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By:

Sumit Ranjan Das

21 August 2024 at 4:08:59 pm

EPFO’s Big Wage-Band Reset

Twelve years is a long time for a wage ceiling to remain unchanged. The last revision came in September 2014, when the limit was raised from Rs.6,500 to Rs.15,000. Last week, the Union Cabinet approved another increase, taking the ceiling to Rs.25,000 a month with effect from 17 September 2026. The government’s estimate is that more than 51 lakh additional employees will come within mandatory EPFO coverage as a result of the change. For employers and payroll professionals, however, the...

EPFO’s Big Wage-Band Reset

Twelve years is a long time for a wage ceiling to remain unchanged. The last revision came in September 2014, when the limit was raised from Rs.6,500 to Rs.15,000. Last week, the Union Cabinet approved another increase, taking the ceiling to Rs.25,000 a month with effect from 17 September 2026. The government’s estimate is that more than 51 lakh additional employees will come within mandatory EPFO coverage as a result of the change. For employers and payroll professionals, however, the headline number is only the starting point. The more important questions are who will be covered, which wages will be taken into account and how the revised provisions will be implemented. Wage Ceiling The existing wage ceiling of Rs.15,000 a month is being raised by Rs.10,000, or 66.7 percent, to Rs.25,000. The change takes effect from 17 September 2026 and marks the first revision since September 2014. The government expects more than 51 lakh additional employees to be covered. Estimated expenditure is about Rs.56,696 crore over five years, while annual government outgo is expected to rise to approximately Rs.11,339 crore. The standard contribution remains 12 percent each from the employee and employer, subject to applicable provisions. The Cabinet said the decision will expand access to provident-fund savings, pension protection under the Employees’ Pension Scheme (EPS) and insurance protection under the Employees’ Deposit Linked Insurance Scheme (EDLI), in accordance with the applicable scheme provisions. The wage ceiling is not merely an administrative threshold. It determines the point at which mandatory EPF coverage applies under the existing framework. At present, a fresh employee joining employment at wages above Rs.15,000 a month is not automatically brought within mandatory EPF coverage and may remain outside mandatory provident-fund, pension and associated insurance protection, subject to applicable statutory provisions. The revised ceiling will bring a substantial section of employees earning between Rs.15,000 and Rs.25,000 within the mandatory coverage framework. The government has also quantified the fiscal impact. The estimated expenditure is about Rs.56,696 crore over five years, while annual government outgo is expected to rise to approximately Rs.11,339 crore, compared with existing annual budgetary support of about Rs.10,250 crore. The Labour Ministry has linked the revision to sustained wage growth, rising incomes and the continued expansion of formal employment since the previous revision in 2014. Payroll Illustration Consider an employee earning Rs.22,000 a month who becomes subject to mandatory coverage under the revised ceiling. At the standard 12 percent contribution rate, if the full eligible wage is used as the contribution base, the employee’s contribution would rise from Rs.1,800 to Rs.2,640 a month, while the employer’s contribution would similarly rise from Rs.1,800 to Rs.2,640. Total monthly contributions would therefore increase from Rs.3,600 to Rs.5,280 — a combined increase of Rs.1,680. However, this should not be treated simply as Rs.1,680 of additional employee savings. Contributions are allocated between EPF and EPS components as prescribed, with the EPF component accumulating in the employee’s account and the EPS component providing pension benefits subject to scheme conditions. The Rs.22,000 example is illustrative, not a universal payroll formula. The final treatment of wage components, existing employees in this band, EPS allocation and transitional matters will depend on the statutory notification and EPFO implementation instructions. For payroll professionals, the immediate task is to assess the operational impact. Key questions include the effective date for existing employees and new joiners, which wage components will count towards PF, whether the 10 percent concessional rate for notified establishments will continue, how the revised ceiling will interact with EPS pensionable wages, and what changes will be required in payroll systems. The Cabinet approval establishes the policy decision; the formal Gazette notification and EPFO instructions will determine how it is translated into payroll processes. The revised ceiling is the first increase since September 2014 and is expected to bring more than 51 lakh additional employees, particularly those in the Rs.15,000-Rs.25,000 wage band, under mandatory EPFO coverage. For them, the change can expand access to provident-fund savings, EPS pension and EDLI insurance, subject to scheme provisions. For employers, it means reviewing payroll costs, employee data, eligible wage components, contribution calculations and compliance systems. The government has described the move as part of efforts to extend statutory social security and strengthen formal employment. The policy has been announced. For payroll professionals, the next chapter is implementation. (The writer is a Cost and Management Accountant and founder of TaxoDas. Views personal

Why India’s Farms Need Smarter AI

Writer: Parashram Patil
Parashram Patil
Aug 5
3 min read

Predictive and agentic intelligence can identify vulnerable farmers early, coordinate timely interventions and make agricultural supply chains more resilient.

