New moves in regulating warehouses

by Anirudh Burman and Iravati Damle.

Warehousing is an important aspect of supply chains. Good warehousing has direct advantages in terms of storage of value, facilitating pledge financing, reduction in wastage, and reducing price shocks by allowing market participants to anticipate future demand and plan for the same. Warehousing has to be trustworthy for these benefits to accrue. Market participants have to be confident that the information provided in warehouse receipts (proof of deposit of a commodity) is accurate at all times. The warehousing sector in India does not reflect these attributes at present. Government regulation can correct some of these inefficiencies.

The warehousing sector in India is unorganised and fragmented (link). The organised segment is dominated by the public sector undertakings such as the Central Warehousing Corporation (CWC) and State Warehousing Corporations (SWC). Over the past decade, private firms have ventured into the business of warehouse services (warehouse service providers or "WSPs"). A large number of small and state level entities dominate the private warehousing segment (link). Under the current constitutional framework, state governments make legislation to regulate licensing of warehouses(For example, West Bengal Warehousing Act, Punjab Warehousing Act, Karnataka Warehousing Act). States across the country have different licensing norms; with different standards (See page 16 of a note on the proposed framework for registration of warehouses for comparison of State warehousing laws of Punjab, West Bengal and Karnataka)


This has resulted in the following problems in the warehousing sector:

• Lack of national standards and uneven levels of enforcement action on offences committed by Warehouse Service Providers (WSP).
• The absence of an integrated information system prevents high quality warehouse service providers from sharing information with potential customers. Therefore, they are unable to differentiate their services from the other players in the market and charge higher rents. As a result, the sector is currently in a low level equilibrium. This will ultimately hamper the growth of the warehousing market, with high-quality warehousing services withdrawing.

The market has started creating its own systems for dealing with inefficiencies. For instance: Banks undertake lending activities against warehouse receipts through Collateral Management Companies (CMC). These banks pay a premium to the collateral managers to insure themselves against the risks of bad warehousing. The collateral managers provide a range of services, including:

• Locating good warehousing facilities;
• Taking delivery of pledged commodities and/ or verifying their quantity and quality;
• Ensure the physical security and quality of the stock for the bank;

Establishment of WDRA

In 2006, the government passed the Warehousing Development Regulation Act, to make provisions for the development and regulation of warehouses, negotiability of warehouse receipts, establishment of a Warehousing Development and Regulatory Authority and for matters connected therewith or incidental thereto. The Act allows warehouses registered with the WDRA to issue Negotiable warehouse receipts (NWR), which are backed by a legal sanction. The WDR Act and accompanying rules and regulations stated registration requirements with the objective of keeping a check on the warehouse service provider. However, it has failed to receive the expected response from the market and bridge the information gap, with the use of NWRs being low.

How do we solve the problem?

WDRA was established to be a national regulator that would build trust and credibility in the instrument of warehouse receipts by ensuring warehouse operators are competent, follow the required processes correctly, and adhere to minimum standards set by WDRA. This is intended to add comfort to those trading in NWRs. As an agent of the State, WDRA has to do so by following the rule of law. Regulations drafted by it must follow a procedure where all stakeholders are informed of the rationale of the proposed regulatory system, the problems that the regulation proposes to solve, and the proposed method of solving these problems.

Lessons from FSLRC for WDRA

The recommendations of the Financial Sector Legislative Reforms Commission (FSLRC) are important in this regard (link).  The Financial Sector Legislative Reforms Commission drafted the Indian Financial Code (IFC) which makes substantial recommendations regarding the procedure that financial regulators must follow while making regulations (Report of FSLRC, Vol 2). By Section 52 of the IFC, if a financial regulator proposes to make any regulations:

• it must publish a draft of the proposed regulations
• this must be accompanied by a statement listing the objectives of the regulations, the problem that the regulations seek to address, the underlying principles, expected outcome, cost benefit analysis, process of making representations.
• it must list the reasons for preference of one principle over another.
• it must publish the representations received, take them into consideration and also state its response to the representations received.
• if the final regulations differ from the proposed regulations, it must list the details and reasons for the difference and also undertake a cost benefit analysis of the differing provisions.

