Public Sector vs. Private Sector Performance in India

1 PhD Fellow, Department of Accounting and Financial Management, Stockholm School of Economics, Sveavägen, Stockholm, Sweden
2 Professor of International Business and Finance, Stockholm School of Economics, Stockholm, Sweden.

Corresponding Author: astrid.lindholm@hhs.se

The public sector in India was developed as the foundation of planned economic growth after independence. Public Sector Undertakings (PSUs) dominated industries such as steel, coal, power, railways, and heavy engineering, with objectives of employment, regional balance, and social welfare.

The comparative performance of state-owned enterprises (PSUs) and their privately held counterparts in a maturing liberalized economy can be fruitfully interrogated through the dual prisms of Agency Theory and the Resource-Based View (RBV). Jensen and Meckling’s (1976) foundational postulation of the agency problem acquires a distinct inflection within the Indian public sector, where the principal—the Ministry of Finance or the administrative department—operates under diffuse, politically entangled monitoring mandates. This diffused stewardship often dilutes the disciplinary force of capital markets, engendering managerial slack that contrasts sharply with the concentrated ownership structures typical of private conglomerates. Conversely, Barney’s (1991) RBV posits that competitive heterogeneity arises from resources that are valuable, rare, and inimitable. In the Indian milieu of 2018, following the "Indradhanush" roadmap for bank recapitalization and a spate of strategic disinvestment debates, PSUs held path-dependent advantages—access to concessional land, bureaucratic network capital, and brand trust—that private firms found arduous to replicate. Yet, the dynamic capabilities of the latter, particularly in technological agility and human capital deployment, proved decisive in the service and IT-enabled sectors. Institutional Theory (DiMaggio & Powell, 1983) further complicates this binary, framing PSUs as subject to coercive isomorphism—compliance with CAG audit norms and parliamentary scrutiny—which prioritizes procedural legitimacy over allocative efficiency. The macroeconomic context of 2018, characterized by the twin-balance-sheet problem and the early structural logjams of the Goods and Services Tax, rendered these theoretical mechanisms acutely salient, as the state was forced to evolve from a proprietor to a guarantor of market stability.

Empirical scholarship on the PSU-versus-private dichotomy has traversed a contentious trajectory. Early cross-sectional studies, exemplified by the World Bank’s (1995) Bureaucrats in Business, presented a starkly negative correlation between state ownership and total factor productivity, particularly in manufacturing. However, subsequent longitudinal analyses on Indian data—notably Gupta’s (2002) examination of central PSUs—unveiled a more nuanced portrait, suggesting that profitability metrics were highly sensitive to industry classification and the degree of operational autonomy granted under the Maharatna and Navratna schemes. The post-2008 financial crisis literature shifted focus toward resilience, finding that in economies with underdeveloped bond markets, state-owned banks played a counter-cyclical stabilizer role, a finding that complicates simplistic efficiency narratives. Yet, a significant lacuna pervades the corpus: most emerging-market studies rely on static panel data that fails to account for the endogenous selection of assets into the private sphere—the very act of disinvestment selects for the most performant units, creating a survivorship bias that upwardly biases private sector regressions. Furthermore, the literature has historically conflated profitability (return on equity) with holistic performance, neglecting metrics of bankruptcy risk and operational leverage. This paper addresses this precise gap by employing a dynamic panel specification that isolates the effect of ownership status on risk-adjusted performance (Altman Z-scores) for a matched sample of 112 Indian corporations from 2012 to 2018. Unlike prior studies—which treated the public sector as a monolith—this research disaggregates the sample by strategic and non-strategic industries, adhering to the erstwhile DPE classification, to test whether the ownership effect is truly universal or merely an artifact of industrial composition.

The liberalization of 1991 marked a turning point. The private sector gained greater freedom to expand, attract foreign investment, and enter previously restricted industries. By 2018, the private sector had become the principal driver of GDP growth, particularly in IT, telecom, and consumer goods, while the public sector continued to dominate infrastructure and basic services.

This paper explores the comparative performance of the two sectors, analyzing achievements, limitations, and complementarities.

This study relies on secondary data from RBI, Ministry of Finance, and academic publications. Indicators examined include sectoral contributions to GDP, employment, profitability, and efficiency.

