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.

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.

How competitive is bidding in infrastructure public procurement? A study of road and water projects in five Indian states

by Charmi Mehta and Diya Uday.

Introduction

Competition is central to the functioning of a market economy. Market power is a market failure, and governments around the world work hard to fight anti-competitive behaviour and market capture by firms. When competitive pressure is lacking, firms fail to achieve efficiency in production.

An efficient system for government procurement is one where the government obtains purchases for the lowest possible price. In the international literature, studies have shown the linkage between higher competition and lower procurement prices (Estache et al. 2008; Hanak and Muvchova 2015), greater efficiency (Adam et al. 2021) and a lower rate of corruption and kickbacks (Knack et al. 2017).

It is difficult to make normative claims about what is the adequate level of competition. Economists have emphasised contestability of a market as the underlying source of efficiency; simple proxies like concentration ratios do not correctly evoke the level of competition. Researchers in the field of competition levels in government contracting have used the number of bids received for a tender as an empirical measure.

There is some international evidence from developing countries about the desirable numbers of bidders in infrastructure public procurement. There are thumb rules, such as desiring eight-or-more bidders for a roads contract (Gupta 2002, Estache et al. 2008) or seven-or-more for water projects (Estache et al. 2008). These normative numerical values would of course, not readily carry forward across locales, but we use them cautiously in the present work.

In the field of government contracting in India, there is anecdotal evidence of anti-competitive behaviour in the market with reports of bid-rigging and collusion. In this article, we aim to step up from this to some statistical evidence. We ask: How much competition do we see in Indian infrastructure procurement? How does this vary across states and sub-sectors?

Methodology

We hand-construct a novel data-set with a sample of tenders from five states: Tamil Nadu, Odisha, Maharashtra, Uttar Pradesh and Kerala. This choice of states was shaped by the levels of spending on infrastructure, geographical heterogeneity, and data availability. We extracted data from 1000 randomly sampled, awarded e-tenders published by state governments on the Central Public Procurement Portal (CPPP) in the water and roads sector for 2018 and 2019.

CPPP is a centralised repository of tender data at the union and state level. Procuring entities across union and state tiers are obliged to publish their tenders on the portal. We use data solely from this portal to ensure consistency in variables and recording. The data-set includes:

1. States in the sample: Kerala, Maharashtra, Odisha, Tamil Nadu, Uttar Pradesh
2. Sectors covered: roads, water
3. Years covered: 2018, 2019
4. Total sample size for each sector: 500 tenders
5. Key words used while searching through the CPPP to select tenders: "road", "water".

We extract the number of bids received for each tender in order to examine the level of competition.

In the research literature, it is argued that three factors shape competition in public procurement:

The value of the contract

Larger contract sizes require greater capital and expertise, which act as an entry barrier for small/mid-size players in the market (Estache et al. 2009; McEvoy 2020).

Structure of the tender

Bundled tenders, where multiple works of different types are bundled into a single tender document, restrict competition to only those firms that can undertake the varied components of bundled tender (Estache et al. 2009). Dividing contracts into smaller lots bolsters MSME participation and thus competition (Hoekman et al. 2022).

Time taken to award contracts

Lengthy award schedules require participating firms to lock-in capital and resources for the bid amidst the uncertainty of winning the bid; this adversely impacts private sector enthusiasm towards bidding (World Bank 2020).

We will examine the extent to which the number of bids per tender correlates with these features.

Results

Table 1 summarises the statistics on the number of bids received across the five sample states for the years 2018 and 2019.

