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Draft Paper. Please do not quote or cite

EDUCATION, SKILLS AND DEVELOPMENT DYNAMICS IN RURAL INDIA

Ravi Srivastava∗∗∗∗

Swati Sachdev∗∗∗∗∗∗∗∗

1. INTRODUCTION

At present, the brunt of backwardness and poverty is borne by rural populations in developing countries, which contain about 70 per cent of the world’s poor (FAO and UNESCO 2003).1 Diverse theoretical frameworks recognise the contribution of education and skills to growth, human development and poverty reduction. Human capital theories which became prominent since the major contributions of Schulz and Becker, and more recently the recognition of the role of the knowledge sector to growth have, in the form of the new and endogenous theories of economic growth, cemented our understanding of this relationship (Schulz, 1961; Becker, 1964; Romer, 1986; 1990). In a narrower perspective, education and human capital can also explain wage and productivity growth, including in sectors such as agriculture, which are the dominant employment sectors in the rural areas of developing countries as well as rural development more generally (FAO and UNESCO, 2003.).

Rural India constitutes about 71 per cent of India’s population whose education and skill base are very low on average and are also much lower than the urban population. This has major implications for both the growth of the important rural production sector (agriculture) but also for the absorption of the rural population in jobs requiring education and skills. Moreover, this has severe implications for those groups which are especially disadvantaged in terms of access to education and skills. At the same time, there are large regional differences in the education and skill endowment of the rural population and generally, as we show in this paper, regions with higher levels of education and skills have better development credentials.

Human capital includes both formal learning (education and training) as well as informal on the job training and learning. A large part of the returns from investment in education and training are not immediately realizable by either workers or employers (if they have one). This has to be appropriately addressed by policy. In this paper, we speak of

∗ Professor of Economics, Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi (ravisriv@gmail.com). ∗∗ Lecturer in Geography, Budge Budge College, Kolkata (swatisachdev@gmail.com). 1 In India, the Planning Commission Expert Group (2009) has recently re-estimated rural and urban poverty and fids the former to be 80 percent of total poverty.

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education and skills separately, although these notions may overlap quite significantly, or one of these may also be seen as a prerequisite for the other.2

It is now argued that changes in technology, management practices, economic structure and the nature of global markets have impacted upon requirements of an educated and skilled labour force, creating the need for more flexible labour and also changing the nature of skills in demand. There are, however, asymmetrical impacts occurring in different parts of the evolving economic system. Flexible production according to Seth (2002) leads to a segmentation of the labour markets between a functionally flexible core labour force and the numerically flexible peripheral labour force (Atkinson, 1984). The need to cater to market niches and rapid product innovation has also led to “lean production systems” and acquisition of products through strategic outsourcing, lading to “vertical disintegration” and a growth of small firms and the informal sector (Seth, ibid., Quinn and Hilman, 1994). Similar changes have also occurred in IT and service related sectors (Seth 2006). However, the focus of literature has primarily been on manufacturing and services sector, whereas agriculture will continue to dominate the employment landscape in India and many other developing countries for some time to come.

2. LABOUR AND EMPLOYMENT IN RURAL INDIA

The Indian employment scenario continues to be undeveloped and dominated by rural and agricultural employment despite some changes that have occurred. In rural areas, the percentage of males in agriculture declined by 11 per cent points – from 77.7 per cent to 66.5 per cent over more than two decades (1983 to 2004-05). In the same period, the percentage of total female workers who were employed in agriculture declined by only about 4 per cent – from 87.7 to 83.3. Thus, overall, 72.6 per cent workers in rural India remain dependent on agriculture for employment (Table 1).

Among all workers, in rural and urban India taken together, nearly 57 per cent are still employed in agriculture, either as cultivators or as labourers. In the case of male workers, less than half are currently employed in agriculture, but nearly three-quarter of women workers are still engaged in agriculture and these numbers have fallen by much less than that for men.

2 Broadly defined, skill is the learned ability of an individual to carry out a (set of) predetermined tasks. A large number of concepts referring to types of skills/skill-sets are now in vogue. (Stasz, 2001, p. 386) distinguishes between academic skills (primarily learnt in schools); generic skills (such as problem solving, communications, or working in teams), technical skills which are specific skills needed in an occupation and may need reference to academic skills; and work related attitudes or “soft skills” which include motivation, volition and dispositions. Beechey (1982) suggests that three different elements can be distinguished when we discuss skills: (1) objectively defines competencies; (2) control over conception and execution; (3) socially defined occupational status, which may be more or less independent of objectively defined competencies. There may also necessity of distinguishing between skill in individuals and the skill required for particular jobs, as well as between these and labelling particular jobs as skilled.

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Table 1: Estimate and Percentage Distribution of UPSS Workers by Broad Industry Groups: 1983-2004/05

Industry group Rural Urban Total 1983 2004-05 1983 2004-05 1983 2004-05 Estimated Workers (In Millions)

Male Agriculture 118.94 145.29 4.73 5.56 123.67 150.86 Non-agriculture 33.81 73.15 41.49 84.81 75.30 157.95 Female Agriculture 78.68 102.59 3.68 4.32 82.36 106.91 Non-agriculture 10.94 20.32 8.27 19.67 19.21 39.99 Total* Agriculture 197.62 247.88 8.41 9.88 206.03 257.76 Non-agriculture 44.75 93.46 49.76 104.48 94.51 197.94 Percentage to Total Workers

Agriculture 77.70 66.50 10.30 6.10 62.00 48.90 Male Non-agriculture 22.30 33.50 89.70 93.90 38.00 51.10

Agriculture 87.70 83.30 31.20 18.10 81.00 72.50 Female Non-agriculture 12.30 16.80 68.80 81.90 18.90 27.60

Agriculture 81.60 72.60 14.70 8.70 68.60 56.60 Total* Non-agriculture 18.60 27.40 86.00 91.30 31.60 43.50

Source: Computed using NSS unit level data on Employment and Unemployment, 38th Round, Schedule 10, 1983 and 61st Round, Schedule 10, 2004-2005.

The growth rate of agricultural employment has been lower than that of total rural employment, and that of the latter, lower than urban employment (Table 2). However, between 1983 and 2005, in absolute terms, as the preceding table shows, both agricultural and rural employment has continued to increase for both men and women.

Table 2: Growth Rate (per cent) of UPSS Workers by Industrial Groups: 1983 to 2004/05

Rural Urban Total Industry group Male Female Total Male Female Total Male Female Total Agriculture 0.99 1.31 1.12 0.70 0.83 0.78 0.98 1.28 1.11

Mining and Quarrying 1.72 1.55 1.67 1.81 -2.44 1.12 2.10 0.45 2.02

Manufacturing 1.97 2.84 2.29 2.06 3.63 2.41 2.03 3.17 2.34

Electricity, etc 1.72 N.A -1.56 1.66 3.42 2.06 2.10 N.A 2.02

Construction 6.98 5.21 6.80 6.05 4.25 5.85 6.71 4.89 6.42

Trade and hotels 4.77 2.61 4.41 4.69 4.63 4.67 4.77 3.63 4.53

Transport, etc 5.60 N.A 5.63 3.51 3.08 3.49 4.39 5.14 4.39

Services* 1.97 3.13 2.25 2.89 4.85 3.43 2.49 4.19 2.92

Non-agriculture 3.67 3.03 3.52 3.40 4.26 3.55 3.52 3.62 3.55

Total 1.67 1.47 1.60 3.13 3.26 3.15 2.05 1.72 1.94 Note: *Services include Financial intermediation; Real estate & other business services; Public administration, Social security, etc; Education; Health and Social work; Community, personal social services; Private households with employed persons. Source: Same as in Table 1.

