Digital transformation and the paradox of financial inclusion in India

by Suyash Rai.

India has made great strides in digital technology, becoming a leading exporter of digitally delivered services to the global economy. These capabilities with computer technology fuelled hopes that digital transformation could yield gains for the Indian state that are comparable to those seen in the private sector. The `Digital Public Infrastructure (DPI)' approach, with India's Aadhaar digital ID system as a prime example, is presented as a path to higher GDP growth for developing countries. There is an emerging debate on the role of the state in shaping the development and deployment of DPIs.

Two key pillars of the Indian story with DPIs are identity services ("Aadhaar") and their impact on financial inclusion. In a new working paper, Economic development and digital transformation: Learning from the experience of Aadhaar and financial inclusion in India, I critically examine the Indian progress on financial inclusion between 2011 and 2021, revealing a paradox: while account ownership surged, account usage remained low.

The facts

The paper analyses India's performance compared to other lower middle-income and middle-income countries. The evidence shows:

• Impressive account opening: India witnessed remarkable progress in account penetration, surpassing the average improvement in middle-income countries.
• High inactivity: A significant percentage of accounts in India were inactive, far exceeding the average for middle-income countries.
• Low account usage: India lagged behind in account usage for both consumption smoothing (regular deposits and withdrawals) and digital payments, indicating a gap between account ownership and actual financial inclusion.

The role of government mandates and Aadhaar

We argue that the rapid scale of account opening was caused by a series of government and Reserve Bank of India (RBI)mandates, particularly the Pradhan Mantri Jan Dhan Yojana (PMJDY). While Aadhaar played a role, it was primarily used as a physical ID for KYC, rather than as a digital ID through e-KYC. The gains in account opening may have a lot to do with state coercion and less to do with DPI.

The primary objective driving these initiatives was to facilitate direct benefit transfers (DBT) for welfare schemes. The government's focus on DBT aimed to reduce leakages and improve attribution for its welfare programs in the eyes of voters.

Why did this approach yield disappointing results?

The paper explores several reasons for the limited account usage despite the increase in account ownership:

• The lack of a viable business model: No-frills accounts, with zero minimum balance and free transactions, are commercially unattractive for banks.
• Mismatch between the solution and the problem: The focus on account opening for DBT didn't necessarily translate into accounts that address the richness and complexity of finance for the poor, of meeting the diverse needs of users for consumption smoothing and payments.

Lessons

The top-down approach, with a readiness to utilise the coercive power of the state, has limitations. While the government achieved its objective of scaling up DBT, this came at the cost of genuine financial inclusion and limited the potential uses of Aadhaar as a DPI.

We highlight the need for a more balanced approach, considering market forces and user needs, so as to obtain better outcomes with DPIs. We stress the importance of political creativity, institutional reforms, and a broader understanding of public value, beyond narrow fiscal objectives, when designing and implementing DPIs.

We offers insights into the complexities of digital transformation and financial inclusion, challenging the simplistic narrative of Aadhaar's success. These experiences invite us to rethink the role of the state in shaping DPIs and consider alternative approaches that can truly leverage technology for inclusive and sustainable development.

Suyash Rai is a Fellow at Carnegie India and a Visiting Research Fellow at the xKDR Forum

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The Justice Srikrishna Report and Digital Monopolies

by Jai Vipra.

Much has been said about the recommendations of the Justice Srikrishna Committee Report on Data Protection, particularly with respect to the broad exemptions made for the state on the non-consensual use of people's data. (The Quint, 2018) However, there is little analysis of how a largely consent-based framework affects people's control over data and over the effects arising from the use of their data.

In this article, I make the case that digital monopolies exist and are driven partly by exclusive control over data; that these monopolies can be broken up with free data portability; that the provisions in the Draft Data Protection Bill are strict on consent but lax on portability, and that by being so, they might end up entrenching existing digital monopolies.

The Committee Report takes the approach of fiduciary responsibility of data. In this fiduciary relationship, the generator of data is the 'data principal', and the user or holder of that data is a 'data fiduciary'. The data principle consents to transfer his data to the fiduciary. The data fiduciary then has the responsibility to use the data principal's data in her best interest and in a fair and reasonable manner. This approach was taken because the Committee considers this the best way to protect a person's privacy. (Sengupta, 2018) However, as I will discuss below, privacy is not the sole value to be protected in this case. I also discuss how an exclusive focus on privacy and consent might lead to outcomes inimical to the social good.

Digital monopolies and control over data

To begin with, making the choice to entrust your data to someone with fiduciary responsibility might guarantee privacy. But it does not automatically mean you control the data and the value resulting from the use of that data. One of the harms that can result from massive data collection is the risking of privacy. But another harm is the limiting of competition.

Consider why, while the rest of the market was shrinking, Google and Facebook together captured USD 32.7 billion growth in digital advertising spending (in the first half of 2016). (Holland, 2017) With the large amounts of data being collected by these companies, precision targeting in advertising wins over all other kinds of advertising. Precision targeting means that advertisements are targeted to the individual based on their preferences and activities, rather than to groups of individuals. Companies that are able to offer precision targeting are able to capture the market, while all other companies lose out (Matsakis, 2018).

Coupled with the existence of network economies, only a few large companies are able to offer precision targeting in this manner. The problem here is not that precision targeting is necessarily bad, but that data control with network effects leads to monopolisation of the market. (Hindman, 2009) Hindman's empirical work also shows how the digital economy is more concentrated than traditional media.

The effects of monopolisation are not limited to the advertising market. Some examples might help illustrate the kinds of harms digital monopolies lead to. An entertainment platform like Netflix that controls most of the market can easily refuse content creators a market for their work, or can charge them exorbitantly due to the lack of competition born from the control of viewer data. Proven examples also exist: Google provided Getty Images with a choice of either allowing users to download images directly from search results, or excluding Getty images from search results, both unviable choices for Getty Images. Yelp faced allegations of extorting restaurant owners to purchase ads on the threat of bad reviews.

Due to monopolisation and particularly due to the emergence of Facebook, Amazon and Google, even though more content is being created than ever, less money is flowing to content creators as platforms soak up an ever-increasing share of the returns (Taplin, 2017). It is now mathematically impossible for a small company starting off in a garage to compete with Google. (Hindman, 2009) Network effects in the digital economy are driving predatory pricing and entrenched market power. (Parsheera et al., 2017) A digital monopoly can be as harmful as any other monopoly, and control of data (in addition to network effects) is an important way in which digital monopolies are maintained.

Data portability as a solution

What then are the solutions to monopolisation? One cannot change the fact that network effects exist in two-sided markets. But one can change the other part of the equation, that is, the control of data. If we want to reduce the monopolistic hold that it is possible to have over data, we have to make the data freely portable. This must be contingent on user consent to said portability, given the privacy concerns with data transfer. Thus, we have to create the ability for the user to transfer the data he generates to himself or to another entity, perhaps a competitor of the data fiduciary.

Argenton and Prufer (2012) have proposed something similar in the context of Google biasing its search results to favour its own subsidiaries, such as Google Maps. With a model that analyses indirect network externalities, they establish that search engine data control leads to monopolisation and reduces economic welfare. They propose that search engines should be required to share data on previous searches (with other search engines) to countervail this tendency.

One example of a recent data portability policy is the open banking regulation in the UK. The UK's Competition and Markets Authority found that big banks in the UK had an unfair advantage over other banks and fintech companies because they held valuable data on their customers, for example, transactions data. It also found that consumers would save money by using more suitable financial products if their data was freely portable. Under the Open Banking Regulations, the banks are required to share customer data, with customer consent, with any service provider through open Application Programming Interfaces (APIs). This enables any entity to easily build products that compete with bank products based on that data. It also works to break up the data-driven hegemony of big players in the market.

What might data portability lead to?

However, there are some difficulties here, particularly if we ask who data really belongs to. Consider an example: if I buy a book on Amazon, I create a piece of data. This is not sensitive data, but it is valuable data. Amazon uses this data and derives value from it in multiple ways: with targeted advertising, tailored product pages, by collating it with other pieces of data and observing profiles and trends, etc. To do all this, it processes the data and modifies it. The original data might belong to me, but who does the processed data belong to? What, indeed, is processing? Collection is not a costless activity, and so is collection processing? Even once processed, it is not that straightforward to imagine that the data belongs entirely to Amazon. With respect to personal data, Arghya Sengupta likens processed data to an envelope with a letter in it. The letter still belongs to the user and it is not possible to separate the envelope and the letter, and ownership is made tricky (Sengupta, 2018).

If we decided ownership based on the work put into production, all data (and indeed all property) would be collectively owned. But under current economic and legal arrangements, ownership is decided based on contracts. If I lease my land to you under a year-long contract, legally the land does not automatically belong to you even if you improve the soil quality by cultivating on it. Given these economic and legal arrangements, if I give Amazon my data under a contract that includes portability, Amazon does not own that data even if it processes it.

The Open Banking regulations described above essentially make all contracts conform to this norm of portability. In this way they aim to create a market where the choice of sharing data remains with the user and not the business. In the cases of open banking and search engine data, lack of portability was shown to create monopolies. When this is shown, there is a case for government intervention through mandating contracts with portability in order to fix this market failure.

