Who is "innovative"? Unpacking the process of tax exemption grants to startups

by Aneesha Chitgupi, Karthik Suresh, and Diya Uday.

When private individuals spend resources on innovation, the ideas and benefits that arise spread to society at large. An underspend on innovation results in the market failure of "positive externalities". The state has an important role to play in solving this market failure by encouraging spending on activities that generate innovation (Mashelkar et al., 2024). In India, the government did so by: (i) building research organisations like CSIR and ISRO and hiring career scientists, and (ii) providing tax exemptions. The Income Tax Act, since its inception in 1961, has exempted expenditures on scientific research. Since 1996-97, goods used for R & D have been exempt from customs and excise duties.

In the last ten years, both the Union and various state governments have perceived "startups" as major drivers of innovation. A host of incentives have been put in place for them. These include: (i) reduced fees and priority in processing patent and design applications; (ii) full exemptions on income tax for the startup following approval from an Inter-ministerial Board (IMB); (iii) priority during public procurement, etc. However, in a previous article (Chitgupi et al., 2023), we analysed Indian patent filings and grants and found that, despite enjoying government incentives, the overall share of startups in patent filings and grants --- a proxy for innovation --- remains small. Moreover, only a few startups receive income tax exemptions aimed at spurring innovation (see Table 1).

Table 1: Overview of the landscape of startups

Startups	2023	2024
Total (registered and unregistered)	2,49,107	3,14,492
Registered as startups by DPIIT*	90,939 (36.5)	1,31,191 (41.7)
Granted tax exemptions by the IMB**	1,100 (1.2)	2,976 (2.3)

Table notes: * fraction of total startups;
** fraction of DPIIT registered startups.
Source: Authors' compilation and analysis

The income tax exemption for startups

Section 80-IAC of the Income Tax Act allows a one hundred per cent tax deduction for eligible startups. The aim is to reduce the tax and compliance burden in the initial years of incorporation. A startup is eligible if (i) it is a new business incorporated after 1 April 2016 and is not a reorganisation of an old business with old machinery; (ii) its total turnover does not exceed INR 100 crores; and (iii) it is "engaged in innovation, development or improvement of products or processes or services or a scalable business model with a high potential of employment generation or wealth creation". The body tasked with determining the eligibility of a startup is the IMB. It was set up by the DPIIT in April 2016 with three members, and it is an executive body with delegated powers.

In this article, we study the institutional design and functioning of the IMB under Section 80-IAC. Our findings suggest that the IMB's process is not optimised to deliver the statutory intent of tax exemptions to startups. We identify the bottlenecks that must be targeted for change and question the current incentive structure for startups to innovate.

Methodology

There is a perception that staffing an organisation with technocrats will solve the problems of the organisation. This is not a recipe for lasting success (Kelkar and Shah, 2022). It is instead important to draft rules and procedures that work within and beyond the administrative system and that provide the right incentives to bolster the purpose of the organisation, i.e., promoting innovation. We view the IMB as an executive body tasked with an executive function --- to determine whether a startup is eligible for an exemption under Section 80-IAC. We rely on principles of administrative law while examining the processes and workings of the IMB. In particular, we focus on (i) institutional design, (ii) transparency, (iii) administrative discretion, and (iv) accountability (see Table 2).

We integrate this framework with Adam Smith's Canons of Taxation which sets out design principles for efficient tax administration. These are: (i) the maxim of equality, i.e., the tax must be collected with equality before the law; (ii) the maxim of certainty, i.e., the time, manner, and amount of tax to be paid ought to be clear and plain to the taxpayer, (iii) the maxim of convenience, i.e., the tax ought to be levied at the time and in the manner in which it is most likely to be convenient for the taxpayer and (iv) the maxim of economy, i.e., the tax ought to be so contrived that it takes from the taxpayer as little as possible. Smith's canons are routinely used by Indian courts to test the constitutionality of actions by tax administrations. For example, in South Indian Bank Ltd. vs. CIT AIR 2021 SC 4266, the Supreme Court applied Adam Smith's canons to examine the tax exemption to income arising from interest paid by banks.

Table 2: Our framework for analysing the process of tax exemptions to startups

Parameter	Administrative principle	Smith's canons
Institution design	Clarity of purpose and process	Equality, certainty, convenience.
	Composition of the board
	Reporting of conflicts of interest
Process transparency	Publication of rules and processes	Certainty
Administrative discretion	Issue of reasoned orders	Equality, certainty
Administrative accountability	Procedure for appeals from orders	Equality, certainty
	Audit oversight mechanism

We hand-collected and evaluated a sample of the minutes of the IMB meetings published on the Startup India portal to determine the eligibility of startups for tax exemptions. Our dataset comprises 52 decision documents out of the 72 available on the portal from 2016 to 2023, with the most recent document being from February 2023. There have been no additions to the IMB decisions since February 2023. Table 3 summarises our sample and provides insights into the total number of cases heard and the corresponding board decisions, forming the foundation of our study. Of the 72 IMB decisions published on the IMB website from May 2016 to February 2023, we have hand-collected data from 52 of these meetings, covering a total of 2,102 cases (72.2 per cent) each representing a startup.

Table 3: Overview of the sample data of IMB meetings

Year	No. of meetings*		No. of startup applications
		Granted	Rejected	Deferred	Total
2016	6	9	265	35	309
2017	3	21	201	141	363
2018	3	6	126	17	149
2019	8	109	1	57	167
2020	8	73	10	15	98
2021	10	80	13	9	102
2022	13	651	97	52	800
2023	1	112	2	0	114
Total	52	1061	715	326	2102

Table notes: *No of meetings from which data was used for this article.
Source: Authors' compilation from the Startup India website.

Results

Institution design

Clarity of purpose and process: The IMB has to decide whether a startup is eligible for the tax exemption. We find that the substance of the criteria to be applied by the IMB to determine eligibility for startup tax exemptions overlaps with the DPIIT criteria to register a startup. The need for a body like the IMB to reassess a startup on the same criteria is unclear. Despite this, only a small percentage of firms that have qualified the DPIIT's criteria meet the IMB's criteria. This violates Smith's canons of certainty and convenience because of the uncertainty of receiving the exemption despite having met the DPIIT's criteria. Table 4 compares the criteria for the IMB and the DPIIT to determine the eligibility of a startup for registration and grant of tax exemption. The criteria are substantially the same.

Table 4: Mandate of the IMB compared with the mandate of DPIIT for startups

	IMB (for startup tax exemption)	DPIIT (for startup registration)
Criteria 1	The entity's business involves innovation, development, deployment or commercialisation of new products, processes or services driven by technology or intellectual property	Entity is working towards innovation, development or improvement of products or processes or services, or if it is a scalable business model with a high potential of employment generation or wealth creation.
Criteria 2	The entity was incorporated on or after 1 April 2016 but before 1 April 2025.	Upto a period of ten years from the date of incorporation/ registration, if it is incorporated as a private limited company (as defined in the Companies Act, 2013) or registered as a partnership firm (registered under Section 59 of the Partnership Act, 1932) or a limited liability partnership (under the Limited Liability Partnership Act, 2008) in India.
Criteria 3	The entity's turnover does not exceed one hundred crore rupees.	Turnover of the entity for any of the financial years since incorporation/ registration has not exceeded one hundred crore rupees.
Criteria 4	The entity has not been formed by splitting up, or the reconstruction, or using more than 20% by value of machinery, of a business already in existence.	The entity shall not be formed by the splitting up or the reconstruction of an existing business

Source: DPIIT notification dated 19 February 2019 and Section 80 IAC of the Income Tax Act 1961

This is further highlighted through our analysis of the reasons for rejection of exemption claims. We map the reasons that are recorded in the minutes of IMB's meetings to the eligibility criteria set out in Table 4. We find that in 65 per cent of cases, a startup fails to get an IT exemption from IMB despite having met the same criteria for the DPIIT's requirement. Table 5 presents the percentage of exemption applications rejected based on previously assessed criteria. Further, 51 per cent of cases were rejected because the IMB determined that the startup was incorporated before 1st April 2016. At the outset, this is an objective criterion that the DPIIT and IMB should be able to agree on. Further, the cases in the Others category, which make up nearly 15 per cent of rejections, comprise criteria not specified under the law, such as shareholding patterns or other reasons said to be privately communicated to the startup.

Table 5: Overview of the reasons for rejection of applications for the IT exemption

	Reasons for rejection	No. of rejections	Rejections as a fraction of total rejections (where reasons are given) (%)
Criteria 1	Lack of innovation, scope of scalability, and wealth generation	91	12.7
Criteria 2	Incorporated before April 2016	366	51.2
Criteria 3	Turnover exceeds hundred crore rupees	0	0
Criteria 4	Reconstruction of existing business	12	1.7
	Others*	105	14.7
	Rejection without reasons	141	19.7
	Total	715	100

Table notes: * "Others" includes reasons not part of Criteria 1 to 4 in Table 4 above.
Source: Authors' compilation and analysis

Composition of the board: It is not apparent how the composition of the IMB is relevant for the purpose of the IMB. Since its constitution, the IMB has had a Joint Secretary from the Department for Promotion of Industry and Internal Trade (DPIIT), a scientist from the Department of Science and Technology, and a scientist from the Department of Biotechnology. For a year, between 2018 and 2019, the IMB also included representatives from SEBI, RBI, the Ministry of Corporate Affairs, the Ministry of Electronics and IT, and the Central Board of Direct Taxes. The IMB also has a "technical consultant". This consultant is an employee of the National Research Development Corporation (NRDC), a public sector undertaking owned by the Government of India. We are unable to find documentation that highlights the selection process and requisite qualifications of these members. All of them are ex-officio members. The role of the NRDC consultant has also not been clearly defined.

