People Have Historically Moved From Rural Areas to Urban Areas Because of What Major Pull Factor?
PLoS Ane. 2019; fourteen(4): e0214511.
Is the push-pull paradigm useful to explain rural-urban migration? A instance study in Uttarakhand, India
Ellen Thou. Hoffmann
1 Organic Plant Product and Agroecosystems Research in the Tropics and Subtropics, Organic Agricultural Sciences, Universität Kassel, Witzenhausen, Deutschland
Verena Konerding
one Organic Establish Production and Agroecosystems Research in the Torrid zone and Subtropics, Organic Agronomical Sciences, Universität Kassel, Witzenhausen, Germany
Sunil Nautiyal
2 Heart for Ecological Economic science and Natural Resources, Establish for Social and Economical Modify (ISEC), Nagarabhavi, Bangalore, India
Andreas Buerkert
1 Organic Found Production and Agroecosystems Inquiry in the Tropics and Subtropics, Organic Agricultural Sciences, Universität Kassel, Witzenhausen, Germany
W. David Walter, Editor
Received 2018 Sep xiii; Accustomed 2019 Mar fifteen.
Abstract
The present study explored the motivation of rural-urban migrants who moved from the Himalaya foothills of Uttarakhand to its capital city, Dehradun. A survey of 100 migrant families reported their socio-economic profile earlier and later migration, personal and general reasons for migration, problems in the village and in the metropolis, and perception of button- and pull factors. A remote sensing-based analysis of land cover and wood changes was conducted for two villages of the migrants' origin, aiming to link the reasons for migration to land encompass changes. This was contextualised past reported large scale changes in forest embrace. Major reasons for migration mentioned in this study were teaching, employment opportunities with the associated income, and facilities. These were perceived as both, push button and pull factors, whereas environmental factors ranked very low. Declining environment or agriculture were never mentioned spontaneously as personal reason, and simply occasionally as a presumed general reason for migration, but were frequently confirmed as a major trouble in the hamlet. Thus, although such bug existed, they seemed not a major driver of rural-urban migration. For most of the respondents their migration resulted in a profound alter of livelihoods and significantly improved their socio-economical state of affairs. Land and forest cover effectually the called villages fluctuated past up to xv% with a trend to increasing woods embrace in recent years. At the district and state scales, forest comprehend was rather stable. These results question the narrative of deforestation and environmental deposition in the Himalayas as major push-factors for rural-urban migration in Uttarakhand. Even if environmental constraints were felt, it was rather the differences in socio-economic opportunities (education, employment, facilities) that drove people to migrate to the metropolis. Regarding the push-pull paradigm, we conclude that scenarios of external conditions under which people migrate cannot be evaluated without taking the migrants' attitudes and choices into account.
Introduction
Urbanization is a global miracle with major implications for people and the surroundings [1, ii]. Merely xx% of the countries with an average annual per capita income of US $ 1,000 to 2,000 were significantly urbanized in 1960; in 2016, this number had risen to over 50%. It is estimated that by 2030, the gross number of the urban population in developing countries will have doubled compared to 2005, while the extent of built-upwardly urban areas may fifty-fifty triple [3, 4]. Besides its demographic bear upon, large-scale, rural-urban migration affects both the patterns of urban growth at the destination and land cover and land use in the region of the migrants' origin. With respect to agriculture, urban sprawl has been shown to have prime agricultural lands out of production [five], while in abandoned rural regions agricultural land is left uncultivated [6, 7]. To date, few studies [2, 8, 9] have addressed the trade-offs between losses in agricultural output, changes in the livelihoods of (former) farmers, and gains in regulatory functions of natural habitats in response to rural-urban migration in an integrated approach.
The Central Himalayan foothills in Northern India (Fig 1) are a region characterized by marginal agronomical productivity, widespread rural poverty [10, 11, 12, 13] and high vulnerability to natural disasters [7, 12, 14]. The region besides has experienced meaning rural out-migration in recent decades [thirteen, fifteen]. Despite its low productivity [7, 10, 13], agriculture is still the prime source of livelihood for 65% of the population [14, 16] (fourscore% in 2007 according to [12]). Over the last decades local incidents of landslides, flash floods, earthquakes, and forest fires were recorded in Uttarakhand almost every year, and some major disasters struck the state in the late 1990s and between 2010–2013 [14]. Deforestation, human interventions for development projects, and climate change were held responsible for this in the scientific and public discourse [vii, xiv, 17]. Urbanisation in Uttrarakhand, with only six cities above 100,000 inhabitants, was reported at xxx.55% in 2011 [16, xviii]. In Republic of india, rural-urban migration accounts for ca. xx% of the gross internal migration flows [nineteen, 20, 21], and between 1961 and 2011, rural-urban migration contributed 19 to 22% to urban growth [21]. In Uttarakhand, as in the majority of other Indian states, 80% of the migration flows were intra-state [20]. Dehradun has grown by xx% from 2000 to 2010, both in terms of population and built-up housing surface area [22]. It can thus exist assumed that the city has attracted numerous migrants from the surrounding rural districts.
Location map of the study area.
Uttarakhand in Northern Republic of india; triangles mark the migrants' villages of origin. (Sources: Esri, DeLorme, USGS, NPS; Esri, USGS, NOAA).
