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DataSpot
DataSpot by IndiaDataHub
16 episodes
1 day ago
In the DataSpot podcast, we try to demystify economic data or indicators for you. We will attempt to answer the practicalities of various economic indicators and concepts rather than their theory. We will try and answer questions like, how is a particular indicator calculated or constructed. What are the important assumptions made in calculating that data? And what is its practical relevance? So, the next time you hear about certain data, you appreciate the nuances and limitations of the data rather than be dismissive of it.
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Business
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All content for DataSpot is the property of DataSpot by IndiaDataHub and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
In the DataSpot podcast, we try to demystify economic data or indicators for you. We will attempt to answer the practicalities of various economic indicators and concepts rather than their theory. We will try and answer questions like, how is a particular indicator calculated or constructed. What are the important assumptions made in calculating that data? And what is its practical relevance? So, the next time you hear about certain data, you appreciate the nuances and limitations of the data rather than be dismissive of it.
Show more...
Business
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Ep 6 - Of what practical use is this GDP?
DataSpot
21 minutes 51 seconds
2 years ago
Ep 6 - Of what practical use is this GDP?

In the previous episode we understood what is GDP. Now, let us understand how to interpret the data? But before that we should understand what different types of GDP data gets released? Why it takes three years to finalise the GDP data? How to interpret the data revisions? 

In this episode we also tell you how the GDP data is calculated. Honestly, it is complicated! 

We also know that half of India's GDP relies on unorganised sector. But we don't know exact GDP of unorganised sector. Do you want to know how the GDP of unorganised sector is calculated? Well, then we have explained it too in this episode. 

GDP calculation, in general, involves some hard-core observable data, but most of the times it includes approximations, proxies and modelling. Therefore, its process, interpretation as well as use-case differs from that of something like inflation data. 

We have heard experts contesting over few decimal point growth rates in GDP data. Is such in-depth analysis really necessary? We cover that as well. 

So, in short, this is a compact package on how to interpret the GDP data. Give it a shot and let us know your feedback.

Sources and Further References:

1. NAS Sources and Methods 2012 

2. Measuring Informal Economy in India _ Indian Experience, By S V Ramana Murthy

3. Methodology for Estimating Quarterly GDP

4. Understanding the New Series of National Accounts 

5. Changes In Methodology And Data Sources In The New Series Of National Accounts 

6. Chp 13 - National Account Statistics - National Statistical Commission

7. Notes On Methodology And Revisions In The Estimates

8. Measuring informality, ILO

9. Press Note First Revised Estimates Of National Income,  Consumption Expenditure, Saving And Capital Formation, 2020-21

10. Studying revisions in Indian GDP data, Ideas for India

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IndiaDataHub - Official Website, LinkedIn and Twitter

Ashutosh, Co-founder of IndiaDataHub - LinkedIn and Twitter

Swapnil, Host - LinkedIn and Twitter

Dwiti, Co-host - LinkedIn and Twitter

Feedback/queries/suggestions on podcast@indiadatahub.com

DataSpot
In the DataSpot podcast, we try to demystify economic data or indicators for you. We will attempt to answer the practicalities of various economic indicators and concepts rather than their theory. We will try and answer questions like, how is a particular indicator calculated or constructed. What are the important assumptions made in calculating that data? And what is its practical relevance? So, the next time you hear about certain data, you appreciate the nuances and limitations of the data rather than be dismissive of it.