Process transparency

Publication of rules and processes: The rules or guidelines on the IMB's processes are not available in the public domain. For example, there are no guidelines on the basis of which the most important phrase in section 80-IAC, "innovation, development, deployment or commercialisation of new products, processes or services driven by technology or intellectual property" is determined. The lack of guidelines also violates Smith's canons of certainty and convenience. Guidelines are necessary for predictability for firms and greater accountability from the government. Other departments of the Indian government that carry out similar certification processes, such as the Department of Scientific and Industrial Research which certifies whether an applicant qualifies for tax exemptions for scientific research and R & D under Section 35(2AB) of the IT Act, have prescribed guidelines that companies can use to evaluate their chances of success.

Administrative discretion

Issue of reasoned orders: A central tenet of administrative law is that an order that carries negative consequences for an assessment should be well-reasoned. Unfortunately, non-speaking orders are frequently issued by Indian tax administrations (example, example). We do not have access to the text of the orders that are issued to individual applicants, so it is unclear whether detailed reasons are provided by the IMB while rejecting applications. However, from the minutes of the IMB meetings, we note that many companies are not provided with adequate and specific reasons for why their applications were rejected. This violates Smith's canons of certainty and equality. All deferred or rejected cases must be provided with reasons for their deferment or rejection. Published decisions help other startups better understand and comply with the eligibility criteria.

Figure 1 demonstrates whether the IMB communicated the reasons for the decisions taken on granting or rejecting the IT exemption for startups. We find that not all decisions are published with reasons. Even where applications are rejected, we do not see reasons being provided in every case. Of the 715 startups that were rejected, 19.7 per cent (141 startups) were not given reasons for rejection by the IMB. We further find that of the total 2,102 cases (Table 3) in our sample, deferred cases accounted for 15.5 per cent of them. Nearly 38.7 per cent of such cases were deferred without providing any reasons. Among the 1,061 startups granted the tax exemption, 67.9 per cent were granted without reasons being published. This creates ambiguity. In 15 per cent of cases, the IMB rejected applications for reasons other than those stated in the law (Tables 4 and 5).

Figure 1: Reason provided by application status across IMB decisions as a share of total cases

Figure 2, presents the share of cases where reasons were published across the decisions taken (whether granted, rejected, or deferred) by IMB from 2016 to 2023 across 52 decision documents. Since its inception, the IMB has published reasons for the majority of its decisions --- 67.3 per cent, 61.2 per cent and 73.2 per cent for 2016, 2017, and 2018, respectively. Across the years 2019, 2020, and 2021, IMB published reasons for all of its decisions. However, in 2022 and 2023, IMB published reasons for only 25.7 per cent and 1.8 per cent of decisions, respectively.

Figure 2: Reasons provided by year as a share of total cases

One may argue that since 2019, the IMB decision in favour of grants has increased from 65 per cent in 2019 to 98 per cent by 2023 and that the lack of reasons provided for the grant of IT exemption is not a major administrative challenge. We believe this has happened as the scheme gained maturity and there was a rise in the number of startups incorporated after 2016 that are applying for exemption under Section 80-IAC. It is important to note here that the cases brought to the IMB are recognised as startups by DPIIT, which follow similar criteria for establishing an entity as a startup with differences in the incorporation date, which is restricted to 'after April 2016' for IMB classification. This has been the major reason for rejections in the early years of the scheme (Table 5).

Administrative accountability

Procedure for appeals from orders: The right to appeal is another core tenet of administrative law. Judicial review of an administrative action ensures the lack of arbitrariness and improves administrative accountability. The IMB does not appear to have an appellate or review mechanism. It is unclear whether the decision of the IMB, not being one taken by the Income Tax Department, is appealable under the Income Tax Act. Moreover, this contravenes Smith's canon of equality. Startups that are aggrieved with the IMB's decision have to directly approach the High Court under a writ petition (example from Delhi HC). A case should lie before the High Court only if it involves a substantial question of law (Datta et al., 2017). This is because it is inefficient and costly to bring routine cases such as challenges to IMB's exemption decisions that do not involve a substantial question of law. This only adds further strain on the high courts' already burdened docket.

Audit oversight mechanism: The IMB is subject to audits by the CAG. However, no such audit of its functions has been carried out so far.

Discussion

Our findings indicate that the 80 IAC tax exemption is not working as desired. There are some core challenges in both the design of the exemption and its implementation.

Substantive challenges: The eligibility criteria to become a startup recognised by DPIIT and the eligibility criteria to be granted a tax exemption by the IMB are substantially the same (Table 4). However, startups are being granted recognition by the DPIIT but are being rejected for the tax exemption on the very same criteria by the IMB. If the criteria are substantially the same, a registered startup must automatically gain a tax exemption grant by virtue of qualifying as a startup by DPIIT. It is unclear why a separate application must be made involving both cost and time. Further, conversations with startup founders revealed that the 80-IAC exemption is not the impetus they need to spur innovation.

Implementation challenges: A key challenge is that rules and process guidelines on the grant of the IT exemptions are not available in the public domain. This has two drawbacks: (i) a startup applying for the exemption will not have a full picture of the process, and (ii) it allows room for administrative discretion, reducing the predictability of the outcomes of the applications. Further, the IMB does not publish reasons for rejection of applications consistently. This is imperative, as it will bring about transparency in the working of the IMB and also give potential applicants a sense of the reasons for rejection, acceptance, or deferment. The IMB process must also have a published procedure for appeals. Lastly, the composition of the IMB must be commensurate with its purpose. It is unclear why, for example, SEBI and RBI officers were part of the panel to determine "innovation". Our findings are in line with the recent observations of the Parliamentary Standing Committee on Commerce on the lack of clarity in the process for granting these exemptions. In response, DPITT has proposed to take steps to make the process of granting tax exemptions more "transparent" and "user-friendly".

Alternative strategies to encourage innovation

Building better supply-side incentives: Supply-side incentives are effective strategies to encourage spending on innovation. Tax exemptions could be one way forward, but not in their current form. We compared the Indian scenario to other countries to get some guidance. We find that while tax incentives are common for startups in many countries, and are the recommended way forward, the mechanism by which they are granted is different from the IMB. In the United Kingdom, "knowledge-intensive" companies can avail of tax benefits. Whether a company is knowledge-intensive or not is determined by HM Treasury under Section 252A of the Finance (No. 2) Act, 2015. The provision defines a "knowledge-intensive" company as one that: (i) spent at least 15 per cent of OpEx on research and development or innovation in at least one of the previous 3 years, or spent 10 per cent of OpEx in each of the previous 3 years, and (ii) is either likely to exploit intellectual property that it has created in the previous 3 years, or at least 20 per cent of the company's full-time employees (FTEs) are engaged in research and development or innovation. These FTEs must have at least a masters' degree. In Ireland, under Section 496 of the Taxes Consolidation Act, 1997 there is a negative list of industrial sectors and companies that do not qualify for the Startup Relief for Entrepreneurs (SURE) scheme if their main activities of business are in the specified sectors. Companies self-certify their applicability for the scheme, and there are stiff penalties in case they misinform the Revenue department. In Germany, the INVEST scheme of the Federal Ministry of Economic Affairs and Climate Action mentions that companies must (i) either hold a patent issued to them in any of the EU member states in the previous 15 years, or (ii) must be "innovative". "Innovation" is proved in a manner opposite of the Irish method, i.e. the company demonstrates that it is in fact working in the specified sector. The German Federal Tax Office may carry out random checks on whether a company is "innovative" by hiring an audit firm to independently assess whether it is truly generating output in its stated sector.

In all these countries, the government does not enter into the question of which firm is "innovative". The main reason for this is the difficulty in determining who is "innovative". Instead, countries identify priority sectors and grant incentives to all startups within the sector. That aside, in the Indian context, given the significant overlaps between the eligibility criteria for being a registered startup vis-a-vis the eligibility criteria for tax exemptions by registered startups, the government may consider a single window clearance system in which an eligible startup is registered and then is automatically given a tax-exempt status on account of being registered with the DPIIT. In doing so, the state will free up valuable state resources and capacity, which may be deployed for other purposes aligned with the intention of promoting innovation among startups.

Demand-side interventions through government contracting: As an alternative, some literature suggests that demand side strategies through public procurement will encourage innovation (Rothwell et al. 1981).

While the creation of the IMB and the government's focus on startups appears to be in response to a market failure, the design of the intervention has flaws. We note that "startups" are not necessarily major drivers of innovation in India. Firm size has no role to play in how innovative it can be. Therefore, tax exemptions for startups, even if they are "innovative", have little measurable effect on innovation in Indian society as a whole. Mashelkar et al (2024) recommend that the government "buy" i.e. contract out more research and innovation functions to firms in the private sector. The spillovers this can generate would be substantive and would have a multiplier effect. We already have many examples of the government choosing to do so e.g. the New Millennium Indian Technology Leadership Initiative (NMITLI) program of the Council for Scientific and Industrial Research (CSIR), Department of Space's (DoS) contracting with companies like L & T and Tata Elxsi to build rocket engines and recovery modules for the all-important Chandrayaan and Gaganyaan missions. These kinds of contracts should be done more often and frequently with all kinds of companies to build innovation and production capacity. Our recommendation in all cases is to pursue innovation by contracting out.