Table 1: Number of bids received in the infrastructure sector

	In the Roads sector
	2018				2019
	Min	Max	Median	Average	Min	Max	Median	Average
Maharashtra	1	4	3	3.26	1	11	3	3.94
Uttar Pradesh	1	11	3	4	2	9	3	3
Tamil Nadu	1	3	2	2	1	3	2	2.08
Kerala	1	8	2	2.08	1	7	2	1.80
Odisha	1	27	5	7	1	55	6	9.96
	In the Water sector
	2018				2019
	Min	Max	Median	Average	Min	Max	Median	Average
Maharashtra	1	34	3	5.56	1	17	3	4.17
Uttar Pradesh	1	8	3	3.04	1	16	3	4.05
Tamil Nadu	1	4	2	1	1	12	3	2
Kerala	1	5	2	2.08	1	5	3	2.28
Odisha	1	52	2	2.45	1	27	3	4

Source: Authors' compilation and calculation from CPPP data

 

Q.1. How competitive is infrastructure procurement?

We find that, with the exception of the road sector in Odisha, the level of competition in terms of the average number of firms bidding for the projects is lower than the normative thumb-rules from the literature. This holds across all states, sectors and years. Further, two features about the lack of competition in this data-set merits discussion:

1. Tenders where bids satisfy the thumb-rules of the literature

   We examine the fraction of tenders in our sample that satisfy these thumb rules, and count the tenders that got more than seven bids in the water sector and eight bids in the roads sector. Table 2 summarises these results. Here, we see that the roads sector fares worse than the water sector.

   Table 2: Fraction of tenders that receive bids meeting normative thumb rules (share in per cent of tenders)

   	Maharashtra	Uttar Pradesh	Tamil Nadu	Odisha	Kerala
   Roads	5	5	0	34	1
   Water	14	7	1	8	0

    
2. Tenders that received only one bid and were awarded

   We find several awarded tenders that attracted only single bids. This is true even in states with relatively higher levels of competition such as Uttar Pradesh, Maharashtra and Odisha. But there are more tenders with single bid awards in the states with the lowest levels of competition, namely, Tamil Nadu and Kerala. We find that single bid awards are more prevalent in the water sector.

Table 3: Fraction of awarded tenders that received only one bid (% of tenders in the sample)

	Maharashtra	Uttar Pradesh	Tamil Nadu	Odisha	Kerala
Roads	6	2	4	6	40
Water	8	6	52	8	21

Q.2. How do the findings vary across states and sectors?

The results are fairly consistent results across sectors and years. For instance, three states -- Uttar Pradesh, Maharashtra, and Odisha -- have higher levels of competition across both sectors when compared to the other observed states. Kerala and Tamil Nadu have lower levels of competition across both years for both sectors.

Q.3. What features of tenders correlate with a low number of bids?

In the literature, there has been interest in three features that may shape the level of competition: the value of the contract, the structure of the tender and the time taken to award contracts. In our data-set, however, these three factors do not correlate with the number of bidders.

Discussion

This evidence suggests there is a low level of competition in public procurement, in two sectors and five states. There are some systematic patterns, where some states and sectors fare worse in getting competitive bidding than others.

Competitive conditions seem to be the feature of a given state. This suggests that there are some features in states like Kerala or Tamil Nadu, which are inhibiting competition, and can be addressed in a way that would impact on government purchases across sectors.

A large number of tenders with a single bid that get awarded are a curious phenomenon and merit further research. These tenders are awarded under the previous Central Vigilance Commission (CVC) guidelines, which require that state Public Works Departments (PWD) cancel tenders that receive single bids at the first instance. Single bids could be accepted only if the procuring entity received only one bid, even after re-tendering. However, the recent General Instructions on Procurement and Project Management, allows the acceptance of single bids under certain conditions. These include: (i) the procurement was satisfactorily advertised and sufficient time was given for bid submission; (ii) the qualification criteria was not unduly restrictive; and (iii) the price in the bid is reasonable in comparison to market values.

Further research is required in extending this kind of work to other sectors and locales, to assess the extent to which the lack of competition is a more general phenomenon in public procurement in India. The source of this lack of competition also merit exploration.

There are limitations in how state organisations do procurement (Mehta and Thomas 2021), including potential gaps in the capacity of implementing rules (Roy and Uday 2020), inefficiencies of processes and timelines (Roy and Sharma 2020), and delayed payment of invoices (Mannivanan and Zaveri 2021). Such problems could create an inhospitable environment for bidding firms, and deter many good firms from taking interest in state purchases. Well incentivised state actors should solve these problems. This raises questions about the feedback loops that impinge upon state actors.

