{"id":373817,"date":"2024-03-14T13:23:51","date_gmt":"2024-03-14T07:53:51","guid":{"rendered":"https:\/\/zerodha.com\/z-connect\/?p=373817"},"modified":"2024-03-20T16:57:32","modified_gmt":"2024-03-20T11:27:32","slug":"its-the-economy-stupid-of-lungis-and-february-numbers","status":"publish","type":"post","link":"https:\/\/zerodha.com\/z-connect\/subtext\/its-the-economy-stupid-of-lungis-and-february-numbers","title":{"rendered":"It\u2019s the economy, stupid! Of lungis and February numbers"},"content":{"rendered":"

We love India Data Hub\u2019s weekly newsletter, \u2018This Week in Data<\/a>\u2019, which neatly wraps up all major macroeconomic data stories for the week. We love it so much, in fact, that we\u2019ve taken it upon ourselves to create a simple, digestible version of their newsletter for those of you that don\u2019t like econ-speak. Think of us as a cover band, reproducing their ideas in our own style. Attribute all insights, here, to India Data Hub. All mistakes, of course, are our own.<\/em><\/p>\n

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Hey! Hey Stats-man! I have a quote for you!<\/strong><\/p>\n

Uh-oh.<\/p>\n

Yeah! You know how statistics are like bikinis? They\u2026<\/strong><\/p>\n

Yes, yes, they hide more than they reveal. Old one. Personally, I prefer “Statistics are like lungis, they hide things you might not want to see.<\/em>”<\/p>\n

But it’s true. Take last month. Because this is a leap year, even though the numbers for February are in, we have no idea if they\u2019re any good.<\/p>\n

Wait, what?<\/strong><\/p>\n

Hah, yes. See, stats-folk love doing \u201cyear-on-year\u201d comparisons for monthly data. That is, they love comparing a month from one year with the same month the previous year.<\/p>\n

The raw numbers for a month tell us very little. If I told you that 2 million cars were sold in India in February, for instance, what would you make of it? Is it a high number? A low one? Is the industry thriving? Declining? What does it all mean for our economy? You\u2019d know nothing. You need some base, some benchmark to make some sense of it.<\/p>\n

For such a benchmark, we usually look at the same month of the previous year. In many ways, it\u2019s the fairest benchmark to compare against, because so much in the economy happens in a yearly rhythm. The weather is similar. People make many of the same purchases. Events repeat themselves. The same crops are sowed and harvested at the same time. Businesses chase the same targets. Weddings, vacations, big purchases – there\u2019s a \u201cseason\u201d for everything. Speaking very roughly, an economy repeats the same motions in the same month every year.<\/p>\n

That makes sense. I think. But isn\u2019t this also true for a leap year?<\/strong><\/p>\n

It is, but for one thing: there\u2019s one extra day in February. That is, the month suddenly stretches by ~3%. That\u2019s 3% more time for things to take place. When we\u2019re trying to measure changes in the tenths of a percent, that extra 3% can skew everything. All growth, this month, is slightly exaggerated.<\/p>\n

It\u2019s hard to know how much this changes things. If February 29 didn\u2019t exist, would the same things happen on February 28? Or March 1? Did special things happen because it was the \u201cleap day\u201d? Or was it just an ordinary day for everyone concerned? There\u2019s no true answer to such a question when you\u2019re looking at something as big as an entire economy.<\/p>\n

All one can say is: take everything this month with a pinch of salt. There\u2019s stuff this lungi is hiding.<\/p>\n

Any more pinches of salt from you and I\u2019ll have a BP problem.<\/strong><\/p>\n

Hilarious.<\/p>\n

But let\u2019s look at the high frequency data-<\/p>\n

The what<\/em>?<\/strong><\/p>\n

The \u2018high frequency data<\/strong>\u2019. Those are little bits of information on the economy that we can gather regularly and quickly.<\/p>\n

Think about it: nobody really understands how an economy is doing. Economies are impossible to grasp. They’re made of billions of transactions – people buying coffee, or filling petrol in their bikes, or picking up a copy of the day\u2019s newspaper – each thing, each day, for each person and each business, taken together. If you really want to understand an economy, first, you must know every last thing that happens in the country.<\/p>\n

The best we can do is look at proxies. We take the small trickle of data-points we can get – the \u2018high frequency indicators\u2019 – and use them to make whatever conclusions we can. They’re not perfect, but they give us a crude, low-resolution picture of the country\u2019s economic activity.<\/p>\n

Ahh. But they\u2019re useless this year?<\/strong><\/p>\n

Not useless, no. But fuzzy, yes.<\/p>\n

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\"\"<\/a><\/p>\n

We\u2019ll start with energy.<\/p>\n

Everything in the economy consumes energy<\/strong>: buildings and factories, machines, vehicles, goods carriers, offices, everything. This makes energy a good proxy for the health of an economy. If an economy consumes more energy than before, more things are happening inside it.<\/p>\n

You sound like a yoga instructor.<\/strong><\/p>\n

Haha, maybe. I can see it.<\/p>\n

Although we\u2019re talking about profane, earthly matters. You know, electricity, fuel – that sort of thing. Here\u2019s the news:<\/p>\n