From despots to institutions by Ajay Shah in The Business Standard, 31 October.
Seminar on Quality of Services in Telecom and Data Services: Issues, Challenges and Solutions in NIPFP YouTube Channel.
Issues and arguments: How a key Delhi-Noida bridge went toll-free by Aneesha Mathur in The Indian Express, 31 October.
How the government fixed a mess in India Inc: The case of Satyam by Shaji Vikraman in The Indian Express, 31 October.
Across the aisle: The case for creative destruction by P Chidambaram in The Indian Express, 30 October.
The star of intellectual journalism by Niranjan Rajadhyaksha in The Mint, 29 October.
We Are in for a Pretty Long Civil War by Julia Ioffe in The Politico Magazine, 28 October.
RBI holds sway over India currency market as traders gripe by Kartik Goyal in The Mint, 28 October.
Inside the frozen zoo that could bring extinct animals back to life by Zach Baron in The GQ Magazine, 28 October.
Subversion of law in law-making by Somasekhar Sundaresan, 25 October.
The benefit of the doubt by Jessica Seddon
in The Mint, 25 October.
India is celebrating everything and doing it differently by Sapna Agarwal and Soumonty Kanungo in The Mint, 25 October.
GST: Make haste slowly by Vijay Kelkar, Satya Poddar and V. Bhaskar in The Mint, 19 October.
Insipid fare from the monetary policy committee minutes by Aparna Iyer in The Mint, 19 October.
Starting trouble for payments banks by Vishwanath Nair and Sahib Sharma in The Mint, 19 October.
Hillary Clinton's 3 debate performances left the Trump campaign in ruins by Ezra Klein in The Vox, 19 October.
The power of prediction markets by Adam Mann in Nature, 18 October.
Sovereign Debt Risk in Emerging Market Economies: Does Inflation Targeting Adoption Make Any Difference? by Weneyam Hippolyte Balima, Jean-Louis Combes and Alexandru Minea in Journal of International Money and Finance, 18 October.
New law should have firm foundations by Bhargavi Zaveri in The Business Standard, 18 October.
Support army, but don't mistake it for being above all criticism by Sushant Singh in The Indian Express, 18 October.
How Maharashtra is changing the way farmers sell their produce by Abhiram Ghadyalpatil in The Mint, 18 October.
Why it's time for cautious optimism, not pessimism, in Indian media by Prashant Jha in The Hindustan Times, 14 October.
Monday, October 31, 2016
Saturday, October 29, 2016
Weaknesses of recent moves on capital controls for outbound capital flows
by Gausia Shaikh and Bhargavi Zaveri.
On 30th September, 2016, RBI published a draft framework for allowing Indian residents to invest in overseas technology funds, for public comments. The draft framework lays down certain criteria on the basis of which RBI may allow an Indian resident to invest in offshore overseas technology funds (draft framework). We, at the Finance Research Group at IGIDR, submitted written comments on the draft framework.
Investing abroad offers Indian investors reduced risk through diversification of holdings. The Indian regulatory framework governing capital outflows is complex, fragmented and unduly restrictive, increasing the cost of investing in foreign securities and businesses. For instance, even at this advanced stage of liberalisation, it does not permit any Indian resident, except a listed company and a mutual fund (and in some cases, an alternative investment fund) to make portfolio investments (i.e. in listed securities) abroad. It defines what kinds of instruments may be invested in, caps the amount that can be invested, requires RBI approval to be obtained if the amount of outflow exceeds USD 1 billion, prescribes what kinds of instruments may be invested in, controls what the foreign investee entity can or cannot do, etc. In short, the regulatory framework has several features of central planning, which are not connected with any market failure arising from capital outflows.
Several expert committees constituted by the Central Government and RBI in the past have underscored the need to simplify the regulatory framework governing capital outflows (See the Tarapore Committee Report (2006), the Report of the Working Group on Foreign Investment (2010) and the Report of the Financial Sector Legislativ e Reforms Commission (2013)). For instance, the report of the Raghuram Rajan Committee (2008) states:
The International Monetary Fund has, in its paper titled Liberalizing Capital Outflows and Managing Outflows, summarised the existing literature and understanding of capital flow measures in partially capital account convertible countries (like India) and has suggested a policy framework governing capital outflows. Among other things, the suggested policy framework makes the following recommendations:
In the context of the understanding summarised above, we submitted the following inputs on the draft proposal to allow Indian residents to invest in "overseas technology funds" after obtaining the approval of RBI.
The draft framework makes an artificial distinction between what it calls 'overseas tech funds' and other funds, and then relaxes the existing restrictions on Indian residents from investing in overseas tech funds. There appears to be no economic rationale supporting a case for allowing Indian residents to invest in some kinds of funds and not others. Interventions in the policy governing capital outflows, must be supported by a consistent economic rationale linked to identified market failures. Similarly, relaxations in such policy must be crafted in accordance with sound principles of public economics.
The draft framework gives the following reason for allowing Indian residents to invest in overseas tech funds:
The reason for relaxation is linked to repeated requests from Indian residents to invest in overseas tech funds. This perpetuates the ad-hoc nature of relaxations that has pervaded the Indian regulatory framework governing capital controls in India. On this blog, we have repeatedly written about ad-hoc measures, both restrictions and relaxations, that seem to pervade the fabric of our law governing capital controls (see, for example, here and here.). While such relaxations may bring temporary cheer to a few stakeholders, such ad-hocism is dangerous as it has bad outcomes for the overall predictability of a regulatory regime in the long run.
Moreover, by mandating Indian residents to approach the RBI for approval for investing in overseas tech funds, even where the Indian resident satisfies the criteria specified in the draft framework, the draft framework perpetuates the approval route mechanism under our regulatory regime. A mature regulatory framework governing capital outflows should leave no scope for the exercise of discretion. The criteria for allowing or not allowing investment abroad must be clearly laid out in the law. Once an Indian resident satisfies such criteria, the investment must be allowed without having to approach any authority for approval. This reduces the transaction costs of investing abroad as well as the scope for exercising ad-hoc discretion.
For a framework so limited in its applicability to investment in a specific kind of fund, the draft framework is unclear on fundamental concepts on which it hinges. For instance, although the primary purpose of the draft framework is to regulate investment in an overseas tech fund, it does not define the concept of an "overseas tech fund" and "overseas tech start-up". To the best of our knowledge, there is no globally accepted definition of what constitutes an overseas tech fund or a tech start-up. The vagueness leaves scope for tremendous discretion and potential for abuse.
The draft framework perpetuates the culture of central planning by defining the eligibility criteria for Indian residents who may invest in overseas tech funds. For instance, it allows only listed companies in India with a profit track record and a minimum networth to invest in overseas tech funds. Similarly, it states that Indian companies which have ``long overdue export'' bills are disallowed from investing in overseas tech funds. An investment in a foreign security is an investment decision just like any other investment in the portfolio of an investor. In a mature market economy, long overdue export bills cannot be a ground for disallowing an Indian resident from investing abroad. None of these restrictions are backed by any economic rationale. The requirement of obtaining approvals from RBI for making such investments further perpetuates the central planning culture of 1990s.
In view of the above, we made the following recommendations with respect to the draft framework:
The authors are researchers at the Indira Gandhi Institute of Development Research, Mumbai.
On 30th September, 2016, RBI published a draft framework for allowing Indian residents to invest in overseas technology funds, for public comments. The draft framework lays down certain criteria on the basis of which RBI may allow an Indian resident to invest in offshore overseas technology funds (draft framework). We, at the Finance Research Group at IGIDR, submitted written comments on the draft framework.
Investing abroad offers Indian investors reduced risk through diversification of holdings. The Indian regulatory framework governing capital outflows is complex, fragmented and unduly restrictive, increasing the cost of investing in foreign securities and businesses. For instance, even at this advanced stage of liberalisation, it does not permit any Indian resident, except a listed company and a mutual fund (and in some cases, an alternative investment fund) to make portfolio investments (i.e. in listed securities) abroad. It defines what kinds of instruments may be invested in, caps the amount that can be invested, requires RBI approval to be obtained if the amount of outflow exceeds USD 1 billion, prescribes what kinds of instruments may be invested in, controls what the foreign investee entity can or cannot do, etc. In short, the regulatory framework has several features of central planning, which are not connected with any market failure arising from capital outflows.
Several expert committees constituted by the Central Government and RBI in the past have underscored the need to simplify the regulatory framework governing capital outflows (See the Tarapore Committee Report (2006), the Report of the Working Group on Foreign Investment (2010) and the Report of the Financial Sector Legislativ e Reforms Commission (2013)). For instance, the report of the Raghuram Rajan Committee (2008) states:
We also need to make it easier for our individuals and institutions to invest abroad. For individuals, the primary task may be to simplify procedures, and liberalize the kinds of assets and managers that can be invested in. For our institutions like pension funds, we have to convince various constituencies that a portfolio diversified across the world is safer than a portfolio concentrated only in India, and has better risk properties (for one, it retains value when the Indian economy suffers a downturn). Regulatory authorities then have to allow institutional portfolios to become broadly and internationally diversified.
Restrictions on capital outflows
The International Monetary Fund has, in its paper titled Liberalizing Capital Outflows and Managing Outflows, summarised the existing literature and understanding of capital flow measures in partially capital account convertible countries (like India) and has suggested a policy framework governing capital outflows. Among other things, the suggested policy framework makes the following recommendations:
- In countries that have substantially liberalised their capital account, capital outflows must be managed primarily with macro-economic and financial sector policies.
- Capital flow measures on outflows may be considered in (i) crisis or near crisis situations; or (ii) to provide breathing space while more fundamental policy adjustment is implemented. Such measures ought to be temporary in nature and must be lifted once the crisis is controlled.
- Even when capital flow measures are imposed, they must not be residency-based. Examples of residency-based measures include measures on residents' investments and transfers abroad, restrictions on investments in financial instruments, etc.
Problems with the draft framework
In the context of the understanding summarised above, we submitted the following inputs on the draft proposal to allow Indian residents to invest in "overseas technology funds" after obtaining the approval of RBI.
Allowing Indian residents to invest in funds managed and invested abroad.
The draft framework makes an artificial distinction between what it calls 'overseas tech funds' and other funds, and then relaxes the existing restrictions on Indian residents from investing in overseas tech funds. There appears to be no economic rationale supporting a case for allowing Indian residents to invest in some kinds of funds and not others. Interventions in the policy governing capital outflows, must be supported by a consistent economic rationale linked to identified market failures. Similarly, relaxations in such policy must be crafted in accordance with sound principles of public economics.
Principles of public administration and rule of law
The draft framework gives the following reason for allowing Indian residents to invest in overseas tech funds:
Reserve Bank (sic) has been receiving references from various Indian parties to invest in Overseas Technology Funds which in turn will further invest in overseas technology startups. Such proposals generally do not meet the eligibility norms for making the overseas direct investment under the automatic route ... It is proposed that the Reserve Bank will deal with such requests under the approval route ...
The reason for relaxation is linked to repeated requests from Indian residents to invest in overseas tech funds. This perpetuates the ad-hoc nature of relaxations that has pervaded the Indian regulatory framework governing capital controls in India. On this blog, we have repeatedly written about ad-hoc measures, both restrictions and relaxations, that seem to pervade the fabric of our law governing capital controls (see, for example, here and here.). While such relaxations may bring temporary cheer to a few stakeholders, such ad-hocism is dangerous as it has bad outcomes for the overall predictability of a regulatory regime in the long run.
Moreover, by mandating Indian residents to approach the RBI for approval for investing in overseas tech funds, even where the Indian resident satisfies the criteria specified in the draft framework, the draft framework perpetuates the approval route mechanism under our regulatory regime. A mature regulatory framework governing capital outflows should leave no scope for the exercise of discretion. The criteria for allowing or not allowing investment abroad must be clearly laid out in the law. Once an Indian resident satisfies such criteria, the investment must be allowed without having to approach any authority for approval. This reduces the transaction costs of investing abroad as well as the scope for exercising ad-hoc discretion.
Lack of clarity and perpetuating central planning
For a framework so limited in its applicability to investment in a specific kind of fund, the draft framework is unclear on fundamental concepts on which it hinges. For instance, although the primary purpose of the draft framework is to regulate investment in an overseas tech fund, it does not define the concept of an "overseas tech fund" and "overseas tech start-up". To the best of our knowledge, there is no globally accepted definition of what constitutes an overseas tech fund or a tech start-up. The vagueness leaves scope for tremendous discretion and potential for abuse.
The draft framework perpetuates the culture of central planning by defining the eligibility criteria for Indian residents who may invest in overseas tech funds. For instance, it allows only listed companies in India with a profit track record and a minimum networth to invest in overseas tech funds. Similarly, it states that Indian companies which have ``long overdue export'' bills are disallowed from investing in overseas tech funds. An investment in a foreign security is an investment decision just like any other investment in the portfolio of an investor. In a mature market economy, long overdue export bills cannot be a ground for disallowing an Indian resident from investing abroad. None of these restrictions are backed by any economic rationale. The requirement of obtaining approvals from RBI for making such investments further perpetuates the central planning culture of 1990s.
