1 Introduction

A distinguishing feature of UP’s economy is its regional imbalance. The state is divided into four well-defined economic regions

– western, eastern, central and southern.1 All these regions have different climatic conditions, soil types and infra-structural d evelopment. The western region is still the most prosperous r egion, despite some catching up by other regions in the 1970s and 1980s. Foodgrain yield per acre in the eastern and central regions is only 80% of the yield in the western region. And the west-to-east gap widens when one considers all crops: the west has witnessed greater diversification of output and has more area under high-value commercial crops. The southern region too has made little progress. By the mid-1990s, crop value per acre in this region was on average less than half that for the western region. Consequently, the gap between the southern region and the rest of the state has continued to widen. Thus, to interpret the state as one economic unit in undifferentiated terms would be misleading.

Given the wide variations in the level of development in the state, we can expect that factors driving non-farm employment growth would also vary. It is likely that in the more developed western region, diversification of employment away from agriculture would reflect the role of demand-pull factors generated by agricultural dynamism, while in the eastern, central and southern regions distress-push can play a role in increasing the volume of non-farm employment.

In the light of the above facts, the present study endeavours to evaluate the employment situation particularly in the non-farm sector. The analysis also attempts to bring out some regional v ariations within the state. The study is made up of six broad s ections. Section 2 looks at employment opportunities for male

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and female employment; in the farm and non-farm sectors. The sectoral distribution of employment is discussed in Section 3. The distribution of workers by their employment status is presented in Section 4. Here, we will also examine the employment status of workers as well as the level of income that self-employed workers consider satisfying. Thereafter, in Section 5, we look at nonfarm employment particularly in the unorganised segment. We,

Figure 1: Infrastructure Development in Different Regions of Uttar Pradesh

130 120 110 100 90 80 70 60

Central Eastern Hill Southern Western Source: CMIE (1997).

then, in Section 6 link non-farm employment with factors such as landownership structure, caste and education utilising the latest National Sample Survey Organisation (NSSO) data available for the year 2004-05.

2 An Overview of Employment Growth

Rural employment accounts for a dominant share of the total e mployment in the state of UP. In 2004-05, 52 million workers out of the total number of 67 million in the state worked in rural areas. In rural areas, employment growth has been disparate between the farm and non-farm sectors. For instance, employment growth in agriculture from 1993-94 to 2004-05 was 1.2% as against 4.76% in the non-farm sector during the same

3.56%, 4.61% and 4.03% between the time periods 1983 to 1987-88, 1987-88 to 1993-94 and 1993-94 to 1999-2000, agricultural growth2 fluctuated and stood at 1.9%, 3.4% and 2.7%, respectively, in the corresponding periods. It appears that the diversification of rural employment in the state was primarily an outcome of factors other than the stimulus provided by agricultural prosperity.

Table 1 also shows the segregated participation of male and f emale non-farm workers. Male activities rose considerably from 18.1% to 33.7% over the period 1972-73 to 2004-05. Female employment in the non-farm sector, on the other hand, exhibited only a meagre growth from 15% to 16.8% in the same period. Notably, the proportion of female workers increased by more than 6 percentage points during the 1990s. This seems to demonstrate increased willingness on the part of the female workers to be involved in sectors that were not preferred earlier.

Gender-wise Trends

Various factors could have accounted for increased willingness on the part of the female workers. A welcome explanation would be that the traditional prejudice against women workers was on the wane. Statistics on earnings of women workers, however, do not support this view. The real earnings of women workers’ in regular employment registered a decline of 32% during the period 1999-2000 to 2004-05. An interesting feature was that the quantum of the cut in wages in the case of more educated women workers was higher than in the wages of the illiterate. While the illiterate women, who constituted more than 66% of the rural women workers, suffered an average cut of 20%, those who had studied up to secondary or higher secondary level had to bear a higher cut of 30%.

The second explanation could be that a decline in real earnings in agriculture, the principal source of employment had forced rural women to take up non-agricultural activities to sus

tain family income. This does seem to be of

Table 1: Usual (Principal + Subsidiary) Status

period. The higher employment growth in the relevance, especially since a major form of

Non-Farm Workers in Uttar Pradesh

non-farm sector resulted in an absolute figure (1972-73/2004-05, in percentages) non-agricultural employment growth has

Year Rural Persons Rural Males Rural Females

of approximately 25.5 million workers in 2004- been an increase in self-employment.

