanding explanations. Human Capital Theory (Becker, 1964) holds that education is an investment that raises productivity and should be rewarded with better employment and earnings. Yet empirical evidence increasingly shows that educational attainment alone does not guarantee favourable outcomes: many Indian engineering graduates remain unemployed or work outside their field, suggesting that returns to education depend on labour market conditions and skill relevance as well as attainment (McGuinness, 2006; Tilak, 2020). It is at this point that Human Capital Theory’s assumptions appear to break down, motivating a second, employer-facing explanation. Job Market Signalling Theory (Spence, 1973) supplies that explanation: credentials function as signals employers use to evaluate potential employees when complete information about ability is unavailable, but they do not guarantee employment if employers perceive deficiencies in practical skills, communication, or professional readiness a concern amplified as employers increasingly value internships, project experience, and digital competencies over credentials alone (Sinha et al., 2020). Where Human Capital Theory explains why graduates pursue engineering education in the first place, Signalling Theory offers one account of why that same credential may fail to differentiate them at the point of hiring; together, the two theories provide a lens on both the supply-side motivation and the demand-side screening that can produce underemployment even among qualified graduates.

Labour Market Segmentation Theory (Reich et al., 1973) addresses the stage that follows recruitment, arguing that labour markets divide into primary segments offering stable, well-paid work and secondary segments characterised by insecurity and limited progression; in India, graduates from prestigious institutions typically access primary-segment employment while others, even with nominally identical signalling credentials, struggle to do so a pattern the theory attributes to regional, institutional, and socioeconomic inequalities rather than to any deficiency in the credential itself. The OECD concept of skill mismatch ties these three theories back to the empirical literature on engineering employability rather than leaving them as abstract labour-economics constructs: mismatch is the observable outcome that Human Capital Theory predicts should not occur, that Signalling Theory suggests employers cannot always screen out in advance, and that Segmentation Theory suggests is distributed unevenly by institution, region, and sector rather than by individual merit alone. Underemployment, in this integrated reading, is treated in the present study as the joint product of educational investment decisions, employer screening behaviour, and structural labour-market segmentation, rather than as explained solely by the number of graduates entering the labour market (Quintini, 2011; ILO, 2019). This integrated framework, rather than the three theories treated in isolation, is applied in Sections 4 and 5 to interpret the study’s empirical findings on engineering employability in India.

2.3 Engineering Education and Employability in India

Engineering education has expanded substantially over two decades, driven by demand for technical professionals, industrialisation, and government access initiatives, making India one of the largest producers of engineering graduates globally. This expansion has not been matched by proportional growth in high-quality employment, a pattern associated with a widening gap between educational output and labour absorption (Tilak & Choudhury, 2021). Because labour market signals are weak, many students continue engineering education despite poor prospects, and graduates who do find work are frequently mal-employed; Indian media have documented engineering graduates applying for posts such as peon or watchman, including the 2019 appointment of 450 engineers as peons and bailiffs by the Gujarat High Court.

The most frequently cited explanation is curriculum-industry mismatch. Gokuladas (2010) found non-technical competencies in communication, reasoning, and interpersonal skills to be stronger predictors of campus recruitment than engineering grades. Sinha et al. (2020), applying Expectation Confirmation Theory, similarly found employer satisfaction depends on whether graduates meet expectations around communication, teamwork, and professional behaviour, and called for closer institution-industry collaboration. Lakshmi (2025), studying Kerala graduates, identified inadequate internships, limited industry exposure, outdated curricula, and weak career guidance as key correlates, while finding that demographic characteristics and academic performance were not significantly associated with underemployment. Regulatory capacity compounds these curricular weaknesses: the All India Council for Technical Education (AICTE) has struggled to maintain quality standards amid rapid institutional proliferation since the 1990s (Sharma, 2014). Where reform has been paired with structured experiential learning, evidence is more encouraging: Pandey et al. (2026) note growing employer demand for communication, teamwork, and adaptability alongside technical knowledge, consistent with the India Skills Report 2026’s emphasis on digital literacy and AI awareness, and the Confederation of Indian Industry (2025) reports that 35% of employers now prioritise soft skills over technical proficiency even in specialised technology roles. Programmes such as Amrita’s Live-in-Labs, which embed students in community-based technology projects, and competitive-programming initiatives illustrate institutional responses associated with improved progression to further study and higher-paying employment. Nonetheless, because relevant skills are context-bound and evolve rapidly alongside frontier technologies such as artificial intelligence, curricular responses risk becoming outdated quickly (Kaushal & Vaghela, 2023). Much of this evidence, however, rests on employer surveys or single-institution case studies, limiting generalisability; and although institutional responses such as AICTE’s regulatory efforts and programmes like Live-in-Labs address the signalling and segmentation dynamics described above at the level of individual institutions, none of these studies measure how far such practices reduce underemployment for the national population of technical graduates which is the question the determinants literature and the present study’s research gap take up next.

