Data for policy analysis in times of pandemic

Editor: Manas Gubbi
December 21, 2020

Background

The onset of the Covid-19 pandemic has presented a unique set of challenges, especially in areas where data are needed for policy formulation and impact evaluation. Policy formulation requires a wide range of datasets; agriculture production, industrial and price statistics, employment figures, public finance statistics and the national accounts, among others, that provide a summary assessment of the economy. The range of data also includes a variety of high frequency indicators that capture short term movements in key economic variables. 

Beginning March 2020, the spread of Covid-19 led to an unprecedented situation as containment measurements included lockdowns, restrictions on physical movements of persons, and goods and temporary closure of industrial and commercial establishments. Such administrative decisions have implications for data collection and overall availability of statistics for policy. This article highlights the state of data availability for various policy analysis and discusses key issues that affect data quality, especially in times of a pandemic.

Landscape and State of data

Some of the most important indicators required for a macroeconomic assessment come from industrial statistics, employment and the national accounts. Most national aggregates such as GDP/GVA estimates by sectors, consumption expenditure, Gross Capital Formation, Imports/ Exports, etc. come with considerable delays as the process of compilation is long and cumbersome.1 Given the nature of compilation, these aggregates offer a point comparison (or change) and have limited ability to capture economic shocks (positive or negative) in real time with higher frequency

The broader picture of the economy can only be understood by datasets such as the Economic Census (EC), Supply and Use Tables (SUTs) and Input-Output Tables (IOT). These datasets provide the detailed structure and composition of the economy, inter-industry flows of goods and services, and more importantly, the distribution of value added by economic activities in the economy. However, in times such as the Covid-19 pandemic, most of these datasets serve a limited purpose as they capture the past, whereas the requirement for policy is to make assessments for the present and future. Using these aggregates for current and future assessments implies making a host of assumptions and dealing with limitations. For instance:    

  1. The EC gives a complete enumeration of all kinds of establishments with a distribution upto the district level, however the present 6th EC data are dated by at least six years (2013-14). Thus, while making any assessment, one has to make an untenable assumption that the distribution of establishments has remained unchanged.
  2. SUTs and IOTs are dated by at least four years (2015-16) and thus the last known structure of the economy may not fully capture recent changes.
  3. National Accounts follow a long revision cycle and GDP estimates for any year are revised six times over a three year period. Therefore, while the focus is on making current and future assessments, the past may equally be uncertain as the magnitudes and directions of revisions are unpredictable.
  4. Consumption Expenditure of households is an important demand side macro aggregate and is the single largest component in GDP. The last available household survey is NSS 68th Round 2011-12, which is considerably dated and may not reflect the changes in composition of expenditures that have happened in recent times.
  5. The most recent surveys of the NSSO are from the 77th Round (Jan-Dec. 2019) that are expected to cover socio-economic expenditure, situational assessment of agricultural households, debt and investment are likely to be available in a span of two years.
  6. As of 2020, the annual employment figures from the Periodic Labour Force survey are available only for 2018 and for urban regions on a quarterly basis till 2019.

Given such a data landscape, the data availability for the current year captures pre-crisis situations at different time periods, leaving only high frequency indicators for assessing short term (month-on-month) movements in few important indicators. In addition, the structure of the economy equally imposes data constraints. Assessing the unorganised sectors (which primarily include unincorporated/ household enterprises) is much more complex as information on such entities is available only from sample surveys. Existing surveys of unorganised manufacturing and service enterprises are available with gaps of three to four years and thus information is unavailable for any immediate policy formulation.

Impact due to the pandemic 

One of the major impacts of the pandemic has been on data collection. With restrictions on physical movements, collection of data on prices, agricultural and industrial production, periodic surveys on employment had been severely impacted during the initial phases of the pandemic. In the passage of time, data collection can only capture information from the point of resumption of economic activities, thus leaving out critical information of the extent of pandemic. Such situations can create statistical inconsistencies as data for missing periods are usually interpolated or imputed and do not necessarily provide a fair and accurate picture of the state of affairs. Data users are often agnostic or unaware of the limitations and constraints faced in compilation of statistical data. Thus, in absence of any information, data users may even naively assume that the impact of such errors, omissions or imputations is negligible. The problem gets compounded when such data are put to empirical use without any information on quality and limitations. 

In summary, policy analysis and impact assessment becomes difficult especially when currently available data captures a dated picture. The problem becomes severe in cases of a crisis as past data do not serve any purpose in taking decisions for the present and future.2,3 In times of a pandemic, measurement issues assume far more importance as unavailability of data is usually bridged by statistical methods that may not reflect the state of affairs. Data driven policy requires state of the art collection frameworks and methods for important macro variables that provide valuable assessment of the economy. Till such systems are available, policy analysis has to rely on information that is dated and best used with judgement and intuition.

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.

