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Labour Productivity Dynamics in Indian Agriculture: 2000–2016

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Abstract

This paper attempts to estimate and identify the regional disparity of India’s agricultural labour productivity growth by decomposing it into technical changes, efficiency changes and input accumulation per worker during the 2000–2016, the period marked with the launch of employment guarantee programme in rural India in 2006 and 2007. The study observes a significant growth in agricultural labour productivity despite variation in their sources, across the states in India during the sample period. While growth of India’s agricultural labour productivity mainly depended on the accumulation of inputs and technical progress, efficiency changes contributed more towards regional disparities in agricultural productivity growth. Improving efficiency to promote TFP growth is important for agricultural labour productivity growth for the low-performing states.

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Fig. 1

Source: Authors’ calculation based on data compiled from Commission for Agricultural Costs and Prices (CACP), Directorate of Economics and Statistics, Ministry of Agriculture (various volumes), for the period 2000–2001 to 2016–2017

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Notes

  1. ‘The basic objective of the Act is to enhance livelihood opportunities in rural area by providing at least 100 days of guaranteed wage employment in a financial year to every household whose adult members volunteer to do unskilled manual work’ at the specified minimum wage (Government of India 2005). It generated more than 18 billion person-days of work to around 400 million households between 2006 and 2015 (Shah 2016). The act was initially introduced in 200 of the poorest districts throughout India in February 2006, later extended to 130 additional districts in April 2007, and to the rest of rural India in April 2008. The agriculture sector in developing countries tends to employ the lowest ability workers (Lagakos and Waugh 2013). Based on the twin principles of self-selection and rights based, MGNREGA enhances choice of work options to casual labour in rural areas and is able to create substantial changes in the Indian agricultural production environment due monopsonistic agricultural labour market (Muralidharan et al. 2020). The shortage of labour accompanied by increased agricultural wage was felt by the large farmers in various states in India during the high season (Azam 2012; Imbert and Papp 2015; Basu 2013; Gulati et al. 2014, Bhargava 2014; Berg et al. 2018).

  2. Being resource intensive and sociological homogenous crop, paddy covers more than 50% of total cropped area and contributes more than 60% of the total foodgrain production across Indian states.

  3. The null hypothesis, for example, H0: The labour productivity distributions remain unchanged throughout the period; H1: The labour productivity distribution has improved over the period.

  4. The kernel density of X can be estimated as: \( f\left( {{\hat{x}}} \right) = \frac{1}{nh}{\sum_{t = i}^{n}} k\left( {\frac{x_{i} - X}{h}} \right)\), where \(X_{1} ,..X_{n}\) are samples from X with the probability density function f(x), k(·) is a symmetric probability density, n is the number of observations and h is the optimal bandwidth (smoothing parameter). We use the Epanechnikov kernel to estimate the distribution and the Silverman rule of thumb method for choice of the optimal bandwidth (Silverman 1986).

  5. The output distance function at time t, defined as the inverse of the maximal proportional increase in the output vector, yt, and given inputs, xt, is expressed as:

    $$D_{O}^{t} \left( {x^{t} ,y^{t} } \right) \equiv \inf \left[ {\theta :\left( {x^{t} ,\frac{{y^{t} }}{\theta }} \right) \in S^{t} } \right] = \left[ {\sup \left\{ {\theta :\left( {x^{t} ,\theta y^{t} } \right) \in S^{t} } \right\}} \right]^{ - 1}.$$
    (4)

    Since \(\theta\in [0, \infty\}\), being the maximum proportional expansion of the output vector yt, given inputs xt and technology St and θ = 1 for efficient observations, it is also equivalent to the reciprocal of Farrell’s (1957) measure of output efficiency, which measure TFP ‘catching-up’ of an observation (states in our case) to the best practice frontier technology.

  6. We use the DEAP (version 2.1) programming code developed by Tim Coelli (1996) of University of New England to estimate Malmquist productivity index and its components.

  7. Thanks to the anonymous referee for the suggestion of 2008 as the dividing year for 2000–2016 under study. As understood in Section 2 of this text, during 2001–2008, the distribution of agricultural output per worker in Indian states gradually evolved from a unimodal to more flatter bimodal distribution. The period was buffeted by multiple factors: first, labour scarcity and increased wages incentivised the farmers to invest in labour-saving mechanisation; second, thanks to soaring crop prices in the world economy, the terms of trade also shifted in favour of agriculture; and third, the policy of doubling agricultural credit in every three years announced in 2004 and subsequent expansion of term lending by banks have a positive effect on private investment that promoted private investments in Indian agriculture (Radhakrishna 2020, p. 9).

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  1. Development Management Institute (DMI), 2nd Floor, Udyog Bhawan, 800004, Patna, India

    Surya Bhushan

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Bhushan, S. Labour Productivity Dynamics in Indian Agriculture: 2000–2016. Ind. J. Labour Econ. 64, 371–388 (2021). https://doi.org/10.1007/s41027-021-00318-w

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