In our example, this would mean that I can decide to transfer the data I generate by buying a book from Amazon. I could transfer it to an Amazon competitor, who then would also be able to offer targeted advertising. Or I could transfer it to an app that gives me financial advice based on my spending patterns. Amazon would still retain my data and profile for as long as I want it to. It would simply port a copy of that data.

Amazon, given its current business model, loses out in this deal. The extinguishing of potential property rights by outlawing contracts that do not include portability will not be a costless or seamless move. Open banking regulations may make banking as it exists today unviable. In fact, they are widely expected to change the nature of the banking business, turning banking into either a platform or service for other businesses rather than a standalone activity. (Finastra, 2018)

Likewise, free data portability in the overall economy might mean that Google or Facebook are no longer able to provide their services for free. However, the 'free' nature of these services hides costs that already exist. The free service, such as email, is bundled with an ad, and given network effects, this bundling leads to monopolisation. The costs of this bundling are all the costs of digital monopolies listed earlier. Portability does not directly cause unbundling, but it reduces the advantage of the market leader in bundling, and thus makes providing free services in return for ads (and other uses of data) less viable.

The status quo is that platforms are free and make money from the control over data. This status quo includes monopolisation. Free data portability means choosing a model where platforms charge for use and do not make money from control over data. It also means that innovations that require big data can be made by smaller companies, public bodies, or your friendly neighbourhood programmer. Of course, the absence of monopolies is not welfare-enhancing in every context, and likely social costs and benefits of such an intervention need to be examined in much more detail.

The question of what kind of data should fall under free portability is not straightforward to answer. It is clear that some data would need to be portable - for example, ride history in Uber. But many other kinds of data are collected, such as how quickly you book a cab at certain times of day, when you book a shared ride versus an individual cab, how your phone battery level determines your willingness to pay a certain price, etc. Which of these data points is it reasonable to port?

In the UK open banking example, the Competition and Markets Authority (CMA) determined through a careful study the kinds of data that were driving monopoly and were thus important to port. (Competition and Markets Authority, 2016) Then, an Open Bankng Implementation Entity (OBIE) was formed, governed by the CMA and funded by the banks mandated to share data. The OBIE determines API specifications and standards. A model on these lines, where the government and companies arrive at data to be ported through studies, might work. This data would differ from industry to industry.

Data portability in the Justice Srikrishna Report

The Draft Personal Data Protection Bill, released along with the Report, also has provisions on data portability. Data fiduciaries are obliged to provide to users, on request, data generated or provided by the user.

However, the fiduciary can refuse to share data based on some grounds. These include that sharing would reveal a trade secret, would not be technically feasible, processing of data is necessary for the functions of the State, or processing is in compliance of law. It can also charge a fee for providing the data in some cases. The user has legal recourse in the event that an exemption is used unreasonably. But that would likely be a long, costly process to be undertaken by each principal. Unlike the open banking regulations, in the Indian draft bill, data needs to be shared only with the user, and not with any entity with the consent of the user, creating an additional step for the user. This, along with the broad exemptions to sharing, severely restricts the mobility of data and hence consumer choice, and perpetuates monopolisation, as demonstrated above.

The draft Bill gives us extensive rights to say "Do not share my data". It does not give us nearly as many rights to say "Do share my data". More sharing rights would mean that Amazon would be mandated to automatically share my purchase history, with my consent, to anyone who asks, for free. It would also mean that Amazon would not have as many possible reasons to refuse to share this purchase history. There are both costs and benefits to this, but it is not a choice to be made without examining both.

In a way, we have already seen the effects of limiting data sharing (although not strictly free portability with consent) in favour of protection. After the Cambridge Analytica scandal, Facebook shut down third party access to users' friends' data in response to heavy criticism. This has affected researchers who relied on Facebook data, particularly on interactions with friends, for their research. (AEDT, 2018) The lack of an easy portability option has effectively made Facebook the sole owner of that data and will inhibit research.

The Draft Bill also puts in place collection limitations (only data that is necessary for the purposes of processing can be collected) and purpose limitations (personal data can only be processed for clear, specific and lawful purposes that are reasonably expected). Whether the collection and purpose limitations change the monopolistic nature of this market by themselves depends on (a) whether multiple uses of the same data have been driving monopolisation, or whether the sheer volume of data matters more, and (b) whether most people will withdraw consent to multiple uses of their data. It is possible that these questions have different answers in every case, and these effects need to be studied once these limitations are in place.

Consent in a concentrated market

Further, the onerous requirements of consent - different forms, layered notices, etc. - outlined in the Report are likely to lead to more monopolisation in the market. This is due to the high compliance costs which are easier for a large company to bear, but also because of restrictions on sharing data with third parties, there is now an incentive to own the entire value chain. A social network will find it easier to make its own payment platform rather than transfer data to smaller payment platforms. When a business starts doing everything, it reduces choice for consumers as well as workers. The consent framework incentivises entities that already control data to use it for many other purposes - consent, and not competitive forces, being the only barrier.

A million different consent calibrations - I consent to see ads, but not to predictive text in my emails - do not change the business model of digital two-sided markets, which rely on freely generated data for value creation through targeting and prediction. There will exist creative ways of acquiring consent, and there will be a small subset of people who refuse consent - still changing very little about market structure. Besides, consent is not very meaningful in platform economies as they exist today. We consent to WhatsApp terms and conditions because all our friends are on WhatsApp - and opting out means missing out. In such conditions, consent can hardly mean that the larger effects of data use were chosen by the user.

Competition, interoperability and portability

Standards for interoperability function on the same lines of thinking. Telecom operators are mandated to provide interoperability across networks in order to ensure competition. For example, Airtel cannot refuse to connect calls to Vodafone subscribers. Data portability is somewhat like a way of providing interoperabilty so that markets are not captured. Data portability as such falls under the ambit of the Competition Act and is therefore a question separate from data protection. However, the implementation of data protection standards without consideration of competition issues might concentrate market power, and thus both these issues need to be considered together.

Conclusion

There is a growing body of literature on how consumers perform unpaid labour every time they use an AI product or service, as their use, through data generation, provides feedback to algorithms that make the product better. (Crawford and Joler, 2018; Hesmondhalgh, 2010; Arvidsson, 2008) In this context, continued user control of what is done with data merits consideration; control that might not be achieved through consent requirements. User control of data also means user control over the systemic effects of data use. Consent is important and necessary, but stringent consent provisions together with weak portability requirements in a monopolised market only serve to entrench existing monopolies. With this, all the good that can come out of data used in the public interest is also restricted.

Portability will not fix all the ill-effects of market concentration in the digital world, especially those of network effects due to the existence of platforms. But it will reduce one aspect driving market concentration, that is, data control.

While privacy is a valuable goal and stringent consent requirements do help achieve this goal, we must be careful not to conflate all issues related to technology in our times with the single issue of privacy. Privacy and security need to be balanced with opportunities for society to use its own data for its own benefit. The issue of portability needs to be examined in this context.

References

Adam Arvidsson, The Ethical Economy of Customer Coproduction, Journal of Macromarketing, 2008.

AEDT, Cambridge Analytica scandal: legitimate researchers using Facebook data could be collateral damage, The Conversation, 2018.

Arghya Sengupta, Why the Srikrishna Committee Rejected Ownership of Data in Favour of Fiduciary Duty, The Wire, 2018.

Cedric Argenton and Jens Prufer, Search Engine Competition With Network Externalities, Journal of Competition Law and Economics, 2012.

Committee of Experts under the Chairmanship of Justice B.N. Srikrishna, Personal Data Protection Bill, 2018.

Competition and Markets Authority, Retail Banking Market Investigation, 2016.

David Hesmondhalgh, User-generated content, free labour and the cultural industries, Ephemera, 2010.

Finastra, Bank as a Platform - The Essential Tools for Open Banking and PSD2, 2018.

Jonathan Taplin, Move Fast and Break Things: How Facebook, Google and Amazon Cornered Culture and Undermined Democracy, Little, Brown and Company, 2017.

Kate Crawford and Vladan Joler, Anatomy of an AI System, 2018.

Louise Matsakis, Facebook's targeted ads are more complex than it lets on, Wired, 2018.

Matthew Hindman, The Myth of Digital Democracy, Princeton University Press, 2009.

Smriti Parsheera, Ajay Shah and Avirup Bose, Competition Issues in India's Online Economy, NIPFP Working Paper, 2017.

The Quint, Experts React to Data Protection Bill: Key Concerns and Takeaways, 2018.

Travis Holland, How Facebook and Google Changed the Advertising Game, The Conversation, 2017.

 

The author is a researcher at the National Institute of Public Finance and Policy. The author thanks Anirudh Burman and Shivangi Tyagi for useful discussions. The two anonymous reviewers provided very helpful directions for thinking and insights that have been incorporated into this article.

The LTCG tax will increase the cost of investment in India, but not by much

by Gaurav S. Ghosh.

Section 33 is one of the more talked-about provisions of the Finance Act, 2018. This is the section that reintroduces a tax on long-term capital gains (“LTCG”), where the LTCG arises from the transfer of “an equity share in a company or a unit of an equity-oriented fund or a unit of a business unit” (Ministry of Law and Justice, 2018). Under the new law, a tax of ten percent will be liable on all LTCG exceeding INR one lakh, where the LTCG arises from the sale of assets described above held for over a year (Income Tax Department, 2018).