NIPFP has supported WDRA in drafting two concept notes for making draft regulations pertaining to the registration and grading of warehouses. WDRA has solicited public comments on these notes:

• Cover Note: (link)
• Note 1, pertaining to the proposed regulatory framework for registering warehouses by WDRA: (link)
• Note 2, pertaining to the creation of a grading system for warehouses and the regulation of entities who may be allowed to grade warehouses: (link)

Feedback on the two notes may be sent latest by 20th July, 2015 to the Director (Tech.), WDRA at dirtech.wdra@nic.in or may also be sent to the Director (Tech.), WDRA, 4/1, Siri Institutional Area, August Kranti Marg, Hauz Khas, New Delhi 110016.

This style of rational, consultative and transparent regulation-making is, as yet, unusual in India.

Conclusion

The field of warehousing suffers from uneven regulation and the absence of national standards. Stakeholders argue that registration requirements with WDRA do not provide market relevant information about the credibility of the WSP/warehouses. The costs of registration outweigh the benefits. This is hampering the growth of the warehousing market in the country. The first note envisions a redesign of the registration process to establish minimum standards and provide quality assurance to market participants. The second note makes the case for WDRA to facilitate the process of generating credible information about registered entities through a system of grading warehouses and WSPs, after registration. While better registration norms will establish minimum standards, grading will ensure that the market gets information about the performance of the warehouse/WSP post registration. These interventions can enable WDRA to efficiently solve the market failures.

Lessons from the Indian currency defence of 2013

by Ajay Shah.

In 2013, the Indian government mounted a big defence of the rupee. The authorities appeared to throw everything that they could at the problem: enhanced capital controls on outflows, relaxed capital controls on inflows, exchange rate intervention, restrictive actions which damaged currency market liquidity to make it easier to manipulate the market by trading on the market, and monetary policy tightening. See this article for a narrative from that period, and a set of links to contemporaneous thinking.

It's important to look back at this period and ask: Did the currency defence deliver on its objectives? Can we identify costs and benefits?

As with all episodes of currency defence, we do not observe the counterfactual: What would have happened if the authorities had not mounted this currency defence? In this case, we have some interesting evidence ahead which suggests that the outcome was not influenced by all these actions.

Along the way, we suffered collateral damage of numerous kinds. Many components of a currency defence are effectively an interest rate defence. At a difficult time in the economy, defending the rupee by raising rates hurt the domestic economy further.

In what follows, on each of the graphs, we superpose the dates of various kinds of actions taken by the authorities. This helps us conduct an intuitive causality test: if an action mattered, it should have yielded some impact in its immediate after math. These graphs also help us see through the claims made by authorities. (Event studies have value in this broad area of research, but are problematic here as there is a lot of event clustering).

Level of the exchange rate

USD/INR exchange rate

The graph above shows the USD/INR exchange rate. The first action was undertaken when the USD/INR exchange rate was within range of Rs.60 to the dollar. After that a series of actions were taken, through June, July, August and September. At the bottom, the exchange rate was near Rs.70 to the dollar. After all the actions stopped, the exchange rate got back to the region of Rs.60 to the dollar.

USD/INR exchange rate, indexed to 100 at start

It is useful to see this same graph where the USD/INR exchange rate is indexed to 100 at the left edge. The currency defence began when there was a 5% depreciation and the bottom was a 25% depreciation. After the flow of measures taken by the authorities ended, the exchange rate recovered to a 10% depreciation compared with the starting point.

Did the currency defence work?

When we look at the graph above, we could tell two rival stories. One argument is that the actions had no impact. Another argument is that the cumulative impact of all the actions gave a reversal of the exchange rate.

We are able to resolve this debate using international evidence. Let's start at the J P Morgan Emerging Markets Currency Index:

USD/INR exchange rate superposed with the J P Morgan EM Currency Index

The graph above shows the J P Morgan Emerging Markets Currency Index on the left axis and the USD/INR on the right axis. The two series are remarkably alike!

All the actions taken by the Indian authorities can only have a small impact on the Emerging Markets  Currency Index, reflecting the weight of the INR in that index. That small weight simply cannot account for the extent to which the two graphs move together.

The two graphs are so alike! If the actions by the Indian authorities had scored some impact, then the Indian story would have unfolded differently from the overall average of all Emerging Markets. It did not. Therefore all the actions taken by the Indian authorities had no impact on the USD/INR.

In a similar vein, we look at the Citibank index of policy surprise in the US:

USD/INR versus the Citi US surprise index

The important dates in this series are the dates of policy surprise in the US. These policy surprises -- Bernanke's first speech and then his second speech -- are more likely to have shaped the USD/INR when compared with the actions of the Indian authorities.