2.4 Determinants of Underemployment

Building on the institutional evidence just reviewed, underemployment is better understood as multidimensional, shaped by the interaction of educational quality, employability skills, labour demand, industrial development, and policy rather than any single cause (McGuinness, 2006; ILO, 2019). Skill mismatch with employer expectations is frequently reported: internships, industrial training, and project-based learning are associated with improved employability by narrowing the gap between classroom learning and workplace requirements (Gokuladas, 2010; Sinha et al., 2020; Lakshmi, 2025). Institutional quality also matters, as graduates from well-established institutions benefit from stronger placement support and industry networks, while rapid college expansion has not always been matched by quality improvements (Tilak & Choudhury, 2021). Labour market conditions compound these effects: the India Employment Report 2024 finds that educated youth face relatively higher unemployment than less-educated peers, a pattern read as reflecting a persistent mismatch intensified by automation and changing industrial skill requirements (ILO & IHD, 2024). Regional disparities are pronounced, as technology-intensive employment concentrates in a few metropolitan regions, which may push graduates unable to relocate toward roles outside their field, a pattern Labour Market Segmentation Theory attributes to structural rather than individual factors (Reich et al., 1973). Gender and residence also appear to shape outcomes: women face occupational segregation and family-related constraints, while rural graduates face weaker industrial development and professional networks (ILO & IHD, 2024). Beyond gender, structural and demographic inequities persist along caste lines: Prakash and Yadav (2024) find that caste and gender jointly shape placement outcomes, with marginalised students recording lower placement rates and women remaining underrepresented in male-dominated engineering disciplines despite comparatively favourable outcomes once recruited, suggesting that access to the labour market, rather than performance within it, is where much of the disadvantage may originate. Tilak (2020) similarly reports that education-related and job-related factors, together with gender, are associated with graduates’ employment status and earnings, reinforcing the view that individual credentials interact with social and structural characteristics to shape labour market outcomes. Finally, the changing nature of work itself is an emerging determinant, as AI adoption is associated with displacement of routine engineering tasks while raising demand for AI literacy, data analytics, and critical thinking (George, 2024), and graduates’ aspirations for multinational, IT-sector, or government employment can prolong voluntary unemployment while awaiting preferred openings (Gokuladas, 2010).

2.5 Policy Initiatives

The Government of India and international organisations have introduced reforms emphasising learning outcomes, practical training, innovation, and industry engagement over access alone (Government of India, 2020; Arnhold et al., 2022). The National Education Policy (NEP) 2020 proposes multidisciplinary learning, flexible curricula, experiential education, and stronger industry collaboration, and is described by Jagadish and Rajashekharam (2025) as integrating skill development directly into the structure of the education system rather than treating it as an add-on; but as implementation remains at an early stage, available evidence describes institutional compliance and curricular redesign rather than measured changes in graduate underemployment. Workforce-focused initiatives such as the Skill India Mission and Pradhan Mantri Kaushal Vikas Yojana have trained millions of youth, with earlier-phase evaluations reporting placement rates of roughly 40-45%, (Sharma, 2026) a figure indicating partial effectiveness, since more than half of trained participants were not placed, and it remains unclear whether placements achieved were commensurate with participants’ qualifications. The World Bank similarly argues that improving employability requires stronger institutional quality, research capacity, and university-employer partnerships, with institutions judged by graduate outcomes rather than enrolment (Arnhold et al., 2022) an outcome-based framing that implicitly acknowledges the shortage of verified employment evidence across existing programmes. The India Employment Report 2024 recommends stronger active labour market policies, expanded apprenticeships, and reduced labour market inequalities, while the India Skills Report 2026 stresses digital literacy, AI awareness, and lifelong learning (ILO & IHD, 2024). Collectively, these initiatives reflect a shift toward improving quality and relevance, but with the partial exception of Skill India placement statistics, most remain documented through stated objectives rather than independent, post-implementation evidence of their effect on graduate underemployment specifically.

