By Dr. Nilanjan Banik
One of the contentious issues in the Vikshit Bharat Guarantee for Rojgar and Aleevika Mission (VB-G Ram G) Bill, which was eventually passed in Parliament, concerns allowing states to pause MGNREGA-related work for 60 days of their choice during peak sowing and harvesting seasons. The central government’s argument is that farm jobs are anyway available during this period, and a labour shortage caused by MGNREGA works may drive up agricultural wages, which could in turn lead to higher consumer price inflation, given that food is a major component of the Consumer Price Inflation. The MGNREGA with an average annual expenditure of over $6 billion is so far the largest workfare programme in the world. Critics argue that the new 60:40 funding pattern, where states must bear 40% of the financial cost, will make it harder for financially weaker states to implement the program, thereby undermining the fundamental purpose of providing “guaranteed” employment opportunities to people below the poverty line.
But how effective was the implementation of MGNREGA in the first place? Interestingly, one might have expected the poor in less progressive states such as Uttar Pradesh, Bihar, and Madhya Pradesh to be more impacted, but the data show otherwise. In our research working with Workers Level Schedule (WLS) sourced from the All India Coordinated Report, by the NITI Aayog, we try doing this. The states considered are Andhra Pradesh, Assam, Haryana, Himachal Pradesh, Jammu and Kashmir, Karnataka, Kerala, Odisha, Punjab, Rajasthan, Tamil Nadu, West Bengal, Meghalaya, Tripura, and Uttarakhand. In total, 6580 raw data points were collected from 40 districts, covering a range of 162 Gram Panchayats. These districts and villages are chosen on the basis of a stratified, multistage random sampling method. We find some interesting results.
Impact of MGNREGA in providing employment opportunities and augmenting market wage rates varied enormously across states. The states which did well in terms of implementing MGNREGA programs are Chhattisgarh, Telangana, Mizoram, Sikkim, and Tripura. Although it is natural to assume that the poorer states will introduce more MGNREGA programmes, however, data find no such direct correlation. For example, although there are incidence of poverty in Bihar, Uttar Pradesh and Madhya Pradesh, there seems to be lack of usage of MGNREGA funds. Likewise, among the north-eastern states, Arunachal Pradesh and Manipur did not do well in terms of providing MGNREGA work. On the other hand, some poorer states, like Chhattisgarh and Tripura did well in term of providing MGNREGA work. For the relatively rich states such as Punjab and Haryana, where the demand for MGNREGA work was low, there is apparently not much interest for implementing MGNREGA.
Workers from the states of Himachal Pradesh, Jammu and Kashmir, Odisha, and West Bengal believed that MGNREGA intervention has led to increase in market wage rates. MGNREGA work has created demand for unskilled workers and this had some spill-over effect in raising market wage rates for non-MGNREGA related unskilled work, for instance manual farm labour, porter, etc. The states of Jammu and Kashmir, Odisha, and West Bengal are industrially backward, and MGNREGA work has been helpful in increasing average market wage rates for unskilled workers.
For the industrially advanced states like Andhra Pradesh, Telangana, Tamil Nadu, and Karnataka, the workers felt no drastic improvement in market wage rates to arise from MGNREGA related activities. For instance, although the southern states of Andhra Pradesh, Karnataka, and Tamil Nadu have fared well in terms of implementing MGNREG schemes, however, most of these funds have been used for buying heavy machinery for the construction of MGNREGA assets. As there is a presence of an alternative industrial base (where there is a demand for manual labour), these states were not that successful in terms of providing wage employment related to MGNREGA work. Karnataka, for example, boasts a thriving agricultural sector and is a pioneer in the electronic national agriculture market (e-NAM), with less demand for work under MGNREGA.
| Economically Progressive States | State-wise daily wage rates for unskilled workers (2024-25) | State-wise average daily wage rates for male agricultural workers (2024-25) | Minimum Wage Rates |
| Haryana | 374 | 499.2 | 340 |
| Maharashtra | 297 | 343.2 | 202 |
| Karnataka | 349 | 454.3 | 411 |
| Telangana | 300 | 302 | 327 |
| Tamil Nadu | 319 | 573 | 132 |
| Economically Laggard States | |||
| Madhya Pradesh | 243 | 256.4 | 235 |
| Bihar | 245 | 362.8 | 235 |
| West Bengal | 250 | 347.2 | 166 |
| Odisha | 254 | 368.7 | 280 |
| Uttar Pradesh | 237 | 354.8 | 295 |
Source: MGNREGA Schedule 2025, Ministry of Rural Development, India
We find that level of industrial development is more important in complementing the minimum wage rates in any states. Also, some of the poorer states have showcased element of corruption, with relevant workers not getting MGNREGA works and sometime not getting payment even after working. There are evidence of money being stolen through multiple channels: false documentation, fabricated workers’ lists, and significant asset misappropriation. In many cases, jobs were allocated on a “verbal basis” with no documentation maintained by village bodies. This pattern of missing records reinforces concerns about disparities and irregularities stemming from self-selection bias, which is largely driven by unlawful political interference. For instance, in Kerala and West Bengal, MGNREGA work faces labour shortages, yet panchayat pradhans have been instructed to submit wage bills listing local party workers as beneficiaries rather than genuine workers. Due to corruption, workers sometimes do not demand work, knowing they will either be denied employment or not receive payment even if they do work.
So, it seems the effectiveness of MGNREGA or for that matter VB-G Ram G in providing employment benefits to the needy and poor depends on a host of factors beyond the states’ ability to implement such programs. A pan-India uniform way of implementing it is not going to be effective. If the variations due to the diversity of the different states of India are not incorporated in the Act, implementation cannot be perfect. Both the model and the method of implementation of VB-G Ram G must be customised according to the needs of every region, with minimum leakage of funds due to corruption in various administrative layers. Fortunately for India, the NSSO has divided the country into 88 independent agro-climatic regions depending on soil, rainfall, and agricultural productivities. For the programmes to be effective, it is advisable that the VB-G Ram G is implemented as per the requirements of the geographical characteristics, taking into consideration the occupational patterns of the local people. And this choice should be devoid of politics as some of the opposition parties are claiming. (IPA Service)
(The author is Professor, Mahindra University, Hyderabad).
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