All currency policy is monetary policy

 

The 91-day treasury bill rate

The currency defence was an interest rate defence, as all currency defences are. The short rate surged sharply.

RBI has numerous instruments through which monetary policy is implemented. All these reduce to one summary statistic in the form of the 91-day treasury bill rate. The graph above shows a huge increase in the rate, from roughly 7.5% up to 12%: an increase of 440 basis points.

Volatility

Squared returns on USD/INR exchange rate

These days, it is fashionable for the authorities to claim that they do not actually have a target exchange rate in mind, but they are only intervening to prevent episodes of high volatility. The graph above shows that squared daily returns on the USD/INR were slumbering when the rupee defence began. Volatility seems to have surged after the government got going. Even after October, volatility had not come back to the levels found in May. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Realised volatility of USD/INR futures

Squared daily returns is a poor statistical estimate of volatility. Using IGIDR FRG data, we exploit  high frequency data to construct the daily time series of realised volatility of the USD/INR currency futures traded at NSE. This also shows a similar picture: volatility was slumbering when the currency defence began; it's hard to claim that the authorities were focused on `excessive volatility' and not concerned about the level of the rate. Volatility worsened through the period of the currency defence. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Implied volatility of USD/INR

We use data from IGIDR FRG on USD/INR options trading at NSE to construct the implied volatility every day. This gives a forward looking indicator of future USD/INR volatility as seeen by the market. It shows that volatility was slumbering when the first actions began. Volatility generally worsened after the authorities acted. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Collateral damage

Many people at the time noticed the impact on stock prices. Nifty dropped by 10% and Nifty Junior dropped by 15% before recovering late in the year. But many other elements of the collateral damage have not been widely noticed. Let's start at stock market liquidity.

Round-trip transactions costs for Nifty basket trades on the NSE spot market

The graph above shows the round-trip transactions costs (in basis points) faced when doing basket trades for the full Nifty portfolio on the NSE spot market. These are computed off the full limit order book that's observed at IGIDR FRG. The lowest curve is at a basket size Rs.2.5 million; the next one is at Rs.5 million and the highest one is at Rs.10 million. These `round trip transactions costs' are the sum of impact cost to buy and impact cost to sell.

Stock market liquidity held up reasonably well in the early part of the story. At event 6 (increase in interest rates), stock market liquidity worsened, particularly at larger transaction sizes. As an example, at a transaction size of Rs.10 million, the round-trip impact cost rose from the region of 8 basis points to the region of 12 basis points, a 50 per cent increase. This would, in turn, spill over into an array of downstream consequences such as enlarged no-arbitrage bands, increased costs of dynamic trading strategies, etc.

Market impact cost reflects a combination of implied volatility, funding constraints and asymmetric information. The outcome seen above reflects a deterioration on all three counts.

Implied volatility of Nifty

At first, the currency defence did not seem to make a big difference to the forward looking volatility of Nifty. But from August onwards, the outlook for Nifty became much more risky. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

CMIE Bank Index

Another element of the collateral damage was banks. The increase in the short rates hurt banks who, on average, seem to be carrying an interest rate mismatch. There was a roughly 30% decline in the CMIE banking industry stock price index.

What can we learn from this episode?