Conclusion
In view of the above, we made the following recommendations with respect to the draft framework:
- The framework must not distinguish between overseas tech funds and others. Indian investors must be given the benefit of diversifying their portfolios by allowing investment in all kinds funds managed and investing abroad. Apprehensions of round-tripping and money laundering must be dealt with under the respective taxation and anti-money laundering frameworks that already exist in India. Such apprehensions alone should not be a ground for pre-empting Indian residents from investing in diversified funds abroad.
- The framework must depart from the central planning approach of defining eligibility criteria for Indian residents who may invest in funds managed and investing abroad.
- The framework must lay down the criteria for allowing Indian residents to invest in funds managed and investing abroad, by incorporating them in the regulatory framework (namely, the Foreign Exchange Management (Transfer or Issue of Any Foreign Security) Regulations, 2004 or FEMA 120) itself, in a clear and precise manner. Once these criteria are complied with, there should be no requirement of further approval from RBI.
The authors are researchers at the Indira Gandhi Institute of Development Research, Mumbai.
Friday, October 28, 2016
The Diwali effect in Delhi air quality
by Dhananjay Ghei, Arjun Gupta and Renuka Sane
As Diwali approaches, we have learned to worry about air quality. Over the last few years, several studies have noted the increase in pollution levels during the period of Diwali owing to increase in commercial activity and firework displays. However, as we show in our previous article, there is considerable variation in PM 2.5 levels in Delhi in terms of location/time/month:
It is possible that the bad air that we see in Delhi at the time of Diwali is just the bad air quality in winter, and is not causally impacted upon by Diwali. In this article, we attempt to quantify the increase in the PM 2.5 levels during the Diwali period. Does Diwali have an impact upon air quality? If so, by how much?
The opportunity to identify a Diwali effect comes from the fact that Diwali is a `moving holiday' which takes place on a different day of each year. If this were not the case, it would be strongly correlated with changing climate.
Our ability to analyse these questions is greatly hampered by the lack of data. As of today, the data only runs from 1/2013 to 10/2016.
The air pollution caused by fireworks includes many contaminants. The data that we are studying covers only pm2.5.
The data used for the analysis comes from the US Consulate based in Chanakyapuri and the Central Pollution Control Board for 4 locations (R K Puram, Punjabi Bagh, Mandir Marg, Anand Vihar). The data consists of hourly PM 2.5 levels across the five locations from January 2013 to October 2016. We winsorise the data at 1% on both ends to remove the extreme tail values.
We first estimate the effect of Diwali on daily data using an event study. We aggregate the hourly concentration of PM2.5, at each location, to arrive at the daily numbers. The day of the Lakshmi Puja is taken as the event day. Therefore, we get 3 events for each location. Next, we calculate the percentage change in PM2.5 concentration levels by differencing the logarithm of PM2.5 values. These are then re-indexed to show the cumulative change over a 20 day window.
The solid line represents the average cumulative percentage change in PM2.5 values during the window, whereas the dashed line represents the confidence intervals calculated using the bootstrapped standard errors. We see that pollution levels start increasing one day before Diwali, and increase till two days after Diwali. It is also interesting to note that the increase in the pollution levels is significant during the two days after Diwali. This can be attributed to the fact that Diwali celebrations begin only on the night of Diwali, thereby leading to a significant increase the next day, as well as Diwali being celebrated over an extended period of time.
We now come at the same set of questions using a regression.
Since Diwali is celebrated over a number of days we also define the following models:
The model is as follows:
\[ PM2.5_{it} = \alpha + \beta_1*Diwali_{t}+ \beta_2*Diwali_{t}*l_{i} + m_t + h_t + l_i+\epsilon_{it} \]
where, $i$ is location, and $t$ is time. Here, PM 2.5 is the hourly measured levels of the pollutant. The first model takes Diwali to be only the date of Diwali, second model defines the Diwali days from one day before to one day after and the third model considers Diwali from the preceding day to two days after Diwali. In addition, we have month ($m_t$), location ($l_i$), and hour ($h_t$) fixed effects. The base for the location interaction term is Anand Vihar. Robust standard errors are used for our analysis throughout.
The first model (Column 1) shows that the baseline effect (i.e. at Anand Vihar) is not statistically different from non-Diwali days. For locations, other than Chanakyapuri, there is a differential effect on Diwali relative to Anand Vihar on Diwali. For instance, Diwali adds on an average 69.35 (73.07-3.72) µg/m3 PM2.5 particulate matter in air at Mandir Marg relative to Anand Vihar.
When we consider the second (Column 2) and third (Column 3) specifications, there is a statistically significant effect in Anand Vihar. The average particulate matter is 99 µg/m3 higher when we consider a two day Diwali, and 135 µg/m3 when we consider a three day Diwali period. While this may not seem much, given the already degraded air quality during these months, Diwali makes the pollution level reach alarming levels (>400, the monthly average in October November is around 340) which can have severe impacts on the health of people.
The Diwali effect is lower in other other locations relative to Anand Vihar. Thus, we see, that on the main day of Diwali, Anand Vihar is not too different from other days, while other locations have more pollutants relative to Anand Vihar. However, once we take into account 1-2 days after Diwali, we see that Anand Vihar is the most polluted location, and other locations have lower pollutants relative to Anand Vihar.
Very little is known, at present, about air quality and Diwali. Using the admittedly weak data resources, we have begun analysing this question here.
To the extent that these results are persuasive, they could help individuals plan strategies to avoid being in Delhi on these days. There is also a case for a Pigouvian tax on fireworks, in order to overcome the externality.
Previous work on Diwali, which helps us see other dimensions of Diwali, includes: Seasonal adjustment with Indian data: how big are the gains and how to do it by Rudrani Bhattacharya, Radhika Pandey, Ila Patnaik, Ajay Shah, and IEDs in Diwali and Toxic chemicals in Holi by Ajay Shah.
As Diwali approaches, we have learned to worry about air quality. Over the last few years, several studies have noted the increase in pollution levels during the period of Diwali owing to increase in commercial activity and firework displays. However, as we show in our previous article, there is considerable variation in PM 2.5 levels in Delhi in terms of location/time/month:
- Time Effect: The effect of diwali is not uniform throughout the day and is more prevelant at particular time of the day than other times. We also need to adjust for the confounding effect of time: pollution levels are high during the night and low during the day.
- Location Effect: Several areas of Delhi are severly polluted throughout the time, whereas others see large variations in their pollution levels. All these reasons make it difficult to attribute the entire increase in PM2.5 on Diwali.
- Month Effect: The day of Diwali Festival varies in the Gregorian Calendar between the 17th October and 15th November every year. Existing pollution levels are already high when compared to the annual average. This is a confounding effect.
It is possible that the bad air that we see in Delhi at the time of Diwali is just the bad air quality in winter, and is not causally impacted upon by Diwali. In this article, we attempt to quantify the increase in the PM 2.5 levels during the Diwali period. Does Diwali have an impact upon air quality? If so, by how much?
Issues in research design
The opportunity to identify a Diwali effect comes from the fact that Diwali is a `moving holiday' which takes place on a different day of each year. If this were not the case, it would be strongly correlated with changing climate.
Our ability to analyse these questions is greatly hampered by the lack of data. As of today, the data only runs from 1/2013 to 10/2016.
The air pollution caused by fireworks includes many contaminants. The data that we are studying covers only pm2.5.
Pollution levels on Diwali
The data used for the analysis comes from the US Consulate based in Chanakyapuri and the Central Pollution Control Board for 4 locations (R K Puram, Punjabi Bagh, Mandir Marg, Anand Vihar). The data consists of hourly PM 2.5 levels across the five locations from January 2013 to October 2016. We winsorise the data at 1% on both ends to remove the extreme tail values.
The effect of Diwali on pollution levels
We first estimate the effect of Diwali on daily data using an event study. We aggregate the hourly concentration of PM2.5, at each location, to arrive at the daily numbers. The day of the Lakshmi Puja is taken as the event day. Therefore, we get 3 events for each location. Next, we calculate the percentage change in PM2.5 concentration levels by differencing the logarithm of PM2.5 values. These are then re-indexed to show the cumulative change over a 20 day window.
 |
| Event study showing the change in PM2.5 around Diwali date (in days) |
The solid line represents the average cumulative percentage change in PM2.5 values during the window, whereas the dashed line represents the confidence intervals calculated using the bootstrapped standard errors. We see that pollution levels start increasing one day before Diwali, and increase till two days after Diwali. It is also interesting to note that the increase in the pollution levels is significant during the two days after Diwali. This can be attributed to the fact that Diwali celebrations begin only on the night of Diwali, thereby leading to a significant increase the next day, as well as Diwali being celebrated over an extended period of time.
We now come at the same set of questions using a regression.
Contribution of Diwali on PM2.5: Regression analysis
Since Diwali is celebrated over a number of days we also define the following models:
- Diwali=t: Diwali
- Diwali={t-1:t+1}: 3 Days (day before Diwali, Diwali, day after Diwali)
- Diwali={t-1:t+2}: 4 Days (preceding day to two days after Diwali)
The model is as follows:
\[ PM2.5_{it} = \alpha + \beta_1*Diwali_{t}+ \beta_2*Diwali_{t}*l_{i} + m_t + h_t + l_i+\epsilon_{it} \]
where, $i$ is location, and $t$ is time. Here, PM 2.5 is the hourly measured levels of the pollutant. The first model takes Diwali to be only the date of Diwali, second model defines the Diwali days from one day before to one day after and the third model considers Diwali from the preceding day to two days after Diwali. In addition, we have month ($m_t$), location ($l_i$), and hour ($h_t$) fixed effects. The base for the location interaction term is Anand Vihar. Robust standard errors are used for our analysis throughout.
| Dependent variable: | |||
| Hourly PM2.5 Concentration | |||
| Diwali=t | Diwali={t-1:t+1} | Diwali={t-1:t+2} | |
| (1) | (2) | (3) | |
| Diwali | -3.720 | 98.687 | 134.709 |
| t = -0.177 | t = 8.496*** | t = 13.181*** | |
| Chanakyapuri*Diwali | 17.270 | -75.878 | -87.035 |
| t = 0.638 | t = -5.100*** | t = -6.692*** | |
| Mandir Marg*Diwali | 73.078 | -67.943 | -66.844 |
| t = 2.606*** | t = -4.450*** | t = -4.979*** | |
| Punjabi Bagh*Diwali | 65.630 | -49.033 | -52.254 |
| t = 2.374** | t = -3.254*** | t = -3.945*** | |
| R K Puram*Diwali | 63.348 | -54.228 | -67.094 |
| t = 2.291** | t = -3.589*** | t = -5.055*** | |
| Month FE | Yes | Yes | Yes |
| Location FE | Yes | Yes | Yes |
| Hour FE | Yes | Yes | Yes |
| Observations | 118,847 | 118,847 | 118,847 |
| R2 | 0.264 | 0.264 | 0.266 |
| Adjusted R2 | 0.264 | 0.264 | 0.266 |
| F Statistic (df = 39; 118803) | 1,091.020*** | 1,094.274*** | 1,103.673*** |
The first model (Column 1) shows that the baseline effect (i.e. at Anand Vihar) is not statistically different from non-Diwali days. For locations, other than Chanakyapuri, there is a differential effect on Diwali relative to Anand Vihar on Diwali. For instance, Diwali adds on an average 69.35 (73.07-3.72) µg/m3 PM2.5 particulate matter in air at Mandir Marg relative to Anand Vihar.
When we consider the second (Column 2) and third (Column 3) specifications, there is a statistically significant effect in Anand Vihar. The average particulate matter is 99 µg/m3 higher when we consider a two day Diwali, and 135 µg/m3 when we consider a three day Diwali period. While this may not seem much, given the already degraded air quality during these months, Diwali makes the pollution level reach alarming levels (>400, the monthly average in October November is around 340) which can have severe impacts on the health of people.
The Diwali effect is lower in other other locations relative to Anand Vihar. Thus, we see, that on the main day of Diwali, Anand Vihar is not too different from other days, while other locations have more pollutants relative to Anand Vihar. However, once we take into account 1-2 days after Diwali, we see that Anand Vihar is the most polluted location, and other locations have lower pollutants relative to Anand Vihar.
Conclusion
Very little is known, at present, about air quality and Diwali. Using the admittedly weak data resources, we have begun analysing this question here.
To the extent that these results are persuasive, they could help individuals plan strategies to avoid being in Delhi on these days. There is also a case for a Pigouvian tax on fireworks, in order to overcome the externality.
Previous work on Diwali, which helps us see other dimensions of Diwali, includes: Seasonal adjustment with Indian data: how big are the gains and how to do it by Rudrani Bhattacharya, Radhika Pandey, Ila Patnaik, Ajay Shah, and IEDs in Diwali and Toxic chemicals in Holi by Ajay Shah.
