Interesting readings

One cool idea for improving the Indian police : video monitoring.





We always knew that gender-segregation of society, as is practised in most of traditional India, is deeply damaging. Writing in the New York Times, Adam Grant shows new insights on why men need women.





Are India's policy rates too low? by Niranjan Rajadhyaksha in Mint.



Atul Gawande in the New Yorker at his best: he has deep insights into how new ideas diffuse. In India, with GDP doubling every decade, we need for new ideas to come into every organisation at a hectic pace. The organisations which are more closed have become obsolete and have failed.



A public interest litigation to be imagined by Somasekhar Sundaresan in the Business Standard. Also see this post where I have an interesting comparison against the election commission.



The recent war on rupee depreciation has inflicted enormous damage. Read Devangshu Datta in the Business Standard, and Tamal Bandhyopadhyay in Mint and Sugata Ghosh in the Economic Times.



The Indian State suffers from basic blunders in the field of health: Too much spending on private goods and not enough spending on public goods. Gardiner Harris has a fascinating story in the New York Times about the detective stories in Indian epidemiology which require State attention. These are the public goods in the field of health.



Mobis Philipose on problems faced by exchanges.



Remya Nair reports on RBI and NBFCs. This should be seen in the context of the FSLRC recommendation on shifting NBFC regulation out of RBI.





When evil was a social system by Christopher Caldwell, in the New Republic.





While we have long heard skeptical views while China thundered ahead with high GDP growth, the problems today seem to be a bit worse than usual. See William Pesek, Paul Krugman and Brad Plumer interviewing Patrick Chovanec. In some sense, the market has known this for a long time. Over the last decade, Nifty has generated returns of 291% while the Dow Jones Shanghai Index gave only 62.6% despite faster Chinese GDP growth.



Take a glimpse into the life of Albert O. Hirschman and Christine Granville. They don't make people like this anymore.

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Re-igniting economic reforms in India: Three key principles

I have a column in the Economic Times today on this subject.

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What platforms might work in Indian politics?

High GDP growth has led to structural transformation, with dramatic change in the composition of the labour force. The latest data from the CMIE Consumer Pyramids database pertains to December 2012, and shows the following occupation structure of the Indian workforce:

Occupation	Share (Per cent)
Small farmer	13.39
Organised farmer	8.69
Agricultural labourer	8.70
Industrial worker	7.92
White collar worker	8.44
Manager / supervisor	0.53
Support staff	6.93
Businessman	7.91
Small trader / Hawker	3.32
Self-employed professional	4.92
Home-based worker	1.50
Wage labourer	27.75

How might this influence political platforms?

The success and stagnation of the Left

The Left caters to the interests of tenured employees of manufacturing firms. They were very successful in influencing policies. To a great extent, the Congress stole the Left's thunder by pampering organised labour, and every political party in India treats them as holy cows.



This success undermined Left thinking as a political force. Left-influenced policies hobbled organised manufacturing, and we see only 7.92% of the workforce in `Industrial worker', which most closely fits the support base of the Left. It is ironic that the very success of the Left in shaping the Indian State has marginalised the Left as a political force. The price we pay for being a liberal democracy is the sacrifice of large-scale labour-intensive manufacturing.



The aristocracy of workers in organised manufacturing is grossly overpaid. What matters to their take now is not GDP growth or their marginal product, but their ability to use their privileged position to extract a rent out of the firms that have no choice but to deal with them. Hence, their interest in GDP growth is relatively low.

The role of agriculture

The farm lobby is not a monolithic bloc with unified interests. There are kulaks who seem to correspond to `Organised farmers' and are 8.69% of the workforce. And there are others (`Small farmers' at 13.39% and `Agricultural labourers' at 8.7%) who add up to 22.09% of the workforce. CMIE defines wage labourer as `Wage labourers are those who seek daily wages from non-agricultural sources. Typically, these are industrial workers who work in factories or companies but are not employed on a regular basis in these. Wage labourers also include construction site workers and those working in other non-agricultural activities. This includes a taxi driver who operates the owners' taxi' and hence this excludes agricultural labour. Policies that favour producers of agricultural products in broad terms would thus benefit 30.78% of the workforce (and hurt everyone else, who buys agricultural products). Policies that are more narrowly focused on the interests of kulaks, such as the old-school fertiliser subsidy, are good for 8.69% of voters and bad for everyone else.