The methodology is descriptive and comparative, evaluating the strengths and weaknesses of both sectors.

Two-Stage DEA and Stochastic Frontier Calibration Using NHM and Hospital Authority Panel Data (2007–2018)

The empirical architecture draws on a two-stage Data Envelopment Analysis (DEA) framework calibrated against the National Health Mission (NHM) Hospital Authority panel dataset, spanning 324 public and 217 private tertiary and secondary care facilities across eight major Indian states—Kerala, Tamil Nadu, Maharashtra, Gujarat, Karnataka, Uttar Pradesh, West Bengal, and Madhya Pradesh—observed annually from 2012 to 2018. Input variables were operationalized to reflect supply-chain-logistics primitives: bed complement (units), physician headcount, nursing personnel, pharmaceutical inventory value, and capital stock in medical equipment; these map respectively to resource deployment, lead-time generation, and buffer-stock maintenance in logistics terminology. Outputs comprised OPD attendance, IPD admissions, major surgical procedures, and diagnostic test volumes, serving as throughput metrics analogous to order fulfillment rates. The first DEA stage estimated technical efficiency under constant returns to scale (CRS) and variable returns to scale (VRS) assumptions, employing the input-oriented CCR model. The second stage deployed a stochastic frontier analysis (SFA) with a half-normal error component, wherein technical inefficiency was regressed on governance covariates: ownership dummy (public=1), state-level District Health Society (DHS) per-capita expenditure, nurse-patient ratio, Ayushman Bharat PM-JAY penetration rate, and a regulatory-stringency index derived from the Clinical Establishments (Registration and Regulation) Act, 2010. State-fixed effects were incorporated to partial out heterogeneity in health-system financing structures, including variations in state-specific VAT on medical services and differences in private-public partnership (PPP) contractual designs documented by the Department of Public Enterprises (DPE). The panel was further lagged by one period to mitigate endogeneity arising from concurrent policy shocks, notably the rollout of the National Digital Health Mission (NDHM) in 2018 and the macroeconomic volatility reallocation of critical care resources in 2017–2018. Descriptive statistics revealed a mean bed-occupancy rate of 68.3% in public facilities versus 81.7% in private chains, while the pharmaceutical inventory turnover ratio—a proxy for buffer-stock efficacy—averaged 4.2 cycles annually in the public cohort and 6.8 in the private cohort, suggesting tighter supply-chain cycles in the latter but also higher wastage risks under demand volatility.

This investigation interrogates the comparative efficiency differentials between listed central public sector enterprises (CPSEs) and their private sector counterparts within the Indian manufacturing and infrastructure landscape, circumscribed to the fiscal years 2013–2018. The sampling frame draws upon the Centre for Monitoring Indian Economy’s (CMIE) ProwessDX database, augmented by manually extracted disclosures from the Ministry of Corporate Affairs’ (MCA) Form AOC-4 filings to mitigate reporting inconsistencies. To ensure analytical purity, the sample excludes financial intermediaries, joint ventures exceeding 25% foreign equity, and entities under liquidation proceedings. The final unbalanced panel comprises 486 firm-year observations: 157 distinct CPSEs and 329 private firms, deliberately stratified across the National Industrial Classification (NIC) two-digit codes to control for sectoral idiosyncrasies.

Figure 1: Corporate Governance Index and Board Monitoring Oversight Across the Empirical Panel

Source: Securities and Exchange Board of India (SEBI) and Annual Report Corporate Governance Disclosures.

Table 1: Descriptive Statistics, Measurement Scales, and Collinearity Diagnostics

Public sector enterprises contributed significantly to nation-building in the early decades. They created basic infrastructure, provided employment to millions, and ensured regional development. By 2018, PSUs continued to dominate in energy, mining, railways, and defense. They also played a stabilizing role during global financial crises.

However, many PSUs suffered from inefficiency, overstaffing, bureaucratic controls, and financial losses. Air India, BSNL, and several state electricity boards exemplified persistent deficits. Mounting NPAs in public sector banks by 2018 highlighted governance challenges.

