Making India’s Social Protection Shock Responsive: Lessons from PDS amid COVID-19

The wide-ranging vulnerability induced by the current pandemic has heightened global interest in shock-responsive social protection (SRSP), i.e. adapting social protection (SP) for addressing the impacts of large-scale natural disasters, economic shocks, pandemics and political crises. Figure 1 shows the common SRSP strategies which policymakers can consider for addressing covariate shocks. 

Figure 1. Adapting social protection systems for crises

Until recently, India’s SP system was largely limited to the formal sector. While there is still a considerable degree of fragmentation and multiple federal schemes operate in silos, there is a growing policy recognition for consolidation and convergence backed by integrated systems.1 The last 15 years witnessed a growth in rights-based entitlements and systemic reforms to build a more inclusive system.2 These encompass the Mid-Day Meal (MDM) program, Integrated Child Development Services (ICDS), Public Distribution System (PDS), National Rural Employment Guarantee Scheme (NREGS) and National Social Assistance Program (NSAP). These programs show a greater degree of institutionalization in terms of legal and/or policy backing, benefit design and implementation processes, resulting in improved coverage. 

The COVID-19 crisis has seen unique innovations involving piggybacking on India’s most extensive safety net, the PDS, for shock response, reiterating its relevance for SRSP. For instance, the Government of Bihar piggybacked on the PDS (although with challenges and swift course corrections)3 to provide a one-off transfer of Rs.1000 to ration-card holders during the COVID-19 crisis. This experience needs to be systematically documented, as it will play a crucial role in informing future preparedness actions. Similarly, Uttar Pradesh (UP)4 and Odisha5 piggybacked on the extensive network of fair price shops (FPS) to distribute food grains (in lieu of in-school cooked meals) to beneficiaries of MDM, while Delhi6 and Kerala7 used it to distribute ‘essential item kits’. Leveraging existing delivery systems helped save crucial time and reduce errors in distribution.

PDS also demonstrated flexibility by expanding vertically (topping up entitlements) and horizontally (increasing coverage). Entitlements for over 80 crore ration-card holders were doubled8 and eligibility was relaxed to include non-ration card holders 9 such as migrant workers10 and some families who are above the poverty line11. On March 26th, the government announced the Pradhan Mantri Garib Kalyan Anna Yojana (PMGKAY) for the period of April to June, further extended till November 2020, for providing free ration (5 kg of rice/wheat and 1 kg of pulses), in addition to the pre-existing entitlements of PDS beneficiaries12. Several states announced their own relief packages, which supplemented this quantity of ration and/or expanded the basket of items. 13,14 Under the Atma Nirbhar Bharat Abhiyan, free rations were extended to migrants from May till August. The pandemic and the consequent exodus of migrants also hastened the speed of ensuring inter-state portability of ration cards through the ‘One Nation-One Ration Card’ (ONORC) approach, though challenges persist. 

Although the PDS played a critical role in alleviating the vulnerability induced by the pandemic, several inadequacies of the system were exposed as well. The challenges posed by the PDS need to be addressed in order to respond better to future crises. The most fundamental criticism of the current PDS regime is the exclusion of eligible beneficiaries. This exclusion is layered and hierarchical, shown in Figure 2. The use of outdated 2011 population census figures to determine the extent of the coverage of the scheme has excluded more than 100 million people from the system.15 The second layer of exclusion emanates from the mandate of linking Aadhaar with ration cards.16 Both these shortcomings represent the plight of vulnerable non-ration card holders who suffer disproportionately because of the difficulty in identifying them for delivering immediate relief. As the ONORC does not address the previous two layers of exclusion, it is plagued by their associated drawbacks too. 

Figure 2. The PDS Exclusion Hierarchy: Introducing ONORC without accompanying measures for addressing the deeper issues of large-scale exclusion is merely touching the tip of the iceberg 

Another welfare program that can learn from the PDS and respond better to future shocks is NREGS, which came to the rescue of many distressed workers in the wake of the widespread job losses induced by the current pandemic. 17  While this surge in demand resulted in significant expansion of the program, spatial mapping of the newly issued job cards across rural districts with their population shares of outmigration and poverty revealed substantial unmet demand. 18 At the same time, the lack of a national urban employment guarantee (UEG) scheme left the urban poor unprotec#srsp18ted. The long overdue UEG is finally under consideration19, and its timely implementation will bring urban informal workers within the ambit of wider crisis management. However, the success of both NREGS and UEG depends on the ability of the states to generate sufficient employment opportunities corresponding to the surging demand. Mobilizing local authorities for identifying such opportunities is a prerequisite to yield tangible results, especially during crises. Another important issue is that of inadequate compensation. NREGS wages are lower than the minimum wage for agriculture in many states.20 Times of crisis unquestionably demand a top-up over the guaranteed wage. The recent guidelines on streamlining NREGS wage payments21 is a welcome move, however, the government must still consider switching to cash payment during difficult times at least in remote areas. NREGS therefore presents a case for both horizontal and vertical expansion.

In conclusion, the detrimental consequences of delayed SP response22 witnessed during the current pandemic only strengthens the case for instituting an emergency response framework across these schemes to fast-track assistance deployment when it is needed the most. The starting point for making SP shock-responsive is to map existing SP systems in terms of their coverage, adequacy and comprehensiveness: to understand the reach of routine SP systems, their capacity to deliver relief adequately and the range of risks covered. An efficient way to do this is to transition from multiple independent program databases to an Integrated Social Protection Information System. Additionally, the shortcomings of existing systems that hinder effective coverage during crises demonstrate that successful adaptation of such systems for emergency response requires them to be resilient in the first place. Given that the case for short-term universalization of SP during a crisis rests on fiscal considerations and political will, ensuring minimum exclusion errors in identifying beneficiaries becomes the most effective strategy for increasing the resilience of existing SP systems and improving the coverage of SRSP systems. Flexible delivery mechanisms form yet another critical element of a resilient SP system. 

