Occupational Categorization of female Work Force in Kashmir Himalayas, India

Occupational classification has been a subject of many experiments. From the definition of a worker to their attribution to a particular category of occupation. Multiple attempts have been made in understanding and explaining the same. The present paper attempts to take the case of female workforce and delves into the subject of categorizing their prevalence across the Kashmir valley (geographic unit), which by and large corresponds to Kashmir Division (administrative unit) in a comprehensible manner. The researcher adopts the Standard deviation method to do so. Using the standard deviations observed for each occupational category, the researcher proposes a rank table visualizing the target area employing a standard deviation-based occupational coding. Based on the rank table it is observed that amongst the female workforce the primary sector is dominant in Ganderbal and Kulgam districts, secondary sector in Shopian, Pulwama and Kulgam, tertiary sector in Srinagar and Budgam and the Quaternary/Quinary sectors in Srinagar, Anantnag and Pulwama districts. The standard deviation method adopted in this paper has led to a satisfactory representation of the occupational distribution observed in the sample area. The occupational coding of the target districts using this method not only makes it easier to visualize the occupational trends but also gives us a clear sense of variation (positive and negative) amongst these categories across the districts.


Introduction
Historians usually couch statements about social stratification and social mobility in terms of occupational structure. To trace the movements of a man from occupation to occupation is, to a considerable extent, to trace his vertical movement within social space. The sum of those movements determines the patterns and rate of mobility, the degree of openness, within a region (Katz et al., 1972). Occupational distribution plays a crucial role in global as well as in Indian economy. The occupational distribution of population reflects on the degree and level of development and the diversification achieved in a particular region. After the Industrial Revolution and also in recent times, the East and South-East Asia have been witnessed by the process of development associated with two types of transitions; (a) the movement of workforce from agriculture to manufacturing and services, and (b) migration from rural to urban areas, i.e., urbanization.
However, these transitions and mobility along with increased education, improving health is also associated with falling fertility rates and other socio-economic aspects (Kumar et al., 2018). It is argued that the Female workforce participation along with their role in society would play a critical role in the development process (Kumar, 2018).
The task of analysing the occupational distribution of the working women in India has usually been avoided by experts in the past, and for quite genuine reasons as (Daniel et al., 1961) have summed up such types of difficulties in this regard as follows: "In every Census enumeration of India since 1881, the occupational figures for females are more difficult to interpret than those for males. To a large extent, in the Indian family economy, the role of women has been and still is auxiliary to that of the men. Accordingly, it has always been hard to draw the line between those whose economic contribution has been substantial and those whose work is considered more fruitful; apart from domestic duties of females, their job has been considered as minor or negligible. Variations from Census to Census, either in total female working force or in the number of women recorded as engaged in particular occupations, may reflect shifts in enumeration practice as much as genuine economic changes (Gulati, 1975).
Over the decades, the Indian Census has experimented with various definitions of the concept of a worker. At least part of this experimentation was aimed to get a correct measure of women's work participation. The 1971 Census change was probably directed at women. Consequently, the impact of the definitional change in 1971 on the female participation rate has been much larger than on the male participation rate. The result thus has been to cause further disgust among experts, particularly those wanting to study changes over time (Agarwal, 1985).
Empowerment of women can accelerate economic development and discrimination against them can hinder the same, as there is a bidirectional relationship between empowerment of women and economic development.
Characterizing the workers is a longstanding and core issue. Traditionally, classification has been done manually.
Many attempts have been made by geographers and other workers in the urban field to classify towns and cities according to the functions (El-Bushra, 1969). This paper aims to analyze the role of female workforce and to classify the female workforce into different occupational categories in relation to male workers. It is important to mention that the census of India only gives broad classifications i.e., (cultivators, agricultural labourers, household workers and others) the above grouping (i.e., into cultivation, agricultural labour and other work) does not follow the usual economic classification by sectors (i.e., primary, secondary and tertiary). Under the latter classification, all those engaged in mining, quarrying, livestock, forestry, fishing, hunting, plantations, orchards, and allied activities would qualify for inclusion in the primary sector. But under the Census classification followed here, all of them are banded with other non-agricultural workers (Gulati, 1975). This paper focuses on the further division of the "other sector" i.e., tertiary, quaternary and quinary. Furthermore, this paper attempts to rank the subdivisions of the sample area on the basis of representation of female workforce across the identified occupational categories, arriving at coding that comprehensively summarizes the distribution.

Data Collection
This paper leverages a mix of primary and secondary data sources. The main source of secondary data used herein is the National Census Data, 2011 (conducted every 10 years by the Government of India through its Ministry of Home Affairs). The census data helped identify benchmarks of demographic and socio-economic indicators for the target study area. For collecting primary data, a structured cum semi-questionnaire was devised and used for conducting a field survey across the sample area. While preparing the questionnaire, methodological triangulation has been adopted. This method of blended qualitative and quantitative approach helps in providing confirmation of findings, more comprehensive data, and increased validity of studied phenomena.