India's structural transformation is giving a rapid decline in the share of the workforce in agriculture. While the CMIE data for December 2012 shows 30.78% of the workforce is in agriculture, the oldest available data, for December 2010, shows 32.68%. This is a decline of 1.9 percentage points in just two years. If we guess that on average, in each year, there is a decline of 0.75 to 1 percentage points, then in a decade, we will get to the range of 20.8 to 23.3 per cent of the workforce in agriculture.



Ordinarily, we would expect politics to favour the interests of buyers of food (69.22% of workers) trump the interests of producers (30.78%). Why is agriculture so prominent in Indian politics?  I can conjecture three explanations:

• As with Industrial workers, the priorities of Indian politics reflect the failure of imagination of the gerontocracy.


• The redistricting process is slow and for a long time had stalled. This has given an exaggerated emphasis to constituencies where agriculture is important. That Indian politics is a gerontocracy despite rapid economic and demographic change may partly be a consequence of slow redistricting.


• As with Industrial workers, once unequal policies create a focused beneficiary of distortions, these beneficiaries have a focused interest in lobbying in favour of the status quo. The costs of these policies are dispersed across society and the others don't have an incentive to mobilise politically.

In this context, it is interesting to see that in places like the US, Japan and Europe, there are strong agricultural lobbies that have achieved highly distorted policies even though their vote share has dwindled away to almost nothing. Brad Plumer in the Washington Post has some insights on how this comes about, and suggests that it is a combination of sharp interests of agriculturists in marginal constituencies.

How might competitive democracy construct an interest-based politics

Indian politics has come up with two ideas that transcend caste and religion : catering to agriculture and catering to industrial workers. Both these interest groups are not that salient in today's India. It is interesting to look at the table and puzzle over what might work.



Congress is pursuing the goal of setting up big welfare programs that target agricultural labour (8.7%) and wage labourers (27.75%), adding up to 36.45%. While this is a big chunk of votes, it isn't big enough to close the deal, particularly as in fast-growing India, many individuals within these two interest groups actually want to escape from poverty and welfare programs. Many of them would be interested in a platform that shows a roadmap out of poverty, instead of offering dole while perpetuating it. If even a small fraction comes to mistrust the strategy of dole, it undermines the extent to which this platform will win elections.



I think there is more possibility in a platform of public goods + growth when compared with the conventional wisdom. By definition, public goods (e.g. law and order or the infrastructure of transportation and communications) benefit all. There is no need to split up the vote and pursue narrow constituencies in this. A pro-growth stance is directly good for many sub-components: Organised farmers (8.69%), White collar workers (8.44%), Managers (0.53%), Businessmen (7.91%), self-employed professionals (4.92%), adding up to 30.49% which is similar to the size of the old-fashioned agriculture lobby (at 30.78%). Looking forward, this group will grow while those interested in either agriculture or dole will shrink.



In addition, a large chunk of the remainder of the workforce -- which may have only a weak interest in a pro-growth platform today -- aspires for a better life, particularly the young. The wage labourer of today wants to be a small trader tomorrow, and the small trader of today wants to be a businessman tomorrow.



I would hazard the following guesses:

Occupation	Share (Per cent)	Dole	Public goods	Growth
Small farmer	13.39	Weak	Yes	Weak
Organised farmer	8.69	No	Yes	Yes
Agricultural labourer	8.70	Yes	Yes	Weak
Industrial worker	7.92	No	Yes	Weak
White collar worker	8.44	No	Yes	Yes
Manager / supervisor	0.53	No	Yes	Yes
Support staff	6.93	No	Yes	Weak
Businessman	7.91	No	Yes	Yes
Small trader / Hawker	3.32	Weak	Yes	Weak
Self-employed professional	4.92	No	Yes	Yes
Home-based worker	1.50	Weak	Yes	Weak
Wage labourer	27.75	Yes	Yes	Weak
Total	100.00	36.45	100.00	30.49

It seems to me that a public goods + growth platform would work better than most people in Indian politics think. Public goods are interesting to all, and the constituency that would strongly favour growth is 30.49% and growing. In contrast, a dole strategy is interesting to 36.45%, and is shrinking.





None of this is relevant if the question that is posed to the electorate is about religion, English, or social conservatism. It may well be the case that the 2014 elections will be fought purely on these questions. But in time, the competitive dynamics of democracy will favour platforms that reflect the interests of the populace, and then these kinds of considerations will matter more.