Adapting SP for accommodating the expanded pool of vulnerable population prompts the need for a National Social Registry backed by comprehensive and dynamic socio-economic data in order to cater to those outside the purview of routine SP (urban poor, migrants). Moreover, vulnerability and needs assessments23 can be leveraged to prioritise regions and households for better risk preparedness and response24. Expanding routine coverage in areas frequently affected by shocks along with appropriate monitoring and evaluation can serve as ideal pilot studies for iterative, evidence-based design tweaks. 

SRSP contingency framework must also be incorporated within the ambit of the formal policy, so that readily deployable Standard Operating Procedures are in place in times of need25. This includes an assessment of the fiscal space for shock response in terms of assessing alternative sources and channels of contingency financing26. A final ingredient of successful SRSP systems relates to a context driven approach. Decentralized decision-making enables policy response to be based on local context, which is extremely relevant for crisis management. Hence, states and their local governments need to be empowered, especially financially, and be involved in formulating SRSP as they know the ground realities and local vulnerabilities most thoroughly. 

The current context of COVID-19 has and will throw up many challenges, particularly by amplifying already existing inequalities. In these times, developing strong SRSP systems is paramount to mitigate such adverse impacts. 

References

1. The World Bank. (2019, February 20). Schemes to Systems: The Future of Social Protection in India. https://www.worldbank.org/en/news/feature/2019/11/20/schemes-to-systems-future-social-protection-india

2. Dreze, J. & Khera, R. (2017). Recent Social Security Initiatives in India. World Development, 98, 555-572. https://doi.org/10.1016/j.worlddev.2017.05.035

3. Government of Bihar. (2020, May 8). Directions regarding monitoring of cash transfer Rs 1000 distribution under PDS ration card linking related issues. http://www.manupatrafast.in/covid_19/Bihar/Govt/Directions%20regarding%20monitoring%20of%20cash%20transfer%20Rs%201000%20distribution%20under%20PDS%20ration%20card%20linking%20related%20issues.pdf 

4. Bajpai, N. (2020, May 30). UP govt to disburse ration, food security allowance to school children.The New Indian Express. https://www.newindianexpress.com/nation/2020/may/30/up-govt-to-disburse-ration-food-security-allowance-to-school-children-2150069.html

5. Orissa Post. (2020, March 21). Odisha govt to provide MDM to students through PDS. https://www.orissapost.com/odisha-govt-to-provide-mdm-to-students-through-pds/

6. The Hindu. (2020, June 4). Not discriminating between ration and non-ration cardholders, govt. tells HC.  https://www.thehindu.com/news/cities/Delhi/not-discriminating-between-ration-and-non-ration-cardholders-govt-tells-hc/article31743441.ece

7. Joseph, A. T. (2020, April 6). How Kerala is feeding its 3.48 crore residents, migrants amid the COVID-19 lockdown. The Caravan. https://caravanmagazine.in/economy/keralas-roadmap-to-feeding-its-348-crore-residents-migrants-amid-the-covid-19-lockdown

8. Government of India. (2020, March 20). DO Letter F. No. l-212020 Desk (MDM). http://mdm.nic.in/mdm_website/Files/OrderCirculars/2020/JS_DO-Letters/DO%20Letter_20-3-2020-COVID-19.pdf

9. Government of India. (2020, March 30). PRADHAN MANTRI GARIB KALVAN ANNA YOJANA – Additional allocation of foodgrains to all the beneficiaries covered under Targeted Public Distribution System (TPDS) free of cost for a period of three months. https://dfpd.gov.in/writereaddata/Portal/Magazine/30032020.pdf

10. Government of India. (2020, May 15). Allocation of foodgrain to the migrants @ 5 kg per person per month for two months free of cost as part of Economic measures (Atma Nirbhar Bharat). https://dfpd.gov.in/writereaddata/Portal/Magazine/PolicydecisionMay2020.pdf

11. ANI. (2020, April 9). Gujarat to provide free ration to 60 lakh families amid COVID-19 lockdown. Business Standard. https://www.business-standard.com/article/news-ani/gujarat-to-provide-free-ration-to-60-lakh-families-amid-covid-19-lockdown-120040900138_1.html 

12. Ministry of Finance. (2020, March 26). Finance Minister announces Rs 1.70 Lakh Crore relief package under Pradhan Mantri Garib Kalyan Yojana for the poor to help them fight the battle against Corona Virus. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1608345

13. Telangana Today. (2020, March 22). Telangana Lockdown: 12 kg free rice per person, Rs 1,500 per family to be supplied for each white ration card. https://telanganatoday.com/telangana-lockdown-12-kg-free-rice-per-person-rs-1500-per-family-to-be-supplied-for-each-white-ration-card

14. Angad, A. (2020, May 15). Non-PDS card holders to foodgrains: Jharkhand fears problems in migrant aid. The Indian Express. https://indianexpress.com/article/india/non-pds-card-holders-to-foodgrains-jharkhand-fears-problems-in-migrant-aid-6410354/

