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The Pitfalls of Estimating GDP
Context:
The National Statistical Office (NSO) is considering replacing the Ministry of Corporate Affairs’ MCA-21 database with Goods and Services Tax (GST) data.
More on News:
- The previous reliance on the Annual Survey of Industries and RBI’s sample of large companies was found to be insufficient as the sector expanded rapidly.
- The MCA-21 database was considered a significant improvement because it provides comprehensive data from mandatory corporate filings.The move to GST data aims for even greater accuracy, using the extensive and up-to-date information generated by GST compliance.
Background
- Gross Domestic Product (GDP) represents the market value of all final goods and services produced within a country during a given period.
- While GDP provides insights into economic health, its accuracy is paramount, particularly when adjusting for inflation through “real” GDP estimates.
- The National Statistical Office (NSO) is responsible for revising GDP series, typically every 5-10 years, to reflect changes in output composition and relative prices. Currently, the GDP series based on the 2011-12 base year is due for revision, with the 2020-21 year proposed as the new benchmark.
Key Highlights:
- The shift from the 2004-05 base year to 2011-12 revealed surprising divergences in GDP growth rates, particularly in manufacturing.
- For instance, manufacturing growth rates diverged significantly between the two series, with a 5.4% growth in the new series vs. a -1.90% in the earlier series.
- These discrepancies raise scepticism and questions about the reliability of GDP estimates.
- The systematic overestimation observed with the MCA-21 database raises concerns about the proposed reliance on GST data without thorough validation.
- GST data remains largely unexamined in policy research, emphasising the need for systematic analyses and independent validation to ensure the integrity of GDP estimates derived from it.
Methods of Estimating GDP in India:
- Output Method: Measures GDP by calculating the total value of goods and services produced in the economy. The calculation can be summarised as follows:
- GDP = GVA + Taxes on Products – Subsidies on Products
- This method provides insight into the production capabilities of various sectors, including agriculture, manufacturing, and services which helps to understand the value each sector adds to the overall economy.
- Income Method: Focuses on the total income generated within an economy. The formula for this method can be expressed as:
- GDP=Wages+Gross Profits+Taxes−Subsidies
- This approach highlights how income is distributed among different sectors and helps identify economic inequalities.
- Expenditure Method: Calculates GDP by summing all expenditures made in an economy over a specific period. The formula for this method is:
- GDP=C+I+G+(X−M): Where C represents consumption, I investment, G government spending, X exports, and M imports.
Recommendations:
- NSO should conduct pilot studies to assess the GST dataset’s suitability for value addition estimation.
- Alternatively, consider reverting to the ASI for manufacturing GDP estimates due to its established credibility and shorter time lag in data availability.
Pitfalls
- Data Reliability: The accuracy of GDP estimates heavily depends on the reliability of the data sources.
- For instance, the shift from the MCA-21 database to GST data could introduce inconsistencies if the new data is not adequately verified.
- Underground Economy: GDP calculations often exclude the informal or black market economy, which can be significant in countries like India.
- Base Year Revisions: Periodic revisions of the base year for GDP calculations can result in significant changes in growth rates and economic size.
- Sectoral Discrepancies: Different sectors may show divergent growth trends, leading to conflicting interpretations of economic health.
- For example, a high growth rate in the manufacturing sector might not align with other macroeconomic indicators like bank credit growth or industrial capacity utilisation.