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WAGE INEQUALITY AND SKILL DEVELOPMENT IN INDIA

2026-11-13T04:49:39Z

Title: WAGE INEQUALITY AND SKILL DEVELOPMENT IN INDIA Authors: BHARGAVI; Jain, Neha (SUPERVISOR)WAGE INEQUALITY AND SKILL DEVELOPMENT IN INDIA Abstract: India's labour market has gone through considerable restructuring over the past two decades. Economic liberalisation, rapid technological change, and an expanding service sector have together produced a labour market in which skilled workers capture a disproportionately large share of wage gains, while low-wage workers, who make up the vast majority of the workforce, continue to face stagnant real earnings and precarious employment. Against this backdrop, successive central government administrations have invested heavily in skill development programmes, from the Industrial Training Institutes (ITIs) to the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) and the Deen Dayal Upadhyaya Grameen Kaushalya Yojana (DDU-GKY), disbursing over ₹6,000 crore annually in an effort to close the skill gap and improve labour market outcomes for disadvantaged workers. What remains thin, even so, is robust, distribution-sensitive evidence on whether these interventions actually reach the workers who need them most. This dissertation looks at the relationship between skill development and wages across the full wage distribution of Indian workers, using data from the Periodic Labour Force Survey (PLFS) 2023 to 24, the most recent nationally representative household survey conducted by the Ministry of Statistics and Programme Implementation, Government of India. The analytic sample comprises 46,294 wage workers aged 15 to 65 drawn from both rural and urban areas across all major states. The core estimation strategy is quantile regression (Koenker & Bassett, 1978), which allows the conditional wage effects of education, vocational training, and other worker characteristics to be estimated at five quantile points, the 10th, 25th, 50th, 75th, and 90th percentiles of the log-wage distribution, directly addressing the main limitation of prior Indian studies that rely on OLS and estimate only mean effects. The econometric model is grounded in an augmented Mincerian earnings function. The dependent variable is the natural log of the monthly wage. Key regressors include years of formal education, a binary indicator for formal vocational training (skill dummy, equal to 1 if the worker received certified vocational training), potential labour market experience constructed as age minus years of education minus 6, experience squared, and a set of demographic controls for gender (female dummy), rural/urban location (urban dummy), and social group membership (SC, ST, and OBC dummies), with 'Others' as the reference category. Employment type (self-employed, regular wage, casual labour) is also incorporated in the OLS specifications. Robust standard errors are used throughout. Six main findings emerge. First, returns to formal education rise monotonically across quantiles, from 6.9 per cent per year at the 10th percentile to 11.5 per cent at the 90th percentile, confirming that education is a force for wage divergence at current rates of return. Second, vocational training yields an inverted-U pattern: returns peak at the median (18.2 per cent), are moderate at the lower tail (11.4 per cent at Q10), and decline at the upper tail (10.5 per cent at Q90), making training more egalitarian in its distributional footprint than formal education. Third, the female wage penalty is largest at the bottom of the distribution (−67.9 log points at Q10) and narrows substantially but remains large at the top (−19.4 log points at Q90), consistent with the sticky floor hypothesis. Fourth, SC and OBC wage penalties widen at higher quantile points, indicating that caste-based barriers to high-skill, high-wage employment are the primary mechanism of disadvantage rather than uniform wage suppression. Fifth, the urban wage premium is largest at Q10 (23.2 per cent) and declines modestly to Q90 (17.1 per cent), suggesting urban labour markets provide proportionately greater gains to low-wage workers. Sixth, experience returns are stable across quantiles at approximately 4 per cent per year and do not contribute to distributional divergence. These findings carry direct policy implications. Targeting PMKVY and DDU-GKY enrolment toward low-wage workers, particularly women, SC/ST workers, and rural youth, would concentrate public resources where the relative returns to training are meaningful. Reforming curriculum and certification to align with industry demand, expanding training infrastructure in rural areas, designing gender-disaggregated programmes, and strengthening anti discrimination enforcement in higher-wage employment are complementary interventions that training policy alone cannot substitute.