15. IndiaSpend. (2020, April 16). More than 100mn excluded from PDS as govt uses outdated Census 2011 data. https://www.indiaspend.com/more-than-100mn-excluded-from-pds-as-govt-uses-outdated-census-2011-data/

16. Muralidharan, K., Niehaus, P. & Sukhtankar, S. (2020). IDENTITY VERIFICATION STANDARDS IN WELFARE PROGRAMS: EXPERIMENTAL EVIDENCE FROM INDIA. NBER Working Paper 26744. https://www.nber.org/system/files/working_papers/w26744/w26744.pdf 

17. Bhalotia, S., Dhingra, S. & Kondirolli, F. (2020). City of Dreams no More: The Impact of Covid-19 on Urban Workers in India. Centre for Economic Performance, Paper No. 008. https://cep.lse.ac.uk/pubs/download/cepcovid-19-008.pdf

18. Narayan, S., Oldiges, C. & Saha, S. (2020, December 1). Does workfare work? MNREGA during Covid-19. Ideas for India. https://www.ideasforindia.in/topics/poverty-inequality/does-workfare-work-mnrega-during-covid-19.html

19. Bloomberg. (2020, September 12). India plans to extend rural jobs guarantee scheme to cities, to address urban unemployment. Financial Express. https://www.financialexpress.com/economy/india-plans-to-extend-rural-jobs-guarantee-scheme-to-cities-to-address-urban-unemployment/2072309/

20. Aggarwal, A. & Paikra, V. (2020, October 5). Why are MNREGA wages so low? Ideas for India. https://www.ideasforindia.in/topics/poverty-inequality/why-are-mnrega-wages-so-low.html

21. Department of Rural Development & National Informatics Centre. (2019, December). Standard Operating Procedure (SOP) on Streamlining MGNREGA Wage Payments. https://nrega.nic.in/Netnrega/Data/SoP_TimelypaymentMGNREGA.pdf

22. Ghosh, J. (2020). A critique of the Indian government’s response to the COVID-19 pandemic. Journal of Industrial and Business Economics, 47, 519–530. https://doi.org/10.1007/s40812-020-00170-x

23. O’Brien, C., Holmes R. and Scott, Z., with Barca, V. (2018) ‘Shock-Responsive Social Protection Systems Toolkit—Appraising the use of social protection in addressing largescale shocks’, Oxford Policy Management, Oxford, UK. 

24. Acharya, R. & Porwal, A. (2020). A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. The Lancet Global Health, 8(9), 1142-1151. https://doi.org/10.1016/S2214-109X(20)30300-4 

25. UNICEF. (2019, December). Programme Guidance: Strengthening Shock Responsive Social Protection Systems. https://www.unicef.org/media/63846/file 

26. O’Brien, C., Holmes R. and Scott, Z., with Barca, V. (2018) ‘Shock-Responsive Social Protection Systems Toolkit—Appraising the use of social protection in addressing largescale shocks’, Oxford Policy Management, Oxford, UK. 

The views expressed in the post are those of the author and in no way reflect those of the ISPP Policy Review or the Indian School of Public Policy. Images via open source.

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RAJESH SRIVASTAVA
RAJESH SRIVASTAVA
1 year ago

Excellent. Keep it up.

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[…] Shrivastava, S. and Sanyal, R. (2021, February 1). Making India’s Social Protection Shock Responsive: Lessons from PDS amid COVID-19. Policy Review.https://policyreview.in/making-indias-social-protection-shock-responsive-lessons-from-pds-amid-covid… […]

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Despite a brutal second wave with cases peaking in April-May 2021, India’s Gross Domestic Product (GDP) grew at a record pace of 20.1 percent in the April-June 2021 quarter compared to the corresponding period last year. The GDP, in absolute terms, stood at Rs 32.38 lakh crore (constant prices). This was actually lower by 9.2 percent than the numbers seen in the April-June quarter of 2019-20. In fact, as the figure below shows, the April-June 2021 GDP numbers are closer to the levels seen during the January-March 2017 quarter.

Source: MOSPI (Annual and Quarterly Estimates of GDP at constant prices, 2011-12 series)

While growth in the April-June 2021 quarter is promising and reflects recovery from the deep plunge seen in April-June 2020, comparisons are being drawn with the pre-Covid levels. 

But what are these pre-covid levels? Should numbers of a single quarter, say April-June 2019-20 be used as the benchmark, or an average growth seen in the previous few quarters be considered as a benchmark for comparison? 

An alternate strategy

We propose an alternative way through which, we compare the present Gross Value Added (GVA) numbers (in level terms) with the numbers obtained using simple univariate time-series forecasts. These forecasts are obtained by exploring the time-series properties of the variable of interest. In particular, these forecasts are arrived at using the Autoregressive Integrated Moving Average models (ARIMA models). ARIMA is a statistical analysis model that uses time-series data to better understand the facts and to predict future trends. 

This comparison helps in assessing how distant are the current GVA numbers from the levels which would have been achieved had there been no shock in the form of the COVID-19 pandemic.   

Since GDP includes taxes, we look at the activity-based variable after excluding the impact of taxes. The variable of interest, therefore, is the Gross Value Added (GVA). We use the GVA data available from June 2011 till December 2019 and extend it using the projections obtained from a univariate ARIMA model. As mentioned before, in this model previous observations are used to predict future values. Therefore, we have excluded the period post-December 2019 to ensure that the trend is not influenced by the COVID-19 shock. 

The figure below shows the raw data along with the projections for the subsequent six quarters (from March 2020 onwards) based on the ARIMA model. These projections present a picture of the GVA trends under normal circumstances, i.e. if the economy would not have been subjected to the COVID-19 shock.

