Impact of land acquisition on farmers lifecycle and lifestyle.
The only issue which decides for change of profession and place of survival overnight, Land Acquisition affects the livelihood of individual as well family members. Thus it accelerates to social change and social stratification by relocation due to landlessness and joblessness. This issue has been affecting the land owners worldwide. According to a study conducted by Negi and Ganguly (2011) which suggests that around 50 million people have been displaced in India due to various kinds of development projects in over 50 years, these; dams, mines, industrial development, and others account for over 21 million development-induced IDP as shown in figure-1.
Punjab is popularly perceived as an agricultural-rural state in the light of the strides it made in agricultural development, eventually shaping itself into the green revolution (Grewal, 2005). Now Punjab faces number of protests, agitations against grabbing of their agricultural land. Land of agricultural farmers and labourers has been grabbed by Government of Punjab. This case of acquisition is counted under public purpose by the Indian Railways. This keeps on adding to the problems of displaced people and people whose profession has been shifted/changed due to land grabbing for Dedicated Freight Corridor Project across the State. Since Independence, it is one of the most affected States in case of resettlement and rehabilitation. Thus to meet these problems, the programme of rehabilitation has been an integral part of Five Year Plans. With the passage of time they have been accommodated to rural and urban avocations. Government of India has been providing rehabilitation to displaced persons since 1947. Over the decades nation has been burdened with the cost and cases of rural rehabilitation and urban settlements. According to 1951 Census, about 7.5 million persons had moved into India in search of permanent home, 4.9 million from West Pakistan and about 2.6 million from East Pakistan.
No doubt different types of amenities and facilities which are in our possession today have come after sacrifice of land owners of their time. According to the central agriculture department, the total availability of arable land in the country was 185.09 million hectares in 1980-81. But in 2005-06 it has come down to 182.27 million hectares. Thus, during this period the cultivable land area has decreased by 2.52 million hectares (i.e. 20 lakh 20 hectares).
In recent times we again witness a similar problem relating to land acquisition but for a different reason altogether. Now, a number of developmental projects are affecting settled and rehabilitated population of Punjab. It's a project of Indian Railways named 'Dedicated Freight Corridor Project'. A train crossing over the entrance of the village was never in the thoughts of the villages that on some day it will alarm their lifecycle and lifestyles. This part comes in the Eastern Corridor, from Ludhiana (Punjab) to Dankuni (near Kolkata), total 1839 km. Land has been acquired under Railway Amendment Act (RAA), 2008. Whatsoever is the project, some have lost part or major portion of their agricultural land, and some have lost their residence too. But there are families whose complete land acquisition has ended up their profession and even displaced from their native place.
The objectives of this paper is:
* to study the impact of land acquisition on the life styles of the project affected persons (PAPs);
* to know the category of land owners affected in this project;
* to find how the land owners have utilized the amount of compensation, and;
* to evaluate the knowledge of the PAPs (Project Affected Persons) about the Land Acquisition procedures, its benefits and effects.
The present study is based on the collective views of the people from villages located on either side of the railway tracks. These are Ghaghar Sarai, Madanpur, Khairpur Shekhan, Hashampur, Kheri Gandian, and Kharajpur of Tahsil Rajpura in District Patiala of Punjab. Among these villages coverage of residential area has been reported only in Kheri Gandian during the survey. In this case, they had been demolishing the house structure in due time after notice issued by the State Government. Its impact over ones psychological state is not less than T-Sunami or avalanche. Every planning of life gets torn to shreds. The study is based mainly on the primary data collected through field survey. In total 50 households were selected from these villages. Questionnaire was designed to know the land holding size, type of land acquired, change in profession, primary and secondary occupation, annual income, use of compensation, acquaintance about the acts and policies related to land acquisition, and impact on their social and economic life.
The data was collected through purposive survey technique using structured questionnaire. Later informal and formal discussions were also held in the villages and with individuals. The data has been analyzed using suitable techniques like frequency, percentage and correlation.
Table-1 shows the information about (A) village wise households, (B) household size categorization, and (C) male-female population of the villages enumerated. It represents the number of households taken during direct interaction to canvass the questionnaire designed for the purpose to meet the objective. Out of the 50 households, 17 small households have been with the population up to 4 members, 28 medium households with population from 5 to 8 members, and 5 large households were having population for more than 9 members. This covered both the nuclear and joint families. Among the population of all age groups 159 males and 117 females population have been reported.