References

1. Adam Smith, The wealth of nations, Fingerprint Classics, 2024.
2. Aneesha Chitgupi, Karthik Suresh and Diya Uday, Are startups engaging in innovation in India?, The Leap Blog, 25 April 2023.
3. Pratik Datta, Surya Prakash B.S., and Renuka Sane, Understanding judicial delay at the Income Tax Appellate Tribunal in India, Working Paper, National Institute of Public Finance and Policy, 13 October 2017.
4. Ramesh Mashelkar, Ajay Shah and Susan Thomas, Rethinking innovation policy in India: Amplifying spillovers through contracting-out, Working Paper, XKDR Forum, 21 March 2024.
5. Vijay Kelkar and Ajay Shah, In Service of the Republic: The Art and Science of Economic Policy, Penguin, 2022.
6. R Rothwell and W Zegweld. Industrial Innovation and Public Policy: Preparing for the 1980s and the 1990s. In: London: Francis Pinter Publications (1981).

Aneesha Chitgupi, Diya Uday and Karthik Suresh are researchers at XKDR Forum. The authors thank Ajay Shah and two anonymous referees for their comments and inputs.

Rethinking innovation policy in India: amplifying spillovers through contracting-out

by R. A. Mashelkar, Ajay Shah, Susan Thomas.

Independent India valued science and rationalism at an early stage of social and economic development. The objectives of building the scientific temper and harnessing the power of science and technology were clearly articulated, for example, in the 1958 Science Policy Resolution. These objectives pertain to improvements in the people, in the society. 

The practical aspects of government expenditures on innovation, and the construction of organisations doing frontiers work, emphasised government organisations. The government used taxpayer resources, built science organisations, hired scientists as civil servants, and developed capabilities within these organisations

In a new paper, Rethinking innovation policy in India: amplifying spillovers through contracting-out, we reopen the objectives of innovation policy in India, applying modern knowledge of public economics and public administration to obtain fresh insights.

We start at the foundations of innovation policy, with new clarity on the questions of why (what motivates state intervention in innovation?), what (in what areas should government intervention into innovation take place in India?), how (what mechanisms should be used when spending public money?) and how much (at what point do the incremental gains to society equal the incremental costs).

The market failure that motivates this field is the problem of spillovers, where the full gains from innovative activity by one person are not captured by her, leading to systematic under-investment into innovation by her. There is a case for public expenditure, where taxpayer resources are spent on innovation, but the expenditure needs to be done in a way that induces spillovers into the society.

We undertake four detailed case studies: the US National Aeronautics and Space Administration (NASA), the US National Institutes of Health (NIH), innovation policy in French defence procurement and CSIR's New Millennium Indian Technology Leadership Initiative (NMITLI). In each case, we understand how tradeoffs are made between `make' and `buy'. Make involves building state organisations, scientists as civil servants. Buy involves contracting-out innovative activities into the society, to private firms and particularly to high-spillover sites of universities and research organisations (whether public or private).

While doing more contracting-out is appealing from the first principles of innovation policy -- the purpose is to obtain greater capabilities in the society, not in the state -- there are many difficulties in implementation. We suggest the strategy for implementation which involves (a) Changes to the GFR; (b) Changes to the founding documents of government innovation organisations; (c) Changes to procurement rules and internal process manuals; (d) Resource planning for a gentle reform trajectory; and (e) a sketch of the required project planning.

Many elements of innovation policy in India have been moving in a similar direction. Three recent initiatives should be pointed out. The Union Interim Budget of 2024-25 envisages a Rs.1 trillion fund that would be channeled to research and innovation in the private sector. The `National Research Foundation' has been setup with a law that came to force on 5 February 2024. The K. Vijay Raghavan Committee report, submitted in early January 2024, has important ideas on improving the working of DRDO. There is a harmony between the philosophy of these three moves, and the ideas of this paper. Conversely, the detailed work of this paper can be useful in translating these initiatives from concept to implementation.

R. A. Mashelkar, FRS, was Director General of CSIR. Ajay Shah and Susan Thomas are co-founders of XKDR Forum.

Are startups engaging in innovation in India?

by Aneesha Chitgupi, Karthik Suresh and Diya Uday.

Introduction

What is a startup? The academic literature takes a broad view --- startups:

• have a high growth rate (Moogk 2012),
• have a lower number of employees (Beck et al. 2008),
• are at the early stage of the life cycle of a firm (Eisenmann 2013, Stevenson and Jarillo 1990), and
• are drivers of innovation (Cohen and Klepper, 1996).

However, governments across the world focus on the link between startups and innovation. In the Netherlands, a startup is defined as "a business that translates an innovative idea into a scalable and generic product or service, using new technology." In the United States, a startup is one that "has never been an SEC reporting company, uses invested capital, often from venture capital investors, to build an innovative growth focused, scalable business." The Israeli "innovation model" is "largely based on the creation of technological value, mainly in start-up companies and multinational corporations R&D centres".

This is true of the Indian government as well. The stated objective of the Startup India Action Plan of 2016 is to promote innovation. The idea that startups are innovative is also reflected in the draft Science, Technology and Innovation Policy of 2020) as well as foreign policy initiatives like the Engagement Group on startups at the ongoing G-20 Summit.

The Startup India Policy offers a suite of regulatory exemptions and incentives linked to innovation by startups. Two key components of this policy are: (i) reduced fees and priority in processing patent and design applications for startups, and (ii) full exemptions on income tax to the startup following approval from an Inter-ministerial Board (IMB). The Startup India Policy has been amended several times. Key changes relating to the definition of a startup have been:

1. February 2016: a startup is (i) not older than five years from the date of its incorporation/registration, (ii) turnover in any of the previous five financial years has not exceeded INR 250 million, and (iii) it is working towards innovation, development, deployment or commercialisation of new products, processes or services driven by technology or intellectual property. The startup should develop and commercialise "a new or a significantly improved product or service or process that will create or add value for customers or workflow".
   To be registered with the DPIIT, as well as to qualify for the tax exemption, a startup needs to be recommended by a registered incubator, or an angel/private equity/ accelerator fund with at least 20 per cent funding, or by the Union or state government as part of a scheme to promote innovation, or it should have filed a patent.
2. May 2017: the age of an eligible firm and the period for calculation of turnover was increased from five to seven years from the date of its incorporation/registration (ten for firms in the biotechnology sector).
   In addition to the definition, a startup may now also have scalable business models with a high potential of employment generation or wealth creation to gain benefits.
   To register as a startup and avail of the tax exemption from the IMB, a firm now only has to make an online application by providing the details of (i) certificate of incorporation/ registration and "other relevant details as may be sought", and (ii) a write-up about the nature of business highlighting how it meets the criteria in the definition. The DPIIT would consider "innovativeness" from a domestic standpoint. DPIIT may grant or reject recognition after review.
3. February 2019: age requirement of an eligible startup was relaxed to ten years for firms across all sectors. The turnover limit was increased to INR 1 billion.

Given the emphasis on "innovation", we consider it important to examine whether India's policies are incentivising innovation by startups by asking the following questions:

1. Are startups in India engaging in innovation?
2. How innovative are Indian startups compared to non-startup firms?

To answer this, we require some well-accepted measure for studying startup innovation. We adopt the most popular method i.e. using patent fillings and grants as proxies to measure innovation (Wang 2018; de Rassenfosse 2019; Katila 2000). We chose this over other proxies like expenditure on R&D (Rothwell and Ziegler, 1981; Geroski, 1989). We examine our questions using patent filings and grants to startups. We also use a novel measure i.e. the benchmarks for innovation as defined under the Startup India policy. We found that startups are not driving "innovation" in the conventional sense of the term in India.

We lend new insights into the conventional wisdom on startups and innovation in India and highlight the need for a re-look at the current policy on startups in India.

Methodology

We use two methods to determine whether startups are engaging in innovation:

(i) Measuring innovation using patent applications and grants: We hand-collected data on patent filings and grants from the Indian Patent Office across different categories of entities for the years 2016-17 to 2020-21. We substantiate this data using the annual reports of the Department of Promotion of Industry and Internal Trade (DPIIT). We examined the fraction of patents filed and granted by startups over the years compared to other entities.

(ii) Measuring innovation using startup registration and granted Income Tax (IT) exemptions under the Startup India Policy: The Startup India Policy 2015 requires startups to be innovative to (i) register as a startup and (ii) be granted IT exemptions under the Startup India Policy read with section 80-IAC of the Income Tax Act. We collected data on the number of startups that have successfully received tax benefits (after being classified as innovative). We then calculated the fraction of startups that were granted exemptions versus total startup registrations. For this, we collected data on startup registrations, applications for IT exemptions and approvals to applications of IT applications for all states and UTs in India between 2016-2022. We aim to gain insights into how many startups are "innovative" according to the policy definitions of "innovation".

We also collect currently available data on the total number of startups in India with the number of startups that are registered with the DPIIT. However, this is only available for the current year. We aim to examine how many startups in India qualify under the policy definition of a recognised startup to examine the stringency of the definition of a startup.

