India’s statistical system and the data collection

 The non-availability of credible, coherent data on the economy and society is a problem that needs urgent attention

India’s statistical system and the data  

भारत ने क्या किया ? 

१ इसकी आशा करते हुए, स्वतंत्र भारत ने एक अच्छी तरह से डिज़ाइन की गई सांख्यिकीय प्रणाली की योजना बनाई और इसे केंद्र और राज्यों दोनों में स्थापित करने की यात्रा शुरू की। 

 2 जैसा कि पत्रकार प्रमित भट्टाचार्य ने एक पेपर में लिखा है, "भारत की सांख्यिकीय प्रणाली 1970 के दशक की शुरुआत तक दुनिया के लिए ईर्ष्या का विषय थी"।

 3 हालाँकि, समय के साथ इसमें लगातार गिरावट देखी गई और यह वर्तमान स्थिति में पहुँच गया है, जो वर्तमान बहस का विषय है।

Three issues

 The first is the quality and credibility of data

•  Policy formulation,
•  implementation, 
• and research are not possible without accurate and credible data.

 1 Credibility 

is closely connected with the form of the data and the value of information, and is dependent on the reliability of the source. 

2 Accuracy 

is when the data is a true representation of the expected values. 

surveys 

1 The issue of capturing data correctly is important for surveys .

2 as they are the source for official rates of unemployment, poverty, and other statistics that guide policy. 

3 This data also aids economic research. 

4 Surveys especially household surveys 

are facing questions of credibility in terms of the 

• samples selected, 
• representation of geographical regions,
•  urban and rural strata, 
•  non-response by households.

A recent paper by the EAC-PM argues

that surveys do not adequately capture the urban part of the economy. While this is a crucial issue, the data and definitions of ‘urban’ itself need to be re-examined.

Given the structural changes taking place in the economy, there can be an accurate portrayal of the economy and society only through a realistic sample.

Second, there are multiple agencies providing data

1 on the same set of indicators for the same sector, but giving different numbers.

 2 This poses the problem of choosing an appropriate data set to formulate policies. 

3 example manufacturing data 

Data on this sector for calculating GDP is from the Ministry of Corporate Affairs’ MCA21 portal. 

But this data differs from the data published by the Annual Survey of Industries. 

Moreover, a 2019 report by the National Sample Survey Office on the services sector found that nearly 36% of companies in the MCA21 system that were used in computing GDP were either not traceable or not classified properly. 

4 As of June 2023, the MCA21 portal was plagued / filled  by technical glitches

 There were problems in uploading filings and accessing documents.

 About 800-900 companies were being registered every day on the new portal of the MCA. 

However, there is concern about a large number of shell companies being formed, which undermines the use of this data for GDP calculations.

The competence / capicity  and ability of the system to generate and disseminate / expand high-quality data is the third issue

surveys been delayed 

1  In recent times, not only have surveys been delayed, but the publication of processed data of a completed survey has also been withheld. 

2 This has hampered the generation of micro-level data on important variables such as consumption used for assessing the extent / area of poverty. 

consumer price index are overdue for revisions 

This is also the case with macroeconomic data.

 The wholesale price series used to assess inflation and the consumer price index are overdue for revisions. To assess economic growth, national income estimates need revisions. 

Non-availability of updated data hinders the assessment of economic growth and poverty reduction.

Urgent attention required

1 With the 2021 Census

yet to take place and

 

2 with a re-look required for methods of 

surveys, data are not available for assessing economic performance and development outcomes. 

In the absence of such data, policymakers look for quick fixes and celebrate success on the outcomes derived from thin samples. 

Recently, NITI Aayog’s National Multidimensional Poverty Index 2023

which used data from the National Family Health Survey (NFHS) of 2019-21, showed reduction in multidimensional poverty since 2015-16. 

No doubt the NFHS has a robust methodology for collecting data for a specific purpose,

 but this particular index needs a caveat / modify  as it is based on a sample of 6 lakh households, while India has more than 30 crore households.

It is time for an overhaul of the system.

 We need better communication between the creators and users of data. 

There should be an attempt to move beyond dashboards and disseminate data more transparently.

 Going back to the Rangarajan Committee Report (2001) and taking stock of what has been done could be a good starting point.

source the hindu