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Rural Labour Market and Farmers Under MGNREGA in Rajasthan

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Abstract

This paper analyses changes in labour market in rural Rajasthan after the introduction of MGNREGA in April 2006. The study attempts to answer the following questions: (1) How does MGNREGA influence rural labour market? (2) Which segment of the labour force seeks job under MGNREGA? (3) What are the MGNREGA-induced changes in agriculture and allied sectors? Marx’s concept of relative surplus population is used to analyse different categories of labours participated in MGNREGA. The study is based on a sample survey of 400 rural households randomly selected from four districts in Rajasthan. The study found that there was a substantial fall in the amount spent per rural household under MGNREGA in Rajasthan since 2011–2012. Moreover, MGNREGA has not been effective in attracting crisis-ridden farmers and farm labours because of its irregular employment and a lower daily wage rate as compared to spot wage. Rural labours, whose reservation wage is less than or equal to wage rate in MGNREGA, do supply labour to the Scheme, and a major share of the workforce in MGNREGA has never been part of the labour market nor do they intend to participate outside MGNREGA. It was found that large and medium farmers wanted to stop MGNREGA in agriculturally prosperous districts. Conversely, in agriculturally less developed districts in Rajasthan, large farmers did not hold MGNREGA responsible for labour shortage and wage hike in the farm sector.

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Notes

  1. The unemployment rate in usual status was 2% for rural male and female, 4% for rural females against 3% for rural males under current weekly status and 6% for male and females under current daily status in India during 2011–2012 (Government of India 2014).

  2. The spate of suicides reported from six districts in Vidarbha region in the state of Maharashtra, Wayanad and Idukki Districts in Kerala state, Andhra Pradesh and Karnataka States stirred a hornet’s nest in India by early 2000s. The forerunner of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is therefore the Vidarbha Package of Rs 3570 million earmarked for helping indebted farmers in Maharashtra, announced in June 30, 2006. As distress in the countryside mounted up in the middle of 2000s, the then union government resolved to supplement the source of livelihood of workers in the country side by providing not less than 100 days of employment to a rural household through public employment programme. The right to employment has been made an Act in the Parliament under the title National Rural Employment Guarantee Act, 2005 (NREGA). The said Act was notified on 7 September 2005, and was opertionalised on 2 February 2006 in 200 selected districts in the country in phase 1 in 2006. The second phase of NRGEA was initiated to include another 130 districts in April 2007, and in phase III, NREGA has been extended to all 614 districts covering 6096 blocks and 2.65 lakh Gram Panchayats (GPs) in the country with a budgetary allocation accounting for about 5% of the total budget outlay of the central government in 2008. The NREGA was renamed in 2009 as Mahatma Gandhi National Rural Employment Guarantee Act or MGNREGA. The MGNREGA is a job guarantee scheme enacted to provide 100 days of employment to adult members of rural labour households in India every year for a statutory minimum wage. The central government defrays the wage cost of unskilled manual workers, while wage cost of skilled and semi-skilled workers shall be shared between central and state government on 75:25 basis, and the same formula will be applicable in sharing the material cost as well. The multiplier effect of a 5% allocation of the central government budget in rural India is significant on account of the fact that the amount is mostly expended on wage goods (Patnaik 2005). In the first phase of MGNREGA, six districts, viz. Banswara, Dungarpur, Jhalawar, Karauli, Sirohi and Udaipur, were included and in the second phase, which was commenced in April 2007, covered another six districts, viz. Tonk, Sawai Madhopur, Chittogarh, Barmer, Jaisalmer and Jalore, in Rajasthan.

  3. There is no secondary data source available to state the average landholding size in the village, but it was reported that 20 bigha is the average size of holdings in the GP. Although there are a few farmers with more than 100 bigha of land, land distribution in the GP is less skewed as compared to Shivpur in Sri Ganganagar. One acre of land in the GP or 1.56 bigha fetches between Rs 4 lakh and Rs 10 lakh depending on the location of the land.