We conducted a detailed analysis of startup policies in India to give us further insight into our results from (i) and (ii) above.

Results

Impressive growth rate in patent filings by startups but their overall share remains small: We examined patents filed and granted by Indian startups versus other Indian entities which include small firms, private and public firms, and natural persons. We did not include foreign firms and institutions filing for patents in India or Indian entities filing for patents abroad. We found, across the years, that the number of patents filed by startups has increased possibly on account of the fee waiver and fast-tracking of applications. We also see specific increases in the years in which these interventions were made (May 2017, February 2019) when patent filings doubled (see Figure 1). The CAGR for patents filed by startups and other entities show a disproportionate growth rate for startups at 54 per cent for the period between 2016-17 to 2020-21 which was nearly 12 per cent for other entities for the same period. We found that startups constitute a small proportion of the total patents filed in India when compared to other entities. Patent filings were largely driven by large firms and universities.

Figure 1: Fraction of patents filed by startups over non-startups (2016-17 to 2020-21)

Disproportionately fewer startups were granted patents: The share of patents granted to startups peaked at 8.8 per cent during 2017-18, remained the same the following year and has declined since then. One reason for this could be that startups were obliged to file for a patent to receive registration under DPIIT as well as for applying for IT exemption. The reason for the drop in shares of both patents filed and granted during 2020-21 could be the removal of patents as a condition for registration of a startup and for IT exemption (in May 2017). We also believe that there could be an overall decline in the quality of patents filed. It appears that while the current policy has incentivised firms to file patents, their applications do not pass the more stringent test of proving innovation and hence they fail. The threshold required to grant a patent is strict and requires a firm to prove novelty, which is not the case at the application stage where anyone may file for a patent.

Figure 2: Fraction of patents granted to startups over non-startups (2016-17 to 2020-21)

Source: Annual reports of Indian Patents Office

Figure 1 showed that the share of patents filed by startups in total patents filed was rising during the period 2016-17 to 2019-20. This is not the case for the share of patents granted (Figure 2).

Less than two-fifths of startups registered with DPIIT qualify for benefits: We find that since 2016, the number of companies registered as startups under the Startup India Policy with the DPIIT has increased in absolute terms. However, the growth rate over time has reduced. We further find that out of all the startups that exist in India, only a percentage of them qualify as "startups" under the Startup India Policy and have been registered as such. For instance, there are 2,49,107 startups in India (as on February 2023) out of which only 90,939 (36.5 per cent) are registered by the DPIIT as startups. It is possible that the unregistered startups have either not applied to be registered or have not qualified as startups as per the definitions. This raises the question: is our current definition of a startup under the Startup India Policy the right one? Should we rethink the definition to extend the benefits of the policy to more startups on the ground?

Low grant percentage of IT exemptions for startups: We found that out of the total number of registered startups, less than 2 per cent of startups have been granted the IT exemption, signifying that few startups have been certified as innovative as described in the Startup Policy on external scrutiny by the IMB. We validated this with data on the number of applications for the IT exemption for the year in which this data is available (2017) and found that 90 per cent of registered startups applied for the IT exemption in that year. This indicates that the low fraction of startups receiving IT exemptions is not for the lack of application on the part of registered startups. This has even prompted questions in Parliament.

To be registered as a startup under DPIIT, a startup has to only declare that they are working towards innovation, whereas to obtain an IT exemption, the fact of innovation is scrutinised by the IMB based on specific criteria because of which a startup may not qualify. It is possible that, at registration under the policy a startup need not demonstrate innovation but only declare it, however, for the IT exemption it must now demonstrate and prove innovation in the manner specified in the policy. It appears that few startups are actually being innovative according to the Startup India Policy. Table 1 summarises our findings.

Table 1: Total startups registered and granted IT exemptions based on whether they are "innovative" (2015-2016 to 2020-21).

Year	Number of startups registered	Growth rate (%)	No of startups granted 80-IAC	Fraction of total (%)
2015-16	471	--	7	1.5
2016-17	5233	1011	69	1.3
2017-18	8775	68	18	0.2
2018-19	11417	30	162	1.4
2019-20	14596	28	83	0.6
2020-21	20160	38	70	0.3

Source: Authors' calculations from DPIIT data

Limitations: (i) We do not have access to consistent yearly data on the number of total startups v. those which are registered. (ii) We do not have data on the pre-policy period. (iii) Our present study is not focused on industry-level features. We intend to pursue this in the next leg of our study.

Discussion

Our findings indicate that both measures --- IT exemption grants based on innovation and patents filings and grants --- suggest that innovation in India does not consistently emerge from startups. Instead, our findings are in line with studies in other jurisdictions which suggest that large firms undertake most innovation on account of their risk appetite and R&D capacity (Cohen and Klepper 1996, Symeonidis 1996). Our findings are also aligned with reports that indicate large firms and universities engage most in innovation if measured by patent filings in India. Is this, however, a true picture of innovation on-ground? And what are the implications of our findings for current innovation policies for startups?

The literature makes the case for government intervention on startup innovation citing the disparity in the ability to compete as a market failure (Wang 2018, Symeonidis 1996). The argument is that startups require a boost to even out the playing field as they are unable to compete with larger firms with more resources. Our findings lend some support to this by demonstrating that (i) startups in India are not innovating as much as large firms, and (ii) patent filings by startups have increased since the Startup India policy came into effect. We also, find that patent grants to startups have not increased. Therefore, despite government intervention in India, startups are not driving innovation. Some explanations for this are as follows:

• The current set of incentives may not be sufficient to drive startups to innovate more. We find some support for this in the literature that finds that supply-side policies alone (e.g. subsidies) are not sufficient to stimulate innovation (Geroski 1989). Focusing on additional demand-side measures such as public procurement of innovation from startups may trigger greater innovation as it reduces the market risk for innovators (Rothwell et al. 1981; Tiwari 2017).
• Conventional notions of innovation are linked to "novelty" through patenting which is a very high standard for measuring innovation. In reality, startups in India may be engaging in innovation which is not eligible for conventional patents such as technological improvements or modifications suited to the domestic context. Reports suggest that startups in India adopt rather than innovate in the conventional sense. For instance, India is using the technology adoption route for developing Web3.
  Another reason could be that Indian firms are innovating but are not registering patents in India. Reasons for this range from poor enforcement in India to sector-specific commercial preferences. An example of the latter is the semiconductor sector --- India has a large chip design industry but this work is done on a contract basis for US semiconductor firms which file their patents in the US.
  Therefore, patents may not be the best way to measure innovation in India. Current startup policies in India should re-think the definition of "innovation" and make it more suited for the Indian context.

We gain some insights from the innovation-linked incentives that are offered by other countries. In South Korea, which has the highest per-capita granting of patents in the world, all startups irrespective of how innovative they are qualify for reduced fees in patent filings and certain tax exemptions available to SMEs. South Korean policy appears to focus more on promoting linkage between large and small firms to promote networking and market access. In the Netherlands, which ranks ninth in the world in patent filings, vouchers are given to SMEs for patent filing that cover up to 75% of costs. The Dutch Tax Office evaluates and grants specific tax incentives for "technical-scientific research" and "development projects". Both these countries, considered to be highly innovative, have tax schemes that are targeted at specific outcomes and there are some general exemptions for patent filings. India could perhaps learn from these policies.

Conclusion

We set out to answer two questions in this article: Are startups engaging in innovation? How innovative are startups compared to non-startup firms? Our findings using both measures indicate that startups are not driving "innovation" in the conventional sense of the term in India. However, many Indian startups have scaled up by engaging technology towards creative solutions in many industries such as payments (Paytm), e-commerce (Meesho), credit cards (CRED) and healthcare delivery (PharmEasy). While these firms may not do well on the conventional measures of "innovation", they have played a role in encouraging entrepreneurship to solve everyday challenges, all while benefiting their shareholders.[1] Policy in India must, therefore, be suitably modified to recognise such contributions towards innovation. This is an emerging idea that Indian policymakers are increasingly acknowledging. For instance, the Economic Advisory Council to the Prime Minister of India noted the importance of FDI from tech transfers as a key source of promoting innovation in India. We need to think harder about what "innovation" means in India and what role should the government play in encouraging innovation.

In further research, we will analyse the pattern of patents filed and granted across various industries to understand which sectors are more innovative in the traditional sense. We will also examine the firms that have received the IMB's certification of being "innovative" to (i) study the characteristics of these firms and the industries to which they belong, and (ii) study the trends in the grant of certification by the IMB for innovation to startups. This will help us gain a more nuanced understanding of what drives innovation among startup firms in India.

Footnotes

[1] According to its Red Herring Prospectus filed at the time of its IPO (November 2021), Paytm does not own any patents.