EMPIRICAL ANALYSIS OF FACTORS INFLUENCING BOX OFFICE REVENUE OF ANIMATED FILMS: EVIDENCE FROM AI-MEDIATED DIGITAL MARKET SIGNALS

2026-11-13T04:22:33Z

Title: EMPIRICAL ANALYSIS OF FACTORS INFLUENCING BOX OFFICE REVENUE OF ANIMATED FILMS: EVIDENCE FROM AI-MEDIATED DIGITAL MARKET SIGNALS Authors: AHMAD, SUHEAL; Aggarwal, Deepti (SUPERVISOR) Abstract: The global animated film sector experienced a significant structural shift between 2015 and 2023, largely spurred by the rise of AI-driven digital platforms, the swift growth of subscription streaming services, and the increasing commercial impact of algorithmic recommendation systems on audience behaviour. In this changing environment, the traditional framework of film economics, which primarily focuses on production budgets and critical reviews, fails to adequately explain the commercial success of animated films. This dissertation empirically investigates the factors that affect worldwide box office revenue for animated films released during this timeframe, paying particular attention to whether AI mediated digital market signals have independent commercial value beyond conventional production-related factors. The research utilises a secondary dataset comprising 13,440 observations of animated films obtained from The Movie Database (TMDB), the Internet Movie Database (IMDb), and Box Office Mojo, encompassing animated theatrical releases globally from 2015 to 2023. The dependent variable in this study is worldwide box office revenue. In contrast, the four independent variables include production budget, the TMDB algorithmic popularity score, total audience vote count, and average vote. The primary analytical method employed is Ordinary Least Squares (OLS) multiple linear regression, conducted using IBM SPSS Statistics (Version 26) with the Enter method. A Pearson bivariate correlation analysis is performed as a supplementary diagnostic step before regression estimation. The regression model exhibits considerable explanatory power, accounting for roughly 69 per cent of the variance in worldwide box office revenue (R² = 0.690, Adjusted R² = 0.690, F = 7,475.594, p < 0.001). All four predictors are statistically significant at the 0.001 level. Vote count is the most influential predictor (β = 0.607, t = 81.434, p < 0.001), indicating that the level of AI-mediated audience interaction on digital rating platforms is the primary driver of commercial success. Production budget follows in importance (β = 0.218, B = 0.989, p < 0.001), with an unstandardized coefficient near 1, suggesting that animated films tend to recoup their production costs through box-office iv earnings at the margin. The TMDB algorithmic popularity score, which serves as the main AI-mediated signal variable in this study, reveals a notable positive independent coefficient (β = 0.146, B = 140,205.597, t = 28.866, p < 0.001), indicating that visibility generated by AI on platforms significantly influences box office revenues, independent of budget and audience engagement. The vote average shows a small yet statistically significant negative partial effect (β = −0.024, t = −4.961, p < 0.001), reflecting a market-segmentation dynamic rather than a negative correlation between film quality and commercial performance in absolute terms. The hierarchy of standardised effects ranks vote count (0.607), budget (0.218), popularity (0.146), and vote average (0.024 in absolute value), providing a clear, evidence-based ranking of the commercial drivers for the animated film industry in the digital age. This study contributes in three main ways: it offers a large-sample empirical analysis of the global animated film market for a contemporary period not previously studied at this scale; it explicitly operationalizes and empirically tests an AI-generated algorithmic metric as a distinct category of revenue predictor; and it provides directly interpretable economic magnitudes that producers, investors, and digital marketers can utilize to assess the commercial returns on investments in digital engagement. Keywords: Animated films; box office revenue; AI-mediated digital signals; TMDB popularity score; vote count; film economics; OLS regression; digital platform economics; algorithmic visibility; audience engagement.