1. USD/INR seems to have responded to global events and all the actions of the authorities seem to have had little impact.
2. The entire focus of economic policy in that period was on fighting the INR depreciation. Every day, new tools were being bandied about and implemented. There was a reverential approach to the power of central banks -- currency intervention, capital controls, choking off financial markets, arranging lines of credit, etc. But when we look back, we see that the central bank was ineffectual in delivering on the goal.
3. The cost of all the actions taken by the government was large, and the payoff obtained from the actions was elusive. Ex post, we see that the cost of mounting the defence was much larger than the costs envisaged at the outset by proponents of the currency defence.
4. The Ministry of Finance debated the views of many advisors, and chose to go with the folks who advocated a big muscular currency defence.
5. I feel that the academic literature on capital controls and currency policy does not adequately come to grips with the mess that we get in the real world when we try to do these things. It's easy to bandy about capital controls or currency trading. Such `heterodox thinking' has become fashionable. Capital controls are not irrelevant; they can deliver pricing distortions and reduce market efficiency. What's important to ask is: Do capital controls deliver on the goals of macroeconomic policy? The answer in India seems to be: No.
6. Some advisors at the time said that it was possible to `squeeze the shorts' and `hit the speculators' without contaminating monetary policy. But all currency policy is monetary policy, and all real monetary economists know this. Every currency defence becames an interest rate defence. The policy rate went up by 440 basis points, dealing a body blow to a weak economy. By August 2013, I felt the UPA was going to lose the next election.
7. When the authorities defend the rupee, this protects foreign currency borrowers. In addition, raising rates hurts local currency borrowers.  This is different from the conventional moral hazard argument, that currency policy creates moral hazard in favour of more unhedged foreign currency borrowing. The new thing I understood in 2013 was that a currency defence is not only good for unhedged foreign currency borrowers; it hurts local currency borrowers. These two factors come together to shape the incentives of firms on choosing between borrowing in local currency vs. borrowing abroad. If you borrow in USD, you will get support from the government in extreme events; if you borrow in INR, you will get hit by the government in extreme events.
8. Episodes like this interact with the bureaucratic politics of Indian finance. As an example, currency futures trading started in 2008, against the protests of RBI which feared the loss of turf (the role of SEBI on exchanges). RBI was also concerned about the possibility that exchanges might achieve liquidity and market efficiency, and thus undo decades of hard work in preventing a genuine currency market. The currency defence of 2013 was used as an opportunity to hamper the working of currency derivatives trading on exchanges. Even today -- June 2015 -- some of those restrictions imposed in the summer of 2013 have not been reversed.
9. Market liquidity got worse once the currency defence got going. This may have implications for the decision to stay exposed through a currency defence.
10. It takes a long time for the damage caused by a currency defence to heal. Some economists think you can switch off and switch on finance at a whim. But the working of the financial system is ultimately about the organisational capital in financial firms. When private firms want to build a business,  organisational capital can only be built slowly, and after such episodes, private firms may not feel like committing resources to build a business. Private firms lose respect and trust in the authorities when they see such episodes, and hold back from investing in building the business.

In the  1980s, an extensive literature worked on capital controls and found generally bad outcomes. In recent years, a new literature is utilising the improvements of econometrics of recent years and reopening the same questions. Forbes & Klein 2015 say:

large interest rate increases, major reserve sales, large currency depreciations, and new controls on capital outflows are ineffective at improving these three outcome variables and often imply substantial tradeoffs. More specifically, sharp increases in interest rates and new controls on capital outflows appear to significantly reduce GDP growth, have no consistent effect on unemployment, and are correlated with higher inflation. Large depreciations also appear to initially reduce GDP growth and be correlated with higher inflation, but there is some evidence that they can yield a lagged improvement in growth and reduce unemployment, especially during the 2000s.

We have a few papers in this rediscovery of the orthodoxy: Patnaik and Shah 2012, Patnaik et. al. 2012,  and Pandey et. al. 2015.

A political economy literature in the US suggests that the last one year prior to an election matters disproportionately (link, link, link, link, link, link). The currency defence of 2013 may have been unusually important in securing a clear majority for the BJP in May 2014. But this same currency defence also damaged the economy, and has helped worsen things in the first year of the BJP.

Concerns about RBI's `Strategic Debt Restructuring Scheme'

I have a column in the Indian Express today on this.

Has the recession which began in 2012 ended?

The question

The erstwhile GDP series, with a base year of 2004-05, has been discontinued. The new series has a base year of 2011-12. On paper, the Central Statistical Organisation (CSO) has made improvements in methods for the new series.

A significant debate is taking place about the soundness of the new data, on two counts. First, some experts, such as Prof. Nagaraj of IGIDR, have expressed concerns about the methods used in the new series. Second, the new series seems out of sync with other directly measured indicators.

This methodological debate about GDP measurement comes at a time when the question of the age is: Has India recovered from the business cycle downturn which began in 2012, or has that downturn continued into the present? The high growth rates visible in the new GDP data suggests that the downturn has, indeed ended.

A trusted output proxy

The output measure which is unambiguously well measured is the sum of revenue of listed non-financial non-oil firms, drawn from the CMIE database. SEBI regulations require that listed companies release quarterly summary financial statements. We see records for every firm, so there is no opaque methodology for extrapolation. It's mere addition of unit level data.

Financial firms are excluded as their accounting concepts are not comparable with non-financial firms. Oil firms are excluded as their output fluctuates a lot owing to changes in the petroleum product subsidy regime and the fluctuations of the world oil price.