2.6 Research Gap

The literature reviewed above demonstrates considerable progress in understanding underemployment among engineering graduates in India, using an integrated Human Capital–Signalling–Segmentation framework to explain why educational attainment alone does not guarantee suitable employment. Three specific gaps, however, remain unaddressed by the evidence surveyed. First, the empirical base is dominated by employer surveys and single-institution or single-state case studies (Gokuladas, 2010; Sinha et al., 2020; Lakshmi, 2025), none of which measure underemployment prevalence using a sample that is representative of India’s population of technical graduates as a whole. Second, while gender, location, and institutional quality are repeatedly cited as determinants, no study reviewed here reports comparable underemployment rates for these groups computed from the same national dataset; the claims remain qualitative or drawn from disparate sources rather than quantified side by side. Third, no reviewed study disaggregates underemployment by type of technical qualification (engineering degree, diploma, certificate course, ITI/vocational training) at national scale, despite the theoretical expectation, from the vertical-mismatch literature, that qualification tier should matter. This study addresses these three specific, currently unmeasured gaps using PLFS (2023-24) microdata, an unweighted, nationally representative sample of 5,767 technical graduates across 34 states/UTs, to compute and compare underemployment rates by gender, rural/urban residence, qualification type, and state.

2.7 Objectives

1. To estimate the overall rate of underemployment among engineering and technical graduates (aged 18–30) in India using PLFS 2023-24 data.

2. To examine variation in underemployment by gender.

3. To compare underemployment rates between rural and urban graduates.

4. To assess how the type of technical qualification (engineering degree, diploma, certificate course, ITI/vocational training, etc.) is associated with underemployment.

5. To identify state-level and regional disparities in underemployment among technical graduates.

3. Methodology

3.1 Study Design

This study employs a quantitative, descriptive, cross-sectional design to examine the prevalence and correlates of underemployment among engineering and technical graduates in India. Rather than testing a causal model, the design describes the magnitude of underemployment and how it varies across gender, sector of residence, qualification type, and geography, using nationally representative secondary survey data.

3.2 Participants

The sample comprises 5,767 individuals aged 18 to 30 years, drawn from 34 Indian states and union territories, each reporting at least one technical qualification: engineering or technology degree, engineering diploma, ITI/vocational certificate, agriculture technical degree, medical degree, or allied technical credential. Participants were not sampled directly by the research team; they constitute the age and qualification-restricted subset of a larger nationally representative household survey, retained after removal of incomplete and duplicate records. All counts and percentages reported for this subset are unweighted (see Section 3.4).

3.3 Tools and Data Source

The empirical basis for the analysis is the Periodic Labour Force Survey (PLFS) 2023-24, conducted by the National Statistical Office (NSO), Ministry of Statistics and Programme Implementation, Government of India (NSO, 2024). The PLFS is a nationally representative, quarterly household survey administered through a structured schedule by trained field investigators, recording detailed employment and unemployment status across urban and rural India. No additional instrument was administered; the PLFS employment-status schedule served as the sole data-collection tool, and its official unit-level microdata files were used as the raw input for this study. Employment status was derived from the usual principal activity status variable recorded in PLFS Schedule 10.4, Block 5.1 of the unit-level file. This variable assigns each respondent a two-digit activity code (11-98) describing their principal activity during the reference year. Codes 31, 41, 51, 61, 62, 71, and 72 (regular/casual wage or salaried work, including those temporarily absent from such work) were classified as Regular/Casual Employment; codes 11, 12, and 21 (own-account workers, employers, and unpaid family helpers in household enterprises) were classified as Self-employed; codes 91, 92, and 93 (attending an educational institution, or engaged in domestic duties) were classified as Student/Domestic Duties; code 81 (did not work but was seeking and/or available for work) was classified as Unemployed; and codes 94, 95, 96, 97, and 98 (rentiers/pensioners, persons unable to work due to disability, the residual “others” category, and casual workers not working due to temporary illness) were classified as Other.