Table-2 gives information about the age and marital status of the population in these villages. As seen 26.8 percent of the population is up to the age of 18 years and 9.4 percent of the population has been reported under old age group. Virtually the onus lies over the remaining categories. The very purpose of this table is to present the dependency over the young and middle aged group. Diagram (Figure-3) showing age group of the households has been placed below.
Table-3 reveals that the percentage of literate population is 69.2 percent near to literacy rate (71.4 percent) (2) of rural Punjab. This also makes the human being to handle the situation in a best possible to plan either by way of change in profession or by relocating themselves. The level of literacy among the villagers made the survey easier and comfortable.
Information collected was initially not expected in such a small survey. 12 percent of the households, whose land has now been acquired, had already seen the face of displacement and resettlement in their earlier life. However this project has not affected these families at a big scale. Only one case of migrated household has been seen who is hailed from other district of Punjab purchased land in village Khairpur Sheikhan years back. Even after his land was acquired, household has remained in the same (semi-medium) category. Remaining 86.0 percent households have been living there over the decades. (Table-4).
Since land is the only commodity in the lifespan of an individual which makes men richer in various aspects. As discussed in table 4, 86.0 percent of the population is residing there over the decades and 14.0 percent of the population has moved later on. Respondents have their own views about the land depending upon their financial status, future liabilities and commitments. Most of the responses have resulted recommendation to the protection of land for the future of their families (56.0 percent). (Table-5).
In Table-6 we look from the point of view either the land is fertile or not. Respondents said their complete land is fertile and can be used for agricultural purposes; however, people have been using it for residential or commercial purposes also. Land thus acquired has been utilized by the land owners for the use as required. Before land acquisition, 74.0 percent households land was purely serving the agricultural purpose, 16.0 percent for the residential purpose and 10.0 percent for the both. Between 84.0 percent of the household's in terms of percentage points 50.0 had been managing cultivation either by themselves or their family members, and remaining percentage points 34.0 of households were either getting the cultivation done through contract or by labour.
* In this category total land in possession of farmers has been acquired. ** Increase noticed with decrease in Semi-medium category. *** Increase in Semi-medium and decrease in Medium category after land acquisition noticed simultaneously.
Table-7 provides the information about the change in farmer's category after the land acquisition. Crux of this table is to know the households who are now landless. This is the situation next to table 4 and 5 when population after land acquisition got displaced and looks forward for resettlement and rehabilitation schemes, and finally relocating. There are 5 households in the category of marginal farmers, who are now landless (16.7 percent). Increase in small category farmer is the result of decrease in semi-medium category. Category of semi-medium farmers thus also remains maintained with the decrease in medium category.
It can be seen from the Table-8 that compensation has been awarded in full to all the land owners as per the 'Entitlement Matrix' for Dedicated Freight Corridor Project Based on RAA 2008 and NRRP 2007. 10.0 percent of the households have opposed the compensation amount and only 60.0 percent out of the households who did not agree to the award of compensation have appealed in the court for enhancement of the compensation amount.
Table-9 depicts that as life takes u-turn especially for the families whose complete land or in-partial, complete house structure or in-partial has been acquired; the onus lies with the head of the household for the utilization of the compensation amount. Truly the same does not happen in all the families due to various reasons such as joint family or joint registry of the land acquired. Therefore the compensation amount is largely distributed.
Percentage shown above will not add up to 100 percent as amount of compensation have been utilized for more than one purpose as shown in last column (Options > 1) indicating that amount has been utilized for clearance of outstanding loan by two households, besides purchase of land. However, majority of households have purchase land, cleared outstanding loans, and invested on education of their children. Households who have bought luxury items have kept the in safe side for their children's marriage.
As shown in Table 9 it reveals that 12 households have utilized the compensation amount for the purchase of land in the neighbouring districts depending upon their necessity and affordability. Change in farmer category can be seen as decreased in Marginal Farmers with the increase ** in Small farmer, Medium, and Semi-medium category in Table-10.
* There is fall of percentage in the category of Marginal Farmers as compared to table 7 (from 16.7 to 12.0 percent) after utilization of compensation for land. ** However, rise in percent has come up in the categories of small, semi-medium and medium.