The broad categories of workers that the employment surveys distinguish between are the (i) self-employed (comprising of employers, employees and unpaid workers); (ii) the regular workers; and (iii) casual labourers. In the economy as a whole, the self-employed were by far the most predominant (56.6 per cent), followed by the casual workers (28.1

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per cent), while the regular workers formed the smallest category (15.3 per cent) (Table 3). In rural areas, however, 60.4 per cent of workers are self-employed, and these are principally farmers (ibid.). Regular workers in rural areas are only 7 per cent while casual workers are 32.5 per cent of total workers in rural areas.

Table 3: Percentage Distribution of Workers by Sector and Type of Employment, 2004-05

Rural Urban Total Industry /status Male Female Total Male Female Total Male Female Total Agriculture Self-employment 64.2 64.8 64.4 69.8 62.3 66.5 64.4 64.7 64.5 Regular Workers 1.3 0.5 1.0 5.4 2.0 3.9 1.5 0.6 1.1 Casual Labour 34.5 34.8 34.6 24.8 35.7 29.6 34.2 34.8 34.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Non-agriculture Self-employment 47.2 59.7 49.9 43.0 43.9 43.1 44.9 51.9 46.3 Regular Workers 24.1 19.7 23.1 43.2 43.4 43.2 34.3 31.4 33.7 Casual Labour 28.7 20.5 27.0 13.9 12.7 13.6 20.8 16.7 19.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 All Self-employment 58.5 63.9 60.4 44.6 47.2 45.2 54.4 61.2 56.6 Regular Workers 8.9 3.7 7.0 40.8 36.0 39.8 18.3 9.0 15.3 Casual Labour 32.6 32.4 32.5 14.5 16.8 15.0 27.3 29.9 28.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Computed using NSS unit level data on Employment and Unemployment, 61st Round, Schedule 10, 2004-2005.

The traditional division of labour in Indian society has been along lines of varna or jati. This has generally been carried across religious lines. Those who are at the bottom of the job pyramid usually belong to the lower castes and share a higher degree of poverty and deprivation. This is confirmed by poverty studies as well as studies of the Indian labour market. The NCEUS (NCEUS, 2007) has analysed the social characteristics of the labour force in depth. Table 4 shows the employment status of rural workers in India, classified by socio-religious groups. Regular employment is the highest among the Hindu upper castes and religious groups other than Hindu or Muslim. Self-employment in rural areas is highest among Hindu upper castes, OBC, and Muslims. Casual wage employment is the highest among Hindu SC workers (more than half of whom are casual workers) and ST workers.

Table 4: Distribution of Rural Workers by Type of Employment and Socio- religious Groups, 2004-2005

Hindu

ST Hindu

SC Hindu OBC

Hindu Others Muslims

Other Religious Groups Total

Self Employed 53.0 42.1 64.7 73.2 63.6 60.7 60.1 Regular Workers 3.9 6.3 6.6 10.4 6.1 10.7 7.1 Casual Labour 43.1 51.6 28.7 16.3 30.3 28.7 32.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Same as in Table 3.

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We have already shown some of the gender characteristics of Indian labour markets in the preceding tables. As already pointed out, women workers are much more concentrated in rural and agricultural work, and as Table 3 shows, a much larger percentage of these workers are either self-employed or employed as casual wage workers than their male counterparts.

India, like sub-Saharan Africa and the rest of South Asia (except Sri Lanka) has a very large informal sector (and conversely a small formal sector). The National Commission for Enterprises in the Unorganised Sector (NCEUS) has estimated the size of employment in the formal/ informal sector in India after defining the latter as individual or partnership based unincorporated enterprises employing less than 10 workers. The relative size of the formal and informal sector is shown in Table 5.

Table 5: Informal and Formal Sector in Urban and Rural India, 2004-2005

Rural Urban Total A&A NA Total A&A NA Total A&A NA Total

Unorganised 97.8 76.0 91.8 93.5 67.5 69.8 97.6 71.6 86.3 Organised 2.2 24.0 8.2 6.5 32.5 30.2 2.4 28.4 13.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Note: A&A Agriculture & Allied; NA Non-agriculture Source: Same as in Table 3. In the urban areas, nearly 70 per cent of employment is in the informal sector, but this increases to almost 92 per cent in rural areas (Table 5). Informal workers are principally either self-employed or employed as casual wage workers with no stable employer-employee relationship. In rural areas, most of these workers are in agriculture. The Commission has also shown that there are large income differentials between the formal and informal sectors and has further included private farm based agriculture in the concept of the informal sector.

3. EMERGING INTER-SECTORAL INEQUALITIES

We do not have data on trend figures for rural and urban incomes. But a large scale national survey carried out in 2004-05 gives us an estimate of the disparity in rural-urban incomes. Urban household incomes were estimated to be 2.05 times rural household incomes – Rs. 26,734 in rural areas and Rs. 41,554 in urban areas. However, given the slow rate of diversification of the rural workforce, NDP per worker in the different sectors gives us an insight into growing rural-urban income gaps (Figure 1). Agricultural incomes per worker were 44 per cent of per worker manufacturing incomes in 1983, but this figure declined to 35 per cent in 2004-05. As a per cent of service sector incomes per worker, agricultural incomes were 27 per cent in 1983 but this shrank to only 17 per cent in just over two decades.

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Source: CSO (2007)

4. EDUCATION AND SKILLS IN RURAL INDIA

The education and skill characteristics of the Indian economy reflect a low level of development and are not in line with the rapidly industrialising developing economies. But there is no doubt that needs in this regard are changing both due to developmental and growth requirements. The broad shifts in the occupational structure of the Indian economy – from farm to non-farm, have, as we have shown, reduced the dependence of the workforce on agriculture, although changes have only been profound for male workers. This has created new skill requirements for the emerging workforce. The share of services in the occupational structure has increased, and modern services led by IT and IT enables services, and sectors such as tourism have gained rapid prominence. Globalisation and the requirements of the “knowledge economy” (which would require a sizeable numbers of knowledge workers) have also created new requirements.3 The Indian economy itself has grown quite rapidly, and at an accelerated pace since 2002-03, creating additional demand for skilled workers in a number of sectors. Capabilities and skills of workers thus need to be redefined both to improve development and growth outcomes.