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A better output proxy for the Indian economy

by Akhil Dua, Pinaki Mukherjee, Radhika Pandey, Ila Patnaik, Pramod Sinha, Ajay Shah.



India's emergence as a market economy has been accompanied by the emergence of business cycle fluctuations that are similar to those seen in market economies [link, link]. In understanding business cycle conditions, and in crafting institutional arrangements for stabilisation, it is essential to properly measure output and prices.

The problem

India is in reasonably good shape on measurement of prices, with older CPI-IW data and now the new CPI. (Using WPI as a measure of prices is wrong, but you don't have to make this mistake; the statistical system does have CPI-IW and then CPI). Measurement of output, in contrast, has presented serious difficulties.



The index of industrial production is widely used as a measure of business cycle conditions. However, it reflects only manufacturing, which is a small part of the economy. In addition, it is riddled with serious difficulties of measurement.



The other key measure that has been widely used is quarterly GDP data. However, contrary to what textbooks teach us, in India, quarterly GDP data is constructed without information about the demand side. In addition, there are two important concerns about the quarterly GDP data from the viewpoint of business cycle analysis:

• Agriculture is included in the overall GDP data -- as it should -- but to a significant extent, fluctuations in agriculture reflect weather shocks and do not reflect underlying business cycle conditions.


• Spending by the government is counted as output in GDP data. However, it does not reflect underlying business cycle conditions. See Robert Higgs on this subject.

As a consequence, quarterly GDP data in India is not a good reflection of business cycle conditions.

Two steps towards measuring output

In order to address these issues, we have constructed two new series which, we feel, do a good job of measuring nominal output.

The first of these is GDP excluding agriculture and excluding government. This focuses upon the output of individuals, small firms and large firms, which is what the market economy and the business cycle is all about.

The second strategy consists of utilising firm data. Listed companies are required to release quarterly results. These results are pored over by accountants, auditors, senior managers, tax collectors, shareholders, etc. They are thus likely to have few mistakes of the sort which have plagued government statistics and survey-based information.

Finance companies have very different concepts underlying their accounting data, and are hence excluded. Oil companies sometimes experience very large jumps in their revenues owing to decisions by the government about administered prices. These fluctuations are not a feature of underlying business cycle conditions. Hence, oil companies are excluded. In short, we focus on all listed firms observed in the CMIE database other than finance and oil companies.

For each pair of quarters, we construct a panel of firms observed in both quarters, and work out the percentage change in the sum of net sales across all the firms. These percentage changes are used to construct a net sales index.



Non-agricultural and non-government GDP is the business cycle. It is made up of production by small firms (going down to one employee) and large firms doing both industry and services. This measure captures large firms in both industry and services and is thus a good proxy for what is going on in business cycle conditions.

Net sales of non-finance non-oil firms, and GDP ex-agriculture and ex-government.

Nominal indexes, non-seasonally adjusted.

The graph above shows these two time-series. The fact that the two series -- which are constructed from completely unrelated sources -- agree with each other across long periods of time is a source of increased confidence.

Net sales of non-finance non-oil firms, and GDP ex-agriculture and ex-government.

Nominal indexes, seasonally adjusted.

 Our first step is to seasonally adjust both series. Once again, it is satisfying to see how well the two series agree with each other, even though they are quite unrelated on their underlying sources.

2-Q moving average of growth of seasonally adjusted levels.

Annualised per cent.

Using this, we are able to compute nominal GDP growth. Once again, it is striking how well the two series agree with each other. Two major recessions are visible: 2001 and 2009.



This series shows much more macroeconomic volatility when compared with what we are used to with conventional data. This is perhaps unsurprising as government expenditure is a fairly stable series, and fluctuations of agriculture are noise. When these two are removed we see substantial macroeconomic volatility. This is not surprising, as India presently lacks the frameworks of stabilisation through either monetary or fiscal policy.

Conclusion

We feel that we now observe a good measure of output in India, with two different measures that are gratifyingly close to each other. These series are a valuable starting point for business cycle research.

The key flaw of this work is that it takes us till a nominal output index. The next big hurdle to cross is that of converting to real. The simplest strategy would be to just use the CPI-IW and then the new CPI.

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Low price-points for new kinds of computers

An entry level game console

A console with quirks, for tinkerers by David Pogue in the New York Times: a gaming computer which is a cube with a 3 inch side, at $100. It runs Android. It is an open design; you can hack the software and hardware.

