{"id":205112,"date":"2026-07-01T16:25:15","date_gmt":"2026-07-01T10:55:15","guid":{"rendered":"https:\/\/www.practicemock.com\/blog\/?p=205112"},"modified":"2026-07-01T16:25:19","modified_gmt":"2026-07-01T10:55:19","slug":"vishleshan-for-regulatory-exams-1st-july-2026","status":"publish","type":"post","link":"https:\/\/www.practicemock.com\/blog\/vishleshan-for-regulatory-exams-1st-july-2026\/","title":{"rendered":"Vishleshan for Regulatory Exams 1st July 2026 | VB-G RAM G: India should aim to universalize its job guarantee scheme instead of limiting it"},"content":{"rendered":"\n

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Home<\/a><\/span> \u00bb Vishleshan<\/a><\/span> \u00bb Vishleshan for Regulatory Exams 1st July 2026<\/span><\/span><\/div>\n\n\n

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India\u2019s rural employment landscape entered a new phase on July 1, 2026, with the launch of the Viksit Bharat\u2013Guarantee 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\u2019s social safety net.<\/p>\n\n\n\n

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Sign up to Download RBI Grade B PYQ PDF<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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VB-G RAM G: India should aim to universalize its job guarantee scheme instead of limiting it<\/h2>\n\n\n\n

Context<\/strong>: On July 1, 2026, India formally replaced MGNREGA (2005) with the Viksit Bharat\u2013Guarantee for Rozgar and Ajeevika Mission (Gramin), or VB-G RAM G \u2014 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 \u2014 and the right response is universalisation, not retrenchment.<\/p>\n\n\n\n

Link to the Article<\/strong>: Mint<\/a><\/p>\n\n\n\n

MGNREGA \u2014 The Architecture It Replaced<\/u><\/strong><\/h2>\n\n\n\n
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  • 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 \u2014 making it the world’s largest public works programme by beneficiary count.<\/li>\n\n\n\n
  • 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 \u2014 demand-driven and legally enforceable as a right, not a discretionary benefit.<\/li>\n\n\n\n
  • Financing: 100% Centre-funded for wages; states bore material costs. The Centre’s liability was open-ended \u2014 if demand exceeded budget, the Centre was obligated to fund it.<\/li>\n\n\n\n
  • 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 \u2014 raising the minimum that agricultural workers would accept from private employers.<\/li>\n\n\n\n
  • Scale at peak (2020\u201321): Over 111 million unique workers participated; \u20b91.11 lakh crore in central expenditure; average days worked approximately 51 days per household \u2014 well below the 100-day guarantee but significant as a consumption support floor.<\/li>\n\n\n\n
  • Structural weaknesses of MGNREGA: chronic payment delays (average 40\u201360 days in several states), weak asset quality (low productivity of created infrastructure), leakage through inflated muster rolls, and inability to address urban poverty.<\/li>\n<\/ul>\n\n\n\n

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    India’s Rural Distress Context \u2014 Why This Transition Matters Now<\/u><\/strong><\/h2>\n\n\n\n
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    • India’s rural unemployment rate stood at 6.1% (PLFS 2024\u201325, usual status); underemployment \u2014 those working fewer days than desired \u2014 is structurally higher and not captured in headline unemployment figures.<\/li>\n\n\n\n
    • Kharif 2026 outlook: The India Meteorological Department (IMD) has flagged below-normal southwest monsoon distribution in key agricultural states \u2014 raising the risk of crop stress and reduced agricultural labour demand in the very season the new scheme’s 60-day suspension window applies.<\/li>\n\n\n\n
    • Rural wage growth: Real agricultural wages grew at under 2% annually in 2023\u201325 (Labour Bureau data) \u2014 below rural CPI inflation in several states, meaning real rural wages have been stagnant or declining.<\/li>\n\n\n\n
    • Urban poverty: According to the Socio-Economic and Caste Census (SECC) estimates and World Bank poverty tracking, approximately 80\u201390 million urban residents live below or near the poverty line \u2014 a population for whom no employment guarantee equivalent exists.<\/li>\n\n\n\n
    • State fiscal stress: 15 of 28 states currently carry fiscal deficit levels above the FRBM-recommended 3% of GSDP (CAG data, 2024\u201325) \u2014 raising a direct structural concern about their ability to absorb the new co-financing responsibility under VB-G RAM G.<\/li>\n<\/ul>\n\n\n\n

      Decoding the Article: Analysis<\/u><\/strong><\/h2>\n\n\n\n

      1. The 60-Day Suspension Breaks the Foundational Logic of an Employment Guarantee \u2014 and 25 Extra Days Cannot Compensate<\/strong><\/p>\n\n\n\n

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      • The defining feature of MGNREGA \u2014 the characteristic that distinguished it from all previous wage employment schemes \u2014 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.<\/li>\n\n\n\n
      • 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 \u2014 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.<\/li>\n\n\n\n
      • The net arithmetic: 125 available days minus 60 suspended days = 65 effective guaranteed days in the worst-case suspension window \u2014 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.<\/li>\n\n\n\n
      • So-what: An employment guarantee that can be suspended precisely when it is most needed is not a guarantee \u2014 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.<\/li>\n<\/ul>\n\n\n\n