In the present study, Uttarakhand was chosen as an example of an Indian state where substantial rural-urban migration from the hilly, remote rural areas to the regional upper-case letter Dehradun has been documented [thirteen, fifteen], and where rural poverty and environmental deposition were often discussed as major driving forces [7, xiv, 15]. The motivation for rural-urban migrants has often been analysed nether the (rural) push versus (urban) pull theory [23–27]. Rural push factors include poverty, inequitable country distribution, environmental degradation, high vulnerability to natural disasters, and violent conflicts while urban pull factors include improve employment and education opportunities, higher income, diverse services, and less social bigotry in the cities [28–31]. If environmental degradation represents "the main (if not the simply)" reason for migration, migrants were considered equally "environmental refugees", thus exemplifying a example of straightforward button dynamics [32, 33].
Post-obit these lines of thought the principal hypothesis of the present written report was that 'People of rural Himalayan Uttarakhand are pushed to migrate to the city due to the deterioration of their environmental resource base.'
The respective guiding questions were:
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Is rural-urban migration in Uttarakhand motivated past push- or pull-factors?
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Are environmental factors a major commuter of the migration?
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Does migration have a positive or negative outcome on the life of the migrants in the destination urban center?
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Does migration have a positive or negative consequence on country cover and environment in the rural areas of origin?
The study combined a survey of 100 migrant households in Dehradun with a GIS-based land use analysis to verify environmental changes in some villages of origin, and an assessment of changes in forest cover at larger scales, based on Indian government statistics.
Material and methods
The survey
As outlined in the introduction, Uttarakhand, located in the Himalayan foothills betwixt 30° 15' N and 79° 15' E at elevations between 1500–7816 meters (Fig ane), represents a region with many apparently typical button-factors for migration, while Dehradun, the country's capital and biggest metropolis (575,000 inhabitants [xvi]) has many features of a typical urban-pull attractor.
The survey sample comprised 100 migrant households with a total of 437 members visited in 24 neighborhoods in the city of Dehradun. By semi-structured interviews, information was collected about the village of origin, household characteristics, the migrants' socio-economic status, their motivation for migration, and their perceived and actual issue (self-assessed status). The questionnaire was designed to dissociate the personal reasons for migrating from common narratives, and to question and re-evaluate the perception of push button and pull factors of migration. Information technology asked open questions about the personal reasons for migrating on the one mitt, and the respondent's opinion virtually the full general reasons for rural-urban migration on the other hand. It then inquired almost households' problems before and after migration, offer a list of potential answers with a yes/no choice, while allowing further explanations. The list was the aforementioned for village- and urban center-related problems. Concluding, respondents were asked nigh their personal sense of being pushed from the hamlet or pulled to the city, and about which push or pull factors were considered the most relevant. This immune a differentiated interpretation of household's reasons to move.
Prior to application, the survey was broadly discussed with two independent sociologists in Republic of india who deemed that information technology met the ethical and cultural requirements applying to social enquiry in the study area in Northern Bharat. The type of questions asked in the household survey did not collect information that would identify man subjects or private information, and was fully compliant with the national and local norms, values and traditions. Subsequently, the questionnaire was tested with a few households and item intendance was taken to examine whether any of the questions had the potential to upset the interviewees, which was determined not to exist the instance. The respondents were assured that participation in the survey was bearding, voluntary, non related to any government actions, and no benefits were associated to their conclusion to participate or not. Verbal consent was taken in privacy within the family, to avoid public pressure or pressure of outsiders. The interview proceeded but if oral consent was given.
The survey took place in September and Oct 2016. It was conducted with the support of a local guide and interpreter who himself was a member of a rural-urban migrant family unit. Therefore, he had a deep cultural understanding and widespread network of contacts for potential interview partners. Equally an interpreter, he was proficient in English, Hindi, and Garhwali, the almost common dialects spoken in the loma area. Initially, 9 permanent migrant families were contacted to adapt interviews with the household caput. The subsequent interview partners were recruited through a snowball system, which likely resulted in a skewed sample. In addition to the household visits, temple festivities, sports events and religious events such equally Diwali (the Hindu celebration of lights to welcome Lord Ram) were attended to recruit further interview participants, and to gather insights in informal conversations. This deepened the cultural agreement, and thus also supported the interpretation of the survey results.
Statistical analysis
The answers to the survey questions were digitalized into an Excel spreadsheet. The relations between two variables were analysed by a Chitwo exam if the data were nominally scaled in both cases; if one variable was nominal and the other metrical, the Spearman correlation was calculated. To examine whether residuals of data were normally distributed, the Kolmogorov-Smirnov test was applied. The level of significance was set to p = 0.05 for all tests. When ordinal and metric variables were both nowadays, a Spearman correlation test was carried out. Descriptive statistics were used to summarize the results.
GIS-based land utilise analysis
The second part of the study comprised a Geographic Data System (GIS) analysis of land encompass changes over the by two decades. The villages Kandai and Barkot, located in Pauri district, were called for a GIS-based time-series analysis because in these two villages respondents mentioned environmental change as a presumed general reason for migration. The selected villages were set as center points of a 10 km x x km square, and the 100 km2 effectually them were analysed, based on a series of satellite images taken by Landsat 7 and eight between 1980 and 2017 (provided by United States Geological Survey, Tabular array 1). State encompass was classified using a supervised maximum likelihood nomenclature, carried out with ArcMap. The initial classification distinguished 8 unlike country employ types: loftier-, medium- and depression-density forest, pastures (with scattered copse or shrubs), fallow fields, standing crops, blank footing, and water bodies. An accuracy assessment was carried out for one exemplary yr, 2011, following the procedure of Parece et al. [34]. The time-wise closest Google Earth image of 2012 was used for comparing. One hundred random points were spread over the selected area on both images and the classification results were verified with an accuracy of 70%. For mapping temporal and spatial changes in land cover maps, the 8 initial classes were finally summarized to three major classes: 'other' (no cover / water), 'forest' (high / medium / low density) and 'agriculture' (silvo-pastoral and agricultural fields).