Adjustment for seasonality 

An economy, over the long term, experiences a concept known as seasonality. These are seasonal fluctuations, movements, that recur with similar intensity in a given period (such as months) each year, thus showing a clear pattern of peaks or troughs over a sufficiently long time period. Broadly, seasonality arises from several calendar related events such as – weather-based factors: monsoon, winter or summer months, agricultural seasons: harvest or sowing season, administrative procedures: tax filings, financial year closure, working days, festivals: Diwali, Christmas, etc., institutional: Annual budgets or Fiscal year ending, social and cultural factors: Statutory holidays, etc.

Such seasonality needs to be adjusted to comprehend the underlying trend, cyclicality, and other movements for a better understanding (Pandey et. al, 2020). 

The quarterly GVA series shown above exhibits seasonality and therefore we seasonally adjust the extended GVA series (GVA values till December 2019 along with the forecasted values) and compare with the seasonally adjusted actual data post-December 2019.  

The difference between the series till December 2019 extended with time-series forecasts and actual series post-2019 (both adjusted for seasonality) would give an assessment of the shortfall in economic activity arising due to the COVID-19 shock. 

Shortfall due to COVID-19

The table below shows the differences between the estimates based on the time-series forecasting and the actual values. We present this exercise for the overall GVA as well as its components. The key highlights of the comparison exercise are as follows:

Table 1: Difference between the Actual values and Estimated values (Rs. Lakh Cr)

*Both Actual and Estimated Values are seasonally adjusted

1. In the January-March 2020 quarter, the difference between the forecasted (estimated) values and the actual values is small. This is due to the limited impact of the pandemic during this quarter. 

2. However, the difference widened to Rs 8.7 lakh crore in the April-June 2020 quarter. This was the period of the nationwide lockdown. As a result, the economic activity was adversely impacted. The major difference was seen in the contact-intensive trade, hotels, and transport sectors. Since agriculture was not impacted by the pandemic, the projected and the actual agricultural GVA is the same. 

3. With the gradual opening up from the July-September quarter, we see that the gap between our estimates and actual values is reduced. However, the financial sector continued to reel under the impact of the pandemic. While some improvement was seen in the GVA of the trade, hotels, and transport sectors in the July-September quarter, there still was a significant shortfall of Rs. 1.4 lakh crore.4. In the October-December 2020 and the January-March 2021 quarter, a distinct improvement is seen in the actual overall GVA numbers. The gap between the estimated and the actual values for the overall aggregate GVA narrowed to Rs 0.8 lakh crore and Rs. 0.3 lakh crore for Oct-Dec 2020 and Jan-Mar

2021 quarter respectively. Except for the trade, hotels, and transport sector, the gap was less than Rs 1 lakh crore for all the sub-sectors. 

5. But, the April-June 2021 quarter revealed that the gap has widened to the tune of Rs. 5.3 lakh crore. This shows that while the recovery was underway, the onset of the second wave and the consequent partial lockdowns pulled back the growth momentum to some extent. The sectoral variations are also worth noting. While agriculture, mining, and manufacturing showed stellar performance despite the second wave, the contact-based services sector (trade, hotels and transport) pulled down the growth. The construction sector also bore the brunt of the second wave.

The above exercise presents an alternative approach to assess the shortfall in GVA numbers due to the COVID-19 shock. There are sectoral variations: while agriculture posted a robust growth and the manufacturing sector was relatively less impacted, it is the contact-intensive sector that primarily got affected due to the shock. Our exercise shows that after the April-June 2020 quarter, the economic recovery was gaining momentum. However, the second wave led to a pause in the recovery process. 

Going forward, with a sustained pick-up in the pace of vaccinations, we should see economic recovery getting back on track. The high-frequency variables such as exports, PMI manufacturing and services, petroleum products consumption, electricity consumption, and GST collection, etc., also suggest a pick-up in economic activity since the beginning of the second quarter. 

The authors are Senior Fellow and Fellow respectively at the National Institute of Public Finance and Policy (NIPFP), New Delhi. Views are personal.

On January 30, 2020, India reported its first COVID-19 case – a medical student in Kerala who had been evacuated from Wuhan. Exactly a year later, the country has recorded more than 1.5 lakh deaths and 1.07 crore positive cases. As of January 29, 2021, 33 lakh healthcare workers have been vaccinated against the virus. With the daily mass vaccination programme set to cover other frontline workers and people with co-morbidities in its upcoming phases, the beginning of the end of the pandemic seems to be in sight. 

Public health officials warn that besides vaccinating vulnerable populations, contact tracing and tracking remain crucial steps in avoiding future outbreaks as reports of new variants of the virus emerge. Until sufficient herd immunity is achieved in the population, testing, tracing and treatment of infected persons, along with mask-wearing and physical distancing measures, are required to break the chain of transmission of the virus. 

Digital Technology Tools for Contact Tracing

Contact tracing refers to a range of methods used to identify, alert and monitor those who may have been exposed to the disease through close contact with an infected person. Contact tracing has been used in the past to manage epidemics such as AIDS, MERS and Ebola, among others. It involves three basic steps of identifying, listing and following up with those who have come in contact with an infected person. Contact tracing efforts were traditionally carried out manually through door-to-door surveillance and in-person interviews. Besides being manpower-intensive, physical contact tracing efforts also rely heavily on human memory and are hence prone to error and omissions. 