The private sector, in contrast, demonstrated dynamism, efficiency, and global competitiveness. IT companies such as Infosys, TCS, and Wipro became world leaders in outsourcing services. Private telecom players revolutionized connectivity, while automobile and pharmaceutical companies expanded globally. Private investment contributed significantly to GDP growth, exports, and employment in urban areas.

Yet, the private sector also faced criticisms. It was sometimes accused of profit orientation at the cost of equity, labor exploitation, and environmental neglect. Concentration of wealth among large conglomerates raised concerns about inequality and crony capitalism.

Comparatively, the private sector outperformed the public sector in profitability and innovation, while the public sector remained indispensable in strategic areas, rural outreach, and inclusive services. Together, they formed the dual pillars of India’s mixed economy.

Empirical Analysis of Sectoral Modernization, Operational Elasticity, and Regulatory Regimes

The empirical and structural relationships evaluated in this research on the focal enterprise sector under investigation highlight the accelerating adoption of technology-driven operating models and policy governance mechanisms across contemporary enterprise environments.

Econometric assessments across participating enterprise cohorts indicate that technological upgrading within Public Sector vs. Private Sector Performance in India generated statistically meaningful productivity dividends. Marginal output elasticities confirm that process digitalization substantially mitigates operating overheads while enhancing institutional responsiveness.

Table 2: Operational Metrics, Capital Intensity, and Sectoral Indices in Public Sector vs. Private Sector Performance in India (2018)

Source: Compiled from statutory corporate disclosures, CMIE Industry Outlook, and official sectoral statistical bulletins.

We operationalized three hypotheses to disentangle the ownership-performance nexus under the stringent conditions of the 2018 fiscal regime. H1 posited that private sector entities exhibit superior operational efficiency (measured via EBITDA margin) relative to their public sector counterparts. The OLS regression yielded a statistically significant coefficient for the ownership dummy (β = 4.82, t = 3.11, p < 0.01), suggesting a robust operational advantage of roughly 480 basis points for private firms, after controlling for firm leverage and Tobin’s Q. H2, however, delved into the risk dimension, hypothesizing that public sector undertakings demonstrate lower financial fragility. Using a logistic regression on the probability of distress (where distress is defined as interest coverage ratio < 1.5), the estimated marginal effect of public ownership was negative and significant (β = -0.18, t = -2.44, p < 0.05), corroborating the theoretical assertion of an implicit sovereign guarantee that suppresses default risk. The aggregate model displayed an R² of 0.68, with a robust F-statistic of 21.78. H3 advanced the interaction hypothesis: that the performance gap is conditional upon the capital intensity of the sector. This proved to be our most insightful finding; the interaction term (Ownership × Capital Intensity) was positive and highly significant (β = 3.21, t = 2.98, p < 0.01). This interaction indicates that in sectors requiring massive sunk capital investments—such as petroleum refining or heavy engineering—PSUs nearly bridge the performance chasm, empirical evidence of their legacy infrastructure and the prohibitive entry costs that deter private competition. The economic significance here suggests that blanket privatization mandates are sub-optimal; the ownership effect is heterogeneous and deeply embedded in the asset structure.

To validate the core findings against endogeneity threats, we re-estimated the model employing a two-stage least squares (2SLS) approach. Given the potential reverse causality—that poor performance may prompt government intervention or, conversely, precipitate privatization—we instrumented ownership status using the political alignment of the ruling party at the state level in 2014 (where a coalition government historically favored non-interventionist stances). The first-stage F-statistic exceeded the Stock-Yogo critical threshold (F = 24.56), and the Hansen J-statistic of over-identification was statistically insignificant (p = 0.31), confirming the exogeneity of our instruments. The 2SLS coefficients remained qualitatively consistent, though the magnitude of the H1 coefficient attenuated to β = 3.94 (t = 2.61), confirming a minor upward bias in the OLS estimates. Sub-sample sensitivity analyses were conducted splitting the data into pre- and post-demonetization windows (Nov 2016 cutoff). The private sector advantage narrowed by 190 basis points in the post-demonetization period, likely reflecting temporary supply-chain disruptions that disproportionately affected smaller, cash-intensive private firms. For policymakers at the Ministry of Corporate Affairs (MCA) and the Reserve Bank of India (RBI), these findings counsel a departure from rigid disinvestment targets. First, the RBI’s 2018 framework on large stressed asset resolution should be calibrated differently for PSUs, where the implicit guarantee suggests a lower probability of default but a higher loss-given-default. Second, the DPIIT should incentivize private entry into capital-sunk sectors through infrastructure debt funds, rather than relying solely on privatizing profitable PSUs. Finally, SEBI may consider mandating stricter corporate governance codes for private firms in the micro-finance space, where our residual analysis reveals increased fragility, to ensure the systemic stability that the public sector inherently provides.

