1. The asset monetisation push needs careful calibration to evade future hazards
Context: Following through on the Budget’s plan to monetise public assets in order to fund fresh capital expenditure on infrastructure, the Government has released an exhaustive list of projects and facilities to be offered to private investors over the next four years.
- About Asset monetization: It is the process of creating new sources of revenue for the government by unlocking the economic value of unutilized or underutilized public assets.
- National Monetization pipeline: NITI Aayog has prepared a National Monetisation Pipeline for FY21-24. Plan includes awarding 150 passenger trains to private players; divestment of the equity stake of Airports Authority of India in the joint ventures that operate the Delhi, Mumbai, Bangalore and Hyderabad airports; and leasing out stadiums such as the Jawaharlal Nehru Stadium in the national capital.
Features of the New Public sector disinvestment Policy:
- No change of ownership is envisaged: As Finance Minister has emphasised, these assets or the land therein will not be sold but private players will be asked to pay for operation and management rights and expected to modernise assets that are either languishing or are simply under-utilised.
- Securitization Transactions: Government estimates these assets — airports, coal mines, highway stretches, even urban tracts, stadia and hotels — to fetch around ₹5.96-lakh crore through structured leasing and securitisation transactions.
- Utilization of this fund: This, in turn, could help fund the National Infrastructure Pipeline with new projects worth ₹100-lakh crore, although the Government has said fiscal constraints are not the trigger for this plan.
- Newer Mechanisms: For example:
- Infrastructure investment trust (InvIT) structure has already been used this year by the PowerGrid Corporation to raise funds against its transmission lines network and could be used for highways, gas pipelines and railway tracks, including the Dedicated Freight Corridor.
- PPP: For ports, mining, railway stations, concession agreements laying out the contours for a PPP are proposed.
Logic of Monetization Program:
- Promoting Private sector: because with subdued private sector investments, the burden of driving investment activity in the economy falls upon the public sector.
- Financing Fiscal Deficit: Shortfall in tax revenue. Government’s need to spend in welfare and development projects.
- Multiplier effect: This would have larger multiplier effect than the revenue expenditure, could also help crowd-in private sector investments.
- Financial Sustainability: Such a mechanism creates avenues for public sector undertakings to raise resources regularly for new investments without being overly dependent on budgetary support from the Centre.
Revenue collections this year:
- Monetization: About ₹88,000 crore is expected from the National Monetisation Pipeline (NMP) in this year itself,
- Privatization: ₹1.75-lakh crore already estimated in the Budget from the sale of public firms such as Air India and BPCL.
Challenges:
- Government is yet to complete a single PSU sale,
- The risks of adverse audit paras about valuations and processes hang over monetisation deals too.
- Long term uncertainty: With proposed concession periods running up to 60 years for some assets, NMP deals, by contrast, could pose a long-term headache if they are not structured with end-user interests in mind, balancing the profit and utility motives.
- Utilization of Proceeds: The proceeds from the monetisation programme, which should be sequestered so that they are not used for financing consumption expenditure.
- Market capacity to absorb addition asset is limited: disinvestment deals during a downturn could crowd out new investments and risk the tag of ‘fire sales’.
- Poor value realization: revenue projections for PPP assets could be deflated now leading to lower bids followed by super-normal gains for the operator in the future.
Way Forward:
- Careful Planning: The sharing of risk and rewards between the public and private partners needs to be weighed carefully for each sector.
- Checks and balances are needed for actual infrastructure usage versus projections at the time of bidding.
- Creating an institution to monitor private partnership: If the Government had implemented its 2014 Budget promise to set up an apex body to devise new PPP models, learning from past mistakes, India’s institutional capacity for the NMP would have been more mature by now.
Conclusion: The success of the government’s monetisation drive is critical not just for the resources it generates for the public sector in the near term, but also for becoming a feasible option for financing a sustained public sector investment push.
2. The ‘statistical vacuum’ in India can be bridged with decentralisation and if States build their own quality databases
Context: Government prevents data from getting released that shows it in a bad light. There is frequent denial of availability of data which goes against the right to information.
Importance of Data for the state:
- The interconnectedness of power and knowledge and its use by States to control populations has long been expounded by many.
Challenges in Data-based policy making
- Lack of robust data: data-centric policy making pre-supposes the existence of reliable, rigorous and validated data with or without demonstrated impact or outcomes. There is a need to ensure a good, robust and reliable database which is difficult to achieve.
- Invalid data: States collect enormous amounts of administrative data. However, these administrative data are often not validated. For example, it is well known that the flow of funds below the block level is often opaque and the data that is submitted by local bodies are generally not validated.
- Implementation challenges: The task of trying to match funds with functions at the panchayat level is rather challenging. While there is a critical need to link the databases of various departments, it is not easy as territorial jurisdictions and household-level identifiers are likely to vary from department to department.
- Inter-operability: There is a need to bring some mechanism to homogenise these various data sets with a single identifier; but more importantly, there is a need to validate these data sets through urban local bodies and rural local bodies.
- Misused/abused/manipulation of data: For instance, absence of data in certain domains does not necessarily indicate better governance. During the novel coronavirus pandemic, some States were not testing enough. Consequently, the data on COVID-19 positive cases were interpreted to seem that some States, especially in South India, were unable to control COVID-19 cases compared to their North Indian counterparts. In such cases, making resource allocations and decision-making based on data are likely to have adverse impacts.
- Complex issues cannot be seen from simple data: For example, a 2012 academic study on assessing the quality of governance across States had an indicator under a ‘Law and Order’ variable that aimed to measure police behaviour, and the indicator was “Complaints against Police behaviour”. It certainly is a wrong way to measure Police behaviour.
- Stigma topics are difficult to capture: an issue such as mental health, that comes with enormous social stigma in India, needs careful measurement as higher incidences of mental health (from institutional sources) can indicate better access to institutional care as well as a social context that is less beset with stigma.
Conclusion:
- As the game of data shows, we are in a data-driven world. While on the one hand, there is a move towards data-based governance and decision-making, on the other, concerned about the ‘statistical vacuum’ due to a number of national statistical bases getting eroded either through delays or data suppression, scholars like Jean Drèze and others have been calling for decentralised systems of data collection processes, with States building their own databases. This requires States to invest heavily in both human and technical infrastructure with built-in quality control measures to ensure an interesting twist to the game of data that is now ongoing.