1972-73 NA 18.1 15.0

05. Out of these workers, nearly 14.7 million Finally, a third and complementary explana

1977-78 NA 19.8 10.9

non-farm workers came from rural areas. Con- tion could be that women’s employment in

1983 18.0 22.1 11.3

sequently employment in rural non-farm sec- non-agriculture has risen in recent times

1987-88 17.8 21.1 8.7

tor notably took place during the 1990s only. b ecause the earnings from these activities is far

1993-94 20.0 23.8 10.7

The share of this sector rose from 17.8% in below the reservation wage for men, leaving

1999-2000 23.5 28.2 12.5

1987-88 to 20% in 1993-94 and subsequently these sources of income open to women. The

2004-05 30.1 33.7 16.8

to 27.2% in 2004-05. Thus, the rural non-farm NA implies not available. gender-wise trends in rural non-agricultural em -Source: Relevant Quinquennial Rounds of NSSO.

sector in the state was distinctly augmented in the late 1990s. Here the evidence seems to suggest that a factor responsible for the growth in non-farm employment was the i nadequate expansion of employment opportunities in agriculture, strengthening the case of those who see push-factors and distress elements as being important to a far greater extent in UP than at the all-India level.

The relationship between employment increases in the nonfarm sector and agricultural growth also suggests that agricultural prosperity had a limited role in the promotion of non-farm employment. This is borne out by the fact that even though the annual average increase in rural non-farm employment was

ployment seem to point to the role of distressdriven increases in such employment, especially among women.

For examining the role of external factors that trigger nonfarm employment, we make use of the infrastructure index3 constructed by the Centre for Monitoring Indian Economy (CMIE) for the year 1995. This index has been computed for all states and districts, permitting an assessment of infrastructural development trends at the regional level. Figure 14 shows that these development indices were the highest in districts in the western

(104.8 and 122.1) and hill regions (98.2 and 117.1) followed by districts in the central (90.5 and 112.8), eastern (83.5 and 103.0) and southern regions (77.1 and 87.6) in that order.

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Figure 2: Regional Distribution of Rural Non-Farm Employment in Uttar Pradesh (in%)

20.1 23.0 20.7 22.5 26.9 Central Eastern Hill Southern Western

Source: Computed from the household level data on CD-ROM supplied by NSSO, Government of India.

Figure 3: Region-wise Commercialisation Index in Districts of Uttar Pradesh

(area under non-food crop in%)

50

40

Kheri

30

Kusinagar

20

10

-10

Central Eastern Hill Southern Western Source: Data on CD-ROM supplied by the Directorate of Economics and Statistics, Ministry of Agriculture, Government of Uttar Pradesh.

Figure 2 presents the NSSOs 55th round survey (1999-2000) r egional level estimates of employment in the state. It shows that rural non-farm employment was the highest in the western r egion at 26.9%. The eastern and southern regions followed it at 23% and 22.5%, respectively. On the other hand, the proportions were the least in the hill and central regions at 20.7% and 20.1%.

A comparison of Figures 1 and 2 indicates that there is no mono tonic relation between the level of infrastructural development and the volume of non-farm employment. While there is an association between the ranks in terms of infrastructure development and the proportion of non-farm employment in the western region, this was not true elsewhere. Thus factors other than a stimulus provided by infrastructure appears to explain the level of non-farm employment in the hill region and possibly also in the eastern, central and southern regions. This implicitly endorses the conclusions of Ranjan (1994) and Singh (1994) that development factors appear to make an impact on non-farm employment only in the western region while in all other regions, distressi nduced factors may have been at work in driving the expansion of non-farm employment.

3 Industrial Distribution of the Workforce

It would be significant to survey the structure of rural employment at the state level. Table 2 lays down the situation from 1983 to 2004-05. Table 2 shows that the bulk of the workforce stayed in the primary sector over the period, though after 1987-88 its share declined by 9.4 percentage points; from 82.2% in 1987-88 to 72.8% in 2004-05. A closer examination of the table reveals that the decline in the level of employment in the primary sector was largely because of a fall in the proportion of male workers rather than female workers.