  4. Total population in Dabla GP is 2607, of which 1596 are males and 1011 are females. The relative shares of SC (28.69%) and ST (10.01%) are relatively low in Dabla GP in Jaislmer compared to other sample villages like Mandwa GP in Dungarpur. Meghwal caste group dominate SC, and Bhil (Nayeek) caste group is the predominant ST in Dabla GP. Rajput (20%) and other upper caste population are major land holding caste group in Dabla. Important castes included under Other Backward Castes are Lohar, Darzi, Jogi and Swami. The work participation rate is 49.82%, of which 82.22% are main workers and 17.78% are marginal workers. In the total workforce, 8.9% are cultivators and 2.77% are agricultural labourers. It was observed that the proportion of cultivators and agricultural labourers in the GP was unexplainably lower than state’s average. Jaisalmer was one of the six districts included in the second phase of MGNREGA in Rajasthan along with Tonk District, while Dungarpur was one among the first six districts included in Phase 1.

  5. MGNREGA time schedule has been changed to 9 am to 6 pm with effect from July 2015.

  6. Conventionally, wage rate for workers in the construction sector is defined as a function of capital stock, labour productivity and collective bargaining. The present study does not intend to fit a wage determination model, but detects breaks, if any, in a series of monthly real wage data for agricultural labours for a period of 14 years from April 2000. It is hypothesised that there are several breaks in the linear movement of daily wage for agricultural labours after the introduction of MGNREGA as compared to the period before its introduction. It meant that daily wage rate has undergone structural change induced by an exogenous factor, MGNREGA in Rajasthan from 2006 to 2007. It means break dates in the real wage variable (W*) is presumed to be a priori known. The method of dividing the sample series into two sub-periods is based on the critical assumption that break date(s) is known a priori and if the break date is a priori unknown, Chow Test is inappropriate because of arbitrary fixing of a break point in the sample (Balakrishnan and Parameswaran 2007; Hatekar and Dounge 2005). Arbitrarily fixed break date in the sample need not necessarily exist or if at all it exists, the true break dates could be a different one (Hatekar and Dounge 2005). For series with unknown breaks, Bai and Perron (1998) suggested an alternative approach to statistically identify multiple structural breaks in a time series (for details, see Mohanakumar 2012). Once the breaks are identified in the linear movement of real wage for farm workers in Rajasthan, it is crucial to know the direction of the movement of real wage against time and it can be estimated using kinked exponential growth function (Boyce 1986). The kinked exponential function takes the following form, and the model eliminates the discontinuity between the trend line by imposing a liner restriction at the break point (k)1. The final growth equation for a series with ‘n’ breaks takes the following form:

    ln yt = ậ1 + á1 (d1t + d2k) + á2 (d2t − d2k) + án1 (dnt + dnk)  + án2 (dnt − dnk) + ut

    where ln yt is (natural) logarithmic value of real wage of different type of workers in Rural Rajasthan; ậ = intercept; á1 to án = growth rate for the sub-period identified with structural break equation. In this case, án varies from 1 to 6 months to represent a crop production cycle. k = breakpoints (varies between 1 and 6); d1 to dn = dummy variable for 1 to n breaks, ut = Error term.

  7. It is rather difficult to obtain daily wage rate on a monthly basis for different categories of wage labours in rural Rajasthan at the district level. The state-level monthly wage data were used in the absence of district-wise data.

  8. For the analysis, 2011–2012 was chosen for two reasons: (1) participation of workers in MGNREGA peaked in 2011–2012 and after that it started declining; (2) data on DDP from agriculture for Rajasthan are available for 2010–2011.

  9. It is rather difficult to elicit data on reservation wage through household sample survey, especially from those who have withdrawn from the labour market long ago or have never been active in the rural casual labour market. Available option is to capture the reservation wage employing proxy variables.

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Acknowledgements

We are thankful to Kushboo Sharma, for comments. The comments and critical observations of the anonymous referee were useful for the paper, and we are thankful for it.

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  1. Institute of Development Studies, Jaipur, Rajasthan, India

    S. Mohanakumar

  2. Public Policy Research Institute, Thiruvananthapuram, India

    R. Vipin Kumar

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  1. S. Mohanakumar
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Correspondence to S. Mohanakumar.

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Mohanakumar, S., Vipin Kumar, R. Rural Labour Market and Farmers Under MGNREGA in Rajasthan. Ind. J. Labour Econ. 61, 131–155 (2018). https://doi.org/10.1007/s41027-018-0125-4

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