Knowledge and skills are not the monopoly of those who are formally educated/ trained, but education and formal training is likely to open avenues for workers which may not be available otherwise for those workers who are engaged in the informal economy. Certainly, the differential availability of education and formal skills could mean that only a small number of individuals (who have such privileged access) are able to improve their life chances. But apart from the mobility consequence, a number of studies demonstrate the impact of education and skills on worker productivity and incomes.4

3 The total number of workers engaged in the knowledge sector in India is still very small. In the information technology sector, including workers in routine operations, the total workforce was estimated at one million in 2004-05 (Chandrasekhar, 2005). 4 See for example, Tilak (1989) and Psacharapolis (1994). We revert to this theme in Section VI of this paper.

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4.1. Education

There are systematic differences in education levels across rural and urban areas, agriculture and non-agriculture, men and women, unorganised and organised sectors, occupation types, social groups, and so on. Moreover, the differences one observes are unadjusted for quality, and adjustments for quality amplifies these differences across the board. Although the job market does discriminate between social groups and between men and women even after controlling for levels of attainment, we show later in this paper that education nonetheless does improve chances of a person moving to a better job.

We focus on two levels of education (primary and secondary) and mean years of education to bring out the low educational capabilities of the population and the workforce in rural areas. It is to be noted that the educational capabilities are higher for the population than for the workforce, and the gap is larger for females, reflecting both labour force participation rates and unemployment rates among men and women.

We first note that the mean years of educational attainment for the population aged 15 years and above was only 3.7 in rural India in 2004-05 (4.7 for males and 2.6 for females) compared to 7.3 years for the urban population (8.2 years for males and 6.3 years for females). For workers in the 15 and above age group, the mean educational attainment was only 3.5 years in rural India and (only 1.9 years for females) and 7.5 years for urban India (Table 6).

In rural areas, in 2004-05, only 44 per cent of the 15 years and above population and 42.8 per cent of workers had primary education, compared to 72.9 per cent and 73.3 per cent respectively for urban 15+ population and workers respectively (the higher attainment among workers compared to population reflects the differential participation rate of different groups in employment). As far as secondary education is concerned, only 15.7 per cent of the 15+ rural population and 14.3 per cent of workers had educational attainment to this level in 2004-05 whereas these figures were about twice as high in urban areas (Table 6).

Table 6: Mean Years of Education of Population and Workers and Percentage of Population and Workers with Educational Attainment Level Primary and Above and Secondary & Above, 2004-2005

Rural Urban Total Male Female Total Male Female Total Male Female Total Mean Years of Education Population 4.7 2.6 3.7 8.2 6.3 7.3 5.8 3.7 4.8 Workers 4.4 1.9 3.5 7.9 5.6 7.5 5.5 2.5 4.5

Percentage with Primary & Above Education Population 55.5 32.3 44.0 80.3 64.4 72.9 63.3 41.7 52.8 Workers 52.8 24.9 42.8 78.2 54.6 73.3 60.2 29.8 50.5 Percentage with Secondary & Above Education Population 21.1 10.2 15.7 48.3 35.6 42.3 29.7 17.6 23.8 Workers 18.7 6.4 14.3 44.8 30.5 41.8 26.4 10.3 21.2

Source: Same as in Table 3.

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No doubt, an age group wise static cohort analysis for the same year indicates that younger age groups are acquiring higher education. This is also consistent with high educational participation rates for 6 to 14 year olds recorded in recent surveys. But discontinuation rates between primary and secondary level are still very high so that improvement in secondary level educational attainment is very low among successive rural cohorts. Thus, computation for ten year cohorts in 2004-05 indicates that primary school attainment was only 15.5 per cent among 65-74 year olds but increased to 66.4 per cent for 15-24 year olds (capturing change over 50 years). But secondary school attainment, which was only 3.8 per cent among 65-74 year olds, was 18.8 per cent among 25-34 year olds in 2004-05, reflecting a much smaller change (Table 7).

Table 7: Percentage of Population with Educational Attainment Level Primary and Above and Secondary & Above by Age-groups, 2004-2005

Primary & Above Secondary & Above Rural Urban Rural Total

Age Groups Male Female Total Male Female Total Male Female Total Male Female Total 15 – 24 75.0 57.2 66.4 78.9 64.5 72.1 28.4 20.4 24.5 34.6 28.3 31.6 25 – 34 62.7 35.9 48.7 69.6 46.1 57.7 26.4 11.8 18.8 35.2 20.6 27.8 35 – 44 49.1 24.7 37.1 58.0 35.6 47.1 18.6 6.1 12.5 27.6 14.1 21.1 45 – 54 42.9 16.1 30.2 53.3 27.3 41.1 14.8 3.2 9.3 25.4 10.5 18.4 55- 64 33.9 9.0 21.4 44.7 17.9 31.3 11.6 1.5 6.5 21.7 6.3 14.0 65 – 74 25.3 5.3 15.5 36.1 12.5 24.3 7.0 0.5 3.8 16.1 3.4 9.8 75 – 84 22.1 4.1 13.4 31.8 9.5 20.8 3.6 0.5 2.1 11.5 2.3 7.0 85 – 94 19.6 4.8 12.0 28.1 7.5 17.4 5.1 1.1 3.0 10.0 1.1 5.4 >= 95 13.8 0.9 6.1 13.1 5.1 8.1 6.3 0.5 2.8 6.4 0.8 2.9 Total 55.5 32.3 44.0 63.3 41.7 52.8 21.1 10.2 15.7 29.7 17.6 23.8 Source: Same as in Table 3.

The low educational attainments in rural areas conceal caste and class differences. For instance as we move up the social gradient, from Scheduled Caste and Scheduled Tribe to Other Backward Classes and General Castes, the mean years in male education increases from 3.1 years (ST) to 3.8 years (SC), 4.7 years (OBC) and 6.1 years (Others) (Table 8). There are much larger differences still among the top and bottom quintiles in terms of per capita consumption expenditure.

Table 8: Mean Years of Education of Persons (15+) across Social Groups, 2004-2005

Rural Urban Total Male Female Total Male Female Total Male Female Total

Population ST 3.1 1.5 2.3 7.2 5.3 6.3 3.5 1.9 2.8 SC 3.8 1.8 2.8 6.2 4.0 5.2 4.4 2.3 3.4 OBC 4.7 2.5 3.6 7.3 5.2 6.3 5.5 3.2 4.3 Others 6.1 3.8 4.9 9.6 8.0 8.8 7.7 5.6 6.6 Workers ST 2.7 1.1 2.0 6.5 3.5 5.6 3.1 1.2 2.3 SC 3.5 1.3 2.7 5.6 3.1 5.0 3.9 1.6 3.1 OBC 4.5 1.9 3.5 7.0 4.4 6.4 5.1 2.2 4.2 Others 5.8 3.3 5.0 9.5 8.4 9.3 7.4 4.6 6.7

Source: Same as in Table 3.

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Differences in education levels are further magnified when learning outcomes are taken into account. Desai et. al. (2010) administered simple tests of reading or writing a sentence and subtraction to students aged 8 to 11 years. Outcomes systematically varied between rural and urban areas, caste groups (outcomes for dalits, adivasis and Muslims were about 60 per cent the levels of Hindu upper castes) and quintile groups (outcomes for students in the bottom quintile were again about 60 per cent of those in the top quintile). (ibid., p. 93).

