Income Tax Scorecard: Can there be a holistic view of the Budget proposals?

by Surya Prakash B S and Kangan Upadhye.

Is it possible to have a unified view of a legislation that pieces together its various provisions? In our paper we present a novel methodology that measures direct tax provisions of the Finance Bill, 2017 (Government of India Budget, 2017) presented by the Union Government of India to the Lok Sabha, against accepted principles of taxation and tax system design.

The Finance Bill seeks to amend many parts of the Income Tax Act and consequently impacts sections of the society differently. Popular media coverage tends to focus on impact on some sectors or a few controversial measures. This is natural given that budget making is a contentious exercise that needs to address concerns from all quarters. Our methodology avoids analysis either from the perspective of the state (revenue mobilisation) or the taxpayers (revenue minimisation). It measures each direct tax provision to see how well they perform against principles of taxation.

Our method consists of a set of “attributes” and “impacts” for which we assign scores. Attributes relate to the objective features of the provisions: we categorise provisions/amendments into compliance, substantive, procedural, exemptions, collection and recovery, anti-avoidance, penal provisions, international taxation and adjudication machinery. A total of 97 provisions in the Finance Bill 2017 are categorised under these attributes.  A single provision could have more than one attribute. For example, the amendments proposed to section 13A which is related to exemption from paying income-tax for political parties, to discourage the cash transactions and to bring transparency about funding political parties is an example of a provision categorised under more than one attribute. It is categorised not only under compliance but also recognised as substantive.

A summary of the above step is depicted below. It can be observed that the Finance Bill, 2017 contained 26 provisions relating to ‘Exemptions’, 22 that were ‘Substantive’ and 21 that made changes to ‘Computation’.

Since the provisions could have various levels of impact, we go on to score them on a seven point scale (-3 to +3) against each of the following seven principles of an ideal tax system design:

1. Transparency: Whether there have been any prior public consultations.
2. Simplicity: Whether the provisions makes levy and collection simpler.
3. Stability: Whether the provisions are prospective or retrospective in nature.
4. Discretionary power: Whether and to what extent discretionary power of tax officers have been enhanced or decreased.
5. Tax rates: Whether and by how much have tax rates have been decreased. Lowering tax rates get higher scores.
6. Tax base: Is the income on which tax is levied increased or decreased. As a principle when more types of income are charged, a higher score is given. As a corollary, exemptions are scored lower.
7. Number of taxpayers: Provisions that extend the levy to more taxpayers have higher score. If a few of them are exempted it gets a lower score.

The scores are calculated in percentage terms (after converting negative scores to positive for ease of comparison) and the results are as depicted in the figure below.

The provisions in the bill score fairly well on simplicity, stability and discretion parameters with moderate scores on taxpayers, tax base and rates relative to the others. The provisions perform poorly on the transparency parameter.

Our results from the framework do support the popular thinking about the 2017 financial bill the way industry experts and practitioners have interpreted in the budget discourse (Chakrabarti et al. 2017).  For example, the amendment to section 132  which empowers relevant authorities under the Income Tax Act, 1961 to carry out a search or seizure without having to declare reason to believe such person or any authority or appellate tribunal, previously required under section 132 of the Income Tax Act, 1961 (Government of India Budget, 2017).

The earlier provisions empowered authorities to enter and search any building, person if they had a reason to believe that the person had failed to disclose material facts. As critics argue without having a reason to declare for search or seizure this power can be misused to conduct arbitrary investigations leading to harrassments and tax terrorism. This provision was rated low on all the parameters. By adopting such a systematic approach to evaluating tax amendments, this could serve as an evidence informed input to the design of taxes in our budgeting system.

To the best of our knowledge, we have not come across any similar methodology in use in any major economy. The methodology is objective, the impact parameters and the attributes categorised are transparent, and these assumptions can be revised by those that seek to view the results based on alternate views or perform a sensitivity analysis.

We are aware that scores given can be made more accurate through data based post hoc impact assessments. Further research is required on this aspect.
The practical value of the results from our approach are many. It would a) enable us to base the study of the Finance Acts against principles of a good tax system b) provide a comprehensive view of the taxation system rather than a view traditionally restricted to revenue objectives or taxpayer hardship; and c) enable a mapping of the trajectory of tax policy by allowing us to compare across years. It can be viewed as a first step towards making the budget-making process transparent, empirical, and inclusive. The methodology used in this paper can potentially also be used to study other legislation and amendments.