References

1. Tom Eisenmann, Entrepreneurship: A Working Definition. Harvard Business Review, January 10, 2013.
2. Stevenson, H. H., and Jarillo, J. C., A Paradigm of Entrepreneurship: Entrepreneurial Management. Strategic Management Journal, 11 (1990), 17-27.
3. Dobrila Rancic Moogk, Minimum Viable Product and the Importance of Experimentation in Technology Startups, Technology Innovation Management Review, March 2012.
4. Beck, Thorsten and Demirguc-Kunt, Asli and Maksimovic, Vojislav, Financing patterns around the world: Are small firms different?, Journal of Financial Economics, Volume 89, Issue 3, September 2008, Pages 467-487.
5. Jue Wang. Innovation and government intervention: A comparison of Singapore and Hong Kong. In: Research Policy 47.2 (Mar. 2018), 399-412.
6. Wesley M Cohen and Steven Klepper, A Reprise of Size and R&D. In: Economic Journal (1996), 106 (437), pp. 925-51.
7. Gaetan de Rassenfosse, Adam Jaffe, and Emilio Raiteri. The procurement of innovation by the U.S. government. In: PLOS ONE 14 (Aug. 2019), pp. 1-11.
8. Katila, R. Measuring innovation performance. In: International Journal of Business Performance Measurement (2000), 2: 180-193.
9. P. A. Geroski. Entry, Innovation and Productivity Growth. In: The Review of Economics and Statistics 71.4 (1989), 572-578.
10. R Rothwell and W Zegweld. Industrial Innovation and Public Policy: Preparing for the 1980s and the 1990s. In: London: Francis Pinter Publications (1981).
11. G. Symeonidis, Innovation, Firm Size and Market Structure: Schumpeterian Hypotheses and Some New Themes, OECD Economics Department Working Papers, 161 (1996).
12. S.A. Low and M.A. Isserman. Where Are the Innovative Entrepreneurs? Identifying Innovative Industries and Measuring Innovative Entrepreneurship. In: International Regional Science Review 38.2 (2015), 171-201.

Aneesha Chitgupi, Karthik Suresh and Diya Uday are researchers at XKDR Forum. We thank Devendra Damle, Josh Felman, Dr. R. A. Mashelkar, Amey Mashelkar, Megha Patnaik, Arjun Rajagopal, Anjali Sharma and the anonymous referees for their feedback and comments.

The market for Covid-19 vaccines and the tipping point to herd immunity

by Ajay Shah.

Many firms are developing Covid-19 vaccines. Enormous resources have to be deployed, up front, to develop a vaccine and to build manufacturing capacity. It is likely that many vaccines will get through to approval in mature regulatory regimes. Not all vaccines will work identically for all situations, e.g. some vaccines may work better for an elderly person than others.

It is commonly assumed that the global market size for a Covid-19 vaccine is about 6 billion people. In this article, we argue that this might not be the case. Let's think about the situation in the market once one or more vaccine reaches the market.

The buyers perspective before vaccine sales have commenced

The private gain for an individual from buying a vaccine are shaped by the probability of getting sick when leading an unconstrained life. This is shaped by the extent to which Covid-19 has burned through the communities that the person plans to engage with. As an example, in the slums of Bombay or Delhi, herd immunity has set in. A person living there knows that few people in her circles are now getting sick, and she feels relatively safe. Well known factors such as age and co-morbidities will also shape the threat perception of each person. Therefore, for her, the gains from a vaccine are relatively modest, and the willingness to pay is small.

In each city of the world, there is a different numerical value for the attack rate (the fraction of people who are infectious) and the extent of immunity. The state of the epidemic in Pune is different from that in Bombay. As time passes, each city is inching towards herd immunity, and the passage of time thus diminishes interest in paying for a vaccine. Vaccine IP and manufacturing facilities are wasting assets.

It it were possible to develop a combination of tests that add up to an `immunity passport', then the price of this test and the odds of coming out positive would shape the demand function for the vaccine.

Progress on immunisation and herd immunity

Into this world, let us imagine that the sale of multiple vaccines commences. At first, there would be a rush of demand and high prices. As immunisation progresses, the attack rate would go down and the gains from buying the vaccine would further go down. In places like Bombay and Delhi, where a considerable proportion of the population has already been exposed to the disease, when a modest fraction of the population is vaccinated, this could tip the population over into herd immunity, and the disease could die down.

In such a world, vaccine makers face the prospect of a short hot market. At first, vaccine demand will be high and the factories will not be able to keep pace. Competition will come about and that will exert pressure on prices. In a city like Bombay, with about 20 million people, after (say) 5 million persons buy the vaccine, this may significantly change the threat perception in the eyes of the average individual. Vaccine demand would then decline.

Under such numerical values, the market potential in Bombay is not roughly \$50 $\times$ 20 million people or \$1 billion, but perhaps more like \$25 $\times$ 5 million people or about \$125 million.

All of this reduced revenue potential will go to the first few firms that get 5 million doses into the Bombay market. Competition would exert downward pressure on the price, demand would tail off as herd immunity sets in, and there would be a price crash. The late comers would flood the market with output but would obtain low revenues in return.

The vaccine demand collapse in a simple model and in the real world

We have always known that a vaccine is not just a private good; there is a positive externality. The novel idea of this article is about tipping points.

Consider a simple model in which herd immunity is achieved at 60%. Suppose 50% of the population is already immune and knows it. The first 10% that gets the vaccine tip the system over to $R_0<1$ and then the fires start dying out. Once the fires start dying out, the attack rate goes down, the threat perception changes, and the incentive for private people to buy the vaccine drops a lot. Under these conditions, the positive externality imposed by vaccine purchase by the early vaccine buyers, upon the overall system, is particularly large.

A key factor that drives behaviour in this model is that when a person is immune, she knows it and then has no incentive to buy a vaccine. In the real world, people don't know whether they are immune, and would be more inclined to buy a vaccine just to be safe. In the limit, the veil of ignorance is complete, nobody is able to assess the threat, and everyone wants to buy a vaccine.

In the real world, the veil of ignorance is not complete. At every place, people do have a personal judgement about the threat level based on the extent to which their friends and family are getting sick (or not) per month. Age and co-morbidities will also shape vaccine demand. As a general principle, it is always wise to think that humans are sentient optimising creatures. Individuals have a noisy estimator of the threat that they face and this will shape their willingness to pay for a vaccine.

Wall street tells Main street what to do

These problems feed into the thought process of private firms and shape the commitments of capital to the problems of vaccine development and manufacturing when faced with a novel epidemic. 

Numerous vaccines are under development. The process of vaccine approval is necessarily slow. At present, we generally think that over time, one by one, many of these vaccines will get through to the market. By the reasoning of this article, the first few will get through, within a few months the market will collapse, and all funding will be yanked for other projects. This will be a bit reminiscent of how funding for vaccines against Sars-Cov-1 was abruptly yanked when the funders realised that Sars-Cov-1 had reached $R_0<1$.

The numerical values used here (e.g. 60% for herd immunity, 5 million immunised in Bombay to tip over into herd immunity, $50, etc.) are of course purely illustrative. To translate these ideas into practical calculations requires data on the extent to which immunity has come about. In many places worldwide, there are good estimates of the persons who have antibodies, but there is more to immunity than measured antibodies. In India, the information available about the state of the disease in (say) Bombay is rather poor.

If we take this dynamics of the vaccine market seriously, vaccine makers have an incentive to create such datasets. Alongside the construction of such datasets, there is a need for derivatives trading on underlyings such as the fraction of Bombay residents who have antibodies.

The argument of this article is a special case of the long-standing problems of incentives for vaccine development. An effective pathway for state intervention, and philanthropic capital, lies in offering contracts for R&D and manufacturing which change the incentives of private persons to engage in these activities.

Implications

To the extent that this reasoning is correct, individuals will at first face a vaccine market with high prices and shortages. For many individuals, particularly for low-risk persons, there is a tradeoff between paying more to get the vaccine early versus paying less to get it late or even to not get vaccinated if the pandemic has subsided.

For firms with a vaccine under development, this article paints a winner-takes-all scenario, where the first few vendors who get output on scale will capture all the revenue. To the extent that this reasoning is correct, plodding along to the finish line late will induce low revenues.

For policy makers and philanthropic capital, it is important to avoid a `coronavirus winter', a collapse in coronavirus research of the kind which happened after the SARS epidemic achieved $R_0<1$. There is enormous knowledge, and capable teams, which has been created by the early gold rush of building vaccines against SARS-Cov-2. This knowledge should not be lost. As an example, it would be nice if research groups will publish research papers and release code before they put out the lights. We need to think of the sustainable frameworks, where we achieve a new normal of high R&D into pathogens that can trigger pandemics.

The economics of releasing the V-band and E-band spectrum in India

by Sudipto Banerjee, Mayank Mishra and Suyash Rai.

Internet usage in India has witnessed an enormous growth in last few years. The wireless data usage in 2017 has increased from 20,092 million GB per year from 828 million GB per year in 2014, showing a growth of more than 24 times. This increasing use of data is causing congestion in the existing bands which finally affects the quality of services provided to consumers. In order to cater to this surge in data consumption, it is essential that there should be a commensurate increase in supply of broadband internet. Perhaps the approach to the supply also needs to change. Availability of additional spectrum is an important piece of this puzzle. V-band (57 GHz - 64 GHz) and E-band (71-76 GHz and 81-86 GHz) are two microwave bands which can be useful for bridging this need for additional spectrum. Spectrum in these bands can be used for high capacity data transmissions for last mile connectivity over short distances ranging from 200 metres to 3 km. These bands can be put to a variety of backhaul (i.e. for connecting towers). V-band can also be used for access under the Wi-Gig standards.

It has recently been reported that the Department of Telecommunications is weighing administrative allocation as the method for releasing the spectrum in the E-band and V-band, and it will take the Attorney General's opinion in this regard. This legal opinion is considered necessary because of the Supreme Court's judgment in the 2G case. The allegations of irregularity in 2G spectrum allocation led to judicial scrutiny of the method of allocation and also much public discussion. The Supreme Court quashed several spectrum licenses granted due to irregularities in the manner of allocation of spectrum to licensees on first-come-first-served basis.