Monday, October 17, 2016
Interesting readings
Ministry vs. regulator: How to draw the lines by Ajay Shah in The Business Standard, 17 October.
Unprepared for bad days by Ila Patnaik in The Indian Express, 17 October.
Could SWIFT and CLS Bank become obsolete? by Prof. Jayanth R. Varma in Prof. Jayanth R. Varma's Financial Markets Blog, 14 October.
The Nation Learns to Move On by James Grimmelmann in The Laboratorium (2d ser.), 13 October.
Express Economic History Series: 18 years ago, how the idea of a 'bad bank' ended up in cold freeze by Shaji Vikraman in The Indian Express, 12 October.
It isn't enough to focus on Doing Business rankings by Rajeswari Sengupta in The Mint, 12 October.
India loosens drug oversight as more Indian companies make substandard medicines by Roger Bate in The AEIdeas, 11 October.
Clear the air before enforcing Bankruptcy Code by Anirudh Burman and Rajeswari Sengupta in The Business Standard, 09 October.
A judge wants to make patent trolling a first amendment issue by Sarah Jeong in The Verge, 07 October.
Encryption App Signal Wins Fight Against FBI Subpoena and Gag Order by BeauHD in The Slashdot, 04 October.
What Chinese corner cutting reveals about modernity? by James Palmer in The aeon, 04 October.
Unprepared for bad days by Ila Patnaik in The Indian Express, 17 October.
Could SWIFT and CLS Bank become obsolete? by Prof. Jayanth R. Varma in Prof. Jayanth R. Varma's Financial Markets Blog, 14 October.
The Nation Learns to Move On by James Grimmelmann in The Laboratorium (2d ser.), 13 October.
Express Economic History Series: 18 years ago, how the idea of a 'bad bank' ended up in cold freeze by Shaji Vikraman in The Indian Express, 12 October.
It isn't enough to focus on Doing Business rankings by Rajeswari Sengupta in The Mint, 12 October.
India loosens drug oversight as more Indian companies make substandard medicines by Roger Bate in The AEIdeas, 11 October.
Clear the air before enforcing Bankruptcy Code by Anirudh Burman and Rajeswari Sengupta in The Business Standard, 09 October.
A judge wants to make patent trolling a first amendment issue by Sarah Jeong in The Verge, 07 October.
Encryption App Signal Wins Fight Against FBI Subpoena and Gag Order by BeauHD in The Slashdot, 04 October.
What Chinese corner cutting reveals about modernity? by James Palmer in The aeon, 04 October.
Sunday, October 16, 2016
Household finance in India: The state of the art, 2016
by Renuka Sane.
Household finance studies how households use financial instruments and markets to achieve their objectives. The Reserve Bank of India has recently set up a committee to look at the various facets of household finance in India. What have we learned so far about household engagement with Indian finance? What are the barriers? What kind of portfolios do we see, if households do enter markets? In this article, we present a brief overview of the research evidence on household finance in India. We also lay out areas for future research that will feed into evidence based policy and regulation making as we move along the path of greater financialisation of the economy.
In the popular discourse in India, household finance has been seen through the lens of financial inclusion, which has come to mean access of low income households to finance with an accent on credit and savings products. However, there is much more going on in the field of household finance:
Poor people have intermittent incomes and face high risk. They need sophisticated finance, but are very often cut off from it. Most wealth is with the rich, where faulty financial decisions at the level of the household, and failures of the financial system, can give inefficient resource allocation at the level of the country. Research on household finance, should not thus not restrict itself to the problems of low-income households. Household finance is a part of finance as a field, not just a part of poverty studies.
Research suggests that five factors shape household engagement with financial markets. These are: Supply side barriers, Knowledge, Trust, Irrationality, and Mismatch between preferences and market offerings.
There are three kinds of barriers:
Research has shown that removal of these barriers does make a difference to participation. More bank branches, door-to-door services are seen to improve the use of finance (Burgess and Pande, 2003; Ananth, Chen and Rasmussen, 2012). The design of new products such as micro-pensions and micro-insurance is making headway into both physical access and transactions costs (Sane and Thomas, 2015; Sane and Thomas, 2016).
At the same time, forcing access through coercing financial firms does not get the desired results, as staff mechanically check regulatory compliance boxes (and sometimes not even that), without really improving access (Mowl and Boudot, 2014). This experience suggests that we need more competition, and more space for innovative product structures, and company structures (such as technology platforms) that can improve on the barriers of access in an incentive compatible way.
Suppose we solve the access issues. Will that be enough? Not really. The next big barrier to household participation is knowledge of products. Just because a broker or a bank exists, does not mean households will easily engage with them.
Households face knowledge constraints, both in terms of the ability to calculate their expected cash-flows and requirements over long horizons, and the understanding of the suite of products available in the market that match their requirements. The literature thinks of this as the lack of financial literacy. A huge literature has therefore developed to measure the financial literacy of households, and whether improvements in literacy lead to improvements in use of financial products - both on the intensive as well as extensive margin.
Early evidence from India shows that financial education improves take up of products (Gaurav, Cole and Tobacman, 2011), but evidence across the world is inconclusive on how effective literacy is (Hastings, Madrian and Skimmyhorn, 2012). The problem with research on financial literacy is that it can mean many things, and we don't know enough about what aspect of literacy really matters. Households across the income spectrum will also struggle with different aspects of financial literacy - a rich educated household may understand compound interest, but may still not know how to calculate the IRR of a complicated product.
Perhaps, we need to think about literacy not as numeracy, but as awareness. We need to focus on making customers know what they don't know, so that they can at least begin to ask the right questions, and seek advice.
Households may have all the knowledge about finance. And yet, they may be unwilling to engage. Lack of trust is often claimed to be a reason why households do not access financial markets. There seem to be two kinds of trust deficits - about the advisor/distributor selling the product, or about the product itself. For example, Cole et. al., (2013) find that lack of trust was an important reason for low take up of rainfall insurance.
One way in which trust gets affected is if financial intermediaries mis-sell products, and customers become wary of anything remotely related to finance. Solving this problem is the main plank of the Indian financial reforms, where the draft Indian Financial Code places consumer protection at the heart of financial regulation.
A lot of recent research in India has focused on documenting the mis-sale of products by distributors (Anagol and Kim, 2012; Halan, Sane and Thomas, 2014; Halan and Sane, 2016). In fact, this is an area that has also received a lot of policy attention. Two Committees set up by the Ministry of Finance (Swarup Committee Report, 2009; Bose Committee Report, 2015) have identified poor regulation of distribution and advice as a key cause of mis-sales. The belief here is that because product providers pay agents, the agents work in the interest of the product provider and not the client. The advice provided by the agents, is therefore, biased.
One way to improve trust is the process of disintermediation through robo-advisory services, where the conflict of interest between advisor and client is removed through regulation of the technology companies who offer robo-advice. This can be connected to platforms like Amazon or Ebay for doing product distribution. This is a dimension where the new world of fintech can have transformative impact. However, before the fintech revolution can impact upon India, a great deal of work is required by way of financial reform.
It may be that households have all the access, and knowledge they need, trust the system, and yet, are unable to make decisions in their long term interest. People forget, can be lazy, can be myopic. The behavioural finance literature has shown a lot of instances where irrationality influences participation in markets.
In India, we don't know too much about this issue. Campbell, Ranish and Ramadorai, (2013) and Anagol, Balasubramaniam and Ramadorai, (2015) have shown evidence of behavioural biases using stock market data on Indian investors. There is, however, not yet enough evidence on all kinds of households, and all kinds of financial products.
An example of irrationality that we see often in India is that the very same people who think stock markets are not to be trusted, will put their money in emu farming. How does one explain this?
The discussion so far has assumed that customers are making a mistake in not engaging with the market. But perhaps, it is most rational for customers to shy away if the market is unable to provide them what they need. There may be several reasons and we speculate on some of them here:
Of course, one could ask, why does the market not respond with products designed to meet household requirements? This may come about as a combination of regulatory barriers to innovation, entry barriers that inhibit competition, and the race to the bottom with high profit rates in the hands of intermediaries who do bad things for households.
Household finance in India is a field where the literature is just beginning to emerge. There is one strand of literature which treats micro data in India as a laboratory where questions of interest in the US can be addressed. However, it is far more interesting to have roots in local knowledge, to identify the important questions relevant to local conditions and constraints, and to craft credible research designs that can feed into important questions in India. This would involve being at the interplay between new datasets, innovations in the industry, and the policy process.
Ananth, B., G. Chen, and S. Rasmussen (2012). The pursuit of complete financial inclusion: The KGFS model in India. In Access to Finance Forum, Reports by CGAP and its Partners, vol. 4.
Burgess, R., and R. Pande (2003). Do rural banks matter? Evidence from the Indian social banking experiment. Evidence from the Indian Social Banking Experiment (August 2003)., Vol (2003).
Anagol, S., V. Balasubramaniam and T. Ramadorai (2015), Endowment Effects in the Field: Evidence from India's IPO Lotteries, Available at SSRN.
Anagol, S. and H. Kim (2012), The Impact of Shrouded Fees: Evidence from a Natural Experiment in the Indian Mutual Funds Market, The American Economic Review, 102(1).
Bose Committee Report (2015), Report of the Committee to recommend measures for curbing mis-selling and rationalising distribution incentives in financial products, Ministry of Finance, Government of India.
Campbell, J., B. Ranish and T. Ramadorai, (2013), Getting better: Learning to invest in an emerging stock market, Available at SSRN.
Cole, S., X. Gine, J. Tobacman, P. Topalova, R. Townsend, and J. Vickery (2013). Barriers to household risk management: Evidence from India, American Economic Journal: Applied Economics 5, no. 1 (2013): 104-135.
Gaurav, S., S. Cole, and J. Tobacman (2011). Marketing complex financial products in emerging markets: Evidence from rainfall insurance in India, Journal of Marketing Research 48, no. SPL (2011): S150-S162.
Halan, M., R. Sane and S. Thomas (2014), The case of the missing billions: Estimating losses to customers due to mis-sold life insurance policies, Journal of Economic Policy Reform, October 2014.
Halan, M., and R. Sane (2016). Misled and mis-sold: Financial misbehaviour in retail banks? NSE-IFF Working paper.
Hastings J., B. Madrian, and W. Skimmyhorn (2012). Financial literacy, financial education and economic outcomes. No. w18412. National Bureau of Economic Research.
Mowl, A. and C. Boudot (2014). Barriers to Basic Banking: Results from an Audit Study in South India, NSE Working Paper Series No. WP-2014-1, NSE-IFMR Financial Inclusion Research Initiative 2014-2015.
Sane, R. and S. Thomas (2015), In search of inclusion: informal sector participation in a voluntary, defined contribution pension system, Journal of Development Studies.
Sane, R. and S. Thomas (2016), From participation to repurchase: Low income households and micro-insurance, IGIDR Working paper, June 2016.
Swarup Committee Report (2009), Financial Well-Being: Report of the Committee on Investor Awareness and Protection, Ministry of Finance, Government of India.
The author is an academic at the Indian Statistical Institute, Delhi Centre. I thank Monika Halan, Anjali Sharma and Susan Thomas for useful discussions.
Household finance studies how households use financial instruments and markets to achieve their objectives. The Reserve Bank of India has recently set up a committee to look at the various facets of household finance in India. What have we learned so far about household engagement with Indian finance? What are the barriers? What kind of portfolios do we see, if households do enter markets? In this article, we present a brief overview of the research evidence on household finance in India. We also lay out areas for future research that will feed into evidence based policy and regulation making as we move along the path of greater financialisation of the economy.
Household finance and financial inclusion
In the popular discourse in India, household finance has been seen through the lens of financial inclusion, which has come to mean access of low income households to finance with an accent on credit and savings products. However, there is much more going on in the field of household finance:
- The first dimension is the intensive vs. extensive margin. We should be asking: What households are cutoff from formal finance, and why? And, for the households that have a non-zero engagement, what determines the depth of that engagement? Do households really use the market as much as they should, or as efficiently as they could?
- A second dimension is the range of products. This is related to the kind of products that households use. These range from credit (accessing loans through informal sources instead of formal), payments, insurance (relying on kinship networks or savings instead of insurance products in times of crisis), derivatives (risk transfer), and investment (using real estate or gold instead of financial products). In each of these areas, we need to understand what households are doing and why.
- A third dimension is the income heterogeneity. Most often, access to finance is seen as low income households not being able to access bank accounts, or formal credit. However, access to finance issues also pervade middle income and rich, rural and urban households as well.
Poor people have intermittent incomes and face high risk. They need sophisticated finance, but are very often cut off from it. Most wealth is with the rich, where faulty financial decisions at the level of the household, and failures of the financial system, can give inefficient resource allocation at the level of the country. Research on household finance, should not thus not restrict itself to the problems of low-income households. Household finance is a part of finance as a field, not just a part of poverty studies.