The authors are researchers at Daksh. The authors are thankful to Shreya Rao and Shweta Mallya for their contribution during the conceptualisation phase of this paper. This paper was presented at the APU-NIPFP workshop Strengthening the Republic #1, January 11, 2020.

Policy uncertainty in Indian e-commerce

by Megha Patnaik.

Reduced investment in India by private persons is a key part of the present growth challenge. Investment is shaped by macroeconomic uncertainty, sectoral uncertainty, and regulatory risks that firms face. In this article, we think about the risks that an E-commerce firm such as Amazon perceives in India. These include the changing FDI rules, unresolved issues of data localisation and code disclosure, the multiple reports on technology-related activities that various government agencies are releasing, and the problems of rule of law in licensing and investigation.

Economic policy uncertainty

Shrinking investment in the Indian economy is a concern. Investments in new projects fell to a 15 year low in the last quarter according to the Centre for Monitoring the Indian Economy (CMIE) Capex data which tracks large investment projects.

Firms are deterred from investing by policy uncertainty (Bloom, 2009). When firms are unclear about the future economic environment, they hold back on investing till uncertainty declines. This delays the pickup of the investment cycle, where firms generates jobs and business for linked firms, fueling aggregate economic activity. Uncertainty particularly affects long-term investments that are irreversible in nature, and for which horizons for cost recovery run into years. These can be investments in new technologies or market segments, or investments in infrastructure. Such investments are particularly important, as they can benefit other firms in the economy, fueling productivity and long-term growth in addition to their business cycle effects.

Private sector investment is adversely affected by three kinds of policy uncertainty - macroeconomic uncertainty, sectoral policy uncertainty, and regulatory risk. The role of Economic Policy Uncertainty at the macroeconomic level has been measured globally (Baker et al, 2016). In the original measure, an index is created by quantifying newspaper coverage of policy-related economic uncertainty mentions in the national newspapers, through combinations of keywords related to policy and uncertainty. Macroeconomic policy uncertainty has been applied to understand global events. Brexit-driven policy uncertainty in the UK moved closely with the GBP Real Exchange Rate in recent times, and the uncertainty surrounding US trade policy affected importing firms. This measure of macroeconomic policy uncertainty correlates strongly with stock market volatility.

Firms face much more than macroeconomic uncertainty. They also face uncertainty at sectoral, geographical and individual levels. Sectoral-level policy uncertainty can be measured through surveying firms sampled across sectors, asking them about expectations about future growth and costs at various horizons (Altig et al, 2019). For example, firms can report not just their expectations about future profits, but the distribution across the possible profit outcomes that they can expect.

An additional source of uncertainty that firms operating in India face is regulatory risk. Even when regulations are formulated, there is a lack of predictability, and excessive executive discretion, in how a stated regulation will be enforced. For example, the licensing by the RBI of 11 payment banks from 41 applicants who wanted to start payment systems was a non-transparent process inconsistent with the rule of law (Roy and Shah, 2015). Another example is the Copyright Board order of 2010 on statutory licensing fees paid by Radio stations. This order arose out of nine one-on-one disputes between radio stations and music producers, but was applied as an in rem order rather than an in personem order. Thus, music producers who weren't part of the original disputes also became governed by the order, despite the appeals by T-Series and SIMCA against the Copyright Board order applying to them. Aggarwal and Zaveri (2019) show the uncertainty induced for private persons through executive discretion in enforcement at SEBI.

Drivers of uncertainty in the E-commerce sector

In the recent Q2 earnings announcement, the Amazon CFO Brian Olsavsky mentioned uncertainty in India's e-commerce policy. He expressed hope for `stable' and `predictable' policy, for the company to continue with its investments in technology and infrastructure in India. This explicit mention about policy uncertainty in India is a unusual moment, and requires attention by policy thinkers. What is the uncertainty associated with investing in India, as seen by Amazon?

