62 iea conference_rnfe_2016

Diversification Towards Rural Non-farm Employment : Impact on Poverty Reduction in

India

P Shinoj Anjani Kumar

99th Annual Conference Indian Economic Association

27-29 December 2016Tirupati, Andhra Pradesh

India

Outline of the presentation Background Data & Methodology Findings Conclusions and policy implications

Background Agriculture has been playing a prominent role in drawing up

India’s economic development trajectory Share of workforce in agriculture shrunk from close to 70

per cent in 1951 to 52 per cent by 2011. But, the share of agriculture in GDP shrunk from nearly 55

per cent to less than 15 per cent during the same period. There is consensus on the roles played by technological

progress, globalization, urbanization, commercialization, etc. that fuelled the surge of rural non-farm employment (RNFE) in India.

However, divergence of opinion remains on what exactly are the pathways through which the farm sector is making way for the growing non-farm sector.

Contd. Evidence from many developing countries suggest that diversification

of rural economy towards non-farm activities has considerable potential to augment income and reduce poverty.

(Reardon et al., 2007; de Janvry et al., 2005; Adams, 2001; Barrett et al., 2001; Lanjouw, 1999; Reardon et al., 1998; Adams and He ,1995; Himanshu et al, 2013). RNFE diversification overcomes land constraint to income growth,

enables farmers cope with income shocks and improves their capacity to invest in productivity-enhancing agricultural inputs and technologies (Reardon and Taylor, 1996; Collier et al., 1986).

At macro level, a growing rural non-farm economy can absorb surplus labour, slowdown rural-urban migration, reduce rural-urban disparities and promote farm-non-farm linkages.

Understanding this process is crucial to chart out the future developmental agenda that is inclusive, equitable and pro-poor for the country.

Contd. The emergence of a thriving non-farm sector across the country in the

wake of deepening urbanization and globalization along with increasing market orientation of the rural economy has brought into fore several questions.

The prominent ones among them include whether the farm and non-farm sectors are complementary or substitutable

in the larger context of economic development of the country?

what are the main drivers that catalyze the process of RNFE diversification?

how the transformation is getting reflected among the various strata of rural population on the basis of their income as well as farm-ownership?

is such a transformation beneficial in reducing poverty in the country as is being reported in several developing countries and in parts of rural India?

Data This study makes use of the unit level data on employment and

unemployment collected at 5 yearly intervals by the National Sample Survey Organization (NSSO), Government of India to examine the trends in farm as well as non-farm employment of rural households.

The data pertains to the 50th (1993-94), 61st (2004-05) and 68th (2011-12) rounds of NSSO surveys.

We focus only on the rural households within the sample as employment diversification from farm to non-farm sector as a social and economic phenomenon has been more relevant for the rural areas.

ANALYTICAL FRAMEWORK

Determinants of non-farm employment diversification To understand the household-level determinants of non-farm

employment diversification in rural India, we fit three binary probit regression models;

……. (1)

denotes the binary dependent variable that assumes a value of 1 if the primary activity (principal industry of activity) of the ith household belongs to the non-farm sector and 0, if otherwise.

K’ indicates the subset of RNFE that includes ‘SE’, ‘CL’ and ‘ALL’ depending on the type of dominant non-farm activity the household is engaged in

Among the set of explanatory variables, denotes the time variable that captures the year of survey. represents a vector containing variables on sociological and demographic characteristics of the household, represents a variable that capture the relative attractiveness of non-farm sector with respect to the farm sector, is a vector that represents the farm-size ownership status of the ith household, the expenditure quintile to which the household belonged to is represented by the vector

ANALYTICAL FRAMEWORK

Impact of non-farm employment diversification on rural poverty Generalized propensity score matching (GPSM)

We use GPSM to assess the impact of nonfarm diversification on household budget and poverty of sample households belonging to various states of India.

More clearly, we attempt to estimate the DRF and marginal treatment effects (MTE) of various levels of non-farm employment diversification (treatment) on

consumption expenditure and poverty status of households (outcomes).