In the secondary sector, manufacturing continued to be the largest mainstay of non-farm employment. However, the rise of employment proportion in the construction activities during the 1990s adds strength to our conjecture that growth of employment in the rural non-farm sector could be due to distressinduced factors. It is argued so because the construction sector d epends essentially on casual labour, absorbing labour seasonally and not necessarily depending upon specialisation. In the remaining two sub-sectors, namely, mining and quarrying and electricity, gas and water, employment of both male and female workers, remained marginal as well as static during the whole period.

In the tertiary sector, trade, hotels and restaurants emerged as an important source of employment. The other services sector employed a substantial proportion of 4.9% of workers in 1983 but its share remained almost constant at 4.5% in 2004-05. The

Table 2: Sectoral Distribution of Usual (Principal and Subsidiary) Status Rural Workers in Uttar Pradesh (1983/2004-05, in percentages)

* Data exclude the state of Uttaranchal. Source: As of Table 1.

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transport and communications sector also gained in importance during the 1990s and registered an increase from 1.1% in 1987-88 to 2.1% in 2004-05. Overall, the importance of construction and trade, hotels and restaurants in explaining the increase in the proportion of non-farm employment indicates that dynamism in

Figure 4: Cumulative Percentage of Self-Employed Workers Who Consider the Specified Income Remunerative in Rural UP (in %)

12.7 30.1 45.7 59.4 74.0 10034.9 56.4 70.1 82.7 91.2 100 Male workers Female workers

the commodity-producing sectors was not primarily responsible for increases in employment. While this may have positive implications in the western region, in the rest of the state it strengthens the view that distress could have played a role in changing the structure of employment over time.

A comparison of the sectoral distribution in non-farm employment separately for males and females brings out the following similarities and dissimilarities. Both the male and female workers were mostly employed in the manufacturing sector. The overall proportion of both the male and female employment also rose in other services sector by almost 1 percentage point between 1983 and 1999-2000. Since, it is to be expected on the basis of gender-based wage disparities that as the reservation wage for women is likely to be significantly lower than for males, the tendency for women to take on jobs in the primary sector as men move into non-farm employment, and for increases in female non-farm employment in manufacturing sector, indicates that employment opportunities in the commodity producing sectors were in fact becoming available at the relatively lower wages that women were willing to accept. On the other hand, the differences between the male and female employment trends are visible for sub-sectors trade, hotels and restaurants and construction. Furthermore, only male workers were present in transport and communications and their share rose during the period. The number of female workers was negligible.

Regional Variations

In addition, we find considerable regional variations across the state. Table 3 highlights the following regional differences in the sectoral distribution of employment. First, the data shows that the western region of UP has the highest number of workers in the secondary sector followed by the southern region at 18.3%. This high share of southern region in total employment is attributable to the considerable size of construction activities. These activities accounted for 13.9% of employment in the southern region. The southern region is characterised by deforested and degraded land. The condition is further compounded by problems of hilly landscape, high winds, scarcity of water and poor soil. Much of the workforce in the region is consequently engaged in soil and water conservation measures such as water harvesting structures. This is a clear indication of distress-induced factors at work, urging workers to rural non-farm employment in the southern region. In contrast with this, the western, eastern and central regions have substantial employment in manufacturing activities to the tune of 12.1%, 9.2% and 8.6%, respectively.

Second, the proportion of employment in the tertiary sector in the western, eastern and central regions was 18%, 15.1% and 11.9%, respectively, but in the southern region it was 6%. Trade and other services sectors provided the highest proportion of employment while transport and communications provided the least in all the regions.

Third, the gender distribution of employment discloses further regional disparities. In the secondary sector, the male workers prominently worked in manufacturing activities in all the regions except the southern region. The female workers, on the other hand, were largely employed in manufacturing activities only in the western and central regions. It implies that the increasing employment opportunities in the manufacturing

Table 3: Region-wise Sectoral Distribution of Usual Principal Status Rural Workers in Uttar Pradesh (2004-05, in percentages)

Source: Computed from the household level data supplied by NSSO (2004-05), Government of India.

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a ctivities offered employment to women at relatively lower wages that were acceptable to them. Besides this, a significant proportion of male and female workers were employed in the construction sub-sector in the southern region only. It was observed, however, that employment of both male and female workers in electricity, gas and water was negligible.

