Archive for April 12th, 2019

Will countercyclical capital buffers help in the next crisis?

April 12, 2019

My new piece in moneycontrol.


Should civil servants be allowed to serve in their home areas? Evidence from India

April 12, 2019

Interesting research by Profs. Guo Xu (Berkeley Haas School of Business), Marianne Bertrand (University of Chicago) and Robin Burgess (LSE).

Our main finding is that officers allocated to their home state perform worse than comparable officers who are allocated to non-home states. On average looking across the entire nation, we find that officers allocated to their home states are deemed to be more corrupt and less able to withstand illegitimate political pressure. The extent to which home allocations worsen performance, however, varies substantially across states. Home-state officers perform worse in states that score higher on corruption as measured by the Transparency International score. Consistent with this subjective evidence we find that, in the more corrupt Indian states, home-allocated officers are more likely to be suspended primarily due to having court cases pending against them. 

These findings thus resonate well with the historical literature which highlights the tension between the need for local knowledge and the challenge of capture and clientelism in settings ranging from the administration of Empire to the allocation of modern-day civil servants and ambassadors. 

Perhaps more importantly, the results also have important implications for a whole host of less-developed countries that are in the process of building state capacity (Besley and Persson 2009). In these contexts, assigning officers back to environments which they are most socially proximate with may actually limit their ability to effectively serve the nation. This is an interesting result, as we know that such home avoidance rules run counter to the preferences of the officers themselves. While related research has suggested that rewarding performance with individually preferred postings may be an effective incentive mechanism (Khan et al. 2018), our results suggest that a policymaker may nonetheless seek to deliberately mismatch subordinates’ preferences and location assignments if their private gains are misaligned with public gains – in this case, due to greater likelihood of local capture.


How mobile telephones are fundamentally changing the profile of India’s imports…

April 12, 2019

Nice piece by Rekha Misra and Anand Shankar of RBI in the April-2019 Bulletin.

….mobile telephones are fundamentally changing the profile of India’s imports. The composition of India’s import basket has largely been dominated by gold and petroleum products. These two commodities, with a combined share of close to 40.0 per cent, have virtually defined the trajectory ofIndia’s overall merchandise imports.

In recent years, however, electronic goods imports increased from a little over US$ 0.9 billion in 1993-94 to US$ 51.5  billion in 2017-18, an annual growth rate of over 15.0 per cent. Consequently, the share of electronic goods increased from less than 4.0 per cent of India’s total merchandise imports to over 11.0 per cent during the same period. In fact, from 2013-14 onwards, electronic goods imports have had a higher share in merchandise imports than gold and currently constitute the second largest import item for India!

This article undertakes an incisive examination of the phenomenon of India’s electronic imports and implications for the viability of India’s external balance. Specifically, the article studies the behavior of imports of mobile phones and parts thereof which lie at the heart of the surge in electronic goods imports. It seeks to highlight the role of policy initiatives in driving the phenomenon and its composition, with longer-term implications for domestic production.


What it means for an economist to work in tech firms: Interview of R. Preston Mcafee

April 12, 2019

Tech firms are becoming ‘the workplace’ for economists.  This article reported how Amazon has hired 150 PhDs, Niranjan wrote a piece on the opportunities, Susan Athey also wrote on Economists in the tech space.

Here is an addition to this series of write-ups. Richmond Fed has a must read interview of R. Preston McAfee, who quit academia to work as an economist in firms such as Yahoo, Google and Microsoft.

He says the opportunities at tech firms provide opportunities to microeconomists:

EF: You were one of the first academic economists to move to a major technology company when you joined Yahoo as chief economist. You’ve since spent more than a decade as an economist at major technology companies. What has changed in the way that economic research is used in these firms?

McAfee: The major change is the relevance of microeconomics — the study of individual markets. 

Economists have had a big role in companies doing macroeconomics for forever, worrying about inflation, GDP, and how those broad aggregates influenced demand for the firm’s products. Microeconomists bring a very different skill set and answer very different questions.

That’s a major change in roles. Amazon, for instance, has more than 150 microeconomists. A really big thing there, and at Microsoft and at Google, is the problem of causality. Microeconomists have been studying how to get at causality — what caused something as opposed to what’s just correlated with it — for 40 or 50 years, and we have the best toolset.

Let me give an example: Like most computer firms, Microsoft runs sales on its Surface computers during back-to-school and the December holidays, which are also the periods when demand is highest. As a result, it is challenging to disentangle the effects of the price change from the seasonal change since the two are so closely correlated. My team at Microsoft developed and continues to use a technology to do exactly that and it works well. This technology is called “double ML,” double machine learning, meaning it uses machine learning not once but twice.

This technique was originally created by some academic economists. Of course, as with everything that’s created by academic economists, including me, when you go to apply it, it doesn’t quite work. It almost works, but it doesn’t quite work, so you have to change it to suit the circumstances.

What we do is first we build a model of ourselves, of how we set our prices. So our first model is going to not predict demand; it’s just going to predict what decision-makers were doing in the past. It incorporates everything we know: prices of competing products, news stories, and lots of other data. That’s the first ML. We’re not predicting what demand or sales will look like, we’re just modeling how we behaved in the past. Then we look at deviations between what happened in the market and what the model says we would have done. For instance, if it predicted we would charge $1,110, but we actually charged $1,000, that $110 difference is an experiment. Those instances are like controlled experiments, and we use them in the second process of machine learning to predict the actual demand. In practice, this has worked astoundingly well.

The pace at which other companies like Amazon have been expanding their microeconomics teams suggests that they’re also answering questions that the companies weren’t getting answered in any other way. So what’s snowballing at the moment is the acceptance of the perspective of economists. When I joined Yahoo, that was still fairly fragile.

He speaks about several things such as firms vs markets, big data, machine learning, regulation, antitrust, work culture at top tech firms and so on.

This bit on the tech industry is fascinating:


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