The LTCG tax has come in for scrutiny and criticism in the financial press. Some commentators have predicted negative consequences for small investors (Arora, 2018; Sampath & Thomas, 2018). Others have noted that the tax will lead to double taxation because some securities transactions will bear both the LTCG tax and the already existing securities transaction tax (Sampath & Thomas, 2018; Business Today, 2018). Yet others have pointed out that there is a lack of clarity about the application of the LTCG tax to specific transaction types, including share inheritances, mergers, and initial public offerings (Upadhyay, 2018). The central government has defended the tax by pointing out that it reduces distortions in investment incentives by reducing discrepancies in tax rates across asset classes (The Hindu, 2018; The Telegraph, 2018).

The opinions have been manifold and heterogenous but have been, in the end, no matter how well reasoned, only opinions. There has been a shortage of quantitative economic analysis of the LTCG and its effect on the Indian economy. In this article, we address this gap by presenting our estimates of the impact of the LTCG on the cost of investment in the main sectors of the Indian economy and at the all-India level.

Intuitively, one would expect the LTCG tax to increase the cost of investment since it increases the hurdle rate – the minimum return that an investment project must earn for financial viability – of a project. Consider a simple example where an investment project is financed only by equity and dividends are not distributed. Suppose the investors expect a 7.0 percent post-tax return from the project. Before the LTCG tax, the project would be feasible if it had a pre-tax return of 7.0 percent. Under the LTCG tax, the project feasibility would require a pre-tax return of at least 7.7 percent: 7.0 percent for the investor as capital gains and 0.7 percent for the government as the LTCG tax. The LTCG tax has thus increased the project’s hurdle rate from 7.0 percent to 7.7 percent.

In the example above, the cost of investment increased by the full extent of the LTCG tax. This is not true for a real-world investment whose tax cost is a function of all the taxes associated with the investment, macroeconomic conditions, and the structure of the investment itself. Apart from the LTCG tax, other taxes affecting the investment cost are the corporate income tax and indirect taxes. There are also tax incentives, which may be asset- and sector-specific, that reduce the tax cost of investment. The investment will have a particular distribution of assets – some investments require more transportation assets, others more machinery assets – and this structure too affects the tax cost of investment. This is because each asset has a different tax treatment: if highly taxed assets are a major cost of investment, then the tax cost will be high and vice versa. And macroeconomic conditions like inflation affect the value of benefits like depreciation allowances, and therefore affect the tax cost of investment.

Given the complex relationships that define the tax cost of investment, it is not feasible to predict with certainty what the relationship between the LTCG tax and the cost of investment might be. But two hypotheses might be formulated on the basis of the discussion above. First, the LTCG tax will increase the tax cost of investment; after all, it does increase the cost of project finance. Second, the impact of the LTCG tax will be minor; considering the wide array of factors affecting the tax cost.

Measuring the tax cost of investment requires an adequate modelling framework. The framework should at least have the three following characteristics. First, it should be flexible enough to model the impacts of all taxes and tax incentives on the cost of investment. Second, the model should be well targeted, i.e. it should measure the tax impact on investment, but not non-investment activities. Finally, the model should allow aggregation, such that firm-level data can be used to estimate tax costs at the sectoral or national levels.

One model that meets these criteria is used to measure Marginal Effective Tax Rates (“METRs”). This model uses firm-level and macroeconomic data to estimate the tax wedge on investment, the difference between the pre-tax and post-tax rates of return earned by a marginal investment project. By construction, the METR model only focuses on marginal investment to the exclusion of other aspects of a firm’s business. The METR itself is a function of all taxes and tax incentives levied. These include direct taxes on businesses and investors, indirect taxes on capital purchases, and incentives like accelerated depreciation and tax credits. And the model allows for aggregation from firm-level METRs right up to one national level number.

We have estimated the tax cost of investment using the METR model, which we have developed for India and discussed earlier on this website (see Ghosh & Mintz (2017)). Specifically, we have estimated the tax cost of investment before and after the implementation of the LTCG tax. A comparison of the before and after values provides an estimate of the impact of the LTCG tax on the tax cost of investment.

Figure 1: METRs before and after implementation of the LTCG tax

Source: Own calculations

METRs in the main sectors of the Indian economy and at the overall all-India level are shown in Figure 1. These sectors together comprised 96.5 percent of all investment in the Indian economy in FY2015-16 (Prowess, 2018). Each sector in Figure 1 has three data points. The left-hand column is the METR before the LTCG tax was reinstated, i.e. LTCG tax rate = 0.0 percent. The right-hand column is the METR after the LTCG tax was reinstated, i.e. LTCG tax rate = 10.0 percent. The line shows the change in the METR after the imposition of the LTCG tax.

The results indicate that the LTCG tax has increased the tax cost of investment – as measured by METRs – in all sectors of the Indian economy. The only exception is the agriculture sector, which is the beneficiary of multitudinous direct and indirect tax exceptions. These combine to ensure that the LTCG tax has no effect on the agriculture METR.

Overall, at the all-India level, the METR has increased from a pre-LTCG-tax value of 20.3 percent to a post-LTCG-tax value 21.1 percent: implementing an LTCG tax of 10.0 percent has yielded a 3.9 percent increase in the METR. This is equivalent to an elasticity of 0.03 in the neighbourhood of the LTCG tax rate. One may infer that the METR is barely sensitive to the LTCG tax rate.

The minimal response of the METR to the LTCG tax is confirmed by the result in Figure 2, where a METRs are plotted for a sequence of the LTCG tax rates. Increasing the LTCG rate from zero to 20.0 percent only increases the METR from 20.3 percent to 21.9 percent, an increase of 8.1 percent. The relationship between the LTCG tax and the METR is roughly linear (the relationship is slightly convex, but this is not very evident in Figure 2).

Figure 2: Relationship between the METR and the LTCG tax

Source: Own calculations

Coming back to Figure 1, we see heterogeneity in the METR change at the sectoral level where – not counting agriculture – the change in METRs range from 1.8 percent in the “other” sector to 7.8 percent in manufacturing. The largest [smallest] METR changes are in the sectors with the lowest [highest] METRs. In other words, the LTCG tax has the greatest [smallest] impact on METRs in sectors where investment is least [most] burdened by taxes. The sectors most affected by the LTCG are manufacturing and transportation, which have the lowest METRs. Manufacturing has a low METR because of generous tax depreciation allowances on machinery. The low METRs for the transportation sector depend on firms charging GST under the forward charge mechanism. If the reverse charge mechanism were used, then the METR is much higher because of significant blocked input tax credits. The least affected sectors – “other” and finance” – are also those with the highest METRs. These sectors suffer high METRs because of adverse asset compositions. Overall, it seems that the LTCG tax reduces tax load discrepancies across sectors by a small amount, and thereby contributes marginally to a levelling of the playing field for investment.

In summary, the following can be said about the impact of the LTCG tax on investment incentives in the country. First, the overall relationship between the LTCG tax and the tax cost of investment (as measured by the METR) is positive, but weak. Since the LTCG tax does not change the METR in any significant way, one may infer that it will not affect investment decisions to any significant degree. Second, there is some heterogeneity at the sector level, with sectors with hitherto low METRs worse affected by the LTCG tax change than sectors with high METRs. The LTCG tax therefore makes a (very) small contribution in levelling the playing field for investment. This second result provides weak support for the government’s contention that the LTCG tax will reduce investment distortions (The Telegraph, 2018), although the mechanism differs from that suggested in the referenced article.

Bibliography

Arora, I. (2018, Mar 14). Government receives requests to drop planned long-term capital gains tax.

Business Today. (2018, Feb 05). How LTCG tax affects mutual fund investors.

Ghosh, G., & Mintz, J. (2017, Nov 23). Measuring the pre and post GST tax cost of investment.

Income Tax Department. (2018). Tax on Long-Term Capital Gains, Income Tax Department, Ministry of Finance, New Delhi.

Ministry of Law and Justice. (2018). The Finance Act, 2018 (No. 13 of 2018), Ministry of Law and Justice (Legislative Department), New Delhi.

Prowess. (2018). [cross tabulation of data].

Sampath, A., & Thomas, S. (2018, Feb 09). Long-term capital gains tax on equity: Will it scare away small investors?.

The Hindu. (2018, Feb 06). For more equity: on long-term capital gains tax.

The Telegraph. (2018, Feb 06). LTCG exemption for equities was a risk for small investors, govt says.

Upadhyay, P. (2018, Feb 19). Union budget 2018: Long-term capital gains tax - the unanswered questions.

 

Gaurav S. Ghosh is an economist and Senior Manager at Ernst & Young LLP

CCI's order against Google: infant steps or a coming-of-age moment?

by Smriti Parsheera.

The Competition Commission of India (CCI) recently concluded its six year long investigation into allegations of abuse of dominance by Google in India. It found that Google had utilised its dominance in general web search services to limit user choice (specifically in the context of Google flight search) and impose restrictions on its search syndication partners. It also noted a third violation relating to Google's historic practice of setting fixed positions for a particular category of search results (referred to as "universal results") that were sourced from its other verticals like images, videos and maps. A few months ago, the European Commission had also levied a penalty of Euro 2.42 billion on Google for offering preferential treatment to its comparison shopping service and demoting rival services in its search results.