We construct an index as follows. For each pair of consecutive quarters $t_1$ and $t_2$ we identify the firms $I_2$ which are observed in both quarters. We sum up the net sales at both time points to get $y_1 = \sum_{i \in I_2} y_{i,1}$ and $y_2 = \sum_{i \in I_2} y_{i,,2}$. This yields an estimate of the growth $y_2/y_1$. These percentage changes are cumulated to make a nominal index and then seasonally adjusted.

This yields a quarter-on-quarter seasonally adjusted nominal growth rate. This is an unambiguously well measured output growth, which does not require getting into debates about measurement of inflation, or the CSO's procedures for measurement of GDP. This series is observed from 1999 onwards.

This output proxy is useful because the listed companies are an important subset of the economy. As an example, a large fraction of the taxes paid by all firms emanate from these firms. Many Indian laws are excessively restrictive on large firms. This has created incentives for large firms to organise themselves as a hub of contracts, where many elements of production are done by satellite firms. I have anecdotal evidence about one large listed firm: for each employee seen on the books of the large firm, there are two employees in the surrounding ecosystem of small firms where sub-components of the production process are contracted out. Hence, a particularly large swathe of the economy is observed when we see the output of these large firms.

Identifying recessions in this data

The heuristic used for identifying downturns is to look for a contiguous period of substantially below-median growth of which at least one quarter has near-zero nominal growth. This yields the following dates of recessions:

1. Q4 2000 to Q1 2002, the downturn that was associated with the IT bust and the 9/11 attacks.
   
2. Q4 2008 to Q1 2009, the downturn associated with the global crisis.
   
3. A downturn that began in Q1 2012.

This is not the NBER approach of identifying peaks and troughs. In a high growth economy like India, we get growth cycles and not business cycles in the conventional sense of the term. Hence, for the purpose of identifying dates of recessions, it is more interesting to identify the periods of extremely low growth.

Figure 1: Quarter-on-quarter annualised growth of the seasonally adjusted index of nominal net sales growth of non-financial non-oil large firms in the CMIE database

The graph above shows seasonally adjusted quarter-on-quarter nominal growth of the revenues of large companies. This shows sluggish growth all through the present downturn. In the Jan-Feb-March 2015 quarter, growth was particularly weak.

This is the measure of macroeconomic conditions that involves the least assumptions about measurement. It admittedly only measures what is going on with the large firms. So far, we have not shifted from nominal to real.

Figure 2: Figure 1 expressed in real terms

The graph above shifts from nominal to real, using CPI-IW as the measure of inflation. The broad picture observed is similar. Both series suggest that the recession which began in Q1 2012 has not ended.

How does the trusted measure compare against old and new GDP data?

So far, we have looked at quarter-on-quarter seasonally adjusted growth rates. We now switch to year-on-year growth rates, because the new GDP data has a short time series and seasonal adjustment is not possible. We use nominal data to avoid the controversies of converting to real. The key graph is placed below.

Year-on-year growth of three alternative output measures (all nominal)

The red line is the old quarterly GDP series, with base year 2004-05. The blue line is the new series with a 2011-12 base. The black line is constructed from firm data. The three recessions are shown as shaded regions. This graph shows three things:

1. Output growth as seen in the sales of large firms has more extreme fluctuations when compared with GDP growth. In good times, revenue growth of firms has gone all the way up to 25% nominal year-on-year growth, and in bad times, this has dropped to near-zero levels.
   
2. The downturn which began in Q1 2012 has not ended. There was only temporary improvement, perhaps aided by the decline in crude oil prices and the optimism associated with the election outcome of May 2014. The latest data, for January-February-March 2015, is the worst observed within this recession.
   
3. The new GDP data is more optimistic than the old GDP data.
   

In the period leading up to June 2012, there was a good agreement between the GDP (2004-05 base) and the firm data. On average, the GDP data was lower by -.39 percentage points.

Something changed in the methods used by the CSO in June 2012 (we know not what). From this date onwards, GDP data (old base) started showing larger values when compared with the firm data, with a gap of +5.1 percentage points.

This gap has risen with new GDP data, where the appropriate and comparable measure is the GVA. This is showing growth which is 7.9 percentage points higher, on average, than the firm sales data.

This evidence does not suggest that the new GDP data is off by 7.9 percentage points. As the graph shows, firm sales data has a bigger amplitude when compared with the GDP data. In good times, the firm data shows very high growth and vice versa. The problem is perhaps limited to the discrepancy between the old data and the new data, which seems to be 190 basis points.

Is growth better with smaller firms?

