A Regulatory Framework for Viksit Bharat

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1. Abstract

India intends to become a USD 30 trillion economy by 2047. Reaching that mark from a base of roughly USD 3.96 trillion implies sustained nominal growth near 9.6 percent in dollar terms, underpinned by real growth of about 7.8 percent a year for more than two decades. This paper argues that the binding constraint on such growth is not the volume of investment, which has already returned to about 30 percent of GDP, but its productivity, and that productivity is set overwhelmingly by the regulatory environment. Using a Harrod-Domar capital-efficiency identity and a Solow growth-accounting decomposition, we show that India’s required total factor productivity (TFP) growth of around 3 percent a year far exceeds its recent realised rate of roughly 1.2 percent, and that lowering the incremental capital-output ratio (ICOR) from a blended 4.9 toward 4.0 would, on its own, add about 1.4 percentage points to trend growth without a single additional rupee of investment. We then broaden the lens beyond intellectual property to the full gamut of cross-sectoral regulation, namely factor markets, network infrastructure, contract enforcement, approval and clearance regimes, and appropriability, and we trace, through sector vignettes in semiconductors, agricultural biotechnology and life sciences, exactly how regulatory latency, weak enforcement and approval uncertainty raise the ICOR and suppress TFP. Intellectual-property-intensive sectors are not the only productivity lever, but they are the highest-upside frontier lever for a high-income transition. The paper closes with a concrete reform matrix mapping each design principle to a current problem, a reform instrument, a responsible institution, a productivity channel and a measurable indicator and a scenario model of 2047 outcomes.

1. Introduction

In 2025 India became the world’s fourth-largest economy, overtaking Japan, with nominal output of approximately USD 3.96 trillion.¹ The political economy of the next quarter-century is organised around a single round number, namely a USD 30 trillion economy by 2047, the centenary of independence, conventionally labelled Viksit Bharat or developed India.² The arithmetic of that ambition is unforgiving. Multiplying output roughly sevenfold in twenty-two years requires compound nominal growth of close to 9.6 percent in dollar terms, which, after netting out domestic inflation and a modest rupee depreciation, corresponds to sustained real growth of about 7.8 percent a year.³ No large economy has maintained that pace for two decades without a decisive and durable improvement in productivity.

The conventional policy conversation treats this as a problem of mobilising capital. Build more roads, ports and factories, raise the investment rate, and growth will follow. That framing is incomplete. India’s gross fixed capital formation has already recovered to about 30 percent of GDP,⁴ a level historically associated with rapid catch-up growth in East Asia. Yet India’s trend growth sits below the Viksit Bharat requirement. The gap is therefore not primarily about how much the economy invests, but about how much output each unit of investment yields, and how efficiently the factors of production are combined. These are questions of productivity and capital efficiency, and both are governed, to a degree that is easy to underestimate, by the rules under which firms invest, hire, build, contract, innovate and scale.

This paper makes four arguments. First, that the binding constraint on Viksit Bharat is total factor productivity, not the capital stock, and that the incremental capital-output ratio is the single most policy-sensitive number in the growth equation. Second, that productivity is overwhelmingly a regulatory variable, set not by one statute but by the whole architecture of factor-market rules, network-infrastructure regulation, contract enforcement, approval regimes and intellectual property protection that determines whether capital and ideas reach their most productive use. Third, that intellectual-property-intensive sectors, while not the only place productivity can be raised, are the highest-upside frontier lever for a high income transition, because the marginal product of an idea is uniquely large and uniquely dependent on the rules. Fourth, that the required reforms can be specified concretely, mapped to institutions, and measured, because the productivity and capital-efficiency gaps are themselves measurable.

A word on scope and balance. It would be a mistake to claim that intellectual property alone carries the productivity burden. Services, logistics, agriculture, power distribution, urban governance and finance all have large productivity gaps, and several of them are quantified in this paper. The correct framing is not that IP-intensive activity is the only lever, but that it is the lever with the steepest payoff per unit of reform effort at the income frontier, precisely because it is where the gap between India and high-income economies is widest and where the returns to good rules compound most steeply. The paper therefore treats cross-sectoral regulation as the body of the argument and the IP frontier as its sharpest edge.

The remainder proceeds as follows. Section 2 sets out the Viksit Bharat arithmetic. Section 3 develops the analytical framework. Sections 4 and 5 confront it with Indian data on capital efficiency and productivity. Section 6 widens the regulatory lens across sectors and sizes the economic impact. Section 7 grounds the macro argument in three sector vignettes that trace the path from a specific rule to the ICOR and the TFP residual. Section 8 reframes the innovation deficit as the highest-upside frontier lever. Section 9 sets out a five-principle reform framework with a detailed implementation matrix. Section 10 presents the scenario model with its assumptions explained, Section 11 is a methodology annex, and Section 12 concludes. All computations are reproduced and documented in the companion workbook and in the annex.⁵

2. The Viksit Bharat Arithmetic

The target can be stated as a simple compound growth problem. Let base-year output be USD 3.96 trillion and the 2047 target USD 30 trillion, a span of twenty-two years. The required nominal compound annual growth rate (CAGR) in dollar terms is

g = (30 / 3.96)^(1/22) minus 1, which is
approximately 9.6 percent a year.

