Decomposition of Changes in Gross Value Added in Indian Agriculture: An Index Number Approach using National Accounts Statistics
1 / 0
Authors
-
Gulab Singh
Formerly with Central Statistics Office, Government of India and United Nations Statistics Division, New York, USA
Keywords:
Gross Value Added; National Accounts Statistics; Index Number; Decomposition Analysis; Input-Output Ratio; Indian Agriculture.Abstract
The present study examines the structural composition and decomposition of Gross Value Added (GVA) in Indian agriculture using the National
Accounts Statistics framework. A mathematical decomposition methodology based on index number theory is employed to separate the changes in
agricultural GVA into output effect, input intensity effect, and price-relativity effect over the period 2011-12 to 2022-23. The analysis utilizes data
from National Accounts Statistics published by the National Statistical Office (NSO), Government of India, with 2011-12 as the base year. The results
indicate that the output effect has been the dominant contributor to GVA growth, accounting for approximately 62.4 per cent of the total change,
followed by price-relativity effect at 24.1 per cent and input intensity effect at 13.5 per cent. The input-output ratio in agriculture has shown a rising
trend from 0.423 in 2011-12 to 0.440 in 2022-23 at current prices, suggesting increasing intermediation in the agricultural production process. A
Törnqvist-type decomposition index has been constructed to measure the year-on-year contribution of each factor, revealing significant inter-year
variability linked to monsoon performance and policy interventions. The findings have implications for agricultural policy formulation and for
improving the methodology of GVA estimation in the agricultural sector.
JEL Classification: C43, E01, E31, O13, Q10
Downloads
References
1. Ang, B.W. (2004). Decomposition analysis for policymaking in energy:
2. which is the preferred method? Energy Policy, 32(9), 1131-1139.
3. Ang, B.W. (2005). The LMDI approach to decomposition analysis: a
4. practical guide. Energy Policy, 33(7), 867-871.
5. Balakrishnan, P. and Parameswaran, M. (2007). Understanding
6. economic growth in India: a prerequisite. Economic and Political
7. Weekly, 42(27/28), 2915-2922.
8. Bhalla, G.S. (2007). Indian Agriculture Since Independence. National
9. Book Trust, New Delhi.
10. Central Statistical Organisation (2012). National Accounts Statistics:
11. Sources and Methods. Ministry of Statistics and Programme
12. Implementation, Government of India, New Delhi.
13. Central Statistical Organisation (2015). Changes in Methodology and
14. Data Sources in the New Series of National Accounts, Base Year
15. 2011-12. Ministry of Statistics and Programme Implementation,
16. Government of India, New Delhi.
17. Chand, R., Saxena, R. and Rana, S. (2015). Estimates and analysis of
18. farm income in India, 1983-84 to 2011-12. Economic and Political
19. Weekly, 50(22), 139-145.
20. Coelli, T.J., Rao, D.S.P., O’Donnell, C.J. and Battese, G.E. (2005). An
21. Introduction to Efficiency and Productivity Analysis (2nd ed.).
22. Springer, New York.
23. Diewert, W.E. (1976). Exact and superlative index numbers. Journal of
24. Econometrics, 4(2), 115-145.
25. Dietzenbacher, E. and Los, B. (1998). Structural decomposition
26. techniques: sense and sensitivity. Economic Systems Research,
27. 10(4), 307-324.
28. Fisher, I. (1922). The Making of Index Numbers. Houghton Mifflin,
29. Boston.
30. Narain, P., Pandey, R.K. and Sarup, S. (2004). Role of agricultural
31. statistics in national accounts. Journal of the Indian Society of
32. Agricultural Statistics, 57(Special Volume), 190-204.
33. O’Donnell, C.J. (2012). An aggregate quantity framework for measuring
34. and decomposing productivity change. Journal of Productivity
35. Analysis, 38(3), 255-272.
36. Syrquin, M. (1988). Patterns of structural change. In H. Chenery and
37. T.N. Srinivasan (Eds.), Handbook of Development Economics,
38. Vol. 1. North-Holland, Amsterdam.