The consequences for Google in India? CCI has directed Google to (i) desist from assigning fixed positions to universal results; (ii) add a disclaimer while presenting its commercial flight results; (iii) not enforce unreasonable restrictions on syndication partners; and (iv) pay a penalty of Rs. 1.36 billion (USD 21.1 million) for its anti-competitive conduct. To put this in perspective, the penalty translates to less than 0.02% of USD 110.9 billion, Google's worldwide revenues in 2017.

Are these the uncertain first steps of an infant CCI venturing into technology-ville or the coming-of-age moment of a regulator that has learnt to balance innovation and competition in the digital era? This post attempts to answer this question by tracing CCI's analysis on issues of universal results, Google flights and restrictive agreements, highlighting some concerns and summarising the takeaways from this decision.

Allegations against Google

CCI's order arises from two separate cases filed by online matchmaking portal Matrimony.com and Consumer Unity & Trust Society against Google Inc. and Google India Private Limited. In the course of the investigation, Google Ireland Limited, a key contracting entity for Google's advertising agreements, was also added as a party.

The informants alleged an abuse of dominance by Google in the general web search and search advertising markets in India, with the following specific claims.

• Search bias: Google was using its dominance in the search engine business to promote its own results like videos (YouTube), news (Google News) and maps (Google Maps) in its search results.
• Unfair advertising terms: Google has the largest number of search users which makes it an unavoidable partner for all advertisers who want to target their ads at those users. The informants alleged that Google was using this position to impose unfair and discriminatory conditions on its AdWords customers (advertisers who bid on keywords for ads to be displayed in Google's search results).
• Denial of access: Google was using its dominance in the search and search advertising markets to impose unfair conditions that restricted its partners from contracting with other competing search engines. As a result, it was denying access to the market to competing businesses.

As per the scheme of the Competition Act, 2002 (Act), the complaints were first examined by the CCI to assess their prima facie merit. Finding a prima facie case of abuse of dominance by Google, CCI referred the matter to its investigative arm, the office of Director General (DG), for detailed investigation. The DG's office took about three years to complete its assessment and submitted an investigation report to CCI in March, 2015. It found Google to be guilty on all the counts raised by the informants in addition to a few others that were discovered by it in the investigation process.

CCI's reasoning and verdict

An abuse of dominance case under Section 4 of the Act requires CCI to establish that the entity in question held a dominant position in a defined relevant market and had abused that dominance through activities like imposing unfair or discriminatory conditions on others, limiting the supply of goods or services in the market, or using its dominance in one market to protect its position in another.

Relevant market analysis

CCI agreed with the findings of the DG that Google was operating in the following relevant markets: (a) market for online general web search services in India and (b) market for online search advertising services in India.

Google contested both these market definitions, arguing instead for a broader relevant market -- the wider the market the lesser the chances of Google being found to be dominant in it. Regarding general web search, Google claimed that it competes with all possible sources of information that can answer a user's specific query (about people, places, recipes, etc.) and there is no separate market for "general web search". This however ignores the fact that there are billions of webpages on the Internet, a majority of which are not known to users. Users therefore commonly rely on search engines to identify new sources of information and even access relatively well known ones. For instance, Alexa's web traffic analytics shows that 66.6 percent of the traffic to Wikipedia, the fifth most popular website in the world, comes through search engines.

Curiously, Google is also reported to have argued that "[b]ecause search is free, Google has no trading relationship with the users of its search service, and so the basis for establishing dominance is absent". CCI rejected this argument, noting that "it is not only flawed but altogether ignores the role of big data in the digital economy". In the multi-sided platform operated by Google, users offer their "eyeballs" and data in exchange for Google's "free" services, which are in turn monetised by Google through advertising revenues. To claim that only services that are directly paid for by users can constitute a relevant market would render large parts of the digital ecosystem outside the purview of competition laws, an outcome that is neither legally tenable nor socially desirable.

Assessing Google's dominance

The determination of dominant position depends on a number of factors, market share being one of them. CCI's order notes that Google has maintained a high market share in both the relevant markets but does not disclose the exact figures (these are blacked out as confidential information). Publicly available information from statcounter, however, clearly shows that in the period since 2010, Google has consistently held over 96 percent of the market share among search engines in India.

CCI also looks at other factors beyond market share. On the search side, it refers to Google's head start in crawling and indexing the web and resulting scale advantages. As explained by Matt Turck, Google benefits from significant "data network effects" -- "the more people search, the more data they provide, enabling Google to constantly refine and improve its core performance, as well as personalize the user experience". As we discuss in this paper on Competition Issues in India's Online Economy, multi-sided platforms like Google are also characterised by strong indirect network effects. CCI uses a similar logic to observe that Google's stronghold in general web search supplements its dominant position in the market for online search advertising, resulting in situation were advertisers are left with little countervailing powers over Google. In summary, CCI notes that this leads to a situation where "[t]he structure of the market is both indicative of and conducive to Google's dominance".

Abuse of dominance

Next, CCI turned to examine the specific allegations relating to the abuse of its dominant position by Google. This is also the point where the Commission significantly digresses from the findings made by its investigation unit. Unlike the DG's report, which found Google to be in violation of the Act on almost all the grounds examined by it, CCI limits its findings to the following three grounds.

1. Fixed positions for universal results:
   

   Findings: Google's search results pages often contain certain "universal results" that are sourced from its other search verticals (See Figure 1 for an example). As per Google, these universal results compete with other generic blue links for the most favourable position on the results page based on their relevance. However, it also admitted that when the service was initially introduced, the display of universal results was limited to certain fixed (1st, 4th or 10th) positions as its systems were not advanced enough to determine the relevant position for such results. CCI dismisses this argument to hold that Google's historic practice of adopting a fixed position for such results was unfair and misleading to its customers who were led to believe that the responses were being ranked solely on the basis of their relevance.

   Consequences: Since Google has already discontinued this practice, CCI limits itself to issuing a desist order directing Google not to resort to such position fixing in the future.



Figure 1: Universal image results in response to search term "Kullu"






2. Commercial unit for flight results
   

   Findings: Google also places various "commercial units" in its search results. This refers to a demarcated ad space for displaying sponsored results relating to shopping, hotels and flights. CCI focused in particular on Google's flight unit (See Figure 2 for an example). It noted that Google provides a prominent placement to its flights unit in general search results with a link that takes the user to Google's own specialised flight search service. It found that this practice results in either pushing down or pushing out other competing vertical search services with the result of misleading users and denying them the opportunity to access the other websites.

   Consequences: CCI directed Google to display a disclaimer in the commercial flight unit box indicating clearly that clicking on the relevant link would lead to Google's flights page and not the results of any other third party service provider.
   



Figure 2: Google Flight Unit in response to search term "Delhi to Kullu flights"






3. Restrictions in syndication agreements
   

   Findings: In addition to the search and advertising services offered on Google's own website, it also enters into syndication agreements with other websites to offer its search and advertising services to them. These agreements can either the take the form of standard online contacts or directly negotiated agreements. CCI found that Google imposes certain unreasonable restrictions on its negotiated search intermediation partners -- websites that enter into negotiated agreements to use Google's services on their websites are restricted from implementing any search technologies that are "same or substantially similar" to those of Google. CCI found that this restricts Google's partners from using the services of competing search engines. It thus "creates conditions for extending and preserving Google's dominance in search intermediation".

   Consequences: CCI directed Google not to enforce the restrictive clauses in its negotiated direct search intermediation agreements with Indian partners.

As noted above, the DG's investigation had found Google to be guilty on many other counts. This included questions about Google's conduct in relation to its AdWords customers; its practice of allowing bidding on trademarks owned by competitors; and its arrangements with distributors like Apple and Mozilla to make Google the default search engine in their products. CCI, however, disagreed with the DG's findings on all these other counts.

Some questions and concerns

CCI's decision raises many important issues regarding Google's conduct in the search and search advertising markets. The design of ranking algorithms to provide preferential positions to certain types of results (without sufficient disclosures) and the imposition of unfair restrictions in commercial contracts are certainly critical issues that can have far reaching implications for online competition in India. Yet, despite agreeing with the principles behind these ends, it is hard to ignore some issues with the means adopted by CCI to reach them. This section focuses on the lack of sufficient evidence-based analysis, selective focus on flight search and CCI's own uncertainty about calculation of the penalty, all of which are factors that could expose the order to subsequent scrutiny.

Absence of robust data and evidence

The first issue, which has also been emphasised at length by the two dissenting members of CCI, relates to the absence of robust data and evidence to support the findings against Google. While the discussions in the order are sufficient to develop a strong intuition about Google's anti-competitive conduct, this intuition should ideally have been followed through with supporting data to build a water tight case. Specifically in the context of flight search, the dissenting members point to the absence of actual data about the traffic flows to the Google flight unit or to competing websites, the positions on what these websites actually appear on Google's results page and the impact that it has on consumer behaviour.

