In today’s Finshots, we break down India’s Periodic Labour Force Survey (PLFS). Yup, that’s it!

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The Story

Imagine writing your grocery list only once a year.

You’d probably run out of tomatoes in March, forget sugar in July and end up hoarding too much rice by December. It’s chaotic, unpredictable, and honestly, just not the best way to plan a kitchen.

Well, that’s kind of how India’s old job data system worked — the one known as the Periodic Labour Force Survey or PLFS.

For the uninitiated, PLFS is the main survey that tells us how Indians are working. Whether they’re employed, unemployed or actively looking for work. It was launched in 2017 and would show up five times a year: four quarterly reports with data only for urban areas, and one annual report combining both rural and urban data.

But it had some serious flaws.

For starters, it leaned heavily towards cities, leaving rural India, home to nearly 65% of the population, largely in the shadows. No rural data in quarterly reports meant that seasonal employment patterns, underemployment and migration in villages weren’t captured well.

Then there was the odd timing. The annual report tracked data from July of one year to June of the next. Not only did this make India’s labour data look out of sync with most global datasets, but it also made it tough to make international comparisons or update global databases on time.

And let’s talk about size. The earlier survey covered about 7,000 villages and 6,000 urban blocks. That’s simply not enough to get a reliable read at the district level, especially in big, diverse states.

And all these limitations meant one thing. We didn’t have a clear, real-time picture of how rural India was working or not working.

So in January 2025, the government decided it was time for an upgrade. From April 2025, it rolled out a revamped PLFS.

The idea? 

Start capturing monthly employment data across both cities and villages, bring rural areas into the quarterly fold and still retain the annual report for long-term trends.

And they didn’t stop there. The sample size got a massive boost, jumping from around 13,000 locations to nearly 22,700. That’s a leap from 1 lakh households to 2.72 lakh. In short, the data just got a lot more representative.

They also gave the survey a new structure. Earlier, in cities, selected households were visited four times a year — once every three months. So if you were surveyed in January, you’d see surveyors again in April, July and October. This setup, known as the quarterly rotational panel, ensured that 75% of the households stayed the same between two quarters, giving some sense of continuity. But rural households got just one visit a year. No revisits, no quarterly check-ins, no updates. This meant that rural job patterns stayed fuzzy and anything seasonal slipped through the cracks.

The new design fixes that.

Now, both rural and urban households are surveyed once a month for four months. Every month, 75% of the households remain the same, creating a smoother month-to-month picture. And 50% of them stay the same across quarters too.

It’s like switching from an annual grocery list to checking your pantry every week for better planning. You know exactly when things are running low and where to restock.

There’s more. They’ve also added a few thoughtful questions like how much land a household owns or leases, whether there’s rental or pension income and what kind of vocational training people have had. It’s richer, deeper data that helps fill in the gaps.

But why does all this matter, you ask?

Let’s look at April’s monthly data that was released a few days ago to understand that.

First up, we have the Labour Force Participation Rate (LFPR) or the share of people who are working or want to work. In in a typical week of April 2025, that number stood at 56% for the country overall. If you zoom in, rural areas did slightly better at 58%, while urban areas lagged behind at 51%.

If you split this by gender, the gap becomes clearer. Among men, nearly three out of four were in the labour force. But for women, it was a different story. In villages, just 38% were working, with urban women faring worse.

Next comes the Worker Population Ratio (WPR) or the share of people who are actually working. It stood at 53% overall in April. That’s 55% in villages and 47% in urban areas. And when you look at women specifically, the numbers drop even more. Only 37% of rural women and just 24% of urban women were working, which pulls the overall female employment rate down to 33%.

Finally, there’s the Unemployment Rate (UR) or the percentage of people who were jobless but actively looking for work. In April, that number was 5%. So roughly one in every twenty people looking for a job didn’t find one.

But zoom into urban areas and you’ll see the real problem. Urban female unemployment was at 9%, while for men it was 6%. This is where monthly PLFS data can make a real difference. Because now, instead of waiting a year to spot trouble, policymakers can respond in real time.

Take urban women’s employment, for instance. The data clearly shows that they face higher joblessness and lower participation rates. With fresh insights every month, the government can quickly tweak or launch programmes aimed at them. Be it skill development, entrepreneurship support or even safer public transport options to make commuting to work easier.

They could even revisit old schemes like Mahila Shakti Kendra. For context, it was launched in 2017 to empower women and connect them to welfare services. But the scheme fizzled out because barely anyone knew it existed. A NITI Aayog study even found that only 13% of people surveyed had even heard of it.

Which means that monthly PLFS can help identify where such schemes failed and where they can be revived and how.

The same applies to schemes like STEP (Support to Training and Employment Programme for Women). With monthly data, the government could refocus efforts on urban areas and sectors where women’s participation is lowest and track progress almost instantly.

It also shines a light on another pressing concern — youth unemployment. April’s data shows that 14% of young people are jobless. For young urban women, that number shoots up to 24%.

That’s huge. And it opens the door to targeting programmes like Skill India Mission more effectively. The government can now double down on urban youth training, market-linked courses, apprenticeships and job fairs, which of course it’s already doing. Initiatives like the National Apprenticeship Promotion Scheme (NAPS) or PM Kaushal Vikas Yojana (PMKVY) can be tailored to the cities and sectors that need them the most and tracked in record time.

Even MGNREGA could benefit. While rural LFPR and WPR are higher than in urban areas, rural youth unemployment still hovers at 12%. With monthly data, the government could increase workdays during job slumps, add new types of projects or support young people in starting agri-businesses.

Basically, monthly PLFS could be a game changer if used well.

It tells us who needs work, where and when. It allows the government to move from reactive to proactive. And takes the guesswork out of policy and brings in real-time evidence.

The only limitation?

Since the entire method has been revamped, comparing these new monthly numbers with older annual reports isn’t a neat apples-to-apples deal. That’s something we’ll have to live with, for now.

But all said and done, it’s still a leap forward.

Until then…

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