1. India's draft e-commerce policy rules earlier this year preventing firms from influencing prices or selling products in which they hold stakes disrupted business plans for e-commerce companies. It bring companies back to the drawing board to ensure they can comply with the current regulations while limiting losses that rose from lack of clear direction from the start. The final e-commerce policy has been held back for another year, putting existing investments of firms in this sector at risk during the interim months, and deterring further investments.
2. The uncertainty around data localisation is another deterrent. The recent announcement by a high-level government panel to do away data localisation for non-critical data, and the upcoming announcement of the position of the Prime Minister's Office on data localisation are policy announcements that drive sentiments on this debate, though none are legal instruments. Under data localisation requirements, companies would need to redesign internal algorithms to access data locally, pay up for new servers, and face costs to protect data in less-secure environments. The predictive power of firms' algorithms would weaken with fewer data points to train models on. The due process of discussions with various government bodies and stakeholders on this issue is still in process. The RBI's requirement for financial data localisation despite existing provisions (Bailey and Parsheera, 2018) for access under the Payments and Settlements Act (2007) suggest that any Indian regulator can step in with special requirements at unforeseen times.
3. A related issue is the disclosure requirement of source code under the draft e-commerce policy. E-commerce firms depend on data-driven marketing and use of collaborative filtering for customer recommendations. A code submission requirement is a coercive technique aimed at achieving `the transfer of technology and local needs' described under the proposed e-commerce policy. Technology transfers cannot and should not be coerced: they happen in an organic and legitimate manner through managers and employees developing skills and passing them onward in data communities or by workers moving across companies (Bloom et al, 2019). It is also doubtful how technological transfers can be achieved with segments of code without underlying data. Will code disclosure requirement be combined with data localisation to pass on core business value to competitors? Will companies need to invest in staff and technologies to find workarounds to be able to mask their key assets? Whether such a code disclosure requirement will come into effect remains unresolved. In mid-2020 the final e-commerce policy will describe the stand of the government on this issue, but this is not definitive either.

Multiple guidelines on the same subject can cause delays in the resolution of uncertainty. The RBI Report of the Working Group on FinTech and Digital Banking includes E-aggregators, Robo advisors and Big Data all under Fintech. E-commerce firms, which are data intensive and provide multiple services, will be included under this description. The fintech steering committee report of the Ministry of Finance is still pending. Each of these reports is a statement about how government agencies are likely to move in the future but these are not legal instruments. Government reports can only suggest but not surely state how future laws will change.

Infirmities of the regulatory processes in India also exacerbates uncertainty. As an example, data localisation requirements by RBI for payments firms were translated from an early idea into an enforced law within a matter of days. There was no due process surrounding how officials could change the law.

The last leg of the legal system -- how laws are enforced -- also suffers from concerns about non-equal application of law, as shown in the examples from RBI (Roy and Shah, 2015) and SEBI (Aggarwal and Zaveri, 2019). For a prospective investor, the risk of investing in India lies in how the law might change in the future through an undemocratic process, and in how the law will be applied to her.

Conclusion

For India to have a stable investment environment, we need to provide firms a stable and predictable policy environment. Investments from firms in various sectors will boost the investment cycle for India. Resolving policy uncertainty both at the macroeconomic level as well as in different sectors, and reducing regulatory risk through better rule of law is critical for India in the current investment scenario as well as for long term growth.

References

Aggarwal, Nidhi and Zaveri, Bhargavi. Problems with evidentiary standards for proving securities fraud in India, The Leap Blog, 23 August 2019.

Altig, David, Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, Brent H. Meyer and Nicholas Parker. Surveying Business Uncertainty University of Chicago Working Paper (2009)

Bloom, Nicholas. The impact of uncertainty shocks. Econometrica (2009)

Bloom, Nicholas, Erik Brynjolfsson, Lucia Foster, Ron Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen. What Drives Differences in Management Practices? American Economic Review (2019)

Baker, Scott R., Nicholas Bloom and Steven J. Davis. Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics (2016)

Bailey, Rishab, and Smriti Parsheera. Data localisation in India: Questioning the means and ends, The Leap Blog, 22 February 2018.

Roy, Shubho, and Ajay Shah Payment bank entry process considered inconsistent with the rule of law, The Leap Blog, 1 September 2015.



Megha Patnaik is faculty at the Indian Statistical Institute, Delhi and Fellow at the Esya Centre. The author thanks Radhika Pandey and Ajay Shah for useful inputs.