EMPLOYMENT AND LIVELIHOOD DIVERSITIES

EMPLOYMENT AND LIVELIHOOD DIVERSITIES I. Introduction Employment generation has been the central problem of development in the face ... 1.91 61…...

0 downloads 66 Views 2MB Size
CHAPTER VI

EMPLOYMENT AND LIVELIHOOD DIVERSITIES I. Introduction Employment generation has been the central problem of development in the face of growing labour force and rightly so. However, employment growth has always lagged behind the growth of labour force that resulted in a huge backlog of unemployment across rural and urban areas. The problem is more serious in rural areas that are confronted with seasonal and intermittent nature of employment (as most of the workers are either engaged in low paid agricultural activities as self-employed or as casual labour), and limited stable employment opportunities outside the agriculture sector on a sustainable basis. Over the years the employment generating capacity in agricultural sector too has also reached near saturation level in absorbing further work force. All this has aggravated the unemployment situation more so in the rural areas. It is widely known that rural households, individuals derive their incomes from

diverse and multiple sources of activities and often large official surveys provide little or no information about the informal nature of activities, in particular secondary sources of employment and incomes. In the state, migration is the important phenomenon and it has strong social and economic transactions to the local economy that are rarely captured in the usual surveys. Understanding this complexity is so important and so central to comprehend the extremely complex and heterogeneous character of labour market. In this chapter, we discuss activity status of population, employment structure, status of employment, occupational structure, multiplicity of employment and diverse income sources based on primary survey results across different household characteristics. Analysis has been done both for migrants and non-migrants with a view to understanding the differentiated activity patterns. II. Activity Status of PopUlation Total population can be broadly categorized in to workforce, labour force and nonworkers according to activity status pursued during the specified reference period. Workforce is the economically active population, participating in different economic activities for earnings, while the unemployed are those who are actively seeking and available for job and are thus included in the labour force. Non-workers are those 163

who are neither available nor seeking for work, the main constituents are students, home workers, retired/pensioners, rentiers, too old or too young, beggars, etc. However, it is not easy to classify a person in to a single category on the basis of hislher single occupation. Since a person may be involved in different (generally, more than one) types of economic activities, his or her occupational status may be determined by taking one or more occupations into consideration. Following broadly the above classification, sample population has been categorized in to principal and principal and subsidiary status occupations. Table 6.1 presents the activity status of total population. Activity status of each individual has been determined on the basis of principal occupation as well as principal and subsidiary occupation of the population and in doing so time criterion has been considered. Activity status of migrant population has also been considered along resident population that gives an account of their differentiated activity patterns (Table 6.1A). Each component of population has been broadly grouped in to few distinctly identifiable categories to have a broader view of population dynamics. The detailed classification of workers and non-workers will be discussed at later stage. Few important features emerge prominently from the activity status. It can be observed that only 42 per cent of total population (including migrant population) is employed on the basis of principal occupation. The proportion rises steeply to 64 per cent if subsidiary workers are also considered. Majority of workers constituted cultivators and animal husbandry workers followed by white-collar workers, shopkeepers and other production workers. Female working force is predominantly engaged in cultivation and related activities followed by weaving. The unemployed constituted about 5 per cent as per principal status. The proportion of non-workers constituted about 53 per cent by principal status and it declines steeply to 36 per cent by principal and subsidiary capacity. Non-availability of durable employment forces workers to engage in subsidiary capacity to such a large extent. Activity status for the non-migrant population changes significantly if migrants are excluded. The proportion of workers declines to 38 per cent according to principal status and subsidiary employment rises to 62 per cent. Proportion of non-workers is substantially higher (53-57 per cent) of which students' proportion is the highest. Unemployment is low among females compared to males resulting low supply of labour force. Distinct feature of segmented labour market is observed for males and 164

females among resident workforce. While females are predominantly engaged in cultivation and animal husbandry activities their male counterpart are engaged more in non-agricultural activities. Considering alone the migrant population, their work participation is as high as 79 per cent (84 and 45 per cent respectively for males and females) almost all are engaged in non-agricultural activities. Table: 6.1 Activity Status of Total Population Occupation

Principal occupation Male

1.

2. 3.

4.

5.

6. 7. 8 9.

Cultivators and animal husbandry workers White collar workers Shopkeeper, sales worker and other selfemployed workers Weavers, tailors and other production workers Labourers and other workers Total workers Unemployed Labour force Students Pensioners Other nonworkers Total nonworkers Total Population

Source. FIeld Survey, 2004-05.

Female

Person

Principal + subsidiary occupation Female Person Male

12.72

34.28

22.73

27.48

58.90

42.07

14.44

0.66

8.04

14.52

0.66

8.09

10.25

0.19

5.58

10.66

0.19

5.80

4.35

4.17

4.26

4.92

7.10

5.94

2.13 43.89 7.71 51.60 33.31 4.10

1.04 40.34 1.52 41.86 28.03 0.09

1.63 42.24 4.84 47.08 30.86 2.24

2.54 60.13 1.15 61.28 26.74 1.07

1.14 67.99 0.19 68.18 17.90 0.00

1.88 63.78 0.70 64.48 22.64 0.57

10.99

30.02

19.82

10.91

13.92

12.31

48.40 1219

58.14 1056

52.92 2275

38.72 1219

31.82 1056

35.52 2275

165

Table: 6.1A Activity Status of Resident Population Occupation

Principal occupation Male

1. 2. 3.

4. 5.

6. 7. 8 9.

Cultivators and animal husbandry workers White collar workers Shopkeeper, sales worker and other self-employed workers Weavers, tailors and other production workers Labourers and other workers Total workers Unemployed Labour force Students Pensioners Other non-workers Total non-workers Total Population.

Female

Person

Principal + subsidiary occu1!..ation Male Female Person

15.1 5.88

35.12 0.68

25.13 3.28

32.25 5.98

60.49 0.68

46.41 3.33

9.22

0.20

4.69

9.61

0.20

4.89

3.53

3.22

3.37

4.22

5.85

5.04

2.35 36.08 8.53 44.61 37.55 4.9 12.94 55.39 1020

0.98 40.20 1.37 41.57 27.9 0.1 30.43 58.43 1025

1.66 38.14 4.94 43.08 32.72 2.49 21.71 56.92 2045

2.75 54.80 1.27 56.07 29.80 1.27 12.84 43.92 1020

1.07 68.29 0.20 68.49 17.46 0.00 14.05 31.51 1025

1.91 61.56 0.73 62.29 I 23.62 0.64 13.45 37.70 2045

Source: Fleld Survey, 2004-05.

(i) Labour force characteristics among sample population From the perspective of household unit analysis, it is necessary to discuss the labour force characteristics including the migrant population. Migrant population has strong social and economic transactions back to the place of origin and therefore forms an integral part of household unit. However, from the perspective of local economy migrant population needs separate treatment in terms of its implications to the local economy. Labour force participation rate (LFPR) of population in our sample stands out to be much higher in comparison to what has been generally reported in the large surveys such as NSS and Census. This variation is obvious, as large surveys often do not capture the population dynamics minutely as they are being captured in micro level studies, which are more of investigative and probing in nature. In the hill region of the state where the migration is prominent phenomenon, often large surveys fail to capture this peculiar feature. Labour force in the sample population constitutes 47 per cent, males accounting for about 52 per cent and females 42 per cent. LFPR steeply rises to 64 per cent taking in to consideration principal and subsidiary workers. For

166

females, the LFPR is much higher (68 per cent) compared to males (61 per cent) (Table 6.2). Table: 6.2 Labour Force and Workforce Participation Rates Activity status

Principal status and subsidiary workers Male Female Person

Principal status workers

Male Female Including migrants and non-migrants Labour force 51.60 41.86 Workforce (All) 43.89 40.34 14.94 Unemployed* 3.62 Non workers 48.40 58.14 100.00 100.00 Total Population number 1219 1056

Person 47.08 42.24 10.27 52.92 100.00 2275

61.28 60.13 1.87 38.72 100.00 1219

68.18 67.99 0.28 31.82 100.00 1056

64.48 63.78 1.09 35.52 100.00 2275

*Relates to unemployment rate (I.e. number of unemployed persons expressed as a Percentage of the total labour force). Source: Field Survey, 2004-05.

Age-specific labour force participation rates show that none of the children below 14 years has been observed to be participating in the labour force by principal status. However, in subsidiary capacity 6.4 per cent children in this age group have been participating with a majority of are female children (8.7 per cent) compared to male children (4.8 per cent). Children assisting parents in agriculture and allied activities is most common feature in poor families in the hill region. Participation rates gradually increases in the higher age groups, highest being in prime age group (30-59) and then starts falling significantly in the old age group (60 and above) (Table 6.3). Table: 6.3 Age-specific Labour Force Participation Rate Age-group

0-4 5-14 15 - 29 30 - 59 60 and above All age groups

Principal status

Male Female 0.00 0.00 0.00 0.00 61.08 39.44 95.75 82.13 54.17 27.03 51.60 41.86

Principal plus subsidiary status

Person 0.00 0.00 51.01 89.00 41.13 47.08

Male 0.00 4.79 77.84 99.72 74.17 61.28

Female 0.00 8.70 94.10 97.12 50.45 68.18

Person 0.00 6.44 85.40 98.43 62.77 64.48

Source: Field Survey, 2004-05.

Workforce constitutes 42 per cent, (male comprising 44 per cent and female 40 per cent) according to principal status. Workforce participation rates (WPR) increases significantly (64 per cent) if subsidiary workers are also included and participation rate for males rises to 60 per cent while for females it increases to 68 per cent. Overall female WPR is higher than males by about 8 percentage points (Table 6.2). 167

Unemployment rate turns out to be 10.3 per cent in aggregate terms, which is very high. Male unemployment rate, in particular, is alarmingly high at 15.0 per cent while the female unemployment rate is comparatively much lower at 3.6 per cent (Table 6.2). Such a high rate of unemployment is an indicative of low economic base in turn resulting in low absorptive capacity of the labour force. Non-working population constitutes about 53 per cent and a large part of it includes students. (ii) Labour force characteristics among non-migrants

From the viewpoint of local economy, the labour force characteristics show quite distinct patterns from that of household economy, when migrant population is excluded. Overall labour force participation declines from about 47 to about 43 per cent by principal status and from 64 to 62 per cent by UPSS. Male LFPR in particular shows decline while female LFPR does not show any change. Similarly, overall WPR by principal status declines steeply from 42 to 38 per cent and male WPR declines by 6 percentage points. Employment opportunities for the resident population are limited and a large part of workforce is engaged in subsidiary occupations. Unemployment rate also increases and for males it goes up from15 per cent to 19 per cent (Table 6.4). The low rate of labour force participation (UPSS) among males is not a demographic phenomenon, rather it is purely an economic phenomenon where a large percentage of males do out-migrate for earning cash income; and females replace their labour, thereby increasing their overall participation. The high LFPRs among the sample population also show that in a subsistence economy like the mountain region in Uttarakhand every able-bodied person has to engage himself to support household income.

.

T a ble: 64 L a b our F orce an dW orlUorce P articlpation R ates Residents (excluding mh~rants) Principal status Principal and subsidiary Activity status workers status workers Male Male Female Person Female Person Labour force 44.61 43.08 68.49 62.30 41.56 56.08 Workforce 36.08 40.20 68.29 61.56 38.14 54.80 0.28 19.12 3.29 2.27 1.18 Unemployed* 11.46 Non workers 55.39 58.44 56.92 43.92 31.51 37.70 100.00 100.00 100.00 100.00 100.00 100.00 Total Population (number) 1020 1025 2045 1020 2045 1025 *Relates to unemployment rate (I.e. number of unemployed persons expressed as a percentage of the total labour force. Source: Field Survey, 2004-05.

168

(iii) Labour force characteristics across spatial units and household levels WPR (UPSS) across the development blocks varies between 59 to 67 per cent with relatively developed blocks showing higher WPR compared to less developed blocks, albeit marginally. Female WPR is reported higher than the male WPR in all the blocks. It is primarily because females are often disproportionately engaged in agriculture and allied activities because they are grossly under represented in the nonfarm activities and for overwhelmingly large majority of female work force in agriculture and allied activities thus acts as sponge. WPR varies significantly across caste groups, lowest being in the case of SCs (around 61 per cent) and highest among STs (68 per cent). Male WPR is highest among STs and lowest among Kshatriya while female WPR is highest for STs and lowest for the SCs (Table 6.5). In the case of SCs where the lowest WPR is reported can be explained on two counts. Firstly, lack of assets, particularly land asset having lowest average size of land and lowest level of literacy has constricted their greater participation in the labour market. Secondly, many castes based occupations in which males and females were engaged have been gradually vanishing that have also impacted their participation adversely. WPR tend to increase with the higher age groups, much lower in the lowest age group (5-14) and then goes up with consistently, highest being in 30 to 59 age group and then declines in the older age group (60 and above) (Figure 6.1). Figure 6.1 Work Participation Rate by Age-Groups 1~~------------------------.