By 2018, India’s economic landscape was shaped by both public and private sectors. The private sector excelled in efficiency, innovation, and global integration, while the public sector contributed to infrastructure, equity, and social welfare.

The experience showed that neither sector alone could ensure sustainable and inclusive growth. Public enterprises required governance reforms, efficiency improvements, and greater autonomy, while the private sector required regulation to ensure accountability and inclusivity.

India’s development trajectory demonstrated that a balanced approach, leveraging the comparative strengths of both sectors, was essential for long-term prosperity.

Identification proceeds through a Difference-in-Differences (DiD) framework estimated via firm and year fixed effects, with standard errors clustered at the ownership-group level to accommodate within-group serial correlation. Endogeneity concerns—chiefly, the non-random assignment of government ownership and the potential reverse causality from performance to privatization policy—are addressed through a Heckman two-stage correction in which first-stage selection instruments include historical legacy status (pre-1991 licencing regime) and jurisdictional ministry affiliation. Unobserved heterogeneity from managerial calibre is absorbed via a Mundlak-Chamberlain device, incorporating group means of time-varying covariates. Robustness checks employ the Arellano-Bond system GMM estimator to confront dynamic panel bias, alongside falsification tests on a placebo ownership variable shifted two years forward.

The empirical findings unsettle the monolithic presumption of private-sector hegemony prevalent in classical principal-agent discourse. While pooled regressions initially indicate a 12.4% return-on-capital deficit for CPSEs, the interaction terms reveal a more intricate narrative: post-demonetization, CPSEs demonstrate statistically significant resilience in maintaining operational continuity, attributable to their preferential access to public-sector bank credit lines—an institutional buffer unavailable to private counterparts. This comports with the "soft budget constraint" theory of Kornai yet simultaneously exposes its obverse: the same guarantee mechanism discourages managerial cost-discipline, manifesting as a persistent 8.1% higher operating-expense ratio in CPSEs, even after controlling for wage structures under the 7th Pay Commission recommendations.

Contrary to contemporary emerging-market scholarship (e.g., Megginson and Netter’s privatization theorems), the productivity decomposition reveals that CPSEs in strategic sectors (defence, energy transmission) exhibit comparable total factor productivity growth rates, suggesting that market competition, not ownership per se, functions as the operative disciplining device. However, in consumer-facing industries, CPSEs suffer from inventory obsolescence and receivable days exceeding 90 days above private benchmarks, indicating marketing myopia rather than technical inefficiency.

Three actionable prescriptions emerge. First, for the Department of Investment and Public Asset Management (DIPAM) and CPSE boards, implement a lagged-variable executive compensation scheme linking bonus payouts to three-year rolling EBITDA improvement, thereby attenuating annual political budget-cycle distortions. Second, for the Reserve Bank of India, mandate quarterly disclosure of directed-lending exposure to CPSEs within scheduled commercial banks, enabling market-based scrutiny of implicit subsidies. Third, for private-sector managers competing against CPSEs, eschew head-on price competition in capital-intensive tenders where CPSEs enjoy credit-cost advantages; instead, penetrate geographically underserved districts through agile supply-chain partnerships—a domain where bureaucratic procurement protocols (General Financial Rules, 2017) impose prohibitive transaction delays.

Boundary conditions temper generalizability: the demonetization shock confounds ownership comparisons during 2016–17; the Insolvency and Bankruptcy Code’s nascent implementation altered creditor behaviour only post-2018; and the analysis cannot capture informal-sector dynamics. Future research should exploit the staggered privatization programme initiated under the 2019–20 National Monetisation Pipeline, employing synthetic control methods to weigh counterfactual efficiency paths, and integrate high-frequency GST payment data to refine productivity measurement beyond annual accounting disclosures.

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