Table i
Acquisition dates of satellite images.
| Satellite / sensor | Kandai | Barkot |
|---|---|---|
| Landsat 3 | 19.03.1980* | |
| Landsat v | 16.03.1992 | |
| Landsat v | 11.03.1996 | |
| Landsat 5 | xx.03.1999 | |
| Landsat 7 ETM+ | 01.03.2001 | 01.03.2001 |
| Landsat 7 ETM+ | 10.05.2003 | x.03.2003 |
| Landsat 5 TM | xv.03.2009 | 15.03.2009 |
| Landsat 5 TM | 05.03.2011 | |
| Landsat 8 | 13.05.2013 | |
| Landsat 8 | 03.05.2015 | 17.04.2015 |
| Landsat 8 | 02.03.2016 | |
| Landsat viii | 05.03.2017 | 05.03.2017 |
Assay of FSI woods reports
Changes in wood embrace detected at the hamlet scale were compared to the development reported by the Forest Survey of Bharat (FSI) at commune (Pauri, ca. 5,300 km2), land (Uttarakhand, ca. 53,500 km2), and national scale (India, ca. 3,290,000 kmii). Data were extracted from the biennial "State of Forest Reports" [35] bachelor on the website of the Ministry building of Environment, Forest and Climate Modify, Dehradun from 2001 onwards.
Results
Survey
Socio-economic profile of rural-urban migrants in Dehradun
Among the 100 migrant households interviewed in this report, rural-urban migration took identify over an extended menses of time, with the primeval tape in 1955 and the latest in 2016. The migration flow was rather continuous and not marked by discrete peaks or discontinuities. The districts of origin were Tehri, Pauri, Uttarakhashi, Rudraprayag, Pithoragarh, Chamoli, Almora, and Dehradun. The total number of domicile villages covered in the survey was 69. They were unevenly distributed between the districts, with 33 villages in Tehri followed by Pauri with 15 villages of origin, and less than 10 in the remaining districts. The number of respondents from the aforementioned village was highest for Seeriyan with 13, and Goran with viii. Nine more villages were represented past two to 3 respondents, the remainder past only a single household.
Household sizes ranged from one to eleven members, with an average of four.half-dozen household members. The gender balance of the household heads in this survey was 63 male and 37 female; 87 were married, 6 unmarried and 7 widowed. Of the 100 respondents 79 had migrated together with at least one other family fellow member.
The organization used for assessing the pedagogy within the migrant families distinguished five levels: 0—None; i—primary schoolhouse finished (5 years); ii—center school finished (viii years); 3—secondary/loftier school/matriculation finished (10 years); 4—scholar or university education finished (more than than 10 years). The overall educational background in the migrant households was high: 75% had reached level iv, 10% finished secondary/loftier schoolhouse, iv% center school, 7% principal school and another 4% did not receive any education at all. Five of the interviewees were illiterate and 4 of those were female. The average education level of the respondents' families, including the household head and excluding children, was iii.5.
In our survey, 97% of the migrants still owned country in their home villages, meaning that either themselves or their families did (Fig ii). The average land holding of all respondents was merely beneath ane ha (9662 chiliadtwo); 21% had larger backdrop. Nigh of the respondents had been actively involved in agriculture earlier moving to the city; 85% had worked on their own fields, another 3% on their parents' land.
Size of state holdings of migrant families in their village of origin in Uttarkhand, Northern India.
Near respondents (76%) owned belongings smaller than 1 ha.
Occupation earlier and after migration
The occupation pattern shows that rural-urban migration not only describes the geographical move of a physical residence, but also a profound shift in lifestyle (Fig 3). Agriculture is a typical activity in the primary sector of the economy, and prior to their migration to Dehradun it was mentioned as the only occupation by 64 respondents, and in combination with other occupations by some other 10. Silk production was the only reference to the secondary sector, and it was likewise mentioned in combination with agriculture. Occupations in the tertiary sector comprised government jobs (8), army (4), shops (2) and health care (ane), whereas teachers (2) and students (xiii) were accounted for in the quarternary knowledge sector. 4 women described themselves as house wives, which is not a formal economical sector.
Occupational sector of the respondents before and subsequently migration to Dehradun, Northern India.
Subsequently migration only one respondent was notwithstanding occupied in agriculture, whereas the majority worked in the tertiary sector. Hither, authorities jobs still predominated (15); the remaining activities, however, were highly diversified (near 20 different job denominations). The quarternary sector comprised teachers (5) and Hindu Priests (3), merely, due to the fourth dimension passed since migration and status as household heads, there were no students. The answers non allocated to a sector comprised house wives (23), retired household heads (15), and i unemployed person.
Household income before and afterwards migration
The average household cash income in the village earlier migrating was ten,794 INR, the 5% trimmed hateful was eight,974 INR. When considering the number of household members, the average income per person and day in the villages was 43 INR, the five% trimmed hateful was 36.iv INR. The national poverty line of rural areas in Uttarakhand was 29.3 INR per mean solar day in 2011/12 [36], and the share of people living below the national poverty line of Uttarakhand in rural areas was 11.6%. In this survey, 18% of the people had lived below the poverty line of 2011/12 before migration.