Data Analysis
The present analysis of occupational distribution of the female population is based on primary and secondary survey as highlighted above. Before devising any methodology, a pilot survey with the help of secondary sources (census, 2011) was done to categorize the large sample area into simpler classes. 5 % of the total villages have been taken and with the help of purposive stratified sampling Female occupational data has been extracted and classes made on the basis of highest and the lowest range using the secondary source (census, 2011). To select the sample size, Cochran's Formula (Formula 1 below) was used for large sample size: Based on the responses from the sample population, the prevalence of various occupational categories across the sample area (district-wise) was recorded. Geographers and other workers in the urban sphere have given considerable weightage to the question of classification, and the general trend of thought has been if an occupation is concentrated in a town to such a level that it dominates the town's economic activity, then that occupation will be its major function (Daniel et al., 1961). However, this leads to possibilities of overshadowing the degree of variation, thus, limiting our understanding of the representative occupational categories in the target study area.
As a consequence of that several functional classifications of regions have been adopted. But perhaps the most successful scientific classification was that worked out by (Nelson, 1961) estimating the degree of variation, which can be described and measured in several alternative ways of which the most popular and most fundamental is the standard deviation referred to as SD (Gregory, 2014). The idea behind the standard deviation (SD) method is to limit the number of towns in any one of occupations to those which are outstanding in providing that service; Otherwise, if a simple technique of percentages is used of the marginal towns or even those below the margin will be included (Nelson, 1961). The SD method (formula 2) employs a mathematical construct to represent the degree of variance within the data under study - Taking inspiration from the SD method, Standard deviations from the average were prepared for each occupational category have been recorded in Table 1. The variations from the average were recognized and the degree of variation as a factor of SD is also recorded. The districts in the target study area were therefore put in their proper categories and ranked based on their SD scores. Two observations were recorded here for each district in terms of the prevalence of a particular occupational category; (a) Their deviation from the SD for that occupational category e.g., 1SD, 2SD, 3SD, and (b) Their deviation in terms of higher representation or lower representation i.e., more than SD or less than SD. Districts that had more than average representation in primary sector, for instance, were given ranking over +1SD, +2SD, +3SD, denoted as P1(p), P2(p), P3(p) ranking. Similarly, districts with less than average representation in primary sector were given ranking as -1SD, -2SD, -3SD, denoted as P1(n), P2(n), P3(n). Higher the ranking of a district in providing a certain function, higher would be its significance within that category. The same procedure has been followed for each occupational category and each district has thus been encoded to depict the occupational diversification and provide a ranking for the same. It is important to mention that the NA category in the table is referred to housewives. This section is included in the section because the researchers believe that housewives also play a vital role in adding monetary value to the economic system.

Results
The target study area, as detailed in the methodology above, was broken down into representative sample villages.
A sample village map highlighting the sample area is covered in Figure 2. Based on the findings from the sample area, the occupational distribution of female workforce has been charted.
Under the occupational categories, as specified earlier, housewives have also been given due consideration along with primary, secondary, tertiary, quaternary and quinary sectors. It was found that women participation in workforce encompasses all sectors, which indicates an improvement in female representation. On the aggregate level, it was observed that the female populace in the valley is largely engaged in tertiary activities, followed by a large segment serving as housewives, closely followed by females engaged in skilled quaternary activities, largely in the medical and research fields. In the study area, however, stark differences were observed in the distribution of female workforce across the sectors. For instance, Ganderbal exhibited a higher concentration of workforce in the primary and secondary sectors, 55 percent and 33 percent respectively. On the other hand, Srinagar saw the majority of respondents engaged in the tertiary, quaternary & quinary sectors, 52 percent, 13 percent and 10 percent respectively.
The detailed distribution of female workforce across the sectors in the districts of the valley is summarized in Figure 3.

Figure 3 Occupational distribution across districts
As detailed in the Methodology section, the percentage distribution recorded above was transformed into an SD model to gauge the degree of variation and thereby arrive at a holistic representation in the form of an occupational coding pattern. The SD and average calculations for the occupational categories based on the results in Figure 2 have been recorded in Table 1.  (Table   2) highlighting the representation of occupational distribution for the female workforce across the 10 districts of the valley using the SD method applied on the responses collected from the respondents. Table 2 Occupational ranking of sample area using the SD Method