A dollar growth rate is not the same as a real growth rate. Nominal output measured in dollars decomposes, approximately, into real output growth, plus domestic price inflation, less the rate at which the rupee depreciates against the dollar. On the assumptions set out in Section 11, namely real growth of 7.8 percent, a GDP deflator near 4 percent, and rupee depreciation of about 1.5 percent a year, the implied nominal dollar growth is approximately 10.4 percent, comfortably above the 9.6 percent the target requires. The USD 30 trillion figure is therefore internally consistent with the World Bank’s estimate that India must grow at about 7.8 percent in real terms to reach high-income status by 2047.⁶ The headline number is achievable, but only at a real growth rate India has rarely sustained for more than a few consecutive years.

Metric	Value	Basis
Base nominal GDP (2025)	USD 3.96 tn	NSO First Advance Estimates, FY26
Target nominal GDP (2047)	USD 30 tn	Viksit Bharat vision
Horizon	22 years	2025 to 2047
Required nominal USD CAGR	9.6 percent	(30 / 3.96)^(1/22) minus 1
Required real GDP growth	7.8 percent	World Bank India CEM (2024)
Investment rate (GFCF / GDP)	30.0 percent	Economic Survey 2025-26

The significance of restating the target this way is that it converts a slogan into a constraint that can be tested against the structural drivers of growth. If real growth must average 7.8 percent, and if the investment rate is already near 30 percent, then the entire burden of the residual falls on how efficiently that investment is converted into output, and on the productivity with which capital and labour are combined. The next section makes that relationship explicit.

3. Analytical Framework: Capital Efficiency and Productivity

3.1 The Harrod-Domar capital-efficiency identity

The simplest bridge between investment and growth is the Harrod-Domar relation, which states that the rate of output growth equals the investment rate divided by the incremental capital-output ratio:

g = s / ICOR

where s is gross fixed capital formation as a share of GDP and the ICOR measures the units of investment required to generate one additional unit of annual output. A low ICOR signals that capital is being used efficiently. A high ICOR signals waste, in the
form of stalled projects, misallocation, regulatory friction, and capital tied up in low-return uses. The identity is stylised, but it isolates a policy-critical quantity. At a given investment rate, growth is inversely proportional to the ICOR, so reducing the
ICOR is, in effect, growth obtained for free.

3.2 Solow growth accounting

The ICOR identity says nothing about why capital efficiency varies. For that we use the neoclassical growth-accounting framework, which decomposes output growth into the contributions of capital, labour and a residual, namely total
factor productivity, that captures everything not explained by factor accumulation:

g_Y = TFP + alpha times g_K + (1 minus alpha) times g_L,

where g_Y is output growth, g_K and g_L are the growth rates of capital and labour input, and alpha is the capital share of income, taken here at 0.45, a conventional value for India. TFP is the part of growth attributable to better technology, organisation, allocation and institutions rather than to more inputs. It is the statistical residue of everything that makes a given bundle of capital and labour more productive, and at the frontier it is overwhelmingly about innovation and its diffusion.

3.3 Why the two are one problem

The two frameworks are not rivals; they are two readings of the same instrument. A falling ICOR is itself largely a manifestation of rising TFP, because the same regulatory and institutional improvements that let firms innovate and reallocate resources also reduce the capital absorbed per unit of output. When a regulator clears a project faster, when a court enforces a contract more reliably, when a factor market moves land or labour to its highest-value use, or when a distribution utility stops losing a sixth of the power it buys, the economy produces more output from the same investment. That shows up as a lower ICOR in the Harrod-Domar identity and as a higher residual in the Solow decomposition. Productivity and capital efficiency are therefore a single problem viewed from two angles, and the policy lever for both is the regulatory environment. The empirical question that follows is simple. If India must grow at 7.8 percent in real terms, what ICOR and what TFP growth does that imply, and how do those requirements compare with what the economy has actually delivered? The distance between the required and the realised is the size of the reform task.

4. Capital Efficiency in India

India’s ICOR has been volatile. It stood near 7.5 in FY12, in the aftermath of the post-crisis investment overhang, when large quantities of capital were locked in stalled infrastructure and over-leveraged balance sheets. It fell to about 3.5 by FY22, although that figure flatters the economy because it reflects a cyclical rebound from a pandemic-depressed base. A more defensible working value lies in the 4.5 to 6.5 range cited in the policy literature, and we adopt a blended 4.9 as a conservative central estimate, treating 4.0 as the efficient-frontier objective consistent with the 2047 ambition.⁷ The implications are stark. At an investment rate of 30 percent and an ICOR of 4.9, the Harrod-Domar identity yields trend growth of only about 6.1 percent. At an ICOR of 4.0, the same 30 percent investment rate yields about 7.5 percent. The improvement in capital efficiency alone delivers roughly 1.4 percentage points of additional growth, without raising the investment rate at all.

Scenario	Investment rate	ICOR	Implied real growth
Current efficiency	30.0 percent	4.9	6.1 percent
Target efficiency	30.0 percent	4.0	7.5 percent
Efficiency ceiling for 7.8 percent	30.0 percent	3.85	7.8 percent
Efficiency dividend	no change	4.9 to 4.0	plus 1.4 points

Turning the identity around clarifies the alternative. To reach 7.8 percent real growth while leaving the ICOR at its current 4.9, India would need to lift its investment rate to roughly 38 percent of GDP, an eight-percentage-point increase that would demand enormous additional savings mobilisation, larger external deficits, or sustained fiscal expansion. The efficiency route is not merely cheaper. For a capital-scarce economy it is the only realistic route. The maximum ICOR consistent with 7.8 percent growth at a 30 percent investment rate is about 3.85, so India must move from a blended 4.9 to under 4.0, a roughly 20 percent improvement in the productivity of its investment, simply to keep the Viksit Bharat target in view. Every reform that reduces the time and capital absorbed before a project begins to produce, from faster clearances to quicker dispute resolution to deeper bond markets, is in the most literal sense a growth policy.

