Vishleshan for Regulatory Exams 1st July 2026 | VB-G RAM G: India should aim to universalize its job guarantee scheme instead of limiting it

India’s rural employment landscape entered a new phase on July 1, 2026, with the launch of the Viksit Bharat–Guarantee for Rozgar and Ajeevika Mission (Gramin), replacing MGNREGA after two decades. While the new scheme promises more targeted asset creation and additional workdays, it also introduces structural changes that have sparked debate among policymakers and economists. This article examines the key differences between the two schemes, their implications for rural workers, and whether the new framework strengthens or weakens India’s social safety net.

VB-G RAM G: India should aim to universalize its job guarantee scheme instead of limiting it

Context: On July 1, 2026, India formally replaced MGNREGA (2005) with the Viksit Bharat–Guarantee for Rozgar and Ajeevika Mission (Gramin), or VB-G RAM G — the most significant restructuring of India’s rural employment guarantee architecture in two decades. This editorial is a policy opinion piece that evaluates the new scheme’s structural changes, identifies the weakening of its core safety-net promise, and argues that India should extend employment guarantee coverage to urban areas. The core argument: VB-G RAM G improves purposefulness but dilutes the unconditional fallback guarantee that was MGNREGA’s foundational design — and the right response is universalisation, not retrenchment.

Link to the Article: Mint

MGNREGA — The Architecture It Replaced

• The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) was enacted in 2005 under the UPA-I government, initially covering 200 districts and expanding nationwide by 2008 — making it the world’s largest public works programme by beneficiary count.
• The scheme guaranteed 100 days of paid wage employment per financial year to every rural household whose adult members volunteered to do unskilled manual work — demand-driven and legally enforceable as a right, not a discretionary benefit.
• Financing: 100% Centre-funded for wages; states bore material costs. The Centre’s liability was open-ended — if demand exceeded budget, the Centre was obligated to fund it.
• Wage mechanism: MGNREGA wages were notified separately by state, indexed to agricultural inflation, and functioned as a de facto wage floor in rural labour markets — raising the minimum that agricultural workers would accept from private employers.
• Scale at peak (2020–21): Over 111 million unique workers participated; ₹1.11 lakh crore in central expenditure; average days worked approximately 51 days per household — well below the 100-day guarantee but significant as a consumption support floor.
• Structural weaknesses of MGNREGA: chronic payment delays (average 40–60 days in several states), weak asset quality (low productivity of created infrastructure), leakage through inflated muster rolls, and inability to address urban poverty.

India’s Rural Distress Context — Why This Transition Matters Now

• India’s rural unemployment rate stood at 6.1% (PLFS 2024–25, usual status); underemployment — those working fewer days than desired — is structurally higher and not captured in headline unemployment figures.
• Kharif 2026 outlook: The India Meteorological Department (IMD) has flagged below-normal southwest monsoon distribution in key agricultural states — raising the risk of crop stress and reduced agricultural labour demand in the very season the new scheme’s 60-day suspension window applies.
• Rural wage growth: Real agricultural wages grew at under 2% annually in 2023–25 (Labour Bureau data) — below rural CPI inflation in several states, meaning real rural wages have been stagnant or declining.
• Urban poverty: According to the Socio-Economic and Caste Census (SECC) estimates and World Bank poverty tracking, approximately 80–90 million urban residents live below or near the poverty line — a population for whom no employment guarantee equivalent exists.
• State fiscal stress: 15 of 28 states currently carry fiscal deficit levels above the FRBM-recommended 3% of GSDP (CAG data, 2024–25) — raising a direct structural concern about their ability to absorb the new co-financing responsibility under VB-G RAM G.

Decoding the Article: Analysis

1. The 60-Day Suspension Breaks the Foundational Logic of an Employment Guarantee — and 25 Extra Days Cannot Compensate

• The defining feature of MGNREGA — the characteristic that distinguished it from all previous wage employment schemes — was its unconditional, demand-driven character: a household could seek work on any day within the year, within the 100-day limit. The scheme was legally a right, not a seasonal programme.
• The VB-G RAM G’s 60-day annual suspension converts a year-round entitlement into a seasonally rationed benefit. The suspension is timed precisely to the harvest season — which is also the period of maximum rural economic activity and, in a distress year, maximum displacement risk when crop failure reduces on-farm employment.
• The net arithmetic: 125 available days minus 60 suspended days = 65 effective guaranteed days in the worst-case suspension window — which is actually fewer than the original 100-day guarantee if the suspension falls during the months a household most needs the fallback. The 25-day increase is a headline improvement that conceals a structural regression in guarantee reliability.
• So-what: An employment guarantee that can be suspended precisely when it is most needed is not a guarantee — it is a conditional entitlement. The policy rationale (protecting farm employers’ labour supply) is legitimate, but it is achieved at the direct cost of the scheme’s foundational promise to workers.