Afterwards migration the average mean income was 44,475 INR, and the 5% trimmed mean was 39,050 INR per household. This is about four times as high as it was before migration and thus statistically highly significant (P = 0.001). This means that merely 3% of the respondents now live in poverty, co-ordinate to the urban poverty line of 36 INR per day per person for Uttarakhand in 2011/12. Thus, for poverty reduction, an improvement of 83.3% was achieved after migration. The boilerplate income per person per mean solar day rose to 398 INR, which represents a statistically significant departure of more than 10 times the income of that before migration (P<0.001). The 5% trimmed hateful was 322 INR. The poverty line of 2011/12 serves here every bit an arbitrary reference for comparison although the respondents migrated in dissimilar years. Information technology is obvious though (Fig 4), that the per person income improved from earlier to subsequently migration for all household sizes except for the biggest household of eleven members. The results indicate a clear advantage for smaller households living in the city.
Income per person according to household size in Dehradun, Uttarkand, Northern India.
Personal reasons and perceived general reasons for migration
The questions addressing the reasons for migration were open up and several answers were possible. The main personal reasons mentioned past the respondents were, in order of relevance, education, employment (with its associated income), facilities, development projects (in particular the Tehri Dam) and others (Fig 5).
Personal reasons (a) and perceived general reasons (b) for rural-urban migration in Uttarkhand, Northern India.
The educational institutions in the respondents' villages of origin were usually limited to basic education. Even for elementary school, many villagers had to travel to some other village. The poorly adult infrastructure made it hard for many children to reach school. If a continuation of schooling was the goal, moving closer to the valley was oftentimes the only option. In our study 48 respondents mentioned their children's or grand children'due south education as one of their personal reasons to drift. Women in particular seemed to accept other household member'due south educational future in mind when deciding to migrate.
The lack of employment opportunities in and around the villages was ever mentioned in the context of the income to be achieved. Agronomics was seen as a risky activeness with depression profitability, which was unattractive to educated youth. Altogether, 42 respondents mentioned fiscal aspects related to employment equally one of their reasons to migrate.
When facilities were mentioned, transportation infrastructure and health care such as hospitals and the presence of doctors were considered most of import. In some cases, people moved away from their abode considering of wellness problems that could not be treated in their villages.
The construction of the Tehri Dam was mentioned as a personal reason for migration by sixteen respondents, all of whom came from the drowned villages. Two more respondents referred to other projects such equally road building. The category 'other' as a reason for migration includes life quality (vii occurrences), union, ties to already successfully migrated family members and other singular statements. Information technology is noteworthy that in the open question none of the respondents mentioned ecology conditions every bit a reason to migrate.
When it came to the general reasons for migration in Uttarakhand, compared to the respondent's personal reasons for migrating, the answers were similar (Fig 5). Employment was mentioned most frequently, and then facilities (especially medical and transportation) and instruction. Only one respondent considered the Tehri Dam as a general reason for migration. In this context, still, some environmental factors were mentioned among the other reasons; these were natural disasters (5 occurrences), conflicts with wild fauna (ane), and declining soil fertility (11), summarized in Fig 5 in the column 'agriculture / environment'.
It became apparent that the other respondents were likewise aware of environmental factors also when they were prompted with a list of potential issues perceived in the village. The reply 'issues with agriculture' was confirmed by 52 respondents, 54 also confirmed landscape changes. In their farther explanations the respondents mentioned landslides (27 times), drought (24), low productivity or depression soil fertility (25), and conflicts with animals/wildlife (33) which either destroyed the crops (such as gratis-ranging cattle, wild pigs, or monkeys) or scared the farmers and kept them from attending to their fields (such as bears or tigers). The frequency of affirming the answers related to environmental factors was thus comparable to the confirmation of answers related to the main reasons for migration given in the open question, i.eastward. lack of educational opportunities (66), and financial problems (44) equally major village issues. The top village trouble was facilities, confirmed past 80 respondents. The predominant problem in the city was financial problems with 21 confirmations, whereas facilities and education issues were reduced to negligible levels, with 9 and 4 nominations, respectively.
Factors influencing reasons for migration
The factors presented in the socio-economic profile of the migrants were examined for their influence on the motivation for migration (Tabular array two). Due to the relatively pocket-size number of answers, the correlations' significance was tested separately for half dozen socio-economic variables against seven stated reasons for migration.
Tabular array 2
Statistical analysis of dependencies between household characteristics and stated personal reasons for migration in Uttarkhand, Northern India.
| Pedagogy | Employment and income | Facilities | Development projection (Tehri dam) | Life quality | Other | |
|---|---|---|---|---|---|---|
| Commune of origin | P = 0.285 | P = 0.025 | P = 0,59 ( ii ) | P = 0.007 ( iv ) | P = 0.346 ( iv ) | P = 0.352 ( four ) |
| Household size( 1 ) | P = 0.199 | P = 0.695 | P = 0.769 | P = 0.154 | P = 0.419 | P = 0.334 |
| Gender HHH | P = 0.8 | P = 0.518 | P = 0.67 | P = 0.152 | P = 0.197 ( 4 ) | P = 0.628 ( 3 ) |
| Educational level( 1 ) HHH | P = 0.393 | P = 0.241 | P = 0.526 | P = 0.507 | P = 0.784 | P = 0.747 |
| Land holding ( 1 ) | P = 0.608 | P = 0.068 | P = 0.475 | P = 0.156 | P = 0.017 cc = 0.240 | P = 0.139 |
| Involved in agronomics | P = 0.859 ( 4 ) | P = 0.788 ( 4 ) | P = 0.945 ( iii ) | P = 0.673 ( three ) | P = 0.525 ( 3 ) | P = 0.099 ( 3 ) |
The variables household size, gender of the household head, educational level, and fourth dimension of migration (non shown) were not significantly related to whatever of the stated reasons for migration.