References

Antonio Estache and Atsushi Iimi, (Un)bundling Infrastructure Procurement: Evidence from Water Supply and Sewage Projects, Policy Research Working Paper No. 4854, World Bank, March 2009.

Antonio Estache and Atsushi Iimi, Procurement Efficiency for Infrastructure Development and Financial Needs Reassessed, Policy Research Working Paper No. 4662, World Bank, March 2008.

Bernard Hoekman and Bedri Taş, Policy and SME participation in public procurement, Vox EU - CEPR, 23 March 2022. 

Charmi Mehta and Susan Thomas, Lessons from the COVID-19 vaccine procurement of 2021, The LEAP Blog, 15 November 2021. 

Emma McEvoy, Small and Medium-Sized Enterprises (SME) Participation in Public Procurement, Maynooth University, 2020. 

Isabelle Adam , Alfredo Hernandez Sanchez and Mihály Fazekas, Global Public Procurement Open Competition Index, Government Transparency Institute, Working Paper Series: GTI-WP/2021:02, April 2021. 

Pavithra Mannivanan and Bhargavi Zaveri, How large is the payment delays problem in Indian public procurement?, The LEAP Blog, 22 March 2021. 

P. Manoj, Govt lifts the ‘fear’ on accepting single bids during public procurement tenders, The Hindu, 2 November 2021. 

Srabana Gupta, Competition and collusion in a government procurement auction market, Atlantic Economic Journal 30, 13–25, 2002. 

Shubho Roy and Anjali Sharma, What ails public procurement: an analysis of tender modifications in the pre-award process, The LEAP Blog, 26 November 2020. 

Shubho Roy and Diya Uday, Does India need a public procurement law?, The LEAP Blog, 19 August 2020. 

Stephen Knack, Nataliya Biletska and Kanishka Kacker, Deterring Kickbacks and Encouraging Entry in Public Procurement Markets : Evidence from Firm Surveys in 88 Developing Countries, World Bank Working Paper, May 2017. 

Tomáš Hanák and Petra Muchová, Impact of Competition on Prices in Public Sector Procurement, Procedia Computer Science, Volume 64, Pages 729-735, 2009. 

World Bank, Contracting with the government, World Bank Doing Business, 2020

 

 

Charmi Mehta and Diya Uday are CMI-XKDR Forum researchers. The authors thank Shailesh Phatak, Susan Thomas and Ajay Shah for their valuable inputs on this work; and Abhinav M from the Indian Institute of Human Settlements for his valuable research support.

Measuring institutional capacity in property tax systems: A case study of ten cities in India

by Diya Uday.

Property tax is ubiquitous with municipal finance. It provides local governments with the means to execute development strategies. In theory, property tax is an ideal candidate for supporting fiscal strategies in decentralised economies because the tax base is immobile making base identification and enforcement relatively easy (Kelly 2013). There are indications, however, that in India, we have not succeeded in doing property taxation well.

A national-level indicator of the performance of property taxes is the percentage of revenue generated from property taxes to the national GDP. Studies indicate that the proportion of revenues from property tax to GDP in India is low when compared with other countries. At the state-level, where property tax is a major source of revenue, there is evidence of revenue shortfalls, indicating the need for reforms. The policy responses for increasing revenues from property taxation, include increasing tax rates, revising taxation criteria and suggesting floor tax rates. But will these interventions be successful in improving the performance of the property tax system in cities?

A key factor in determining the success or failure of any policy intervention is institutional capacity. Policy interventions such as increases in property tax rates assume that ULBs are operating at optimal levels of institutional capacity and therefore increases in tax rates, property values or even improvements in tech infrastructure will optimise revenues from property taxes. In particular, these policies are founded on two main assumptions:

• that ULBs have adequate human resources and the technical capacity to assess and demand taxes correctly;
• having assessed taxes correctly, ULBs have the enforcement capacity to collect the entire tax demanded.