We can contrast this with the European Commission's approach in its similar case against Google. In a press release issued in June, 2017, the Commission announced that its order against Google was supported by evidence from various sources, including "(i) significant quantities of real-world data including 5.2 Terabytes of actual search results from Google (around 1.7 billion search queries); and (ii) experiments and surveys, analysing in particular the impact of visibility in search results on consumer behaviour and click-through rates". Based on this evidence, it was able to gauge the precise effects of Google's prominent placement of its comparison shopping service.

• The traffic to Google's comparison shopping service increased 45-fold in the United Kingdom, 35-fold in Germany, 19-fold in France, 29-fold in the Netherlands, 17-fold in Spain and 14-fold in Italy.
• There was a sudden drop of traffic to certain rival websites, to the tune of 85% in the United Kingdom, up to 92% in Germany and 80% in France.
  

In the present case, it is clear that the dissenting members are not disagreeing with the merits of the case against Google but the absence of sufficient data to make those claims. That being the case, the logical course for the Commission would have been to direct further investigation by the DG on these specific grounds or pursue an inquiry on its own. Both these courses were open to the Commission under Section 26(7) of the Act but their adoption would of course have meant a further delay in an already long pending decision.

Questions about the flight search analysis

The next set of issues revolves around CCI's focus on Google flight search and its prominent display on the search results page as a ground for abuse of dominance. The order examines the impact of the prominent real estate given to Google's flight unit vis-a-vis third-party travel sites like MakeMyTrip.com or Yatra.com and its misleading impact on users. However, as noted here, Google's flight service (at least at present) only offers users the option of comparing flight prices and not of directly making the bookings through Google. Therefore, the market in question is that of flight fare comparison websites and it would accordingly have been relevant to consider the impact on competing fare aggregation sites like "skyscanner" and "farecompare" instead of only those that provide flight booking services. Such an analysis would have also enabled CCI to examine a potential violation of Section 4(2)(e) of the Act, which relates to the use of dominant position in one market to protect its position in another.

Further, the order does not clarify as to why the flight search functionality is more problematic than similar commercial units displayed by Google in response to shopping or hotel related searches in India. In case of shopping results, the Commission makes a passing remark that "Google's display of Shopping Unit may not per se affect the ranking of free search results". In case of hotels, the order states that Google does not offer this feature in India even though a search for hotels on Google's India site does display a commercial unit, which has similar features to its flight search function -- it leads to another Google page that contains advertisements from hotels and hotel booking websites.

Penalty imposed by CCI

Relying on the Supreme Court's decision in the Excel Crop Care case, CCI decided to limit its penalty to Google's "relevant turnover" from India. For this purpose CCI sought information from Google regarding its revenues from different segments of its India operations, which the Commission notes was provided in an unsatisfactory manner. For instance, it is unclear from the order whether the information supplied by Google under the head "Relevant Turnover from Direct Sales in India" was based on (i) the income earned by all Google entities from end-users based in India; (ii) the income earned by Google India Private Limited from advertisers in India; or also (iii) the income earned by Google Ireland, Singapore and others from Indian advertisers.

Unfortunately, CCI acknowledges these infirmities and still goes on to determine the final penalty amount based on the unclear information furnished by Google. Given the criticality of this point, it would have been appropriate for CCI to seek more specific information from Google and, if required, provide further time for the same. It could also have used its statutory powers to elicit this information.

Conclusion

The Google case is an important development in India's competition jurisprudence on abuse of dominance in multi-sided technology markets. CCI's order acknowledges at the outset that "intervention in technology markets has to be carefully crafted lest it stifles innovation". It also highlights CCI's intent to refrain from interfering in specific product design elements unless the conduct in question is particularly egregious and an intervention becomes necessary to correct certain distortions.

Despite its well intentioned attempts to balance the interests of innovation, competition and consumer welfare, the decision falls short on some counts. Firstly, in an industry centered around click-through-rates, analytics and ranking measurements, the order primarily relies on qualitative and descriptive accounts to establish Google's violations. Secondly, CCI chooses to intervene in certain product design elements (universal results, commercial units) but not in others (like the AdWords ranking mechanism and trademarks bidding policy). While doing so, it fails to offer any broader guidance on the basis for demarcating general product design elements (that could also negatively impact competition) from particularly egregious conduct that merits competition intervention.

Yet, irrespective of the fate of this particular decision, actions such as these serve an important function in taming the conduct of big tech -- the threat of external regulation creates an impetus for better "self" regulation. In the past, Google amended its AdWords terms to make it easier for advertisers to simultaneously manage advertising campaigns on competing ad platforms. This was done through voluntary commitments offered by Google in relation to an inquiry by the Federal Trade Commission. Similarly, a press release by the European Commission notes that in the context of its anti-trust proceedings, Google had modified its direct AdSense contracts (with websites who use Google's services to display ads on their pages) to give its partners more freedom to display competing search ads. Recent moves by Facebook and others to control fake news on their platforms are also grounded in similar concerns.

Finally, the case lays important ground work for subsequent cases against Google and other dominant players in India's online ecosystem. As CCI's orders get challenged before the appellate tribunal and eventually the Supreme Court, we will see new jurisprudence around issues of competition in the digital economy. This will hopefully create a feedback loop for increased rigour and evidence-based analysis in future cases in this sector.

 

Smriti Parsheera is a technology policy researcher at the National Institute of Public Finance & Policy. She has previously worked as a researcher with the Competition Commission of India, including on the Google case. The views are personal.

Measuring the pre-and post-GST tax cost of investment

by Gaurav S. Ghosh and Jack Mintz.

The most wide-ranging change to the Indian tax system in decades is the introduction of the goods and service tax (“GST”). But is this tax beneficial or harmful for investors, especially for new investment? Is the GST impact uniform across sectors, or does it favour in some sectors while harming investment in others?

India’s GST reform has integrated state level sales taxes and central excise and service taxes into the GST, which is a centralized value added tax (“VAT”) and enables businesses to claim more refunds of taxes paid on purchases from other businesses. It removes a significant amount of taxes on business inputs that are cascaded into business costs and passed on to consumers or businesses purchasing goods and services from other businesses. Some non-refundability of input taxes remain for exempt sectors such as agriculture, petroleum and alcohol. Generally, though, the Indian VAT reform will result in a signficant reduction in VAT on business input costs as we show below.

We report results from our recent study where we evaluate the cost burden placed by India’s tax code upon potential investors, both before and after the introduction of the GST. We do this by developing an economic model that is calibrated to represent the Indian tax system, and then using this model to simulate GST impacts. The model and its implementation are described below. This is followed by discussion of our results.

The METR model

Our tool for evaluating the impact of the Indian tax system on investment incentives is the Marginal Effective Tax Rate (“METR”), which measures the tax wedge imposed upon investment. The METR is an analytical framework developed in the 1980s to evaluate tax systems in their aggregate and to facilitate cross-country and cross-sectoral comparisons. Seminal METR studies from this era are Auerbach (1983), Boadway et al. (1984) and King & Fullerton (1984). The METR has since been used by academics and governments to evaluate tax competitiveness, gauge the economic impacts of changes to the tax code, and design investment-friendly tax policies. The METR analytical framework is useful because it provides a strong empirical basis to tax policy debates. It has proved itself over the years by being used by policy makers worldwide. Ours is the first in-depth implementation of the METR framework to the Indian economy.

The tax wedge, $\omega$, estimated under the METR framework, is the difference between the pre-tax rate of return earned by a marginal project, $r_g$, and the post-tax rate of return that accrues to the marginal project’s investors, $r_n$. The METR itself is the tax wedge divided by the pre-tax rate of return.

\[ METR = \frac{\omega}{r_g} = \frac{r_g - r_n}{r_g}\]

The sizes of the tax wedge (and the related METR) is affected by direct taxes, sales taxes on capital purchases and other capital-related taxes like stamp duties. The METR also accounts for tax incentives including accelerated depreciation, initial allowances, and tax credits. High METRs imply high tax loads and low returns to investors and vice versa. High METRs therefore indicate that the associated tax systems are less competitive when it comes to attracting capital investment.

Estimating the METR for a marginal project requires the estimation of $r_g$ and $r_n$ for that project. In the METR framework, both variables are functions of a wide array of tax rates and incentives; the functional forms are derived through recourse to standard microeconomic principles and assumptions. We do not provide technical details here. Interested readers can refer to Ghosh & Mintz (2017). We only note that the post-tax rate of return $r_n$ is equal to the inflation-adjusted market interest rate for issuing bonds and equity finance, which is the same across all businesses net of risk. The pre-tax rate of return, $r_g$, is estimated by estimating the user cost of capital model (Jorgenson, 1963) net of depreciation and risk. The Indian tax system therefore affects the estimation of $r_g$ and $r_n$ – and by extension, the estimation of the METR – because of its impact on the real market rate of return and the user cost of capital within the structure of our model.

Aggregation principles

METR estimation requires consideration of the marginal investment that earns a pre-tax rate of return sufficient to cover taxes and the market rate of return. The marginal investment depends on the characteristics of its industry and the choice of production technology. Every sector, in effect, has a multitude of marginal investments varying across characteristics like industry, asset class and financing structure. Each marginal investment has a different METR because it faces a different tax treatment once all relevant taxes, exemptions and their interactions are considered. Debt-financed transportation in the power sector will, for example, have a different METR than equity-financed machinery in the agriculture sector. There are multiple reasons for this: the differential treatment of debt and equity in the Indian tax code where interest payments are deductible, but dividends are not; the different incentives available to investors in the power and agriculture sectors; and the differential tax treatments of asset classes.