100 I----.:====:;~:::---I oo+-------~~~----~-~~~ oo+------++-----------~~--~

-+- fv'ale

_ _ Female

~+-----u-----------------~ ~+---~------------------~

O+-__--~----~----~----~ 514

15-29

30-59

00+

Age-group

It is also observed that medium land size class has the highest WPR followed by

landless class. Male WPR is highest among medium land size class followed by 169

landless and lowest WPR among landless class has the lowest female WPR and very high male WPR (Table 6.5). The reason for high WPR in these two different land classes is altogether different. Landlessness forces male labour force to work for offfarm and non-farm jobs while females due to limited opportunity in the non-farm sector and also due to social inhibitions have to remain idle for want of work outside very limited segments of labour market. In most of the agricultural and related activities females tend to have greater participation than males barring limited farming operations like ploughing and terracing that are generally male dominated operations. Table: 6.S Work Participation Rates (UPSS) Across Different Socm ·IG roups an dL an delasses District Including migrants and Block non-mi!!;rants Male Female Person Hawalbagh Almora 63.48 70.83 66.77 Salt 56.21 67.65 61.31 Pithoragarh Dharchula 56.55 67.14 61.75 Berinag 67.14 65.48 64.12 T. Garhwal Chamba 61.59 66.94 64.12 Kirtinagar 55.47 64.29 59.70 Uttarkashi Bhatwari 59.84 75.89 67.36 Dunda 61.94 64.93 63.32 Brahmin Social category 60.00 71.53 65.20 Kshatriya 59.52 67.92 63.40 SC 60.71 59.71 60.26 ST 62.30 73.50 67.78 Others 62.50 69.05 65.85 5-14 4.79 Age-group 8.70 6.44 15-29 74.59 93.48 83.38 30-59 99.15 97.12 98.14 60+ 74.17 50.45 62.77 Landless 70.21 Land class 63.33 67.53 Marginal 59.37 68.33 63.56 Small 60.19 66.29 63.02 Medium 77.78 66.67 72.73 Developed Development 60.54 70.00 64.98 Less developed 59.75 status of blocks 66.04 62.64 Total 60.13 67.99 63.78 733 Nos 718 1451

Source: FIeld Survey, 2004-05.

170

There are significant differences in male and female participation rates across development blocks and male participation consistently throwing low rates compared to their female counterparts. Developed blocks showing higher WPR compared to relatively less developed blocks and this is reflected both for males and for females (Table 6.6). Table: 6.6 Work Participation Rates ~ UPSS) Across Development Blocks Residents (excluding migrants) Block District Person Male Female 65.15 70.83 60.12 Hawalbagh Almora 67.65 57.30 47.10 Salt 63.57 69.70 57.14 Pithoragarh Dharchula 62.54 58.04 67.14 Berinag 61.00 54.70 66.94 Chamba T. Garhwal 44.23 64.29 55.22 Kirtinagar 69.00 58.82 79.59 Bhatwari Uttarkashi 59.13 Dunda 55.91 62.40 66.15 62.07 Landless 69.44 Land class 61.44 Marginal 53.98 68.79 60.34 Small 55.43 65.52 66.67 66.67 Medium 66.67 71.29 64.51 Developed 57.87 Development Less developed 51.76 65.46 58.71 status of blocks 54.80 68.29 61.56 Total Source: FIeld Survey, 2004-05.

(iv) Employment status of workers Employment status of workers has been ascertained based on the principal and subsidiary status occupation and accordingly workers have been categorised in to self-employed" regular and casual statuses. It can be observed that the selfemployment is the principal mode of livelihood of the workforce as 82 per cent workers are engaged in numerous self-employment activities within agriculture and non-agricultural pursuits including migrants. About 13 per cent workforce is engaged in regular/salaried employment and another 6 per cent in casual works. The employment status of the workers is also distinctly different for male and female. Women are disproportionately engaged in self-employment activities (98 per cent) with negligible share in regular and casual employment (1 per cent each respectively) while their male counterpart have 66 per cent share in self-employment, about one fourth share (24 per cent) in regular employment and one tenth in casual employment (Figure 6.2). 171

Status of employment changes significantly with the exclusion of migrant workers. The proportion of self-employed increases to about 91 per cent while the share of regular employment declines to 6 per cent and that of casual employment to 3 per cent. The status of employment hardy changes in the case of female workers, however for males the share in self-employment increases to 82 per cent and regular employment declines to 13 per cent and casual employment to 5 per cent (Figure 6.3). About 15 per cent principal and subsidiary workers are migrants and almost all among them are regular employed and with their inclusion the status of employment of workforce changes significantly. Figure 6.2: Status of Employment of All Workers (UPSS) Male

10%

IC Self employed I_Regular

!oCasual

1%

Female

1%

I-

IcSelf employed

I

Regular oCasual _ _

98%

172

J

Figure 6. 3: Status of Employment of Resident Workers (UPSS) Mile

5%

IJ Self elll>loYed~1 • Regular [0 Casual

I

Female

1%

IJ Self elll>loyed

• Regular Casual

98%

III. Structure of Employment at Disaggregated Levels and by Household Groups

(i) Structure of employment Structure of employment of the workforce can be analysed usmg standard occupational and industrial classifications. In this section the structure of employment is analysed using industrial classification of workers and in the later part of analysis we will use the occupational classification. In order to maintain simplicity, structure of employment by industrial classification is analysed according to principal status of workers and as such subsidiary status of workers has not been taken in to account due to limitation in the primary data set. It has been observed that the analysis based on principal status gives clearer picture of work activity and combining subsidiary activity with the principal status subsides the status of subsidiary worker. Also, those who are non-workers according to their principal status are mostly engaged in the cultivation and animal husbandry activities in their subsidiary capacity. 173

Distribution of workers in major sectors of economy shows that agriculture and allied segments is the dominant sector that employs nearly 55 per cent of the workforce including migrant popUlation. Women workforce, in particUlar, is disproportionately engaged (87 per cent) in it that constitutes a backbone to the hill agriculture while men workforce is comparatively much lower (29 per cent). Within agriculture sector, animal husbandry employs nearly 2.8 per cent with a higher female share (4.9 per cent) than the males (1.1 per cent) and it is one of the important and dependable sources of cash income for the poor households besides being an integral agricultural activity that provides main drought power and manure. While a great majority of women are engaged in agricultural and allied activities the men, on the other hand, tend to dominate the non-agricultural pursuits. The share of male workforce in the non-agriculture sector constitutes 71 per cent while for women it is limited to only about 13 per cent, indicating the distinctly segmented labour market for male and females (Table 6.7). However, excluding migrants the share of farm employment rises steeply from about 55 to 67 per cent and that of non-agriculture share declines correspondingly from 45 per cent to 33 per cent. Female share in agriculture and allied activities increases to about 89 per cent with little occupational diversification to non-agricultural activities indicating their vulnerability to subsistence agricUlture. Agriculture, therefore assumes the predominant sector for employment for large majority of labour force with little surplus generating capacity and by and large this sector acts as labour sponge, in particular for the females. Female work force accounts for pitifully low share in all other segments of non- agricultural activities except in manufacturing that employs 8.0 per cent, which mainly consists of household based activities. The other important sectors are trade, hotel & restaurant (constituting petty trade and business), public administration, manufacturing and transport & communication accounting for about 39 per cent and 28 per cent employment respectively for migrant and non-migrant workers. Construction activities have been by and large male dominated activities and female have a very low share in these activities. Thus, accessibility to non-farm employment opportunities is mainly in the domain of males. This further accentuates the vulnerability of females, though they are the main producers in agriculture yet have no direct access to income howsoever little, accrues from agriculture. The hill economy thus presents a typical case of distinctively 174

identifiable labour markets for males and females separately, while men are mostly engaged in non-agricultural activities the women seem to be largely concentrated in agricultural activities. Such a low mobility of women labour force is primarily attributed to the cultural factors that inhibit them to go beyond limited segment of cultivation and related activities. This raises the issue of one kind of limited labour mobility of females and men assume the major source of cash needs working in nonagricultural activities. Another issue is structural rigidity in agricultural sector, in particular the female labour force with high amount of drudgery, that provides a little or no opportunity for upward mobility and skill formation, that raises issue of another kind.

Table: 6.7 Distribution of Workers by Major Industry (UPS) Industry

Agriculture and allied activities Manufacturing Electricity , water, gas etc. Construction Trade, hotel & restaurant Transport & communication Finance, business activities etc. Public admin, education etc.

Total Total workers

Migrant and non-migrant workers

Non-migrant workers

Male

Female

Person

Male

Female

Person

29.21 8.61

86.62 10.33

54.69 9.38

42.12 8.70

89.08 8.01

66.92 8.33

2.06 5.81

0.00 0.23

1.15 3.33

1.90 7.61

0.00 0.24

0.90 3.72

24.72

0.47

13.96

19.57

0.49

9.49

8.24

0.00

4.58

7.88

0.00

3.72

2.62

0.00

1.46

0.27

0.00

0.13

18.73 100.00 534

2.35 100.00 426

11.46 100.00 960

11.96 100.00 368

2.18 100.00 412

6.79 100.00 780

Source: FIeld Survey, 2004-05.

(ii) Employment structure at spatial levels Distribution pattern of workers within local economy presents distinct variations across development blocks, social category and land class and indeed it makes sense to understand this diversity within the local economy. Agriculture and allied activities are principal mode of sustenance in all the development blocks. This is primarily

175

because the subsistence nature of agriculture that does not ensure better employment and income opportunities. The disaggregated picture of industrial distribution of workers across development blocks show that the less developed blocks showing higher share of workforce in agriculture and allied activities than the relatively more developed ones, barring Dunda block which show relatively lower share of workforce in agriculture and allied activities primarily due to its close proximity to block and district headquarters that helps to provide more employment opportunities in non-farm activities (Table 6.8). This is partly attributed to a comparatively higher extent of commercial farming (as reflected in higher percentage of gross cultivated area under commercial crops) in these blocks that is able to retain male workforce gainfully in agriculture. Employment share in manufacturing is generally higher in developed blocks compared to less developed blocks in the districts. Three blocks namely Dharchula, Bhatwari and Dunda have relatively higher share of workforce in manufacturing. This is primarily because of concentration of tribal population in some of the villages where almost every household seems to be engaged in manufacturing activity (e.g; weaving and manufacturing woolen garments). Electricity, water and gas constitutes very low share (less than 1 per cent) in employment. Though construction activity provides employment to about 3.6 per cent workforce yet it is an important activity for employment generation particularly for the poor and unskilled ones. This is particularly due to increase in the building of infrastructure like roads, school buildings, and drinking water under various government funded development schemes in rural areas of Uttarakhand. At the

.~ame

time, there has been a rapid

increase in the building construction activity both within the villages and nearby rural bazaars, which generated a demand for labour. Spurt in construction activity is mainly due to government support for building infrastructure like roads, culverts, bridges, buildings and wage employment programmes. However, it needs to be noted here that most of such rural non-farm jobs in the construction sector were not necessarily located within the boundaries of villages. A large proportion of these jobs were available through daily commuting to nearby towns/ rural bazaars. It is plausible that in these places people do not have other better opportunities and construction works almost immediately provides a relief to them. Such wage employment opportunities are more in relatively less developed blocks compared to developed blocks. Trade, 176

hotel & restaurant are another important activity after agriculture etc that provides employment to large number of people (9.50 per cent), primarily because low investment requirements and low risks associated in such types of activities. Most common activities are retail shops catering to the food and non-food needs, small tea stalls and restaurants (dhabas), which have sprung up in large numbers almost immediately in the villages nearby road heads and nearer to market places and block headquarters. Most of the developed blocks have higher share in these activities. Transport and communication is emerging yet another important activity employing relatively higher share of workforce in the relatively developed blocks than the relatively less developed ones. Employment in fmancial activities is abysmally low. As regards public administration, education etc. no defmite pattern is observed. In two relatively developed blocks have higher employment share while another two relatively less developed blocks have low employment share. Overall pattern of nonfarm employment in developed blocks show higher share than the less developed blocks (Table 6.8). By and large it is observed that the villages nearer to block or district headquarters help to create more employment opportunities, these centres being the hub of development activities for the adjoining villages and number of business activities get agglomerated near the block and district centre, thus providing a market to spur. The block or district has numerous institutions such as schools, health centre, veterinary centre, training centre, demonstration farm centre etc. All these establishments become strong force to spring up a market. The most common business activities around these villages observed are teashop, grocery shop, transport business Geep transport), petty contractor, grain milling, STD booths etc. These activities are common and wherever such growth points have occurred deliberately or otherwise, numerous such activities have .sprung up almost immediately. (iii) Employment structure by social class