5. The Productivity Gap

Growth accounting tells the same story from the supply side. Imposing the 7.8 percent target on the Solow decomposition, with a capital share of 0.45, capital growing broadly in line with output along a balanced-growth path, and labour input growing at about 2.1 percent a year (a demographic contribution of roughly 1.3 percent plus human-capital improvement of about 0.8 percent), the residual TFP growth required to close the equation is approximately 3.1 percent a year. India’s realised TFP growth over 2011-19 was about 1.2 percent, and on a more generous measure about 2.2 percent over 2010-19, with TFP contributing roughly 30 percent of GDP growth in the 2010s.⁸ The required rate therefore exceeds the realised rate by between roughly one and two percentage points. That gap, on the order of 1.9 percentage points against the central estimate, is the quantitative statement of India’s productivity challenge, and it is precisely the magnitude the World Bank attributes to its accelerated-reforms scenario, in which TFP growth runs 40 to 50 basis points above the business-asusual path.

TFP scenario	Assumed TFP growth	Implied real GDP growth	Gap to 7.8 percent
Business-as-usual	1.2 percent	5.9 percent	minus 1.9 points
Reform	2.0 percent	6.7 percent	minus 1.1 points
Accelerated modernisation	2.5 percent	7.2 percent	minus 0.6 points
Frontier	3.0 percent	7.7 percent	minus 0.1 points

The message of Tables 2 and 3 is identical and mutually reinforcing. Whether read as a capital-efficiency problem or a productivity problem, the Viksit Bharat target cannot be met by accumulation alone. A productivity gap of this size cannot be closed by any single ministry or scheme. It is the cumulative product of thousands of regulatory settings that determine whether the most productive firms can grow, whether resources flow to their best use, and whether the returns to innovation accrue to those who generate it. The next section opens that black box.

6. The Regulatory Drivers of Productivity: A Cross-Sectoral View

Productivity does not rise or fall in the abstract. It is the aggregate of decisions taken by millions of firms, each of which operates inside a dense lattice of rules. Five layers of that lattice bear most directly on the ICOR and the TFP residual, and each is a domain of active, quantifiable policy reform. The point of this section is to show that the productivity argument is not a claim about intellectual property alone, but about the regulatory state as a whole, and to attach numbers to the drag wherever the evidence allows.

6.1 Factor markets: land, labour and capital

Productivity growth depends on the continuous reallocation of land, labour and capital from less productive to more productive uses. Where land acquisition is slow and contested, where labour regulation discourages firms from growing past size thresholds, and where capital is intermediated through a banking system biased toward incumbents and collateral, that reallocation is throttled. The visible result is a profusion of small, sub-scale firms that never reach the size at which research and process innovation become viable. The four labour codes enacted between 2019 and 2020, the digitisation of land records under the SVAMITVA and DILRMP programmes, and the deepening of the corporate-bond and insolvency frameworks are all, in growth-accounting terms, productivity reforms, because they raise the share of resources that reach their best use. Reform examples that would move the needle further include timebound single-window land assembly for industrial use, the operationalisation of the labour codes with simplified compliance, and a corporate-bond market deep enough to fund long-horizon projects without tying them to bank collateral.

6.2 Network and infrastructure regulation: logistics and power

Two network sectors illustrate how regulation shows up directly as a cost on every other sector. India’s logistics cost, long cited at 13 to 14 percent of GDP, has been re-estimated by the government at about 7.97 percent of GDP, with the National Logistics Policy of 2022 targeting about 8 percent by 2030 through multimodal integration and digitisation.⁹ Every percentage point of GDP spent moving goods that a better-regulated system would not require is a deadweight loss to the competitiveness of tradable sectors, and it raises the effective ICOR of manufacturing by inflating the working and fixed capital tied up in inventory and transit. In power, the aggregate technical and commercial losses of distribution utilities fell from 25.5 percent in FY13 to about 15.4 percent in FY23, but accumulated discom losses still stood at roughly Rs 6.5 trillion, sustained by tariffs that do not reflect cost and by under-metering.¹⁰ A power system that loses a sixth of the electricity it buys and cannot recover its costs raises the price and lowers the reliability of an input that every factory uses, depressing manufacturing TFP across the board. The Revamped Distribution Sector Scheme, with an outlay of Rs 3.04 lakh crore tied to lossreduction milestones, is the reform vehicle; the productivity payoff is economy-wide rather than sectoral.

6.3 Contract enforcement and the rule of law

Innovation and long-horizon investment require confidence that contracts will be honoured and disputes resolved within a commercially meaningful time. On the last comparable international measure, India took about 1,445 days to enforce a commercial contract and ranked 163^rd of 190 economies, with roughly 30 million cases pending for over a year in the district judiciary.¹¹ Slow and uncertain enforcement raises the risk-adjusted hurdle rate for exactly the patient, intangible-heavy investments that lift productivity, and it raises the ICOR by lengthening the time capital is exposed before it can earn a return. Reform examples with a direct productivity channel include the expansion of dedicated commercial courts, statutory timelines for commercial suits, the strengthening of arbitration enforcement, and the digital case-management systems being deployed under the e-Courts programme. Judicial and arbitral capacity is, in this sense, an innovation input, even though it appears in no research budget.