The COVID-19 pandemic witnessed the adoption of digital technology tools for disease management at an unprecedented scale globally. Several countries around the world, including India, launched mobile phone applications and other digital tools to aid disease surveillance and contact tracing. Such applications make use of mobility reports, real-time monitoring of wearables and devices, bluetooth proximity tracking, GPS location data and cellular network data, among others, to alert people to possible exposure, track the spread of the disease, and predict future hotspots. 

The use of digital technology promotes optimal usage of time and manpower in contact tracing. However, these apps in their current forms are not capable of capturing all possible scenarios under which a person can contract the disease. With about 41% of the world currently lacking access to the internet, digital technology tools cannot entirely replace the need for manual contact tracing. Such technology also carries an inherent risk of excluding already underserved populations, and furthering other existing prejudices. In the absence of proper data protection safeguards, they also pose a serious threat to individual privacy. Several countries such as Singapore, China, Taiwan and South Korea used advanced digital contact tracing tools. 

How India Fared in Contact Tracing

India’s tracing policy remained consistently comprehensive from March 2020 with the Ministry of Health and Family Welfare recommending contact tracing for all confirmed cases. Detailed guidelines for the same have been issued by the Integrated Disease Surveillance Programme, National Centre for Disease Control. The protocol emphasises the need to trace all contacts as early as possible, besides clearly defining the ‘low risk’ and ‘high risk’ contact categories. In July 2020, the Union health ministry directed states to ensure that at least 80% of new cases are traced and quarantined within 72 hours of testing positive. However, several news reports suggest, state health authorities failed to keep pace with the burgeoning caseload and scaled back on contact tracing over time. Indian Council for Medical Research (ICMR) and several state authorities stopped putting out contact tracing data after the first few months. 

State and district authorities implemented different models to carry out contact tracing in the initial days of the pandemic. For instance, as many as 2000 contact tracing teams were put together in Bhilwara district in Rajasthan to screen almost 92% of its 24 lakh-strong population within nine days. A team of 16,000 screened Himachal Pradesh’s population of 68 lakhs. Several states such as Delhi, Punjab, Uttar Pradesh and Maharashtra failed to keep track of domestic and international travellers. The large-scale movement of migrant workers to their home states during and after the nationwide lockdown further hampered these efforts. 

Cities such as Pune, Agra and Bengaluru with high population densities put together ‘war room’ teams comprising healthcare department, police department and collectorate officials to trace suspected cases and track the disease spread. Karnataka’s contact tracing capacity dropped from 47 per patient in June to less than six primary contacts per patient in July. The Government of Karnataka developed eight in-house apps to manage tracing and tracking during the pandemic. 

Once the pandemic entered the community transmission stage, a large share of the responsibility of contact tracing and isolating on exposure shifted to individuals and communities. However, the social stigma attached to testing positive for COVID-19 along with an enforcement-oriented approach (lockdowns, demarcation of containment zones, putting up banners outside the homes of those testing positive, mandatory use of Aarogya Setu, etc) adopted by authorities to fight the disease failed to encourage self-assessment and self-reporting by the average citizen. With reports emerging of discrimination against marginalised communities, public trust in contact tracing efforts eroded in the absence of congruent public messaging across political and social divides. 

A Review of Aarogya Setu App 

India launched its Aarogya Setu app in April 2020 to aid its ongoing contact tracing efforts. The app uses a combination of GPS (Global Positioning System) and Bluetooth to track other Aarogya Setu-enabled phones in close proximity of a user, and alerts them about possible exposure to an infected person. It can be used to self-report suspected symptoms for risk assessment before availing various services such as entry to public places and travel, and for public messaging on quarantine and treatment guidelines as well.

As of October 2020, the app had recorded over 15 crore individual downloads. In May 2020, government officials said 1.4 lakh Aarogya Setu app users had been alerted via Bluetooth contact tracing about possible risk of infection due to proximity to infected patients. The app had also helped narrow down on 697 potential hotspots. In June 2020, the government said one in every four to five positive cases were using the app with a total of 13.5 crore downloads. As many as 1.33 lakh of those users had tested positive. For each positive case, the app was able to trace an average of 28 possible contacts, resulting in tracing of over 28 lakh suspected cases. More than 11,000 potential hotspots were also reportedly identified between three to fourteen days before the disease began spreading in those areas in this period. 

However, as public fatigue set in and various legal challenges related to the app’s usage emerged, its uptake remained an issue. For such an app to be successful in contact tracing, at least 50% of the total population must be using it. The existing digital divide and lack of enough digital literacy posed major hurdles to the app’s uptake and affected the quality of data being fed into it. Moreover, concerns over privacy of the captured data and fears of it being repurposed for commercial or other surveillance measures also emerged. With the use of the app being made mandatory for work and travel, it failed to encourage user behaviour or incentivise self-reporting of symptoms. As cases began to peak in densely-populated areas, the app failed to provide accurate results on possible exposure. 

What Lies Ahead

The way forward for contact tracing in India lies in a hybrid model. Digital contact tracing measures must be complemented with manual contact tracing efforts in rural and socio-economically weaker communities. Care must be taken so as to ensure that manual contact tracing duties do not overburden Accredited Social Health Activists (ASHAs) and other healthcare providers, affecting the non-COVID-19 work they perform. 

An effective contact-tracing model must be built on the principles of public benefit and trust, scientific validity and ensured efficacy with enough safeguards in place to avoid discrimination. Personal privacy and individual autonomy in the use of the digital technology tools must also be preserved. Data captured through such apps or tools must not be repurposed in any manner. 