2. State Co-Financing Will Structurally Disadvantage the States Where Rural Distress Is Worst

• The shift from Centre-only to Centre-plus-state wage financing appears administratively rational — shared fiscal responsibility should theoretically improve state-level accountability and reduce Centre’s open-ended liability. However, it creates a perverse geographic outcome: the states with the highest MGNREGA demand (Bihar, Jharkhand, Odisha, Uttar Pradesh, Madhya Pradesh) are precisely the states with the weakest fiscal positions and the highest debt-to-GSDP ratios.
• Under the new architecture, a fiscally stressed state facing a distress year — high rural unemployment, crop failure, migration pressure — faces a simultaneous demand surge and budget constraint: the more workers seek employment, the greater the state’s co-financing liability, creating an incentive to suppress demand registration rather than expand programme access.
• MGNREGA’s history shows this pattern was already present under the old scheme (states manipulated muster rolls and delayed job cards to manage costs); the new co-financing structure institutionalises and amplifies the incentive. The 10% vs. 40% differentiation by state category mitigates but does not eliminate this problem.
• So-what: The fiscal architecture of VB-G RAM G is designed as if India’s poorest states have comparable fiscal capacity to its richer states. They do not. A uniform co-financing ratio applied to fiscally heterogeneous states is not equitable cost-sharing — it is a mechanism that will reduce effective coverage precisely in the geography where coverage is most needed.

3. Universalisation to Urban Areas Is the Right Direction — But the Absence of a Concrete Proposal Leaves the Argument Incomplete

• The editorial’s call for urban employment guarantee coverage is supported by three converging realities: urban poverty affects 80–90 million people; the pace of formal job creation in manufacturing and services is insufficient to absorb urban migration (India’s formal sector employment grew at approximately 2.3% annually in 2022–25 per EPFO data, against urban labour force growth of 3.5–4%); and urban infrastructure — roads, drainage, sanitation, housing — has a documented maintenance and construction deficit that unskilled labour could address.
• The precedent exists: Rajasthan’s MGNREGA Urban Pilot (2022–23) and Tamil Nadu’s Urban Employment Scheme have demonstrated operational feasibility of urban public works at scale. The CAMPA (Compensatory Afforestation Fund Management and Planning Authority) and Smart Cities Mission provide institutional channels through which urban employment-linked infrastructure work could be routed.
• However, urban employment guarantee design involves structurally different challenges than rural: heterogeneous skill requirements, higher urban wage floors, stronger trade union presence, more complex land and contracting arrangements, and greater fiscal pressure on urban local bodies. None of these design challenges are acknowledged in the editorial.
• So-what: The universalisation argument is directionally correct and increasingly urgent, but a call for urban coverage without a financing model, institutional architecture, or design framework is advocacy without architecture. The editorial identifies the destination correctly but provides no map.

The Fine Print — What the Article Does Not Say Loudly Enough

• The 125-day figure applies to one member per household, not per adult worker — the article treats the 25-day increase as a straightforward expansion of coverage, but the per-household (not per-worker) structure means a household with three working-age adults still receives the same aggregate entitlement. The effective employment guarantee per adult worker in a multi-adult household has not increased at all.
• The article does not quantify the wage-floor effect of removing MGNREGA’s year-round wage competition. Agricultural economists (Himanshu, JNU; Dreze, Ranchi) have documented that MGNREGA wages set the effective minimum that agricultural workers would accept from private employers. The 60-day suspension removes this competition during harvest — the period when farm employers have maximum bargaining power over migrant and daily-wage labour. The article notes this as a distortion that favoured the poor but does not estimate the magnitude of the wage suppression the suspension enables.
• The scheme’s technology upgrades (biometric authentication, geospatial tracking) are presented as unambiguous positives — but the history of Aadhaar-linked payment authentication in MGNREGA shows significant exclusion errors. LibTech India’s longitudinal studies (2018–2023) documented that biometric authentication failures excluded genuine beneficiaries at rates of 8–15% in some districts. The article does not flag that technology-dependent authentication, without robust grievance redressal, can reduce coverage for the most vulnerable.
• State fiscal capacity to absorb 40% wage co-financing is not uniform — but the article treats it as a single national policy variable. The 10% vs. 40% differentiation exists in the legislation but the article does not specify which states fall in which category or what the fiscal stress distribution looks like. The critical question — whether the 10% category covers Bihar, Jharkhand, and Odisha (the highest-need states) — is left unaddressed.
• The editorial’s urban employment guarantee proposal does not engage with the fiscal architecture question. Urban local bodies in India collect less than 1% of GDP in own revenues (Finance Commission XV data) and are largely dependent on state devolution. An urban employment guarantee without a dedicated Central funding stream — modelled on the original MGNREGA’s Centre-first financing — would effectively be unfunded.

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