6.4 Approval and clearance regimes

The productivity cost of regulation is frequently less about its content than its speed. Environmental clearances, product approvals, building permissions and licensing all tie up capital in projects awaiting a decision, which raises the effective ICOR directly. A regulation that is reasonable in substance but slow in operation imposes a real tax on output, and uncertainty about the outcome compounds the tax by deterring the investment altogether. The reform repertoire here is well understood, namely digitisation and single-window systems, statutory timelines with deemed approvals where risk is low, risk-based regulation that concentrates scrutiny on genuinely hazardous cases, and regulatory sandboxes that allow controlled experimentation. The National Single Window System and the Jan Vishwas decriminalisation of minor offences are steps in this direction; the unfinished agenda is to make speed a statutory entitlement rather than an administrative aspiration.

6.5 Intellectual property and appropriability

The fifth layer, and the one with the steepest payoff at the frontier, is the protection of intangible assets. India’s gross research intensity is about 0.65 percent of GDP, against roughly 2.4 percent in China and 3.5 percent in the United States, and the private sector funds only about 36 percent of it.¹²¹³ Patent throughput has risen sharply, with 110,375 filings in FY2024-25 and a domestic majority for the first time, but only about 30.4 percent of grants went to residents and average pendency has exceeded four years against a global best of two to three.¹⁴¹⁵ Weak and slow appropriability lowers the private return to invention and therefore the quantity of it. This layer is developed in full in Section 8; here it takes its place as one of five regulatory domains, the others being equally real and, in network and factor markets, equally quantified.

6.6 The economic impact of regulation

These layers are not rhetorical. They can be sized. The capital-efficiency channel alone is worth about 1.4 percentage points of trend growth, the difference between an ICOR of 4.9 and 4.0, and that improvement is overwhelmingly a regulatory achievement, accomplished by clearing projects faster, enforcing contracts sooner, and cutting the losses and frictions in network sectors. The productivity channel is worth a further structural lift of roughly 1.9 percentage points of TFP growth, the gap between the realised 1.2 percent and the required 3.1 percent. On the input side, lifting research intensity from 0.65 to 2.0 percent of GDP would require additional annual research spending of roughly USD 53 billion, rising from about USD 26 billion today to about USD 79 billion, most of which must come from private firms responding to better rules.¹⁶ The common thread is that regulation operates as a wedge between potential and realised output. Reducing the wedge raises the level and the growth rate of GDP simultaneously, and because the wedge is largest in the highest-value activities, the gains are concentrated where they compound most. The next section makes the mechanism concrete in three sectors.

7. Sector Vignettes: From Rule to ICOR and TFP

The macro argument rests on a causal claim, namely that specific regulatory features raise the ICOR or suppress TFP. The claim is most persuasive when it is traced through individual sectors. Three vignettes follow, chosen to span a capital-intensive manufacturing frontier, a science-intensive agricultural frontier, and a research-intensive services frontier. In each case the mechanism is the same, even though the rule differs: latency, uncertainty, weak enforcement or thin appropriability lengthens the time and raises the capital required to convert investment into output, or lowers the private return to the innovation that would raise productivity.

7.1 Semiconductors and electronics

Semiconductor fabrication is the archetype of a high-ICOR activity. A modern fab costs billions of dollars and yields revenue only after years of construction, qualification and ramp-up, so the capital-output ratio during the build phase is extreme and the project is acutely sensitive to delay. India’s response has been the India Semiconductor Mission, with an outlay above USD 10 billion and fiscal support of up to 50 percent of project cost, under which the Tata-PSMC fab at Dholera (Rs 91,000 crore, 28 nanometre) was approved in February 2024 and Micron’s USD 2.75 billion assembly-and-test facility at Sanand broke ground, with ten projects approved by 2026.¹⁷ The episode is instructive in both directions. It shows that decisive, fast, well-capitalised policy can pull a frontier industry into the country, and it shows how much the outcome depends on regulatory velocity and certainty, because every month of approval delay or incentive ambiguity raises the effective ICOR of a multi-billion-dollar asset. The productivity channel runs through appropriability and speed: stable incentives, fast clearances and reliable contract enforcement lower the risk-adjusted cost of capital for assets whose entire value lies in the future. The reform lesson generalises beyond chips to every capital-intensive, long-gestation investment.

7.2 Agricultural biotechnology

Agricultural biotechnology is the clearest Indian example of how regulatory uncertainty suppresses TFP outright. Bt cotton, commercialised in 2002, remains the only genetically modified crop in cultivation, grown on about 10.8 million hectares, and it delivered large, well-documented productivity gains. Yet Bt brinjal, approved by the Genetic Engineering Appraisal Committee in
2009, was placed under a moratorium in 2010, and GM mustard (DMH-11), granted environmental clearance in 2022, remains stayed pending Supreme Court proceedings, with the Court in 2025 directing the government to frame a national GM policy.¹⁸ The economic effect is a frozen frontier. Research and investment in a science where India has genuine capability are deterred not by a prohibition but by the absence of a predictable, time-bound, science-based approval pathway. The suppressed TFP is invisible in the statistics precisely because the innovations never reach the field. The reform required is not a verdict for or against any particular crop, but a transparent, evidencebased regulatory process with statutory timelines, so that the appropriable return to agricultural research is not held hostage to indefinite delay. Approval uncertainty, here, is the binding tax on productivity.