4.2. Formal Skills

We can describe formal skills as those which are developed in a structured and standardised manner and, more importantly, where the training outcomes are measured/assessed according to some objective parameters. The prerequisites for skill education and training, the contents and period of training can be quite varied. Skill training can be integrated with the school system in some form of vocational education or can be provided independent of it. At the highest end of skill provision, we may have technical or engineering education lasting over several years. At the low end, the training may last only for a few weeks and be quite limited in scope. In the Indian case, this may result in degrees; diplomas or certificates through technical or engineering institutes, polytechnics, Industrial Training Institutes or other training institutes/trainers providing training certified or recognised by government departments.

Only a small proportion of the Indian population receives vocational education or training in any form. Moreover, such training is directly related to the residence status of the household and to gender, economic and social status of the individual. The characteristics of formally trained individuals in the population can be indirectly inferred from the NSS 1993-94 survey on Employment and Unemployment and more directly from the questions canvassed in the 2004-05 NSS survey.

The National Sample Survey on Employment and Unemployment, 50th Round, 1993-94 canvassed a separate question on skill along with the questions on education and technical education. The question was thus put to the whole population. The question on skill involved asking the individuals to choose from a given list of 31 skills types including an option to choose ‘No skill’ and a miscellaneous category of ‘Others’.5 The skills asked in 50th round pertained to the traditional skill set, barring certain exceptions, and the skill set associated with the newer trades were not adequately represented. The survey did canvass a separate question on technical/vocational education leading to certificates, diplomas or degrees which could be used to glean information on upper end skill acquisition.

Analysis of this data in Table 9 reveals that approximately 10 per cent of population reported as having any of the skills (91.2 million). In rural areas, only about 10 per cent 5 The various skills included in the list were Stenographer; machineman; fitter, die-maker; electrician; repair of electronic goods; motor vehicle driver; fisherman; miner, quarryman; spinner including charkha operator; weaver; tailor, cutter; carpenter; mason; bricklayer; shoemaker, cobbler; moulder; blacksmith; goldsmith; silversmith; boatman; potter; nurse, midwife; basket maker, wick product maker; toy maker; brick maker; tile maker; bidi maker; book-binder; barber; mud-house builder & thatcher and Others.

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of the men (34.2 million) and 6.3 per cent of the women (20.3 million) possessed specific marketable (formal or non-formal) skills. The percentages are reported higher in urban areas, but still quite low -- only 19.6 per cent for men (24.3 million) and 11.2 per cent for women (12.4 million).

Among workers, 19.4 per cent reported being skilled, 21.7 per cent among male workers and 14.6 per cent among female workers. However, among urban workers, a third reported as being skilled.

For the purpose of analysis, the skills reported in the 50th round, have been further classified by us into predominantly formal and predominantly informal skills. The skill type and general educational attainment levels of the persons with skills was examined and taken as a guideline to classify the skills.6 According to our categorization, in 1993-94, approximately 2 per cent of the population and 4 per cent of the workers had predominantly formal skills. In the urban areas, 10.3 per cent of the workers were formally trained, while in rural areas only 2.3 per cent workers were so trained. There were large differences between the percentage trained male and female workers. Among the former, 5.6 per cent had formal skills, while among the latter, only 0.7 per cent had formal skills (Table 9).

Table 9: Percentage of Population and Workers with Skills, 1993-94

Rural Urban Total Male Female Total Male Female Total Male Female Total Population Formal skill 2.1 0.2 1.2 7.2 1.3 4.4 3.5 0.5 2.0 Informal skill 8.0 6.1 7.1 12.4 9.9 11.2 9.2 7.1 8.2 Any marketable skill 10.1 6.3 8.3 19.6 11.2 5.6 12.6 7.6 10.2 Usual Status Workers Formal skills 3.4 0.2 2.3 12.1 3.9 10.3 5.6 0.7 4.0 Informal skills 13.9 11.9 13.2 22.4 25.9 23.2 16.1 13.9 15.4 Any marketable skill 7.3 12.1 5.5 34.5 29.7 33.5 21.7 14.6 19.4 Note: Excludes cases with sex not reported. Source: Computed using unit level data of NSS, 50th Round, Schedule 10 on Employment and Unemployment, 1993-1994.

The NSS Survey Round on Employment-Unemployment for 2004-05 collected information about general and technical education status of the entire population (which included technical degrees, diplomas and certificate education). It also canvassed information on the skill profile only of youth (15-29 years population), with enquiries as to whether the individuals had or were undergoing non-formal or formal training.

6 Based on the above analysis the skills considered predominantly informal are: fisherman; miner, quarryman; spinner including charkha operator; weaver; tailor, cutter; carpenter; mason; bricklayer; shoemaker, cobbler; moulder; blacksmith; goldsmith; silversmith; boatman; potter; midwife (rural); basket maker, wick product maker; toy maker; brick maker; tile maker; bidi maker; book-binder; barber; mud-house builder & thatcher and Others. Skills considered predominantly formal are: Stenographer; machineman; fitter, die-maker; welder; electrician; repair of electronic goods; motor vehicle driver and midwife (urban).

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Among workers in the age group 15 and above in rural India, 1.3 per cent had technical education compared to 1.2 per cent in 1993-94. This was much lower than the urban figure of 7.4 per cent (Table 10). The rural male-female gap was somewhat narrower in 2004-05 compared to 1993-94 and in urban India the gap appeared to have narrowed down quite significantly.

Table 10: Technical Education, 1993-94 and 2004-05

Rural Urban Total Rural Urban Total 1993-94a 2004-05 b

Male 1.7 7.6 3.2 1.6 7.6 3.4 Female 0.5 5.3 1.2 0.7 6.7 1.7 Total 1.2 7.1 2.5 1.3 7.4 2.8

a. % of 15+ workers with either graduate & above general education in agriculture, engineering/technology, medicine or with any technical diploma/certificate.

b. % of workers with technical degree or diploma in any stream. Source: Computed using NSS unit level data on Employment and Unemployment, 50th Round, Schedule 10, 1993-94 and 61st Round, Schedule 10, 2004-2005.

Regarding skill acquisition, in the total population of 289.5 million in the young age group, 3.9 million (1.4 per cent) were estimated as receiving formal training, while 7.2 million (2.5 per cent) were already trained, giving a total of 11.2 million (3.9 per cent). The numbers of persons informally trained was twice as large – 22.3 million (7.8 per cent). In rural areas, the percentage of formally trained youth was 2.2, compared to 7.6 per cent in urban areas. Among young workers, the percentage of those with formal training was only 1.9 in rural areas, compared to 8.3 in urban areas (Table 11).