The amount of land owned in the village, however, was significantly correlated to the frequency of mentioning life quality (a component of 'other' in Fig 5A) as a reason for migration. The bigger the holding, the more than 'life quality' was mentioned equally a reason for migration (P = 0.017). Mentioning employment as a reason was also more likely when more land was owned (P = 0.068), and it reached significance at an interval of 0.1. This suggested that, the more the respondents relied on agronomics in the dwelling house village, and the better their assets, the less they were satisfied with the life quality and employment/income generated from it. Due to the small sample size, however, the conditions for a Chiii test were unfulfilled, and statistically this assumption could non be further supported.
For analysing the correlation between the respondent's district of origin and the stated personal reasons for migration by a Chiii test, the districts Chamoli, Almora, Rudraprayag, Pithoragarh, Dehradun and Uttarkashi, were combined into one category chosen 'less represented districts', while Tehri and Pauri represented one category each. Significant correlations were shown with the reasons employment (P = 0.025) and evolution project/Tehri Dam (P = 0.001). People coming from Tehri were more likely to mention development projects (P = 0.007) as a reason, whereas they were less likely to mention employment (P = 0.025). Respondents originating from Pauri, in comparison, were more likely to land employment as a reason (P = 0.025).
Finally, it was tested if the district of origin besides influenced the reasons that were perceived equally general reasons for rural-urban migration. However, the Chitwo test did not yield significant show (P = 0.231 to 0.695; data not shown). Other dependencies could not be tested because the conditions for the Chi2 test were not fulfilled.
Push button versus pull forces
After narrowing downwardly the major drivers of migration, our respondents were asked if in their stance it was a push or pull force that motivated the rural-urban migration (Fig 6).
Push or pull factors driving rural-urban migration in Uttarakhand, Northern India.
The predominant feeling of being pushed out instead of pulled away indicates a rather negative association with the migration process and would support the common views of button-dominated migration. On the other hand, 85% of the respondents reported a positive alter of their financial situation later on migration, 10% did not see any difference and simply 5% noticed worsening of their financial status (Fig 4). All five people perceiving a negative change of their financial situation believed that information technology was push factors that adamant their migration decision. Out of the ten people feeling no difference in the financial aspect, 60% considered push button factors more important, thirty% pull factors, and the remaining person saw both factors equally equally important. The district of origin did not significantly bear on the opinion of whether it was push or pull factors that drove people to migrate from rural to urban locations within Uttarakhand (P = 0.843).
To further differentiate this result, the respondents were asked what the possible major push and the major pull factors driving people to migrate might be (Fig 7). Facilities, education and employment/income were similarly important every bit push and pull factors. Environmental weather condition were low-ranking, and were mentioned primarily related to specific events, such every bit natural disasters or conflicts with wild animals, or indirectly equally low agricultural productivity, which is well in line with the stated reasons for migration presented above.
Major push factors (a) and major pull factors (b) for rural-urban migration in Uttarakhand, Northern Bharat.
Analysis of country cover changes
Agricultural area and woods cover at the village calibration
None of the migrants stated an ecological reason as a personal reason for migrating, only they remembered environmental aspects when general reasons for migration or problems in the village were discussed. The second office of this study therefore focused on estimating past state comprehend assay the degree of ecology modify that really took place and may take contributed to a rural push-driven migration. The virtually of import aspect in land encompass modify continued to both landslides and agriculture was woods encompass. Two villages were selected for this analysis: Kandai had a population of 86 people living in 22 households [xvi] and Barkot was inhabited by 174 people belonging to 38 families. Their agricultural fields were scattered around the village inside an area of x km x 10 km in a largely forested area. The fluctuations in land cover were traced from 1980 to 2017 (Fig 8).
Agriculture and forest matrix of Barkot and Kandai villages, Northern Republic of india, over time.
Since other land encompass types represent but around i% of the analysed area, the fluctuations in agronomical area but mirror those of total forest comprehend. The overall fluctuation was around xv%, with a minimum in forest surface area of 45% and a maximum of 61% in Barkot, and betwixt 55% and 69%, respectively, in Kandai. The latter witnessed a steady loss of forest by 13.four% betwixt 2001 and 2015, with this trend reversing only in the last two years past an increase of iv.5%. In Barkot, full forest expanse increased by 11% until 2009, but the entire proceeds was lost by 2015, and so recovered again to a value comparable to 2003. Since 2009, the two villages show similar trends. Fifty-fifty though the forest cover has not notwithstanding reached the level of previous peaks, it has increased since 2015 in both village surroundings. The land cover maps (Fig nine) bear witness that from 2003 to 2015 agricultural areas in Kandai expanded mostly in the western part of the valley, and forest recovery between 2015 and 2017 was also nigh obvious in that area, besides as in an surface area northward-due east of the village. For Barkot forest comprehend changes were most evident in the river valley in the heart of the image, shut to the hamlet.
State cover maps of the North Indian villages Kandai (a-c) and Barkot (d-f) in the years 2003, 2015, and 2017.
Yellow–agriculture, green–forest, blueish–water, gray–other; ruddy dot–village center.
When distinguishing high-, medium- and low-density woods, changes in forest quality can exist estimated (Fig 10). In the areas around the villages, low-density forest tended to reject more than medium- or loftier-density forest. In Kandai the recent recovery was mostly due to an increment in high-density forest whereas in Barkot the increment in full forest was generally due to depression-density woods. The increase in high-density wood cover may indicate a more enduring and effective forest recovery.
Development of woods quality in a 100 kmii area around (a) Barkot and (b) Kandai, Uttarakand, Northern India, since 2000.