To achieve revenue optimisation from property tax it is important to first get tax administration right. Without this, it is unlikely that local governments will be able to capture the full extent of the property tax potential even with tax rate increases or technological interventions. This raises the important question: what is the current capacity of ULBs in property taxation?

In the literature we see indications of deficiencies in the institutional capacity of ULBs in performing some major tax functions like tax collections (World Bank 2004; Mathur et. al 2009; Bandyopadhyay 2014). While these studies give us valuable insights, this literature is not recent. Institutional capacity may have improved over time given the recent concentration of schemes to improve local governance such as the Smart Cities Mission and the Jawaharlal Nehru National Urban Renewal Mission (JNNURM). There is a need for new studies that will give us insights into the current state of institutional capacity.

In this article, we therefore measure the institutional capacity of some property tax functions in a sample set of cities in India. Our aim in doing so is two fold:

• to gain insights on the current level of institutional capacity in some property tax functions in a sample set of cities.
• in doing so we attempt to demonstrate that policy interventions must not presume the existence of adequate institutional capacity.

Our findings contribute to the existing literature on the state of property tax administration in India. In addition to this, we question the current approach to measuring administrative functions in the property tax system. We suggest an alternative approach for a more accurate diagnosis of the problems in administration.

A case study of ULB capacity in ten cities

We undertake two levels of analysis. We first examine the institutional capacity of ULBs in property tax collections in a sample set of cities. We then analyse the human resource allocation in the property tax departments in some ULBs. We use our findings to gain insights on institutional capacity in ULBs in a set of sample cities.

Sample selection: Our selection of the cities was driven by the location of the city and the availability of data. Our final selection includes a list of metropolitan and tier-2 cities located across ten different states in India. The selected cities are Chennai, Pune, Indore, Vishakapatnam, Shivamoga, Varanasi, Surat, Warangal, Kota and Bilaspur.

1. Measuring collection capacity

Methodology: We measure collections by calculating the Tax Collection Ratio (TCR), a commonly used method for measuring tax collections. Applying this method, we calculate the TCR as the difference between the tax demand made and the actual tax collected across each of the five years for which the data was available in each of the sample cities (2013-2018). We then calculate the TCR as a percentage value. We use this percentage value as a proxy to demonstrate the level of administrative capacity of a given city by taking 100 per cent as the benchmark. For instance, if the TCR percentage of a given city is 90 per cent, we interpret this to mean that the city has 90 per cent institutional capacity. Such a city has a higher level of institutional capacity when compared with a city in which the TCR percentage is 80 per cent, indicating a higher deficit in tax collections.

Table 1 sets out (i) the average property tax collected in ten cities across five years and (ii) the minimum and the maximum property tax collection across years in the period of study.

Table 1: City-wise average property tax collections (2013-2018)

City	State	Average TCR (%)	Minimum tax collection (as a % of tax demanded in that year)	Maximum collection (as a % of tax demanded in that year)
Chennai	Tamil Nadu	90	74 (2013-14)	106.60 (2017-18)
Pune	Maharashtra	96.36	87 (2017-18)	109.77 (2015-16)
Indore	Madhya Pradesh	80.25	72.16 (2014-15)	106.48 (2016-17)
Vishakapatnam	Andhra Pradesh	114.29	24.42 (2017-18)	265.52 (2015-16)
Shivamoga	Karnataka	98.36	97.86 (2014-15)	99.30 (2016-17)
Varanasi	Uttar Pradesh	96	92 (2013-14)	98.97 (2017-18)
Surat	Gujarat	84.65	76.65 (2015-16)	84.21 (2014-15)
Warangal	Telangana	79.87	75.12 (2013-14)	82.75 (2016-17)
Kota	Rajasthan	58.86	37.04 (2013-14)	96.14 (2016-17)
Bilaspur	Chattisgarh	90.2	75.52 (2017-18)	122.95 (2013-14)

Source: Author's calculations from Smart Cities Mission data

Findings: We find that no city in the sample has achieved 100 per cent TCR. Only one city i.e. Shivamoga has close to 100 per cent of tax collections. There is a deficit in property tax collection across all the cities in the sample (distance from 100 per cent collection of tax demanded). We do find, however, that half the ULBs in the samples have achieved the goal of 90 per cent efficiency as set by the JNNURM. We also find that there are variations in property tax collection across cities. While in some cities the collections are below sixty per cent (Kota), others have a much higher percentage of collection (Shivamoga and Pune).