Table 11: Percentage of Population and Workers (15 – 29 Years) with Skills, 2004-2005

Rural Urban Total Skill Male Female Total Male Female Total Male Female Total

Population Formal: Receiving 1.0 0.5 0.8 3.4 1.9 2.7 1.8 0.9 1.4 Formal: Received 1.5 1.3 1.4 5.2 4.5 4.9 2.7 2.2 2.5 Formal skills 2.6 1.7 2.2 8.6 6.4 7.6 4.5 3.1 3.9 Informal skills 9.6 6.3 8.0 9.3 4.9 7.3 9.5 5.9 7.8 Any skills 12.1 8.0 10.1 17.8 11.4 14.9 14.0 9.0 11.6

Usual Status Workers Formal: Receiving 0.5 0.4 0.4 1.6 2.5 1.7 0.8 0.7 0.8 Formal: Received 1.5 1.4 1.5 5.9 9.2 6.5 2.8 2.7 2.7 Formal skills 2.0 1.8 1.9 7.4 11.7 8.3 3.6 3.4 3.5 Informal skills 12.2 11.2 11.9 13.8 13.6 13.8 12.7 11.6 12.3 Any skills 14.2 13.0 13.8 21.2 25.3 22.0 16.3 14.9 15.8 Note: Excludes cases with skill not reported. Source: Computed using unit level data of NSS, 61st Round on Employment and Unemployment, Schedule 10, 2004-2005.

Analysis of the percentage of workers in the age group 15-29 who had either received formal training or were currently receiving such training, classified by their level of education (Srivastava 2008) shows that the largest percentage of trained workers are again in the trade and services sectors, followed by manufacturing/mining/electricity.

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Further, there is a strong correlation between level of education and formal training. The percentage of workers with below primary level of education with formal training was only 0.3 per cent, rising to 10.8 per cent for those with secondary or higher secondary level of education, and 19 per cent for those with graduate of higher level of education.

Analysis also shows quite clearly that the likelihood of being formally trained is not only higher for those with high levels of education, but also among the economically better off, males, those living in urban areas, and those belonging to upper castes (ibid.). In order to find out whether these factors exercise an independent influence on the probability of being formally trained after accounting for the influence of the other factors, Srivastava (2008) fitted a logit regression to the data. The results shown in Table 12 represent the likelihood of certain variables which impact the formal training by sex, residence, education. It was observed that the likelihood that an individual gets formal training is 91 per cent more if he comes from an urban area in comparison to his rural counterparts. Similarly, a male has a greater chance to be trained in comparison to his opposite sex. The likelihood of an educated person being trained is 300 per cent if he is educated up to middle and jumps to 2500 per cent if he is secondary educated as compared to illiterate persons. The social group also has an influence on the chances of being formally trained. The SC, OBC and Others all have a higher likelihood of being formally trained as compared to the STs.

Table 12: Logit Model: Dependent Variable - Formal Vocational Training

Variable Coefficient Odds Ratio Constant -7.298 Sector Rural(R) Urban

- 0.648*

- 1.913

Sex Female(R) Male

- 0.133*

- 1.142

Education Illiterate & below Primary(R) Primary & Middle Secondary & Above

- 1.177* 3.229*

- 3.244 25.243

Poverty Status Below Poverty Line(R) BPL to 2*Poverty Line More then 2* Poverty Line

- 0.457* 1.250*

- 1.579 3.492

Socio-religious ST(R) SC OBC Others

- 0.851* 0.684* 0.529*

- 2.342 1.983 1.698

*- Significant at .01 Source: Srivastava (2008).

Informal Skills

In most cases in India, skills are acquired through inter-generational learning and/or systems of informal apprenticeships. The lack of formal education does not stop workers

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from acquiring such skills. Although one may expect lack of basic literacy and numeracy to be a constraint, operationally workers are able to bypass even this barrier. Informal skills, as we show below, are distributed across a wider cross section of the population than are formal skills and are more widespread nearer the base of the socio-economic pyramid. While the Employment-Unemployment Surveys in 1993-94 and 2004-05 canvass questions on informally acquired skills, one problem with this data is that skills required in agriculture are not canvassed. Thus the questions relate primarily to skills required for non-agricultural vocations.

As shown earlier in Table 9, we estimate that in 1993-94, 8.2 per cent of the population and 15.4 per cent of the workers were informally trained while in rural areas, the incidence of formal training among population and workers was estimated to be 1.2 per cent and 2.3 per cent. Overall, 16.1 male workers and 13.9 per cent female workers were informally skilled but in rural areas, the corresponding percentages were 13.9 and 11.9 per cent.

For the year 2004-05, the NSS survey reports informal skills in the 15-29 age population. In this segment, twice as many people (7.8 per cent) were imbued with informal skills than formal skills (3.9 per cent). In terms of total numbers, the number of informally trained youth stood at 15.7 million in rural areas, compared to only 6.6 million in urban areas. The percentage of informally trained young workers was 11.9 among rural workers compared to 13.8 among urban workers. But, as noted earlier informal skills in agriculture were more or less excluded from the purview of the survey. As with formal skills, there are gender differences in the percentage of the population with informal skills, both in rural and urban areas. But these differences are virtually eliminated when we consider the percentage of skilled to total workers. In other words, although education does not have an overall favourable impact on women’s participation in the workforce, both formal and informal skill acquisition positively affects a woman’s participation in the workforce.

The interesting finding is that unlike formal skills, informal skills are much more likely to reside in workers who are at the bottom of the pyramid. Analysis shows further that the highest proportion of informally trained people is in the lowest consumption expenditure categories, whereas the highest expenditure category has the lowest proportion of such people. Since traditional manufacturing skills are the highest in the caste groups presently classified as Other Backward Classes (OBC), the highest proportion of people with such skills are in this group, followed by the ST, SC and Other Caste groups (Srivastava 2008).

The fairly wide dispersion of informal skills in a much broader segment which is socio-economically less well placed, than that in the case of formal skills, is also borne out by the results of logit regression (Srivastava, 2008) which shows that the likelihood of possessing informal skills is lower in urban India (odds: 0.88). It is substantially higher for males (odd: 1.69) and lower for secondary or higher level of education (odds: 0.60). It is also lower for groups with high consumption levels. Finally, it is highest for the OBC groups, followed by “Other” castes (odds: 1.47 and 1.24 compared to ST groups).

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The fact that workers with informal skills are still poor and vulnerable shows that the strong association with existence of informal skills and poverty and vulnerability is because many of the skills reside in workers in languishing trades/sectors. Moreover, the valuation of traditional skills is also an issue. Taken together, workers with informal skills are not able to effectively exploit the emerging labour market conditions as a result of which such skills are not able to provide a way out of poverty to them.

We summarise the analysis in this section as follows:

Although there is evidence of improvement in educational attainments of the rural population as shown by higher attainments of younger cohorts, this improvement is very slow beyond the primary level. The average educational attainment of the rural population and of the rural workforce in India continues to be very low and is marked by rural-urban and intra-rural disparities. The same situation exists with regard to formal skill training. Informal skills are more widely prevalent but these do not clearly translate into better jobs for the rural as well as urban population, probably because many of the trades in which they reside, are languishing. In the next section, we turn to a closer analysis of the relationship between education and skills and the nature of employment in rural areas.

5. THE LINKS WITH JOBS IN RURAL INDIA

In this section we explore the impact of formal education and skills on the type of employment secured by workers in rural India. We hypothesise that educated and skilled workers who also have better social and physical capital (land) are much more likely to have regular jobs. We further hypothesise that low levels of education, landlessness or near landlessness, belonging to a low ascribed caste status and being female is likely to relegate workers to low paid and insecure agriculture wage employment.