Accurate estimation of the METR, whether at the sectoral or all-India level, requires consideration of this heterogeneity across different types of marginal investment. Consistent with the literature (King & Fullerton, 1984; Mintz, et al., 2016), we estimated METRs by using a bottom-up approach.

First, we identified four key tax and economic characteristics of a marginal investment in India. These were sectors, asset types, investment sizes, and whether the marginal firm was paying the corporate income tax (“CIT”) or the minimal alternate tax (“MAT”). Each marginal investment would have some level of each of these characteristics. Second, we identified the number of levels for each characteristic. There were nine sectors, six asset types, two firm sizes and two types of tax payers, as shown in Table 1, leading to 216 unique marginal investments. The identified sectors (except “Others”) were the main investment destinations in India, accounting for 93 percent of investment in 2015 among firms in the Prowess database. The asset types were those identified for differential treatment under the Indian tax code. Firm size was selected on the basis of the investment threshold for initial allowances: these are only allowed for investments exceeding INR 250 million.

Table 1: Characteristics and levels of marginal investments in India
Industry / Sector	Asset type	Firm Size	Tax payer type
Agriculture, forestry & fishing	Buildings	Small firms, with investment < INR 250 million in 2015	CIT payer
Construction	Furniture & fittings
Electricity, steam, gas & AC supply
Finance & insurance	Inventory
Information & communication (“Infocom”)	Land	Large firms, with investment > INR 250 million in 2015	MAT payer
Manufacturing	Machinery
Wholesale & retail trade, repair of motor vehicles & motorcycles	Transport
Transportation & storage
Other

Third, METRs were estimated for each of the 216 marginal investment types. This required collection of tax and economic data for each type, and then the incorporation of this data into the formal METR model. Finally, METRs were calculated at different levels of aggregation as weighted averages of subsets of the 216 types. For example, the all-India METR was a weighted average of all 216 METRs, while a sectoral METR was a weighted average of the 24 METRs relevant to that sector. The weights used were the capital shares associated with each marginal investment type. The capital share was the proportion of all new investment capital in a given year that was allocated to a given marginal investment type. The capital share data and certain other data were obtained from the Prowess database. Other data sources were the Indian Income Tax Act, tax guides published by EY and other accounting firms, official Indian government communications, the Thomson Reuters EIKON financial database, and publications by the Central Statistical Office.

Results

Some results from our analysis are presented below. We begin with a comparison of METRs before and after the implementation of the GST, which is shown in Figure 1. We compare METRs both at the all-India level and at the sectoral level. The blue (red) bars represent pre-GST METRs (post-GST METRs).

We see that the GST leads to a fairly large drop in METRs at the all-India level, from 28% to 22%. This is because the GST removes pre-GST blockage of many input tax credits. Many of the blockages arose because different indirect taxes – such as state VAT and central excise and service taxes – could not be set off against each other. Consider the plight of a services firm, which paid state VAT on some inputs, but collected central service tax from its customers. Since state taxes were not creditable against central service taxes they were blocked, leading to tax cascading. Unable to claim credit for state taxes paid, the firm’s input costs would be higher by the amount of the blocked taxes. This higher tax burden would lead to a higher METR. Post-GST, the distinction between central and state taxes vanished and therefore many blockages and cascades also vanished.

Other blockages arose because of exemptions granted under the pre-GST system. It is a common misconception that tax exemptions are business-friendly. Nothing could be further from the truth. Since the exemption recipient does not pay the output tax, it cannot set off its input taxes. All input taxes in exempt sectors are therefore blocked and lead to a higher METR on investment. The GST has retained some previous exemptions, such as in the agriculture sector, but has removed others. This has contributed to the overall drop in the METR.

We also see that METRs are heterogeneous across sectors. Pre-GST, METRs were lower in production-related industries than in service industries. Production sectors may have had lower METRs, but for different reasons. Manufacturing benefited from relatively low central and state indirect tax rates, as well as few input tax credit blockages. This is because most investment in the manufacturing sector is into machinery and equipment (“M&E”), which had relatively favourable tax treatment. Agriculture and electricity, on the other hand, were exempt sectors and therefore had blocked credits. However, these sectors received other benefits from the tax code that, together, brought down their METRs. Agriculture was exempt from the corporate income tax, while electricity benefited from large tax depreciation allowances and (like manufacturing) low indirect tax rates on M&E. The service industries had higher METRs because they faced higher taxes on their inputs as well as greater blockages on input tax credits. The asset mix for the service sectors consisted of comparatively less M&E (with the exception of finance) and more of other assets like land, buildings, transport and inventory. The pre-GST tax treatment of these other assets was relatively less favourable than for M&E.

When comparing pre- and post-GST METRs, two results stand out. First, METRs have reduced for all sectors with the sole exception of electricity (to be explained below). Second, the size of the METR reduction varies across sectors with the reduction being higher in the service sectors than in the production sectors. As a result, although variations in METR persist in the post-GST era, the size of these variations has reduced, with the specific result that the tax competitiveness gap between the production and service sectors has also reduced.

These results can be unpacked. Perhaps the most notable result in the figure above is that the GST raises the METR in the electricity sector, while reducing it in others. Pre-GST, the electricity sector faced two distortions: sector-specific indirect tax incentives and blocked input tax credits. The former was beneficial and the latter harmful from an investment (and METR) perspective. The two distortions cancelled each other out, leading to a relatively low METR of 29%. Post-GST, the beneficial sector-specific incentives were removed while the harmful blockages remained. As a consequence, the METR increased sharply in this sector to 38%. The services sectors benefited more from the GST because they were the ones for whom blocked credits were a greater problem in the pre-GST era. This led to a reduction in the tax competitiveness gap between the services and production sectors.

Summary

The merits and demerits of the GST have been debated vigorously in the press and among the policy community in recent months. We contribute to this debate by presenting the results of the first (to our knowledge) empirical investigation of the impact of the GST on the incentive to invest in India. We find that the GST does improve investment incentives at the all-India level by reducing the marginal effective tax rate or METR from 28% to 22%. This is achieved through a reduction in the blockages of input tax credits across value chains and a reduction in indirect tax exemptions. The all-India METR numbers mask heterogeneity at the sectoral level. Pre-GST, the Indian tax code incentivized investment in production sectors like manufacturing and electricity through lower METRs, while the tax cost of investment was relatively higher in service sectors like transport, information & communications and trade. Post-GST, this pattern of incentives has changed. While METRs for manufacturing remain low, METRs for the power sector have increased significantly. METRs for the service sectors have also come down sharply post-GST. METRs in some service sectors like finance and trade are now roughly equal to that for manufacturing.

In summary, the GST has reduced the overall tax cost of investment in India and reduced investment distortions in the tax code, somewhat levelling the playing field between the production and service sectors as destinations for investment. This is good news, but we caveat it by pointing out that the full potential of the GST as an incentive for investment has not been reached. We have unreported results that show that METRs would come down further – particularly in the electricity sector – if the remaining exemptions were removed.

References

Auerbach, A., (1983), Taxation, corporate financial policy and the cost of capital, Journal of Economic Literature, 21(3), pp. 905-940.

Boadway, R., Bruce, N. & Mintz, J., (1984), Taxation, inflation, and the effective marginal tax rate on capital in Canada, Canadian Journal of Economics, 17(1), pp. 62-79.

Ghosh, G. & Mintz, J., (2017), Investment and the Indian tax regime: Measuring tax impacts on the incentive to invest in India, Bangalore: EY.

Jorgenson, D., (1963), Capital theory and investment behavior, American Economic Review, Volume 53, pp. 247-259.

King, M. & Fullerton, D., (1984), The taxation of income from capital: A comparative study of the Unites States, the United Kingdom, Sweden and West Germany, Chicago: University of Chicago Press.

Mintz, J., Bazel, P. & Chen, D., (2016), Growing the Australian economy with a competitive company tax, Sydney: Minerals Council of Australia.

 

Gaurav S. Ghosh is Senior Manager, Ernst & Young, LLP and Jack Mintz is Palmer Chair in Public Policy and Director, School of Public Policy, University of Calgary.

Building blocks of Jio's predatory pricing analysis

by Smriti Parsheera.

In a recent post on predatory pricing and the telecom sector Ajay Shah questions whether the subsidised user base of Reliance Jio can set off a network effect. The post makes two claims. The explicit claim is that the combined effect of interconnection regulation; mobile number portability and open standards of TCP/IP ensures that there are no real network effects in the telecom sector. The underlying implicit claim is that the existence of network effects is central to a predatory pricing analysis in this context. This piece takes a closer look at both these claims and the other factors that should inform the Competition Commission of India (CCI)'s analysis in the complaint filed by Airtel against Jio's pricing practices.

Network effects in telecom

Modern day tariff plans, including that of Jio, comprise of three main components - voice, data and access to content - all bundled into one product. A competition law analysis of Jio's pricing strategy must focus on each of these segments individually, and then their collective effect.

