Amey Sapre


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.


  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.