A multinomial logistic regression was fitted to data from the Employment-Unemployment Survey of the National Sample Survey Organisation for 2004-05 for the rural workers aged 15 and above to explore the likelihood of individuals being employed either as self-employed in agriculture/non-agriculture or as regular workers in non-agriculture in rural areas and to find out the role of education and skills attainment and whether factors like socio-religious identity and economic class exercise an independent influence on the probability of an individual being employed as regular worker in non-agriculture (Table 13). The dependent variable is the type of employment which is categorised into regular non-agricultural employment, self-employment and other wage employment; other wage employment being the reference group/ base outcome. The reference variables are illiterates for education, no formal skill/ technical education for training, Hindu Others for socio-religious groups and land owned less than 1 acre for land ownership.

Our analysis shows quite clearly that the likelihood of being self-employed or in regular salaried work in non-agriculture compared to other wage labour is significantly higher for higher educated compared to illiterates. Especially, the odds of being in regular salaried work increases manifold and is almost 177 times higher for those graduates and above

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than for illiterates. The odds of being in regular work in non-agriculture are also significantly higher for those with technical education or any formal skills compared to being employed as other wage labour. Any formal skill or technical education even increases the odds of being in self-employed compared to other wage labour, although not to as much extent as regular non-agricultural work. Thus, education post secondary level is a much surer guarantee of regular non-agricultural employment specifically.

The social group also has an influence on the chances of being in regular salaried employment in non-agriculture or in self-employment. All social groups Hindu ST, SC, OBC, Muslims and even other religions have lower odds of being in regular work in non-agriculture compared to the Hindu Others.

However, individuals from households with higher land ownership have higher odds of being self-employed and in regular salaried work in non-agriculture than being in other wage employment compared to those with land owned less than 1 acre. The likelihood is especially higher of being in self-employed for those with land owned more than 5 acres. Thus, it is evident that education and skills as well as socio-economic situation of an individual have an influence on an individual’s labour market situation in rural areas and education and skills do tend to improve an individual’s chances of being in regular better off forms of employment.

Table 13: Multinomial Logistic Regression: 15+ Workers in Rural India 2004-2005 Dependent Variable: Workers – Type of Employment; Base Outcome/ Reference Group is Other Casual Wage Workers

Relative Risk Ratio (RRR) Std Error Z P>z [95% Confidence Interval] Illiterate (R) Primary Middle 1.35 0.02 19.31 0.00 1.31 1.39 Secondary HS 2.69 0.07 35.79 0.00 2.54 2.84

Education

Graduate & Above 6.54 0.59 20.79 0.00 5.48 7.80 No (R) Formal Skill /

Technical Education Yes 1.20 0.09 2.36 0.02 1.03 1.39 Others (R) Hindu ST 0.30 0.01 -42.09 0.00 0.28 0.31 Hindu SC 0.33 0.01 -43.86 0.00 0.32 0.35 Hindu OBC 0.58 0.01 -23.35 0.00 0.56 0.61 Muslims 0.85 0.03 -5.62 0.00 0.80 0.90

Socio- religious Group

Other Religions 0.52 0.02 -19.66 0.00 0.48 0.55 < 1 acre (R) 1 – 5 acres 4.72 0.08 97.09 0.00 4.57 4.87

Self-employed

Land Ownership (acres)

>= 5 acres 21.53 0.80 82.44 0.00 20.01 23.16 Illiterate (R) Primary Middle 4.23 0.13 45.95 0.00 3.98 4.50 Secondary HS 24.83 0.93 86.07 0.00 23.08 26.72

Education

Graduate & Above 176.77 16.36 55.91 0.00 147.45 211.93 No (R) Formal Skill /

Technical Education Yes 2.32 0.18 10.75 0.00 1.99 2.71 Others (R) Hindu ST 0.44 0.02 -16.98 0.00 0.40 0.49 Hindu SC 0.49 0.02 -19.08 0.00 0.45 0.52 Hindu OBC 0.57 0.02 -17.23 0.00 0.53 0.60

Regular Salaried Non-agricultural Workers

Socio- religious Group

Muslims 0.91 0.04 -2.12 0.03 0.83 0.99

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Other Religions 0.54 0.03 -12.32 0.00 0.49 0.60 < 1 acre (R) 1 – 5 acres 1.89 0.05 24.41 0.00 1.80 1.99

Land Ownership (acres)

>= 5 acres 4.48 0.21 31.43 0.00 4.08 4.92 Number of Observations: 147477; Log likelihood = -98370.692; LR chi2(22) = 47948.80; Prob > chi2 = 0.0000; Pseudo R2 = 0.1960.

Another exercise using logistic regression was undertaken for the rural workers aged 15 and above to explore the likelihood of individuals being employed as casual workers in agriculture and to find out the role of education and factors like gender, socio-religious identity and economic class (Table 14). The dependent variable is the type of employment which is categorised into casual employment in agriculture and other workers. The other workers were considered as the reference group. The reference variables are male for gender, illiterates for education, Hindu Others for socio-religious groups and land owned less than 1 acre for land ownership.

Our analysis shows quite clearly that women have a higher likelihood of being employed as casual labourers in agriculture than being employed as any other worker in rural areas. Education significantly reduces the odds of an individual working as agricultural casual labourer compared to any other worker, indicating that illiteracy tends to accentuate the likelihood of an individual working in more vulnerable forms of employment. Socio-religious identity also has an influence and Hindu STs, Hindu SCs have very high odds of being in casual agricultural employment compared to Hindu Others. The odds of being a casual agricultural labour is higher for all socio-religious groups than Hindu Others. Similarly, individuals from households with higher land ownership have lower odds of being casual labourer in agriculture as they tend to be more self-employed on their own farms.

Table 14: Binary Logistic Regression: 15+ Workers in Rural India 2004-2005 Dependent Variable: Workers – Type of Employment; Reference Group (0) Other Workers; (1) Casual Workers in Agriculture

Odds Ratio Std Error Z P>z

[95% Confidence Interval]

Sex Male (R) Female 1.22 0.02 12.49 0.00 1.19 1.26

Illiterate (R) Primary Middle 0.53 0.01 -34.49 0.00 0.51 0.55 Secondary HS 0.18 0.01 -46.59 0.00 0.17 0.20

Education

Graduate & Above 0.04 0.01 -22.78 0.00 0.03 0.05 Others (R) Hindu ST 3.17 0.11 34.68 0.00 2.97 3.38 Hindu SC 2.74 0.08 34.50 0.00 2.58 2.90 Hindu OBC 1.82 0.05 21.42 0.00 1.72 1.92 Muslims 1.06 0.04 1.65 0.10 0.99 1.14

Socio- religious Group

Other Religions 1.75 0.07 14.05 0.00 1.62 1.90 < 1 acre (R) 1 – 5 acres 0.26 0.00 -71.77 0.00 0.25 0.27

Land Ownership (acres) > 5 acres 0.04 0.00 -58.67 0.00 0.04 0.05

Number of Observations: 147477; Log likelihood = -53082.787; LR chi2(11) = 24216.59; Prob > chi2 = 0.0000; Pseudo R2 = 0.1857.