Employment share across social class shows that Brahmins, SC and ST have relatively low share in agriculture and allied activities while Kshatriya and other caste have high share. Employment in manufacturing is significantly higher among STs and SCs than other social groups. SCs are mostly engaged in repair related works, mainly caste-based occupations like basket weaving, black smithy, copper smith, etc. which are essentially low paying activities while the STs are engaged in weaving and manufacturing woollen garments. STs have distinctly higher share as their presence in 177

household manufacturing is very high. Employment share in construction is highest among SCs followed by Kshatriya. In trade, hotel & restaurant all the social groups have fairly high share in employment for males but females are grossly underrepresented. In transport and communication too all the social groups are represented barring other caste. In public administration, education etc., Brahmins have highest share followed by SC and Kshatriya. It can be observed that the upper castes and STs have relatively higher share in employment in every branch of activities while the lower social classes have much lower share in employment barring construction (Table 6.8). Scheduled tribe have highest share in nonagriculture employment (68 per cent) followed by Scheduled castes (40 per cent). The Brahmins and Kshatriya have 32 and 29 per cent share respectively while the other caste have the lowest share (12.5 per cent). The reason for highest share of nonagriculture employment among STs is primarily due to their engagement in traditional occupation like weaving, manufacturing of woollen garments and trading. Low average size of cultivable land among SCs forces some people (mostly illiterates and unskilled) in to casual type of employment in construction sector on the one hand and reservation in employment also seem to have helped others (better educated lot) to get regular/salaried employment on the other hand. Brahmins are observed to have highest literacy both among males and females in the present survey and also have traditionally been most forward class both in terms of education and employment in the region. Rightly so, their share in public public administration, education etc constitutes the highest share (18 per cent). The other dominant activity in which they are engaged is trading. (iv) Employment structure by land size class

Apparently, the occupational structure of the workforce is determined to a large extent by the availability of productive asset like land. This is obvious as land is the only asset, which retains the workforce in the absence of other productive employment opportunities. As regards the relationship between land size class and share in rural non-agriculture employment is concerned, distinctly negative relationship between the land size class and the dependence on non-agriculture employment is observed. It can be noted that the landless class depends to a significant extent on non-agriculture activities while the share considerably declines 178

in higher land sizes. This is plausible in the context of rural setting where land is prime asset and landlessness or lack of cultivable land forces people to fmd livelihoods in non-agricultural pursuits whatsoever income or wages they receive. Interestingly, in the highest land size class none of the household member is engaged in other non-agriculture activities except in public services (Table 6.8).

179

Block

AgricuItu re and allied

Blocks Hawalbagh 63.06 Salt 71.13 Dharchula 51.43 Berinag 77.08 Chamba 78.13 Kirtinagar 81.25 Bhatwari 64.13 Dunda 54.37 Social category Brahmin 67.59 Kshatriya 73.91 SC 60.20 ST 32.22 Others 87.50 Land class Landless 23.33 Marginal 68.01 Small 72.06 Medium 90.00 Status of development blocks Less developed 70.21 Developed 63.86 Total 66.92 Source: FIeld Survey, 2004-05.

Table: 6.8 Structure of Employment by Industry Groups Across Spatial Units an d H ouse h0 ld G roups ManufacElectr Constr Trade, hotel Transport Finance, Public turing icity uction & & communi Business admin, water, restaurant cation activities education gas etc. etc. etc

Total

2.70 1.03 15.24 1.04 4.17 1.25 21.74 18.45

0.00 3.09 0.00 2.08 1.04 0.00 0.00 0.97

6.31 5.15 4.76 4.17 0.00 . 5.00 0.00 3.88

10.81 12.37 11.43 11.46 11.46 2.50 7.61 6.80

3.60 2.06 10.48 2.08 4.17 1.25 3.26 1.94

0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12.61 5.15 6.67 2.08 1.04 8.75 3.26 13.59

100.00 (111) 100.00 (97) 100.00 (105) 100.00 (96) 100.00 (96) 100.00 (80) 100.00 (92) 100.00 (103)

2.78 0.87 15.31 46.67 4.17

0.93 1.09 1.02 0.00 0.00

0.93 3.91 8.16 2.22 0.00

7.41 10.87 5.10 10.00 8.33

2.78 4.13 2.04 5.56 0.00

0.00 0.00 1.02 0.00 0.00

17.59 5.22 7.14 3.33 0.00

100.00 (108) 100.00 (460) 100.00 (98) 100.00 (90) 100.00 (24)

53.33 6.85 4.41 0.00

3.33 0.89 0.00 0.00

3.33 4.02 1.47 0.00

3.33 10.42 4.41 0.00

3.33 3.57 5.88 0.00

0.00 0.15 0.00 0.00

10.00 6.10 11.76 10.00

100.00 (30) 100.00 (672) 100.00 (68) 100.00 (10)

5.85 10.64 8.33

1.60 0.25 0.90

4.52 2.97 3.72

8.51 10.40 9.49

1.86 5.45 3.72

0.00 0.25 0.13

7.45 6.19 6.79

100.00 (376) 100.00 (404) 100.00 (780)

180

(v) Educational status of workers Among the non-migrant workers, 33 per cent are illiterates (constituting 16 per cent males and 49 per cent females) while about 17 per cent are educated up to secondary school (14 per cent up to primary and 18 per cent up to middle). Another about 8 per cent workers are educated up to higher secondary and 7.0 per cent workers are graduates and above. A little over 1 per cent worker possesses technical qualification. Educational attainment among females is very low as about 80 per cent workforce is educated up to middle level of education. A cursory look at the educational status of the workers shows that the educational level attained by them is directly linked with their activities. The workers with higher levels of education are by and large concentrated more in public administration, education etc; trade, hotel & restaurant and transport and communication while those with lower levels of education are mainly engaged in agricultural and allied activities, manufacturing and construction (Table 6.9). There are sharp differences in the levels of education and industry groups between males and females. Females are disproportionately represented in the agriCUlture and allied activities (89 per cent) and almost half of them are illiterates. Similarly, in manufacturing majority of females are illiterates. This only shows the less importance attached to female education in a household, as they are forced to join agricultural and low paid manufacturing activities even without basic schooling. Almost 51 per cent workers in agriculture are educated only up to secondary level. The level of education in manufacturing is even more precarious. In construction sector, 10 per cent workforce is educated up higher secondary levels. Understandably, lack of employment opportunities forces many educated persons to take up such jobs under duress particularly in public works, whatsoever wages are paid in lieu of their physical labour. The level of technical education is almost negligible and the illiteracy among female worker is also quite significant (49 per cent) despite the fact that literacy rate stands out to be very high in the rural area of the state. From the policy perspective, low level of literacy among female worker has serious implications for their employability outside the agriCUlture. Their low educational and skill levels can hardly help them to secure employment, outside agriculture. Therefore efforts need to be made to improve their productivity in agriculture itself through imparting them knowledge and training 181

of market-oriented farming. At present hill women largely lack training in marketoriented farming and agro-processing. They have hardly been benefited with the scientific agriculture, which could have otherwise reduced their drudgery.

182

Table: 6.9 Educational Status of Non-migrant Workers (UPS) by Industry and Education Levels Industry

Agriculture and allied activities

Manufacturing

Electricity, water, gas etc.

Construction

Trade, hotel & restaurant

Transport & communication

Finance, business activities etc.

Public admin, education etc.

Total

Sex

Male Female Person Male Female Person Male Female Person Male Female Person Male. Female Person Male Female Person Male Female Person Male Female Person Male Female Person

Illiterate

Up to primary

Up to middle

Up to secondary

21.94 49.32 41.19 31.25 54.55 43.08 0.00 0.00 0.00 21.43 100.00 24.14 9.72 0.00 9.46 6.90 0.00 6.90 0.00 0.00 0.00 0.00 11.11 1.89 16.03 48.79 33.33

22.58 15.26 17.43 15.63 12.12 13.85 14.29 0.00 14.29 17.86 0.00 17.24 5.56 50.00 6.76 0.00 0.00 0.00 0.00 0.00 0.00 4.55 0.00 3.77 14.13 14.81 14.49

24.52 15.80 18.39 12.50 21.21 16.92 28.57 0.00 28.57 28.57 0.00 27.59 22.22 50.00 22.97 17.24 0.00 17.24 0.00 0.00 0.00 6.82 0.00 5.66 20.65 16.02 18.21

22.58 11.17 14.56 31.25 12.12 21.54 14.29 0.00 14.29 14.29 0.00 13.79 23.61 0.00 22.97 27.59 100.00 27.59 100.00 100.00 100.00 22.73 11.11 20.75 23.37 11.17 16.92

Source: Field Survey, 2004-05.

183

Up to higher secondary 4.52 5.45 5.17 6.25 0.00 3.08 28.57 0.00 28.57 10.71 0.00 10.34 20.83 0.00 20.27 24.14 0.00 24.14 0.00 0.00 0.00 15.91 33.33 18.87 11.68 5.58 8.46

Graduate and above 3.23 3.00 3.07 0.00 0.00 0.00 0.00 0.00 0.00 3.57 0.00 3.45 16.67 0.00 16.22 17.24 0.00 17.24 0.00 0.00 0.00 45.45 44.44 45.28 11.68 3.64 7.44

Technical

0.65 0.00 0.19 3.12 0.00 1.54 14.29 0.00 14.29 3.57 0.00 3.45 1.39 0.00 1.35 6.90 0.00 6.90 0.00 0.00 0.00 4.55 0.00 3.77 2.45 0.00 1.15

Total

100.00(155) 100.00(367) 100.00(522) 100.00(32) 100.00(33) 100.00(65) 100.00(7) 0.00 100.00(7) 100.00(28) 100.00(1.00 100.00(29) 100.00(72) 100.00(2.00 100.00(74) 100.00(29) 0.00 100.00(29) 100.00(1) 0.00 100.00 (1) 100.00(44) 100.00(9) 100.00(53) 100.00(368) 100.00(412) 100.00(780)

(vi) Employment pattern among youth workers

Youth workers constitute around 27 per cent of the total workers according to principal activity. About 56 per cent workers are engaged in non-agricultural activities, but there are sharp differences in the relative share of male and female workers in the agriculture and non-agriculture activities (Table 6.10). While male youth workers have disproportionate share in the non-agriculture activities (92.5 per cent) the female workers, on the other hand, has minuscule share (7.2 per cent) in this sector. Female workers' extra-ordinary engagement in agriculture and allied activities is most conspicuous phenomenon that shows their vulnerability as well as drudgery. Share of male workers in the trade, hotel & restaurant (36 per cent); public admin, education etc (23 per cent); transport & communication (17 per cent) is significantly higher. The proportion of youth workers declines to 21 per cent with significant change in work pattern if the migrant workers are excluded.

The share in non-

agriculture sector employment falls steeply from 56 per cent to 32 per cent and that the share of agriculture and allied activities rises from about 44 to about 68 per cent, indicating a very high dependence on non-agriculture employment opportunities outside the region mainly through out-migration. The corresponding share of male and female workers in the non-agriculture activities declines and that of agriCUlture and allied activities rises in almost every branches of activity. Majority of local employment opportunities (65 per cent) in which male youth are engaged are low pay-off and petty activities like trade, hotel & restaurant; transport & communication and construction activities. Only about 10 per cent male workers are engaged in regular employment in public administration and education etc. The female youth have disproportionate share in agriculture and allied activities (95 per cent) primarily because they are grossly under-represented in non-agriculture activities. Their share in non-agriCulture activities is pitifully low and most of them are engaged in manufacturing (illiterate or less educated) and some are in regular/salaried jobs in public administration and education etc (educated ones). It is obvious that almost all the agriculture and allied activities are primarily in the domain of females while male have sole domination in the non-agricultural activities. Such a clear-cut segmentation of work pattern in the labour market between the males and females raises certain issues at the policy plane from long-term perspective that has wider ramification on human resource development. Out of the total female workers engaged in agriculture 184

and allied activities, 26 per cent of them are in the young age group (15-29) and 45 per cent of them having educational levels high school and above. Such a huge educated workforce engaged in low productive activities merely act as sponge and is indicative of lack of correspondence between the education system and the needs of labour market. Education system with little content of marketable skills produces a variety of obstacles in the labour market in terms of unemployment or under-employment.