Figure 5: Proportion of Self-Employed Workers Who Consider Their Own Income Remunerative by Income Range (in %)

<1,000 1,000-1,500 1,500-2,000 2,000-2,500 2,500-3,000 >3,000 Income range Source: As of Table 3. 79.4 66.6 61.1 47.0 42.6 49.4 82.0 55.1 51.9 39.9 34.3 39.7 MalesFemales

In the tertiary sector, the male rather than female workers mainly carried out activities of trade and transport and communications. Nonetheless, a small proportion of female workers were employed in trade activities in central, eastern and western regions. Otherwise, the female workers primarily worked in

o ther services and this proportion was substantial in western r egion at 11.8% in the year 2004-05.

The preponderance of male-preferred superior employment in construction and services leads us to investigate whether there is a link between employment in the tertiary sector, particularly in the western, eastern and central regions of the state and their commercialisation5 indices. Figure 3 (p 65) would suggest a bsence of any such relationship, except for the western region where most of its districts had higher commercialisation levels. Any such relationships appear to be completely missing in districts of eastern region, except for Allahabad and Kushinagar districts where commercialisation was as high as 47.7% and 26.8% in 1999-2000. In the rest of its districts, it was no higher than 10%. In the districts of central region too, the impact of commercialisation on tertiary sector employment appears weak, except in case of Kheri district which is an outlying district.

On the whole, the figures indicate that in rural UP, commercialisation does not make an impact on rural non

is not clear and the matter needs to be examined further. With these observations, we move on to investigate the status of nonfarm workers from the perspective of self-employment and the d egree of regularity of employment so as to obtain a more precise picture of non-farm employment in the state.

4 Status Distribution of Rural Non-Farm Workers

A prominent feature of Indian employment situation is that a large part of reported employment is composed either of the selfemployed or casual labour. The regular salaried employees/wage labourers form only a small proportion of the total workforce, particularly in rural areas. However, the proportional size of selfemployed workers in the state and its regions was considerably higher than the all-India level. For instance in 2004-05, the proportion of self-employed workers in the state was 72.8% in comparison of nearly 60% at the all-India level. The regular mode of employment, however, was close to the all-India estimates. Consequently, the incidence of casual employment was lower in the state than at the all-India level. In 2004-05, out of the total workers, 20.7% were casually employed as against the all-India figure of 32.7%.

The pattern of status distribution differed in the non-farm sector from the pattern in rural economy of the state of UP as a whole. As compared with the proportion of self-employed rural

Figure 6: Estimated Annual Gross Value Added Per Worker in Unorganised Manufacturing and Service Sectors (in ‘000 Rs)

OAMEs NDMEs DMEs Total OAMEs Establishments Total Manufacturing Sector Services Sector 20 16 12 8 4 0 Sector of Employment Source: NSSOs 56th and 57 rounds.

workers in the state, the proportion of self-employed rural workers in non-farm employment was considerably lower even though it still dominated the other categories (i e, regularly employed and casually employed in Table 4). The proportion of regularly employed workers in the non-farm sector was higher and consecutive to it, the share of casually em

adequate remuneration for the labour put in by them. In rural UP, the minimum wages prescribed as per the Minimum Wages Act is Rs 58 per day. Assuming that there are 24 working days in a month, the minimum monthly wage at the prescribed rate comes to Rs 1,392. The data points out that a larger proportion of workers in the state would consider wages even lower than the legally prescribed minimum wages as adequate compensation for their labour. Nearly one-fourth of the male workers and half of the f emale workers in the state would feel gratified if they receive

Figure 7: Relationship between Landownership and Percentage of Households in Non-Farm Employment (2004-05)

64.7 45.8 17.0 7.0 6.8 8.0 Landless Sub-Marginal Marginal Small Medium Large

Landownership

Source: As of Table 3.

wages much less than the prescribed minimum monthly wages under the act (Figure 4, p 66). If the next immediate income group of Rs 1,500-2,000 were also taken into account, there would be nearly 45.7% workers in particular that speak of their poor conditions and strengthen the view that they are in miserable circumstances with extremely low incomes compelling them to resort to mean jobs. The situation is no better in the non-farm activities too which evidences a similar position.