One element of the methodological improvement made by the CSO in the new data is reaching beyond large firms traded on stock exchanges (``listed companies'' in the Indian parlance) to smaller firms. It is argued that these smaller firms are growing much faster. In order to test this hypothesis, we utilise annual data for a much larger universe of private firms (``unlisted companies'' in the Indian parlance). The CMIE database now has strong coverage of unlisted firms, which makes this analysis feasible.

Have unlisted companies done much better?

The results show that in recent years, the performance of the unlisted companies was worse. This cannot be an explanation for the high GDP growth seen in the official data.

Corroborating evidence: Export demand

Export growth, as seen in the BOP and as seen in the firm data

The graph above superposes goods and services exports from the Balance of Payments data against the exports of goods and services seen in the firm data. The BOP data is well measured and hence trustworthy. This gives us an opportunity to compare the picture seen through firm data versus the picture for the full economy. Here also, the large firms often seem to have more volatilility of boom and bust in exports growth, when compared with the overall data.

This shows a period of sluggish exports growth in the post-2012 period.

Corroborating evidence: Investment demand

We obtain trusted evidence about investment by firms through the CMIE Capex database, which  tracks all projects presently under implementation.

Quarter-on-quarter growth of infrastructure projects `under implementation'

The graph above shows point-on-point growth in infrastructure projects which are classified as `under implementation'. As there is no seasonality in this series, the simple point-on-point growth measure is useful. Many of these projects have been stuck on account of policy bottlenecks, and it was hoped that the new BJP government would be much more effective at removing these bottlenecks. In the period after 2012, in most quarters, the growth has been below the long run median (the dashed line).

Quarter-on-quarter growth of all projects `under implementation'

Infrastructure is only one component of overall investment. The graph above shows point-on-point growth in all projects that are `under implementation'. As there is no seasonality in this series, the simple point-on-point growth measure is useful. This shows a slight recovery from the depths experienced prior to the elections. However, we remain near or below the long-run median value (the dashed line).

Quarter-on-quarter growth of all projects `announced'

Project announcements are a measure of business confidence. When firms are profitable, and financing is not a constraint, more projects get announced. The graph above shows sluggish growth in project announcements in the third recession. In most quarters, sub-median values have been observed. (There are some methodological issues in how we construct this measure, which tend to slightly overstate growth in the latest quarters).

Conclusion

The new GDP data features many improvements in methodology. However, there is something going on, which we do not yet understand, which is a cause for concern. The extremely optimistic picture shown in the new GDP data does not square against what is seen in other data. Firm data released at quarterly and annual frequencies is micro data where every record can be perused and checked. Investment data is the aggregation of facts about individual projects which can be perused and checked. BOP data is a sound measure of goods and services being sold by India as we measure foreign exchange going into the country.

This examination shows slow growth. Investment data is sluggish. Export demand is sluggish. These are consistent with the most trusted output measure -- revenue growth of large firms.

The picture appears to be one where a recession began in Q1 2012 and has not yet ended.

Release of information in machine-readable format

by Ashish Aggarwal.

All data starts out in computers. All data is analysed using computers. However, all too often, materials are produced and placed on websites which are not readable by computers. This dramatically drives up the cost of using the data. There is much to gain from an insistence that the materials which appear on websites -- of financial firms and of regulators -- are machine readable

One example of a success story is mutual funds who provide data like NAV (Net Asset Value) of their schemes as an electronic feed. Third party websites are able to use this to provide annualised return on portfolios, or analysis and comparison of historical data. None of this would have been possible if mutual funds had a chaos of diverse presentation with different fund houses giving out data in different ways.

The above-mentioned electronic feed is an example of machine-readable data. "A computer file" does not constitute machine readable data. Machine readability is obtained where the data can be read and processed by a computer for further analysis and interpretation. Comma Separated Values (CSV) is one example of a machine-readable data format. Other examples include XML files.

The gains from machine readable data

The value of even minimal information, when made accessible in machine readable form, is remarkable. As an example, suppose a government releases adequate information for all consumer courts to be placed on google maps. Once this is done, consumers can start rating the courts. This can support policy analysis and improvement of the courts which are laggards. As more information is released, more sophisticated applications become possible. If case load data about consumer courts is made available, third parties could build software and systems through which one could get a fairly accurate estimate of the queue and expected hearing slot if a complaint were to be filed on any given day. If data on financial firms against whom complaints are filed is also loaded, one would know which firms are generating more complaints.