After a Presidential reference, the Supreme Court had clarified that it is the prerogative of the Government to decide the methodology of alienation of other public resources, provided the method is transparent, fair and backed by social or welfare purpose. The Court also stated that revenue maximisation need not be the sole objective while alienating public resources and in fact this is subservient to the goal of serving common good of the society. Therefore, as the Government decides to release this presently unreleased spectrum, it should consider the overall economic impacts of the alternative strategies for releasing the spectrum. On this issue, we recently published a Working Paper that provides an overview of the uses of V-band and E-band spectrum, and how we may think about choosing a suitable method for releasing this spectrum. This blogpost gives an overview of the paper.

Licensing approaches

There can be four main approaches to releasing the spectrum. First, Individual authorisation with individual licensing is the conventional link-by-link allocation involving individual frequency planning/coordination. In this method, the allocation of spectrum is usually done using traditional procedures for issuing licenses, which involves a selection process by the administration. Sometimes, the administration delegates this task to the operators, but it keeps control of the national and cross-border interference situation. Second, individual authorisation with light licensing is also a method of giving exclusive usage authorisation to certain service providers for a period of time, but the method of licensing is simpler, and may involve 'first come first served' procedures. In this method, allocating authority typically places a limitation on the number of users in a given area.

Third, general authorisation with light licensing is a combination of license-exempt use and some degree of protection of users of spectrum. There is no individual frequency coordination, but the user is required to notify the authorities with the position and characteristics of the links. The database of installed stations containing appropriate technical parameters is publicly available. Importantly, in this method, there is no limitation on the number of users. Fourth, the license exempt method offers the most flexible and low cost usage of spectrum. It does not require even notification or registration, and does not mandate any individual frequency coordination. This method has worked well in specific bands (e.g. 2.4 and 5.8 GHz used for Wi-Fi access) where short range devices are allocated, but fixed service applications may also be accommodated. Although this does not guarantee any interference protection by the regulator, alternate interference management techniques are available to deal with the issue. In addition to these approaches, there are block assignment regimes, where assignment is made through renewable licensing or through permanent public auctions, or through other allocation mechanisms.

Although auction is often assumed to be the most suitable method for allocating spectrum, studies on unlicensed spectrum suggest that such availability of spectrum can also lead to significant benefits for the economy, many of which seem to be arising out of unpredictable uses, which may not have occurred if the spectrum had been auctioned. One interesting example is the use of RFID in clothing sector. According to a study done in 2014, this use generated about USD 100 billion of annual benefits for the US economy. Arguably, if the usage of this spectrum had been restricted, this usage would not have scaled up in such manner. It is important to understand that absence of auction is not the same as giving arbitrary benefits to hand-picked service providers. If the regime is open and transparent, the spectrum can be made available for use by a variety of potential service providers and users without giving unfair advantage to anyone in particular.

Most countries acknowledge that certain spectrum bands are best left unlicensed, or may be subjected to a "light touch" licensing regime, with minimal regulation. In India also, a number of spectrum bands are unlicensed, like 2.4 GHz and 5.8 GHz spectrum bands used for Wi-Fi access; 865 MHz - 867 MHz band used by RFID devices; 402 MHz - 405 MHz spectrum band used for medical wireless devices; and so on. A number of countries have adopted license free frameworks for adopting the V-band, including USA, UK, Switzerland, Japan, Korea, China, Canada, Malaysia and Philippines. Although international experience is useful, we also need to consider Indian context, and how this spectrum may be put to use in this context. In the paper, we consider the various potential uses of this spectrum, and attempt to quantify the scale of this usage in an optimal scenario.

The potential uses of V-band and E-band in India

The context of broadband Internet in India will determine the kinds of benefits that India can get from V-band and E-band. There are certain key aspects of this context. First, in India, most users access broadband internet through wireless networks. However, wireless broadband networks has certain limitations. In densely populated areas, as more people get on to wireless broadband, the mobile spectrum bands may get congested. This necessitates more cell sites and higher backhaul speeds. Compared to wired connections, especially fiber optic connections, mobile broadband provides lower speeds and less consistent connectivity. Further, a low density of wired connections constrains the potential of developing community hotspots, where residential or business short-range networks are made available for use by other users of the network. Globally, the proliferation of such hotspots is one of the most remarkable stories in the growth of Internet in recent years. Such hotspots provide high speed, consistent connectivity, while offloading from mobile networks - a benefit that India will not be able to realise without proliferation of fixed broadband connections.

Second, a negligible percentage of the fixed broadband connections are fibre optic-based. Most of the wired connections use DSL, Dial-up, or Ethernet, all of which offer potentially lower speeds than fibre optic. This situation is very different from what is seen in developed countries, and also in comparable developing countries. Third, India has a low density of commercial Wi-Fi hotspots. Such hotspots can help augment the mobile broadband and private residential and commercial hotspots as well as mobile broadband. Use of public Wi-Fi can help offer consistent, reliable and high speed Internet to users, while decongesting mobile broadband. India has only about 36,270 public Wi-Fi hotspots. The total number of public Wi-Fi hotspots in the world is over 12 million.

In the coming years, the main challenges for internet access in India are likely to be round consistency and quality of access. To address these challenges, the intermediate policy goals for broadband Internet India should be: expanding access to fixed broadband; decongestion of mobile broadband in dense urban environments; and proliferating Wi-Fi hotspots. Achieving these intermediate goals would help improve quality and consistency of internet access in India. This will not happen automatically, and requires policy focus, which may begin with rethinking spectrum allocation methods for these spectrum bands.

In the paper, we have identified the following key uses of the V-band and E-band spectrum, and tried to quantify the scale of these uses:

• Support proliferation of commercial Wi-Fi and Wi-Gig hotspots: these bands can help backhaul the commercial Wi-Fi infrastructure in a cheaper and quicker manner, especially in dense urban locations. These bands can also promote the proliferation of Wi-Gig hotspots. Wi-Gig networks use V-band which provides wider channels than standard Wi-Fi, resulting in significantly faster data speeds.
• Support expansion of fixed broadband Internet in urban areas: these bands can help solve the last mile problems of getting high speed wired broadband Internet into dense urban locations.
• Backhaul for mobile broadband: these bands can provide higher capacity backhaul for mobile broadband, thereby easing congestion.
• Other uses: these bands can be put to a variety of other uses. These, inter alia, include: extension of local area networks between buildings within a building complex; Internet of Things (IoT); Vehicle to vehicle communication and Augmented Reality (AR)/Virtual Reality (VR) Systems among others.

Given India context of high urban population density, and many urban areas with old and dense construction, the scale of usage of these spectrum bands is likely to be quite high. We tentatively find that if these spectrum bands are allowed to be used optimally, they could lead to improved speed of internet, increased consistency in internet access, and greater volume of internet usage. However, there are many potential uses of this spectrum that are difficult to predict at present. For instance, the potential for IoT is very difficult to predict at the moment, albeit a lot is being said about how far IoT can go. These are early days for the adoption of this spectrum and the allocation method should take into account this uncertainty about the potential uses.

Mapping the economic benefits arising from the uses

In choosing a method for releasing this spectrum, the focus should be on maximising the net benefit for the society as a whole. Given the paucity of relevant data and earlier studies, we are unable to reasonably monetise the economic value of these benefits. However, we attempt to map the types of uses with various types of economic benefits that may accrue from them.

If the V-band and E-band spectrum is delicensed or lightly licensed, the pass-through cost of this spectrum will be zero or very small, and only substantial cost will be installation costs. In a competitive market, ceteris paribus, reduction in costs will lead to lower prices for consumers. Given the price elasticity of demand for internet, and the rapid evolution of technology, this availability will lead to higher usage of broadband internet by consumers, allow new consumers to use broadband internet and enable innovative business models and technologies.

The use of these spectrum bands will lead to a reduction in costs and create opportunity to reach hitherto unreachable locations in dense urban environments with high speed Internet. This will lead to a shift in the supply curve, so that more quantity is made available at a given price. If the quality of Internet access improves, as is expected from the use of V-band and E-band, there may also be a shift in the demand curve, as users may be willing to pay more for the connection. Quality improvement also has larger economic benefits. For instance, if the speed of Internet usage increases, users will be able to put their connections to a wider variety of uses, especially in commercial contexts.

Following are the key economic benefits expected to arise from the uses of V-band and E-band spectrum. It should be noted that all these benefits cannot be fully attributed to these spectrum bands. Some of them, such as benefits from Wi-Gig devices, may be fully attributed to these spectrum bands, because they rely completely on the availability of this spectrum. Other benefits can be partially attributed to these bands.