Research suggests that five factors shape household engagement with financial markets. These are: Supply side barriers, Knowledge, Trust, Irrationality, and Mismatch between preferences and market offerings.
Supply side barriers
There are three kinds of barriers:
- Physical: That is, financial institutions do not exist in the vicinity of the households residence.
- Transactions costs: That is, the minimum amount required for a transaction is higher than what households can afford, or the cost of low-valued transactions is too high making them unviable.
- Documentation: That is, households do not have enough documents for formal registration e.g. KYC procedures.
Research has shown that removal of these barriers does make a difference to participation. More bank branches, door-to-door services are seen to improve the use of finance (Burgess and Pande, 2003; Ananth, Chen and Rasmussen, 2012). The design of new products such as micro-pensions and micro-insurance is making headway into both physical access and transactions costs (Sane and Thomas, 2015; Sane and Thomas, 2016).
At the same time, forcing access through coercing financial firms does not get the desired results, as staff mechanically check regulatory compliance boxes (and sometimes not even that), without really improving access (Mowl and Boudot, 2014). This experience suggests that we need more competition, and more space for innovative product structures, and company structures (such as technology platforms) that can improve on the barriers of access in an incentive compatible way.
Knowledge
Suppose we solve the access issues. Will that be enough? Not really. The next big barrier to household participation is knowledge of products. Just because a broker or a bank exists, does not mean households will easily engage with them.
Households face knowledge constraints, both in terms of the ability to calculate their expected cash-flows and requirements over long horizons, and the understanding of the suite of products available in the market that match their requirements. The literature thinks of this as the lack of financial literacy. A huge literature has therefore developed to measure the financial literacy of households, and whether improvements in literacy lead to improvements in use of financial products - both on the intensive as well as extensive margin.
Early evidence from India shows that financial education improves take up of products (Gaurav, Cole and Tobacman, 2011), but evidence across the world is inconclusive on how effective literacy is (Hastings, Madrian and Skimmyhorn, 2012). The problem with research on financial literacy is that it can mean many things, and we don't know enough about what aspect of literacy really matters. Households across the income spectrum will also struggle with different aspects of financial literacy - a rich educated household may understand compound interest, but may still not know how to calculate the IRR of a complicated product.
Perhaps, we need to think about literacy not as numeracy, but as awareness. We need to focus on making customers know what they don't know, so that they can at least begin to ask the right questions, and seek advice.
Trust
Households may have all the knowledge about finance. And yet, they may be unwilling to engage. Lack of trust is often claimed to be a reason why households do not access financial markets. There seem to be two kinds of trust deficits - about the advisor/distributor selling the product, or about the product itself. For example, Cole et. al., (2013) find that lack of trust was an important reason for low take up of rainfall insurance.
One way in which trust gets affected is if financial intermediaries mis-sell products, and customers become wary of anything remotely related to finance. Solving this problem is the main plank of the Indian financial reforms, where the draft Indian Financial Code places consumer protection at the heart of financial regulation.
A lot of recent research in India has focused on documenting the mis-sale of products by distributors (Anagol and Kim, 2012; Halan, Sane and Thomas, 2014; Halan and Sane, 2016). In fact, this is an area that has also received a lot of policy attention. Two Committees set up by the Ministry of Finance (Swarup Committee Report, 2009; Bose Committee Report, 2015) have identified poor regulation of distribution and advice as a key cause of mis-sales. The belief here is that because product providers pay agents, the agents work in the interest of the product provider and not the client. The advice provided by the agents, is therefore, biased.
One way to improve trust is the process of disintermediation through robo-advisory services, where the conflict of interest between advisor and client is removed through regulation of the technology companies who offer robo-advice. This can be connected to platforms like Amazon or Ebay for doing product distribution. This is a dimension where the new world of fintech can have transformative impact. However, before the fintech revolution can impact upon India, a great deal of work is required by way of financial reform.
Irrationality
It may be that households have all the access, and knowledge they need, trust the system, and yet, are unable to make decisions in their long term interest. People forget, can be lazy, can be myopic. The behavioural finance literature has shown a lot of instances where irrationality influences participation in markets.
In India, we don't know too much about this issue. Campbell, Ranish and Ramadorai, (2013) and Anagol, Balasubramaniam and Ramadorai, (2015) have shown evidence of behavioural biases using stock market data on Indian investors. There is, however, not yet enough evidence on all kinds of households, and all kinds of financial products.
An example of irrationality that we see often in India is that the very same people who think stock markets are not to be trusted, will put their money in emu farming. How does one explain this?
Mismatch
The discussion so far has assumed that customers are making a mistake in not engaging with the market. But perhaps, it is most rational for customers to shy away if the market is unable to provide them what they need. There may be several reasons and we speculate on some of them here:
- Perhaps formal finance is not able to deal with households, especially seemingly illiterate (financially or otherwise) households with respect and fairness.
- Perhaps households in India are extremely risk averse, and there aren't products that provide an appropriate risk-return trade off.
- Perhaps, products in the market are too rigid, and not account for liquidity constraints that households may face.
- Perhaps the tax structure is such that households end up over investing in one kind of product only for the tax break, leading to a sub-optimal portfolio allocation.
- Perhaps, several households are constrained by the need to not disclose income for fear of paying higher income (or other) taxes, and prefer to lock money in informal products.
Of course, one could ask, why does the market not respond with products designed to meet household requirements? This may come about as a combination of regulatory barriers to innovation, entry barriers that inhibit competition, and the race to the bottom with high profit rates in the hands of intermediaries who do bad things for households.
Where do we go from here?
- Improving the regulatory environment
- How do we design regulatory frameworks that will foster competition and bring about innovation? How do we let the market freely design products that match customer need? How will these be balanced by the need for consumer protection? What, in the current framework needs to change? The draft Indian Financial Code has provided a blue print in terms of a principles based law on solving these issues. We need to develop a policy research agenda that helps us translate that law into regulations, and build enforcement capacity at the regulatory bodies.
- Improving the market for advice
- Given the complexity of financial products, and low levels of financial literacy (that are likely to not get better soon), a class of intermediaries in the form of advisors will come to play an important role in channeling household savings to financial markets. How do we build this market for advice? How do we integrate robo-advisory with human distribution? How do we understand households use of such advice? What makes households follow advice?
- Understanding risk preferences
- While a lot of our energy so far has been consumed by understand the supply-side problems in India in the form of distribution, or weak regulation, a crucial component in making this market work is the preferences of households themselves. What is the level of risk aversion among Indian households? How does this vary with income, education, location, employment? How does this then determine financial choices? Does this evolve over time? A detailed study on risk preferences of Indian households will go a long way in understanding the determinants of choices made by households.
- Dissecting financial literacy
- If households have to engage with financial products, then they must be able to understand what kind of a product matches their need, and what a product truly offers. This requires a basic understanding of finance, and the ability to engage with a financial intermediary. An important area of research, therefore, is to understand what financial literacy means, and what aspect of financial literacy is likely to get us the most gains? Can we isolate a few factors that will give us disproportionate gains in how households understand financial products?
Household finance in India is a field where the literature is just beginning to emerge. There is one strand of literature which treats micro data in India as a laboratory where questions of interest in the US can be addressed. However, it is far more interesting to have roots in local knowledge, to identify the important questions relevant to local conditions and constraints, and to craft credible research designs that can feed into important questions in India. This would involve being at the interplay between new datasets, innovations in the industry, and the policy process.
References
Ananth, B., G. Chen, and S. Rasmussen (2012). The pursuit of complete financial inclusion: The KGFS model in India. In Access to Finance Forum, Reports by CGAP and its Partners, vol. 4.
Burgess, R., and R. Pande (2003). Do rural banks matter? Evidence from the Indian social banking experiment. Evidence from the Indian Social Banking Experiment (August 2003)., Vol (2003).
Anagol, S., V. Balasubramaniam and T. Ramadorai (2015), Endowment Effects in the Field: Evidence from India's IPO Lotteries, Available at SSRN.
Anagol, S. and H. Kim (2012), The Impact of Shrouded Fees: Evidence from a Natural Experiment in the Indian Mutual Funds Market, The American Economic Review, 102(1).
Bose Committee Report (2015), Report of the Committee to recommend measures for curbing mis-selling and rationalising distribution incentives in financial products, Ministry of Finance, Government of India.
Campbell, J., B. Ranish and T. Ramadorai, (2013), Getting better: Learning to invest in an emerging stock market, Available at SSRN.
Cole, S., X. Gine, J. Tobacman, P. Topalova, R. Townsend, and J. Vickery (2013). Barriers to household risk management: Evidence from India, American Economic Journal: Applied Economics 5, no. 1 (2013): 104-135.
Gaurav, S., S. Cole, and J. Tobacman (2011). Marketing complex financial products in emerging markets: Evidence from rainfall insurance in India, Journal of Marketing Research 48, no. SPL (2011): S150-S162.
Halan, M., R. Sane and S. Thomas (2014), The case of the missing billions: Estimating losses to customers due to mis-sold life insurance policies, Journal of Economic Policy Reform, October 2014.
Halan, M., and R. Sane (2016). Misled and mis-sold: Financial misbehaviour in retail banks? NSE-IFF Working paper.
Hastings J., B. Madrian, and W. Skimmyhorn (2012). Financial literacy, financial education and economic outcomes. No. w18412. National Bureau of Economic Research.
Mowl, A. and C. Boudot (2014). Barriers to Basic Banking: Results from an Audit Study in South India, NSE Working Paper Series No. WP-2014-1, NSE-IFMR Financial Inclusion Research Initiative 2014-2015.
Sane, R. and S. Thomas (2015), In search of inclusion: informal sector participation in a voluntary, defined contribution pension system, Journal of Development Studies.
Sane, R. and S. Thomas (2016), From participation to repurchase: Low income households and micro-insurance, IGIDR Working paper, June 2016.
Swarup Committee Report (2009), Financial Well-Being: Report of the Committee on Investor Awareness and Protection, Ministry of Finance, Government of India.
The author is an academic at the Indian Statistical Institute, Delhi Centre. I thank Monika Halan, Anjali Sharma and Susan Thomas for useful discussions.
Saturday, October 15, 2016
SEBI's proposal to regulate social media: Where did we go wrong?
by Ajay Shah and Bhargavi Zaveri.
On 7th October, 2016, SEBI issued a consultation paper proposing stricter regulation of investment advisory and research activity in relation to securities. Of the 17 odd proposals in the consultation paper, two proposals directly affect the right of hitherto unregulated persons to opine on securities on public platforms. These proposals are:
This is sought to be done by modifying the SEBI (Prohibition of Fraudulent and Unfair Trade Practices Relating to Securities Markets) Regulations, 2003 (FUTP Regulations), which deal with securities market abuse, to reflect these prohibitions.
In this article, we (a) critique these proposals for being excessive and not backed by empirical research; and (b) analyse how a similar proposal would be dealt with under the draft Indian Financial Code which was written by the Financial Sector Legislative Reforms Commission (FSLRC).
In the field of public economics, there are four classes of market failure: public goods, market power, asymmetric information and externalities. The field of consumer protection in finance is rooted in market power and asymmetric information. The former is the subject of competition policy. The present discussion is about the latter.
In any market, consumers obtain information from three sources (a) from the manufacturer of a product; (b) from an advertiser or distributor of the product; and (c) from third persons, such as friends, family and personal acquaintances whose advice they generally rely on in making decisions in life.
Conventional consumer protection policy has regulated the sources of information mentioned in "(a)" and "(b)" because information disseminated from these sources is likely to be biased (Shapiro (1983)). For example, the State requires that a seller of a television in a multi-brand showroom must not be vested in a specific brand of TV, and must equally advertise TVs of all brands. On the other hand, a friend or personal acquaintance who opines on a new brand of TV, on social media or on other telecommunication channels, is not regulated. If the person to whom the opinion is given knows that the opinion giver is associated with the brand of TV that she recommends, she can choose to make her own decision on whether or not to purchase the TV. Other factors such as reputation and previous experience with the opinion giver, will also factor in the decision making process of the person to whom such opinion is given.
Why do we not regulate personal acquaintances as an information source? There are four reasons: (a) such regulation deters people from speaking freely, which in turn, adversely affects decision-making by all the users of the market, (b) it burdens people with the obligation to act in a fiduciary capacity every time they express an opinion on a market, (c) it is inconsistent with a core element of enlightenment values, free speech, and (d) the State does not have the capacity to regulate communications made in ordinary conversations (notwithstanding the platform).
Similarly, in the market for financial products and services, the regulatory strategy that has always been adopted is a controlled one: Only issuers of financial products, and financial intermediaries, must be regulated with respect to the information that they disseminate on financial products, because such information is vulnerable to bias. All communications made by such persons must be regulated, whether the communication be in the form of television or radio advertisements or posts applications or on social media. However, regulating persons who are neither the issuers of financial products nor in the business of financial intermediation, is akin to regulating a relative or personal acquaintance having an opinion on a specific brand of a TV. SEBI's proposal to regulate any person who opines on the securities market, as an investment advisor, falls in this category.