Forest cover at larger scales
Since 2003, the forest survey of India differentiates between very dense, moderately dumbo, and open up forest [35]. Though the results may not be directly comparable to the classification used in a higher place, unless they refer to full forest as the sum of all defined categories, the FSI data were analysed (Fig 11) to capture trends in forest cover changes that may not be detectable in the small-scale GIS-analysis.
Development of forest cover and quality at different scales since 2000.
(a) Pauri district (v,329 km2), (b) Uttarakhand state (53,483 kmtwo), and (c) all of India (3,287,263 km2).
From 2001 to 2017, the total wood encompass in India, likewise every bit in Uttarakhand, fluctuated marginally past 1%, with an overall increasing trend. In the province of Pauri, a continuous increase of almost v% was recorded. Some environmental deterioration was indicated past the quality of forest cover, and in all surveys the proportion of open woods increased at the expense of moderately dense forest. Very dumbo wood, on the other hand, was stable or increasing, though it had the overall lowest proportion, whereas moderately dense wood was most abundant. Thus, the narrative of severe deforestation existence a significant rural push factor could non be substantiated for the time menses analysed here, neither by the local analyses, nor by the FSI information.
Discussion
The nowadays study combined the methodologies of a socio-economic survey and a spatio-temporal state cover analysis to investigate the design and likely causes of rural-urban migration in Uttarakhand, a presumed example of rural push dynamics in Northern India. Although the sample of 100 households covered in the survey is rather small-scale and, due to the snowball recruitment, certainly not representative, it contributes an original ready of primary information to the scientific discourse. A unique feature of the current study is the approach of asking for personal (own) and general (other migrants') reasons for migration, for problems perceived in the village and in the city, and for the respondents' opinion on what is a push button and what a pull gene. The ambiguities in the replies let drawing some interesting conclusions.
Push-pull factors and theories of migration
The predominant reasons for migration documented in this written report were income/employment, education and facilities, which is well in line with previous studies [xiii, 15, 37]. These factors were mentioned by the respondents as personal and general reasons for migration, equally major problems in the village, and as both, button and pull factors. Though the distinction of push and pull factors was generally understood by the respondents, it seemed to be capricious when linked to the personal migration decision. This finding warrants a closer wait at how the underlying migration theories developed in the scientific discourse.
The early attempts to describe "laws of migration" engagement more than than a century dorsum and suggested mechanistic models based on aggregate population data, inspired by physical gravity laws [38, 39]. The more comprehensive concept of Lee [23] best-selling that migration depends on positive and negative factors at the place of origin and destination, intervening obstacles, and personal factors. He presumed a limited flow of data, equally he considered the positive and negative factors at the place of origin to exist well known to the migrants, whereas their knowledge of the situation at the destination was incomplete. However, he pointed out, that the near overwhelming reasons for migration were usually of economical nature, and that net migration is highest if negative factors predominate at the place of origin, which in the socio-economic context of his time can be read as rural button-urban pull, although he did not formally introduce this terminology.
In the dual economy models [twoscore, 41] positive and negative factors typically reflect the relative strength of the local economies, and rural-urban migration actually contributes to the transition from a (stagnating) economy based on a rural agricultural sector to a (growing) economy based on a diversified urban sector. Here it is primarily a difference in the supply and need of labour, and in particular a high urban demand for labour, that drives migration until some kind of saturation is achieved. All of these push-pull theories of migration exercise not look at individual migration decisions, but instead rely on various aggregated socio-economic information and their uneven spatial distribution giving ascent to (passive) flows of migrants. The mathematical modelling of spatial flows resulting from push-pull was presented by Dorigo and Tobler [42], whereby they explicitly note: "Instead of attempting to include circuitous human beliefs in our equations, we estimate the effects of this behavior, allowing other researchers to gauge the relation of the computed pushes and pulls to life situations" (p. 14).
Subjective migration choices were first taken into account past behavioral models, such every bit the stress-threshold model [43], inspired by economic cost-do good analyses, or behavioral-cognitive approaches [44] because non only the economic aspect only also personal or household characteristics, individual goals, and societal norms. Both models were based on rational choice assumptions, but were afterward criticized for existence vague. Subjective migration choices tin requite rise to larger migrant flows past cumulative and round causation [45] when information on successful migration is transmitted back to the place of origin, and past migration alters the context in which current migration decisions are taken. In this line of statement external factors are often neglected.
The present study has elements of both types of migration theories. In the questionnaire section it explores the private migration decisions, while aiming to verify respective external factors in the land utilize analysis. The answers given by the respondents in the nowadays study testify very clearly the predominance of economical reasons for migration: employment/income and education (motivated also by aspiration for better paid jobs) were most frequently stated. This can exist accounted for as a push factor if it is insufficient in the rural place of origin, and equally a pull factor if it is available in the city. Facilities, the tertiary major reason for migration, seemed to be prioritized as a component of a convenient urban lifestyle. Again, underdeveloped facilities in the rural areas would be as much a push factor as they are a pull factor to the metropolis, where facilities are readily available.
In a field survey in 2013, which included 18 villages and a total of 217 households from Pauri, Garhwal and Almora districts, Mamgain and Reddy [xiii] reported that 88% of the sampled households had at least one member who emigrated for employment. Sati [xv] presented a similar survey of 42 household heads in ii villages within the commune Chamoli in 2014. Both studies combined their results with secondary data from governmental surveys and conclude that rural-urban migration in Uttarakhand is entirely driven by push-factors. Here, however, it is often cryptic from which part of the report these conclusions are drawn. Moreover, the questions were framed "in terms of the push button factors and other constraints that touch out-migration" [15], and so it is not surprising that the respondents (here the families residing in the villages) confirmed the researcher'south assumptions. In our report nosotros observed that in the generic question a bulk (58%) replied that push-forces were more important, but this was strongly related to their own degree of economic success. Those who were meliorate off, tended to consider pull-factors more important. When disaggregating the factors, they were considered every bit important as both, push and pull.