• Producer surplus due to offloading from mobile broadband: Producer surplus usually increases if the cost somehow falls without change in price charged. It can also increase if the price increases without corresponding increase in costs. The use of these spectrum bands would enable offloading from mobile broadband which will generate producer surplus.
• Producer surplus from lower backhaul costs for mobile broadband: Lower backhaul costs could lead to producer surplus for mobile broadband service providers. This can be calculated by comparing the costs of backhaul using V-band and E-band with the cost of establishing infrastructure for a similar quality of service using other backhaul solutions, such as fibre optic cables. Most of this surplus would arise in congested areas.
• Consumer surplus from use of commercial Wi-Fi hotspots and fixed broadband: Consumer surplus is the benefit that consumer derive from use of a service or good. A variety of sources for consumer surplus can be identified on the basis of the uses of V-band and E-band presented in the previous section: consumer surplus from commercial Wi-Fi in dense locations; consumer surplus from free Internet given by Wi-Fi hotspot providers; consumer surplus from greater use of Wi-Fi and Wi-Gig devices; consumer surplus from indoor use of fixed broadband.
• GDP contributions: In addition to consumer surplus and producer surplus, there are also GDP contributions that may arise from the use of this spectrum. These are mostly in terms of new or improved businesses and technologies that are enabled by this spectrum band, such as usage of commercial Wi-Fi and Wi-Gig hotspots, higher speed of internet, Wi-Fi and Wi-Gig device sales, new or modified businesses and technologies (eg. IoT).

Since most of these benefits will accrue to consumers and producers, this will also create potential for the Government to extract part of this benefit as additional tax collection. For instance, sale of devices and provision of services will create opportunities for the Government to collect taxes from these activities. Further, to the extent that these activities will lead to additional profits for service providers, part of that profit will be taxed by the Government. The concern that the Government may lose out on some non-tax revenue if it chooses to delicense this spectrum may be overcome by these revenue opportunities. Further, in light licensing regimes, some fees may also be levied on the usage of the band. However, as discussed earlier, keeping the fees high may impede usage of these bands, and may discourage some types of usage that may generate significant economic benefits. The experience of Wi-Fi and RFID spectrum supports this contention.

Conclusion

In conclusion, four key takeaways emerge from this analysis. First, while choosing a method for releasing this spectrum, the focus should be on ensuring maximum aggregate benefits for the society, and not short-term revenue maximisation for the Government. Among other things, this means that the potential of these bands to help improve India's overall system of broadband Internet access should be realised. Some of the major limitations in the present system could be partially overcome by use of these spectrum bands, along with other suitable policy measures.

Second, although the economic benefits of these spectrum bands are likely to be substantial, studies on economic benefits of previously unlicensed spectrum bands suggest that the variety and scale of economic benefits may increase over a period of time if easy access to spectrum is enabled. As has happened with other unlicensed spectrum bands, innovation and competition may lead to many types of uses that are difficult to anticipate at present. Hence, it would make sense to liberalise the spectrum without any cumbersome procedures or high fees.

Third, many of the benefits are not realised by the service providers, and accrue in terms of consumer surplus and GDP contributions of businesses and technological innovations spurred by the availability of this spectrum. If the Government decides to target revenue maximisation while allocating the spectrum, it will mainly be able to extract part of the producer surplus. However, this will have effect on proliferation and therefore, on consumer surplus and GDP contributions. This may lead to significantly lower economic benefits of the spectrum for the economy as a whole. So, it may be better to not allocate this spectrum based on methods of individual authorisation. Instead, general authorisation with light licensing or license-exempt approaches may be explored. These approaches also allow the government to extract part of the economic benefits later, especially in the form of taxes.

Fourth, in thinking about the strategy to release the spectrum, it is important to align with global device ecosystems and standards, so that India can benefit from economies of scale in production of devices, and potentially become a manufacturing hub for the devices.

 

The authors are researchers at the National Institute of Public Finance and Policy.

Estimating the impact of the draft drone regulations

by Devendra Damle and Shubho Roy.

The Directorate General of Civil Aviation (DGCA) recently released draft guidelines for regulating civilian drones, for public comments. Clause 12.21.e) of the guidelines establishes a no-fly zone in all areas within 50 km of India's land border. In this article we try to estimate the footprint of this clause on the economic activity in these areas and on the residents.

The draft guidelines lay out the legal requirements for drone operations in India. They include provisions to classify, license, set safety requirements, and operational parameters for drones including drone pilot licensing. One of the provisions: Clause 12.21.e) of the guidelines states:

12.21 No RPA shall be flown:
...
e) Within 50 km from international border which includes Line of Control (LoC), Line of Actual Control (LAC) and Actual Ground Position Line (AGPL);

[The draft guidelines refer to drones as ``Remotely Piloted Aircraft'' (RPA).]


The drone guidelines are a type of delegated-legislation (regulations are another). The legislature of a modern economy is usually neither equipped, nor has the time to legislate all details of a law. Therefore, the legislature usually creates the broad legal framework, and the authority to fill in the details is delegated to the executive or statutory bodies (like regulators). Since government agencies, unlike legislators, are not elected representatives of the people. Therefore, the regulations/guidelines made by such agencies are not strictly democratic.

To address this democratic deficit, legislatures place certain requirements on government agencies making regulations. Two common requirements are to (i) invite public comments on draft regulations, and (ii) conduct a Cost-Benefit Analysis (CBA). The agency is required to estimate the costs of complying with the regulations, and the benefits arising out of the regulations. Neither inviting public comments nor conducting CBAs is a universal requirement in India. The DGCA, should be commended for inviting public comments on the draft regulations. It has, however, not conducted a CBA of the regulations.

A CBA can sharpen the decision making of a government agency. Even before public consultation is done, a CBA provides the government agency an idea of potential costs and benefits. Consider the way the New Zealand government did a CBA for a proposed regulation on drones. It gave five options for regulation (including ban) and analysed the impact of each one of them. The impact of each option was then measured against the stated objective of the regulation. Another example is the US Federal Aviation Authority's (FAA) proposed regulation on training and licensing of drones. Even on this narrow point the FAA carried out a detailed analysis of the total costs and benefits to society. On the side of costs, the FAA estimates that each pilot will have to spend USD 150 to be trained. This training is expected to result in social benefits of USD 733 million (pessimistic estimate) to USD 9 billion (optimistic estimate) over five years. The FAA provides detailed information about assumptions and methodology for interested parties to do their own calculations.

In this article we try to analyse the impact of one provision of the regulations: the no-fly zone. Such analyses can be used in a CBA of drone regulations.

Impact of the no-fly zone

It is difficult to predict the impact of any new technology. Before the Internet, mobile phones or GPS became ubiquitous it would have been impossible to predict all the innovative ways they would change human life. Similarly, drones are a disruptive innovation that may have a profound impact on us. To estimate the impact of 12.21.e), we examine three sectors in which drones are already changing established processes or hold great promise to do so: (1) general services to the population, (2) agriculture, and (3) infrastructure monitoring.

General services to the population: Drones will change the way goods and services are delivered to the masses. They might be especially effective in border areas which typically suffer from low connectivity. For example, Zipline, a private company that uses drones to deliver blood to hospitals in the mountainous region of Rwanda, has cut down the delivery-time from 4 hours to 45 minutes. Facebook plans to use drones to provide internet connectivity in remote areas. In urban areas, drones can be used for governance. In Gurgaon, for example, drones are being used to conduct land-use surveys, for assessing property tax, checking encroachments, and urban planning. In the private sector, drone have multiple applications which go beyond the obvious courier and delivery services. For example, private construction companies can use them to monitor construction and maintain a safe working environment. Drone photography and videography are a new source of economic activity, which will be denied to people living in border areas.

Agriculture: Many Indians still derive their income from agriculture and drone technology is already changing agriculture in India. In Karnataka and Haryana, drones are going to be deployed for spraying pesticides on crops. Drones can identify plant disease before any visible signs show and alert farmers or spray crops with appropriate pesticides. In Gujarat, Maharashtra, Rajasthan and Madhya Pradesh insurance companies are using drones for quick assessment of crop damage for crop-insurance payouts.

Infrastructure: Drones can be used for inspection and monitoring of infrastructure projects. The Prime Minister, Narendra Modi, recently suggested using drones to monitor rural road construction projects and to keep illegal mining in check. Power companies are using drones to monitor power lines in remote and inaccessible areas. Similarly, the Gas Authority of India Ltd. is using drones to inspect sections of gas pipelines that pass through difficult terrain. The no-fly zone effectively bans this kind of drone usage in the border districts, many of which have difficult terrain.

Methodology

Our approach to measuring the impact was mapping the 50 km no-fly zone using geo-spatial analysis, and then, estimating how many people, how many urban areas, and how much land, agricultural area and infrastructure are situated in the zone.

Estimation scheme:

As sub-district level geo-spatial data for administrative borders are not available, we estimate at the district-level for population, agricultural workers, agricultural area, and operational land holdings in the no-fly zone. To account for the lack of sub-district level data we make three estimates: pessimistic, realistic and optimistic. To make our estimates—

1. We split the districts into three categories, based on the percentage area of the district covered by the no-fly zone as: X, Y & Z. Category X districts are those where the no-fly zone covers less than 50% of the land area. For Category Y districts, the coverage is between 50–90%. Category Z are districts where the coverage is more than 90%.
2. For the pessimistic estimates, we assume that the percentage of the population, agricultural workers, agricultural area, and operational land holdings falling in the no-fly zone are the same as the percentage of the district's land area covered by it. For example, if the no-fly zone covers 40% of a district's land area, then we assume that 40% of its total population, agricultural area, and operational land holdings lie in the no-fly zone.
3. For the realistic estimates, we halve the pessimistic estimates for all Category X districts. For Category Y and Z districts we take the same values as the pessimistic estimate.
4. For the optimistic scenario, we use a scheme similar to the realistic estimate, but we halve the pessimistic estimates for Category X and Category Y districts. For Category Z districts we use the same value as the pessimistic estimates.