Changes in profile of rural households based on their principal industry of activity

Sector Share of households (%) Change, %(1993-94 to

2011-12)1993-94 2004-05 2011-12

Crops 68.8 60.30 52.9 -23.1

Livestock 0.9 1.45 0.6 -33.3

Other farm 1.5 1.12 2.0 33.3

Non-farm 25.7 31.67 39.0 51.8

Other*3.1 5.46 5.5 66.7

Employment participation rate (%)-Usual status

Sector

Employment participation rate (%)-Usual Status Change, %(1993-94 to

2011-12)1993-94 2004-05 2011-12

Lowest quintileCrops 79.1 73.4 61.7 -22.0Livestock 3.9 4.0 1.8 -53.8Other farm 2.4 2.0 2.5 4.2Non-farm 14.5 20.7 33.9 133.8

Highest quintileCrops 59.4 52.2 47.2 -20.5Livestock 9.8 11.4 5.8 -40.8Other farm 1.0 0.8 2.0 100.0Non-farm 29.8 35.6 45.0 51.0

Employment participation rate (%)-Usual status

Sector1993-94 2004-05 2011-12

Change, %(1993-94 to 2011-12)

Marginal (0.5-1.0 ha)Crops 77.5 75.0 69.2 -10.7

Livestock 6.5 7.9 3.8 -41.5

Other farm 0.9 1.0 2.4 166.7

Non-farm 15.0 16.1 24.6 64.0

Large (>4.0 ha)Crops 81.7 76.9 78.2 -4.3

Livestock 9.6 11.6 3.8 -60.4

Other farm 0.3 0.8 1.8 500.0

Non-farm 8.5 10.8 16.1 89.4

Share of RNFE across statesShare in RNFE (%)

1993-94 2004-05 2011-12

≤ 20 Andhra Pradesh, Arunachal Pradesh, Bihar, Chhattisgarh, Jharkhand, Karnataka, Meghalaya, Mizoram, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Uttar Pradesh, Uttarakhand,

Arunachal Pradesh, Chhattisgarh, Karnataka, Meghalaya, Mizoram, Madhya Pradesh, Maharashtra, Uttarakhand,

21-40 Assam, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Kerala, Manipur, Nagaland, Punjab, Sikkim, Tripura, Tamil Nadu, Andman & Nicobar, Puducherry

Andhra Pradesh , Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Manipur, Nagaland, Odisa, Punjab, Rajasthan, Sikkim, Tripura, Tamil Nadu, Uttar Pradesh, West Bengal, Andman & Nicobar, Puducherry

Arunachal Pradesh, Andhra Pradesh , Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Manipur, Mizoram, Madhya Pradesh, Maharashtra, Nagaland, Odisa, Uttar Pradesh, Uttarakhand

41-60 Goa, Lakshadweep, Tripura Goa, Kerala, Lakshadweep, Puducherry, Tripura,

Lakshadweep, Jammu & Kashmir, Manipur, Meghalaya, Punjab, Rajasthan, Tamil Nadu, West Bengal,

> 60 Goa Goa Goa, Kerala, Tripura, Andman & Nicobar, Daman & Diu, Puducherry

Employment participation rate (Usual Status) in non-farm sector by its sub-sectors in rural India, 1993-94 to 2011-12

Sector

Employment participation rate (%)-Usual Status

Change, %(1993-94 to

2011-12)1993-94 2004-05 2011-12

Mining 2.9 2.0 1.1 -62.1Manufacturing 32.2 28.8 20.9 -35.1Electricity & water 0.9 0.5 0.5 -44.4Construction 11.4 20.0 39.9 250.0Trade & hospitality 20.2 22.5 15.8 -21.8Transport, storage & communication 6.7 8.8 7.2 7.5Financing, real estate, insurance & others 1.4 1.8 1.0

-28.6

Community, social & personal 24.3 15.7 13.5 -44.4

Random Effects Probit estimation on factors contributing to non-farm employment diversification in rural IndiaDependent variable – Primary activity of the household (Non-farm =1, otherwise = 0)

Variable

RNFE (SE) RNFE (CL) RNFE (ALL)

CoefficientStandard error

Coefficient

Standard error

CoefficientStanda

rd error

Constant -2.724*** 0.114 -3.712*** 0.059 -1.803*** 0.035Time 0.234*** 0.051 0.272*** 0.007 0.371*** 0.011Age of the household head (year) -0.002*** 0.000 -0.010*** 0.000 -0.005*** 0.000Gender of the household head (male = 1, female = 0) 0.335*** 0.012 0.213*** 0.013 0.355*** 0.010Household size (no.) 0.048*** 0.001 0.003 0.002 0.067*** 0.001General education of household headIlliterate (No formal schooling = 1, otherwise=0) -0.085*** 0.013 0.847*** 0.022 -0.968*** 0.012Primary (1-4 years of schooling = 1, otherwise = 0) 0.161*** 0.012 0.837*** 0.021 -0.713*** 0.012Secondary (5-10 years of schooling = 1, otherwise = 0) 0.207*** 0.012 0.621*** 0.022 -0.488*** 0.011Technical education of household head ( Yes = 1, otherwise = 0)