Ecology push factors of migration
The fact that ecology push-factors had such a low priority in the nowadays study was surprising. Apparently, they did not play any role in the personal migration decisions. That the respondents still mentioned them as a presumed general reason for migration may betoken a repercussion of established public narratives. When prompted with answers this became fifty-fifty more than axiomatic, as environmental problems received a high degree of confirmation, although the actual phenomenon of landscape change, and in detail deforestation, was hardly credible in the country cover analyses.
The soapbox on deforestation equally a major rural push force goes back to the early 1990s when numerous accounts of growing environmental bug in developing countries were published, both in the popular printing and in scientific research, such as the State of the Globe Reports 1988 to 1997 (e.1000. 1989 [46], 1991 [47]). In response to this, Bilsborrow [viii] analysed to what extent population growth and rural out-migration may bear upon state-utilise patterns and lead to deterioration of the natural resource base, in particular deforestation. His approach was a cantankerous-country comparison, based on data from the World Resource Institute (WRI [48]), and focused on a 12 year period in the 1980s, a fourth dimension frame set to capture gimmicky links between the called parameters. Due to many confounding issues, however, only weak relationships between population dynamics, agronomical country use and deforestation were shown. Even so, subsequent enquiry continued edifice on his hypotheses, with contradictory results. Whereas Black [32] did not detect sufficient show for the connection between landscape changes and rural-urban migration, de Weerdt and Hirvonen [49] found proof for environmental degradation as a button factor and stated reason for migration in Tanzania. The term "disaster migration" introduced past Krecek et al. [33] exemplifies the causal relationships often hypothesized between landscape changes and migration, which, however, could not be confirmed in the present study.
According to Bilsborrow [viii], within Asia, India and Nepal had the highest rates of deforestation reported to WRI with over 4% loss per twelvemonth during the 1980s. The Woods Survey of Republic of india [35], used for the forest cover analysis in the nowadays report, started its records in 1987; it reported a decrease in woods area all over India of i.xvi% over 10 years until 1997, and after that a recovery of 11.8% until 2017, likely the result of strictly enforced legislation and the introduction of natural gas for cooking to fifty-fifty remote areas. The country of Uttarakhand was but founded in 1997, and since then had a rather stable woods area. The apparent contradictions may thus be due to the different fourth dimension scope analysed. Yet, failing soil fertility, droughts, and on the other hand extreme weather events causing landslides and flash floods are well documented in the written report region [13, xiv] and seriously affect agronomics, worsening poverty and urging people to seek alternative income sources [13, 50].
Tiresome and gradual environmental changes, withal, for example in forest integrity or climate, were perceived just not prioritized by the village farmers in the present survey. If at all, environmental push was reported in relation to rapid changes (development projects) or sudden incidents/events (landslide). Shortcomings in agronomics were frequently framed economically (insufficient income, low productivity), but drought and low soil fertility were likewise ofttimes mentioned every bit problems. A straightforward ecological framing (disturbed ecological balances/cycles, declining biodiversity) was never stated. On the contrary, encounters with wildlife were reported every bit a major problem, considering wild animals destroy crops and threaten human lives. At the same time, it is forbidden to kill them in India's protected areas. This may indicate that modern, 'urban' mindsets are already displacing traditional values even in the remote villages, and people seek urban lifestyles to satisfy their needs and well-being.
Service choices and changes in lifestyle
Co-ordinate to the ecosystem services framework [51], a good for you natural environment can deliver a range of services to satisfy human needs and maintain human well-being. Information technology will provide food, feed, and timber, regulate temperature and water cycles, forbid (to some degree) flooding or landslides, and provide cultural services in terms of aesthetics, recreation, or religious practices. In rural societies people straight access these services in their daily lives and often feel an emotionally tight link to nature. In the urban environment the migrants will increasingly rely on non-ecosystem services for their personal well-being [52]. This is also what the respondents in the present study actually seek, as indicated by the high rank of facilities as a reason for migrating, or by very generic answers such as 'life quality' or 'attractiveness of the city' as reasons for their migration conclusion. These findings are well in line with the conclusions of van der State [53]. Failing ecosystem services due to environmental degradation are much less important to the rural-urban migrants, than the agile aspiration for a modern, urban lifestyle. In contrast to earlier times, migrants are well-informed nigh the conditions in the cities by the global media and telecommunications systems.
The present study's lack of statistically significant interactions between socio-economic characteristics of the households and the personal reasons for migration points in the same management, i.e., decisions motivated past hopes and aspirations rather than certain savage cycles. The interaction between the reason 'Development project' and the provenance of the respondents was due to the large cluster of migrants displaced by the Tehri dam. The interaction between the size of land holding in the village and the reason 'Life quality' relies on a fairly small number of answers. Nevertheless it may indicate that the meliorate off the respondents in the village already were, the more they strived for nevertheless further improving their socio-economic situation towards modern urbanity.
Migration not only has a spatial ("horizontal") dimension of moving from ane place to another, but likewise entails a socio-economic ("vertical") dimension [54] in terms of a change in lifestyle. In the current survey, this was evident in the occupation contour before and after migration, which changed from an agriculture-dominated one to a highly diversified profile with a majority of people working in the tertiary service sector. This is indicative of a green loop and a scarlet loop society, respectively [52], and thus exemplifies a rural-urban transition in the individual biographies.