Data Sources:

For the analysis we used the following openly-accessible data:

• Census 2011 — for population, and for number of cultivators and agricultural labourers;
• Agricultural Census 2010–11 — for agricultural area and number of operational land holdings;
• Datameet — for shape-files of borders of States and Districts in India based on the 2011 Census (Note: Shape-file is a commonly used file format for storing and exchanging geo-spatial data. See here for details.);
• Open Street Maps — for the shape-files of Actual Ground Position Line, for infrastructure, and locations of urban areas; and
• ESRI's A-16 Federally Derived Data, Open Data Catalogue — for shape-files of Line of Control and Line of Actual Control.

The number of districts has increased since 2011, from 640 to the current number of 707. We have considered the population, number of districts, and district boundaries as given in the 2011 Census.


Plotting the no-fly zone
 

• We made a base-map using state and district borders from Datameet;
• We then added the LoC and LAC, downloaded from the ESRI database and AGPL from Open Street Maps;
• To demarcate all areas in India within 50 km from the land border (and from the LoC, LAC and AGPL in Jammu and Kashmir), we plotted a 50 km inward buffer. This represents the no-fly zone.

Calculating the impact
 

• We calculated the area of overlap between the no-fly zone and each district, to calculate what percentage of the district's land area is inside the no-fly zone.
• To estimate the population, number of farmers, agricultural area, and operational land holdings in the no-fly zone, we multiplied the respective totals for the district by the percentage of the district's land area lying within the no-fly zone, along with the applicable discounts.
• We used the previously plotted no-fly zone as a filter, to extract urban areas (cities and towns), canals, roads, railway stations and bridges falling in the no-fly zone from the Open Street Maps data dump for all of India (downloaded on 17/12/2017). The operation is analogous to using a cookie-cutter to cut out a shape from a flat piece of dough.

Results

Here is the data (geo-spatial and tables) to reproduce the results.

The following figure shows the total area of India covered under the 50km no-fly zone.

Figure: Area covered by drone ban

More than a quarter of India's districts (168 out of 640) across 18 states fall at least partially within the 50 km no-fly zone. In more than 10% of India's districts (65 out of 640) the no-fly zone covers more than 90% of their land area. Of these, 39 districts fall completely inside the no-fly zone. Table 1 shows the population, number of farmers, land area, and agricultural land covered by the no-fly zone.

Table.1: Summary of area covered by the 50 km no-fly zone

Indicator	Pessimistic estimate		Realistic estimate		Optimistic estimate
	Total	% of India	Total	% of India	Total	% of India
No. of Districts	168	26.21	-	-	-	-
Land Area*	420.88	12.80	-	-	-	-
Agricultural Area*	122.56	8.89	106.85	7.75	88.98	6.45
Population^	141.27	10.67	129.22	9.76	115.82	8.75
Cultivators^	10.95	8.60	9.92	7.79	8.44	6.63
Agricultural Labourers^	14.25	9.88	13.06	9.05	11.44	7.93
Operational Land Holdings^	13.35	9.65	12.29	8.88	10.64	7.69

* in '000 sq.km.
^ in millions

As Table 1 shows, 8–10% of the total population of India will be affected by the no-fly zone. It will impact between 6–9% of all the farmers in India. The total affected population in the pessimistic scenario (141.27 million), is greater than the total population of the 75 largest cities in India put together (140.33 million).

Jammu & Kashmir (20 out of 23), and Assam (20 out of 27) have the highest number of affected districts followed by Uttar Pradesh (15 out of 71) and Bihar (14 out of 38). In terms of percentage of total number of districts affected, Mizoram, Sikkim, Tripura are at the top, at 100%. This means that every single district in these states is at least partially by the no-fly zone. These three states are followed by Jammu & Kashmir (91%), Manipur (89%), and Meghalaya (86%).

In terms of percentage of total land area covered, the ban disproportionately affects the northeastern states. Sikkim and Tripura are entirely covered by the no-fly zone. The no-fly zone covers 86% of the land area in Mizoram, more than 60% in Manipur, Arunachal Pradesh and Meghalaya, and more than 50% in Nagaland. These are all small states, which one would expect to have high coverage, but some of the larger states are also heavily affected. The no-fly zone covers nearly 44% of the total area of West Bengal, nearly 39% of Bihar, and nearly 33% of Punjab.

While a large chunk of the population affected by the no-fly zone will be from rural areas and small towns, some large cities will be affected as well. Table 2 gives an overview of the infrastructure and urban areas located inside the 50 km no-fly zone.

Table.2: Urban areas and infrastructure inside the 50 km zone no-fly zone

Item	Quantity
State Capitals	4
	Agartala, Gangtok, Shillong, Srinagar
Cities (other than state capitals)	9
Towns	325
Canals	3127 km
Roads	70829 km
Railway stations	550
Bridges	3349

As Table 2 shows, a total of 13 cities fall in the no-fly zone. Of these, four are state capitals. Amritsar and Jammu are among the other major cities that fall inside the no-fly zone. A significant amount of infrastructure also lies in it.

With such large areas affected by the proposed ban, it becomes necessary to ponder the costs and benefits of such a blanket ban.

Drawbacks of blanket bans

These draft regulations will exclude a substantial part of India's population from the benefits of drone technology. An example of a similar blanket ban, based solely on geography, is the Ministry of Defence's map restriction policy of 1967 (revised in 2017). It restricts the sale of high-resolution topographical maps of all border areas to civilians. The restricted zone covers all areas within approximately 80 km of the border, which is nearly 40% of India's total land area. This ban, like the drone ban, was also enacted due to national security considerations. However, with the advent of satellite imaging technology, the same maps are easily available from international vendors. This means the ban is not only redundant, but has also resulted in lost revenue for the Government of India. It also means that foreign nationals have easier access to high-resolution topographical maps of restricted areas in India than agricultural cooperatives, gram panchayats, municipal bodies, companies and Indian citizens residing in these areas.

For the government, a blanket ban seems attractive because it (apparently) requires the least amount of state capacity to enforce. In the case of the no-fly zone, all the government has to do is penalise any person flying drones in the no-fly zone. It does not have to determine whether the drone use was legitimate or not. The government also does not have to invest in setting up offices and systems to license and monitor use. However, blanket bans are also the most expensive form of regulatory intervention. They do not distinguish between legitimate and illegitimate activity, and treat both the same way. In doing so blanket bans impose huge costs on those they regulate.

India's economic history is peppered with instances where blanket bans were imposed, only to later realise they were hampering economic development. Banning entry of foreign investors, financial derivatives, and private participation in banking and insurance are a few notable examples. Thankfully, the country has begun to undo them gradually, but the damage has already been done.

In some cases, India has not taken the ban approach. India did not ban mobile phones and internet near the border. Instead, in many border areas, the government has worked harder to provide last-mile internet and mobile connectivity. While mobile phones and internet also pose national security concerns, the country did not choose to go down the banning route for them.

Similarly, for drones, we might need a more nuanced approach to regulation that tries to balance national security with the legitimate needs of residents in the no-fly zone. For example, even today, farmers in Punjab are allowed to grow crops in no-mans-land, beyond the border fence with Pakistan. The security concerns there are addressed by security checks rather than a complete ban on farming. Farmers in 10 districts of Punjab (situated well away from the same border) will be unable to use drones for agriculture. In Punjab, a state which already suffers from overuse of pesticides, drones can decrease their use by only spraying affected crops. The security concerns, like in the case of farming in no-mans land, can be met with monitored use.

The blanket ban also ignores India's border policy. 33 districts (in Uttarakhand, U.P, Bihar and Sikkim), which lie in the no-fly zone, are on or near the border with Nepal (but not China). Similarly, 12 districts in Assam which are in the no-fly zone are on or near the border with Bhutan (but not China or Bangladesh). India has good relations and an open border policy with both these are nations. Using the same standard (i.e. blanket ban) as the one used for districts on "sensitive borders" is a disproportionate response. It demonstrates a lack of risk-based regulatory approach.

The ban, as it stands is inequitable. It disproportionately affects states sharing a land border with other countries, especially the north-eastern states. If drone technology starts impacting quality of life, persons in the no-fly zone may be deprived of economic opportunities. Such a deprivation is worse when it is done through regulation. Since a regulation is issued by an un-elected government agency; it denies Indians in the no-fly zone their right to participate in the legislative process.

Conclusion

India needs a regulatory framework for drones. The advent of any new, disruptive technology creates tension between the freedom of people (to use to it, to improve their lives) and national-security concerns. Building state capacity is hard, and building it close to borders is harder. However, bans cannot be a substitute for it. In the case of civilian drones in border areas, closer monitoring, cooperation with border forces, involvement of local authorities, and higher security clearances are some alternative approaches that could better balance the tension. Our drone regulations need to create this balance.

References

Cost of compliance for clinical establishments, by Manya Nayar and Shubho Roy, Ajay Shah's Blog (September 2017)

India needs drones by Shefali Malhotra and Shubho Roy, Ajay Shah's Blog (June 2016).

A cost-benefit analysis of Aadhaar, National Institute of Public Finance and Policy (November 2012).


The authors are researchers at the National Institute of Public Finance and Policy, New Delhi. We thank Shekhar Harikumar for valuable inputs.