It is therefore paramount importance to make the education

system relevant to the market needs and create workforce that has the flexibility to acquire new skills for new jobs in the labour market.

Table: 6.10 Employment Pattern among Youth Workers (Aged 15 to 29 years) Industry

Total workers (migrants and non-migrants) Female

Person

Female

Person

7.43 7.43

92.79 5.41

44.02 6.56

17.54 7.02

95.28 2.83

68.l0 4.29

2.03 2.70

0.00 0.00

1.16 1.54

0.00 5.26

0.00 0.00

0.00 1.84

35.81

0.90

20.85

28.07

0.94

10.43

16.89

0.00

9.65

31.58

0.00

11.04

4.73

0.00

2.70

0.00

0.00

0.00

22.97 100.00 148

0.90 100.00 111

13.51 100.00 259

10.53 100.00 57

0.94 100.00 106

4.29 100.00 163

Male Agriculture and allied activities Manufacturing Electricity, water, gas etc. Construction Trade, hotel & restaurant Transport & communication Finance, business activities etc. Public admin, education etc. Total Nos.

Non-migrants workers Male

Source: FIeld Survey, 2004-05.

IV. Occupational Structure of Workforce (i) Occupational structure of migrant and non-migrant workers

Detailed occupational structure for all the workers (including migrants) and for the non-migrant workers is presented in Tables 6.13 and 6.l4 both by principal and principal and subsidiary

statu~es.

Cultivators form the major occupational group both

for males and females. According to principal status, the other major occupational groups are service workers followed by shopkeepers & sales workers and weavers 185

and related workers. Clerical and related workers and teachers also accounts for about one tenth of workforce. Majority of female workers (80 per cent) are engaged as cultivators and about one tenth of them are also engaged in weaving and related occupations.' Their share in non-agriculture occupational groups is very low (15 per cent). While majority of male workforce are engaged in non-agriculture activities. Service workers accounts for about 17 per cent, shopkeepers and sales workers to about 14 per cent, clerical workers about 10 per cent and teachers about 6 per cent in the occupational structure. The transport workers (drivers) also accounts for about 6 per cent. In the principal and subsidiary capacity, the overall agriculture and nonagriculture workers share is about 66 and 34 per cent respectively. Share· of female workers in non-farm activities hardly changes but in the case of male workers, the share of non-agriculture occupation declines from· about 71 to 54 per cent .A large majority of male workers are engaged in agriculture and related activities in their subsidiary capacity (Table 6.11). Table: 6.11 Occupational Structure of Migrant and Non-migrant Workers Occupation

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Principal + subsidiary occlipation Person Male Female Person 50.99 40.44 76.32 58.21

Principal occupation

Male Female Cultivator 27.85 80.05 Animal husbandry 1.12 4.93 2.81 worker 5.33 Technical and managerial----- ---2-:-62- ---O:DU- ----1~46- --1~9T5.98 4.06 4.37 Teacher 1.64 Clerical and 10.09 0.00 5.62 7.51 related workers Shop keeper and 14.02 0.47 8.01 10.52 sales worker 17.20 0.00 Service worker* 9.57 12.84 Weavers and 10.33 7.28 4.51 related workers 4.86 Other production 5.05 0.00 2.81 3.69 worker** Transport workers 6.36 0.00 (drivers) 3.54 4.78 Labourers 4.11 1.88 3.12 3.55 0.75 0.70 0.55 Other workers*** 0.73 535 961 732 426 Total workers

Source: FIeld Survey, 2004-05.

186

10.31

7.79

-----mm- --

0.97-

0.97

2.69

0.00

3.79

0.28 0.00

5.45 6.48

10.45

7.45

0.00

1.86

0.00 1.25 0.42 718

2.41 2.41 0.48 1450

(ii) Occupational structure of non- migrant workers

The occupational structure gets significantly changed if only non-migrant workers are considered. The share of non-migrant workers in agricultural activities rises and that of share in non-agriculture activities declines to a substantial proportion. This is primarily because almost all the migrant workers are engaged in non-agriculture activities. It is important from the point of view of local economy to understand the occupational diversity of workforce. The occupational structure clearly shows the segmented labour market feature between male and female workforce. A little over 87 per cent female are primarily engaged in cultivation and animal husbandry activities according to principal status while about 58 male workers are engaged in non-agriculture activities (Table 6.12). The occupational structure remains almost same for females by principal and subsidiary status. Other than cultivation and allied activities, weaving, labouring and teaching are other occupations for female workers. For males, shopkeepers and sales workers account for the highest share (15 per cent) followed by clerical and related workers and teachers (7 per cent each). The other occupational groups are drivers, weavers and related workers, labourers and service workers constituting about 6 per cent each.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

13.

.

Ta ble: 6120ccupationaIS tructure 0 fN on-ml2rant W orkers Principal occupation Principal + subsidiary occupation Male Female Person Male Female Person Cultivator 40.49 82.28 62.56 52.42 78.00 66.64 Animal husbandry worker 5.10 3.33 6.44 10.57 8.74 1.36 Technical and managerial 1.63 0.00 0.77 1.07 0.00 0.48 Teacher 1.70 4.10 4.47 1.00 2.54 6.79 Clerical and related workers 7.07 0.00 3.33 4:83 2.14 0.00 Shop keeper and sales worker 15.49 0.49 7.56 10.55 0.29 4.85 Service worker* 4.35 0.00 2.05 3.04 0.00 1.35 Weavers and related workers 5.98 8.01 7.05 5.19 8.57 7.07

Occupation

Other production Transport workers Labourers Other workers***

Total workers

worker** (drivers)

3.80 6.52 5.71 0.82 368

0.00 0.00 1.94 0.49 412

1.79 3.08 3.72 0.64 780

2.50 4.47 4.47 0.54 559

0.00 0.00 1.29 0.29 700

* ServIce workers constItute hotel room boys, cooks, domestIc servants, securIty servIce workers etc. ** Other production worker comprises grain miller, food preserver, bakery workers, brewers, dairy workers etc. *** Other includes veterinary assistant, ANM, Dai and religious workers. Source: Field Survey, 2004-05.

187

1.11 1.99 2.70 0.40 1259

(iii) Occupational structure and householdfeatures

The occupational structure shows the distinct pattern of labour market segmentation in terms of castes, land classes, income groups and across development blocks (Table 6.13). In the case of social category, there are huge variations in the share of nonagriculture occupational structure. Scheduled caste and scheduled tribes have fairly high share in non-agriculture occupations (34 and 66 per cent respectively) while the Brahmins (21 per cent) and Kshatriya (17 per cent) have comparatively low share. The other caste has the lowest share in non-agriculture occupations (8 per cent). There are distinct patterns of non-agriculture occupations in each social group. Brahmins have comparatively higher share in teaching and technical & managerial occupations while Kshatriya have relatively high share in occupations like shopkeepers, clerical and related workers, transport workers (drivers) and service workers. The scheduled caste has concentration in occupations like weavers, labourers and other production workers and the scheduled tribes have concentration as weavers, shopkeepers and transport workers. The land size classes and the structure of occupations in the non-farm sector have distinctly negative relationship throughout and the proportion of cultivators increases with the increase in land size classes and rightly so. However, it is observed that in the landless and the highest land size classes the proportion of workers in animal husbandry is very high. The reasons seems to be different for both the classes, for the landless class animal husbandry provides instant cash source of income particularly in rural setting and in the highest land size class animal husbandry becomes an integrated activity of cultivation and also it is one of the income enhancing sources. In the landless category, majority of workers have concentration as weavers followed

by animal husbandry workers, labourers and teachers. In the marginal land holding class, after cultivation majority of workers are engaged as animal husbandry workers, weavers and shopkeepers. In the small landholding class, weavers and teachers are major occupational categories in the non-agriculture occupations and in the highest land size class cultivators, animal husbandry workers and teachers are the major occupational groups.

188

Table: 6.13 Occupational Structure of Non-mierant Workers across Household Household features

-'0"'

~

~

=

U

~

-;a,g~ = '"' ..-<-=~ '" '"' == "t:l

0

..-= ~ ~

-·c ~ ~

~ ~

-=a:l "t:l= = ~ ~ ~ a

-='"'

-.. ~

~

-u ~

~.

~

'"' ~

~

~

'"' ~

~

'" '"'

~ ~ ~ ..::.=~~

~"'''::':::

-= = ~ O"t:l

00.

'"'

~

~ ~

'"' ~

•• ..::.=

t~ 0'"'

00. ~

=

0

:I:l

~

~

~

~ ~

Features~SS)

~

~~

'"' ..::.= ~

'"'