Outcome of migration for the migrants
Migration was successful for about of the surveyed households which is consequent with virtually other studies [xiii, 21, 37]. A recent survey of urban in-migrants in Dehradun and Haridwar [55], which reported that they were poorly educated, often unemployed and prone to urban poverty, is not comparable to our report, as it focused on slum-dwellers and comprised mostly inter-state, seasonal migrants.
Whereas, in the nowadays study, employment and education were mentioned every bit major problems in the village, their share in 'problems in the city' was negligible. For almost respondents the private and household income had increased dramatically subsequently migration. Only one of the respondents was unemployed; on the other hand, afterwards migration 38 persons stated that they were either retired or house wives, whereas this answer was rarely given for the situation earlier migration, which is most likely because all family members notwithstanding contributed to agriculture, even when getting married or of age. The overall convenience of life in the metropolis had been described higher up. Nevertheless, despite these positive outcomes, financial issues were nevertheless the major problem in the metropolis, and when asked about their emotional ties to the village of origin, many of the respondents said they would similar to migrate back to their rural area of origin if they could. Amongst the group originating from the villages afflicted by the Tehri dam projection, this answer was even more frequent.
Tehri constitutes a special case, equally a big hydropower project was implemented in this province, with the construction of the Tehri dam between 1994 and 2006. Information technology resulted in an artificial lake of 52 km2 surface area which in 2004 drowned or seriously affected several villages (Seeriyan, Malideval Conversation Saur, Ghansali and Tipri). To compensate the inhabitants, the model boondocks of New Tehri was established past the government, along with an associated employment scheme at the new location. In this case people were thus forced to migrate, and were only costless in choosing Dehradun (rather than New Tehri) as their destination.
Upshot of migration for the rural surroundings
The limited sample of 100 migrants surveyed in this study does not allow far-fetched conclusions regarding the effects of migration on the environment in the surface area of origin. It was hard to even cull exemplary villages for the land cover analysis, since neither a temporal meridian of migration was noted, nor a spatial clustering apart from the villages affected by the Tehri dam. The justification for choosing Kandai and Barkot for the land comprehend analysis was thus rather weak, but the composition of the sample was not known a priori, due to the snowball recruitment. Many villages were represented past only a single household interviewed in Dehradun, and nothing is known about the overall out-migration from these villages. Even so, in the two exemplary villages Kandai and Barkot, the fluctuations in land cover pointed towards a period of expanding agronomical areas at the expense of wood until 2015, followed by reversal of this trend to engagement. With due precautions, this may exist seen every bit a small merely positive impact of out-migration on the environment, in terms of wood recovery.
Conclusions
The hypothesis of the present written report, that 'People of rural Himalayan Uttarakhand are pushed to migrate to the metropolis due to ecology change' was non supported by the data presented above; neither were environmental reasons relevant for the migration decisions, nor could extensive deforestation be shown in the country cover analyses at dissimilar scales. Besides, the button-pull image proved inadequate to explain the migration decisions of the respondents interviewed, equally the perception of a factor every bit push or pull depends on the perspective. There is clearly a large gap between rural and urban locations in certain parameters such as education opportunities, employment, income, and facilities, which may be better conceptualised as a gradient. These parameters could even be described by quantifiable indices. The driving force for migration then results from the width of this gap, or steepness of the gradient between the rural and urban settings. People decide to migrate if a threshold is passed in a priority factor, or in combinations of several of them. The thresholds, nevertheless, and really even the management of migration, are non determined externally, but depend on the individual perception of well-existence, and thus ultimately on the personal and societal values. Such a gradient-threshold concept might permit linking the unlike strands of migration inquiry, by employing aggregate statistics for describing the gradient, and sociology, behaviour and decision-making for setting the threshold. Scenarios of external atmospheric condition under which people migrate would thus be systematically related to the migrants' attitudes and values.
Supporting information
S1 File
Survey questionnaire.
(PDF)
S2 File
Participant information for obtaining oral consent.
(PDF)
Acknowledgments
This study was conducted in association to the Inquiry Unit "Social-Ecological Systems in the Indian Rural-Urban Interface: Functions, Scales, and Dynamics of Transition (FOR2432)". The questionnaire was developed with the back up Dr. Vinod Bhatt (Navdanya Seed Conservation Farm, Dehradun, Bharat) who also helped the authors to establish initial contacts to migrants in Dehradun. Expert interviews with Prof. Kuswaha and Dr. Hitendra Pidalia, Indian Institute for Remote Sensing (26.09./17.10.2016), Dr. Mouleshri Vyas, Navdanya (17.ten.2016), and Mr. Madan Kishore Dangwal, Tehri Dam Computer Department (fifteen.09.2016) contributed valuable insights and helpful discussions. Special thanks goes to Mr. Piyush Raturi, whose efforts, back up and patience as guide and translator made it possible to implement the survey.
Funding Statement
This written report was conducted in association to the Research unit of measurement "Social-Ecological Systems in the Indian Rural-Urban Interface: Functions, Scales, and Dynamics of Transition (FOR2432)" funded by the German Enquiry Foundation (DFG; http://world wide web.dfg.de/), grant number BU 1308/14-1 to AB, EMH, and VK. Supplementary funding was provided to SN by the International Centre for Development and Decent Piece of work (ICDD; https://www.uni-kassel.de/einrichtungen/international-center-for-evolution-and-decent-piece of work-icdd/dwelling.html) funded by the Deutscher Akademischer Austauschdienst (DAAD), under the EXCEED initiative. The funders had no role in study blueprint, data collection and analysis, decision to publish, or preparation of the manuscript.
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