As discussed, land has been purchased away from the home town by most of the farmers, looking forward the return on investment in future and hike of land prices depending on their financial commitments. Four households who could not afford to purchase land at some other place are now landless. Change as increase in other category of farmers is timely investment. Investment of 12 households is quite visible from the above table as increase by 8 (in 'Net change' column) in Small, Semi-medium, and Medium category. Rest, the difference of four households is noticeable in 'After Acquisition and 'After Utilization of Compensation' total (See Table-11). Graph (Figure-4) below shows the changes in farmer category during all the three stages i.e. before acquisition, after acquisition and later in utilizing of compensation for land purchase.
While the literacy level of the villagers is high, the knowledge level about the Land Acquisition process, land pooling scheme, and rehabilitation process less than expectation has been reported. Either the organization and responsible authorities of the area are not making them aware about the facts before the acquisition or the willingness to sale land for the sake of money is higher. However, as per respondents awareness about Land Acquisition Act is higher (66.0 percent) as compared to schemes and policies (30.0 percent). It is understood that most of the villagers are not aware about issuance of notifications rather they came to know from the markings and sign boards placed by the railway authorities as Table 12 shows.
Table-13 indicates that there is lesser percentage (6.0 percent) of population who said that economic status is better after acquisition, possibility of two categories reflects here one who was under debt or has constructed their own house. Worst (54.0 percent) has been responded after land acquisition much as it was assumed. People got displaced are now landless, houseless and some are now in search of job to fulfill their family needs. Though compensation has been awarded as per the market rate but the loss of immovable asset has affected their heart and mind that their economic status has diminished.
The following Table-14 shows the relationship level:
Details of Bivariate Correlation have been obtained by choosing the variables 'Economic Status before Acquisition' and 'Economic Status after Acquisition'. In defined cell of the correlation matrix, in each cell Pearson's correlation coefficient, p-value for two tailed test of significance, and the sample size is obtained. The correlation coefficient between economic status before and after acquisition is 0.332 and the p-value for two-tailed test of significance is less than 0.05. Concluding that there is a strong positive correlation between 'Economic Status before Acquisition' and 'Economic Status after Acquisition' and this correlation is significant at the significance level of 0.05.
As per Table-15, lesser change is seen in change of primary occupation as per the information provided by the respondents. Also, total frequency of primary occupation (from codes 2 to 6) is higher (23.2 percent) than cultivation (i.e. 12.7 percent). As discussed in Table-13, smaller changes in cultivation and agricultural labour frequency has necessitated the families to look for a job/business prospect considering that their economic status has decreased. Noticeable decreased in frequency of prime occupation of cultivation has decreased (from 12.7 to 12.3 percent) and simultaneous increase in frequency of agricultural labour from (4.7 to 5.1 percent) after land acquisition. It also affected the frequency of 'No work' in before and after the land acquisition.
Table-16 shows the effect of joblessness as well landlessness in the cultivation and agricultural labour. Income in some cases has ceased due to demolition of house structures. Percentage of 'No work' has gone down with the increase in search of job/business prospects. This mainly has come up with the fall in secondary occupation where more than one source was also available for income purpose. Change in 'No work' frequency does not indicate to loss of job rather it indicates cases where earning has reduced after the acquisition of land. It can be observed from the following table below (Table-17) depicting details after correlating data for annual income before and after land acquisition.
Details of Bivariate Correlation have been obtained by choosing the variables 'Annual Income Primary Source Before Acquisition' and 'Annual Income Primary Source After Acquisition'. The two-tailed test was used because there was no prediction about the direction of relation between the variables have been tested. In defined cell of the correlation matrix, in each cell Pearson's correlation coefficient, p-value for two tailed test of significance, and the sample size is obtained. The correlation coefficient between annual income before and after acquisition is 0.970 and the p-value for two-tailed test of significance is less than 0.0005. Concluding that there is a strong positive correlation between 'Annual Income Primary Source Before Acquisition' and 'Annual Income Primary Source After Acquisition' and that this correlation is significant at the significance level of 0.01.
Details of Bivariate Correlation have been obtained by choosing the variables 'Resident Status before Acquisition' and 'Economic Status after Acquisition'. In defined cell of the correlation matrix, in each cell Pearson's correlation coefficient, p-value for two tailed test of significance, and the sample size is obtained. The correlation coefficient between resident status before acquisition and economic status after acquisition is 0.393 and the p-value for two-tailed test of significance is less than 0.05. Concluding that there is a strong positive correlation between 'Resident Status before Acquisition' and 'Economic Status after Acquisition' and this correlation is significant at the level of 0.01.