3.4 Procedure

Two unit-level PLFS datasets were merged, cleaned, and filtered to isolate respondents aged 18–30 reporting at least one technical qualification. After removing incomplete and duplicate records, a final analytic sample of 5,767 valid observations was retained. Each respondent’s recorded employment status was classified into five mutually exclusive categories: Regular/Casual Employment, Self-employed, Student/Domestic Duties, Unemployed, and Other. Underemployment was operationalised as membership in the Unemployed (activity code 81) or Other (activity codes 94, 95, 96, 97, 98) categories, on the reasoning that individuals in these groups lack suitable paid engagement despite holding a technical qualification. This is a composite indicator combining two conceptually distinct groups: individuals actively seeking work who could not find it (Unemployed, code 81; n = 1,237, 21.5% of the sample) and individuals recorded in the residual activity codes 94-98 (Other; n = 222, 3.8% of the sample), which the PLFS schedule does not further disaggregate by reason (e.g., disability, temporary illness, or unrecorded informal engagement) within this technical-graduate subsample. Readers should therefore treat “underemployment” as reported here as an indicator of the absence of any current suitable paid employment, rather than as a direct measure of skill mismatch, over-education, or involuntary part-time work in the sense used elsewhere in the literature (McGuinness, 2006); we report the two component counts separately in Table 1 so their relative contribution to the composite rate is visible. Respondents classified under Student/Domestic Duties (activity codes 91, 92, 93) were excluded from the underemployed count, since voluntary further study cannot be distinguished from study undertaken for want of employment; this is acknowledged as a conservative bound on the true rate of underemployment.

All percentages reported in this study are unweighted sample proportions rather than population-weighted estimates; PLFS multi-stage stratified sampling weights were not applied in the present analysis, and no adjustment was made for differential non-response across strata. Because technical graduates are not evenly distributed across states, sectors, and demographic strata, unweighted percentages may not generalise directly to the national population of technical graduates. 

Descriptive statistics frequencies and percentages were computed for the overall sample and cross-tabulated against gender, sector (rural/urban), type of technical qualification, and state/union territory. The underemployment rate for each subgroup was calculated as the number of underemployed individuals divided by the subgroup total, multiplied by 100. Subgroup sample sizes (n) are reported alongside rates.

4.Results

This section presents the descriptive findings on underemployment among engineering and technical graduates aged 18–30 years, based on the Periodic Labour Force Survey (PLFS) 2023-24 dataset (Ministry of Statistics and Programme Implementation [MoSPI], 2024). After merging, cleaning, and filtering the data by age and technical qualification, the final working sample consisted of 5,767 observations (MoSPI, 2024). Results are presented for the overall sample and across gender, place of residence, type of technical qualification, and state; as noted in Section 3.5, none of the subgroup comparisons below have been tested for statistical significance, and all figures are unweighted.

4.1 Overall Employment Distribution

Table 1. Distribution of Respondents by Employment Category (N = 5,767)

Figure 1. Employment category distribution among engineering graduates (N = 5,767).    

Of the 5,767 engineering and technical graduates included in the sample, 1,459 respondents (25.30%) were classified as underemployed, 1,237 (21.5%) recorded as Unemployed and 222 (3.8%) recorded in the residual “Other” category  meaning they were either unemployed or engaged in employment classified as “Other” under the study’s classification scheme (MoSPI, 2024). The remaining 4,308 respondents (74.70%) were classified as employed in regular/casual work, self-employment, or Student/Domestic Duties. 

4.2 Underemployment by Gender

Table 2. Underemployment Rate by Gender

Figure 2. Underemployment rate by gender.  