Pursuant to the enactment of the SEBI (Investment Advisers) Regulations, 2013 (Investment Advisors Regulations), SEBI gave itself powers to regulate only those persons who were in the business of rendering investment advice. This is evident from the definition of "investment adviser", which is defined thus: investment adviser means any person who for consideration, is engaged in the business of providing investment advice to clients....
The proposal to regulate any person who opines on a specific security or financial product on social media or other telecommunication channels, as an investment advisor, is inconsistent with the concept of investment advisor (as defined above). Regulation as an investment advisor triggers several costs and obligations such as minimum capital adequacy requirements, fiduciary obligations, suitability and disclosure obligations. Mandating every person expressing a view on a security to take on such obligations would be a case of regulatory over-reach. Adding sanctions in the form of including similar provisions in the FUTP regulations would be stifling free speech. In any market, consumers benefit from more availability of information rather than less. Free speech by trusted persons is a great check against consumer abuse. Consumers are in a `marketplace of ideas' and will choose what experts, friends and relatives to believe.
Identifying any market failure requires research. Even after a market failure has been identified, every market failure does not warrant regulation. A regulator with limited State capacity must establish priorities. These priorities must be driven by what regulation is the most effective (in terms of generating benefits) and least costly (for both the regulator and the regulated). Only where the benefits of regulation outweigh the costs, should the regulator expend its energies and resources on trying to resolve it. Hence, FSLRC recommended that all regulation making must do a formal cost-benefit analysis.
In the present context, what kind of research ought to have taken place before a proposal to make a specific policy directed to communications by financial intermediaries on social media plaforms? It would need to be backed by research on inter alia the following questions:
IOSCO has conducted research on similar lines for financial intermediaries spanning across 22 countries (including India). While the research exercise was done in 2014 and involved a relatively small survey sample set of 100 financial firms, some of the findings from the cross-country research are revealing:
The IOSCO survey is not perfect. However, the report, in itself, contains useful hints for a research agenda that must feed into an exercise for making a regulation that proposes to intervene so extensively with the rights of persons to express opinions on public media.
SEBI's document should have approached the question in this fashion. In a sound regulatory organisation, regulation-making should be primarily driven by research into the working of the economy, understanding the anatomy of market failure, identifying the lowest use of coercive power in addressing them, and analysing past experiences of the introduction of interventions.
Prof. J. R. Varma of IIM-A has rightly criticised these proposals as cases of regulatory over-reach. He correctly says that if everybody needs a license from SEBI to post any stock specific thing on any social media, SEBI would quickly become one of the richest regulators in the world with a market capitalisation rivalling that of Facebook. He also conjectures that these proposals would have gone through even within the regulatory framework proposed by the FSLRC in 2013. We now enumerate elements of the Indian Financial Code (the law that was drafted by FSLRC) which, we feel, would have correctly blocked such proposals.
On a plain reading of the provisions of IFC version 1.1, we find that the scheme of the said law does not allow a regulator to mandate registration or authorisation for a person who voices her opinion on the value of a security on a public platform (including on social media), unless such person is in the business of providing investment advisory services:
To avoid instances of regulatory overreach of the kind attempted by SEBI in the consultation paper, FSLRC incorporated the registration requirement in the primary law itself. Mandating registration or authorisation from a regulator is the single most important entry barrier. It is the point at which regulation begins, and the point of commencement of the jurisdiction of the regulator. Allowing this point of commencement of regulation to be defined in delegated legislation creates perverse incentives, as it gives a regulator the power to define its own jurisdiction.
Contrast this position with that in the SEBI Act. The jurisdiction of SEBI under section 11(1) of the Act has been defined thus:
This leaves both the scope of who can be regulated and in what manner, open to interpretation by the regulator. Using public choice reasoning, we would predict that SEBI would use this provision to grow its own turf.
Section 11(2) of the SEBI Act goes on to say: Without prejudice to the generality of the foregoing provisions, the measures referred to therein may provide for --
(ba) registering and regulating the working of ....such other intermediaries as the Board may, by notification, specify in this behalf;
The SEBI Act does not define the term 'intermediaries'. This leaves it open to SEBI to define the scope of its own jurisdiction.
IFC 1.1, on the other hand, instilled checks and balances against such discretion, in the primary law itself:
The world over, communications between financial intermediaries and consumers are regulated through codes of conduct and disclosure requirements. Such regulation would apply, notwithstanding the channel of communication. Monitoring and supervising compliance with such regulations is hard work. However, a regulation that is sensibly crafted to address the difficulties of surveillance, such as requiring financial advisors to maintain records of social media postings and internal approval policies within the firm, may resolve the problem. Regulations that hamper the working of the marketplace of ideas are best avoided.
SEBI's mistakes are grounded in the faulty drafting of the SEBI Act and would not arise under IFC 1.1.
Carl Shapiro, Consumer Protection Policy in the United States, Journal of Institutional and Theoretical Economics, Bd. 139, H. 3., Regulation: Analysis and Experience in West Germany and the U.S.A.: A Symposium (October 1983), pp. 527-544.
The authors thank Smriti Parsheera for useful discussions and Gausia Sheikh for research assistance.
Ajay Shah is a researcher at NIPFP and Bhargavi Zaveri is a researcher at IGIDR.
On 7th October, 2016, SEBI issued a consultation paper proposing stricter regulation of investment advisory and research activity in relation to securities. Of the 17 odd proposals in the consultation paper, two proposals directly affect the right of hitherto unregulated persons to opine on securities on public platforms. These proposals are:
- No person shall be allowed to provide trading tips, stock specific recommendations to the general public through short message services (SMSs), email, telephonic calls, etc. unless such persons obtain registration as an Investment Adviser or are specifically exempted from obtaining registration.
- No person shall be allowed to provide trading tips, stock specific recommendations to the general public through any other social networking media such as WhatsApp, ChatOn, WeChat, Twitter, Facebook, etc. unless such persons obtain registration as an Investment Adviser or are specifically exempted from obtaining registration.
This is sought to be done by modifying the SEBI (Prohibition of Fraudulent and Unfair Trade Practices Relating to Securities Markets) Regulations, 2003 (FUTP Regulations), which deal with securities market abuse, to reflect these prohibitions.
In this article, we (a) critique these proposals for being excessive and not backed by empirical research; and (b) analyse how a similar proposal would be dealt with under the draft Indian Financial Code which was written by the Financial Sector Legislative Reforms Commission (FSLRC).
1 Principles
In the field of public economics, there are four classes of market failure: public goods, market power, asymmetric information and externalities. The field of consumer protection in finance is rooted in market power and asymmetric information. The former is the subject of competition policy. The present discussion is about the latter.
In any market, consumers obtain information from three sources (a) from the manufacturer of a product; (b) from an advertiser or distributor of the product; and (c) from third persons, such as friends, family and personal acquaintances whose advice they generally rely on in making decisions in life.
Conventional consumer protection policy has regulated the sources of information mentioned in "(a)" and "(b)" because information disseminated from these sources is likely to be biased (Shapiro (1983)). For example, the State requires that a seller of a television in a multi-brand showroom must not be vested in a specific brand of TV, and must equally advertise TVs of all brands. On the other hand, a friend or personal acquaintance who opines on a new brand of TV, on social media or on other telecommunication channels, is not regulated. If the person to whom the opinion is given knows that the opinion giver is associated with the brand of TV that she recommends, she can choose to make her own decision on whether or not to purchase the TV. Other factors such as reputation and previous experience with the opinion giver, will also factor in the decision making process of the person to whom such opinion is given.
Why do we not regulate personal acquaintances as an information source? There are four reasons: (a) such regulation deters people from speaking freely, which in turn, adversely affects decision-making by all the users of the market, (b) it burdens people with the obligation to act in a fiduciary capacity every time they express an opinion on a market, (c) it is inconsistent with a core element of enlightenment values, free speech, and (d) the State does not have the capacity to regulate communications made in ordinary conversations (notwithstanding the platform).
Similarly, in the market for financial products and services, the regulatory strategy that has always been adopted is a controlled one: Only issuers of financial products, and financial intermediaries, must be regulated with respect to the information that they disseminate on financial products, because such information is vulnerable to bias. All communications made by such persons must be regulated, whether the communication be in the form of television or radio advertisements or posts applications or on social media. However, regulating persons who are neither the issuers of financial products nor in the business of financial intermediation, is akin to regulating a relative or personal acquaintance having an opinion on a specific brand of a TV. SEBI's proposal to regulate any person who opines on the securities market, as an investment advisor, falls in this category.
2 Concerns about over-reach
Pursuant to the enactment of the SEBI (Investment Advisers) Regulations, 2013 (Investment Advisors Regulations), SEBI gave itself powers to regulate only those persons who were in the business of rendering investment advice. This is evident from the definition of "investment adviser", which is defined thus: investment adviser means any person who for consideration, is engaged in the business of providing investment advice to clients....
The proposal to regulate any person who opines on a specific security or financial product on social media or other telecommunication channels, as an investment advisor, is inconsistent with the concept of investment advisor (as defined above). Regulation as an investment advisor triggers several costs and obligations such as minimum capital adequacy requirements, fiduciary obligations, suitability and disclosure obligations. Mandating every person expressing a view on a security to take on such obligations would be a case of regulatory over-reach. Adding sanctions in the form of including similar provisions in the FUTP regulations would be stifling free speech. In any market, consumers benefit from more availability of information rather than less. Free speech by trusted persons is a great check against consumer abuse. Consumers are in a `marketplace of ideas' and will choose what experts, friends and relatives to believe.
3 What is sound homework by a regulator?
Identifying any market failure requires research. Even after a market failure has been identified, every market failure does not warrant regulation. A regulator with limited State capacity must establish priorities. These priorities must be driven by what regulation is the most effective (in terms of generating benefits) and least costly (for both the regulator and the regulated). Only where the benefits of regulation outweigh the costs, should the regulator expend its energies and resources on trying to resolve it. Hence, FSLRC recommended that all regulation making must do a formal cost-benefit analysis.
In the present context, what kind of research ought to have taken place before a proposal to make a specific policy directed to communications by financial intermediaries on social media plaforms? It would need to be backed by research on inter alia the following questions:
- How many financial intermediaries use or allow the use of social media and public platforms (not covered by the usual Advertisement Code) for dissemination of scrip-specific information?
- To what extent do consumers rely on advice disseminated on public platforms and make investment decisions on the basis of such information?
- Are these retail or sophisticated consumers? To what extent do they need to be protected?
- What has been the experience of consumers who have relied on financial advice disseminated on public platforms? Would they be entitled to challenge the advice rendered by their financial advisors, had it not been disseminated on social media?
- What kind of internal policies or quality controls do financial intermediaries who use social media platforms, put in place for their authorised employees and representatives?
- What kind of records do financial intermediaries who use the social media for advising and advertising, maintain, to allow regulatory surveillance?
- What is the cost that SEBI has expended in enforcing the Investment Advisors Regulations and Research Analyst Regulations over a given span of time? How many enforcement orders have been passed? How many were challenged and overturned? The assumption would be that the cost of enforcement would multiply as more people would require registration as investment advisors.
IOSCO has conducted research on similar lines for financial intermediaries spanning across 22 countries (including India). While the research exercise was done in 2014 and involved a relatively small survey sample set of 100 financial firms, some of the findings from the cross-country research are revealing:
- About 25% of the surveyed firms did not allow the use of social media for business purposes. Of those that did, general usage by sales staff is not allowed.
- Amongst the firms that did allow usage of social media, none of them allowed the staff to use social media to deliver product recommendations or investment advice. The usage was limited to seeking potential clients by disseminating business profiles.
- Of the 75 intermediaries that permitted the use of social media on behalf of the firm, all of them implemented some type of registration within the firm to track users, postings, training for users of social media, pre-approval and monitoring mechanisms.
- An overwhelming majority of survey respondents treated social media communications like all other business communications and in this regard, false or misleading statements and unjustified promises were prohibited.
- Majority of the firms required social media communications to be supervised by trained individuals within the firm. Most of these utilised staff from compliance, legal or branch managers to conduct this supervision.
The IOSCO survey is not perfect. However, the report, in itself, contains useful hints for a research agenda that must feed into an exercise for making a regulation that proposes to intervene so extensively with the rights of persons to express opinions on public media.
SEBI's document should have approached the question in this fashion. In a sound regulatory organisation, regulation-making should be primarily driven by research into the working of the economy, understanding the anatomy of market failure, identifying the lowest use of coercive power in addressing them, and analysing past experiences of the introduction of interventions.
4 How would these proposals be treated under the framework proposed by FSLRC?
Prof. J. R. Varma of IIM-A has rightly criticised these proposals as cases of regulatory over-reach. He correctly says that if everybody needs a license from SEBI to post any stock specific thing on any social media, SEBI would quickly become one of the richest regulators in the world with a market capitalisation rivalling that of Facebook. He also conjectures that these proposals would have gone through even within the regulatory framework proposed by the FSLRC in 2013. We now enumerate elements of the Indian Financial Code (the law that was drafted by FSLRC) which, we feel, would have correctly blocked such proposals.
