3.1. Linkages between Agriculture and Non-Agriculture: Review of Selected
3.1.2 Vaidyanathan’s Hypothesis
agriculture, rupee 0.64 of value gets added to the rural non-agricultural sector. He found that value added in the rural non-agricultural sector was 0.93 in Punjab and Haryana and 0.64 in Madhya Pradesh and Bihar. The impact of the multiplier was influenced by rural infrastructure, rural population density and per capita agriculture income.
Chandrasekhar’s (1993) seminal work on agrarian change and rural occupational diversification in West Bengal provides a non-linear relationship between agricultural prosperity and growth in rural non-agricultural development. His framework is discussed in section 3.1.3.
Vaidyanathan advanced two propositions for establishing the validity of his hypothesis.
His empirical observations that spanned the period from the early 70s to the mid-80s across fourteen states, got encapsulated in his first proposition that states that the rural unemployment rate (RUR) is positively related to the share of rural non-agricultural employment (RNAE). In other words, when the rural unemployment rate rises due to lack of employment opportunities in agriculture, then the labour force seeks employment in the rural non-agricultural sector out of distress. The eventual outcome is an increase in the size of the rural non-agricultural workforce and a decline in the rural unemployment rate. The second proposition states that RUR is negatively related to wage rate in the rural non-agricultural sector or the ratio of the wage rates in the rural non-agricultural sector and the agricultural sector. The second proposition is a fallout of the first proposition. When RUR is high the labour force joins the rural non-agricultural sector out of distress and they bring down the ratio of the wage rates.
3.1.2.1 Critique of Vaidyanathan’s Hypothesis
The first proposition got validated by several scholars. Unni (1989, cited in Sen, 1998) and Dev (1989, cited in Sen, 1998) substantiated the first proposition using disaggregated data across several regions of India. It got validated by Singh (1989, cited in Bhalla, 1993b) in his study spanning the eastern districts of Uttar Pradesh.
Murty (2005) furnishes several reasons as to why the second proposition may not be valid even if the rural non-agricultural sector is residual in character. He cites that rural government schemes may stymie fall in rural wage rates and those pushed out of agriculture may get self-employed. In these scenarios there will be no impact on the prevailing wage rates. The view that Vaidyanathan’s hypothesis cannot be tested using cross-section data has gained ground (Dev, 1990, Unni, 1991, Basant and Parthasarathy,
1991, cited in Murty, 2005; Visaria, 1995, cited in Murty, 2005). Unni (1991) and Dev (1990) were unable to test the second proposition as data on wage rates at the regional level were unavailable.
The other reason for the residual sector hypothesis not gaining ground, is the argument that the wage rates in the rural non-agricultural sector have been better than the agricultural sector. This implies that a move out of agriculture is for better quality employment rather than distress (Sen, 1998). Papola’s (1991, cited in Sen, 1998) analysis based on NSS data revealed that all-India average wage of regular workers in the rural non-agricultural sector was 2.7 and 2.4 times the wage of regular agricultural workers in 1977-78 and 1987-88 respectively. The wage of casual rural non-agricultural workers was 40 per cent more than casual agricultural workers in both years. Bhalla (1991, cited in Sen, 1998) found that barring the segments construction, mining and quarrying, workers in other segments of the rural non-agricultural sector were better off than their counterparts in the agricultural sector. However, Sen (1998) points out that the data set used by Bhalla and Papola, do provide some contrary indications that reconfirm the residual character of the rural non-agriculture sector. Dev (1989, cited in Sen, 1998) found that agriculture was the sector with the highest poverty incidence at the all-India level in 1977-78 but at the disaggregated level this was true only in the states of Andhra Pradesh, Tamil Nadu and West Bengal. In the rest of the states, one of the industrial segments in the rural non-agricultural sector had the highest incidence of poverty. The identified segments were construction in seven states, mining and quarrying in three states and manufacturing in two states. In 1983 at the all-India level agriculture had the highest poverty; but it is evident from the state-wise data given by Bhalla (1991, cited in Sen, 1998) that this was true in Rajasthan, Madhya Pradesh and in the above-mentioned
the highest incidence of poverty. This is no longer so as construction happens to be a prosperous segment. Sen (1998) remarks that barring the segments like electricity, gas, etc., (which generated regular jobs) the other segments of the rural non-agricultural sector, (which generated casual work) were residual in character. Sen (1998) also points out he does not agree with the argument that a sector with a higher wage cannot be a residual sector19.
Given these divergent views on Vaidyanathan’s hypotheses, we look at other analytical frameworks in the subsequent sections. These studies examine other parameters to ascertain if the growth of the rural non-agricultural sector in many regions in India can be viewed as extensions of distress driven spill-over due to stagnation and low employment elasticity of the agricultural sector.