'"' = o a. ~ ~0 J~,", _ 0

"t",-. "'''::'= '"' ~ '"' .=: ,",0,",

o~ '"' ~ '" ~

~~~

'= "' 0

,.Q ~

~

'"'

0

~

'"'"' ~

Social cate20ry

-='"'

~

0

Brahmin

68.65

10.81

1.08

5.95

2.16

3.78

0.54

1.08

1.08

1.62

1.62

1.62

Kshatriya

74.16

8.75

0.40

2.02

2.42

5.38

1.88

0.27

0.54

2.29

1.88

0.00

SC

57.23

8.81

0.63

3.14

1.26

2.52

0.63

10.53

4.40

1.26

8.81

0.63

ST

28.57

5.26

0.00

0.75

2.26

6.77

0.00

50.38

0.75

2.26

2.26

0.75

Others Land class

82.05

10.26

0.00

0.00

0.00

2.56

2.56

2.56

0.00

0.00

0.00

0.00

,

Landless

9.30

11.63

2.33

4.65

2.33

2.33

0.00

55.81

2.33

2.33

4.65

2.33

Marginal

67.80

9.08

0.37

2.11

2.02

5.32

1.47

5.41

1.19

2.02

2.84

0.37

Small

75.00

3.70

0.93

5.56

3.70

1.85

0.93

5.56

0.00

1.85

0.93

0.00

11.11

0.00

5.56

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Medium 83.33 Income 2roup (MPCI Up to Rs. 74.19 250

9.68

0.00

0.00

0.00

1.08

0.00

0.00

0.57

0.57

3.41

1.70

4.26

1.99

74.15

9.09

Rs.500 to 1000

69.35

10.51

0.45

0.00

1.57

4.70

1.79

7.83

1.12

15.05

0.00

3.98

0.28

1.79

0.45

0.45

4.46

1.19

0.60

0.00

'"

'0~"'

-~

0

~

(100.00) 185 (100.00) 743 (100.00) 159 (100.00) 133 (100.00) 39 (100.00) 43 (100.00) (1090 (100.00) (108) (100.00) (18) (100.00) (93) (100.00)

0.00

0.00

250 to Rs. 500

Rs.1000 to 2500

0.00

'"' ~

(352) (100.00) (447)

(100.00) 53.87

6.25

1.19

6.55

5.06

8.33 189

0.60

11.31

0.60

I

More than Rs.2500 3.23 58.06 Development status of blocks

0.00

25.81

3.23

0.00

0.00

3.23

0.00

6.45

0.00

(336) (100.00) (31) 0.00 (100.00)

Less developed

67.54

9.84

0.33

3.11

1.80

4.59

0.82

4.75

1.15

1.15

4.26

0.66 (610) (100.00)

Developed

65.79

7.70

0.62

2.00

2.47

5.08

1.85

9.24

1.08

2.77

1.23

0.15

Total

66.64

8.74

0.48

2.54

2.14

4.85

1.35

7.07

1.11

1.99

2.70

0.40 (1259)

Source: Fleld Survey, 2004-05.

190

(649) (100.00)

The higher the income groups the dependence on agricultural related occupations is lower and opposite is also true. In the lowest income group, the major occupational groups are cultivators and animal husbandry workers in the agriculture based occupation and labourers in the non-agriculture based occupation while in the highest income groups the major non-agriculture occupations are teachers and transport workers. Generally, in the higher income groups, the dominant income groups are teachers, shopkeepers, clerical and related workers and weavers while at the lower end the concentration is more on weavers, shopkeepers and labourers. (iv) Occupation and industrial distribution of workforce

The occupational distribution of workers by industry shows a very narrow base with little diversification across industry groups. Majority of workers are concentrated in a few industry groups. The cultivators, animal husbandry workers, teachers, shopkeepers & sales workers, weavers and transport workers are concentrated in a single industry, the remaining occupational groups are bunched in to a few industry groups (Table 6.14). Such a narrow base of occupation across industry groups is an indicative of a less diversified base of the hill economy. Table: 6.14 Occup_ation and Industrial Distribution of Workers_(!JPS Manufa Electrici Constructi Trade, Transpo Occupation Agriculture chotel & and allied ty on rt& group turing restaura commu water, gas etc nt nication

Finance, business activities etc

Public admin., educatio n etc

All

100.00 (488) 100.00 (26)

Cultivator

100.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Animal husbandry. worker Technical and managerial Teacher

100.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Clerical and related workers Shop keeper and sales worker Service worker Weavers and related workers

66.67 0.00

0.00

0.00

0.00

0.00

0.00

15.38

11.54

33.33 0.00

0.00

0.00

0.00

100.00

0.00 19.23

53.85

100.00 (6) 100.00 (32) 100.00 (26)

0.00

0.00

0.00

0.00

100.00

0.00

0.00

0.00

100.00 (59)

0.00

0.00

0.00

0.00

87.50

0.00

6.25

6.25

0.00

100.00

0.00

0.00

0.00

0.00

0.00

0.00

100.00 (16) 100;00 (55)

191

Other production worker Transport workers (drivers) Labourers and other worker Total

0.00

0.00

0.00

0.00

100.00 (14)

0.00

100.00

0.00

0.00

100.00 (24)

0.00

0.00

0.00

0.00

100.00 (30)

0.13 (1)

6.79 (53)

100.00 (780)

0.00

0.00 71.43

21.43

0.00

0.00

0.00

0.00

26.67

7.l4 0.00

73.33

66.92 3.72 9.49 8.33 0.90 3.72 (29) (74) (522) (65) (7) (29) Note: Figures in parenthesis show absolute number. Source: Field Survey, 2004-05.

V. Employment Status of Workers

The structure of employment status of the workers also shows divergences across caste, land holding size and income groups among the non-migrants. Across caste groups, some differences in the share of self-employment are observed but the variations are not that large, the Brahmins and scheduled caste have lower share compared to others caste groups. The share is over 80 per cent in all the social groups and the share of females is extraordinarily high primarily because huge contribution of agriculture and allied activities. Brahmins have highest share in regular employment both for males and females and relatively low share in casual employment (Table 6.15). The scheduled caste has the highest share in casual employment (both for males and females), particularly for the illiterates and less educated. Interestingly, reservation policy has also helped them (better educated ones) in regular employment particularly in public employment. The scheduled tribe has the lowest share in casual employment and fairly high share in regular employment, next to Brahmins. The reason for the high share in regular employment is primarily due to reservation policy that has also helped them in to regular employment and in fact literacy among scheduled tribe in the hill region is fairly high. While the other caste is the most backward caste and their share in regular employment is the lowest and in casual employment the share is high, next to scheduled caste. By and large the scheduled caste and the other caste are socially and economically disadvantaged sections primarily because of their low literacy levels compared to other social groups. It has been observed that the size of land class generally showing positive relationship

with the share of self-employment and negative relationship with that of share in 192

casual employment. The share of regular employment is the highest in the small land holding class and the lowest in the highest land size class and in other land holding classes the variations are low. In the higher income groups, the share of self-employment declines and that of regular employment increases and casual employment shows secular decline both for males and females. As regards employment status of educational levels, over one fifth of illiterate workforce is engaged in self-employment activities and the majority of them are females. A large majority of workforce (81 per cent) has educational levels high school and below. Conversely, in the regular employment status a large majority of workforce (80 per cent) has educational attainments high school and above. A little over one third (34 per cent) male workforce and one half (50 per cent) of female workforce in the regular employment status has educational levels graduate and above.

None of the female worker has been reported in regular employment status

having educational level below high school. Over one third (36 per cent) illiterate workforce has casual employment status and a large majority of them (78 per cent) are females. None of the casual workers have educational levels beyond graduate and above. The employment status by levels of educational clearly indicates that a large proportion of those having regular employment status (80 per cent) are educated high school and above levels while majority of workers (77 per cent) having casual employment status have educational levels middle and below. Also, large majority of self-employed status workers (81 per cent) have educational levels below high school. Across status of development blocks, relatively developed blocks have higher share of self-employment and regular employment both for males and females while the share of casual employment is lower compared to the less developed blocks.

193

Table: 6.15 Employment Status of Non-migrant Workers across Household Feature (UPSS) Self-employed Household Regular Casual features Male Female Person Male Female Person Male Female Person Male Social cate20ry Brahmin 76.83 97.09 88.11 20.73 10.27 2.44 0.97 1.62 100.00 1.94 Kshatriya 92.19 0.96 5.65 3.96 0.72 84.45 98.31 11.59 2.15 100.00 74.70 92.11 83.02 10.84 SC 1.32 6.29 14.46 6.58 10.69 100.00 ST 82.14 98.70 91.73 14.29 6.77 1.30 3.57 0.00 1.50 100.00 Others 80.00 94.87 10.00 100.00 0.00 2.56 10.00 0.00 2.56 100.00 Land class Landless 76.00 12.00 0.00 100.00 86.05 6.98 12.00 0.00 6.98 100.00 Marginal 0.98 6.06 81.89 97.56 90.73 12.63 1.46 3.21 100.00 5.47 Small 80.39 96.49 88.89 17.65 3.51 10.19 0.00 0.93 100.00 1.96 Medium 0.00 87.50 100.00 94.44 12.50 5.56 0.00 0.00 0.00 100.00 Income 2rouP (MPCI) 0.00 0.00 20.93 12.00 Vpto Rs. 250 79.07 83.87 88.00 0.00 16.13 100.00 Rs. 250 to 500 86.58 98.52 93.47 2.68 1.14 10.74 0.00 1.48 5.40 100.00 89.74 100.00 95.53 8.21 3.58 2.05 0.00 0.89 100.00 Rs. 500 to 1000 0.00 69.62 84.23 . 29.75 2.81 Rs. 1000 to 2500 0.00 97.19 15.48 0.63 0.30 100.00 More than Rs. 2500 0.00 0.00 100.00 57.14 82.35 70.97 42.86 17.65 29.03 0.00 Educational levels 2.47 23.33 77.78 35.90 100.00 Illiterates 12.50 38.95 28.36 2.74 0.00 22.22 0.00 12.82 100.00 Primary 12.28 12.45 12.38 8.22 7.41 10.00 0.00 28.21 100.00 0.00 9.88 36.67 Middle 19.96 19.49 10.96 19.18 0.00 12.82 100.00 12.50 High school 28.29 20.54 26.03 24.69 16.67 15.37 10.26 100.00 Intermediate 17.28 13.33 0.00 37.50 16.45 9.52 12.29 15.07 Graduate and 0.00 100.00 35.80 0.00 0.00 50.00 above 4.54 6.23 34.25 8.77 0.00 0.00 100.00 2.74 0.00 2.47 0.00 Technical 0.00 0.70 1.75 Development status of blocks 5.08 100.00 1.16 5.41 8.30 2.61 10.94 96.23 89.51 Less developed 80.75 1.23 100.00 1.13 7.40 2.72 0.00 98.87 91.37 14.97 Developed 82.31 3.10 100.00 1.14 6.43 1.29 5.37 13.06 90.47 81.57 97.57 (559) (9) (39) (8) (81) (30) (73) (683) (1139) (456) Total Note: FIgures 111 parentheses show absolute figures. Source: Field Survey, 2004-05.

194

Total Female

Person

100.00 100.00 100.00 100.00 100.00

100.00 100.00 100.00 100.00 100.00

100.00 100.00 100.00 100.00

100.00 100.00 100.00 100.00

100.00 100.00 100.00 100.00

100.00 100.00 100.00 100.00

100.00

100.00

100.00 100.00 100.00 100.00 100.00

100.00 100.00 100,00 100.00 100.00

100.00 100.00

100.00 100.00

100.00 100.00 100.00 (700)

100.00 100.00 100.00 (1259)

VI. Multiplicity of Employment

People deriving income from different sources are the most common features in poor regions as single source of income is hardly sufficient to eke out living. It is common to

our understanding

that

poor

regions

are

generally

characterized by

underdevelopment in terms of its resource base both physical and human and various factors reinforces it to remain at low equilibrium trap in development trajectory. This is more so in agrarian economies, which have abundance of labour supply that is manifested in terms of huge unemployment and underemployment in various forms. Lewisian development framework precisely deals with this kind of phenomenon and it is argued that industrialization process would eventually help to graduate backward economy in to development process with near full employment. However, such development hypothesis has not proved correct and people in poor regions continue to have their dependence on multiple sources of income for living, despite the fact that there has been industrial development in other regions. Engagement in multiple sources of livelihoods is most common not only among poor but relatively well-off households in order to augment their incomes. The two different processes seem to work simultaneously; first as a deliberate household strategy to enhance income as an involuntary response to crisis situation (Stark, 1991) and secondly to seize opportunities as a means of accumulation (Davies, 1996). In poor regions people depend on various sources of employment primarily because of sustenance in the .absence of any sole option of employment. Multiple economic activities are most pervasive and widespread among different groups of population in the survey areas in Uttarakhand, manifesting symptoms of backward economy. It can be observed that about 26 per cent among all the workers have single or sole

activity while a large majority of them (74 per cent) resort to more than one activity (Table 6.16). It is most common feature in all the blocks and districts in the hill region of the state. Apparently, there does not seem to be operating a single pattern of activities in different workers' group, rather different processes seem to work simultaneously across that govern the pluriactivity of work pattern. These processes are governed by the opportunities and options of livelihood available to the population by operating in several labour markets. Interestingly, landless workers seem to be depending primarily on the single activity (68 per cent), probably because the lack of opportunities and alternative options available to them due to entry 195

barriers in the labour markets. This could precisely be the reason for lowest household incomes in this group.

While in the case of marginal and small