Raspberry Pi

This is a complete computer for $35. You have to see it to believe it. Connect in an ordinary smartphone power supply, an Ethernet cable, a USB keyboard, a USB mouse and an HDMI screen, and you're up and running. It boots linux and runs a browser. [link] [link] [link]

A bit more money and a lot more compute

Arduino is going beyond the geeks to the artists.



Beaglebone Black is at the $45 pricepoint.



Utilite is a quad-core computer that consumes 3-8 watts. At $99.

Implications

In the West, these things are cute. But in developing countries, where individuals and firms are more resource-constrained, these new approaches can be valuable alternatives to conventional bloatware. These open up a new world of exploration and R&D in India, as a large number of tinkerers and researchers can afford these pricepoints. Many workplaces can shift away from conventional desktops and save money on hardware and on electricity.

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Taking a stand on the equity risk premium in India

by Suyash Rai.

What is the Equity Risk Premium and why it matters

The basic intuition in investing is that over and above the time value of money, the return on an investment must compensate for the risk it adds to the portfolio of an investor. In the Capital Asset Pricing Model (CAPM), this rate of return can be computed based on two variables: the risk premium of the market on the whole (ERP), and the sensitivity of an asset to the market (Beta). The asset must generate returns equaling the time value of money (a.k.a. the risk free rate) plus ERP*Beta. This tells us the required rate of return for an asset of systematic risk Beta.



The Equity Risk Premium (ERP) is a key variable in many decisions in corporate finance and asset pricing. The equity index is a diversified portfolio where the bulk of gain from (domestic) diversification has been accomplished. How much higher is the return on this portfolio, on average, when compared with taking zero risk by investing in government bonds? This value is termed the `equity premium' or the `equity risk premium':




$\mathrm{ERP}=E(r_M-r_f)$


The equity risk premium is at the heart of finance, shaping the behaviour of everyone buying, selling or regulating publicly traded assets. For example, monopoly regulators the world over use some version of the CAPM to decide how much return is fair for regulated monopolies. The ERP is an essential component of this decision. For example, the Airport Economic Regulatory Autority (AERA) uses the standard CAPM to determine the fair rate of return on capital for private airports in India. All these applications require arriving at a numerical estimate for the ERP.

A warning about the risk free rate

Estimating the ERP inherently requires taking a stand on what the risk-free rate is. For the risk free rate, the yield on a government of India bond can be used. For short-term decisions, treasury bills (maturity of under 1 year) can be used, but for most decisions with long term horizons, it makes sense to use yield on the 10-year government of India bonds. The 10-year bond has a deeper, more liquid market, and therefore provides a more reliable estimate of the risk free rate, compared to government bonds of other maturities.



We have to, however, keep in mind the problem that under the Indian system of financial repression (forced purchases of government bonds), the observed interest rates on all government bonds are understated: given the level of inflation, default risk and inflation risk in India, voluntary buyers would require a higher rate of return.



In the future, many things are likely to happen to the risk-free return in India. Progress on easing financial repression will remove the forced purchases of bonds and tend to push up the required rate of return for government bonds. On the other hand, access to the Indian bond market for foreign buyers of bonds will give lower interest rates. Finally, establishing a sound central bank, as envisaged under the draft Indian Financial Code, will give low and stable inflation which will give a lower cost of borrowing for the government.



Another problem is that prior to 2000, market data on the government bond market is highly spotty. Some analysts (for example, see this paper by Prof. Rajnish Mehra) use the bank deposit rate as the risk free rate in the pre-2000 period. However, this is also a rate that was distorted by regulation and did not reflect market forces.

Three alternative approaches for estimating the equity risk premium

Even though the ERP is extremely important, it is quite difficult to arrive at a good numerical estimate for it. There are three stylised approaches of estimating ERP, each with a few variations (for a detailed discussion on these approaches, see this review paper on ERP). They are:

1. Asking around
   
   


2. Utilising forward looking estimates
   
   


3. Looking back

In this article I review these three approaches as a mechanism for estimating the ERP in India, and offer some views on what the most sensible estimates might be.

1. Asking around

If two alternative estimates are unbiased and imperfectly correlated, then a combination of these estimates is generally better than either of the two. Hence, we can survey investors, portfolio managers, and other people we consider relevant, and ask them about what they think is a reasonable spread of stock returns over time value of money. The average value will be a good estimate as long as each person has an unbiased estimate.



