The root cause of environmental degradation in India is attributed to rapid growth of population. India has about 18% of worldís population but only 2% of the geographical area. The availability of medical and social care has lowered the death rate, while the birth rate remained constant resulting a three fold increase in expectation of life at birth (60.3 years during 1991-95) as compared to the first decade of twentieth century. Though the population of Indian Himalayan Region (IHR) contributes only 3.6% of countryís total population, it is spread across 16.16% of nationís total geographical area, thus average population density in the region is very low. But the decadal growth rate of IHR is alarmingly high (29.05%) as it is much higher than the nationís growth rate (23.85%) during 1981-91. So in this section, the district lavel data of decadal growth rate and the huge idle manpower (non-working population) for IHR were analysed using sampling theory. In conventional sample survey analysis of data only means and proportions and sampling errors are computed and the findings described, rather than analysed. But testing of hypothesis needs to be done and the theoretical formulations are to be accepted or rejected. In such studies one can not control and manipulate X, the independent variable (female literacy of rural people and literacy rate respectively as discussed in first two correlations), so the experimental design is of limited utility. As in the above two cases more than 70% of the sampled districts fall within upper and lower confidence limits (assumed as +1SE, and -1SE, respectively, of the regression line), thus theoretically the hypothesis is accepted.
An attempt is made to findout whether any correlation exists between female literacy of rural people and decadal population growth (1981-91). The result indicate a marginally negative relationship, where more than 75% of the sample districts exhibit lower decadal growth rate in districts with comparatively higher female literacy. Apart from the natural increase of population which depends on several factors viz., birth rate, death rate, fertility, and mortality rate, the region is highly exposed to several international boundaries. So immigration/migration is more important factor, which influence the higher growth rate. In the South Asian regional context the immigration into India is much more than the migration due to socio-economic and political factors. All the highly dispersed districts whose population growth rate is much higher than the expected, situated in north-east India and are known centres of population immigration. For example, districts like Kohima, Phek, Wokha, Mokokchung, Tuensang, and Mon (all in Nagaland) have shown a very high decadal growth rate although the statehood (Nagaland) birth rate is lowest (average 19.25 per 1000) in comparison to others states in the region during the period of 1988-93. The remaining 3 high growth districts are Changlang, Lohit (Arunachal Pradesh), and Chhimtuipui (Mizoram) are also exposed to international boundaries. The deviation of moderate literate districts of Uttar Pradesh hills (Pauri, Tehri, Almora, and Pithoragarh) have shown a very low growth rate as compared to IHR. Apart from fertility changes due to family planning programme, a large number of males of these districts migrate to north Indian cities for their livelihood and this might have contributed to the low growth rate. The decadal growth rate of less populous districts such as East Kameng (Arunachal Pradesh) and Lahul-Spiti (Himachal Pradesh) may be due to the harsh climatic conditions, specifically the Lahul-Spiti district which is showing a negative trend of population growth rate.
The second analysis emphasises on the workforce (includes main and marginal workers) of IHR, which comprises about 40.83% of total population of the region. Though the workforce participation rates are satisfactory as compared to the nationís total workforce, but a large part of manpower (more than 59%) remains idle. The large proportion of main workers are engaged primarily in agricultural activities which signifies that static position of the economy so far as, intersector occupational mobility is concerned. Change in occupational structure is closely related with the pace of modernisation in the production system, the quality and availability of productive assets, the level of literacy, the know-how among the people, the availability of technical institutions and necessary infrastructure, the rate of population growth and migration/immigration, the influence of existing social institutions and local culture of the people. Among all these factors the rate of population growth has only the direct impact on non-working population as a whole of the region, and hence there is no need to discuss about it. The overall literacy rate, as a quantitative measure also plays a significant role on occupational structure and dependency rate which is calculated by taking the proportion of non-workers to total workers. The picture of IHR clearly shows that above 73% of the sample districts have higher percentage of non-worker population in districts with high literacy. The reason behind the higher workforce participation rates are economic, geographical, and demographic. The vast hilly areas, the very rurality of the people and low income compel the inhabitants to depend on work participation of all family members and not on individual work. So, the lack of literacy and backwardness, almost all adult members and most of the young ones of a family work in the field, or in the case of village artisans, all adult members participate in the family craft and hence the dependency rate of these districts are comparatively low. In contrast to this the densely populated districts like North, South, West Tripura (Tripura), Darjeeling (West Bengal hills), Dehradun (Uttar Pradesh hills), Imphal (Manipur), and Kangra, Una (Himachal Pradesh) have a very high percentage of non-working population which is normal due to different occupational structure resulting individual work. The only exception is West Siang (Arunachal Pradesh), which is low density area with higher number of non-working population, having a very little impact of literacy rate. The impact of literacy rate on working population and dependency rate is also less on very low density areas spreading over few districts of Arunachal Pradesh, Manipur, Mizoram, and Himachal Pradesh. The percentage of non-working population and dependency rate of Tripura is alarming, implies a high intensity of unemployment problem and comparatively a lower rate of per capita income.
Though these analyses provide indications of positive correlation ship between female literacy in rural area and decadal population growth rate and total literacy to non-working population, a long-term data-set analyses are needed to support these inferences. To sum up these analyses indicate the potential of data-sets and shows how they could be used.