landholding classes, dependence on multiple activities increases in order to augment their household income. However, in the medium landholding class such dependence on multiple activities declines, probably because household have relatively higher incomes from agriculture and animal husbandry and other assured sources of incomes such as from pension and other incomes. Income group (MPCI) shows inverted U shape curve, initially dependence on singular employment rises with rise in income levels but later tapers off as income levels rises. In the higher income group dependence on single employment declines and that of multiple employment rises. Educational levels also indicate somewhat similar pattern, initially dependence on single employment increases as we move to higher levels of education (up to intermediate) but then such dependence gradually declines and multiplicity of employment rises. Such a huge dependence on multiple activities seems to be common feature in the mountain economy where cultivation is not only seasonal but the income from this activity is low (20 per cent) that is not enough to provide livelihood beyond a few months in a year. Animal husbandry, though more or less perennial activity, at best supplements the household incomes. Therefore, multiple engagements in activities form an important part of household strategy. Though there does not seem to be perceptible variations in the activities pursued in the developed and less developed blocks yet developed blocks showing greater intensity of multiplicity of activities. This kind of phenomenon indicates relatively better livelihood opportunities available in the developed blocks. This fmding of present study dispel the commonly derived hypothesis that poor households derive their income from multiple sources as part of their survival strategy while the relatively better off households and individuals depend on few and stable sources of incomes (Ellis, 2000; Gordon & Craig, 2001). It can be argued that in relatively less developed regions, lack of opportunities constrict the people to enter in the labour market while in the relatively developed region people seize the existing opportunities for better remunerated activities.

196

Table: 6.16 Multiplicity of EmpJoyment I among AIIWork ers Two employment Three Household feature One employment employment Social categ()ry_ Land class (acre) 23.26 67.44 Landless 24.68 51.56 Marginal 52.34 19.63 Small 50.00 27.78 Medium Income group (MPCD 19.35 62.37 Up to Rs.250 23.58 55.40 Rs.250 to 500 29.82 46.86 Rs.500 to 1000 24.70 Rs.1 000 to 2500 47.32 22.58 51.61 More than Rs.2500 Educational levels 52.96 Illiterates 25.44 19.08 50.00 Primary 24.48 50.21 Middle High school 29.73 49.81 31.01 47.47 Intermediate Graduate and 22.00 54.00 above Technical 20.00 30.00 Development status of blocks Less developed 27.70 52.62 48.77 Developed 23.92 25.76 50.64 Total 324 Number of workers 637

9.30 23.76 28.04 22.22 18.28 21.02 23.32 27.98 25.81 21.60 30.92 25.31 20.46 21.52 24.00 50.00 19.67 27.31 23.61 297

Source: FIeld Survey, 2004-05.

Extent of multiplicity of employment increases sharply among principal workers. About 92 per cent principal workers are such who are engaged in more than one type of employment. In the rural areas such multiplicity of activities is not unusual phenomenon and almost every principal worker is engaged either in cultivation or in animal husbandry activity.

Even those who are in regular employment such as

teachers and clerical & related workers have also been working in agriCUlture and animal husbandry activities to a very large extent. In fact, all the principal workers in the category of the clerical and related workers, labourers and other production workers are also working as cultivators and animal husbandry workers in their subsidiary capacity. Extent of multiplicity of employment is relatively low among transport workers (drivers), weavers, other workers and shopkeepers & sales workers (Table 6.17). 197

.

Principal occupation

Teacher Clerical and related workers Shop keeper and sales worker Service worker Cultivator Animal husbandry worker Weavers and tailors Other production worker Transport workers (driver) Labourers Other workers Total No. of workers

T a bl e: 6 17 P rIDcIpaI W ork ers P ursuID2 Su b SI°d Iary A ctivIbes Subsidiary occupation Weavers Shop Other None Cultivator Animal husbandry and Labourers keeper workers worker tailors and sales worker ·9.38 84.38 3.13 0.00 0.00 3.13 0.00 o

0

00

0

Total

100.00

0.00

96.15

0.00

3.85

0.00

0.00

0.00

100.00

16.95 6.25 3.48

77.97 81.25 15.98

5.08 0.00 68.24

0.00 0.00 0.82

0.00 0.00 7.99

0.00 6.25 0.61

0.00 6.25 2.87

100.00 100.00 100.00

15.38

65.38

0.00

7.69

11.54

0.00

0.00

100.00

32.73

60.00

1.82

0.00

3.64

1.82

0.00

100.00

0.00

85.71

7.14

7.14

0.00

0.00

0.00

100.00

33.33 0.00 27.27 8.21 64

66.67 75.86 63.64 37.95 296

0.00 20.69 9.09 44.36 346

0.00 0.00 0.00 1.15 9

0.00 3.45 0.00 5.77 45

0.00 0.00 0.00 0.64 5

0.00 0.00 0.00 1.92 15

100.00 100.00 100.00 100.00 780

Source: Field Survey, 2004-05.

vn Extent of Employment and Unemployment (i) Average days of employment

Average annual days of employment available in agriculture and allied activities are indeed very low in the hill areas. This is primarily because the nature of hill agriculture that poses insurmountable problems in terms of small, fragmented and scattered land holdings where only limited traditional crops can be had in a year and there is little scope for expansion either in terms of area or bringing about radical technological transformation. Irrigation facilities are very low and agriculture is mainly rainfed and use ofHYVs and other chemical fertilizers is very low resulting in low productivity and low returns from agricultural crops. All these factors lead to limited food availability hence the food insecurity that is common feature in many parts of the hill districts in the state. Although, people seem to be working very hard in agriculture activities yet it does not provide employment beyond few months in a 198

year. On an average agriculture and allied activities provide employment to about 56 days in a year for males while for females it provides for about 106 days employment (Table 6.18). Generally, in the context of hill agriculture animal husbandry provides more days of employment than agriculture particularly for females. This is principally due to the fact that agriculture is seasonal activity while the animal husbandry is perennial activity through out the year. It is quite intriguing to observe that despite heavy engagement in agriculture and allied activities it provides employment not beyond two months for males and three and half months for females. Across development blocks one does not find any perceptible variations. However, it can be observed that the relatively developed blocks have generally higher person day's employment compared to less developed blocks. The reasons for higher days of employment in the relatively developed blocks could be due to more areas under horticulture crops leading to commercialisation of agriculture (vegetables, fruits and other non-food crops) compared to relatively less developed blocks (Table 6.19). This supports our hypothesis that diversification in to high value crops promotes more employment and incomes. Across caste category, scheduled caste has the lowest person day's employment available in agriculture and allied activities both for males and females and highest being for upper castes Kshatriya and Brahmin. SCs have the lowest days of employment available in agriculture and allied activities primarily because of lowest asset base (land, for example) that makes difficult to enhance their endowment base hence provide limited days employment in agriculture and animal husbandry. STs have relatively higher day's employment available, particularly for males, despite the fact that they have also lower per capita land available for cultivation. The reason for this is that STs are generally settled in higher altitude where agro-pastoralism is dominant and certain horticultural crops such as potato, radish, apple, razma (kidney beans) are grown in abundance along with animal husbandry (herd of sheep and goats) that provides raw wool for making woollen garments, which is the traditional occupation of the tribal community. Availability of days of employment and size of land class are positively correlated, higher the land size class the higher is the availability of man days and rightly so. This clearly indicates that land endowment is the most important factor in determining the intensity of employment of a worker.

199



Higher size of land obviously helps to employ more number of people, whatsoever the level of productivity of people engaged in it. Table: 6.18 Average Annual Person Days of Employment Per Worker District

Almora Pithoragarh T.Garhwal Uttarkashi Social category

Land class

Age-group

Development status of blocks Total

Source: FIeld Survey, 2004-05.

Agriculture and allied{days) Male Female Hawalbagh 57 118 Salt 53 108 Dharchula 114 56 Berinag 55 102 Chamba 112 63 Kirtinagar 51 99 Bhatwari 88 60 Dunda 54 98 Brahmin 56 108 Kshatriya 59 113 SC 45 80 ST 58 87 Others 55 94 Landless 35 70 Marginal 55 105 Small 63 114 Medium 74 118 0-5 6-14 25 40 15-29 41 100 30-59 70 121 59+ 62 93 Developed 59 109 Less developed 54 102 56 106 Block

Across age groups, days of employment increases gradually in the higher age-group, lowest being in the age-group 6-14 and highest in the age-group 30-59 but then drops in the highest age-group 59 and above and rightly so. It is common in the hill districts that even school going children (age-group 6-14) help their parents in agriculture and allied activities, particularly in harvesting season when the pressure of work increases. Female children are more conspicuously engaged than their male children counterparts. While agriculture being the seasonal activity, the animal husbandry is perennial one that requires constant care and rearing animals. Children perform various activities such as taking animals for grazing, giving fodder and water, cutting fodder and bringing from the fields, cleaning the place of stay of these animals and 200

sometimes milching the cows and buffaloes etc. It is also observed that relatively developed blocks have higher intensity of employment than the less developed ones, albeit marginally. The intensity of employment, therefore (per person annual person days of employment) is very low in the hill areas ofUttarakhand. Table: 6.19 Area under Horticultural Crops (Acres) Block Gross Area under cropped horticulture area 23.89 Hawalbagh 64.33 Almora Salt 85.85 0.00 Berinag 72.57 5.47 Pithoragarh Dharchula 12.50 61.10 T.Garhwal Chamba 20.75 18.41 Kirtinagar 46.63 2.66 24.83 Uttarkashi Bhatwari 12.00 32.62 Dunda 1.95 395.85 Total 89.71

District

Per cent

27.08 0.00 7.00 16.98 47.01 5.39 67.42 5.64 22.66

Source: FIeld Survey, 2004-05.

Average days of employment in casual works turns out to be about 51 days for males and 43 days for females per annum (Table 6.20). It is very low indeed and the nature of employment is such that people have to fmd multiple sources of employment to cope their living. Casual employment is mostly available in construction activities both in private and public domain. Most common activities are construction of individual houses and construction of roads, culverts, bridges and buildings in public works. However, it needs to be mentioned here that such jobs in the construction sector are not necessarily available withiri the boundaries of villages. The fact is that some of these jobs are available through daily commuting to nearby towns and rural bazaars. However, the access to such jobs for females is somewhat limited owing to various socio-economic barriers and inhibitions. It is interesting to note that days of employment in casual works is higher in relatively in less developed blocks and female casual employment in particular is distinctly visible only in less developed blocks. For females, casual employment in construction activities is not very encouraged due to social and cultural inhibitions, however casual works in agricultural activities is most common for landless and poor families. However, agriculture and allied activities does not ensure such employment beyond few days in weeding and harvesting season and casual employment in construction then becomes 201

economic necessity particularly for poor and landless families. It can be seen that females are employed in construction activities only in two blocks, which are relatively less developed varying from 30 to 45 days employment. Across social category, the dependence on casual employment is highest (in terms of days of employment available) among SCs (both males and females) followed by Kshatriya and Brahmin in that order and STs, on the other hand, have lowest days of employment available. One of the reasons for higher and lower dependence on casual type of employment is lack of assets (land, for example), low educational levels and non-availability of other regular sources of earnings that forces people to go for casual type of jobs involving hard physical labour. Also, availability of such employment is important, and such opportunities are most likely available in rural bazaars and nearer to block and district .headquarters. Proximity to such centres is perhaps the most important factors that generate variety of development activities and earning sources in construction and related activities. Dependence on casual works is higher among the sub-marginal, landless and marginal land holding classes and dependence on such employment gradually declines in the higher land size classes and the fact that none from the medium land holding class is available for casual type of employment. No one is reported having engaged in casual employment up to age 14 years and the days of casual employment for males increases with higher agegroups while declines for females.

.

I P er W ork er T a ble: 620 Average An nuaIP erson D ays 0 fE mpjoyment Casual employment (days) Block District Female Male 59 Hawalbagh Almora 30 42 Salt Pithoragarh Berinag 62 Dharchula 43 T.Garhwal Chamba 53 45 Kirtinagar 54 Uttarkashi Bhatwari 40 42 Dunda 57 Brahmin 42 25 Social category Kshatriya 53 40 SC 56 49 ST 33 Others 37 no land 48 Land class 35

202

Age-group

Development status of blocks Total

>0 <= .50 >.50 <= 2.50 >2.50 <= 5.0 0-5 6-14 15-29 30-59 59+ Developed Less developed

52 52 45

44

-

-

-

47 51 56 49 53 51

-

-

44 43 34

43 43

Source: FIeld Survey, 2004-05.

(ii) Extent of unemployment Unemployment rate among the principal workers works out to be about 11.5 per cent