The underemployment rate is descriptively higher among male graduates (25.75%) than among female graduates (24.55%), a difference of about 1.2 percentage points (Table 2). Given the small size of this difference and the absence of a formal significance test (Section 3.5), this pattern should not be interpreted as evidence of a meaningful gender disparity in underemployment.

4.3 Underemployment by Sector (Rural vs. Urban)

Table 3. Underemployment Rate by Rural/Urban Sector

Figure 3. Underemployment rate by sector (rural vs. urban).                 

Rural technical graduates show a higher underemployment rate (27.55%) than their urban counterparts (24.01%), a gap of roughly 3.5 percentage points (Table 3). 

4.4 Underemployment by Type of Technical Education

Table 4. Underemployment Rate by Type of Technical Qualification

Figure 4. Underemployment rate by type of technical education.

Underemployment appears most acute among holders of shorter or lower-tier qualifications: certificate-course graduates recorded the highest rate (40.00%), followed by agriculture technical degree holders (32.26%) and ITI/vocational trainees (30.00%). However, these three subgroups are based on very small samples (n = 15, 31, and 10 respectively, as shown in Table 4), so their point estimates are unstable and should not be ranked against one another, or against the larger subgroups, with confidence. Engineering diploma holders form a substantially larger subgroup (n = 712) and recorded a rate of 30.76% a comparison more suitable for weighing against the largest subgroup, engineering/technology degree holders (n = 1,508, 25.80%), whose rate is close to the all-India average. Medical degree holders (n = 245) reported the lowest rate of any group (18.78%). 

4.5 Geographic Variation in Underemployment

Table 5. Illustrative State-Level Variation Relative to the All-India Average (25.30%)

Figure 5. Underemployment Rate by State and Union Territory Among Engineering and Technical Graduates        

Note. Underemployment rate (%) is shown for all 34 states/union territories in the sample, calculated as the number of respondents classified as Unemployed or Other divided by the total respondents from that state, multiplied by 100. States are ordered from highest to lowest rate, ranging from Nagaland (77.78%) to Delhi (3.45%). Data are unweighted and based on varying, sometimes very small, state-level sample sizes; estimates for states with few respondents (e.g., Nagaland) should be interpreted with caution. 

Figure 6. Highest-ranked state (Nagaland) compared with the all-India average underemployment rate.                                                                                                  

Nagaland records the highest rate in the sample (77.78%), though this figure should be treated cautiously given the small number of respondents from the state. Jammu & Kashmir, Lakshadweep, Himachal Pradesh, Rajasthan, and Kerala also report rates well above the all-India average of 25.30% (Table 5).

5. Discussion 

Every year, India produces approximately 1.5 million engineering graduates; this rapid expansion of technical education has been accompanied by increasing concerns regarding graduate underemployment (Wheebox et al., 2025). The present study examined the prevalence of underemployment among technically qualified young adults using nationally representative, though unweighted, data from the Periodic Labour Force Survey (PLFS) 2023-24. By analysing employment outcomes across gender, place of residence, type of technical qualification, and state, the study sought to provide a descriptive picture of how underemployment varies across different demographic and educational groups.

The findings indicate that underemployment remains a considerable challenge for engineering and technical graduates in India. Among the 5,767 technically qualified young adults included in the analysis, 1,459 (25.30%) were classified as underemployed on the composite indicator described in Section 3.4. In practical terms, this suggests that approximately one in every four technically qualified graduates in the sample was unable to obtain employment that adequately matched their qualifications or remained without suitable employment. Although nearly half of the respondents (46.9%) reported regular or casual employment, a considerable proportion continued to experience difficulties in entering appropriate occupations. These findings are consistent with the view that obtaining a technical qualification alone may no longer be sufficient to ensure favourable labour market outcomes in the current economic environment (NSO, 2024), though the cross-sectional design cannot establish this as a causal claim.