7.3 Pharmaceuticals and life sciences

Pharmaceuticals show how a regime optimised for one objective can constrain another. India is the third-largest drug producer by volume and a global supplier of affordable generics, an achievement built on process capability and a patent regime, notably the anti-evergreening provision of Section 3(d) of the Indian Patents Act, 1970 and the compulsory-licensing power of Section 84 of the Indian Patents Act, 1970 designed to maximise access. The same architecture, however, narrows the appropriable return to incremental and original innovation, which is part of why Indian pharmaceutical firms have historically invested a far smaller share of revenue in research than global innovators. The productivity question is not whether the access safeguards should be abandoned, which they need not be, but whether the surrounding environment, namely regulatory data protection, clinical-trial approval timelines, patent-examination quality and speed, and predictable enforcement, can be modernised so that the returns to genuine invention are sufficient to pull the sector up the value chain toward novel therapeutics and biologics. The emergence of contract research and development organisations and of multinational capability centres shows the latent capacity; whether it is realised depends on appropriability and approval velocity. The mechanism, once again, is that slow approvals raise the ICOR of research-intensive projects and thin appropriability lowers the TFP they would otherwise generate.

A summary of the three vignettes, with the regulatory friction and the productivity channel made explicit, appears in Table 4.

Scenario	Investment rate	ICOR	Implied real growth
Current efficiency	30.0 percent	4.9	6.1 percent
Target efficiency	30.0 percent	4.0	7.5 percent
Efficiency ceiling for 7.8 percent	30.0 percent	3.85	7.8 percent
Efficiency dividend	no change	4.9 to 4.0	plus 1.4 points

8. The IP Frontier: The Highest-Upside Lever

Having placed intellectual property among five regulatory domains, it is worth stating precisely why it merits special, though not exclusive, attention. The claim is not that IP-intensive sectors carry the productivity burden by themselves. Services, logistics, agriculture, power distribution, urban governance and finance all have large productivity gaps, several of them quantified above, and a balanced reform programme must address all of them. The claim is narrower and, properly stated, stronger: IP-intensive activity is the highest-upside frontier lever for a high-income transition, because the marginal product of an idea is uniquely large, reproducible at near-zero cost once discovered, and uniquely dependent on the rules.

The supporting evidence is the size of the gap. India’s research intensity of about 0.65 percent of GDP trails China, the United States, Germany and South Korea by a factor of four to seven, and the private sector funds barely a third of it, the inverse of the pattern in research-intensive economies.¹⁹²⁰ In advanced economies, IP-intensive industries account for roughly two-fifths of all value added, 47.9 percent of GDP in the European Union and 38.2 percent in the United States; India has no equivalent official measure, but the gap is not in doubt.²¹ A frontier economy raises its TFP growth from 1.2 to 3 percent not by building more of what it already builds, but by expanding the share of activity in which ideas, rather than machines, are the binding input. That expansion is governed almost entirely by regulation, because intangible assets are creatures of law in a way that physical assets are not.

It is equally important to recognise that intellectual property is an input to productivity, not productivity itself. The deeper mechanism is absorptive capacity, namely the ability of firms, universities and the state to generate, adapt and diffuse knowledge. Research intensity and patent quality are proxies for that capacity, and India’s weakness on both is mirrored in thin university-industry linkages, a small base of doctoral researchers per capita, and limited venture financing for deep technology. A regulatory framework that raises the private return to invention is a necessary condition for closing the gap, but it must be paired with investment in the human and institutional capital that turns protected ideas into products. The IP frontier is therefore the lever with the steepest payoff, but it must be pulled alongside, not instead of, the broader cross-sectoral reforms of Section 6.

9. A Regulatory Framework for Modernisation

The diagnoses above translate into a compact set of design principles that apply across the whole class of IP-intensive and network sectors. They are intended as a framework for legislative and administrative reform rather than a list of sectorspecific measures. Five principles follow, each stated as a sentence and then operationalised in the implementation matrix of Table 5.

The first principle is appropriability with proportionality. Innovators must be able to capture a sufficient and predictable share of the returns to invention, through patent quality, regulatory data protection and reliable enforcement, while the state retains narrow, rules-based override powers for genuine emergencies rather than broad discretionary ones. The second principle is administrative velocity. Every regulation should be designed and resourced to operate quickly, because latency is a direct tax on output, and speed should be a statutory entitlement secured through digitisation, deemed approvals and riskbased scrutiny. The third principle is institutional capacity. The state must invest in the scientific, legal and administrative talent of its patent offices, sectoral regulators and courts, because no statute delivers good outcomes through an under-staffed institution. The fourth principle is incentive alignment between public and private effort, shifting the composition of national research and investment toward the private firms that commercialise it. The fifth principle is evaluability, the discipline of assessing every regulation against a single question, namely whether it raises or lowers total factor productivity and the efficiency of capital, and institutionalising that test through a standing productivity audit.