Moreover, contact tracing efforts must evolve at the community level such that citizens feel individually responsible and safe to report symptoms and seek treatment in case of suspected exposure. Going ahead, an interrelated digital eco-system that combines the internet of things (IoT), big data analytics, artificial intelligence (AI), and blockchain technology can help create a technology-aided model for effective tracing and tracking of other communicable diseases even after the COVID-19 pandemic ends.

The views expressed in the post are those of the author and in no way reflect those of the ISPP Policy Review or the Indian School of Public Policy. Images via open source.

Author:
Editor: Samir Pius George
February 1, 2021

Background

The power sector consists of five stakeholders- energy sources, power generators, transmitters, distributors, and consumers (figure 1). The draft National Electricity Plan 2016 projects that the peak demand at the end of 2021-22 would be 235 GW.1 As per the 2019 CRISIL report, India’s installed capacity of power generation is 344 GW. Produced electricity could only be transported to the region of demand if the transmission network is capable. The current transmission line capacity in India is 3.9 Lac km. It grew at a compound annual growth rate of 7.2% from 2012 to 2018.

Figure 1. Present structure of power sector, Source: “Overview of the power sector,” PRS report

The power generation capacity is sufficient, and the transmission capacity is rapidly growing, but the power distribution is the weakest link in the value chain. Since 1991, the power sector has seen several reforms to improve the status quo, but they failed to address the issue of the DISCOMs being in perpetual losses. The fundamental issue is that DISCOMs fund their operational losses by debt. As of March 2015, the state DISCOMs had accumulated outstanding debt of approximately ₹4.3 lakh crore and roughly ₹3.8 lakh crore losses.2 The government launched UDAY (Ujjwal DISCOM Assurance Yojana) to allow the states to help DISCOMs overcome these losses. The scheme helped the DISCOMs clear their books, but it did not resolve the loss accumulation’s fundamental issue. DISCOMs are in losses because of a substantial mismatch between fixed cost and the recovered fixed charges. The mismatch reflects due to the electricity consumption subsidy and the Aggregate Technical and Commercial (AT&C) losses. Electricity consumption subsidy is the largest share (49%) in India’s pie of total energy subsidies. In the financial year 2017-18, the total electricity consumption subsidy was ₹86400 crores.3

Table 1  Increase in main “direct” subsidies

The government subsidizes electricity for some consumers, such as metro rails, railways, and agriculture consumers. DISCOMs provide subsidized electricity to these consumers and get compensated by the government later. However, governments tend to delay payments, which creates a non-recovery of the fixed utility costs.

The central and state regulators (CERC and SERC) and the DISCOMs recover these ‘losses due to subsidies’ by cross-subsidizing the low-tariff users by increasing the variable charge of the high-tariff users. This practice increases the cost of electricity.

What is the fixed and variable charge?

The supply tariff (payable by the consumer) is divided into two categories: fixed charge and the variable charge. When the consumer books a connection, the DISCOM sanctions a specific load to that consumer. The fixed charge corresponds to that sanctioned load. Variable charge corresponds to the actual consumption of the consumer.

The fixed charge includes capacity charges payable to power generators, transmission charges, operation and maintenance expenses, depreciation, interest on loans, and equity return. The variable charge recovers the variable utility costs, such as the variable cost component of power purchase.

Looking at the power sector from the first principles

According to Shah and Kelkar, the state is an inefficient institution. They establish the fact by giving a law of unintended consequence, which causes the state inefficiency- “A government intervention that carries an intention to have a certain outcome will very often end up yielding a very different result.”4 Market freedom works well in producing goods and services efficiently. The state should intervene where the market fails to provide efficiency. The market fails when there is information asymmetry between the producer and the buyer, a monopoly in the market, any externality (non-negotiated outcomes) involved, or in the case of public good.5

An individual can be prohibited from consuming electricity (excludable), and its consumption by an individual affects the overall supply (rival). Since the production, transmission, and distribution of electricity are excludable and rival, therefore principally it should be produced, transmitted, and distributed by the market, i.e., the private players. There is no state intervention required until a market failure could be quantified in terms of externality (positive and negative), market monopoly by a private player, information asymmetry, and public good.

With the electricity act of 2003, the government tried to create a free market in the power sector. The intention was to harness market efficiency in the sector by inviting private players to create competition, negotiation, and choice. As per the power sector’s status quo, the government-owned utilities generate 54% of the total power. 92% of the transmission utilities are government-owned, and the government owns almost all the DISCOMs (distribution companies) except in Delhi, Mumbai, and in a few other cities.5 Hence the government did not succeed in their original intention.

For a free market to function, price control by the government distorts the supply and demand equilibrium as it creates a deadweight loss6 to the economy. A free market does not keep entry barriers, and the market mechanism decides the profit margins.

Central Electricity Regulatory Commission (CERC) and State Electricity Regulatory Commission (SERC) regulates the tariffs of DISCOMs heavily. As per section 79 and section 86 of the electricity act 2003, CERC and SERC control the tariffs of the generation, transmission, and distribution companies and issue licenses for the market entry, fix trading margins, and specify the grid technology, and adjudicate upon the disputes.7

The case of state intervention in the power sector

Let us consider that there is no state. The market will have the responsibility to produce, transmit, and distribute the electricity. The state will then intervene through either financing or regulating the market. It should not produce because, in this sector, there is no public good. There are four possible market failures

1. Negative Externality: 64% of the electricity production in India is by coal-based thermal power plants (Source: Ministry of Power; PRS). The production of electricity by coal-based thermal power plants requires coal burning, which pollutes the air. Air pollution is a negative externality because there is a channel of influence between the polluter and the citizens that are not negotiated.