Figure 5 (p 67) further bears out the fact that nearly 20% of workers who fail to earn less than even the lowest of the monthly wages prescribed under the Minimum Wage Act feel satisfied with the compensation they get for their labour. This is clear evidence of extremely low expectations of workers especially women, of r ewards of their labour in self-employment. Predictably, the level of satisfaction falls as the required level of income increases. There are 33.4%, 38.9%, 53% and 57.4% of male workers and 44.9%, 48.1%, 60.1% and 65.7% of female workers who are not able to achieve the income range of Rs 1,000-1,500, Rs 1,500-2,000, Rs 2,000-2,500, Rs 2,500-3,000 and more than Rs 3,000 per month, respectively. As seen here, for women workers, the reality of selfemployment is apparently even more depressing. It has been noted that the female workers already had the lowest expectations of income from self-employment, and most would have considered even still lower levels to be remunerative but even these are b eyond reach. It is noted, however, that the proportion of selfemployed workers with income above Rs 3,000 are a fairly ambitious lot. Nearly 49.4% of male workers and 39.7% of female workers in this income group consider themselves fairly rewarded for the labour they put in, while 49.6% of males and 60.3% of females have higher expectations. In the aggregate, we find that a substantial number of workers are unable to earn even the minimum prescribed wages. That is evident enough of their deplorable conditions. Moreover, there is an increasing number of selfemployed workers in the successive income groups who are dissatisfied with their present income. Here again, the deplorable conditions of these workers are discernable. With these observations, for a clearer and more precise picture of non-farm employment in Uttar Pradesh, we now turn to a study of the employment scenario in the organised and the unorganised sectors.

5 RNF Employment in Organised and Unorganised Sectors

An analysis of rural non-farm employment in terms of organised and unorganised6 sectors may throw some light on the “prosperityvs-distress-induced” debate. However, it may be noted at the outset that there is no rural urban breakdown available separately for the organised and unorganised segments of the Indian economy. This is because, first, the organised segment data is published only for rural and urban areas combined and second, the unorganised segment estimates are derived as a residual by subtraction of orgnaised segment estimates from the National Sample Survey (NSS) estimates which cover employment in both segments without distinction. However, an overview of the relative role of the two sectors is important because of the following reasons. First, the share of workers employed in the organised sector has remained more or less constant at a low level for many years. And second, there has been a continuous increase in the relative importance of unorganised segment employment particularly in the non-farm sector.

Employment in the state centred on the unorganised sector which accommodated nearly 95% of the employment. Within the unorganised non-farm sector, the employment growth was substantial in the manufacturing sector. During the period 1993-94 to 1999-2000, employment in this sector recorded a growth rate of nearly 33%. Moreover, productivity per worker in the rural

Figure 8: Distribution of Non-Farm Workers of Different Educational Levels in Rural UP (2004-05, in %)

30.3 36.9 36.9 32.5 43.1 14.6 37.8 34.2 25.5 24.9 37.0 36.8 32.2 44.4 Male FemaleTotal

Illiterate Primary Middle Higher Secondary Graduate and above Source: As of Table 3.

u norganised manufacturing sector also rose continuously during the period. For instance, as per 56th round of NSS report, estimated productivity per worker (at 1993-94 prices) was Rs 4,169 in 1989-90. It increased to Rs 5,613 in 1994-95 and further to Rs 7,293 in 2000-01 which is to be welcomed.

It is to be noted here that the rural unorganised manufacturing segment is essentially of own account manufacturing enterprises (OAMEs) – which do not hire any labour on long-term basis and largely depend on family labour. On the other hand, the establishments employing up to five workers are known as non-directory manufacturing establishments (NDMEs); while the large sized ones with more than five workers are known as directory manufacturing establishments (DMEs). In most of the rural OAMEs, in 2000-01, the average gross value added per worker

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(GVAPW) in manufacturing units was Rs 7,268 which was considerably lower than the average GVAPW of rural NDMEs and DMEs. Similarly, the GVAPW of in the unorganised services in 2001-02, the OAMEs which operated without engaging any hired worker on a regular basis, was Rs 14,194 which was also lower than the rural establishments which employed at least one hired labourer on a regular basis (Figure 6, p 67). To conclude, the low levels of GVAPW in both the manufacturing and services sectors clearly

Figure 9: Level of Educational Attainments of Rural Non-Farm Workers (2004-05, in %) 38.1 23.6 18.0 7.2 13.0 Illiterate Up to primary Middle Secondary Graduate

Source: As of Table 3.

suggest the fact that in most of the unorganised non-farm sectors, employment was of distress-induced type.