Table 5 makes these principles concrete by mapping each to a current problem, a reform instrument, a responsible institution, a productivity channel and a measurable indicator. The matrix is the operational core of the paper, and it is deliberately specific enough to be tracked.

Principle	Current problem	Reform instrument	Responsible institution	Productivity channel	Measurable indicator
Appropriability with proportionality	Resident grant share only 30.4 percent; thin enforcement; broad override powers	Patent-quality reform; regulatory data protection; predictable, narrow compulsory licensing rules	DPIIT; Patent Office; Ministry of Commerce	Raises private return to invention; lifts TFP residual	Resident grant share; enforcement time for IP suits; R&D as percent of sales
Administrative velocity	Patent pendency over 4 years; slow product and environmental clearances	Statutory timelines; deemed approvals; risk-based scrutiny; regulatory sandboxes; single-window systems	MeitY; MoEFCC; sectoral regulators; NSWS	Lowers effective ICOR by shortening capital lock-up	Patent disposal time; median approval timeline; share of deemed approvals
Institutional capacity	Examiner and controller vacancies; under-resourced regulators and courts	Recruitment and training; scientific staffing of regulators; commercial-court expansion	Patent Office; sectoral regulators; judiciary	Converts good rules into outcomes; raises TFP and lowers ICOR	Examiners per 1,000 filings; commercial-court disposal rate; vacancy ratio
Incentive alignment	Private sector funds only 36 percent of GERD	Research-promotion schemes; public-private missions; in-house R&D tax treatment	DST; AnusandhanNRF; Ministry of Finance	Raises and reorients research toward commercialising firms	Private share of GERD; GERD as percent of GDP; patents per R&D rupee
Evaluability	No standing measure of regulation’s productivity impact	IP-intensity and TFP audit embedded in regulatory impact assessment	NITI Aayog; line ministries	Guards against regulatory drift; sustains the gains	Published TFP-impact assessments; ICOR and TFP tracked annually

10. Reform Scenarios and 2047 Outcomes

To make the stakes concrete, the companion model combines the two levers developed above, namely the investment rate and the ICOR, into a real growth rate for each of five scenarios, then compounds nominal dollar output from the FY26 base to 2047 and compares it with the USD 30 trillion target. The mechanics are fully transparent and are set out in the Methodology Annex. For each scenario, real growth is computed from the Harrod-Domar identity as the investment rate divided by the ICOR. Real growth is then converted into nominal dollar growth by applying a constant uplift that reflects domestic inflation net of rupee depreciation, specifically a GDP deflator of 4 percent less depreciation of 1.5 percent a year, which adds about 2.4 percentage points to the real rate. Nominal dollar GDP in 2047 is the FY26 base of USD 3.96 trillion compounded at that nominal rate over twenty-two years, and the final column expresses it as a share of the USD 30 trillion target.

The five scenarios are defined as follows, and the assumptions behind each are deliberately conservative and explicit. The stagnation scenario holds the investment rate at 28 percent and lets the ICOR drift up to 5.5, representing a failure to reform factor and network markets; it yields about 5.1 percent real growth and leaves India at roughly two-thirds of the target. The Business-as-usual scenario keeps the investment rate at the current 30 percent and the ICOR at the blended 4.9, with no improvement in capital efficiency; it yields about 6.1 percent and reaches about four-fifths of the target. The Reform scenario lifts investment modestly to 32 percent and improves the ICOR to 4.4 through the crosssectoral reforms of Section 6; it yields about 7.3 percent and crosses the target. The Accelerated modernisation scenario combines a 31 percent investment rate with an ICOR of 4.0, the efficient-frontier value, and yields about 7.8 percent, the headline requirement. The Frontier scenario pushes investment to 33 percent and the ICOR to 3.85, the efficiency ceiling, representing full IP-led modernisation, and yields about 8.6 percent. The scenarios are not equally likely; they are designed to isolate the effect of capital efficiency, which is why the investment rate varies only modestly across them while the ICOR does most of the work.

Scenario	ICOR	Real growth (in percent)	2047 GDP (USD tn)	Share of target (in percent)
Stagnation	5.5	5.1	20	67
Business-as-usual	4.9	6.1	25	83
Reform	4.4	7.3	32	105
Accelerated modernisation	4.0	7.8	35	116
Frontier (IP-led)	3.85	8.6	41	137

Two features of the table deserve emphasis. First, the difference between stagnation and frontier is not marginal. It is the difference between a USD 20 trillion economy and a USD 41 trillion economy, a gap larger than the entire current output of India, generated by differences in capital efficiency and productivity rather than in the volume of investment. Second, the scenarios that reach the target are precisely those in which the ICOR falls below 4.5, which is to say those in which cross-sectoral regulatory modernisation has done its work. The model therefore does not merely restate the target; it shows that the target is a regulatory-reform target, because the parameter that moves the outcome from failure to success is the efficiency of capital, and that efficiency is set by the rules under which the economy invests and innovates.

The model is deliberately transparent and conservative. It abstracts from external shocks, demographic turning points and the precise sectoral composition of growth, and its scenario parameters are illustrative rather than forecast. Its purpose is to size the prize and locate the lever, not to predict the path. On that limited but important question it is unambiguous. India reaches Viksit Bharat if, and broadly only if, it raises the productivity of its investment, and it raises the productivity of its investment chiefly by modernising the regulation of its economy across factor markets, network sectors and the IP frontier together.

