• Sanskriti IAS - अखिल मूर्ति के निर्देशन में

Reliable data, good policy

  • 24th November, 2021

(MainsGS2:Government policies and interventions for development in various sectors and issues arising out of their design and implementation.)


  • An appropriate understanding based on reliable and timely public data is critical to ensure effective policy intervention and becomes the cornerstone of good governance.

Twin challenges:

  • Policy making is facing twin challenges in collating reliable high frequency data and interpreting the same as the impact of COVID-19 pandemic is proving to be an unprecedented disaster.
  • During the period of shocks or volatility like the one caused by COVID-19 pandemic, understanding the economic  and social performance provides better insight than analysing year-on-year performance.

Robust public data:

  • The reliable and timely public data have a direct bearing on the state’s capability to design and implement programmes effectively.
  • Among the emerging economies, India is credited to have a relatively robust public data system generated through its decennial Census and yearly sample surveys on specific themes.
  • Though errors continue to be higher than in high-income countries, Census data are recognised for their reliability.


Delayed data:

  •  Despite having adopted the latest data processing technologies, there has been a growing delay in the release of the collected data which makes data less useful for policy intervention.
  • The delay also implies less public scrutiny and hence undermines accountability. 

Comparability issue:

  • In recent years, the  government introduced changes to the estimation of GDP that made comparisons over time impossible.
  • Adjustments to computation and survey methods are always welcome when they are meant to improve accuracy.
  • In this instance, the arguments for revision and the revisions undertaken do not improve the quality of estimates.
  • Therefore, the revisions, some claim, are driven more by political considerations than by the need to improve accuracy.

Conduct of sample:

  • The statistical bureau has been revising the sample surveys almost every year and among many one crucial sample survey is the quinquennial ‘Monthly Household Consumer Expenditure’ (MHCE).
  • The Government of India (GoI) in November 2019 announced that the MHCE data collected in 2017-18 could not be released due to ‘data quality issues’.
  • The MHCE provides the data base to compute the weightage assigned for commodities in the calculation of Inflation Index, the poverty line and poverty ratio, nutritional standards of people based on their consumption of various food items, and consumption expenditure in the national accounts system.
  • The government also uses the poverty estimates to decide on the State-wise allocation of foodgrains to be sold at subsidised prices through the Public Distribution System. 

Reasons for Withholding data:

  • Hands off responsibility: Restraining data leads to breach of public confidence and accountability. Denial of data on important markers of governance, delivery and issues that matter to people keeps responsibility at bay.
  • Bounced to States: Not acknowledging facts or numbers leads to deflect accountability to the other unit of power i.e. States and avoidance of responsibility from vital issues.

Challenges to be addressed:

  • If digital data collection tools are to be used as announced earlier, several challenges need to be addressed. 
  • The loss of  a precious data base affects the framing of policies relating to food and nutrition security, among others. 
  • Impact of pandemic both quantitative and qualitative on education and health is also unmeasurable due to lack of sufficient tools of data collection.
  • Moreover, the robust estimation of individual items in the national accounts system also awaits the Census and the subsequent sample survey results.


  • In the absence of timely and reliable public data, the policy mechanism of the government will hamper.
  • Thus, government need to ensure that the data generation possibilities opened up by new technologies are embedded in a robust system of public data production and use.

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