We also see a variation in the TCR within the same city. For instance, Kota has a maximum TCR of 96.14 per cent in one year (2016-17) but a low TCR of 37.04 per cent in another (2013-14). Similarly, Vishakpatnam has an over collection of 265.52 per cent in the year 2015-16 but under collection of 24.42 per cent in 2017-18. Even in cities like Pune or Chennai, which have a high average TCR across five years (column 3), the minimum TCR (column 4) and maximum TCR (column 5) vary. In half of the cities in the sample, we also see tax collection exceeding the maximum tax demand in a single year (column 4) for Chennai, Pune, Indore, Vishakapatnam and Bilaspur.

2. Examining human resource allocation

Our second level of analysis examines the human resource capacity in the property tax departments in a set of sample cities. The human resources could affect the TCR in two ways: First, the technical capacity of the human resources to apply the rules correctly. For instance, the ability to correctly identify taxable properties, ascertain property values, apply the assessment formula to a given assessee and determine amounts due. Second, the number of personnel in the department could potentially affect the level of accuracy in tax functions. For instance, an inadequate number of resources could increase inaccuracies. In this analysis, we focus on the second aspect of human resources, the number the personnel to examine whether a higher number officers alone leads to a better TCR.

Methodology: We collected data on the number of officers in the property tax department in the sample cities for which this data was readily available. The cities for which this data was readily available were Chennai, Pune, Vishakapatnam, Shivamoga, Varanasi, Warangal and Bilaspur.

Given the paucity of data on the number of taxable properties in the city, we device an indicator to estimate the number of taxable properties in the city using proxies. For this, we first collect Census 2011 data on the number of households living in permanent structures within the municipal area. We then calculated the number of officers per 10,000 households. We also collect data on the total area (sq. km) of the city and compare this to the administrative strength.

Table 2 sets out the administrative strength of the property tax department, the number of households living in permanent structures within the municipal area, the estimated officer to households ratio (per 10,000 households) and the city area in the sample cities for which this data was available.

Table 2: Comparing city-wise human resource allocation and TCR

City	State	Average TCR (%)	Adminis-trative strength (no. of officers)	No. of households in permanent structures	Allocation of officers (per 10,000 households)	City area (sq. km)
Chennai	Tamil Nadu	90	276	10,40,948	3	1,189
Pune	Maharashtra	96.36	41	6,83,267	1	7,256.46
Vishakapatnam	Andhra Pradesh	114.29	56	4,25,409	1	6,501
Shivamoga	Karnataka	98.36	125	58,826	21	8,477.84
Varanasi	Uttar Pradesh	96	321	1,64,014	19	1,535
Warangal	Telangana	79.87	68	1,41,750	5	406
Bilaspur	Chattisgarh	90.2	80	57,292	14	6,377

Source: City municipal websites and Census 2011

Findings: We find that some cities with higher a TCR, also have a higher officer to households ratio. For instance, Shivamoga has the highest TCR and the highest level of administrative strength. However, we see that cities with a low TCR, do not have the lowest administrative strength. For instance, Warangal is has the lowest TCR in the sample, but not the lowest officer to households ratio.

We observe that cities with similar TCR scores do not have similar personnel to households ratios. For instance, the officer to household ratios for similar TCR cities such as Varanasi and Pune or Chennai and Bilaspur are false, demonstrating a variation in human resource allocation even across cities with the same TCR levels. Further, cities with a larger area also do not always have a higher allocation of officers. For instance, Varanasi has a smaller area than Vishakapatnam, but a higher number of officers. Chennai has a smaller area than Shivamoga, but a higher number of officers than Shivamogga. We find not consistent pattern in the manner in which human resource allocation is done across cities.

