Abstract
Agricultural productivity growth is key to rapid structural transformation of the economy. More than 50 years after the start of the green revolution, India’s reliance on agriculture as the mainstay of livelihoods continues to be high. Against this backdrop, this paper examines trends in labour and total factor productivity in Indian agriculture. We first examine trends in the differences in employment-output shares in rural India, and show that these have not narrowed enough, and are particularly wide for women workers. Next, at the aggregate level, we compare estimates derived from the India KLEMS and USDA data sources, and find that while both reflect very similar labour productivity trends, the USDA estimates of the share of TFP in output growth are much higher. The omission of water as an input is a serious limitation in both cases. The changing composition of agricultural growth may drive much of the TFP growth, but it is likely occurring on a relatively small base in terms of livelihoods. We then estimate farmer-specific TFP indices for wheat and paddy as these crops continue to be the single large source of rural livelihoods. We use unit record data and estimate production functions in two states each. We find that there is spatial heterogeneity in the extent of shift in the estimated densities of TFP. Finally, we examine sources of change in farm business incomes and show that in the past, productivity growth has been able to sustain farmer incomes despite adverse trends in market prices. We conclude by highlighting the need for a subsectoral analysis of agricultural productivity, that accounts for a degrading resource base, and discuss implications for sustained productivity growth.
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Source: Computed from the INDIA KLEMS and USDA databases.
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Notes
As computed from the INDIA KLEMS database https://www.rbi.org.in/Scripts/KLEMS.aspx.
According to the Total Economy Database (TED) constructed by the The Conference Board that uses alternative (non-official) GDP estimates for China, the Chinese economy grew by 7.5 ppy whereas India grew by 6.4 ppy.
The terms nonfarm and nonagriculture are used synonymously.
Although the NSS has now been merged into the National Statistical Office, we continue to use the older name.
A fuller examination of structural transformation of the Indian economy would require a detailed analysis of terms of trade, trends in agricultural and non-agricultural wages, nature of rural land markets and land fragmentation, convergence in economic growth across states, among other indicators. Space considerations preclude a detailed discussion of these issues.
It was collated and complied by the United States Department of Agriculture (USDA) from several data sources, including the FAO and the ILO. This has information for all countries; here we extract and use only the Indian figures.
The two databases also account for quality of inputs in different ways. For example, the KLEMS considers quality of labour and composition of capital stock; the USDA explicitly accounts for agricultural land that is used as pasture, and that under irrigation. Further, while KLEMS reports estimates for the fiscal year, the USDA uses the calendar year; the period of coverage also differs slightly. An updated USDA database became available recently, but a check of the calculations using the updated figures suggests that our results and conclusions remain unaffected. Further details on how inputs and output are measured can be found for KLEMS at https://rbi.org.in/Scripts/PublicationReportDetails.aspx?UrlPage= &ID=1186 and for USDA at https://www.ers.usda.gov/data-products/international-agricultural-productivity/documentation-and-methods/#data.
Unlike the industry sector, where there are many inter-industry transactions calling into question the validity of the gross output method, the extent of such transactions are far more limited within agriculture. The main exception is the use of cereals as feed in the livestock subsector.
The KLEMS figures refer to gross output in agriculture as computed by the Central Statistics Office, and includes the crop, livestock/animal husbandry and fisheries subsectors, The USDA database uses the value of production of 189 crop and livestock commodities as reported by the FAO.
We retain this periodisation even though the KLEMS data in Table 5 suggest no difference in the rate of growth of output between the PR and REC periods.
Note however that the USDA data source attempts to account for it indirectly by incorporating irrigated area and area under pasture in its measure of land use.
The \(R^2\) from a regression of log changes in TFP on rainfall is slightly over 0.40. Further, the trend in rainfall in the first subperiod (WTD) was negative, suggesting that the TFP growth of \(-\)0.11 ppy would likely have been positive but for adverse rainfall conditions. Similarly, the rainfall in the last subperiod (ACC) was good, indicating the 2.22 ppy rate of growth in TFP is likely overestimated.
Even when inputs such as seeds are taken into account (both data sources do so), they do not reflect a distinction between the more productive hybrids relative to open varieties. Thus seed quality is also absorbed in the residually-estimated TFP growth.
There were definitional changes in what constitute agricultural households, but these are unlikely to affect the overall trend suggested here.
For details of sampling design and definitions, see chapter IV of http://desagri.gov.in/wp-content/uploads/2021/06/manual_cost_cultivation_surveys_23july08_0.pdf.
In brief, the approach involves the assumptions that (a) \(\varepsilon\) consists of two additive components: an idiosyncratic productivity shock that affects the farmer’s decision rules \(\mu _{it}\), and an iid component \(\nu _{it}\) that is realized only after intermediate input decisions are made; productivity evolves exogenously as a first-order Markov process; (b) The intermediate input demand function is strictly positive and is invertible in the productivity shock and (c) that investment decisions are made in previous periods and are not influenced by the productivity shock. While Levinsohn and Petrin propose a two-stage estimator, Woolridge’s method involves a single-step GMM estimation. Of course, the Woolridge method also has limitations, especially given the relative short panels that we employ. Whether estimated TFP is sensitive to choice of estimation method is a topic for further research.
An alternative approach used in the literature is to use the farmer-fixed effect coefficient as reflecting the part of output not explained by inputs. This approach has been followed for example by Aragon et al 2021.
We acknowledge that this is a rather large increase, especially given its implications for relative factor shares. We also do not claim statistical significance for this increase.
In the interests of space, and given our focus on TFP, we do not report the estimated coefficients. These are available from the authors on request.
Yields are based on crop-cut experiments; farm harvest prices are based on state reports that in turn rely on price reports from not less than ten villages in each district. As with DR, costs in this paper are deflated using the CPIAL, while returns and prices are deflated using the WPI. Also, area weights have been used to aggregate state-level figures to the national average.
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Acknowledgements
We are grateful for the excellent research support provided by Deepak Varshney in “Changes in the Probability Density Function of Total Factor Productivity in Paddy and Wheat: Evidence from Unit Record Data” and Abhishek Arora in “Drivers of Farm Incomes: Wheat and Paddy”. We also thank Isha Chawla for helpful discussions. Finally, we greatly appreciate the perceptive and constructive comments of the two referees. All errors are our own.
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Krishna, K.L., Meenakshi, J.V. Agricultural Productivity Growth and Structural Transformation in Rural India: Some Recent Evidence. J. Quant. Econ. 20 (Suppl 1), 277–302 (2022). https://doi.org/10.1007/s40953-022-00321-y
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DOI: https://doi.org/10.1007/s40953-022-00321-y