Business of rendering financial service v. opining on financial markets
On a plain reading of the provisions of IFC version 1.1, we find that the scheme of the said law does not allow a regulator to mandate registration or authorisation for a person who voices her opinion on the value of a security on a public platform (including on social media), unless such person is in the business of providing investment advisory services:
- IFC requires only those persons who are engaged in the business of rendering advice on financial products, for a consideration, to be registered with the regulator. Section 154 of IFC1.1 states: No person may carry on the business (emphasis supplied) of providing a financial service or purport to do so, without an authorisation from the Regulator to provide that financial service. The expression `financial service' has been defined in section 2(76) to include: rendering or agreeing, for consideration, to render advice on or soliciting for the purposes of (i) buying, selling, or subscribing to, a financial product; (ii) availing a financial service; or (iii) exercising any right associated with a financial product or financial service;...
- During the deliberations among the members of the FSLRC, the language of section 154 of IFC1.1 (corresponding to section 141 of IFC1.0) was specifically debated. Two possibilities were considered:
- Possibility 1: No person may provide a financial service or purport to do so, without an authorisation from the Regulator to provide that financial service.
- Possibility 2: No person may carry on the business (emphasis supplied) of providing a financial service or purport to do so, without an authorisation from the Regulator to provide that financial service. This option was consciously chosen to ensure that an activity relating to a financial product or a financial service triggers the authorisation requirement, only if one engages in it as a business.Some test cases for this proposition are explained below: (a) A professor and some researchers are debating on the current valuation of a company. Each of them opines on it, and the notes of the class are published on a public platform. This would not trigger the mandate of registration or authorisation, as the opinions were not voiced in the course of carrying on a business of providing a financial service or a financial product. (b) At a seminar, an economist voices her opinion on oil prices. A member from the audience tweets about it, along with a note of endorsement. This would not trigger the mandate of registration or authorisation as the opinions were not voiced in the course of business of providing investment advice on oil futures.
IFC defines the scope of regulatory jurisdiction in the primary law
To avoid instances of regulatory overreach of the kind attempted by SEBI in the consultation paper, FSLRC incorporated the registration requirement in the primary law itself. Mandating registration or authorisation from a regulator is the single most important entry barrier. It is the point at which regulation begins, and the point of commencement of the jurisdiction of the regulator. Allowing this point of commencement of regulation to be defined in delegated legislation creates perverse incentives, as it gives a regulator the power to define its own jurisdiction.
Contrast this position with that in the SEBI Act. The jurisdiction of SEBI under section 11(1) of the Act has been defined thus:
Subject to the provisions of this Act, it shall be the duty of the Board to protect the interests of investors in securities and to promote the development of, and regulate the securities market, by such measures as it thinks fit.
This leaves both the scope of who can be regulated and in what manner, open to interpretation by the regulator. Using public choice reasoning, we would predict that SEBI would use this provision to grow its own turf.
Section 11(2) of the SEBI Act goes on to say: Without prejudice to the generality of the foregoing provisions, the measures referred to therein may provide for --
(ba) registering and regulating the working of ....such other intermediaries as the Board may, by notification, specify in this behalf;
The SEBI Act does not define the term 'intermediaries'. This leaves it open to SEBI to define the scope of its own jurisdiction.
IFC 1.1, on the other hand, instilled checks and balances against such discretion, in the primary law itself:
- As explained above, it defines the starting point of regulation in the primary law itself. Only persons engaged in the business of rendering a financial service can be mandated to obtain regulatory authorisation.
- While the expression financial service has been defined expansively, only the Central Government has been empowered to expand this definition. At the same time, the regulator has been empowered to exclude products and services from the definition of financial services. Thus, checks and balances have been built in the primary law by allowing the Central Government to expand the jurisdiction of the regulator, and allowing the regulator to limit its own jurisdiction. The grounds on which the Central Government can expand the jurisdiction of the regulator are also specifically set out in section 161 of IFC1.1.
5 Conclusion
The world over, communications between financial intermediaries and consumers are regulated through codes of conduct and disclosure requirements. Such regulation would apply, notwithstanding the channel of communication. Monitoring and supervising compliance with such regulations is hard work. However, a regulation that is sensibly crafted to address the difficulties of surveillance, such as requiring financial advisors to maintain records of social media postings and internal approval policies within the firm, may resolve the problem. Regulations that hamper the working of the marketplace of ideas are best avoided.
SEBI's mistakes are grounded in the faulty drafting of the SEBI Act and would not arise under IFC 1.1.
References
Carl Shapiro, Consumer Protection Policy in the United States, Journal of Institutional and Theoretical Economics, Bd. 139, H. 3., Regulation: Analysis and Experience in West Germany and the U.S.A.: A Symposium (October 1983), pp. 527-544.
Acknowledgement
The authors thank Smriti Parsheera for useful discussions and Gausia Sheikh for research assistance.
Ajay Shah is a researcher at NIPFP and Bhargavi Zaveri is a researcher at IGIDR.
Thursday, October 13, 2016
Describing Delhi's air quality crisis
by Dhananjay Ghei, Arjun Gupta and Renuka Sane.
One of the most important elements of public health is regulatory interventions that yield clean air. In late 2016, we await the air quality crisis of the Delhi winter with trepidation. A few attempts at solving the problem have begun. The Government of Delhi experimented with an odd-even policy to regulate traffic between 1 January 2016 to 15 January 2016, and then between 15 April 2016 to 22 April 2016. The results of these experiments have been mixed [here and here].
What you measure is what you can manage. Only when we are able to marshal evidence in a systematic way about the extent and nature of the problem, will we be able to design and deliver a response. The measurement of air pollution in Delhi has begun on a small scale. In this post, we describe patterns seen in the available data.
There are many pollutants in the air such as carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3). The worst among these is small particulate matter, or PM 2.5, which are a mixture of solid and liquid droplets floating in the air whose diameters are less than 2.5 micrometers. These fine particles are produced from all types of combustion, including motor vehicles and power plants and some industrial processes.
The health impact from pollution is a complex transform of exposure to all pollutants. However, of the pollutants, PM 2.5 particles are considered the most harmful as they are able to enter deep into the respiratory tract, reaching the lungs. This can cause short-term health effects such as eye, nose, throat and lung irritation, coughing, sneezing, runny nose and shortness of breath, and in the long-term can affect lung function and worsen medical conditions such as asthma and heart disease. We, therefore, narrow our attention to the measure of PM 2.5. The unit of measurement of PM 2.5 is µg/m3 and the breakpoints of raw PM 2.5 values by the US Environmental Protection Agency are the following:
We fetch raw PM 2.5 values from two data sources on pollution in Delhi. The first is put out by the US Embassy based in Chanakyapuri. In addition, the Central Pollution Control Board also puts out real time data for various locations across India. We select 4 locations which provided us with the most consistent dataset. This gives us a total of 5 locations for which we have data:
We use hourly data from the locations mentioned above for a time period from January 2013 to October 2016. It should be noted that values are missing from certain sections of the data. These missing observations are excluded from our analysis.
Drawing upon the Chinese experience, it's interesting to ask: Do the Indian government sources tally with the US Embassy data? We can't say, as there is no measurement for a location near the US Embassy by the CPCB.
These are three types variations seen in PM 2.5.
Time Effect: Figure 1 above shows the variation in hourly pollution levels during different days of a week. Darker colors represent increased PM 2.5 matter in the air. We see that the pollution levels are low during the day, but start increasing post 6 p.m. and remain elevated till 9 a.m. of the next day. The average PM 2.5 concentration from 6 p.m. to 9 a.m. is 140 µg/m3, whereas the average PM 2.5 concentration from 9 a.m. to 6p.m. is 108 µg/m3. PM 2.5 levels in the range of 101-200 can cause breathing discomfort to anyone with prolonged exposure to the air during these times. This graph suggests that a measure that restricts traffic during the day such as the odd-even policy is unlikely to be as effective as a measure that restricts emissions at night.
Month Effect : Figure 2 shows the hourly variation in pollution levels during different months of the year. Note that the scale for this figure is different from that used in Figure 1. The monsoon months have the lowest levels of PM 2.5 particulate matter. Larger particles are settled in few hours due to gravity, but smaller particles such as PM 2.5 are removed by precipitation. Winters have the highest levels of PM2.5 matter in the air, on account of low wind speed and high relative humidity. PM 2.5 concentration reaches above 200 in the winter months, which can cause respiratory illness to people on prolonged exposure and puts people with respiratory illness, and heart disease on a far greater risk.
Location Effect: Figure 3 shows the hourly variation in pollution levels at the five locations where instruments are available. Chanakyapuri seems to perform better than other areas of Delhi, in terms of PM 2.5 particulate matter. Anand Vihar has the highest pollution levels amongst the 5 different locations, and has severe levels of air pollution in the night. This can cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart diseases.
Thus, we see that there is a strong location effect on pollution levels. This can be due to the varying population densities of these locations as well as the proximity to industries etc. This could lead to location-specific policy initiatives such as closing down factories or modifying vehicular traffic.
Data and R code.
Dhananjay Ghei and Arjun Gupta are researchers at the National Institute of Public Finance and Policy. Renuka Sane is an academic at the Indian Statistical Institute, Delhi Centre.
One of the most important elements of public health is regulatory interventions that yield clean air. In late 2016, we await the air quality crisis of the Delhi winter with trepidation. A few attempts at solving the problem have begun. The Government of Delhi experimented with an odd-even policy to regulate traffic between 1 January 2016 to 15 January 2016, and then between 15 April 2016 to 22 April 2016. The results of these experiments have been mixed [here and here].
What you measure is what you can manage. Only when we are able to marshal evidence in a systematic way about the extent and nature of the problem, will we be able to design and deliver a response. The measurement of air pollution in Delhi has begun on a small scale. In this post, we describe patterns seen in the available data.
Why is PM 2.5 a good measure?
There are many pollutants in the air such as carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3). The worst among these is small particulate matter, or PM 2.5, which are a mixture of solid and liquid droplets floating in the air whose diameters are less than 2.5 micrometers. These fine particles are produced from all types of combustion, including motor vehicles and power plants and some industrial processes.
The health impact from pollution is a complex transform of exposure to all pollutants. However, of the pollutants, PM 2.5 particles are considered the most harmful as they are able to enter deep into the respiratory tract, reaching the lungs. This can cause short-term health effects such as eye, nose, throat and lung irritation, coughing, sneezing, runny nose and shortness of breath, and in the long-term can affect lung function and worsen medical conditions such as asthma and heart disease. We, therefore, narrow our attention to the measure of PM 2.5. The unit of measurement of PM 2.5 is µg/m3 and the breakpoints of raw PM 2.5 values by the US Environmental Protection Agency are the following:
| 24-hr PM 2.5 | AQI Categories | Health Effects Statements |
|---|---|---|
| 0.0-12.0 | Good | None |
| 12.1-35.4 | Moderate | Respiratory symptoms possible in unusually sensitive individuals, possible aggravation of heart or lung disease in people with cardiopulmonary and older adults. |
| 35.5-55.4 | Unhealthy for Sensitive Groups |
Increasing likelihood of respiratory symptoms in sensitive individuals, aggravation of heart or lung disease and premature mortality in people with cardiopulmonary disease and older adults. |
| 55.5-150.4 | Unhealthy | Increased aggravation of heart or lung disease and premature mortality in people with cardiopulmonary disease and older adults; increased respiratory effects in general population. |
| 150.5-250.4 | Very Unhealthy | Significant aggravation of heart or lung disease and premature mortality in people with cardiopulmonary disease and older adults; significant increase in respiratory effects in general population |
| 250.5-500 | Hazardous | Serious aggravation of heart or lung disease and prematuremortality in people with cardiopulmonary disease and older adults; serious risk of respiratory effects in general population. |
Data
We fetch raw PM 2.5 values from two data sources on pollution in Delhi. The first is put out by the US Embassy based in Chanakyapuri. In addition, the Central Pollution Control Board also puts out real time data for various locations across India. We select 4 locations which provided us with the most consistent dataset. This gives us a total of 5 locations for which we have data:
- R K Puram
- Punjabi Bagh
- Mandir Marg
- US Embassy (Chanakyapuri)
- Anand Vihar
We use hourly data from the locations mentioned above for a time period from January 2013 to October 2016. It should be noted that values are missing from certain sections of the data. These missing observations are excluded from our analysis.
Drawing upon the Chinese experience, it's interesting to ask: Do the Indian government sources tally with the US Embassy data? We can't say, as there is no measurement for a location near the US Embassy by the CPCB.