The present findings are broadly consistent with previous research documenting a widening gap between higher education and employment opportunities in India. During the last two decades, engineering education has expanded rapidly, resulting in a substantial increase in the number of graduates entering the labour market each year. Employment generation, however, does not appear to have expanded at the same pace. Sharma (2014), drawing upon NASSCOM data, reported that only about one-fourth of engineering graduates possessed the competencies expected by employers. Similarly, Tilak and Choudhury (2021) argued that while access to engineering education has improved considerably, educational quality and graduate employability have not advanced proportionately. The India Employment Report 2024 also highlights that educated youth continue to experience relatively high unemployment and underemployment, a pattern read as reflecting labour market demand that has been unable to absorb the growing number of graduates (ILO & IHD, 2024). The present findings are consistent with these observations and suggest that increasing enrolment alone may not resolve graduate employment challenges unless educational quality and employment creation improve together, an interpretive, rather than causally established, conclusion given the descriptive design used here.

Gender differences in underemployment were small: male graduates reported an underemployment rate of 25.75% compared with 24.55% among female graduates, a gap of roughly 1.2 percentage points that was not tested for statistical significance and should not, on the present evidence, be read as a meaningful gender disparity in underemployment. Although many previous studies have reported poorer employment outcomes among women, the descriptive findings here are consistent with underemployment affecting male and female engineering graduates to a broadly comparable extent, within the limits of an untested difference this small. One possible explanation is the relatively low representation of women within India’s engineering workforce, where women account for approximately 14% of practising engineers (Majumder & Sarkar, 2025); women who complete engineering education and actively participate in the labour market may represent a comparatively selective group with stronger educational backgrounds or greater family support, which could narrow the observed gap. At the same time, labour force participation among women remains relatively low, so some female graduates who are not actively seeking employment may not be captured within the underemployment category as operationalised here. Future research using weighted data and inferential tests should investigate additional factors such as workplace discrimination, family responsibilities, mobility, and career aspirations that may influence women’s employment trajectories.

The analysis revealed a clearer, though still untested, difference between rural and urban respondents. Underemployment among rural graduates (27.55%) exceeded that of urban graduates (24.01%), a pattern consistent with persistent regional inequalities in labour market opportunities, though the cross-sectional design cannot establish this as a causal relationship. Employment opportunities in engineering remain concentrated within metropolitan regions where information technology companies, manufacturing industries, start-ups, and research organisations are located. Bengaluru alone contributes nearly one-quarter of India’s IT employment, while Delhi NCR represents another major technology hub (KTree, 2023). Previous research has similarly documented that manufacturing and service industries remain geographically concentrated within selected urban regions (Agarwal & Behera, 2022). Limited industrial infrastructure, financial constraints, weaker professional networks, and migration-related challenges may plausibly reduce employment opportunities for graduates residing in rural areas, although the present data cannot isolate residence from correlated factors such as qualification mix or regional demographic composition. Consistent with Labour Market Segmentation Theory (Reich et al., 1973), employment outcomes may be influenced not only by individual qualifications but also by structural characteristics of regional labour markets; confirming this mechanism directly would require multivariate analysis, which we identify as a priority extension of the present descriptive work (Section 4.3).

Variation was also evident across different technical qualifications, though the smallest subgroups warrant particular caution. Certificate holders showed the highest underemployment rate (40.00%, n = 15), followed by graduates with agricultural technical degrees (32.26%, n = 31) and ITI/vocational training (30.00%, n = 10); because these subgroups are very small, their rates are unstable and should be treated as indicative rather than precise. Engineering diploma holders (30.76%, n = 712) form a more reliable comparison group. In contrast, medical graduates reported the lowest underemployment rate (18.78%, n = 245), and engineering and technology graduates reported an underemployment rate of 25.80% (n = 1,508), which closely approximated the national average. These descriptive patterns suggest that labour market demand may differ across professional qualifications. Medical education generally follows a structured pathway characterised by licensing requirements and relatively stable demand, whereas engineering encompasses diverse specialisations with unequal employment opportunities. Consistent with Job Market Signalling Theory (Spence, 1973), employers may use qualification tier and credential type as a screening signal when complete information about a candidate’s practical competence is unavailable; this could help explain why shorter-duration or lower-tier credentials are associated with higher underemployment in this sample, although the cross-sectional design cannot confirm the screening mechanism directly. Employers increasingly seek graduates possessing practical experience, communication skills, internship exposure, digital literacy, and problem-solving abilities alongside technical knowledge (Lakshmi, 2025). Similarly, Mazumder (2017) argued that inadequate faculty development, limited research infrastructure, weak industry collaboration, and outdated curricula continue to reduce the effectiveness of engineering education. Collectively, these findings suggest that employability may depend not only upon educational attainment but also upon the quality and practical relevance of technical education, and upon the credentialing signal that a given qualification conveys to employers.