in the state, which is indeed very high by any measure. As usual, male unemployment rate is exorbitantly higher at about 19.1 per cent while female unemployment is noted to be very low at 3.3 per cent *. The reason for such a gap between unemployment rate among males and females is obvious in the context of hill region where women do not seem to be either available or seeking for work due to their heavy engagement in cultivation and house chore activities and men on the other hand have rather limited involvement in agricultural activities that does not make them gainfully employed. Precisely this is the reason that makes huge gaps of unemployment rate among the males and females. However, there are variations in the intensity of unemployment across blocks, caste groups, land class, age groups and across developed and less developed blocks (Table 6.21). Unemployment rate varies minimum from 7.0 per cent to maximum 21.3 per cent and relatively less developed blocks within the districts are showing much higher rate of unemployment barring. Hawalbagh block. Unemployment rates among males are much higher and surprisingly, in two less developed blocks, namely Berinag and Kirtinagar, unemployment rates are exceptionally higher to the extent of 38.3 and 40.5 per cent respectively. One of the reasons for lower unemployment rates in the developed blocks is relatively higher share of cropped area under horticultural crops that probably have kept people gainfully employed. The Planning Department, Government of Uttarakhand has estimated the rate of unemployment in the State to be 21.63 per cent in the terminal year of the Tenth Five Year Plan i.e. 2006-07, and the increment of unemployed persons to be 3.10 lakh (Directorate of Economics and Statistics, Uttarakhand, 2007).

203

Across social classes, the unemployment rate is highest among the SC (14.8 per cent) and the lowest among the STs (3.2 per cent) and 'Others' (4 per cent). Female unemployment rate is reportedly to be very low while male unemployment rate is disproportionately very high. Male unemployment rate is high for upper classes as well (12-13 per cent). Across land class, landless category has the highest intensity of unemployment (14.3 per cent) followed by small (12.8 per cent) and marginal landholding classes (11.2 per cent). Male unemployment is almost equal in these three categories (19-20 per cent). Medium land holding class has comparatively the lowest unemployment rate. Unemployment rate is positively correlated to the levels of education, higher the levels of education higher are the rate of unemployment and opposite seems to be also true. Unemployment rate in the lower categories of education is indeed very low, particularly among illiterates and primary levels and none of the female is reported as unemployed even up to high school level of e1iucation. The reason seems to be obvious as people with low educational and skill levels can not afford to remain unemployed for any length of time and they have to necessarily find some source of livelihood for sustenance, whatsoever wages they get. Surprisingly, unemployment rates are very high among females in the graduate and post-graduate levels and in fact unemployment rate at post-graduate level are the extraordinarily higher. Understandably, there has been expansion and ease of access to higher educational institutions for girls through private examination system that has helped to acquire higher education even at home. But such education without employable and marketable skills has resulted in huge unemployment particularly among females. As usual, unemployment rate is highest among the young age-group population (15-29) and intensity of unemployment is higher in the less developed blocks than the developed ones (Table 6.21). The survey results clearly show that the problem of unemployment is huge one in the hill districts of the state and it is more pronounced among relatively less developed blocks than the relatively developed blocks. Unemployment is concentrated more among lower social classes and among landless class. Intensity of unemployment is visible among the higher educated categories.

204

Table: 6.21 Unemployment Rate among Non-migrants lPrinc~al Workers) Block Unem~l~ment rate District Total Male Female 12.60 Hawalbagh 14.86 9.43 Almora 6.73 13.46 0.00 Salt 7.08 10.00 3.77 Pithoragarh Dharchula Berinag 4.84 21.31 38.33 10.28 . T. Garhwal Chamba 19.30 0.00 40.54 1.69 16.67 Kirtinagar Uttarkashi Bhatwari 11.11 2.22 7.07 Dunda 13.11 3.85 8.85 20.63 4.92 12.90 Brahmin Social category Kshatriya 20.60 3.50 12.21 SC 24.24 2.04 14.78 ST 4.08 2.27 3.23 Others 10.00 0.00 4.00 Land class Landless 19.05 7.14 14.29 Marginal. 11.23 19.06 3.21 12.82 Small 20.45 2.94 Medium 14.29 0.00 9.09 Illiterate 3.23 0.00 Educational 0.76 Primary 1.85 0.87 levels 0.00 Middle 16.67 0.00 9.62 High school 22.02 15.48 0.00 Intermediate 34.85 8.00 27.47 Graduate 36.84 21.43 32.69 Post-graduate 24.00 69.23 39.47 Technical 18.18 0.00 18.18 0-5 Age-group 0.00 0.00 0.00 6-14 0.00 0.00 0.00 15-29 56.82 11.67 35.32 30-59 4.65 0.00 2.25 59+ 0.00 0.00 0.00 Developed Development 13.88 3.98 9.42 status of blocks Less developed 25.24 2.67 13.56 All 19.12 3.29 11.46

Source: FIeld Survey, 2004-05.

VIII. Income and Earnings: Income is the outcome of diverse portfolio of activities perused by the rural households from cultivation, animal husbandry, casual wage works, regular wage works and other non-farm activities etc. Income is accrued from these diverse activities as well as from transfer income such as pension and remittances. Sources of income are determined by various factors such as assets base of the households 205

(farm and non-farm), level of education and skill attainment and employment opportunities available. Cultivation and animal husbandry activities together contribute to about one third (32 per cent) of the total income. Regular employment is yet another important source of income that constitutes about 25 per cent followed by self-employment activities contributing to about 21 per cent income. Transfer income (pension) also contributes substantively (10 per cent) and remittances accounts for about 9 per cent to the total household income. Uttarakhand hill economy has been postulated as highly dependent on remittance income and often termed as money-order economy, however this line of argument does not seem to bear much empirical evidences. Income from casual income (including government programmes) is minuscule contributing 2 per cent in the share of household income (see Figure 6.4). Share of income varies across household features from different activities. It can be observed that the household income of Brahmins and ST is significantly higher compared to other social categories. Average annual income of 'other caste' is the lowest followed by SC (see Figure 6.5). Majority of Brahmins. and ST are associated with highest income quintiles, while the poorest social groups-- the SC and the 'other caste' -- are concentrated in the lowest income quintiles and the Kshatriya are in the middle quintiles (Table 6.22).

206

Figure 6.4: Proportion of Income from Different Sources

IJ Agriculture II A nimal husbandry [] Self -employment 12% [] Regular salary/wage • Pension

Iii! RemITtances iii Casual labour

[] Crther so urces

Figure 6.5: Average Annual Income by Social Category

Kshatriya

Brahmin

SC

ST

others

Table: 6.22 Distribution of Household Population by Social Category and I ncome Q""I umtl e G roups Per capita income ST Brahmin Kshatriya SC Others quintile group

Bottom 2nd 3rd 4th Top Total

14.55 14.55 14.55 21.82 34.55 100.00

23.14 20.09 21.40 20.09 15.28 100.00

Source: FIeld Survey, 2004-05.

207

25.93 33.33 20.37 9.26 11.11 100.00

2.00 6.00 18.00 34.00 40.00 100.00

33.33 41.67 25.00 0.00 0.00 100.00

The household income of the Brahmins comes from more stable source of earnings such as regular jobs (41 per cent) and pension (20 per cent), which is assured and perennial source of income (Table 6.23). Major sources of income for the ST are selfemployment (40 per cent) agriculture and animal husbandry (31 per cent) and regular salary income (18 per cent). For Kshatriya, agriculture and animal husbandry constitute the principal source of household income (35 per cent) followed by regular salary income (22 per cent). While in the case SC the major sources of incomes are agriculture and animal husbandry activities (33 per cent), self-employment (23 per cent) and regular salary income (22 per cent). Iricome from casual wages is highest for the scheduled caste compared to other social groups (6.50 per cent). For 'other caste', the principal sources of incomes are agriculture and animal husbandry (contributing 47 per cent) and remittance (27 per cent). The main reasons for variations of income across the castes are linked closely to the asset base of the households and also educational and skill levels that have overarching influence on income generating capacity. As regards the land size classes, the share of income from agriculture increases with the land size classes. For landless, the main sources of income are .self-employment (49 per cent), regular income (25 per cent) and from remittance (15 per cent). Though income accruing from casual employment is very low yet the share is highest for landless category. In the higher land size classes the share of income from self-employment declines and that of regular income share rises. Income from animal husbandry form~ important source of income for marginal and small landholding size classes contributing about 12 to 10 per cent of total income respectively. In the lowest monthly per capita income group (Up to Rs. 250), income from animal husbandry forms an important source of income contributing 38 per cent of total income and rightly so as in the poorer family it is an important and instant source of cash income. The other important sources of income are casual wage income (22 per cent) (including government programmes) and remittance (11 per cent). Also for the next income group (Rs. 250 to 500), animal husbandry, casual wage incomes and remittance contributes to 23, 7 and 18 per cent respectively. In the higher income groups (Rs. 500 to 1000, 1000 to 2500 and more than Rs. 2500), the . share of income from agriculture and animal husbandry, casual wage incomes, remittance declines and that the share from regular incomes and pension increases. As regards the levels of education and share of incomes from different sources, share 208

from cultivation, animal husbandry, casual income and remittance declines and that of the share from regular income and pension rises. Income from self-employment generally declines with the higher level of education barring technical education where share of income from self-employment is very high and from regular source the share is low which is plausible. Relatively less developed blocks showing comparatively low share of income from cultivation and self-employment but higher proportion of income from regular income, casual income, pension and remittance compared to developed blocks. The major sources of income in the developed blocks are agriculture and animal husbandry (41 per cent), regular income (23 per cent) and self-employment (22 per cent).

209

Household features

Agriculture

Social category Brahmin 10.77 Kshatriya 21.75 SC 22.64 ST 22.23 Others 29.56 Land class No land 0.69 Marginal 19.86 Small 24.89 Medium 40.30 Income group (MPCI) Up to 250 22.29 250 to 500 26.16 25.35 500 to 1000 1000 to 2500 16.33 More than 2500 7.59 Educational levels Illiterates 21.60 Primary 26.55 Middle 24.07 High school 17.67 Intermediate 19.92 Graduate and above 6.67 Technical 5.61 Status of development blocks Less developed 8.20 Developed 29.70 Total 20.09

Source: FIeld Survey, 2004-05.

Table: 6.23 Share of Income from Different Sources Sources of income Animal SelfRegular Casual Govt. husband employment salary/wage programme ry

Pension

Remitta nce

Others

Total

9.07 13.41 10.16 8.79 17.24

11.77 19.14 22.92 40.13 13.69

41.37 21.63 21.86 18.31 7.47

0.49 1.58 6.50 0.65 1.02

0.18 0.33 1.02 0.29 0.66

20.10 10.08 6.72 0.78 0.00

5.64 11.44 6.63 5.84 26.63

0.60 0.66 1.56 2.98 3.73

100.00 100.00 100.00 100.00 100.00

4.88 12.26 10.20 9.27

49.34 21.38 13.22 0.00

24.84 23.80 27.39 36.37

2.40 1.93 0.60 0.00

0.25 0.42 0.12 0.12

0.00 9.89 15.19 0.00

15.27 9.39 7.59 9.39

2.33 1.06 0.80 4.55

100.00 100.00 100.00 100.00

38.39 22.78 13.56 6.91 3.71

2.34 15.81 23.33 23.91 10.61

0.00 3.62 9.34 36.95 61.58

18.03 6.16 1.53 0.06 0.00

3.64 1.02 0.40 0.07 0.00

3.00 5.19 13.14 9.02 13.37

10.56 18.22 12.21 5.55 2.51

1.76 1.05 1.14 1.20 0.63

100.00 100.00 100.00 100.00 100.00

14.36 12.60 12.37 11.01 10.87 3.98 15.34

20.58 18.45 23.89 25.40 22.35 12.92 43.84

20.41 17.65 18.19 25.18 18.82 57.60 5.72

2.41 1.88 2.91 1.05 0.82 0.00 0.00

0.37 0.60 0.50 0.31 0.23 0.10 0.41

7.33 9.91 9.19 9.47 19.20 11.66 17.15

11.62 11.32 8.20 8.53 6.43 6.09 11.91

1.33 1.04 0.67 1.37 1.36 0.98 0.00

100.00 100.00 100.00 100.00 100.00 100.00 100.00

12.43 11.12 11.71

19.38 22.46 21.08

26.11 23.12 24.45

2.48 1.18 1.76

0.38 0.38 0.38

16.76 4.56 10.02

13.54 6.03 9.39

0.72 1.46 1.13

100.00 100.00 100.00

210

(i) Household income It can be observed that a large number of households (78 per cent) in the less

developed blocks have concentration in the monthly income group below Rs. 1000 while in the

~elatively

developed blocks (77 per cent) have concentration in the

monthly per capita income Rs. 1000 and above, barring Chamba block, which is showing similar case of less developed block. Overall, 36 per cent households have monthly per capita income less than Rs. 500, which is closer to the poverty line threshold (Table 6.24). Across social category, SC and 'other caste' have fairly large proportion (ranging from 56 to 75 per cent) of their household population falling in the income groups less than Rs. 500. The STs and Brahmins, on the other hand, have high concentration in the higher income groups (Rs. 1000-2500 and Rs.2500 and above). In the lower land size classes, the concentration of households is more at the lower ends of income while in the higher land size classes opposite are true. It can be observed that the relatively developed blocks showing low proportion of their household in the low income groups and less developed blocks showing relatively higher proportion of households in the lower income groups. Maximum disparities across income groups, measured as co-efficient of variation, occur in case Chamba (126.65), Bhatwari (120.00) and Dharchula (114.24) and lowest in Salt (57.33) and Kirtinagar (65.19) blocks. Most wide disparity across social category is noted among 'other caste' and ST with coefficient of variation of 140.07 and 119.37 respectively. Medium land holding classes appear to have highest degree of variability with a co-efficient of variation of 136.93. Variability between developed and less developed blocks also appears to be significant.

211