Dimensions of variation
These are three types variations seen in PM 2.5.
 |
| Figure 1: Variation by time of day |
Time Effect: Figure 1 above shows the variation in hourly pollution levels during different days of a week. Darker colors represent increased PM 2.5 matter in the air. We see that the pollution levels are low during the day, but start increasing post 6 p.m. and remain elevated till 9 a.m. of the next day. The average PM 2.5 concentration from 6 p.m. to 9 a.m. is 140 µg/m3, whereas the average PM 2.5 concentration from 9 a.m. to 6p.m. is 108 µg/m3. PM 2.5 levels in the range of 101-200 can cause breathing discomfort to anyone with prolonged exposure to the air during these times. This graph suggests that a measure that restricts traffic during the day such as the odd-even policy is unlikely to be as effective as a measure that restricts emissions at night.
 |
| Figure 2: Variation by month |
Month Effect : Figure 2 shows the hourly variation in pollution levels during different months of the year. Note that the scale for this figure is different from that used in Figure 1. The monsoon months have the lowest levels of PM 2.5 particulate matter. Larger particles are settled in few hours due to gravity, but smaller particles such as PM 2.5 are removed by precipitation. Winters have the highest levels of PM2.5 matter in the air, on account of low wind speed and high relative humidity. PM 2.5 concentration reaches above 200 in the winter months, which can cause respiratory illness to people on prolonged exposure and puts people with respiratory illness, and heart disease on a far greater risk.
 |
| Figure 3: Variation by location |
Location Effect: Figure 3 shows the hourly variation in pollution levels at the five locations where instruments are available. Chanakyapuri seems to perform better than other areas of Delhi, in terms of PM 2.5 particulate matter. Anand Vihar has the highest pollution levels amongst the 5 different locations, and has severe levels of air pollution in the night. This can cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart diseases.
Thus, we see that there is a strong location effect on pollution levels. This can be due to the varying population densities of these locations as well as the proximity to industries etc. This could lead to location-specific policy initiatives such as closing down factories or modifying vehicular traffic.
Reproducible research
Data and R code.
Dhananjay Ghei and Arjun Gupta are researchers at the National Institute of Public Finance and Policy. Renuka Sane is an academic at the Indian Statistical Institute, Delhi Centre.
Wednesday, October 12, 2016
How will your armed forces perform?
by Ajay Shah.
In the world of public policy, there are two dimensions to thinking about performance.
How hard is a problem? "High load problems" are those where there is more discretion, a large number of transactions, and the stakes are high.
Are there easy mechanisms for accountability? Performance is greater when measurement of outcomes is more feasible. As an example, it's relatively easy to get performance from a central bank which is tasked with delivering a 4% inflation target and nothing else, as success/failure are highly visible. Performance is measured every month. If the central bank performs poorly, within a few years, this becomes visible, and that would kick off remedial actions.
Feedback into superficial change or deeper change? When institutions are weak, a government agency is personified into a few individuals at the top. When failure is visible, the response is to seek staffing changes. As an example, the 1962 war led to the sacking of V. K. Krishna Menon; the scandal of 1991/1992 led to the departure of S. Venkitaramanan. These were relatively superficial changes. For a display of better institutional capability, the Indian inflation crisis of 2006-2013 helped trigger off fundamental reform of RBI.
The best feedback loops are those where measurement is frequent, and reports of low performance kick off small steps that address the problem. When measurement is infrequent, the feedback loop is much less effective.
A small number of actions, and thus a weak feedback loop, is something that we've seen in another domain: consumer finance. In the field of consumer protection in finance, there has long been a sense about why consumers do badly when buying certain financial products like home loans or pensions: the transactions are so infrequent. When a consumer engages with toothpaste, there are many transactions through life, and a feedback loop is in place through which performance is improved. In contrast, many consumers buy only one or two home loans or pension products in their life. There isn't enough experience with small course corrections through which the feedback loops kick in. This leads to big mistakes by consumers. While this is an example about consumers and finance, the principle is general: infrequent actions hamper feedback loops.
The armed forces pose a difficult challenge for public administration as it's hard to know whether they are working properly. Using vast public resources, an organisation is built for the purpose of waging war. In peace time, it's hard to know whether the organisation has the advertised capabilities. Wars often have a decisive win/lose outcome, but wars are infrequent, and there also, there are extraneous factors that limit accountability.
With the armed forces, we face the Principal-Agent problem where the Principal (the political and civilian authorities) want to get a target level of capability at the minimum cost, and the Agent wants to pursue self-interest. In this Principal-Agent problem:
In advanced countries, there is a considerable extent of the rule of law, and review of the armed forces, in times of peace and war. For example, see the 19 occurrences of the word `lawyer' in Ghost Wars by Steve Coll, and the long history of the political leadership sacking military leaders. But this is complex institutional machinery which is hard to build. A recurring feature of underdevelopment is that civilian authorities have limited power over the top leadership of the armed forces. When the generals claim excellence, it is hard for civilian authorities to be intrusive, impose management and staffing changes, etc. Weak oversight by the Principal is then the breeding ground for failure by the Agent. Claims of the greatness of the Agent increasingly diffuse through society, unchallenged.
War is an expensive way to resolve a dispute; it seems like a shame that ordinary processes of bargaining are not able to avert more wars. Why do wars take place as much as they do?
Nobody would go to war if they did not expect to win. Yet, half the time, one party has gone to war with the mistaken notion that he was going to win. Why does the Principal make this mistake?
Some ingredients of this excessive willingness to mistakenly wage war may be as follows. In each country, the Agent claims I'm doing a great job of using your money, we're very capable, we're ready for a war, we will win it. Exaggerated respect for capabilities of the armed forces would arise when these claims are not challenged widely in society. There is a toxic interplay between places with high nationalism (which would tend to glorify the armed forces) and low institutional capability (which would tend to leave the Agent free to do as he likes). Under these conditions, the Principal may be prone to inefficiently continue politics by other means.
I thank Shekhar Hari Kumar, Ila Patnaik, Kaushik Krishnan, Renuka Sane and Susan Thomas for useful discussions on this.
Backdrop
In the world of public policy, there are two dimensions to thinking about performance.
How hard is a problem? "High load problems" are those where there is more discretion, a large number of transactions, and the stakes are high.
Are there easy mechanisms for accountability? Performance is greater when measurement of outcomes is more feasible. As an example, it's relatively easy to get performance from a central bank which is tasked with delivering a 4% inflation target and nothing else, as success/failure are highly visible. Performance is measured every month. If the central bank performs poorly, within a few years, this becomes visible, and that would kick off remedial actions.
Feedback into superficial change or deeper change? When institutions are weak, a government agency is personified into a few individuals at the top. When failure is visible, the response is to seek staffing changes. As an example, the 1962 war led to the sacking of V. K. Krishna Menon; the scandal of 1991/1992 led to the departure of S. Venkitaramanan. These were relatively superficial changes. For a display of better institutional capability, the Indian inflation crisis of 2006-2013 helped trigger off fundamental reform of RBI.
Rare events and feedback loops
The best feedback loops are those where measurement is frequent, and reports of low performance kick off small steps that address the problem. When measurement is infrequent, the feedback loop is much less effective.
A small number of actions, and thus a weak feedback loop, is something that we've seen in another domain: consumer finance. In the field of consumer protection in finance, there has long been a sense about why consumers do badly when buying certain financial products like home loans or pensions: the transactions are so infrequent. When a consumer engages with toothpaste, there are many transactions through life, and a feedback loop is in place through which performance is improved. In contrast, many consumers buy only one or two home loans or pension products in their life. There isn't enough experience with small course corrections through which the feedback loops kick in. This leads to big mistakes by consumers. While this is an example about consumers and finance, the principle is general: infrequent actions hamper feedback loops.
The puzzle of building the armed forces
The armed forces pose a difficult challenge for public administration as it's hard to know whether they are working properly. Using vast public resources, an organisation is built for the purpose of waging war. In peace time, it's hard to know whether the organisation has the advertised capabilities. Wars often have a decisive win/lose outcome, but wars are infrequent, and there also, there are extraneous factors that limit accountability.
With the armed forces, we face the Principal-Agent problem where the Principal (the political and civilian authorities) want to get a target level of capability at the minimum cost, and the Agent wants to pursue self-interest. In this Principal-Agent problem:
- Building the armed forces in peace, or using them in war, is a high load problem as it involves a lot of discretion, high stakes. It takes a lot of capability in the political and policy system to pull this off.
- The checks-and-balances of democracy are weak given the opacity that envelops the armed forces.
- The Principal (the civilian political leadership) knows little about the performance of the Agent (the armed forces). In peace time, years and years go by with no feedback loop about performance. For example, Germany knows little about the capabilities of their armed forces as they have been tested so little in recent decades. By this yardstick, the US fares best as there are frequent wars which generate data about performance.
- For countries where nationalist feelings are prevalent, there is a greater risk of civilian authorities glorifying the armed forces and thus failing to employ public choice reasoning. By this yardstick, Germany fares better than the US.
In advanced countries, there is a considerable extent of the rule of law, and review of the armed forces, in times of peace and war. For example, see the 19 occurrences of the word `lawyer' in Ghost Wars by Steve Coll, and the long history of the political leadership sacking military leaders. But this is complex institutional machinery which is hard to build. A recurring feature of underdevelopment is that civilian authorities have limited power over the top leadership of the armed forces. When the generals claim excellence, it is hard for civilian authorities to be intrusive, impose management and staffing changes, etc. Weak oversight by the Principal is then the breeding ground for failure by the Agent. Claims of the greatness of the Agent increasingly diffuse through society, unchallenged.
Mistakes by the Principal
War is an expensive way to resolve a dispute; it seems like a shame that ordinary processes of bargaining are not able to avert more wars. Why do wars take place as much as they do?
Nobody would go to war if they did not expect to win. Yet, half the time, one party has gone to war with the mistaken notion that he was going to win. Why does the Principal make this mistake?
Some ingredients of this excessive willingness to mistakenly wage war may be as follows. In each country, the Agent claims I'm doing a great job of using your money, we're very capable, we're ready for a war, we will win it. Exaggerated respect for capabilities of the armed forces would arise when these claims are not challenged widely in society. There is a toxic interplay between places with high nationalism (which would tend to glorify the armed forces) and low institutional capability (which would tend to leave the Agent free to do as he likes). Under these conditions, the Principal may be prone to inefficiently continue politics by other means.
Conclusion
- The armed forces are one of the hardest problems in public administration: high discretion, high stakes, high opacity. When wars are infrequent, the civilian authorities (the Principal) know little about the capabilities of their Agent. When a country suffers from nationalism, this further damages the ability of the Principal to coldly analyse and reshape the Agent.
- Overconfidence bias would make the Principal over-estimate how well he is handling the Agent. Nationalism is likely to delude the Principal too. Hence, civilian leaderships may have a bias in favour of over-estimating the capabilities of their armed forces.
- Fewer countries would go to war if they had a more accurate assessment about the incompetence of their armed forces.
I thank Shekhar Hari Kumar, Ila Patnaik, Kaushik Krishnan, Renuka Sane and Susan Thomas for useful discussions on this.
Thursday, October 06, 2016
Author: Ila Patnaik
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On this blog:
On this blog:
- On the usefulness of Parliamentary law in achieving fiscal responsibility, 26 October 2023.
- Distribution of self-reported health in India: The role of income and geography, 30 September 2021.
- Thomas Laubach, 3 September 2020.
- Release of v2.0 of the Exchange Market Pressure dataset associated with PFM 2017, 6 April 2020
- Chennai 2015: A novel approach to measuring the impact of a natural disaster, 24 December 2019
- The rise of government-funded health insurance in India, 21 January 2019.
- There be dragons: Off-balance-sheet liabilities of the Indian State, 13 November 2018.
- Diagnosing and overcoming sustained food price volatility: Enabling a National Market for Food, 6 August 2018.
- Fair play in Indian health insurance, 30 April 2018.
- Improved measurement of Exchange Market Pressure (EMP), 27 May 2017
- Monetary policy strategy for 2017, 16 February 2017
- The RBI board: Comparison against international benchmarks, 24 January 2017
- Legislative strategy for setting up an independent debt management agency, 6 October 2016.
- Dating the Indian business cycle, 7 September 2016.
- Motivations for capital controls and their effectiveness, 5 April 2016.
- Foreign Currency Borrowing by Indian Firms: Towards a New Policy Framework, 3 April 2016.
- Seasonal adjustment with Indian data: how big are the gains and how to do it, 16 January 2016.
Legislative strategy for setting up an independent debt management agency
by Radhika Pandey and Ila Patnaik.
Every government requires an institutional arrangement for its borrowing and debt management. The borrowing of the government, i.e. the sale of bonds, is enabled by a capable bond market. To the extent that the bond market is liquid and has wide ranging participation, it becomes easier for the government to obtain low cost financing. Just as resource-raising of a private firm has an `investment banker' for advice and then execution, resource-raising for governments has a `public debt manager' for advice and issuance.
In India, RBI has traditionally been the public debt manager for the Government. RBI owns or controls bond market infrastructure (exchange, clearing house and depository), and also regulates the bond market.