6 Determinants of the RNF Employment

This section carries out an analysis to examine the influence exerted on employment in the rural non-farm sector by factors such as landownership pattern across rural households, the educational level/skill of workers concerned and their social affinity. The intention of such an analysis is to improve our understanding of the impact of these factors on non-farm employment as well as to identify further the broad reasons for their involvement in non-farm jobs.

6.1 Landownership Structure

Land is an important asset in rural areas. It is also an important source of employment to the rural population. Availability of land crucially determines the extent of labour absorption in agriculture. Moreover, the small and marginal farmers may also tend to lease their land to the medium and big farmers instead of taking the latter’s land on lease. The main reason for this is that small fragments of land are uneconomical due to cost of inputs and indivisibilities associated with modern agriculture. On the other hand, medium and big farmers continue to enjoy economies of scale as ploughing an extra piece of land involves only a marginal rise in cost and therefore a lower financial burden. Thus, with little access or noaccess to agricultural land, the majority find in non-farm employment, a major source of employment. This inference is well supported by the available data at the state level.

Figure 7 (p 68) shows that more than a majority (64.7%) of the landless households were engaged in non-farm activities in 2004-05. On the other hand, the proportions of non-farm workers of the landowning classes were lower than that of the landless. Against 64.7% of the workers of landless category, the proportions of workers owning submarginal, marginal, small, medium and large pieces of land were in the descending order of 45.8%, 17%, 7.9%, 6.8% and 8%, respectively. Apparently, unlike the landless/

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submarginal households, the modest proportion of landowning class engaged in non-farm activities would have taken up these activities as they offered lucrative options. However, there a ppears no evidence of pull factors at work for a majority of workers in a scenario characterised by non-accessibility to agricultural land, deceleration in output growth in farm sector, deceleration in wage rates for both regular and casual workers, increasing i nput costs and declining profitability in agriculture.

6.2 Education and Skills

The level of education of the population is a potent instrument in influencing the rural non-farm employment pattern. The workers who are more literate are better informed about the job market than their illiterate counterparts. They are better equipped and proactive in the quest for a job even if it involves migration to urban areas or to activities other than agriculture. As evidenced by Figure 8 (p 68), educational attainments tend to promote a shift to non-farm activities at the state level. Figure 8 shows that 24.9% of workers in UP are illiterate. Their proportion amongst the primary level educated workers was 37%. The category with a higher education (graduates and postgraduates) who took to non-farm employment is in the higher proportions of 44.4% and 47.8% for all of rural UP. A similar pattern of increased non-agricultural employment by workers with higher education level is conspicuous both for the male and female non-farm workers. However, since the share of the educated among all workers was low, the influence of this factor on the aggregate share of non-farm employment would have been low.

Interestingly, it may be noted that the pattern at the state level varied somewhat from the all-India level in respect of proportion of non-farm workers of primary and middle levels of education. In the state of UP, the primary and middle levels of school education did not make such differences, as they did in the rest of the

Figure 10: Distribution of Education Status among the Social Groups Engaged in Non-Farm Activities (2004-05, in %)

64.9 68.4 63.4 50.5 20.8 20.4 19.5 19.5 4.3 5.5 8.9 12.8 9.9 5.7 8.3 17.2 ST SC OBC Others Primary Middle Higher Secondary Graduate and above

Source: As of Table 3.

country. This is accounted for by the fact that in rural areas of UP, the education imparted between primary and middle standards made no impact whatsoever on the learners’ skills that are r equired in rural non-agricultural activities. Various studies have established that the functioning of these schools is far from satisfactory (for example, Drèze and Gazdar 1996).