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To summarise the results in this section: education and formal skill training or a technical education significantly increase the probability of a rural person being employed as a regular worker. On the other hand, the lack of any education or a low level of education increases the probability of a worker being relegated to agricultural casual employment. However, other individual and household characteristics such as the social status of a person, sex, or asset ownership also have an influence on the type of employment of an individual.

6. EDUCATION, SKILLS AND DEVELOPMENT - A REGIONAL PERSPECTIVE

The relationship between education and skill development, on the one hand, and economic development, on the other, has to be strong and bi-directional so that each of these can be sustained. Over a period of time we should expect growth in education and skills to help increase agricultural productivity but more importantly provide an impetus to non-farm and urban growth. This hypothesis appears to be supported by the Indian evidence. The table below gives some relevant state level indicators.

Table 15: Education, Skills, Employment & Incomes across States, 2004-2005

Mean Years of Education

(15+ Workers)

(I)

% Workers with Formal

Skills (15 -29) years (II)

% Workers with

Technical Education (15+) (III)

% Urban Population

(IV)

% Non-farm to Total Rural

Employment (V)

NDP of Agriculture per Worker (1999-2000 Prices) (Rs.)

(VI)

% Regular to Total Workers

(VII)

Mean Wages Casual

Workers (Rs.) (VIII)

Per Capita NSDP at Factor

Cost (1999-2000

Prices) (Rs.) (IX)

Kerala 6.9 16.2 7.5 26.4 58.0 30275.62 16.4 118.68 25118

Maharashtra 4.5 4.4 2.9 43.8 20.1 11129.88 8.0 38.10 26603

Punjab 4.5 2.9 1.9 35.1 33.2 49886.78 13.0 72.59 27905

Haryana 4.4 1.9 2.4 29.9 36.0 36637.50 13.7 71.90 30822

Gujarat 3.9 1.9 1.0 36.6 22.8 16782.95 7.2 49.38 23346

Tamil Nadu 3.7 3.5 1.9 42.3 34.7 14415.79 10.4 55.95 22975

India 3.5 1.9 1.4 28.4 27.4 17047.21 7.1 48.42 19331 Uttar Pradesh 3.5 0.8 1.0 22.3 27.4 15093.86 5.4 50.72 10421

West Bengal 3.5 1.4 0.7 28.0 37.3 28319.34 7.5 47.84 19367

Bihar 3.3 0.3 1.0 10.4 22.1 11426.90 2.7 43.68 6772

Karnataka 3.1 1.9 1.0 34.3 19.1 14474.99 5.0 40.90 19847

Orissa 3.0 1.0 0.9 15.4 31.0 12637.77 5.6 38.12 13329

Chhattisgarh 2.8 1.4 0.6 17.5 13.9 7041.29 3.9 34.35 14070

Jharkhand 2.7 0.8 1.0 19.4 30.1 10089.35 4.2 47.50 12869

Andhra Pradesh 2.5 1.5 1.0 28.8 28.3 18466.66 7.2 41.56 19871

Madhya Pradesh 2.5 0.6 0.6 26.0 17.6 15200.73 5.2 35.99 12032

Rajasthan 2.5 0.3 0.3 26.4 27.2 18001.68 5.5 60.42 14908 Source: Column (I) – (V), (VII), (VIII) - Computed using unit level data of NSS, 61st Round on Employment and Unemployment, Schedule 10, 2004-2005. Column (VI), (IX) – RBI (2009).

We have ranked the states in descending order based on mean years of education of rural workers above 15 years of age and percentage of workers with formal skills. We notice

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from the table that all states with performance exceeding the All India levels also have per capita income levels as also percentage of regular to total workers which are higher than the national average. All these states, except Kerala, where habitation characteristics exhibit a rural-urban continuum, also have urbanisation levels which are higher than the All India levels (Table 15). The percentage of rural non-farm workers and the mean wages of casual workers are also higher than the national average in all these states except for Maharashtra and Gujarat, but both these states the urban pull for workers, as evidenced by the urban levels, is much higher. However, the net domestic product per agricultural worker is lower than the national average in three of the states (Maharashtra, Gujarat and Tamil Nadu). The converse results hold for states which have rural and education levels which are lower than the national level, although variable values for Andhra Pradesh and Rajasthan (and West Bengal which is on par with the national average) are exceptions for some indicators.

The above analysis should not be taken to imply that the development experience is similar across the diverse states but broadly to indicate that higher rural education and skill levels do appear to co-exist with growth processes, which also reflect economic structures with higher demand for higher educated and skilled labour force.

7. IMPLICATIONS FOR POLICY

The emerging structure of the rural labour market in India and the current status of education and skill training and have been highlighted by us in this paper. As pointed out, only 42.8 per cent of the workforce in the rural areas is educated up the primary level (Grade 5) and the mean years of education of the rural workforce in India is only 3.5 years. The percentage of young rural workers with formal training is a low 1.9. This section discusses some policy related issues.

(a) Desirable level of education. Education serves four important objectives. First, as Dreze and Sen (1995) have pointed out, it is intrinsically important and generates high positive social externalities. Second, as discussed in our brief introduction, education also generates strong growth inducing externalities. Further, we show in this paper that higher levels of education in rural areas are also associated with positive externalities generating higher farm and non-farm growth, wages and urbanisation at the regional level. Third, in the absence of a strong redistributive focus in other spheres, education is a strong leveller and generates equity. The literature on this issue indicates a strong relationship between improvement in education and human capital with growth on the one hand, and with income distribution and poverty reduction, on the other (Tilak, 1989). Finally, it generates high private returns to individuals.

Research on returns to education and training in India is hampered by adequate data but limited work does exist (Tilak 1987, 1989, Psacharapolis 1994). Both private and social returns on different levels and types of education have generally been found to be high, although these are sometimes in different directions with for example, the social rates of return indicating being generally higher for primary and school education than for higher education while the reverse is the

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case for private returns (Psacharapolis 1994). Results for the vocational stream of school education are found to be ambiguous (ADB, 2004; Psacharapolis, 1994). But the pattern of demand and supply of private-for-profit education, which has expanded rapidly at all levels and for all types of education as well as training ( although the increase has been most significant in the case of professional and technical higher education), indirectly confirms high perceived returns from education.

One of the ambiguities in present research is returns of education and skill training to self-employed vocations. But, as discussed earlier, the research on education and productivity in agriculture generally demonstrates a positive relationship.

Indian policy is now committed to ensuring schooling up till elementary school and the Right to Education Act has come into force on April 1, 2010. But the threshold level of education in the Indian case appears to be secondary education (Tilak, 1989). This is also borne out by the strong complementarity between secondary and higher levels of education and technical/vocational training discussed earlier in this paper. The high discontinuation rates between primary and high school of education will require substantial policy correction before universal high school levels can be achieved in a definite time frame.