Consider a household survey run by a regulator. The regulator can release a PDF file with a report which analyses the survey evidence. This is useful and interesting. A big jump is obtained when the regulator releases the record level data. This would make possible novel analysis by third parties, of kinds that may have never been envisaged by the regulator.

A revolution is shaking the world of finance globally, the financial technology revolution. This is critically about opening up data access to new kinds of firms, while access is controlled by consumers. This is about shifting ownership of data from financial firms or governments to consumers, and giving consumers access to sophisticated analytical services which add value.

Developments internationally

These ideas are not unique to India; they are changing the way governments and regulations work worldwide. The U.S. Government’s Open Government Directive of 2009 is one early example of a government that created such an obligation. It said that to the extent practicable and subject to valid restrictions, agencies should publish information online in an open format that can be retrieved, downloaded, indexed, and searched by commonly used web search applications. An open format was defined as one that is platform independent, machine readable, and made available to the public without restrictions that would impede the re-use of that information.

Initially this led to resistance, inconsistent formats etc and required government to create capacity to make it happen. After that, the US government has set up data.gov, home to its open data initiative with tools, and resources to conduct research, develop web and mobile applications and design data visualisations. It followed this up in 2013 by making open and machine-readable the new default for government information.

Many countries have embarked on similar initiatives. As an example, see a paper tabled in 2012 in the UK Parliament about unleashing the potential of open data. In 2011, the Open Government Platform (OGP) was launched as an international platform for domestic reformers committed to making their governments more open, accountable, and responsive to citizens. Since then, OGP has grown from 8 countries to the 65 participating countries. India is not yet on that list.

Implications for the draft Indian Financial Code

The Indian Financial Code (IFC) has drafted strong reporting mechanisms so as to achieve accountability of financial sector regulatory institutions. This needs to be pushed further into the direction of the release of machine readable data. Good reporting can be used more effectively, if the data tables and charts can be read and analysed with minimum frictions through computer programs.

In Chapter 16, `Functioning of the financial agency', the first section `Minimum standard for publication of information' (S.74(2)) says:

All information published on the website or other repository of the Financial Agency must be in an easily accessible and text-searchable format.

The phrase `machine readable format' needs to be defined and used in the law. This would encourage innovative financial sector firms and third parties to provide analysis to consumers using tools like mobile based apps, thus helping consumers make better choices in a timely manner.


The author is a researcher at the National Institute for Public Finance and Policy, New Delhi.

Things that the government should not do

The job of government is to address market failures: public goods, asymmetric information, market power, externalities. In addition, a certain focus on welfare programs is the inevitable consequence of universal suffrage. We can debate the appropriate design, and scale, of these programs.

That leaves a third class of issues where neither justification is present: where the government is doing something which is not grounded in market failure and it does not have political salience. The standard Indian example is: Air India. There is no case for government to be in that business.

The impending demise of the US Export-Import Bank [ht: Marginal Revolution] provides another example: Exim Bank [website, law].

Financing and risk-taking in connection with international trade or cross-border investment is the ordinary stuff that the financial industry does. The self interest of financial firms will produce these services. There are numerous anti-competitive provisions in the present policy framework, which are inhibiting entry of financial firms into a diverse array of businesses. Removing these barriers will enhance competition and create a diverse array of services that meet the requirements of customers.

There is no case for the government to subsidise exporters, but even if that were desired, there are more efficient ways to do this rather than subsidised credit.

The only role for government in finance is that of addressing market failures. The 9 components of the Indian Financial Code are:

1. Consumer protection: Left to themselves, financial firms will mistreat consumers. This is the heart of how we should think about financial economic policy.
2. Micro-prudential regulation: Left to themselves, financial firms will take on too much risk and fail too often, which will hurt unsophisticated consumers and impose externalities upon bystanders.
3. Resolution: The ordinary bankruptcy process does not work for financial firms, where failure can be disruptive. The government is required in the field of resolution, to identify financial firms that are not viable before they are insolvent, and gracefully handle the situation in ways that don't hurt innocent bystanders and protect unsophisticated consumers.
4. Systemic risk regulation: This is about seeing the woods and not the trees. Financial regulation induces pro-cyclicality; we need ways to combat this. We need ways to reduce the probability of systemic crises, and better ways to deal with them when they do come about.
5. Public debt management: We need an agency which will do investment banking for the government, and figure out the right ways to organise the market for government bonds.
6. Capital controls: We need to have the rule of law, and equal treatment of non-residents, in the working of capital controls.
7. Monetary policy: We need an accountable central bank which will safely produce fiat money, through a sound monetary policy process. 
8. Development and redistribution: Finance is being used as a tool for redistribution, and this requires sound foundations of governance. There are also some public goods of market infrastructure which require policy attention.
9. Contracts, trading and market abuse: These are adaptions of the standard law on property and contracts which are required for insurance and securities markets.