Two features of non-farm employment in the state should be noted which speak of the nature of non-farm employment in UP. First, although higher educational attainment appears to be an important determinant of non-farm employment, Figure 9 shows that the RNF sector in the state was dominated by workers who

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were either illiterate or those who possessed low grades of educa-It is important to note that most of the non-farm workers in the tion. The percentage of illiterate non-farm workers in 2004-05 rural areas of UP belong to the socially backward category of workwas no less than 38.1% in the state. Another one-third was merely ers; and these workers were poorly educated. Very few of them had educated up to the middle level. The remaining one-third of the studied up to the higher secondary level or above (Figure 10, p 69). non-farm workers were educated up to the secondary level and We have noted that most of the non-farm workers in the state beabove. Such a skewed distribution in favour of illiterates and longed to the OBC category; nearly two-thirds of them had studied poorly educated workers involved in low-skilled non-farm jobs only up to the primary level. Only a small proportion had studied further strengthens the likelihood of their having been driven to beyond higher secondary and a small proportion of 8.3% were RNFS employment under duress. Second, it was also noted that graduates. The educational standards amongst the SC workers were the majority of the rural workers engaged in non-farm activities also low. Of them, 68.4% had studied only up to the primary level were landless and only a small proportion owned medium and and another 20.4% had education up to the middle school level. big farms. Such evidence does confirm our perception of distress-On the other hand, the educational standards among the socially induced employment in the non-farm sector. better off, i e, others, were relatively good. Amongst them, 12.8%

graduates and above. It is significant that the majority of the socially In rural areas, the caste structure plays an important role in the backward non-farm workers, i e, the OBCs and the SCs, were poorly determination of activities, especially in the non-agricultural educated while only a few better educated non-farm workers, parsector. The situation in UP appears to be broadly the same but ticularly from the category of others were likely to have had reasonwith an exception to the all-India trend. At the all-India level, ably economically viable jobs. Singh and Tripathi (1995) also afcastes characterised as “others” who pursued non-farm activities firmed this phenomenon in their village level study in Allahabad came second followed by scheduled castes (SC) and scheduled district. They found in their survey that for the upper caste and tribes (ST). In UP, the Other Backward Classes (OBC) mainly pur-large farmers, a higher level of education and increased per capita sued rural non-farm activities in 2004-05. There were nearly 54% income were the main causes of shift towards non-agricultural acof OBC non-farm workers at the state level. However, the next in tivities. For others, participation was a consequence of uncertainty the participating category were SC workers. Their high propor-of returns to agricultural cultivation. To conclude, a large presence tion is because of the fact that the state of UP accounts for a larger of the landless and marginal landowners pursuing non-farm acti-SC population than any other Indian state. The 2001 Census vity, with many of them having had little education and a majority figures reckoned their proportion at 21.1%. The proportion of belonging to the socially downtrodden classes with limited assets

o thers category workers comes next at 25.7% in the state. The ST confirmed our belief in non-farm workers having been driven to workers were the last in the category. their plight largely by distress factors.

Notes Ministry of Labour, government of India which on Employment and Unemployment Survey Results 1 Prior to 9 November 2000 the hill region was also

covers all establishments in the public sector, ir-– All India (New Delhi: Department of Statistics, a part of the state. Since it is no longer so, our

respective of their size and non-agricultural es-Government of India).

tablishments in the private sector employing 10 or

analysis would exclude the hill region. Therefore, – (1990): “Results of the Fourth Quinquennial the 61st round pertaining to the year 2004-05 is S urvey on Employment and Unemployment, NSS

more persons. Information in respect of all the not comparable with the earlier round data. 43rd Round”, Sarvekshna.

public sector establishments and non-agricultural

establishments in the private sectors employing

2 Net state domestic product at factor cost in agricul-– (1997): Key Results on Employment and Underem

25 or more persons is collected simultaneously

ture is being used to measure growth in agriculture. ployment (New Delhi: Department of Statistics,

and information from small non-agricultural

3 The transport facilities have a total weightage of Government of India).

e stablishments in the private sector employing

26% in the index. Energy sector was assigned a – (2001): Employment and Unemployment Situation

10to 24 persons is collected on voluntary basis.

total weightage of 24%. The other sectors – irriga-in India, 1999-2000, NSS 55th Round (July 1999tion, banking, communication infrastructure, June 2000), NSS Report No 458, May.e ducational institutes and health facilities carried

– (2002): Unorganised Manufacturing in India weightages of 20, 12, 6, 6, 6%, respectively.

References 2000-2001, NSS 56th Round, NSS Report No 479.4 Boxplots are used here as they have advantage

Basant, R and R Parthasarthy (1991): “Inter-Regional

Variations in Rural Non-Agricultural Employment