Why One-Size-Fits-All Doesn’t Work for MGNREGA: Lessons from State-Level Evidence

The passage of the Viksit Bharat Guarantee for Rojgar and Ajeevika Mission (VB-G Ram G) Bill has reopened an old but unresolved debate about India’s flagship rural employment programme: how much flexibility States should have, and whether financial capacity alone determines success. Critics have focused on two changes — allowing States to pause work for 60 days during peak agricultural seasons, and a revised 60:40 Centre–State funding pattern — arguing that both dilute the original spirit of the Mahatma Gandhi National Rural Employment Guarantee Act. But evidence from the ground suggests the real story is more complex.

The core concern: does higher State burden weaken employment guarantees?

At the heart of the criticism is the fear that financially weaker States will struggle to shoulder 40% of programme costs, undermining employment security for the rural poor. In theory, this appears persuasive: poorer States should need MGNREGA the most, yet may be least able to fund it.

However, research published in “Regional Statistics”, based on worker-level data from the NITI Aayog, suggests that financial capacity alone does not explain outcomes. The effectiveness of MGNREGA — or its successor under VB-G Ram G — depends on local economic structures, governance quality, labour demand, and corruption levels. A uniform, pan-India implementation model struggles to accommodate these variations.

What the data across States actually shows

The study draws on Workers Level Schedule data covering 6,580 observations across 40 districts and 162 Gram Panchayats in 15 States, selected through stratified multistage sampling. The findings challenge several common assumptions.

Some relatively poorer States, such as Chhattisgarh and Tripura, performed well in providing MGNREGA employment. Meanwhile, States with high poverty incidence like Bihar, Uttar Pradesh and Madhya Pradesh showed weaker utilisation of available funds. Even within the North-East, performance diverged sharply, with Tripura doing relatively well while Arunachal Pradesh and Manipur lagged.

Equally telling is the experience of richer States. In Punjab and Haryana, demand for MGNREGA work was low, and there appeared to be limited administrative interest in pushing the scheme. This suggests that poverty levels alone do not drive implementation intensity.

When MGNREGA raises wages — and when it doesn’t

One of MGNREGA’s intended spillover benefits is upward pressure on rural wages. The research finds this effect is highly uneven.

Workers in Himachal Pradesh, Jammu and Kashmir, Odisha, and West Bengal reported that MGNREGA led to higher market wages for unskilled labour. In these relatively industrially backward States, the programme created genuine demand for labour, pushing up wages even outside MGNREGA worksites.

By contrast, in more industrially advanced States such as Andhra Pradesh, Telangana, Tamil Nadu, and Karnataka, workers perceived little wage impact. Although these States implemented MGNREGA extensively, a significant portion of funds went into machinery-intensive works rather than labour-intensive activities. With alternative employment options already available, MGNREGA played a weaker role in shaping wage dynamics.

Industrial development matters more than income levels

A key insight from the study is that the level of industrial and non-farm development matters more than per capita income in determining MGNREGA’s effectiveness. Where alternative employment opportunities exist, MGNREGA functions as a fallback option with limited wage influence. Where such opportunities are scarce, it becomes a crucial labour market anchor.

This helps explain why Karnataka, despite a strong agricultural sector and participation in platforms like e-NAM, sees relatively low demand for MGNREGA work: labour is already absorbed elsewhere.

Corruption and political interference as binding constraints

Perhaps the most damaging factor identified is corruption. In several poorer States, workers reported denial of work, delayed or missing payments, and widespread manipulation of records. Practices included fabricated job cards, ghost beneficiaries, asset misappropriation, and verbal job allocations without documentation.

In extreme cases, workers stopped demanding work altogether, convinced that they would either be denied employment or not paid. Such distortions depress both participation and wage outcomes. Evidence from States like Kerala and West Bengal also points to political interference, where local bodies allegedly submitted wage bills in the names of party workers rather than actual labourers.

Why regional customisation is unavoidable

These findings underline a central point: diversity across States is not a design flaw but a structural reality. Implementation cannot be uniform when labour markets, agricultural cycles, industrial bases and governance capacities differ so widely.

India already recognises this diversity institutionally. The National Sample Survey Office has classified the country into 88 agro-climatic regions based on soil, rainfall and productivity. Aligning employment programmes like VB-G Ram G with these regional characteristics — including occupational patterns and seasonal labour demand — would make them more effective.

What this means for the VB-G Ram G debate

Allowing States flexibility, including the ability to pause work during peak agricultural seasons, need not undermine employment guarantees if designed carefully. The real risks lie elsewhere: weak governance, poor monitoring, and political capture.

For VB-G Ram G to succeed, both its design and execution must be customised regionally, while simultaneously tightening transparency and accountability to minimise leakage. Treating all States alike may appear equitable on paper, but in practice it risks reproducing the very failures critics fear.

The evidence suggests that employment guarantees work best not when they are rigidly uniform, but when they are locally grounded, corruption-resistant, and aligned with the economic realities of the people they are meant to serve.