11. Methodology Annex

This annex makes the model fully reproducible. Every figure in the paper traces to a parameter or formula below, and the same content is implemented cell-by-cell in the companion workbook, where blue cells are inputs, black cells are formulas and green cells are cross-sheet links. The annex is intended to satisfy the requirement that a published model state its assumptions on inflation, exchange-rate depreciation, capital growth, labour growth, and the mapping from real growth to nominal dollar GDP.

11.1 Assumptions

Parameter	Value	Source or basis
Base nominal GDP, FY26	USD 3.96 tn	5.1
Target nominal GDP, 2047	USD 30 tn	6.1
Horizon	22 years	7.3
Real GDP growth (target)	7.8 percent	7.8
GDP deflator/domestic inflation	4.0 percent	8.6
INR/USD depreciation	1.5 percent	Assumption, long-run drift
Nominal uplift over real	about 2.4 percent	(1.04) times (1 minus 0.015) minus 1
Investment rate (GFCF / GDP)	30.0 percent	Economic Survey 2025-26
Current ICOR (blended)	4.9	Compiled estimates; range 4.5 to 6.5
Target/ceiling ICOR	4.0/3.85	Policy target; ceiling for 7.8 percent
Capital share alpha	0.45	Growth-accounting convention for India
Labour force growth	1.3 percent	Working-age population
Human-capital growth	0.8 percent	Schooling and quality
Historical TFP growth	1.2 percent	RBI and studies, 2011-19
GERD, India	0.65 percent of GDP	DST R&D Statistics

11.2 Formulas

The model uses four relationships. First, the required nominal dollar CAGR is (target divided by base) raised to the power one over the horizon, minus one, which gives about 9.6 percent. Second, the Harrod-Domar identity gives real growth as the investment rate divided by the ICOR. Third, nominal dollar growth is obtained from real growth by the mapping nominal equals (1 plus real) times (1 plus deflator) times (1 minus depreciation) minus one, so that, for example, 7.8 percent real becomes about 10.4 percent nominal. Fourth, 2047 GDP is the base compounded at the nominal rate over the horizon. The Solow decomposition, used in Section 5, sets required TFP growth equal to target output growth minus the capital contribution (alpha times capital growth) minus the labour contribution (one minus alpha, times labour-input growth), giving about 3.1 percent against a realised 1.2 percent. Capital growth is set equal to output growth along a balanced-growth path, and labour-input growth is the sum of labour-force and human-capital growth.

Two cautions apply. The Harrod-Domar identity is a first-order approximation that holds capital efficiency constant within a scenario; in reality the ICOR and TFP co-move, so the scenarios should be read as comparative statics rather than dynamic forecasts. And the nominal-to-real mapping assumes a constant inflation and depreciation wedge; a different wedge would shift the dollar figures without changing the central finding, which concerns real growth and capital efficiency. These simplifications are deliberate, in the interest of transparency, and the workbook allows any assumption to be flexed.

12. Conclusion

The road to a USD 30 trillion economy runs through productivity, not merely accumulation. India has already done the hard work of raising its investment rate to about 30 percent of GDP. The unfinished task is to raise the return on that investment. The Harrod-Domar identity shows that moving the ICOR from a blended 4.9 toward 4.0 would add roughly 1.4 percentage points to trend growth at no additional cost, and the Solow decomposition shows that meeting the 7.8 percent real-growth requirement demands TFP growth near 3 percent against a realised rate closer to 1.2 percent. Both readings point to the same conclusion. The Viksit Bharat target is a productivity target in disguise, and productivity is set by the rules.

Those rules span the whole regulatory state, namely factor markets, logistics and power, contract enforcement, approval regimes and the protection of intangible assets. The crosssectoral evidence is concrete: logistics costs near 8 percent of GDP, distribution losses still around a sixth of power purchased, contract enforcement measured in years, and patent pendency over four. Intellectual-property-intensive sectors are not the only lever, but they are the highest-upside frontier lever, because the marginal product of an idea is uniquely large and uniquely dependent on the rules. The sector vignettes show the mechanism in operation, and the implementation matrix shows what to do about it, institution by institution and indicator by indicator. The scenario model makes the point starkly. The same investment effort produces a USD 20 trillion economy under stagnation and a USD 41 trillion economy at the frontier, and the variable that separates them is the productivity of capital. Whether India reaches USD 30 trillion by 2047 will depend less on how much it builds than on the rules under which it invests and invents.

Paper Presented at 108^th SKOCH Summit

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1 National Statistical Office, Ministry of Statistics and Programme Implementation (2026), First Advance Estimates of National Income FY2025-26, January 2026 (approximately Rs 357 lakh crore, about USD 3.96 trillion). India ranked fourth by nominal GDP in 2025, overtaking Japan; IMF, World Economic Outlook Database, October 2025, https://www.imf.org/external/datamapper/profile/IND.

2 Government of India, Viksit Bharat @2047 vision (NITI Aayog). PHDCCI (2024), Viksit Bharat @2047: A Blueprint of Micro and Macro Economic Dynamics, projects up to USD 34.7 trillion by 2047, https://www.phdcci.in. The USD 30 trillion figure is the commonly cited round target.

3 World Bank (2024), India Country Economic Memorandum: Becoming a High-Income Economy in a Generation, Washington DC, finding average real growth of about 7.8 percent required to 2047 under the accelerated-reforms scenario, https://www.worldbank.org. Ministry of Finance, Economic Survey 2024-25, cites about 8 percent.