A recent survey (published on June 26, 2013) of 12 finance and economic professors, analysts and managers of companies in India found the average ERP to be around 8.5% (up from 8% in 2012).



This method is used by many practitioners, but its validity is quite suspect. There is a great degree of recency bias in ERP based on surveys, and given the voluntary nature of responses, we usually don't know how representative the surveys are. If the survey respondents are not using a sound basis for estimating the ERP, the survey cannot, in most cases, give an accurate estimate.

2. Looking into the future

One can compute the ERP implied in the present stock valuations and forecasted earnings for firms. The implied return is calculated based on the stock valuations and the forecasted earnings, and then the risk free rate is deducted.



This implied ERP can change quite rapidly, because it is based on the current stock valuations and expected cash flows. For example, in the US, implied ERP (based on Free Cash Flow to Equity or FCFE) was 2.05% in 1999, and doubled to 4.10% in 2002. Someone using the 1999 implied ERP to take a decision with, say, a ten year horizon, would have underestimated the risk premium. A variant of this method, which mitigates this problem, is to take the average implied ERP for the last few years.



To calculate the implied ERP, we require estimates of future cash flows, which, except for a few well-analysed firms, may not always be available. In India, we now have a number of analysts regularly putting out earnings estimates. For the biggest firms in India, there are 20-40 such analyst reports available at any point of time. But for most other firms, these estimates are hard to come by. Taking a handful of prominent firms as representative of the entire market may lead to an under-estimation of risk premium.



The strength of the implied ERP approach is that it yields a reasonably good estimate of the ERP over a short term (over the next few years). For June-end, 2013, one estimate of the implied ERP (by Pitabas Mohanty of XLRI) is 10%.

3. Looking back

The most commonly used method for estimating the ERP is the historical method. This method uses the difference between the average historical return on a stock market index and the returns on the riskless asset. It is a useful method in many contexts, because it yields a good estimate of the long term central tendency of the ERP. However, it has big problems in the Indian context. Two problems stand out: the problem of estimating risk-free rate, and the shortage of historical data on index returns.



We don't have a long span of equity market data available. Though the index returns (on BSE Sensex) are available from 1979 onwards, dividend data in CMIE Prowess only starts from 1990. So, we have reliable data on market returns for about 23 years only (1990-2013).



The full time-series for the BSE Sensex, from April 1979 onwards, has 8100 observations. However, estimating average returns depends only on the span and is not helped by frequency. And the span of only 23 years in the period where dividends are observed, leaves a lot to be desired. The average annual return on BSE Sensex (not including the dividend yield) during this period (July 06, 1990 to July 05, 2013) is about 19% and the annualised standard deviation of daily returns is 27.8%. As a consequence, the mean return is estimated quite imprecisely: the standard error of the mean works out to 5.8%. The 95% confidence interval runs from 7.4% to 30.6%.



A few more years of data is not going to solve this problem. Halving the standard error requires increasing the span by 4 times.



Let's apply the basic historical method of estimating ERP in India. As the risk free rate, I take the average yield from 2000-01 to 2012-13 on the 10-year government of India bond: 7.77%. The average total annual return on the Sensex (stock return+dividend yield) from 1990 to 2013 is 20.7%, but this is an arithmetic mean. For equity returns, geometric mean is a better measure of central tendency, because of the high level of serial correlation in the series of market returns. The geometric mean of total annual returns (stock returns+dividend yield) is 15.9%, which means that the historical ERP is 8.13%. This is an estimate, but not a reliable one.

3a. Looking back in a different way

To work around the problems in the historical method we need to use a variant of the historical method, which helps us make the most of the advantages of the method, while overcoming the measurement limitations we face in India. This can be done if we take the historical ERP for a mature market (or a group of mature markets) over a long span, and adjust it for the premium to be paid for India's country risk. In using this method, the best option is to take long run historical ERP from the US as the base. Stable and reliable equity market time series is available for a fairly long span in the US. Based on equity market returns from 1928 to 2012, the historical ERP for the United States is 4.2% (geometric mean). Adjusting for the country risk premium for India is a bit tricky. It can be done through a number of methods, each with its pros and cons:

1. Based on sovereign rating: There is a default spread implied in India's sovereign rating. India's sovereign rating of Baa3 (Moody's) implies a default spread of 2%. Based on this the ERP in India is 4.2%+2% = 6.2%.
   