References:

  1. See Sapre, A., & Sengupta, R. (2017). Analysis of revisions in Indian GDP data. World Economics Journal, 18(4).
  1. Roy, R. (2020, June 4). Intuition, not Prediction. Business Standard
  1. Ghosh, A., Raha, S., et al. (2020). Jobs, Growth and Sustainability: A New Social Contract for India’s Recovery. CEEW and NIPFP Report, New Delhi: Council on Energy, Environment and Water.

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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.

The Union Budget for FY 2021-22 presented on February 1, 2021 has the distinction of being the first budget after Covid-19 devastated much of the world, including India. India registered a historic contraction of nearly 24% in its Gross Domestic Product (GDP) in the first quarter of the current financial year, unemployment surged, small enterprises suffered acutely and vulnerable households slipped into poverty. During the course of the year, the government announced a series of measures to alleviate the Covid-19 induced stress. Since then, there have been signs of a nascent recovery in the economy. In this context, there has been tremendous anticipation around this budget to put India firmly back on the growth path. 

Towards this end, the budget has made many noteworthy announcements. Two key announcements stand out viz., a push to the privatisation agenda by announcing the privatisation of two public sector banks and an insurance company, and the establishment of an asset reconstruction company to take over the Non-Performing Assets (NPAs) of banks. 

Privatisation of public sector banks

Signalling a clear and key policy shift, the government has announced an ambitious and strategic privatisation policy by proposing to disinvest/strategically sell public sector entities (PSEs). Towards this end, the government has approved four sectors as strategic, where it will retain a minimum number of entities. It will pare down its presence above this minimum in strategic sectors, and completely in non-strategic sectors. Notably, banking, insurance and financial services have been identified as  strategic sectors.  

A number of central PSEs (including Air India, Shipping Corporation of India and the Container Corporation of India) have also been identified by the budget for divestment this fiscal year. Further, the NITI Aayog has been tasked with identifying the next pipeline of central PSEs for disinvestment.  Within this overall context, the current budget has also proposed to privatise two public sector banks (PSBs) in addition to Industrial Development Bank of India (IDBI), and a general insurance company.  

Privatisation of PSBs is not a new idea. It was attempted earlier as well.  The former Finance Minister, Yashwant Sinha, proposed to bring government stake below 51% in PSBs in early 2000s. However, this did not garner enough support. Given the burgeoning requirement of capital by PSBs and the limited fiscal space with the government, it has now become imperative to find other avenues to bridge the gap.  The proceeds of the disinvestment could help release government resources to more productive uses, particularly as government finances too have come under tremendous pressure in the wake of the pandemic.  

Moreover, with the approval of banking as a strategic sector, and the maintenance of public sector presence, the government should be able to avoid any compromise on its social agenda – a key concern that has been flagged earlier on privatisation of banks. 

Resolution of bad assets

Flow of credit is an imperative to meet the needs of a growing economy. A surge in bad or non-performing assets impedes the flow of credit. This is because banks must make higher provisioning to cover their bad assets, reducing the overall credit available to firms and households. It also makes banks risk averse. 

Measures to provide relief to borrowers such as the moratorium on loans – could exacerbate the problem of bad loans. An improvement in the NPA ratio of the banks was visible before the pandemic but the policy support extended to borrowers could impact the asset quality of banks through postponement in recognition of bad assets. 

The Financial Stability Report released by the Reserve Bank of India (RBI) in January this year estimates a sharp rise in the stressed assets on the banks’ books, particularly in the case of public sector banks. A number of measures were taken by RBI to improve the flow of credit by banks; however, the offtake of credit is still slow. The need of the hour therefore is to resolve these bad assets and clean up banks’ balance sheets so they can begin to lend more freely. 

The budget tries to address the problem of bad loans by announcing an asset reconstruction company (ARC) and an asset management company (AMC). This mechanism is expected to take over the stressed assets from banks, manage and eventually dispose them for value. The assets may be disposed of to potential buyers which include alternative investment funds (AIFs).  

The idea of a “bad bank” has apparently been inspired by the experience of countries such as the US and Malaysia. The Malaysian government, for instance, set up “Pengurusan Danaharta Nasional Burhad” – a government-backed AMC – that successfully bought and resolved bad assets in the Malaysian financial system in the aftermath of the Asian Financial Crisis in late 90s. 

While details on the Indian initiative are sparse at this point, the proposed mechanism is understood to not be a government owned entity. Instead, this mechanism would be primarily led by banks, with the government offering some support – perhaps in the form of a guarantee. The success of this proposal would depend on how well the proposed entity is managed. It will also depend on the capital allocation strategy by banks and how much money the government sets aside for this entity.

The reference to Alternate Investment Funds (AIFs) and other entities as potential buyers perhaps hints at measures for improving the efficiency of the stressed assets market – an important step that must go hand in hand with the creation of a “bad bank”.  However, a pitfall that the proposed mechanism must guard against is the potential “moral hazard”. It must disincentivise, rather than incentivise, poor decision making by the banks that led to the bad assets in the first place. 