These nine areas are the only  roles required of the government in finance.

Once we decide that we want a financial agency (e.g. the Public Debt Management Agency, or the Resolution Corporation), full thinking is required on how to make the agency work properly. How to create checks and balances, and appropriate incentives, so as to produce high performance? A well drafted law is necessary but not sufficient in getting to a high performance agency. A badly drafted law can disrupt the organisation, and a well drafted law can nudge things along in the right direction, but constructing a high performance agency is a challenge in its own right, where a distinct set of issues are faced.

Institution building for FSLRC: MOF has begun on the `Financial Redress Agency'

MOF has just begun the `task force' process for constructing the last brand-new element of the financial regulatory architecture envisaged by FSLRC: The `financial redress agency' (FRA).

What is the Financial Redress Agency?

FRA is intended as a one-stop shop for aggrieved consumers. The consumer should not have to navigate between the RBI Ombudsman or the SEBI Scores or the IRDA Ombudsman, etc. It does not make sense for India to have multiple parallel systems run by sectoral regulators: this drives up cost, reduces the reach of the branch network for the same level of expense, and increases confusion in the eyes of consumers. In the FSLRC world, the consumer would first try to resolve her problem with the financial service provider, and when dissatisfied, approach the FRA. The FRA would have access points all over India. It would be a light weight mediation system, where neither the consumer nor the financial firm would have lawyers.

FRA would produce data about its activities, which would show the problem areas faced by consumers. This would help raise the alarm about products and sales practices which are faulty. The mis-selling of ULIPs that began in 2004 should have been stopped before 2011. If FRA and FDMC had been in place, timely data would have been produced and the scandal would have been stopped earlier.

Where does this fit into the Indian financial reforms?

India's financial reforms are working on three tracks:

1. The first element is the legislative process that should, at some point in the future, lead to Parliament enacting the Indian Financial Code. The February 2015 Budget Speech by Arun Jaitley said he will table this in Parliament `sooner rather than later'.
2. The second element is building institutional capacity to enforce the new law. In India, building high performance institutions is difficult. As with SEBI or PFRDA or NSDL, it makes sense to build the institutional capacity ahead of time so that when Parliament passes the law, it can immediately be enforced. When the law is enacted without adequate planning for the institutional capacity, this can lead to difficulties as was seen with the Companies Act, 2013.
3. The third element is to treat FSLRC as the strategy and chip away at incremental changes within the existing legal framework to move towards this goal. This also builds institutional capacity, and reduces the complexities after the law is passed. It front-loads the gains: why not reap the fruits of improved financial sector policy sooner rather than later? Elements of this include: (1) The FSLRC Handbook, (2) the SEBI-FMC merger (backdrop and then Budget 2015), (3) shifting regulation-making power on non-debt capital controls from RBI to MOF (Budget 2015), (4) inflation targeting as the objective for RBI, (5) Finance SEZs, etc.

State capacity is about well drafted laws and sound institutions that enforce these well drafted laws. The Indian malaise with chronically malfunctioning institutions is as much about badly drafted laws as about badly designed organisations. A quantum leap in the law -- the IFC -- will not solve the problem by itself. A similar quantum leap in the working of financial agencies is also required. In order to do this in a systematic way, MOF has invented a new framework involving `task forces' which lay the foundations for a financial agency before the management team is recruited.

At present, four task forces are in motion -- to build the Financial Sector Appellate Tribunal (FSAT) that will hear appeals against all financial agencies, the Public Debt Management Agency (PDMA), the Resolution Corporation (RC) and the Financial Data Management Centre (FDMC). The Task Force for the Financial Redress Agency is the fifth in the sequence. By virtue of starting out 8 months after the first four, it will benefit from the learning of the last 8 months on how this new process of institution building works.

Each of these five projects would take over three years from start to finish. One one hand, this is frustratingly slow. We need the FRA or the  FSAT or the PDMA or the FDMC or the RC yesterday. But it's not possible to do these things in reduced time. And these horizons are consistent with the time horizons for IFC to go through the parliamentary process.