Limitations: (i) We use the number of households living in permanent structures within the municipal limit as a proxy for the number of taxable properties in a city. This does not take into account the commercial property coverage of a city. (ii) Another proxy for the number of properties in a city is the area of a city, however, a larger city may be less dense and have fewer properties than a smaller and more dense city which may have a larger number of properties (iii) The estimates are only as accurate as the data available on government websites.

Learnings for property tax reforms

The findings from our case study offer insights for property tax policy reforms in ULBs:

Presumption of adequate capacity: Our study finds deficiencies in institutional capacity in tax collections across ULBs. From a reforms perspective, even if tax rates are increased, unless the present institutional capacity is improved, revenues from property taxes might continue to be affected. Further, while our study examines the institutional capacity in one tax function - tax collections, it is likely that there are deficiencies even across other functions. This may affect the outcomes from the current set of policy interventions which focus on increasing revenues by changing the design of the tax system rather than fixing the problems in the administration.

Effect of variation across ULBs: Our findings demonstrate a variation in the capacity of ULBs to carry out property taxation. We are therefore likely to see varying levels of success even for the same set of reforms across ULBs because of the different levels of institutional capacity.

Inconsistencies within ULBs: We not only see a variation in the TCR across ULBs, we also see variation in the TCR within the same ULB across different years. This is demonstrated by the variation in the minimum and maximum collection ratios of cities in our sample. This means that even cities with an overall higher average capacity might have low or high collections in a given year. For instance, the minimum TCR in Vishakapatnam is 24.42 per cent across five years and the maximum is 265.52 per cent. Similarly, the minimum TCR in Kota across five years is 37.04 and the maximum is 96.14 indicating a wide variation in the tax collections even by the same authority. While it is unclear why this is the case, this indicates some inconsistencies in capacity levels.

Management of human resources: Our findings indicate that the institutional capacity in property tax systems is not only a function of administrative capacity in terms of the number of personnel. For instance, while we see that Shivamoga has the highest officer to households ratio and the highest TCR, Pune had a lower officer to households ratio but has the second highest TCR. Similarly, despite having a similar TCR, Chennai and Bilaspur have very different human resource allocations. Therefore, increasing the strength of the administration alone may not yield better outcomes in the assessment and collection of property taxes. Instead, improving the technical capabilities of the administration or effective utilisation of the existing human resource capacity by ULBs might yield results. For instance, Bahl et. al 2013, suggest that tax authorities in developing countries are unable to capture economies of scale.

A new approach to measurement

In the course of this study, we found that the existing approach to the measurement of tax functions in the literature has two main problems. First, studies examine tax collections as an isolated administrative function and not as a product of the preceding tax functions. Second, because of this, these studies tacitly assume that the administrative processes that precede tax collections, such as the tax assessment and all the processes that make up tax assessment are accurately done. This in turn affects the diagnosis of the problems in administration.

We posit instead, that the property tax system comprises of a series of interconnected administrative processes that determine the overall outcome of revenue generation from property tax. Each process determines the success of the next. Errors in administering one process will have repercussions for the accuracy and success of the processes and functions that follow. For instance, tax collection is not just a product of the enforcement function of the ULBs. It is also a function of accurately assessing taxes due. Similarly, the accuracy of the tax assessment function is determined by (i) the maintenance of a database of all taxable properties in the city (ii) regular updation of this database, (iii) correct valuation of the properties in the database, (iv) correct application of the tax formula for these valued properties and (v) determining permitted exemptions. Table 3 set outs an indicative list of the functions that work to together form a chain of administrative processes which ultimately determine tax collections.

