Category: Covid-19

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.

In one of the most significant behavioural changes in modern times, the COVID-19 experience brought several challenges to the field of behavioural economics. From the uneasy debate of behavioural fatigue to creating successful norms for restricting the spread of the virus. However, practitioners have a long way to go vis-a-vis making the field more robust in its understanding and in its attempts to contribute effectively to policy making.

Over the years, behavioural economists have caught many governments’ attention in developing behavioural change frameworks to influence citizen behaviour. It started with the UK and USA governments in the 2010s, which established their respective nudge units to help in policy making. The Indian government has also seen some of the benefits in recent years and have started working with behavioural units in the country to deliver some of their services. One of the main reasons that this science has interested governments is the low-cost nature of nudges. 

When the COVID-19 pandemic hit, governments rushed to develop policies with some of their behavioural experts, altering citizen behaviour to restrict communicability. This was the time to test behavioural economics’ competency and see whether it can be a potent tool in policy making as argued by its supporters for many years. 

Governments worldwide adopted varied restrictions. Where the U.K. was taking a more lax response, India adopted one of the strictest lockdowns in the world. In the former, a controversy started when some experts recommended herd immunity based on almost 70% of the population getting affected by the virus. This was based on and complemented by some controversial remarks by behavioural experts, who used “behavioural fatigue” as a reason not to enforce a lockdown early as people will get fed up with it. Immediately, almost 600 behavioural economists wrote a letter questioning the evidence of the behavioural fatigue argument. In the end, the U.K. government enforced a lockdown. 

The episode of “behavioural fatigue” brought controversy to the field, which has frequently been looked at as over-generalising and over-claiming and often faced replication problems. This can be attributed to a false perception about what behavioural economics is and what it offers to public policy. 

Behavioural Economics is the study of humans, organisations, and governments’ behaviour employing disciplines of psychology and economics. It critiques the dominant position of the rational model of the economic agent. Even though the field has been around for decades, it was made popular with the application of “Nudge” — a cost-effective tool to modify human behaviour. 

During COVID-19, various countries adopted nudges in their policy decisions. One of the widely used ones was the 2m distancing signs in public spaces that visually prompts citizens to distance themselves and avoid overcrowding. Another was focusing on creating a clear message to the public on how to behave during a pandemic. As overload of information could be confusing, a salient messaging like ‘Stay Home; Protect the NHS, Save Lives’ helped the public adhere to a particular behaviour. But the most popular was singing the Happy Birthday song for the recommended 20 seconds hand wash, which gave people a reference point. The development of these nudges was by identifying the heuristics and traits of human behaviour. 

Some of these nudges were fruitful in the pandemic as many people changed their behaviour which helped restrict the spread of the virus. However, many of them failed in different environments — a point on how it is difficult to generalise nudges. Therefore, a nudge’s efficacy should be tested extensively across different contexts to make them robust. But often, we fail to focus on the testing part and accept a general theory of nudges, which leads to unintended consequences. For example, the famous nudge discussed by Richard Thaler of auto-enrolment savings program, which leads to higher savings for people, is discussed as a general way to use nudges. In many areas, it has led to that conclusion. However, when a set of US Army civilians were auto-enrolled for a similar savings plan, the research found that it left people with higher mortgage and car debt, and the long-term savings result was inconclusive. 

Nudge as a tool is one part of behavioural economics; however, it has become synonymous with it, leading to problems of misinterpretation. Many behavioural economists argue that sometimes nudges or behavioural intervention are not the best fixes for behavioural problems. This is attuned with the lockdown during the pandemic where governments had to resort to strict intervention due to difficulty in changing human behaviour through nudges. 

Nevertheless, behavioural economics is an efficient tool when assessing behavioural changes in people. For instance, with a new set of social norms and stigma during lockdown, non-compliant individuals could be behaviourally intervened by making these new norms salient. Another point is to understand the pandemic fatigue through evidence and develop behavioural solutions that are contextualised. But a big problem faced by governments is the infodemic of misinformation with the pandemic, particularly by the anti-vaccination movement which can jeopardise the effort to combat the virus. This is the next challenge for behavioural economists, to understand anti-vaxxers’ behaviour and try to modify it. Other challenges lie in making citizens comply with the vaccination drives and continue to adhere to safety guidelines. 

The experience of COVID-19 has been a testing ground for the theories of behavioural economics. Some of them have responded well when it comes to norms, slight behavioural changes, saliency, etc. However, other theories brought controversies like the untested evidence of behavioural fatigue. These limitations should be discussed more and referred to when designing behavioural interventions, particularly nudges, which might not always be the best response for behavioural problems. 

The future of behavioural economics lies in collaboration among diverse teams with local knowledge and a multidisciplinary approach to understanding behavioural problems and avoiding over-generalised theories. More importantly, there is a need for epistemic humility among behavioural economists to lead a more robust and evidence-based behavioural approach. 

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.

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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