        2. State Co-Financing Will Structurally Disadvantage the States Where Rural Distress Is Worst<\/strong><\/p>\n\n\n\n

          \n
        • The shift from Centre-only to Centre-plus-state wage financing appears administratively rational \u2014 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.<\/li>\n\n\n\n
        • Under the new architecture, a fiscally stressed state facing a distress year \u2014 high rural unemployment, crop failure, migration pressure \u2014 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.<\/li>\n\n\n\n
        • 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.<\/li>\n\n\n\n
        • 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 \u2014 it is a mechanism that will reduce effective coverage precisely in the geography where coverage is most needed.<\/li>\n<\/ul>\n\n\n\n

          3. Universalisation to Urban Areas Is the Right Direction \u2014 But the Absence of a Concrete Proposal Leaves the Argument Incomplete<\/strong><\/p>\n\n\n\n

            \n
          • The editorial’s call for urban employment guarantee coverage is supported by three converging realities: urban poverty affects 80\u201390 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\u201325 per EPFO data, against urban labour force growth of 3.5\u20134%); and urban infrastructure \u2014 roads, drainage, sanitation, housing \u2014 has a documented maintenance and construction deficit that unskilled labour could address.<\/li>\n\n\n\n
          • The precedent exists: Rajasthan’s MGNREGA Urban Pilot (2022\u201323) 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.<\/li>\n\n\n\n
          • 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.<\/li>\n\n\n\n
          • 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.<\/li>\n<\/ul>\n\n\n\n

            The Fine Print \u2014 What the Article Does Not Say Loudly Enough<\/u><\/strong><\/h2>\n\n\n\n
              \n
            • The 125-day figure applies to one member per household, not per adult worker<\/strong> \u2014 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.<\/li>\n\n\n\n
            • The article does not quantify the wage-floor effect of removing MGNREGA’s year-round wage competition.<\/strong> 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 \u2014 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.<\/li>\n\n\n\n
            • The scheme’s technology upgrades (biometric authentication, geospatial tracking) are presented as unambiguous positives \u2014 but the history of Aadhaar-linked payment authentication in MGNREGA shows significant exclusion errors.<\/strong> LibTech India’s longitudinal studies (2018\u20132023) documented that biometric authentication failures excluded genuine beneficiaries at rates of 8\u201315% in some districts. The article does not flag that technology-dependent authentication, without robust grievance redressal, can reduce coverage for the most vulnerable.<\/li>\n\n\n\n
            • State fiscal capacity to absorb 40% wage co-financing is not uniform \u2014 but the article treats it as a single national policy variable.<\/strong> 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 \u2014 whether the 10% category covers Bihar, Jharkhand, and Odisha (the highest-need states) \u2014 is left unaddressed.<\/li>\n\n\n\n
            • The editorial’s urban employment guarantee proposal does not engage with the fiscal architecture question.<\/strong> 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 \u2014 modelled on the original MGNREGA’s Centre-first financing \u2014 would effectively be unfunded.<\/li>\n<\/ul>\n\n\n\n

              What to Watch<\/u><\/strong><\/h2>\n\n\n\n
              Indicator<\/strong><\/td>Source<\/strong><\/td>What It Signals<\/strong><\/td><\/tr><\/thead>
              VB-G RAM G worker registration and job card issuance \u2014 July\u2013September 2026<\/strong> \u2014 whether first-quarter demand matches or falls below the equivalent MGNREGA quarter in 2025<\/td>MoRD VB-G RAM G public dashboard, monthly from August 2026<\/td>A drop of more than 10\u201315% vs. the same quarter in 2025 signals that state co-financing pressure is suppressing registration. Demand parity or increase would suggest the 25-day expansion is absorbing transition friction<\/td><\/tr>
              State-wise 60-day suspension notifications for Kharif 2026<\/strong> \u2014 specifically whether Bihar, UP, Jharkhand, and Odisha invoke the full window during the post-monsoon agricultural stress period<\/td>State government gazettes and MoRD circulars, August\u2013October 2026<\/td>If high-distress states invoke the full suspension during a weak Kharif season, it directly validates the editorial’s core concern. No invocation by these states would suggest they are treating the suspension as optional, not structural<\/td><\/tr>
              Union Budget 2027\u201328<\/strong> (February 2027) \u2014 whether a dedicated urban employment guarantee pilot receives a budget line, and whether VB-G RAM G’s wage indexation mechanism is formally confirmed<\/td>Ministry of Finance Annual Budget + MoRD Outcome Budget 2027\u201328, February 2027<\/td>A dedicated urban employment line \u2014 even a \u20b95,000\u201310,000 crore pilot \u2014 signals universalisation has entered the fiscal agenda. Absence confirms the editorial’s call has not moved policy. Wage indexation confirmation would resolve the most critical fine print gap in the new scheme’s design<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

              VB-G RAM G replaces MGNREGA. Know key changes, benefits, challenges, and impact on rural employment in India.<\/p>\n","protected":false},"author":25,"featured_media":205113,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[4022],"tags":[],"class_list":["post-205112","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-vishleshan"],"yoast_head":"\nVishleshan for Regulatory Exams 1st July 2026 | VB-G RAM G vs MGNREGA<\/title>\n<meta name=\"description\" content=\"VB-G RAM G replaces MGNREGA. 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