4 Ministry of Finance, Economic Survey 2025-26 (Gross Fixed Capital Formation 30.0 percent of GDP in FY26). World Bank, Gross Fixed Capital Formation (percent of GDP), India, indicator NE.GDI.FTOT.ZS (29.6 percent in 2024), https://data.worldbank.org/indicator/NE.GDI.FTOT.ZS?locations=IN.

5 Author computations, companion workbook ‘Viksit_Bharat_Regulatory_Framework_Model.xlsx’. Scenario parameters are illustrative model inputs on the assumptions set out in the Methodology Annex, not forecasts. All formulas are transparent and reproduced in Sections 10 and 11.

6 World Bank. Gross Fixed Capital Formation (percent of GDP), India, indicator NE.GDI.FTOT.ZS. https://data.worldbank.org/indicator/NE.GDI.FTOT.ZS?locations=IN

7 ICOR estimates compiled from public sources: about 7.5 in FY12 and about 3.5 in FY22, with an operating range of 4.5 to 6.5 commonly cited and a policy target of 3 to 4. A blended 4.9 is adopted here as a conservative central value. See, for example, PRS and policy commentary summarised at https://www.drishtiias.com.

8 Reserve Bank of India and growth-accounting studies: India TFP growth about 1.2 percent (2011-19) and about 2.2 percent (2010-19, alternative measure), with TFP contributing roughly 30 percent of GDP growth in the 2010s. See Conference Board Total Economy Database and academic surveys.

9 DPIIT, Ministry of Commerce and Industry, Report on Logistics Cost in India (2025): national logistics cost estimated at about 7.97 percent of GDP. National Logistics Policy (2022) targets reduction to about 8 percent of GDP by 2030 from earlier estimates of 13 to 14 percent, https://www.dpiit.gov.in.

10 Power Finance Corporation, Report on Performance of State Power Utilities; Ministry of Power. AT&C losses fell from 25.5 percent (FY13) to about 15.4 percent (FY23); accumulated discom losses about Rs 6.5 trillion (FY23). Revamped Distribution Sector Scheme outlay Rs 3.04 lakh crore, https://powermin.gov.in.

11 World Bank, Doing Business, Enforcing Contracts (India): 1,445 days to enforce a commercial contract and rank 163 of 190; data as of 2019, programme discontinued September 2021, https://archive.doingbusiness.org. Roughly 30 million cases pending over one year in district courts (2022).

12 Department of Science and Technology, Research and Development Statistics at a Glance 2022-23, https://dst.gov.in; PIB, Parliament replies on R&D investment. India GERD about 0.65 percent of GDP; private sector about 36 percent of GERD, against more than 70 percent in advanced economies.

13 World Bank, Research and Development Expenditure (percent of GDP), indicator GB.XPD.RSDV.GD.ZS: China about 2.4 percent, United States about 3.5 percent, Germany about 3.1 percent, South Korea about 4.8 percent, https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS.

14 Office of the Controller General of Patents, Designs and Trade Marks, Annual Report 2024-25: 110,375 filings (up 19.75 percent year on year); domestic share 61.79 percent; resident share of grants 30.4 percent. Summarised in Cyril Amarchand Mangaldas analysis (January 2026), https://corporate.cyrilamarchandblogs.com.

15 Prime Minister’s Economic Advisory Council (2022): average patent pendency exceeds four years against a global best practice of two to three years. Patents (Amendment) Rules 2024 reduced the examination-request deadline from 48 to 31 months, https://ipindia.gov.in.

16 Genetic Engineering Appraisal Committee approvals and moratoria; ISAAA and Drishti IAS summaries on Bt cotton, Bt brinjal and GM mustard.

17 India Semiconductor Mission, Ministry of Electronics and IT: outlay over USD 10 billion (Rs 76,000 crore); fiscal support up to 50 percent of project cost. Tata-PSMC fab at Dholera (Rs 91,000 crore, 28 nm) approved February 2024; Micron ATMP at Sanand (USD 2.75 billion); ten projects approved by 2026, https://ism.gov.in; PIB releases.

18 Genetic Engineering Appraisal Committee approvals and moratoria: Bt cotton commercialised 2002 (the only GM crop in cultivation, about 10.8 million hectares); Bt brinjal approved by GEAC 2009 but placed under moratorium 2010; GM mustard (DMH-11) given environmental clearance 2022, commercial release stayed pending Supreme Court proceedings; Supreme Court (2025) directed a national GM policy. See Drishti IAS and ISAAA summaries.

19 Department of Science and Technology, Research and Development Statistics at a Glance 2022-23, https://dst.gov.in; PIB,
Parliament replies on R&D investment. India GERD about 0.65 percent of GDP; private sector about 36 percent of GERD, against more than 70 percent in advanced economies.

20 World Bank, Research and Development Expenditure (percent of GDP), indicator GB.XPD.RSDV.GD.ZS: China about 2.4 percent, United States about 3.5 percent, Germany about 3.1 percent, South Korea about 4.8 percent, https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS.

21 EUIPO and EPO (2026), IPR-Intensive Industries and Economic Performance in the European Union: 47.9 percent of EU GDP, https://www.euipo.europa.eu. USPTO (2016, third edition), Intellectual Property and the U.S. Economy: 38.2 percent of US GDP (2014), https://www.uspto.gov.