Considerable geographic variation was observed across Indian states and union territories, this variation should be interpreted with the same caution for every state, not only for the highest-ranked one. Nagaland reported the highest underemployment rate (77.78%), an estimate that should be treated with particular caution because of its small sample size; the same caveat applies to the other states discussed here, for which sample sizes and confidence intervals were not computed in the present analysis. Jammu and Kashmir, Himachal Pradesh, Rajasthan, Kerala, and Lakshadweep also recorded rates exceeding the national average, whereas Delhi, Gujarat, and Uttarakhand reported comparatively lower levels. Previous reports have similarly identified persistent regional differences in employment opportunities associated with variations in industrial development, economic diversification, and labour market structure (NITI Aayog, 2025; Sreeraj & Jose, 2025). States characterised by stronger industrial ecosystems and larger service sectors appear, descriptively, to offer more employment opportunities for technically qualified graduates than states with comparatively limited industrial growth, a pattern again consistent with, though not a direct test of, Labour Market Segmentation Theory.

5.1 Policy Implications

The findings also have implications for higher education and public policy, understood as directions consistent with the descriptive evidence rather than causally established prescriptions. The National Education Policy (NEP) 2020 emphasises multidisciplinary learning, internships, experiential education, skill development, and stronger collaboration between higher education institutions and industry (Ministry of Education, 2020); these reforms have plausible potential to improve graduate employability if implemented effectively, though the present data cannot evaluate their effect directly. Likewise, the National Apprenticeship Promotion Scheme has been reported to show positive outcomes in improving youth employability through structured workplace training (Ministry of Skill Development and Entrepreneurship, 2026). The present findings are consistent with a continued need to bridge the gap between engineering education and labour market expectations. Universities could regularly revise curricula, strengthen industry partnerships, promote internship opportunities, and provide structured career guidance with a view to helping graduates acquire competencies aligned with evolving industry requirements.

The present findings are also broadly consistent with recommendations made in the India Skills Report 2026, which highlights digital literacy, artificial intelligence, communication skills, analytical reasoning, and adaptability as competencies likely to matter for future employment (Wheebox et al., 2025). Similarly, the World Bank has emphasised that improving graduate employability requires sustained investment in educational quality, innovation, research capacity, and industry engagement rather than merely expanding access to higher education (Arnhold et al., 2022). These findings are consistent with the view that strengthening the quality and relevance of engineering education is one plausible avenue for addressing graduate underemployment in India, alongside the balanced regional development and qualification-tier-specific measures suggested by the state- and qualification-level patterns. The findings of the present study have implications for educational institutions, policymakers, employers, and students, offered here as directions rather than as evidence-based mandates given the descriptive design. Universities could strengthen industry collaboration, expand internship opportunities, incorporate project-based learning, and regularly update engineering curricula to reflect technological developments. Policymakers could consider promoting balanced regional industrial development, strengthening apprenticeship programmes, and improving employment opportunities in smaller cities and rural regions. Employers could contribute by collaborating with academic institutions in curriculum design and skill development initiatives. Students may also benefit from developing transferable skills, including communication, problem-solving, digital literacy, and emerging technological competencies, alongside technical knowledge.