0.096*** 0.023 0.010 0.039 0.402*** 0.023

Caste dummy (SC/ST = 1, others = 0) -0.259*** 0.007 0.149*** 0.008 0.008 0.006Income per SE worker ratio -0.001 0.000 - - - -Wage ratio - - 0.070*** 0.015 - -Income per worker ratio - - - - 0.003*** 0.000

Variable

RNFE (SE) RNFE (CL) RNFE (ALL)

CoefficientStandard error

Coefficient

Standard error

CoefficientStanda

rd error

Farm-size class of the householdLand class 1 (sub-marginal = 1 otherwise = 0) 1.220*** 0.020 1.327*** 0.041 1.068*** 0.017Land class 2 (marginal = 1 otherwise = 0) 0.783*** 0.021 0.813*** 0.042 1.068*** 0.017

Land class 3 (small = 1 otherwise = 0) 0.548*** 0.022 0.481*** 0.044 0.756*** 0.017

Land class 4 (medium = 1 otherwise = 0) 0.366*** 0.024 0.258*** 0.048 0.463*** 0.019

Expenditure quintile of the householdGroup 1 (first quintile = 1 otherwise = 0) -0.177*** 0.12 0.145*** 0.014 -0.509*** 0.010Group 2 (second quintile = 1 otherwise = 0) -0.079*** 0.011 0.171*** 0.014 -0.358*** 0.010

Group 3 (third quintile = 1 otherwise = 0) -0.014 0.010 0.102*** 0.014 -0.259*** 0.010

Group 4 (fourth quintile = 1 otherwise = 0) 0.011 0.010 0.090*** 0.014 -0.141*** 0.009

Sigma u 0.072 0.02 0.006 0.005 0.015 0.007

Rho 0.005 0.004 0.000 0.000 0.000 0.000

No. of observations 208107 208107 208107

Wald Chi2 12419 11343 35768

Log likelihood -92918 -60287 -114440

Impact of non-farm diversification on rural poverty

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Dose response functions and marginal treatment effect functions of non-farm diversification on MPCE of sample households

Dose response functions and marginal treatment effect functions of non-farm diversification on poverty gap

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Summing up A shift of rural inhabitants from the farm sector to non-farm sector for

employment during 1993-94 to 2011-12. Both in terms of primary industry of activity of the households as well

as usual status of employment of population, the farm sector has been gradually being replaced by the non-farm sector.

Both crop and livestock sub-sectors display a reduction in employment participation, whereas other farm activities maintain status-quo.

Within non-farm, there is a clear predominance of construction sub-sector as its share in employment compared to other non-farm alternatives bulged considerably during the study period.

The broad trends mark a structural shift in the economy characterized by abandonment of agriculture and allied activities particularly by the new entrants in the workforce for gainful employment in the non-farm sector.

Summing up The temporal trend is common across the quintile groups of

households, with the most marked changes being noted in the lowest quintile group.

However, in terms of relative shares in any particular year, households with higher per capita expenditure are more non-farm oriented than their counter parts.

Similarly, a perusal on the trends of employment participation across the five farm-size groups shows that, small and marginal farm-households are more inclined to employment in the non-farm sector as they have smaller land and other resources at their disposal to pursue livelihood in the primary sector.

The farm to non-farm transformation is visible in all states and UTs, but with considerable disparity across them. The pace of emergence of non-farm sector in states like Meghalaya, Madhya Pradesh, Bihar, Rajasthan, Mizoram, Odisha, etc. is remarkable.

Summing up Various socio-economic and demographic factors such as age, gender

and education of the household head, household size, caste affiliation, farm-size and expenditure status of the households, relative income/wage advantage in non-farm sector vis-à-vis farm sector, etc. significantly influence the process.

The shift of working members from farm to non-farm sector for gainful employment helps them in improving their consumption expenditure significantly, and inter alia in escaping poverty.

The pro-poor dimension of non-farm diversification has considerable policy implications, as India.

Careful planning at macro-level is the need of the hour to leverage the benefits of this social and economic transformation that has the potential to transform the lives of millions of rural dwellers in times to come.

THANK YOU

Economy & Finance

62 iea conference_rnfe_2016