Voice services: Telecommunication services are known to generate strong network effects - the value of having a phone number is linked to the number of people who can be called using it. This creates a classic case for concentration of market power in the hands of the incumbent. Telecom regulators have overcome this issue by mandating operators to link their networks with the networks of other operators, allowing users to communicate across networks. Research on telecom networks, however, finds that despite interoperability, users tend to display a preference for being on a larger network, particularly when operators offer lower tariffs for calls made within their networks (on-net/off-net price differentiation). Others suggest that the network effects in telecom are more 'local' in nature - the preference to be on the same network as one's family and friends leads to the formation of calling clubs. This is not necessarily dependent on the overall size of the network.

In summary, even with mandated interconnection norms, traditional telecom services display a certain level of network effects. Arguably, the relevance of being "on the same network" would have gone down with the convergence of voice and data services and availability of various over-the-top calling apps. This requires a deeper study of consumer behaviour and preferences in the post-data world.

Internet services: Network effects on the Internet are not about the provision of Internet access services (i.e. the data services offered by ISPs) but rather about the direct and indirect network effects that define the business models of many Internet-based platforms and businesses.

Telecom service providers are increasingly stepping into the role of Internet platforms by bundling access to online music, TV, movies and news along with their communication services. In Jio's case, every new SIM comes bundled with a bouquet of Jio-branded apps, making it one of the fastest growing content aggregators in the country. Its free offer period from September to March has helped Jio build a massive user base, which in turn helps in attracting other complementary users to its platform. For instance, Uber's recent decision to partner with Jio Money reflects the value that it sees in being able to access Jio's users. The same holds true for other merchants and suppliers, like providers of music, video and news content, who are attracted to platforms with a large number of users.

Integration of data services and content

The vertical integration of data services and content offers Jio many advantages. One, convenient access to free content along with free/discounted data services has helped Jio in promoting higher consumption patterns. The aggressive data usage on Jio's network, particularly of video content, will gradually translate into higher revenues. The company claims that its users "consume nearly as much mobile data as the entire United States of America...and nearly 50% more mobile data than all of China." It would be interesting to see what percentage of this data is being consumed within the Jio ecosystem and the change in consumption patterns after Jio starting charging for its data services.

Two, it promotes faster adoption of in-house services. To take an example, the AT&T/FaceTime case study in the United States found that less than 10 percent of iPhone users downloaded Skype while all of them had automatic access to Apple's FaceTime. Adoption of Jio Money versus rival payment apps (among Jio subscribers) is likely to show similar results. Reports about the launch of Jio's 4G feature phone with built-in Jio apps suggest the possibility of further entrenchment of new users in the Jio universe.

Can Jio's pricing strategy in telecom enable it to indulge in monopolistic behaviour in related markets like mobile payments? Unlike telecom services, the payments sector continues to suffer from the lack of interoperability among providers, leading to significant network effects. Safaricom's M-Pesa service in Kenya offers an example of how the company was able to leverage massive network effects in the mobile-money space to establish its dominance in calls and text messages. The situation in India is certainly different - we have higher levels of competition, both in telecom as well as online payments. Yet, the Kenyan example is a helpful reminder of the extent to which cross-linkages between bundled products can influence their adoption and usage, to the exclusion of other competitors.

Jio's dual role as a telecom provider and platform offering access to online content makes it difficult to outright dismiss the role of any network effects. Moreover, any subsequent recoupment of the losses suffered by Jio in its early days need not necessarily be through a significant markup in data tariffs. Increase in volume of data consumption, future monetisation of Jio apps and opportunities for utilisation of data collected from users, are all factors that must be considered.

The tests of predatory pricing

The law and jurisprudence on predatory pricing defines it as below cost pricing by a dominant firm, with a view to exclude competitors. Sustained discounting practices in a market with strong network effects certainly raises a red flag due to the tendency of a single network to dominate the market. In such a scenario, there is a strong likelihood of recoupment after other competitors have left the market and structural barriers deter the entry of new players. The determination of predatory pricing, however, does not hinge on the existence of these network effects.

When Jio first launched its services in September, 2016 it was a fresh entrant in a market with several established players. Its price point of zero was certainly below cost but there was no question of it being a "dominant player". Any regulatory intervention to stop the pricing plans at that stage, whether by the sectoral regulator TRAI or the CCI, would have been premature.

This position has come to change over the last few months. Jio has managed to acquire a sizable presence in the market for high-speed data services - it holds about one-third of the country's broadband subscriber base and about 85 percent of the market in terms of mobile data traffic. Its share in the overall market for telecom services (voice plus data) still remains small since telecom subscribers continue to outnumber Internet users by a wide margin. The manner in which CCI delienates the "relevant market" will therefore form the crux of its analysis in this case.

Accordingly, the first step for CCI would be to determine whether there is a market for data services that is distinct from the broader cellular services market? This will hinge on a factual analysis of whether users regard voice, data and high-speed data services as being interchangeable in terms of their end-use and characteristics, based on a number of factors. One, voice calls can be made using the Internet but the reverse is not true - this indicates a one-way substitutability between the services. Two, there are some differences in the utility of 2G and 4G networks based on the applications that they are able to support. Three, CCI will need to collect data on Jio's usage patterns, that of its competitors and the switching behaviour of consumers. Four, supply-side constraints (like spectrum holdings) that can make it difficult for providers to switch from one type of service to another will also need to be considered.

In the second stage, CCI will need to examine whether Jio can be regarded as being dominant player in the identified market. Besides looking at its market share, in terms of subscriber base and usage volumes, this analysis must also consider the various other factors that have been given under the Competition Act, 2002. These include:

1. Size and resources of Jio and its competitors - Telecom being a capital-intensive industry has many big players. Each of them has access to significant capital resources, although there may be differences in the extent to which these firms have been leveraged.
   
2. Vertical integration of the enterprise - As discussed above, the bundling of voice, data and content offers Jio certain clear advantages. Other players are also offering similar bundles but not necessarily at the same scale. In many cases, the prices and bundles offered by other players have come about as a response to Jio's entry strategy.
   
3. Entry barriers - Telecom is a heavily regulated sector and there are entry barriers, both in terms of licensing requirements and the availability and price of spectrum.
   
4. Relative advantage through contribution to economic development - The arrival of Jio's 4G LTE network, with its aggressive pricing strategy, could also have some pro-competitive effects. Arguably, it has nudged the telecom market towards greater price competition, resulting in lower tariffs. Over time, this could also push other operators towards faster upgradation of technology.

Assuming that CCI's analysis leads it to delineate a separate broadband market (and Jio is found to be dominant in it), the third challenge would be to assess whether its current prices are in fact "below cost". This again will require data on the costs incurred by Jio for delivering its voice and data services and the free apps that are on offer. Finally, CCI will have to determine whether Jio's current pricing continues to be in the nature of a genuine "promotional strategy" by a new entrant or is it a deliberate attempt to reduce competition in the market.

Many have linked the consolidation that we are seeing in the market today with Jio's entry strategy. One one hand, consolidation reduces the number of players, hence reducing competition. On the other, it might be a sign of the sector's movement towards a more mature market with fewer players who are able to focus better on infrastructure expansion and quality of services. CCI will need to weigh in all these factors while examining the impact of Jio's prices on consumer interests, competition in the market and overall economic development.

These are all complex questions, with no obvious answers. The solution lies in a multi-stage, data-driven analysis of predation that should be rooted in an understanding of competition policy and telecom economics. Co-operation and knowledge-sharing between CCI and TRAI is key to finding these solutions.

 

Smriti Parsheera is a researcher at the National Institute of Public Finance & Policy. The author would like to thank Amba Kak, Kaushik Krishnan and Faiza Rahman for useful discussions.

Predatory pricing and the telecom sector

by Ajay Shah.

1. When there are network effects, we should be cautious about the business strategy of discounting. What looks like a gift to consumers today is often a plan to achieve market power and recoup those gains by extracting consumer surplus in the future.
2. The burn rate at Reliance Jio is likely to be pretty large. However, the question that we should be asking: Can this subsidised user base set off a network effect?
3. In telecom, interoperability regulation is in place. Even if Reliance Jio was able to establish a commanding market position through discounting, there is no way to close off its user base for rival firms. Interconnection regulation by TRAI imply that a phone call from a rival telecom company, to a Reliance Jio customer, will always go through. The open standards of TCP/IP mean that a data packet from a customer of any data communications company in the world will successfully reach a Reliance Jio customer. Even if all my friends and family are on Reliance Jio, it makes no difference to my decision to be on Reliance Jio. There is no network effect.
4. Recoupment test: If in the future, Reliance Jio tries to increase prices, nothing prevents customers from switching to rival firms. There is no reason for a consumer to stay with Reliance Jio at future dates if it turns out that Reliance Jio is expensive.
5. Market power in this industry has been checked by the three key building blocks -- interconnectivity regulation + mobile number portability + the open standards of TCP/IP.
6. In fact, there is a negative network effect, as follows. Suppose a lot of customers switch from rival telephone companies to Reliance Jio. This will clog the airwaves of Reliance Jio's base stations, so the performance of Reliance Jio will go down while the performance of rival companies will go up. Through this channel, if Reliance Jio succeeds a lot in gaining customers, it will fail in delivering the best mobile data services.