5.2 Limitations

The present study has several limitations that should be acknowledged. First, the study relied on secondary cross-sectional data obtained from the PLFS 2023-24; consequently, causal relationships between educational characteristics and underemployment cannot be established, and language throughout this manuscript has been revised to use associative rather than causal phrasing wherever the underlying claim is descriptive. Second, the operational definition of underemployment was based on employment status categories available within the PLFS dataset, combines the Unemployed and Other categories into a single composite indicator, and may not capture qualitative aspects such as skill mismatch, overqualification, involuntary part-time employment, or job satisfaction. Third, all percentages reported are unweighted; PLFS survey design weights were not applied, and this should be corrected before any estimate in this manuscript is treated as representative of the national population of technical graduates. Fourth, no inferential statistical tests were conducted, including the gender, rural/urban, qualification-type, and state-level comparisons, which are descriptive only. Fifth, some subgroup estimates were derived from very small sample sizes most notably the certificate-course (n = 15) and ITI/vocational training (n = 10) qualification groups, and several states, including but not limited to Nagaland and these should be interpreted with particular caution and ideally accompanied by confidence intervals in future work. Sixth, the PLFS dataset does not provide detailed information regarding internship experience, academic performance, institutional quality, socio-economic background, employability skills, or employer expectations, all of which may influence employment outcomes. Finally, although artificial intelligence and automation are increasingly transforming engineering employment, the dataset used here does not include variables measuring AI-related skill requirements or technology-driven displacement. Future research should adopt longitudinal designs to examine how underemployment changes over time as graduates transition into the labour market, apply PLFS survey weights and formal inferential tests (including multivariate models that control for qualification type, gender, age, and region simultaneously) to the comparisons reported here, and report confidence intervals for every subgroup and state estimate rather than only the smallest ones. Studies incorporating primary data could explore additional determinants such as employability skills, internship experience, institutional quality, socio-economic background, career aspirations, and employer perspectives. Future investigations should also examine discipline-specific differences within engineering, as employment opportunities vary considerably across specialisations. Given the rapid integration of artificial intelligence into engineering occupations, future research should investigate how AI adoption is associated with graduate employability, job displacement, skill requirements, and career progression, using data that directly measures AI exposure rather than inferring it from a dataset not designed to capture it. Comparative studies across countries or across different technical disciplines may further improve understanding of graduate underemployment and inform evidence-based policy interventions.

6. Conclusion

India’s aspiration to become a developed and innovation-driven economy depends substantially on its ability to effectively utilise its technically skilled workforce. The present study finds that despite the rapid expansion of engineering education, underemployment remains a considerable concern among technically qualified young adults in the sample analysed. Approximately one-fourth of engineering and technical graduates were classified as underemployed on the composite, unweighted indicator used here, a pattern consistent with the view that educational expansion has not been accompanied by proportional growth in quality employment opportunities, though this cannot be confirmed causally with the present cross-sectional design.

The findings further indicate that underemployment is descriptively associated with multiple, interacting factors, including place of residence, type of technical qualification, and regional context. Although gender differences were small and untested, more substantial descriptive disparities were observed across rural and urban areas, technical qualifications, and states — several of the latter based on small samples that warrant caution. These patterns are consistent with graduate employment outcomes being shaped not only by educational attainment but also by broader structural characteristics of the labour market, read through the integrated Human Capital–Signalling–Segmentation framework.

This study addresses three specific gaps in the existing literature: it draws on nationally representative, if unweighted, data spanning 34 states and union territories rather than a single institution or state; it computes gender, residence, and qualification-type comparisons from one common dataset and definition; and it disaggregates underemployment by qualification tier at national scale. Addressing graduate underemployment more conclusively will require coordinated follow-up work applying survey weights and inferential statistics, alongside coordinated action from higher education institutions, industry, and policymakers. Improving curriculum relevance, strengthening industry-academia collaboration, expanding apprenticeship and internship opportunities, promoting balanced regional development, and investing in emerging technological skills are plausible priorities suggested by these findings for improving graduate employability. As India’s labour market continues to evolve with digital transformation and artificial intelligence a dimension this dataset does not measure directly ensures that engineering education remains aligned with changing workforce demands, and that future analyses of it are weighted and statistically tested, will be important next steps for this research agenda.

Acknowledgment

The authors sincerely acknowledge Saba Suhail Khan for her guidance throughout the preparation of this manuscript and her support in organizing the writing process and providing constructive feedback is greatly appreciated. The authors also acknowledge Aijaz Ahmad Bhat, Pragya Ghosh, Devansh Prasad, and Gourav Pandey for their valuable assistance in drafting, reviewing, and refining various sections of the manuscript.

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