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Day 54 of 95

Dendup W L commits to:
Read for atleast one hour daily irrespective of whatever the day holds in store for me.
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Dendup W L
October 26, 2021, 5:58 PM
Okay so I'm pretty excited today. I took some time to rethink and frame my objectives for my dissertation and ended up thinking of something quite different and radical if I might add.

My dissertation objectives to be honest were (are) not very solid. They seem vague and broad so I needed to rethink.

I started with the thing that I knew I wanted to pursue and my interest in it.
I want to work on Monetary Policy and I definitely want to look into the Banking sector in relation to MP. On this, I am very sure and could not compromise.

Now coming one step deeper,, I also want to determine what factor/variable plays a very important role in hindering the policy transmission in the Banking sector. So this automatically translates into an objective for the dissertation.

From here, I started deviating from the norm and landed upon something quite unique but at the same time difficult to pull off. * drum roll *

Okay, before that, another thing I really wanted to incorporate was the impact of the Covid19 pandemic in my study and how it affected MP and the real output, employment and inflation.
Thinking of this, I had initially decided to model a Monetary Policy shock and see how the economy reacted to this but this seemed obvious given the already available data, and modelling such a shock response would require some really intense hours/days.

But then came another thought today - why not use a differences in differences (DiD) analysis ? But how exactly is the question.

2020 March - Covid hit the Indian Economy and the RBI responded adequately by cutting policy rates and other unconventional measures too.
Typically, Banks should also follow the same rate cut, atleast in their lending rates to borrowers. Even if this is with a lag (I also wanted to estimate the lag from a change in MP transmitting to a change in Banks' rates)

But not every bank changes the rate in the same proportion as the Central Bank does. There are other factors into play such as Banks' own spread of risk which they have to include in their rates.

Let me give an overview again of what we know -
Mar 2020 -> Covid
RBI responds
Banks too respond

But not all Banks set the same rates. Some transmit a large percentage of the change in Policy rates, some transmit a lesser percentage.
In India too, this must have been the case. So, a natural experiment like setting is seen where the aforementioned prevailing condition exists but the responses by the Banks are different. Some set rates in tandem with Policy rate cuts, some don't.

Control group - Banks who kept their rates relatively unchanged
Treatment group - Banks who changed their rates significantly.

At the end of a year, what were the outcomes ?
Did a significant change in Banks' rates in tandem with Policy rates result in a better transmission, resulting in increased loans and funds or not ?
Or did keeping the Banks' rates unchanged result in the same amount of loans ?
What happens in the Banking sector in case of a Monetary shock ? Does its choice to adequately transmit the rates result in any significance ?

This is the reference for a video I saw on a similar DiD analysis.

Now comes the difficult part (and this is not a criticism of the above)
- From where do I procure such data ? Does India have data on Banks having different lending rates ? If so, which banks do I take ?
- I have to learn an entire new Econometric methodology (DiD) and explain it better than a Professor, which obviously takes a lottt of time and effort. Materials to learn from, getting the intuition, trying out, if I get stuck then ?
- The last problem I seem to have is that of the time period. Technically the Covid pandemic isn't over. Will one year before and after i.e 2019,2020,2021 suffice to draw conclusions ? Or to overcome that, I look into the significant Policy Rate cuts and then go quarterly ?

Our Behavioural Economics Professor Akash Krishnan might be able to help me, atleast in checking for the feasibility of such an idea and then if possible, formulating a research design. I don't think anyone has ever done such an analysis with regard to such a problem of Monetary Transmission.

Need to spend some more time on this and chisel the idea into a sculpture worth pitching to my guide. I don't intend to keep a backup incase this fails as I will then be taking the first two above objectives and then simply build upon it.

(If anyone is reading this, do let me know your thoughts please)
Dendup W L
October 25, 2021, 5:46 PM
Oh my. The CIE period made me lazy (thank God I did not commit money otherwise I would have lost a lot)

Starting from today, I'm running multiple regressions to get acquainted with the datasets and variables such as Repo Rates, LAF, and other Monetary components such as M1, M3.

A small start. Will continue now. Gonna keep the limit to 12 now
Dendup W L
October 13, 2021, 6:22 PM
Read "Improving Monetary Transmission through the Banking channel : The case for external benchmarks in bank loans" by Viral V Acharya.

This delved deeper than the previous paper and aimed to examine the problem : Why do policy rate cuts by the RBI not translate fully into Banks' lending and deposit rates ?

The reasons could be many but underlying them all lies the development of the lending rate systems in India. From the PLR of 1994, BPLR of 2003, Base rate system of 2010 and then the MCLR of 2016, all these lending rates suffered rigidities and hence banks took advantage of it, often at the expense of the borrowers.
These rates were mostly internal in nature, whereas developed economies use external benchmarks as these are beyond the influence of Banks and cannot be used for their advantage. India is characterized by a system of Internal benchmarks.

Now an external benchmark is not sufficient as long as banks get to decide and change the 'spread' over the benchmark. The spread is the risk that the bank undertakes and this is in addition to the benchmark rate. There have been several cases where the bank adjusted the spread to offset changes in the MCLR !

The RBI recommended that this spread over the benchmark rate be constant unless the borrower's credit assessment changes.
However, Banks raised several, logical issues with the recommendations which were addressed adequately.

Also, other financial intermediaries operating in the economy such as NBFCs, now growing in number and significance, too need to regulate their rates in tandem with the policy rates and these are aligned with those of banks. An internal study group was constituted to look into these matters.

In conclusion, the reason why transmission is so important is that for a percent change in the policy rate if the banks do not pass even, say a half percent then the changes in Monetary Policy has to be more dramatic ! implying more significant rate cuts or hikes and this only adds more 'jerk' to the economy. The internal benchmark rate did not do much to improve the transmission mechanism over the years.
The external benchmarking case has therefore been made.
Dendup W L
October 11, 2021, 5:54 PM
Today, re-read all of the three papers I've read so far. Had to run a regression model in R but the technicalities of the language code are too complex to understand, and so what would take a simple hour or so took more than 4 hours to do. This took much of my effective time.
But the regression function estimated was related to Monetary Policy - how does changes in increase in the money supply explain changes in Inflation, and the second regression was to estimate a simple money demand function.

Even if one knows the theory properly, while testing it out there is no guarantee that it will work out. Also, there is a major problem of knowing which dataset to take and from where.
The methodology part is gonna be even tougher I guess. I thought having a strong theoretical foundation would suffice. Sigh.
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