Working Capital Management and Profitability in India’s Cement Sector: Evidence and Sustainability Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the impact of working capital management (WCM) on profitability in the Indian cement industry, an energy-intensive sector central to the country’s infrastructure growth. Using a balanced panel of listed firms over 2010–2024, we employ pooled OLS, two-way fixed effects, quantile regressions, and dynamic system GMM to address heterogeneity and endogeneity concerns. The results demonstrate that reductions in the cash conversion cycle (CCC), accelerated receivables collection, leaner inventories, and prudent use of payables significantly improve profitability. Quantile regressions reveal that highly profitable firms capture larger absolute gains from CCC reductions, while size-split analysis indicates that smaller and liquidity-constrained firms achieve proportionally greater marginal relief. These findings represent complementary perspectives rather than unified statistical relationship, a limitation we acknowledge. Dynamic estimates confirm the robustness of results after accounting for persistence and reverse causality. Beyond firm-level outcomes, the study contributes conceptually by linking WCM efficiency to sustainability financing: liquidity released from shorter operating cycles can be redeployed into green and energy-efficient investments, offering a potential channel for ESG alignment in carbon-intensive industries. Policy implications highlight the role of digital reforms such as TReDS and e-invoicing in strengthening liquidity efficiency, particularly for mid-sized firms. The findings extend the international WCM profitability literature, provide sector-specific evidence for India, and suggest new avenues for integrating financial and sustainability strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it