Both the above announcements mark important interventions in the banking sector. Their success will however depend on the actual details – of the institutional structures and enabling frameworks put in place. Implementation will also be key, given the competing interests when it comes to privatisation and the government’s own poor track record on divestment.  This will, therefore, be a keenly watched space in the coming year.  

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.

On February 01, 2021, Finance Minister Nirmala Sitharaman introduced the Budget for FY 2021-22 in the Parliament. Budget announcements are always a highly anticipated event in India; this time the expectations were even higher for the government to provide a credible roadmap for recovery. However, ahead of the Budget, another bill rumored to be proposed during the session was making news. The to-be-proposed Cryptocurrency and Regulation of Official Digital Currency Bill, 2021 has taken the nascent crypto-industry in India by surprise. The Bill has dual objectives of (i) banning ‘private’ cryptocurrencies in India and (ii) creating a framework for the Reserve Bank of India to issue official digital currency. While further details on the Bill are awaited, now is an opportune time to look at the current status of digital currencies in India and around the world.

The Reserve Bank of India (RBI) has viewed cryptocurrencies with a jaundiced eye. In April 2018, the RBI issued a circular, “prohibiting banks and entities regulated by it from providing services in relation to virtual currencies.” The circular was subsequently overturned by the Supreme Court on grounds of being a “disproportionate” response by the RBI. It further asserted that there was no evidence that any regulated entities had indeed incurred losses or instability on account of virtual currencies. 

Understanding Digital Currency

For a preliminary understanding of digital currency, one can look towards its most popular example – BitCoin. It was launched against the backdrop of the 2008 global financial crisis (GFC) as a bulwark against excessive printing of currency by central banks. The mystery surrounding the inventor, the legendary Mr. Satoshi Nakamoto, only added to the allure of the new digital currency. Here was a currency that was decentralized and maintained user anonymity while ensuring complete transparency for all transactions. BitCoin is limited to 21 million units, which are mined by solving complex mathematical problems (a.k.a. proof of work) and can then be traded on BitCoin exchanges. Blockchain technology, upon which BitCoin is built, has a certain democratic appeal; blockchain ledgers are immutable and can be changed only when such a change is validated by a given number of participants. Despite these advantages, there has been criticism against BitCoin or any of the non-fiat digital currencies to be used as a reserve currency, especially on account of limitations to being used as a medium of exchange

Opportunity for a Central Bank Digital Currency

In recent years, and perhaps consequentially, central banks around the world have begun to evaluate the possibility of a sovereign-backed digital currency also known as a central bank digital currency or a CBDC. This begets an obvious question – what indeed is a CBDC? Traditionally, money comprises cash, deposits maintained by commercial banks with the central bank and deposits with commercial banks. A CBDC introduces a new form of digital money which is a liability of the central bank. In theory, even retail participants could hold a CBDC in the future. Secondly, one might wonder, what are the motivations for issuing such a form of money? A report published by the Bank for International Settlements (BIS) in 2020 broadly categorizes the merits and risks of a CBDC as follows:

a. Payment systems – motivations and challenges

This category includes a multitude of motivations such as ensuring continued access to risk-free money in societies where cash is going out of fashion, improving financial inclusion, enhancing efficiency of cross-border payments, etc. Key risks in this category include ensuring cyber resilience and balancing public privacy needs with anti-money laundering requirements.

b. Monetary policy – motivations and challenges

If CBDCs are designed as interest-bearing instruments, then monetary policy transmission would, in theory, be immediate. This could incentivize commercial banks to accelerate passing on the effects of changes in policy rates. Whether CBDCs should indeed be interest-bearing instruments is a design challenge requiring further study.

c. Financial stability – motivations and challenges

A key motivation for central banks to evaluate issuance of CDBCs is to pre-empt the risk of loss of monetary sovereignty on account of displacement by privately issued digital currencies such as Diem (previously called Libra) by Facebook. However, introducing a CBDC introduces the possibility of a bank run in times of crisis from commercial deposits to central bank money.

Way Ahead

Money is an economic, social, and political phenomenon. Introduction of CBDCs requires careful planning, analysis, and balancing risks with efficiency motivations. Design choices abound in terms of technological architecture as well as features embedded in the instrument. In the Indian context, a well-designed pilot project aligned with social and economic realities is paramount. Internationally too, interest in CBDCs has increased, partially on account of the COVID-19 pandemic. A survey conducted by the BIS in 2020 revealed that 86% of central banks (out of a total of 65 respondents) were actively engaged with CBDC research, evaluation, and/or development (see figures below). China famously leads the pack in digital currency development adding a currency dimension to its competition with the United States.

Figure 1 Source: BIS Central bank survey on CBDCs. 1 Share of respondents conducting work on CBDCs.

Adoption of new technology is often scary, and rightly so, especially in cases where it has the power to improve or destroy entire systems. India’s financial system has been revolutionized by fintech, especially in the digital payments space. It is indeed time we re-visited the idea of money in light of the technology now available at our disposal. The to-be-proposed Cryptocurrency and Regulation of Official Digital Currency Bill, 2021 signals India’s willingness in this regard.

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.