Table: 6.24 Household Incomes Across the Blocks, Social Class and Land Class Monthly per capita income group (Rs.) Household CV feature More 250 to 500 to 1000 to than 2500 2500 Up to 250 500 1000 Development Blocks ·13.46 96.30 3.85 7.69 23.08 51.92 Berinag 0.00 6.00 50.00 42.00 2.00 120.00 Bhatwari 2.27 2.27 47.73 47.73 0.00 126.65 Chamba 54.0Q 2.00 114.24 12.00 32.00 Dharchula 0.00 73.42 36.54 21.15 0.00 11.54 30.77 Dunda 80.07 35.29 Hawalbagh 1.96 25.49 33.33 3.92 65.19 40.00 18.00 12.00 6.00 Kirtinagar 24.00 57.33 19.61 37.25 11.76 23.53 7.84 Salt Social category 66.19 32.73 7.27 Brahmin 5.45 21.82 32.73 66.59 21.83 Kshatriya 10.48 27.07 37.12 3.49 86.47 44.44 14.81 11.11 29.63 0.00 SC 0.00 8.00 36.00 54.00 2.00 119.37 ST 66.67 0.00 0.00 140.07 Others 8.33 25.00 Land class 80.89 6.25 31.25 37.50 25.00 0.00 No land >0 <= .50 14.39 34.53 36.69 14.39 0.00 77.16 >.50 <= 69.97 2.50 6.16 24.17 34.12 31.28 4.27 >2.50 <= 13.33 36.67 36.67 13.33 0.00 5.0 80.79 >5.0 <= 0.00 50.00 0.00 50.00 0.00 136.93 10.0 Status of blocks

Less developed Developed Total

15.61 1.03 8.50

32.68 22.05 27.50

29.76 40.51 35.00

17.56 34.36 25.75

4.39 2.05 3.25

57.28 90.59 67.41

Source: FIeld Survey, 2004-05.

There are various factors that determine the income levels of household in a typical hill economy like Utiarakhand. These factors are asset base (primarily, land), educational and skill levels, proportion of non-farm employment, social groups, infrastructure index of villages and out-migration etc. In order to ascertain causality, regression model has been fitted with a view to understanding the relationships between predictor variables and dependent variable. Results of regression are presented in the next chapter. The regression results show that the monthly per capita income of household is positively and significantly affected by proportion of non212

fann employment, area under horticulture, educational attainments, per capita land owned, migrant status of worker (shifted) and development index of villages. While proportion of dependents, households belonging to disadvantaged social groups, casual labour households and migrant households (short and long-term) have negative impact on monthly per capita income of household with statistically significant sign. Signs of the coefficients of all variables are in accordance with our a priori intuition.

(ii)Wages There are variations in wages across different occupations and locations and also within occupations and locations. V ariability ~ wages is widespread in the hill region and wages are typically governed by different factors that have causal relationships with a number of socio-economic variables. Not only the demand for and supply of labour that govern the wage rate in the hill areas but also caste-class composition of the

village,

inter-personal relationship,

locational

advantages

and barriers,

geographical mobility of the labour etc. play their roles in deciding the wage structure and wage rates. Owing to hill specificities, the labour market is not developed and imperfections often create rigidity in the labour market. As can be noted from table 6.25 below that mean earnings per day varies widely across blocks and occupations. Returns to labour input for rural households are very low in agriculture and allied activities as can be observed that the mean earnings per day from this activity is Rs.56, which is the lowest while for self-employed in nonfann activities is the highest at Rs.l45 per day which is higher by over two and half times as compared to those engaged in agriculture and allied activities. Even per person day earnings are much higher for labour input engaged in casual wage works (Rs.76 per day) compared to agriculture and allied activities. It can be observed that 75 per cent workforce engaged in agriculture and allied activities contributes to about 32 per cent income and per capita mean earnings from this source is the lowest which validate our hypothesis that this sector has limited potential for absorption of growing labour force productively. One of the reasons for the lowest mean earnings in agriculture and allied activities is subsistence nature of agricultural that does not produce any surplus for the market and hence generate no demand for labour. Agriculture labour market is by and large absent or dormant in the hill area and mostly family labour is used or some times exchange labour is also practised. However, in the case of shortage of family labour 213

hired labour is used particularly in ploughing, weeding and harvesting seasons but practice of hired labour is not widely prevalent. Such work is generally undertaken by the uneducated and unskilled labour and often has to work at low or depressed wages. But it is also observed that despite low overall mean earning per day, developed blocks (namely, Bhatwari, Chamba, Dharchula and Hawalbagh) have higher average mean earnings per day compared to less developed blocks. Bhatwari block is showing exceptionally very high mean earnings at Rs.l87 per day. The reason for high mean earnings in the developed blocks is primarily due to relatively large area under horticulture crops (see Table 6.19) that has high cash income values and requires more labour inputs per unit of output. Across social category, scheduled tribes have the highest mean earnings followed by 'other caste' and SC. The land size class and earnings are positively co-related; higher the size of land holding higher is the mean earnings per worker. Average mean earnings for males are higher by about one-fifth than females indicating disparity in mean earnings. The developed blocks have about two and half times higher mean earning per day than the less developed blocks. Mean earning per day in self-employed in non-farm activities is observed to be much higher than agriculture and allied activities (61 per cent higher). Earnings are slightly higher (10 per cent) in relatively less developed blocks than the developed ones. Across social category, 'other caste' has the highest mean earnings while the ST has the lowest. The reason for the highest mean earnings for 'other caste' is primarily due to low average man-days employment compared to other caste groups. Mean earnings generally rise as the size the land class increases but in the highest land size class there is no earnings from this source probably because the land assets engage the family labour in cultivation. In the case of casual wage labour in non-farm activities, there are some variations in the mean earnings across blocks, social category, land size class and between male and female but overall wage variations between male and female are not that large. Average mean earnings per day are noted to be higher than the minimum prescribed wages for unskilled workers. Coefficient of variation is highest in agriculture and allied activities followed by self-employed in non-farm activities and least in the case of casual wage labour in non-farm activities (Table 6.25).

214

Household features Blocks

Table: 6.25 Per Person D ay Mean Earmngs fior SeIf-employed an dC asua I W a2e W orks Self-employed in non-farm Casual wa2e Agriculture and allied Male Female Person Person Female Person Male Female Male

;~Berinag

Bhatwari Chamba Dharchula Dunda Hawalbagh KirtinaKar Salt Brahmin Kshatriya SC ST Others Land class -l~o land >0 <= .50 >.50 <=2.50 >2.50 <= 5.0 >5.0 <= 10.0 Status of blocks Less developed Developed Total CV

26.13 182.47 75.22 61.63 49.27 61.97 36.69 26.90 37.62 57.57 60.74 110.22 76.85

27.73 232.88 82.61 50.60 47.49 53.72 37.93 32.55 33.20 43.12 105.09 76.20 55.79

26.67 187.21 76.63 59.42 49.05 60.02 37.03 28.43 36.91 53.60 66.99 103.52 70.74

173.19 111.02 152.86 158.55 130.71 195.71 291.84 156.38 200.45 184.09 135.32 103.54 229.17

110.00 70.12 250.00 92.22 119.35 170.00 129.31 228.57 110.00 192.15 79.76 71.06 0.00

170.74 104.62 157.74 149.26 129.55 191.34 231.41 172.71 196.57 185.32 129.53 98.53 229.17

83.35 81.33 80.66 58.23 85.27 76.92 64.78 80.11 79.93 77.10 76.69 72.50 58.00

66.52 85.91 73.71 74.42 59.84 74.40 68.76 88.97 76.00 65.44 79.21 87.28 92.28

73.84 81.71 79.34 61.48 82.50 76.50 65.43 82.21 79.34 74.37 77.02 75.98 81.54

43.35 59.20 56.41 70.28

35.21 54.37 43.80 50.26

40.57 57.87 53.80 63.09

108.82 128.39 169.75 155.51

112.50 98.55 138.03 178.95

108.94 122.76 166.78 164.10

78.02 74.36 80.49 66.15

80.19 72.13 70.69 69.41

78.80 73.92 78.80 67.15

74.52

0.00

74.52

0.00

0.00

0.00

58.00

0.00

58.00

31.50 84.52 58.70 77.83

32.19 70.72 47.66 95.72

31.69 81.81 56.08 79.40

153.36 144.76 148.09 32.08

162.50 103.67 125.77 44.14

154.58 139.00 145.01 23.56

76.65 76.84 76.73 12.61

70.34 75.83 72.04 13.01

75.21 76.68 75.80 10.62

Source: FIeld Survey, 2004-05.

215

IX. Conclusion

Typically the hill economy of Uttarakhand showing higher female LFPR than the males that has distinctly come out from the field data and also corroborates with the Census and NSS data. Survey data also shows that there is highly gender biased work structure in the rural areas of the state as women overwhelmingly work in agriculturerelated occupations while their male counterparts work in non-farm occupations. This is primarily because the subsistence farming economy with precarious industrial base. Agriculture assumes the predominant sector for employment for large majority of labour force with little surplus generating capacity and by and large this sector acts as labour sponge, in particular for the females. Non-farm activities constituting petty trade and business, wage/salaried employment and casual employment particularly in construction activities have been by and large male dominated activities and female have a very low share in these activities. Generally, developed blocks showing higher share in non-farm activities than the less developed ones primarily because of diversification from cereal crops to horticulture crops and better infrastructure and proximity to block and district headquarters. Agriculture is increasingly becoming an uneconormc family enterprise, and employment opportunities outside agriculture are extremely linlited, particularly for females. Within farm sector there is huge incidence of underemployment in terms of unutilised labour time and majority of rural households are forced to diversify their activities as a part of their survival strategy to cope seasonality and uncertainty of production. Unemployment rate turns out to be 11.5 per cent in aggregate terms, which is indeed very high. Male unemployment rate, in particular, is alarmingly high at 19 per cent while the female unemployment rate is comparatively much lower at 3 per cent. The survey results clearly show that the problem of unemployment is huge one in the hill districts of the state and it is more pronounced among relatively less developed blocks than the relatively developed blocks. Unemployment is concentrated more among lower social classes and among landless class. Intensity of unemployment is visible among the higher educated categories. Females are disproportionately represented in the agriculture and allied activities (90 per cent) with low levels of education and a very high proportion of them are illiterates. Generally low levels of education of a large majority of workers in rural areas of Uttarakhand, partiCUlarly of females, have implications from the policy 216

perspective for their employability outside the agriculture. Their low educational levels can hardly help them to secure employment, outside agriculture. Average annual days of employment available in agriculture and allied activities are indeed very low in the hill areas. Although, people seem to be working very hard in agriculture activities yet it does not provide employment beyond few months in a year. On an average agriculture and allied activities provide employment to about 56 days in a year for males while for females it provides for about 106 days employment. However, it can be observed that the relatively developed blocks have generally higher person day's employment compared to less developed blocks. The reasons for higher days of employment in the relatively developed blocks could be due to more areas under horticulture crops leading to commercialisation of agriculture (vegetables, fruits and other non-food crops) compared to relatively less developed blocks. Availability of casual employment is less than two months (50 days) in a year, providing on an average 51 days employment for males and 43 days for females. Multiple economic activities are most pervasive and widespread among different groups of population in the survey areas in Uttarakhand, manifesting symptoms of backward economy. The average annual income of the households is estimated to be low with significant variations across different castes and land size classes. The principal reasons for variations of income across the castes are linked closely to the asset base of the households and also educational and skill levels that have overarching influence on income generating capacity. It can be observed that the relatively developed blocks showing low proportion of their popUlation in the low income groups and less developed blocks showing relatively higher proportion of population in the lower income groups. In order to know the determinants of household income, regression model has been fitted with a view to understanding the relationships between predictor variables and monthly per capita income (MPCI) as a dependent variable. Signs of the coefficients of all variables are in accordance with our a priori intuition. Coefficients of all explanatory are significant at 1 per cent level of significance, except social group, which is significant at 5 per cent level of significance and proportion of females, which is insignificant even at 10 per cent level of significance. This implies that most of the regressors taken in to our consideration are important determinants of monthly per capita income. Also the p

217

value of the F statistic suggests that jointly all the regressors are significant (Chapter VII). A large majority of workforce (75 per cent) is engaged in agriculture and allied activities contribute to about one third of total household income and mean earnings from this source is lowest compared to that of self-employed and casual wages in non-farm activities that validate our hypothesis that this sector has limited potential for absorption of growing labour force productively ..

218

1