India’s artificial intelligence conversation has largely focused on productivity. Policymakers speak of higher yields, precision farming and digital marketplaces. Technology companies promise smarter irrigation, better weather forecasts and more efficient logistics. Yet one critical variable often remains missing from these discussions: the farmer.


Agricultural supply chains falter when climate shocks, delayed payments, poor health, labour exhaustion and financial distress reinforce one another. Treating each challenge in isolation has produced fragmented solutions. Artificial intelligence offers an opportunity to connect these risks instead of compartmentalising them.


Predicting Vulnerability

The future of agricultural AI, therefore, lies not merely in predicting crops but in predicting vulnerability. Evidence from a primary survey of 150 sugarcane cultivators in Daund taluka of Pune district illustrates why this matters. The study examined production, labour, health, nutrition, healthcare expenditure, borrowing, social protection and Farmer Health Capital (FHC) - a framework on which this author has done considerable work, and that views a farmer’s physical capacity as productive economic capital rather than simply a health outcome.


The findings are sobering. Every surveyed farmer reported crop loss, while 96 per cent experienced delayed sugarcane payments. During the crushing season, farmers worked an average of 10.97 hours a day but slept only 5.23 hours. Average back and joint pain measured 7.73 on a ten-point scale, and more than a quarter identified musculoskeletal problems as their principal illness.


These statistics reveal systemic economic risk. FHC argues that physical health directly influences labour availability and, by extension, agricultural productivity. The survey recorded an average Farmer Health Capital Index of 0.479, while econometric analysis showed that physical health significantly predicted farmers’ confidence about the following agricultural season (p<0.001). Confidence, in this context, reflects expectations about one’s ability to continue farming effectively.


Such evidence provides an ideal foundation for predictive AI.


A Farmer Risk Intelligence System could combine health indicators with weather forecasts, crop conditions, labour demands, payment histories and household financial data. Rather than analysing these variables independently, machine-learning models could identify combinations that signal elevated production or livelihood risk. A farmer simultaneously facing crop loss, chronic physical pain, long working hours, sleep deprivation and delayed payments would receive an early-warning risk score long before a crisis becomes irreversible.


Prediction, however, is only half the story. The next generation of artificial intelligence is increasingly described as agentic because it can coordinate workflows after identifying a problem. In agriculture, that means AI should not simply recognise vulnerability; it should help organise timely responses.


When approved risk thresholds are crossed, an agentic system could notify farmer-producer organisations, extension workers or local administrators. It might recommend hydration and ergonomic interventions during peak labour periods, flag delayed payments requiring administrative attention or connect eligible households with healthcare, insurance or credit-support schemes. Importantly, financial, medical and entitlement decisions should remain subject to human approval and farmer consent. AI should coordinate responses, not replace human judgement.


Survey data show that average out-of-pocket healthcare expenditure reached Rs. 23,233 per season, while 90 of the 150 surveyed farmers reported borrowing to meet medical emergencies. Using these parameters, a calibrated economic simulation evaluated a preventive investment of Rs. 5,000 per household—roughly 1.88 per cent of average seasonal sugarcane revenue. Assuming a conservative 10 per cent recovery in lost labour efficiency, the model estimated approximately Rs. 26,597 in additional household revenue.


The same simulation estimated a potential public saving of around Rs. 7,000 per household when modest preventive expenditure reduces subsequent healthcare liabilities. Again, this represents a modelled scenario rather than realised savings. But it establishes a measurable benchmark against which future AI-enabled programmes can be evaluated.


Nor is this architecture confined to sugarcane. Cotton, grapes, onions, wheat, rice, pulses and other value chains confront similar combinations of climate uncertainty, labour stress and financial vulnerability.


India has declared its ambition to become a global leader in artificial intelligence. Agriculture should be one of the sectors where that ambition delivers its greatest social return. But success should be measured by whether technology helps farmers remain healthy enough, financially secure enough and resilient enough to keep producing.


If predictive intelligence can identify vulnerability before it becomes crisis, and agentic systems can coordinate timely, human-centred interventions, agricultural data cease to be merely descriptive. They become a digital shield by protecting farmers, strengthening supply chains and making India’s food economy more resilient from the ground up.


(The writer is a member of Maharashtra Agriculture Price Commission. Views personal.)

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