(b) Thus, the main way forward in India is clearly ensuring the availability of quality school education to everyone with some possibility of students acquiring occupational skills in the last stages of school education. In other words, a major thrust is required at this stage towards universalising quality school education. Vocationalisation of school education through a separate stream has been followed with success in several countries in Europe, Asia and Latin America but doubts have also been raised about pursuing this approach in India (ADB 2004, World Bank 2007, Morris 1996). This is also because of the several problems faced by the vocational schools students (World Bank 2007). However, strengthening of the vocational components for significant numbers of the school going population; presents an alternative to students who would like to enter the labour market after having acquired some specific skills in the high school stage. The FAO-UNESCO report on education and rural development has correctly emphasised locally relevant and context specific school curricula. This requires a stress on relevant pedagogies and sufficient decentralisation of syllabus formulation.

(c) On the matter of formal skill training, current official estimates put the total skill training capacity at around 3 million while the NSS has estimated that in 2004-05, 3.95 million persons were undergoing formal skill training. Given that a component of this training is in short duration courses, the total formal training capacity in the country would currently range between 4.5 to 5 million and most of this is urban based. The present training capacity compares to an annual

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estimated addition to the labour force of about 9.2 million. India, thus, has a long way to go to achieve the human capital levels of the East Asian economy.7

Since some years, there is a perception that skill shortages have emerged and there is a lot of emphasis on expanding skill training (GOI, 2002; GOI, 2008; Planning Commission, 2001a and b, 2002, 2006, 2007, 2008; National Knowledge Commission, 2006; Teamlease, 2007; World Bank, 2007). However, as pointed out by us earlier, the main objective of an expansion of skill training cannot aim only at meeting the shortages of skill requirements of the organised sector, but rather increasing the productivity and incomes of workers in the informal (agricultural and non-agricultural) economy. In principal, this is now being recognized but there is insufficient attention to the implications of the economic structure for the types of training that would be required and the manner in which ambitious goals in this regard could be achieved.

(d) At a general level, the goal of skill training should be to prepare existing and potential rural workers for the changing needs of the economy and of the different sectors, including agriculture. The training should serve the needs of a modern agriculture and a globalizing economy. A major question in this context is how the appropriate training needs can be identified and how they can be addressed. The latter issue covers both the content of training and also its mode of delivery. Recent reports have pointed to a mismatch between demand and supply for the vocationally trained in India, resulting in higher rates of unemployment of these segments. There is a view that the provision of skills should be market linked and demand led. This has been strongly voiced by the Planning Commission Task Force on Employment (2001) which has called for a complete withdrawal of government delivery of training. This has also been echoed by a more recent Report (Planning Commission, 2007). A similar argument has been put by industry associations. The underlying assumption is that there is no market failure in the supply and/or demand for skills. This is an erroneous conclusion when seen in the light of the experience of developed countries or those developing countries which have rapidly improved their human capital base, or seen in the context of the disparities in the acquisition of skills and the existing labour market structure with a small organised sector. The existing situation is partly due to public training institutions not being sensitive to market signals, and the provision of poor quality training (ILO, 2003, Planning Commission, 2007), which needs to be corrected. However, in the absence of proper information, private institutions and individuals also end up making wrong choices which results in over supply. The aim of building rural training capacity should be to reach out to a dispersed population and workforce. The goal of expanded training capacity should be, in our view to reach both potential entrants into the workforce and the young

7 Planning Commission (2001b), p. 129, shows that the percentage of vocationally trained persons exceeds 60 in a number of developed as well as East Asian economies. In the Korean Republic, more than 95 per cent workers in the age group 20-24 were vocationally trained in 1998.

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workers. As we have shown in this paper, at present formal skill training is usually associated with at least a higher secondary level of education. But given the present characteristics of the workforce and the slow increase in schooling levels, the goal should be to aim at all those who have minimum schooling (say Class 5). Further, full time training courses of a long duration will not suit those for whom the opportunity cost of staying out of the workforce is high. Hence short modular courses for skill training are desirable. Finally, formal training requires a degree of standardisation and proper certification. This is desirable in order to ensure an assurance of quality from the providers as also from the point of view of the trainees. As far as the issue of financing is concerned, given the limited reach of both education and skill training in rural areas, principally confined to a small section of the population, which are from socio-economically better off sections and the highly significant differences in education and training status across locations, gender, and family backgrounds, there is clear evidence of market failure. The state thus needs to step in to make arrangements to expand training, either directly, or through suitable public-private partnerships, and to make it accessible to all sections of rural workers. We can thus envisage a strong role for public funding and coordination.8 In the past few years, the government of India has taken a number of steps to expand formal training. There is a decision to set up 50,000 skill development centres which will take training down to the Block level. A National Skill Development Coordination Board under the Planning Commission and a National Skill Development Corporation under the Ministry of Finance have also been set up. The Ministry of Labour has started a skill development initiative (SDI) which is based on modular employable skill (MES) training courses. The number of identifiable skills for which standardised courses are being developed has increased significantly and accreditation has been given to certification agencies in the public-private mould. However, for a vast country like India, the training system should be vastly decentralised and it should be based on identification of training needs and resources at the local level. Training efforts at and below the district level should be backed by a strong Labour Market Information System (LMIS) which should be integrated nationally. The National Commission for Enterprises in the Unorganised Sector has recommended the setting up of a District Skill Development Council (DSDC) involving all stakeholders and had left it open to the state governments to adopt specific governance models for the DSDC (NCEUS 2009). The Commission had also recommended the setting up of District level labour market information systems which would also maintain an inventory of training providers and their skills (ibid.). At present also, the skills identified under the SDI are almost entirely for the non-agricultural sector, whereas the agricultural research and extension system, along with other bodies and stakeholders should also be involved in identifying and developing modules for training of young agricultural workers.

8 Public support can take several forms such as financing, regulation, coordination. In the East Asian NIEs, in one view, the last of these has been the most important role for the state (Green, Francis, Ashton, James and Sung, 1999).

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Undoubtedly, the general steps by the government of India as well as the specific programmes adopted by the Ministries, notably the Skill Development Initiative of the Ministry of Labour and Employment, the Entrepreneurship & Skill Development Programme by the Ministry of Micro, Small and Medium Enterprises (MSME), and the Skill Development Scheme for the Below Poverty Line & the Rural Development and Self Employment Training Institutes ( RUDESTIs) of the Ministry of Rural Development have taken a quantum leap forward in recognizing and addressing the training needs of informal sector and rural workers. These programmes are also being supplemented by programmes designed and implemented by NGOs. However, the general architecture of a skill development system which can properly address the requirements of informal sector and rural/agricultural workers has still to emerge in India and it is still not backed by an adequate administrative and financial thrust in this area.

8. CONCLUSION

As shown in this paper, the Indian economy is largely rural and employment is dominated by agriculture. Levels of formal education and training are low in rural India, and the low educated, the poor, women and those from low social status groups have much lower levels of human capital. At the individual level, higher levels of education and skills are associated with higher returns and better jobs. At the regional level, there is a strong bi-directional relationship between economic and rural transformation of a region and the level of education and skills. The potential gains that can be derived by low productive workers, who are usually from deprived social groups, can be large, and this can also positively affect income distribution in their favour. These arguments reinforce those that emphasise the importance of education due to its intrinsic value. Given the context of market failure and the significant positive externalities arising from education and training, a large programme of universal schooling up to the high school level and expansion of formal training in rural areas needs to be put into place.

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