ECONOMIC GROWTH AND SUSTAINABILITY: A CASE STUDY OF MINING INDUSTRY IN JHARKHAND, ODISHA AND CHHATTISGARH

2026-11-13T04:15:07Z

Title: ECONOMIC GROWTH AND SUSTAINABILITY: A CASE STUDY OF MINING INDUSTRY IN JHARKHAND, ODISHA AND CHHATTISGARH Authors: TIRKEY, SPRIHA; Arora, Rajiv (SUPERVISOR) Abstract: Jharkhand, Odisha and Chhattisgarh are the mineral rich states of India and contribute major share of production of coal, iron ore and limestone. Despite this wealth of resources, they continue to face high infant mortality rates, low SDG rankings and huge social challenges especially among tribal communities. The study examined the relationship between mining and social sustainability by using data from 2014 to 2024. The main indicator of social welfare was the Infant Mortality Rate (IMR) and a Panel Fixed Effects (LSDV) regression model was used. The factors considered in the analysis were per capita GSDP, mining share of GSDP, DMF expenditure, forest cover and the MMDR 2015 reform. The results are mining intensity is a strong and statistically significant positive predictor of infant mortality (r = 0.713, p < 0.001; β = +0.998, p = 0.006). This supports the idea that structural mineral dependence degrades health outcomes rather than improves them the resource curse, quantified. Second, per capita GSDP has no significant effect on IMR (p = 0.500), meaning the income generated by mining does not reach the communities who pay for it with their health and their forests. Third, the MMDR 2015 reform dummy is the most significant predictor in the model (β = −5.960, p < 0.001) and correlated with about six fewer infant deaths per thousand live births in the post-reform period evidence that structural policy reform can work when properly designed. Fourth, in these states, despite the collection of more than thirty thousand crore rupees across the three states, the impact of DMF expenditure on IMR has been statistically insignificant (r = −0.054, p = 0.776). This is a governance failure, not a design failure. The study highlights the need for urgent reforms in DMF governance structures, a regulation minimum allocation for healthcare from DMF funds, economic diversification away from mineral dependence and consistent enforcement of the MMDR compliance framework.

ESG PERFORMANCE, CARBON DISCLOSURE QUALITY, CARBON COMMITMENT, VOLUNTARY DISCLOSURE AND GREENWASHING: EVIDENCE FROM LISTED COMPANIES

2026-11-13T04:07:34Z

Title: ESG PERFORMANCE, CARBON DISCLOSURE QUALITY, CARBON COMMITMENT, VOLUNTARY DISCLOSURE AND GREENWASHING: EVIDENCE FROM LISTED COMPANIES Authors: SAKSHAM; Kaushik, Aakansha (SUPERVISOR) Abstract: This dissertation investigates whether the quality, commitment, and voluntarism of sustainability disclosure by listed Indian companies—as mandated under the Business Responsibility and Sustainability Report (BRSR) framework—genuinely reflect environmental performance, or whether it constitutes a form of symbolic reporting that masks operational inaction, a phenomenon widely characterised as greenwashing. Three original measurement instruments are developed from BRSR-mandated corporate filings: the Disclosure Quality Index (DQI), a five-dimension composite covering breadth, depth, quantitative substance, implementation evidence, and governance credibility, weighted using Shannon entropy to address the saturation problem endemic to existing binary disclosure measures; the Carbon Commitment Index (CCI), which captures the formal architecture of climate compliance through environmental impact assessment, risk governance, and regulatory conformance disclosures; and the Voluntary Disclosure Index (VDI), which measures the degree to which firms exceed the mandatory reporting floor. The analytical sample comprises 744 de-duplicated firm-year observations drawn from FY2025 BRSR filings of NSE-listed Indian companies spanning 14 sector groupings. All three indices are subjected to standard psychometric and convergent validity tests. The dissertation then develops a Greenwashing Index by cross-referencing index scores against independently observed emission intensity. It assigns each firm to one of six descriptive categories—ranging from Genuine Sustainability Leaders through Transparent Performers and Commitment Leaders to Symbolic Reporters, Potential Greenwashers, and Severe Greenwashers. Empirical results, estimated using median (least-absolute-deviations) regression, Huber-robust regression, and OLS with heteroskedasticity-consistent standard errors on 716 complete-emission observations, yield four principal findings. First, the DQI is positively and significantly associated with emission intensity (DQI_noD3: β = +1.70, p < 0.001), indicating that higher disclosure quality is correlated with higher— not lower—carbon performance, consistent with the legitimacy-theory prediction that high-emitting firms invest disproportionately in disclosure form. Second, the CCI is negatively associated with emission intensity in both Huber and median regressions (β = −0.226, p < 0.001), suggesting that firms with more robust climate-compliance governance achieve lower emission intensity once the scale effect of absolute emissions is removed. Third, the VDI exhibits a flat, non-significant relationship with emissions, reflecting widespread saturation at the mandatory compliance ceiling. Fourth, the quadrant classification identifies 265 of 716 firms (37.0 per cent) as Greenwashers—a proportion materially and statistically above the 25 per cent random benchmark—while only 149 firms (20.8 per cent) qualify as Substantive disclosers whose reported quality is matched by genuine environmental performance. The findings extend the symbolic-disclosure framework and the legitimacy management literature to a mandatory disclosure regime in an emerging economy, and generate actionable implications for SEBI regulators, ESG ratings agencies, and institutional investors.