Primary Sector
Kashmir valley has been a fruit-growing region from ancient times. Horticulture is an old economic activity in the state. Kalhana, the great Kashmiri historian has mentioned fruit culture in Kashmir in his book "Rajtarangini" during the region of King Nara as back as 1000 B.C. Kashmir Valley, as per the survey results, primary sector is seen mostly across the valley with a larger concentration in Ganderbal and Kulgam with no such exception.
According to the census 2011, female employment in Primary sector is majorly seen in agriculture in both the districts. In Kulgam district the total percentage of cultivators is 36.48% and agricultural labourers is 33.12%.
Ganderbal district has 28.6% as cultivators and 21.87% as agricultural labourers. It is noteworthy to mention here that women are not free as men to participate in the formal economy due to social limits. This implies that most part women workforce is engaged in lower-skilled and semi-skilled with low wages and are not owners of capital.
Generally, female cultivators are members of a family that owns the land, rather than being the owners (Alam, 2020). Also, the impact of agricultural commercialization on women employment in primary sector cannot be ignored. District Ganderbal and Kulgam are best known for their maize and paddy cultivation. Kulgam which was famous for the rice productivity once used to be called the rice bowl of Kashmir is now amongst the lowest producers of rice (Raina, 2002). Paddy land is getting converted into horticultural land as farmers seek to earn more revenue against horticultural products (Reshi et  Anantnag conclusion has been that women had an increase in their work burden along with a fall in their nutritional standard (shifting from yam to cassava) and no gain in their control over cash income (Bukh, 1979;Mbilinyi, 1972;Palmer, 1977). In the Indian context, there are virtually no studies on the employment effect of this shift to cash crops. In such a situation, the introduction of certain cash crops may have a very positive impact on employment. For example, the introduction of cigarette tobacco in the early twenties had a dramatic impact on female labour as tobacco cultivation was largely dependent on female labour for transplanting, weeding, harvesting and curing (Mies, 1984).

Secondary Sector
As Gabba and blanket making are well-known Kashmiri heritage popularized since long. These support the findings from the survey especially given that these see higher traction amongst the females, also giving employment opportunities to the Kashmir female workforce.

Tertiary sector
Srinagar District dominates the sector followed by Budgam, Anantnag, Baramulla and Bandipora. Being the Summer capital of the State, Srinagar city is composed of higher service sectors like tourism, trade and commerce and employment in the government sector. Anantnag being in the midst of the trade route connecting the valley with rest of the states, therefore, shows a positive variation.

Quaternary and Quinary Sector
Due to the limited availability of the data in these two sectors the abundance was found in three districts namely Srinagar, Anantnag and Pulwama. Srinagar city, the only Metropolis of the J&K state, constitutes around twothird of the state's urban population and is two times larger than the second largest city of the state-Jammu.
Srinagar, a primate and characteristically diversified as well as unique city, unanimously serves as a regional centre in the vast catchment and is not only the largest urban centre both in terms of demographic size and areal spacing but also rapidly growing city amongst all Himalayan urban centres (Yousuf et al., 2017). It is clearly seen that the high income in urban areas and low income in rural areas in addition to the prevalence of specialized institutions herein acted as the pull and push factors for the economic development of the city thereby providing job opportunities in both the private and government sectors. Similarly, for Pulwama the positives from the developed industrial belt seem to have aided growth in higher sectors with focus on education and skill development.

Housewives
The distribution of housewives, as expected, was significant across the study area.

Problem Statement
A handicap observed in concluding the current study is the lack of existing statistical data to compare the findings across the identified occupational sectors. Furthermore, given the limitation of reliance on primary data, factors like inter-district migration and commuting for work on a daily basis, could not be ignored and may reflect in the findings above.

Conclusion
Women share in formal and informal labor market has gained much importance at national and international level.
It has become the main agenda of national and international organizations for last three decades. However, collecting data for women who are contributing to the household income through their economic activities is a Herculean task (Awan et al., 2015). Female contribution to the informal sector is often neglected. Singh et al., (2012) stated that the role of women in agriculture as female labour is not highlighted in India. Despite of their presence in agricultural activities regarding sowing, transplanting and post-harvest operations they are considered as an invisible worker. In truth, women are involved in all aspects of agriculture, from crop selection to land preparation, to seed selection, planting, weeding, pest control, harvesting, crop storage, handling, marketing, and processing (Ghosh et al., 2014). Same is the scenario with the other sectors as well. Whatever the reason for this neglect, the importance of occupational categorization of female workforce based on the services they render is of utmost importance. The SD method adopted in this paper is a way forward in this approach and has led to a satisfactory methodical representation of the occupational distribution observed in the sample area. The occupational coding of the target districts using this method not only makes it easier to visualize the occupational trends but also gives us a clear sense of variation (positive and negative) amongst these categories across the districts. Focus on occupational categorization can be helpful for the study of other related aspects like female occupational health and environment as well. Cheng et al., (2000) while studying the association between psychosocial work characteristics and health functioning in American women stated that the women in jobs with high demands, low control, and low social support ("iso-strain" jobs) showed the greatest declines in health status and functioning. Towards this end, the systematic approach adopted for occupational categorization and the results therefrom will not only help in understanding the needs better & aid in empowering women as well as to attain related sustainable development goals.