# Resident Status before Acquisition refers that living place is Native place (i.e. belongs to ancestral property), Migrated or Rehabilitated.
Down fall in Primary Source of Income
Graph (Figure-5) depicts that cases of 11 households holding agricultural land and their primary source of income was agriculture. After acquisition of land their primary source of income has been affected. In first two cases whose complete part of the land has been acquired their income has touched the minimum level i.e. zero. Annual income has been shown in millions for clarity in representation, thus indicating for the first household income in INR has decreased from [??]1.20 lacs to [??]0.00 and so on.
The next graph (Figure-6) depicts the case of 4 households holding agricultural land and income from agriculture was their secondary source of income. After acquisition of land their secondary source of income has been affected. Annual income is shown in millions.
Conclusion and Suggestions
The various factors relating to land acquisitions in Punjab on the lives of people have been analyzed, and based on the responses of the project affected persons, this paper concludes following. It has led to a number of changes in the category of the farmers, utilization of compensation amount by the land owners, and changes in the categories and income of primary and secondary occupation. It is evident from the discussions quoted above, that each table that land acquisition was for the public purpose in the interest of the nation. At the same time it is revealed that it was an agricultural land of the private parties acquired by the government for the public purpose. It is a universal truth that everything raises with the time, let it be demand, supply of any of the product. Demand and supply for several products is met after production, distribution and consumption. However, the place of survival rather say productive source also (i.e. land) does not increases with the time. Acquisition either at a small or a large scale has always affected in some percentage from large to the marginal farmers.
(1) CPI views on Land Acquisition Bill (LARR Bill 2011), a CPI publication 12/2011
(2) Primary Census Abstract-India, Census of India 2011
(3) Land Acquisition Act here refers to 'Land Acquisition Act, 1894 (1 of 1894)' published by Govt. of India, Ministry of Law and Justice. (4) LARR Act, 2013 here refers to Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act, 2013 (30 of 2013) published by Govt. of India, Ministry of Law and Justice. (5) Land Pooling Policy here refers to Notification No. 6/23/13-6Hg1/ 1440 dated 19th June, 2013 by Govt. of Punjab, Department of Housing and Urban Development. (6) Rehabilitation Act here refers to Displaced Persons (Compensation and rehabilitation) Act, 1954 [Act 44 of 1954].
Bal, Gurpreet (1995): 'Development and Change in Punjab' Published by National Book Organization, New Delhi.
Census of India, (2011)): 'Primary Census Abstract, India' Published by ORGI, New Delhi. Chakravorty, Sanjay (2013): 'The Price of Land-Acquisition Conflict Consequence', Oxford University Press, New Delhi.
Department of Housing and Urban Development, (2013):"Land Pooling Policy" Notification issued vide No. 6/23/13-6Hg1/ 1440 dated 19th June, 2013 by Government of Punjab.
Jaswinder Singh (2013): "Preliminary Information related Land Measurement', Singla Law Agency, Chandigarh
Entitlement Matrix for "Dedicated Freight Corridor Project Based on RAA 2008 and NRRP 2007" (Revised 13.06.2011) http://dfccil.org/dfccil_app/d0cs/atttcxl1.pdf, accessed on 15 Feb, 2014
First Five Year Plan, "Rehabilitation of Displaced persons, Chapter 38' http://planningcommission.nic.in/plans/planrel/fiveyr/1st/1planch38.html accessed on 3 Feb, 2014.
Gaur Ajai S. and Gaur Sanjaya S. (2014): "Statistical Methods for Practice and Research", Sage Publications, New Delhi.
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Mahalingam, Ashwin and Vyas, Aditi (2011): "Comparative Evaluation of Land Acquisition and Compensation Processes across the World", Economic and Political Weekly, Vol. XLVI No. 32.
Malik, Yudhvir Singh (2012): "Land Acquisition-A National Issue: Lessons from Haryana" Published in The Administrator Journal of LSBNAA, Special Issue December 2012, Vol. 53 No. 2.
Mathur, Hari Mohan (2012): "Resettling Displaced People-Policy and Practice in India", Routledge Taylor and Francis Group, New Delhi.