   


2. Based on bond spreads or CDS spreads: Bond spreads and CDS spreads are often used, but the relevant information is not available for India. For bond spreads, we need a significant amount of sovereign debt denominated in US dollars, which is not there. There is negligible CDS activity on sovereign debt, mainly because very little sovereign debt is held by foreign investors.
   
   


3. Relative standard deviation of the Indian and a benchmark equity market: Another method is to use the relative standard deviation of India's equity market with the US equity market over the last few years. The idea in this approach is that since the standard deviation of returns is a measure of the risk in a market, the relative standard deviation can be used to adjust the mature market ERP to get the ERP in India. In this approach, the relative standard deviation of equity markets in the two countries is multiplied into the ERP of US markets, to get the ERP in India.
   
   
   $\mathrm{ERP}=\mathrm{Default\; spread\; implied\; in\; sovereign\; rating}*({\mathrm{SD}}_{\mathrm{India\; Equity}}/{\mathrm{SD}}_{\mathrm{US\; Equity}})$
   Based on the relative standard deviation of equity returns from Feb 2011 to Feb 2013 (0.99), the ERP in India would be around 4.16. So, as per this estimate, the ERP in India is lower than that in the US. It is difficult to make the case that India is less risky than the US, and should have a negative spread vis-a-vis the US.
   
   


4. Relative standard deviation of domestic equity and bond markets: Another market-based approach is to use the relative standard deviation of the domestic equity and bond markets in India. The intuition in this method is that the default spread implied in the sovereign rating does not fully capture the risk of the equity market, and should therefore be adjusted to reflect the relative risk between the equity market and the debt market. The relative standard deviation is assumed to be a measure of this adjustment. In this approach, the relative standard deviation is multiplied into the default spread implied in the sovereign rating, and added to the mature market ERP.
   
   
   $\mathrm{ERP}=\mathrm{Mature\; market\; ERP}+\mathrm{Default\; spread\; implied\; in\; sovereign\; rating}*({\mathrm{SD}}_{\mathrm{Equity}}/{\mathrm{SD}}_{\mathrm{Bond}})$
   Though in theory this is a good method, in application it gives strange results in some contexts. It is highly dependent on recent data on relative volatility. For example, right now in Greece, given the volatility in debt markets, this approach yields a very low ERP, lower than most developed markets. As of March 2013, India had the highest relative standard deviation of equity and bond markets in the world (4.91), more than many countries usually assumed to be riskier. This is based on two years of weekly returns. I think this high relative standard deviation is because of the relative inactivity in the bond market, which is largely dominated by sovereign bonds that are largely held by captive investors. Volatility in the market would increase if it becomes more vibrant. At present, this method yields an ERP of 4.2% + 4.91*2 = 14.02%.

Any historical method has two general problems: there is a certain degree of survivorship bias in the time series, and the ERP is obtained mainly on the basis of data from firms above a certain size. These biases need to be considered before using any version of the historical method of estimating the ERP.

Choosing a suitable approach

There is no perfect method for estimating the ERP in India, and there is a wide range of estimates (from 4.16% to 14.02%). While choosing the suitable approach for our purpose, we must be cognizant of the fact that in the Indian environment, too often, we are flying blind with weak information. We don't have access to a long span of reliable market data. We don't have reliable estimates of future cash flows for many firms. We don't have reliable sovereign bond and CDS spread information. We don't have good indicators of the risk free rate. But we must make do with what we have; we have to go to war with the data that we have got.



I am thinking mainly about the corporate finance decisions. In corporate finance, the main use of the ERP is to estimate the cost of capital or reasonable rate of return on investments. In such applications, the ERP estimation method one uses is shaped by the horizon. For estimating the long run ERP (say, for more than 5 years), all methods other than the historical method are rife with problems, especially given the data availability in India. The standard historical approach is not suitable, because we really need a much longer span of equity market data, and much better indicators of the risk free rate.



A variant of the historical method can be used: one that takes the ERP from a mature market and adjusts it for India's country risk. In my opinion, for estimating the long run ERP, it is best to take the historical ERP for US, and add to it the default spread implied in India's sovereign rating. This yields the ERP of 6.2%. The other methods of adjusting for country risk premium suffer from serious problems. As of now, the two methods based on relative standard deviations yield very strange estimates, and therefore must be set aside. The ERP in India yielded by one of these methods is lower than the ERP for the US, and the other method puts the ERP in India at a level higher than many economies that are known to be much worse than India. Having regularly observed the results of these methods over the last few years, I have seen them yield some really wonky estimates of the ERP for many countries. We should use these methods carefully. The implied default spread, on the other hand, is a bit too stable (ratings are revised infrequently), but it is not as prone to absurd results.