Historically, RBI managed government debt on paper based ledgers. However, following a scam in the government securities market in 1992 and recommendations by an RBI Committee on Repurchase Agreements, RBI set up an electronic ledger for holding government securities. This ledger, the Securities General Ledger, was legally mandated to be the only depository for government securities through the Government Securities Act. The Act gave RBI exclusive powers to oversee, govern and regulate participation in the depository.
The RBI also set up a trading platform for government securities that was based on an order matching system known as NDS-OM and helped banks set up CCIL, a bank owned clearing and settlement system on which G-Secs could be settled.
In its role as overseer of the G-Sec market, RBI also acquired powers to regulate the G-Sec spot market through a carve-out created through a government notification under Securities Contracts Regulation Act. In 2006 it was given additional powers to regulate derivatives on government securities, through an amendment to the RBI Act by adding a chapter (Chapter III-D) giving it these powers. The amendment mandated that all derivatives transactions on G-secs will be legal only if they are undertaken by RBI regulated entities. The amendment gave powers to RBI to issue directions to agencies dealing in Government securities. These steps ensured that RBI had full supervisory powers over any entity that participated in either G-sec markets or in their derivatives.
In this period, RBI did not have a clear objective, as was emphasised by the preamble of the RBI Act which described the agency as a `temporary provision'. This arrangement came under question from two points of view. On one hand, securities markets underwent legal and institutional reform that improved their market infrastructure and regulatory capacity. In parallel, the objective of inflation targeting was gaining currency as the predominant objective of RBI. This repeatedly led to the proposal that the debt management work, which conflicts with monetary policy, be placed in an independent Public Debt Management Agency, and the bond market be merged into securities markets. In a recent paper we describe the legislative aspects of implementation of the PDMA. We work out the intricacies of a PDMA Act which establishes the PDMA as an agency, and merges the bond market with securities markets.
Existing thinking on the subject, such as the Financial Sector Legislative Reforms Commission, assumes a clean slate in which the PDMA is created as an agency and a unified financial market system is enacted at one go. We work out the complexities of amending existing laws, without the assumption of a clean slate. We also work out the issues of sequencing through which the existing institutional arrangements are transitioned into the new arrangements. In light of some recent developments towards setting up of a PDMA, this paper is useful as laying the groundwork for implementing the PDMA reform.
The establishment of PDMA would yield numerous gains for the Indian macroeconomic and financial system. It would free RBI of the conflict of interest of performing debt management work for the central and state governments. It would yield low cost financing for government debt. It would result in development of the bond market by harnessing the capabilities of the securities market infrastructure. Finally it would yield improvements in the government borrowing program by selling bonds to voluntary buyers in a deep and liquid government bond market.
Every government requires an institutional arrangement for its borrowing and debt management. The borrowing of the government, i.e. the sale of bonds, is enabled by a capable bond market. To the extent that the bond market is liquid and has wide ranging participation, it becomes easier for the government to obtain low cost financing. Just as resource-raising of a private firm has an `investment banker' for advice and then execution, resource-raising for governments has a `public debt manager' for advice and issuance.
In India, RBI has traditionally been the public debt manager for the Government. RBI owns or controls bond market infrastructure (exchange, clearing house and depository), and also regulates the bond market.
Historically, RBI managed government debt on paper based ledgers. However, following a scam in the government securities market in 1992 and recommendations by an RBI Committee on Repurchase Agreements, RBI set up an electronic ledger for holding government securities. This ledger, the Securities General Ledger, was legally mandated to be the only depository for government securities through the Government Securities Act. The Act gave RBI exclusive powers to oversee, govern and regulate participation in the depository.
The RBI also set up a trading platform for government securities that was based on an order matching system known as NDS-OM and helped banks set up CCIL, a bank owned clearing and settlement system on which G-Secs could be settled.
In its role as overseer of the G-Sec market, RBI also acquired powers to regulate the G-Sec spot market through a carve-out created through a government notification under Securities Contracts Regulation Act. In 2006 it was given additional powers to regulate derivatives on government securities, through an amendment to the RBI Act by adding a chapter (Chapter III-D) giving it these powers. The amendment mandated that all derivatives transactions on G-secs will be legal only if they are undertaken by RBI regulated entities. The amendment gave powers to RBI to issue directions to agencies dealing in Government securities. These steps ensured that RBI had full supervisory powers over any entity that participated in either G-sec markets or in their derivatives.
In this period, RBI did not have a clear objective, as was emphasised by the preamble of the RBI Act which described the agency as a `temporary provision'. This arrangement came under question from two points of view. On one hand, securities markets underwent legal and institutional reform that improved their market infrastructure and regulatory capacity. In parallel, the objective of inflation targeting was gaining currency as the predominant objective of RBI. This repeatedly led to the proposal that the debt management work, which conflicts with monetary policy, be placed in an independent Public Debt Management Agency, and the bond market be merged into securities markets. In a recent paper we describe the legislative aspects of implementation of the PDMA. We work out the intricacies of a PDMA Act which establishes the PDMA as an agency, and merges the bond market with securities markets.
Existing thinking on the subject, such as the Financial Sector Legislative Reforms Commission, assumes a clean slate in which the PDMA is created as an agency and a unified financial market system is enacted at one go. We work out the complexities of amending existing laws, without the assumption of a clean slate. We also work out the issues of sequencing through which the existing institutional arrangements are transitioned into the new arrangements. In light of some recent developments towards setting up of a PDMA, this paper is useful as laying the groundwork for implementing the PDMA reform.
The establishment of PDMA would yield numerous gains for the Indian macroeconomic and financial system. It would free RBI of the conflict of interest of performing debt management work for the central and state governments. It would yield low cost financing for government debt. It would result in development of the bond market by harnessing the capabilities of the securities market infrastructure. Finally it would yield improvements in the government borrowing program by selling bonds to voluntary buyers in a deep and liquid government bond market.
Tuesday, October 04, 2016
Law - Economics - Policy conference, 2016
Economics holds basic insights into what government should do. Law is the DNA of government, the code that summons structures of the State. Public administration is the management problem of making government work. These fields are inter-related. However, the three communities have largely stood apart in India so far.
Late last month, NIPFP and INET organised `LEPC 2016', a conference which brought the three communities together. In our knowledge, this was the first such conference in India, that fostered such inter-disciplinary work, brought these communities together, and featured serious research papers.
The conference had 10 research papers, 4 panel discussions and 5 talks. The 10 papers were chosen from a field of 35 submissions that responded to the call for papers in July 2016. The papers address questions of contemporary interest and offer important new knowledge.
The conference program and some materials are up on the web. Some videos from the show will appear on the NIPFP youtube channel in coming days.
Stay tuned for the call for papers for the next LEPC.
Late last month, NIPFP and INET organised `LEPC 2016', a conference which brought the three communities together. In our knowledge, this was the first such conference in India, that fostered such inter-disciplinary work, brought these communities together, and featured serious research papers.
The conference had 10 research papers, 4 panel discussions and 5 talks. The 10 papers were chosen from a field of 35 submissions that responded to the call for papers in July 2016. The papers address questions of contemporary interest and offer important new knowledge.
The conference program and some materials are up on the web. Some videos from the show will appear on the NIPFP youtube channel in coming days.
Stay tuned for the call for papers for the next LEPC.
How to identify manufacturing companies for GDP estimation
by Amey Sapre and Pramod Sinha.
In a recent paper, we worked on the problems of gross value added (GVA) estimation for the manufacturing sector. One of the elements in the procedure is the identification of manufacturing companies from the MCA21 data. The GVA formula changes depending on whether a firm is classified as manufacturing or trading, and hence the classification of a firm into manufacturing or services is a critical question. We highlighted four concerns about the present procedures:
Presently, the extent of distortion in the GVA estimate is unknown. In the paper, we try to estimate the extent of misclassification by looking for the two cases (i) firms that operate as non-manufacturing entities, but have their NIC codes registered in a manufacturing activity and (ii) firms that are into manufacturing, but have their NIC code registered in any other economic activity. However, we need to go beyond measuring misclassification to algorithms for better classification. In this article, we propose one such solution.
Currently, section II in the Form No. MGT 7, [pursuant to section 92(3) of the Companies Act, 2013 and Rule 12(1) of the Companies (Management and Administration) Rules, 2014] requires companies to furnish up to ten principal business activities . The information deals with disclosures of the main activity group, business activity with respective codes and their share in total turnover. Under this arrangement, the main activity has 21 different codes from A to U, each representing a particular activity. For example, a company reporting code C indicates a manufacturing concern, while code G shows trading. However, when non-reporting takes place, these codes alone will not solve the problem. A scrutiny of product schedules and financial statements is still needed.
Looking at product schedules and financial statements, manually, is expensive. This is particularly when low error rates are demanded. It is desirable to automate this work. We can see the rough contours of algorithmic classification as follows.
For a trading firm: Typically, for a trading company, from the revenue side, the income from trading to total turnover ratio would be higher than income from manufacturing. From the expenditure side, the ratio of purchase of finished goods to total expenses would be higher than the expenses on manufacturing.
For a manufacturing firm: In this case, from the revenue side, the ratio of income from sales to total turnover would be much higher than the ratio of trading income to total turnover. Similarly, from the expenditure side, the ratio of purchase of raw materials to total expenses would be much higher than expenses on trading. Also, for a manufacturing company, excise duty would be form a significant part of the indirect tax payments.
A statistical analysis of the ratios can help identify the characteristics of the manufacturing sector, and be used to classify firms effectively. The ratios can be applied to ascertain the highest revenue contribution on a yearly basis and at the same time allows a cross-check with reported codes and declaration under of Form No. MGT 7. The classification algorithm would need to deal with various categories of observation, including procedures that deal with various possibilities of non-reporting.
Amey Sapre is at the Indian Institute of Technology Kanpur and Pramod Sinha is a researcher at NIPFP.
In a recent paper, we worked on the problems of gross value added (GVA) estimation for the manufacturing sector. One of the elements in the procedure is the identification of manufacturing companies from the MCA21 data. The GVA formula changes depending on whether a firm is classified as manufacturing or trading, and hence the classification of a firm into manufacturing or services is a critical question. We highlighted four concerns about the present procedures:
- The use of reported ITC-HS codes can be misleading as the codes identify a product, and not a business activity.
- For the purpose of GDP estimation, identification of companies has to be done every year. In cases where the ITC-HS codes are unavailable, using the NIC digits in the Company Identification Number (CIN) can also be misleading. The CIN code does not change in time, and does not not track the evolution of the firm over time.
- As the top revenue generating products of a company can vary yearly, this will require the statistical authority to identify and re-classify companies on a yearly basis.
- In the absence of a feasible solution, wrongly classified companies will show an incorrect GVA contribution. On the aggregate, both manufacturing and services sector will show a distorted picture. These difficulties are compounded by the fact that the appropriate deflator to be used when converting nominal to real differs between the two cases, and has taken substantially different values in recent years.
Presently, the extent of distortion in the GVA estimate is unknown. In the paper, we try to estimate the extent of misclassification by looking for the two cases (i) firms that operate as non-manufacturing entities, but have their NIC codes registered in a manufacturing activity and (ii) firms that are into manufacturing, but have their NIC code registered in any other economic activity. However, we need to go beyond measuring misclassification to algorithms for better classification. In this article, we propose one such solution.
A potential solution
Currently, section II in the Form No. MGT 7, [pursuant to section 92(3) of the Companies Act, 2013 and Rule 12(1) of the Companies (Management and Administration) Rules, 2014] requires companies to furnish up to ten principal business activities . The information deals with disclosures of the main activity group, business activity with respective codes and their share in total turnover. Under this arrangement, the main activity has 21 different codes from A to U, each representing a particular activity. For example, a company reporting code C indicates a manufacturing concern, while code G shows trading. However, when non-reporting takes place, these codes alone will not solve the problem. A scrutiny of product schedules and financial statements is still needed.
Looking at product schedules and financial statements, manually, is expensive. This is particularly when low error rates are demanded. It is desirable to automate this work. We can see the rough contours of algorithmic classification as follows.
For a trading firm: Typically, for a trading company, from the revenue side, the income from trading to total turnover ratio would be higher than income from manufacturing. From the expenditure side, the ratio of purchase of finished goods to total expenses would be higher than the expenses on manufacturing.
For a manufacturing firm: In this case, from the revenue side, the ratio of income from sales to total turnover would be much higher than the ratio of trading income to total turnover. Similarly, from the expenditure side, the ratio of purchase of raw materials to total expenses would be much higher than expenses on trading. Also, for a manufacturing company, excise duty would be form a significant part of the indirect tax payments.
A statistical analysis of the ratios can help identify the characteristics of the manufacturing sector, and be used to classify firms effectively. The ratios can be applied to ascertain the highest revenue contribution on a yearly basis and at the same time allows a cross-check with reported codes and declaration under of Form No. MGT 7. The classification algorithm would need to deal with various categories of observation, including procedures that deal with various possibilities of non-reporting.
Amey Sapre is at the Indian Institute of Technology Kanpur and Pramod Sinha is a researcher at NIPFP.
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