Since it is a market failure, the state should intervene. There are two modes of intervention that seems possible in this case:

a. Regulation and Tax mechanism: States can regulate the thermal power plants by putting stringent emission norms and imposing taxes on the plants’ amount of pollution. The tax may act as a disincentive to the polluters, and they will pollute less.

b. Create a pollution market: The state can limit the aggregate pollution that all the thermal plants can cumulatively make and make the emission tradable.

To understand the two interventions and their efficiency, let us create an oversimplified hypothetical scenario to understand the market mechanism. There are two power plants X and Y, with a production capacity of 2GW per year. Plant X pollutes 4 million tonnes of CO2 for the 2GW production, and plant Y pollutes 5.33 million tonnes of CO2 for the same production. A plant can earn $100 per GW per year. To safeguard the broader public health and reduce the pollution levels, the state put a ceiling on the amount of CO2 that a power plant can emit. As per the norms, a plant with a 2GW capacity can pollute up to 3 million tonnes per year. Under this regulation, the total power generation in the society would be 2.62GW (X=1.5GW and Y=1.12GW), assuming the proportional relation between emission and power produced. If the cost of pollution to society is $30 per million tonnes, then the overall capital generation would be $82 (X: $150-$90 and Y: $112-$90). The $30 will be charged from the power plants as the carbon tax.

Bringing the market mechanism (Concept of carbon trading): In our oversimplified hypothetical scenario, let us bring the market mechanism and analyze the results. Instead of a simple regulation-tax mechanism, the state decides to sell the total emission quota. The cost of 1 million tonnes is $30. Plant X a and Y bought their quota of 3 million tonnes each for $90. Plant Y knows that plant X has a capacity of 2GW. Hence it is in its interest to sell the quota of 1 million tonnes to plant X. After the trade, plant X will operate at its full capacity and over-pollute, whereas plant Y will under-pollute. The overall pollution will remain at 6 million tonnes. However, the overall capita generation in society would be $95 with the production of 2.75GW.

Tax collection is a costly affair. Tax collection is costly, therefore spending one rupee of the tax money is equivalent to spending three rupees.8 Even if $30 is charged as tax from each plant in the first method, the net value of the tax collected would be one-third of the total, i.e., $10 only.  In the method of carbon trading, this inefficiency is also removed.

2. Positive Externality: When the supply and demand are met, the market reaches an equilibrium price. However, there will still be a marginalized population left that will not be able to afford the electricity at the equilibrium price. Hence either there is less consumption or no consumption. The electricity consumption is directly proportional to the GDP. Access to power increases the overall production, nudges small scale manufacturers to enter the market, boosts the retail market of electrical appliances, and increases the standard of living. Hence there is a case of a positive externality.

Currently, the government provides subsidy and regulates supply tariff structure. This subsidy and regulation model creates complexity in tariff structures that lead to information asymmetry and decreases the overall efficiency of benefits.  Instead, it should use the method of direct benefit transfers to promote consumption. It is a low cost and a less intrusive method.

3. Information Asymmetry: In a free market, the power demand fluctuates frequently. The demand fluctuation depends on various factors like peak hours and seasons. Depending upon the fluctuating demand, market prices could vary. If there is a lack of transparency in these fluctuating prices, the DISCOMs may hide the information and overcharge the consumer. Without information about the price fluctuation in the public domain, the consumer will not know if the DISCOMs are overcharging it.

The government should intervene and monitor the market and make the information available in the public domain so that the consumer could decide to continue with the present service provider or move to another service provider.

4. Monopoly: The distribution business is the combination of content (electricity) and carriage (wires). A distribution company owns the wires in an area and supplies electricity purchased from the producer and drawn from the transmission. Such ownership of wires makes the DISCOM a monopoly in that area. As per the Electricity Act 2003, the consumer can choose a different DISCOM for the electricity supply. If the current DISCOM owns the wires, it will always overcharge the other DISCOM for using the wires. This capability-to-overcharge will give an edge to the wire owner to interfere in the pricing of its competitors. This edge will make the current service provider a monopoly. DISCOMs also tend to pose operational barriers and procedural delays/rejections when switching to other providers on unreasonable grounds.

A study commissioned by the Forum of Regulators in 2015 suggested the separation of content and carriage (C&C). Such separation will make the carriage a separate entity that can provide services to any DISCOM. The wire company will not get involved in the content business. This separation will rule out any possibility of a monopoly creation.

Stakeholders respond to the incentives in a free market. Individuals running the state-owned utilities behave in their best interest. The incentive for a government employee, in this case, is not to optimize the process to compete in the market as there is less and unfair competition. Moreover, substantial political intervention weakens the market further.

In a free market of private players, the incentive structure changes, and it brings competition and efficiency in the market. Below is the predicted incentive structure in the power market that is free and has minimum state intervention.

StakeholdersIncentiveExpected action in a competitive market
ProducerProduce up to its optimal capacity to maximize their profits.Maintain the demand by keeping the prices low and increase efficiency to compete in the market.
TransmissionWin more transmission contracts to survive the market.Keep transmission losses low and maintain the high capacity to maintain low transmission cost.
DISCOMMaximize the consumption of consumers and win more customersReduce outage by investing in technology like smart metering to monitor real-time consumption data. Minimize the tariffs to increase demand and consumption.