So, I would say that 6.2% is a reasonable estimate of the long run ERP in India. If the horizon of asset pricing decision is long, this should be a reasonable estimate of the ERP in India. Take the example of the Airport Economic Regulatory Authority (AERA) of India, which needed an estimate of the ERP over a long horizon (5 years). We at NIPFP worked with AERA to estimate a reasonable rate of return for the private airports, and after due consideration of the options, AERA decided to opt for this method of taking the ERP from the US and adding to it the default spread implied in India's sovereign rating (see this order on the tariff for the Mumbai International Airport).



If the horizon of the investment decision is shorter, one needs an estimate of short run central tendency. In such contexts, one can use survey-based or implied ERP. The ideal survey should have a large sample that is drawn randomly from the surveyed population so as to be representative of it, and for implied ERP, reasonably reliable data on expected cash flows for firms should be available. Since it forces us to do our own math and to think precisely, and because good data is now available for firms in India, I would say it is better to use the implied ERP than to use the survey-based ERP in India.

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Interesting readings

Devesh
Kapur in the Business Standard on the Right to X Act.

Ashutosh
Varshney has a great piece in the Indian Express
analysing the possibilities for a BJP led by Narendra Modi. Also
read Poornima
Joshi in Caravan magazine.

Laurie
Garrett and Maxine Builder in Foreign Policy about
preparations that Indian authorities need to undertake associated
with the Haj this year.

The first universities in India that shed their
xenophobia will
break away from the pack.

Milan
Vaishnav on the extent to which voters in India know and use
caste information when thinking about candidates.

In continuation
of the
commodities transaction tax, Mobis Philipose tells us
about the
impact on day 1.

Menaka
Doshi in the Financial Express on the low quality of
economic policy making in India.

The Ministry of Finance has setup
a Standing Council of Experts on the international competitiveness
of the Indian financial system
[link].

Corporate
governance failure at Ranbaxy? by Asish K. Bhattacharya
in the Business Standard.

The big idea of the Indian equity market reform in the early 1990s
was to have a 3-way separation between shareholding, management and
membership of exchanges. Jayanth Varma has a blog post about how
there
are problems
at the Chicago Board Options Exchange, in 2013 which are
reminiscent of those seen at the BSE in 1993. I'm not surprised, as
the CBOE does not have three-way separation.

The world according to Sheila
Bair. She now leads a non-government group of experts
called
the Systemic
RIsk Council. We need more such groups of experts in
India, who are not backed by the State.

Google
is the General Electric of the 21st century by John Gapper
in the Financial Times.

In continuation
to Let's
not confuse knowledge with college, see ideas for recruiters
by Rory
Sutherland who is vice-chairman of Ogilvy Group.

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The attack on the market for the rupee is a mistake

I have a column in the Economic Times today titled The attack on the market for the rupee is a mistake.



You may like to also see:

• Ila Patnaik in the Indian Express yesterday on macroeconomic fundamentals.


• Do not mourn rupee fluctuations, 11 June.


• An editorial in the Indian Express today.


• Shankar Acharya in the Business Standard.

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Seminar on FSLRC in Delhi tomorrow

PHDCCI has organised a seminar on FSLRC tomorrow (5th July). This is at the PHDCCI Auditorium, from 9:15 AM to 1:15 PM. The persons on the program are:

• Rajiv Bajaj, Co-Chairman, PHDCCI Capital Markets Committee


• Ashish Chauhan, CEO, BSE


• Prithvi Haldea, Chairman, PHDCCI Capital Markets Committee


• Sharad Jaipuria, Sr. Vice President, PHDCCI


• Suman Jyoti Khaitan, President, PHDCCI


• K. P. Krishnan, Government of Karnataka


• Arvind Mayaram, Secretary, DEA, Ministry of Finance


• Ila Patnaik, NIPFP


• Suyash Rai, NIPFP


• Shubho Roy, NIPFP


• Shashank Saksena, Director, DFS, Ministry of Finance


• Ajay Shah, NIPFP


• Shekhar Shah, Director-General, NCAER


• Anup Wadhawan, JS(CM), DEA, Ministry of Finance

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Who should start a bank in India?

RBI has 26 applicants. Which should it choose? I have a column in the Economic Times today on this question. 

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