Table 3: Indicative list of processes involved in tax assessment and collection

Function	Processes
A. Accurate tax assessment	i. Maintaining a property records database of all taxable properties
	ii. Updating the property records database
	iii. Correct valuation of properties in the database
	iv. Correct application of the tax formula
	v. Correct determination of exemptions and concessions
B. Accurate tax collection	i. Making a correct tax demand (= Ai+Aii+Aii+Aiv+Av)
	ii. Enforcement to collect tax demanded

When we break down administrative functions into smaller processes and view each function as being linked to the next, the result of measuring of any one administrative function will provide us with insights on the accuracy of not just the function being measured but also the previous functions in the chain of administration. For instance, the TCR of a ULB is an indication of the institutional capacity of not only tax collection but also of assessing tax correctly and getting the processes associated with the functions of assessment and then collection right. In this view, a TCR of 90 per cent potentially indicates not only a failure by the ULB to recover 10 per cent of the tax demanded but also potential inaccuracies in assessment for 10 per cent of the tax demanded, leading to appeals and pending cases on account of which payment might not have been done by assesses.

Our learnings from the case study, therefore, are not indicative of capacity issues just in tax collection, but could also be on account of inaccurate tax assessments. This analysis, in line with reports on poor tax assessments in ULBs.

This approach has two advantages over the traditional approach. It breaks down and highlights all the processes involved in property tax administration. In doing so, it allows us to more accurately diagnose the specific function at which the process fails.

Conclusion

We carried out this case study to demonstrate the importance of institutional capacity in the property tax system of ULBs. We have two main findings which are as follows:

First, we demonstrate that the problems in institutional capacity exist across a majority of our sample cities. This signals that there are potential capacity problems in many if not all cities across India. It is unclear therefore whether the present set of interventions to increase property tax revenues will yield optimum outcomes. Our findings demonstrate that it is important to precede policy interventions with the measurement of institutional capacity in the property tax system. We cannot presume the existence of adequate institutional capacity. This is in line with the literature that suggests that infrastructure and institutions are the foundation for achieving effective policy outcomes (Kelkar and Shah 2019, Pritchett et al 2012, Subramaniam and Felman 2021).

Second, deficits in the TCR are not just signals for improving capacity in tax collections and enforcement but also in tax assessment and all allied administrative processes. It is therefore difficult to diagnose which part of the property tax administration requires reform. A failure at any one point of the system has repercussions for the remaining functions. We, therefore, need a comprehensive framework for measuring institutional capacity at the level of each process of the property tax system, some of which are illustrated in Table 3.

Our study also demonstrates that while most cities have some way to go, some cities have achieved higher levels of TCR than others, indicating that they have perhaps learnt to do assessments and collections better than others. We also see that some cities appear to have achieved better utilisation of administrative strength than others. There are perhaps lessons in tax assessment and collection in these cities that other ULBs in India can learn from. A case study of the good practices in collection and assessment in these cities might offer insights for better property tax administration in other cities in India.

References

Arvind Subramaniam and Josh Felman, The Economy and Budget: Diagnosis and Suggestions, January 2021.

Matt Andrews, Lant Pritchett, Michael Woolcock, Looking Like a State: Techniques of Persistent Failure in State Capability for Implementation, CID Working Paper No. 239 June 2012.

O. P Mathur, Debdulal Thakur and Nilesh Rajyadhyaksha, Urban Property Tax Potential in India, National Institute of Public Finance and Policy, 2009.

Roy W. Bahl, Johannes F. Linn and Deborah L. Wetzel, Governing and Financing Metropolitan Areas in the Developing World, Lincoln Institute of Land Policy, Pages 1-30, 2013.

Simanti Bandyopadhyay, Municipal Finance in India: Some Critical Issues, ICPP Working Papers 14-21. May 2014.

Roy Kelly, Making the Property Tax Work, ICEPP Working Papers. 42, 2013.

Vijay Kelkar, Ajay Shah, In Service of the Republic: The Art and Science of Economic Policy, 2019.

World Bank, India: Urban Property Taxes in Selected States, 2004.

Diya Uday is a senior researcher at the Finance Research Group, Mumbai. The author would like to thank Ajay Shah, Susan Thomas and the anonymous referee for their valuable insights, comments and guidance for this work.