Figure 2 Incentive structure of the stakeholder in a free market (author’s analysis)

Atma-Nirbhar Package

Prime Minister Narendra Modi announced the Atma Nirbhar package on May 12. It was indicated that this special economic package would be worth 20 lakh crores, which is approximately equal to 10% of the country’s GDP. The Finance Minister Nirmala Sitharaman, in her subsequent conferences, unfolded the specifics of the interventions that the government is planning out of the relief package.

In the power sector, DISCOMs were given special attention, and the following interventions were announced:

1. Intervention: At present, the consumer bears the cost of the inefficiencies in various processes by the DISCOMS. However, as per the announcements, the DISCOMS would have to bear the cost of their inefficiencies. The government may come up with some penalty system for the DISCOMS for the inefficiencies such as load-shedding.8

This could prove as a needed intervention in the market. This is because due to these regulations, the DISCOMS would be disincentivized to use inefficient ways of distributions.

2. Intervention: The government has planned to remove the regulatory asset funds that were provided to the distribution companies.

3. Intervention: It is planned that the distribution companies would be provided the monetary support (liquid) of approximately 90,000 crores. This extra support is given so that the distribution companies could be relieved from the power GENCOS’s liabilities.

This could be a short-term plan but would remain ineffective. Earlier, the government has tried many times to de-burden the DISCOMs but failed in the longer run. This is because we need to develop more freedom in the sector and inscribe it in the Electricity Act 2003.

4. Intervention: The government has decided to privatize the distribution companies in the union territories.

This intervention is needed but not sufficient. Privatization brings market freedom. Even the Electricity Act of 2003 proposes an open market competition. Still, we see that the market mechanism is not functioning as per its full potential. Removing the government’s market monopoly in the market economy is essential, but still, there is a chance of the creation of other kinds of monopolies due to the non-separation of carriers and content. Hence there could be two ways forward:

a. The carrier and content entities should be distinct and different in the power distribution market.

b. The privatization of DISCOMs must be done in all states.

Conclusion

Electricity to the country is like blood to the body. The more efficiently it circulates, more will be the growth of the economy. Unfortunately, the circulation system of electricity is cluttered. India claims to have achieved 100%9 electrification that provides the necessary infrastructure for electricity reach in the country’s remotest part. With the installed capacity of 324GW and demand of the only 270GW, India sits on a surplus production capacity of 54GW per year.

Nevertheless, we have power outages, and DISCOMs are in loss. 17 years ago, the country opened her market for competition, but still, we have a government monopoly in production, transmission, and distribution of the power sector. With the non-alignment of incentives and open yet restricted private players’ entry, the current power structure will continue to disappoint. Even after 73 years of independence and 17 years of significant policy reforms in the sector, many see electricity as a rare and expensive commodity.

The power sector is like a broken machine, and the reforms are acting like a repair or replacement of a broken part. We need to change the entire machine. John Maynard Keynes said that “The important thing for government is not to things which individuals are doing already, and do them a little better or a little worse, but to those things which at present are not done at all.” State intervention is justified only in the zone of market failures. The state should not get involved in either production, transmission, or distribution of power. The first policy problem is to establish a system where the market can work with freedom. The second policy problem would be market failures. The state has a huge role to play in the market failures in the power sector. It must remove the externalities by regulating the market and giving the freedom for carbon trading to prosper. Removing information asymmetry and breaking monopolies will increase efficiency and produce more utility for society.

References

  1. CRISIL Infrastructure Advisory. (2019). Diagnostic study of the power distribution sector. Niti Aayog, Government of India. http://niti.gov.in/sites/default/files/2019-08/Final%20Report%20of%20the%20Research%20Study%20on%20Diagnostic%20Study%20for%20power%20Distribution_CRISIL_Mumbai.pdf
  2. UDAY (Ujwal DISCOM Assurance Yojana). (2015, November 5). Financial Turnaround of Power Distribution Companies. Press Information Bureau, Government of India. https://pib.gov.in/newsite/PrintRelease.aspx?relid=130262
  3. Soman, A. (2018). India’s Energy Transition: Subsidies for Fossil Fuels and Renewable Energy. International Institute for Sustainable Development. https://www.iisd.org/system/files/publications/india-energy-transition-2018update.pdf
  4.  Kelkar, V., & Shah, A. (2019). In Service of the Republic. Penguin Random House India. https://penguin.co.in/book/uncategorized/in-service-of-the-republic/
  5. Mishra, P. (2012, 9). OVERVIEW OF THE POWER SECTOR. PRS Legislative Research. https://www.prsindia.org/sites/default/files/parliament_or_policy_pdfs/Overview_of_the_Power_Sector_final_web.pdf
  6. Samuelson, P. A. (n.d.). Economics (19th ed.). McGraw Hill Education (India) Private Limited.
  7. Ministry of Law and Justice. (2003, June 2). The Electricity Act, 2003 [No.36 of 2003]. Cercind. http://www.cercind.gov.in/act-with-amendment.pdf
  8. Kumar, A. (2020, May 20). Summary of announcements: Aatma Nirbhar Bharat Abhiyaan. PRS Committee Reports. https://www.prsindia.org/report-summaries/summary-announcements-aatma-nirbhar-bharat-abhiyaan
  9. Choudhary, A. (2018, November 13). Modi’s village electrification is among world’s biggest successes this year, says this report. The Financial Express. https://www.financialexpress.com/economy/modis-village-electrification-is-among-worlds-biggest-successes-this-year-says-this-report/1380269/

The views expressed in the post are those of the author and in no way reflect those of the ISPP Policy Review or the Indian School of Public Policy. Images via open source.