A Summer Spent Doing Macroeconomics

Say you’re a student, and you’ve just finished learning a fair bit about macroeconomics. You’ve read and not understood Keynes, you’ve read and think you’ve understood Friedman, and you don’t have the faintest idea what folks in macro have been up to since Robert Lucas.

OK, all that is fine, but how should a budding macroeconomist spend her summer this year?

You could do a lot worse than reading this article, and asking yourself some simple questions.

Such as, do I hear you say? Read on!

Google mobility, for instance, is down more than 40 per cent since the start of April and currently at levels seen a year ago, when the national lockdown was in effect. This dynamic is also visible in the cross-section: states that forced down mobility more strongly have, in general, also seen a larger drop in positivity rates.


What is Google Mobility? What does the data for India look like? How does this data correlate with statewise Covid-19 numbers? Can I create simple tables and charts in, say, Google Sheets that show a link between the two? And write up a blog about how I did it? Or maybe create YouTube tutorials that show how I did it?

That said, there’s growing evidence the impact will not be trivial even if not of the same scale as the first wave. By the middle of May, power demand was down 13 per cent and vehicle registrations were down 70 per cent compared to the start of the quarter, while e-way bills in the first half of the month were at 40 per cent of where they should be. A broader composite index would suggest activity is tracking a 6-7 per cent sequential decline this quarter and, while this is much shallower than the 25 per cent sequential contraction witnessed last year this time, the fact that it comes on the heels of the first shock, and can potentially trigger more hysteresis, remains a source of concern.


Where does the data for power demand come from? Where does the data for vehicle registration come from? Where does GST data come from? What does the phrase “tracking a 6-7 percent sequential decline” mean? What is hysteresis?

Household income uncertainty and precautionary savings can be expected to rise. Even before the second wave, households had signalled caution about future spending (manifested in the RBI Consumer Confidence Survey) likely reflecting both an income hit and a precautionary savings motive. This behaviour is consistent with labour market dynamics wherein the unemployment rate, once adjusted for reduced labour force participation, had increased meaningfully even before the second wave.


What is the RBI Consumer Confidence Survey? How is it calculated (see Annexure A in this document)? Where do we get unemployment data from?

Private investment could also take time to pick up. Even before the second wave, utilisation rates were in the mid-60 per cent range, much lower than needed to jumpstart investment.


What is OBICUS? It stands for Order Book, Inventory and Capacity Utilization Survey. How else do we track capacity utilization?

We have previously found a strong elasticity of India’s exports to global growth and, if that holds, this should drive a strong export rebound in India. Some of this is already visible in the data with manufacturing exports surging in recent months, and currently 18 per cent (in nominal dollar terms) above pre-pandemic levels.


Where might that paper/research be, the one that talks about the strong elasticity of India’s exports to global growth? What does it tell us? What is different between the time that paper was written and today? Is that to India’s advantage or not? How do we tell?

If crude prices average close to $70 this fiscal year, as is expected, that would constitute a 50 per cent increase over last year and serve as a negative terms of trade shock that impinges on household purchasing power and firm margins — a process already underway.


EIA? Or something else? Should we take lagged data? If yes, with what lag? If no, why not? Where do we get information on firm margins? Bloomberg/Reuters? If yes, do we have access to a terminal? If no, whom do we ask for a favor?

When all is said and done, the completeness of an economy’s recovery from Covid-19 — and therefore the level of scarring — is assessed by comparing its post-Covid-19 path of the level of GDP with the path forecasted pre-Covid-19. If the aforementioned forecasts fructify, the level of quarterly GDP at the end of this year would be about almost 8 per cent below the level forecasted pre-pandemic. To be sure, India will not be the only emerging market to be below its pre-pandemic path. In fact, among the large economies, only the US and China will surpass it. But that said, an 8 per cent shortfall is meaningful.


What is the level of GDP, and how is it different from the growth rate of GDP? Which should one use, and how does the answer change depending on the context? Where do we get data on GDP of all countries at one time? Which one of these measures should we use for comparison, and why?

Macro is hard, and in many different ways. Understanding the theory is hard, but piecing together parts of the puzzle from disparate (and at lest in India, gloriously unfriendly) data sources is perhaps harder still. But if you want to “do” macro for a living, being familiar with the answers to these questions is table stakes.

That is, getting familiar with the answers to the questions I have asked here gets you the right to sit at the table. Playing the game better than the others once you’re in is a whole different story. And playing the game means using this data with your knowledge of theory to try and take a stab at the really important questions:

The question, therefore, is how should economic policy respond to this second shock? With fiscal and monetary policy already quite expansive, is there space to respond further? We assess policy options and tradeoffs in a companion piece tomorrow.


Trust me, macro is hard.

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