Second order issues:

1. Interconnectivity regulation imposes costs upon all regulated persons and these costs should be placed in a fair manner.
2. There is an opportunity to obtain market power in JioMoney as payment regulation lacks all three components: interconnectivity regulation + number portability + open standards.

Competition issues in India's online economy

Smriti Parsheera, Ajay Shah and Avirup Bose.

The world of high technology companies is seen as a dynamic area with a rapid pace of creative destruction. There is, however, a class of industries where there are strong network effects, where the market tends to collapse into a narrow set of players. After one burst of innovation where a new online business is born, there is the possibility of entrenched market power with the extraction of consumer surplus.

Many firms, global and Indian, have resorted to the strategy of making large losses by subsidising users, as a way to obtain those network effects. This has created a new class of concerns about predatory pricing, with unprecedented negative profit margins on a sustained basis, being supported by equity capital infusions. In the short run, discounts are popular, but recoupment is inevitable and market power will adversely affect consumers in the future.

In a recent Paper, we argue that the existing competition law regime in India needs to be fine tuned, for technology-enabled markets with significant network effects, to address the possibility of new kinds of abusive conduct. We offer a series of tangible proposals through which the Competition Commission of India can better handle these emerging situations. We also look into the role and responsibilities of the investors who back these online businesses and the impact of their conduct on competition in the underlying markets.

Entry barriers in the new economy

Innovation is the foundation of economic progress. While we normally revere technology companies for their disruptive innovations and the efficiencies that they create, we must recognise that some technology-driven businesses are susceptible to the acquisition and abuse of market power. The Indian competition regime is an evolving one, and has only recently started facing some of these concerns. Our paper brings new evidence and arguments to the table, on these questions.

Internet-based businesses, along with several other high-technology sectors, form part of the 'new economy', characterised by high rates of innovation; low marginal cost; increasing returns of scale; and, in many cases, network effects. Direct `network effects' arise where a user's benefit from a product or service increases with the number of other users on that network. The benefit of being on Facebook or WhatsApp, for instance, corresponds with the number of friends and family who use that service. Contrast this with the benefit of having an email address, where the benefits are not limited to closed proprietary networks. This became possible due to the early adoption of interoperability standards in email protocols.

Network effects are particularly important in two-sided markets where users on each side of the market derive a positive effect from the expansion of users on the other side. Commuters who use taxi aggregation platforms like Ola and Uber will logically be attracted to a service that has a large number of drivers on its network, which yields a lower waiting time. The same is true for the drivers working with these platforms. Similarly, in case of payments wallets, in the absence of interoperability regulation, merchants and customers will both prefer a service that has the most addressable users.

With the use of modern technology, the cost of running the marketplace itself has dropped to near zero levels. As an example, the online classifieds site Craigslist reports that it has about 40 employees who manage a network that sees over 80 million classified ads per month. The marginal cost of a transaction has gone to near-zero levels. This gives a unique class of problems where technological innovation that yields cost reductions cannot be a mechanism to take on an incumbent.

Brain versus brawn

How can market power be established, in this new world? One mechanism through which one player can obtain a competitive advantage is to attract users through technological innovation, and thus get a network effect started. This is an attractive strategy for firms which have deep human capital. Another mechanism is by using financial capital to pay subsidies that entice users. This is an attractive strategy for firms which have superior access to financial capital. Many online businesses have resorted to practices like deep discounting, cash-back offers and other schemes designed to attract new users and establish the network effect. Sometimes, heavy losses have been sustained for years on end.

As an example, the global taxi company 'Uber' made worldwide losses in the first half of 2016 of US\$1.27 billion (approximately Rs.86.5 billion). Uber's behaviour impacts upon the Indian economy as it has applied the strategy of using financial capital as a competitive lever in India also. On a similar note, the Indian taxi company 'Ola' reported a net loss of Rs.7.96 billion in March, 2015. The company's financial records for the periods after that are not yet available although it is reasonable to expect that the losses will be significantly higher due to the higher driver incentives. In the last two years, it is estimated that the two taxi companies, Uber and Ola, burned cash adding up to about Rs. 130 billion in India.

Such behaviour is found in other industries also. In the field of payments, where regulations have blocked interoperability and thus created the opportunity to kick off a network effect, the firm One97 Communications, which owns 'PayTM', reported a loss of Rs.15.49 billion in March, 2016.

The scale of these discounting practices, and the sustained periods for which they are continued, has created new barriers to competition. It is difficult to rationalise these sustained losses as being an introductory offer by a new player. Rather, these practices appear to be a systematic competitive strategy. Capital has become a competitive weapon. This gives rise to concerns that the market may eventually tip in favour of the player that may not necessarily have the most innovative product or service, but one that succeeds in obtaining more capital and enticing more users in the early days, using subsidies. While seeming beneficial for consumers in the short run, such practices raise concerns about competition on account of the creation of market power, and elevated prices for consumers in the following years when losses are recouped.

The FDI guidelines issued by the government in March, 2016 turned the spotlight on pricing practices of e-commerce firms. It clarified that the automatic route of foreign investment would be available only to those e-commerce marketplaces that avoided such subsidies.

These issues have also come to the attention of the CCI in a few recent cases. In April 2015, the CCI passed a prima facie order recommending a detailed investigation into the allegation that, armed with substantial funding received from various investors, Ola had indulged in abusive market practices to garner greater market power in the city of Bengaluru. More recently, the COMPAT directed the Director General of the CCI to initiate a similar investigation to assess Uber's dominance in the market for radio taxi services in the National Capital Region (NCR) of Delhi after the CCI had refused such an investigation. Uber has now challenged this decision before the Supreme Court, citing a 'jurisdictional flaw' in the Tribunal's ability to order such an investigation. Alongside these developments, CCI is also reported to have set up an in-house panel to understand the cash-back incentives being offered by various online companies from the perspective of predatory pricing provisions under the Act.

Our paper explores the recent developments in India in this area, in the light of foundations of economics and competition law. It argues that there are grounds for concern about the harm to competitive dynamics from these new business strategies. At the same time, it is important to avoid intrusive interventions that bring the State into excessive involvement in the world of business.

New economy requires new thinking

There is a need to take into account the distinct economic features of certain high-technology businesses when looking into allegations of anti-competitive conduct by them. Practices like deep discounting and cash back offers may be aimed at building sufficient scale in today's market to ensure that the business is able to fully capture tomorrow's market, to the exclusion of other competitors. A robust economic analysis of the impact of increasing returns to scale, and network effects, is required for understanding the present and future impact of these practices on competition and consumer interests. A novel dimension, which is addressed in the paper, concerns collaboration between the investors in the multiple firms that they invest in.

Transient gains to consumers

We examine the question about gains to consumers from discounting. We suggest that the gains in the short term need to be seen in a larger context. The recoupment test examines the extent to which market power can be achieved in the future, after which prices can be raised. If the CCI were to adopt this test in investigations relating to predatory pricing by online firms it would see that in certain areas, there are network effects, and once a small cartel of firms has acquired market power, it would be difficult for entrants to compete with them in the future. In that future scenario, it would be possible for incumbents to raise prices, and recoup earlier losses.

Interoperability as a tool for competition policy

In some situations, the CCI could rely on the essential facilities doctrine to mandate interoperability between a dominant player that is found to be indulging in the abuse of its position and other operators in the market. For instance, imposing interoperability requirements on a dominant payments network can help extend the network effects of digital payments to the economy as a whole, rather than being limited
to a closed network. The imposition of any such requirements will, however, need to be balanced against factors such as the payment of fair and reasonable access fees, the complexity of institutional arrangements required to monitor such arrangements and assessment of the impact on future innovation. More generally, open standards are an important element of interoperability, and various arms of the regulatory State need to push in favour of competitive markets through interoperable open standards.

Acting within Internet time

Given the fast-changing nature of online businesses, there are concerns about the elapsed time between a full-fledged investigation and the determination of a violation. We suggest a two-pronged approach to address this issue. On one hand, the CCI needs to work towards adopting stricter time frames for the disposal of cases, particularly those relating to new economy firms. On the other, we propose a voluntary settlement process that will allow a business that is under investigation to voluntarily alter its market behaviour, with the concurrence of the authority but without the need for a conclusive finding of violation by the CCI.

Conclusion

In India, technology companies are generally revered as the source of technological progress. However, the problems of competition policy are universal and cut across all industries. The basic principles do not change. The purpose of competition policy is to stave off situations where a narrow set of firms have market power, and new players are not able to enter. Society gains when firms obtain profits and valuation through innovation, not through the crafty use of financial capital to kick off network effects.

These issues were not faced in thinking about Indian competition policy as recently as five years ago. They are, however, likely to become increasingly important in the future. We argue that this calls for fresh think about the legal framework also. There is a case for competition authorities to look into the unilateral abusive conduct of a firm, which, although not dominant at the given point of time, is engaging in anti-competitive practices that create a strong and imminent possibility of its dominance. We highlight some pros and cons of this approach and leave this question open for further research.

Smriti Parsheera and Ajay Shah are researchers at NIPFP, and Avirup Bose is a researcher at Jindal Global Law School.