Ministry of Law and Justice (1894): "Land Acquisition Act, 1894 (1 of 1894)", Government of India, New Delhi.
Ministry of Law and Justice (2013): "Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act, 2013 (30 of 2013)", Government of India, New Delhi.
Siddiqui, Kamal (1997): "Land Management in South Asia-A Comparative Study", Manohar Publisher and Distributors, New Delhi.
Rehabilitation Act 1954, "Displaced Persons (Compensation and Rehabilitation) Act, 1954 [Act 44 of 1954]' http://punjabrevenue.nic.in/dpcract.htm Accessed on 15 May, 2014.
Sharma R. N. and Shashi R Singh (2009): "Displacement in Singrauli Region: Entitlements and Rehabilitation", Economic and Political Weekly, Vol. XLIV No. 51.
Singh, R.Y. (2012): "Geography of Settlements," Rawat Publications, Jaipur.
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Table 1: Detail of Village wise households, Household size and Population Code Category Frequency Percentage A Village wise households 1 Ghaghar Sarai 7 14.0 2 Hashampur 6 12.0 3 Khairpur sheikhan 2 4.0 4 Kharajpur 2 4.0 5 Kheri Gandian 21 42.0 6 Madanpur 12 24.0 B Household Size 1 Small (up to 4 members) 17 34.0 2 Medium (5 to 8 members) 28 56.0 3 Large (9 and above members) 5 10.0 C Population 1 Male 159 57.6 2 Female 117 42.4 Source: Field Survey, February 2014. Table 2: Population Profile Code Category Frequency Percentage A Age 1 Children (0-7 years) 32 11.6 2 Teenager(8-18 years) 42 15.2 3 Adolescence (19-25 years) 42 15.2 4 Young (25-40 years) 65 23.6 5 Middle aged (40-60 years) 69 25.0 6 Old (senior citizens60+ years) 26 9.4 B Marital status 1 Never Married 105 38.0 2 Married 157 56.9 3 Widow 6 2.2 4 Widower 8 Source: Field Survey, February 2014. Table 3: Literacy Status of Population Code Category Frequency Percentage A Education 1 Primary (or passed below 8th) 47 17.0 2 Middle 25 9.1 3 Matric 69 25.0 4 10+2 34 12.3 5 Graduate 11 4.0 6 Post Graduate 2 0.7 7 Professional 3 1.1 8 Illiterate 85 30.8 Source: Field Survey, February 2014. Table 4: Category of Residence Status of the Households Code Category Frequency Percentage 1 Ancestral Property 43 86.0 2 Migrated 1 2.0 3 Rehabilitated 6 12.0 Source: Field Survey, February 2014. Table 5: Respondent Perception about Land Ownership Code Category Frequency Percentage 1 Ancestral Property 6 12.0 2 As Security for Children 28 56.0 3 Kept as Asset for sale 10 20.0 4 Agricultural purpose 6 12.0 Source: Field Survey, February 2014. Table 6: Category of Land Acquired Code Category Usage of land Frequency Percentage A Land Status 1 Agricultural Agricultural 37 74.0 2 Non-agricultural Residential 8 16.0 3 Agricultural Agricultural and 5 10.0 and Non- residential agricultural purpose B Cultivation by 1 Self/ Family 25 50.0 2 Contract 17 34.0 Source: Field Survey, February 2014. Table 7: Change in Category of Farmers Code Category of Before After Land Owner Acquisition Acquisition 1 Marginal Farmer 30 25 (< 2.5 acre) 2 Small Farmer 6 7 (2.5 to 5 acre) 3 Semi-medium 5 5 (5 to 10 acre) 4 Medium 1 -- (10 to 25 acre) 5 Large -- -- (25 acre & above) Total 42 37 Code Category of Increase Decrease Land Owner 1 Marginal Farmer -- 5 * (< 2.5 acre) 2 Small Farmer 1 ** -- (2.5 to 5 acre) 3 Semi-medium -- -- (5 to 10 acre) 4 Medium -- 1 *** (10 to 25 acre) 5 Large -- -- (25 acre & above) Total 1 6 Source: Field Survey, February 2014. Table 8: Award of Compensation Code Category Frequency Percentage A Recipient of Compensation 1 Full 50 100.0 2 Partial -- -- B Compensation Acceptability 1 Yes 45 90.0 2 No 5 10.0 C Appealed in Court 1 Yes 3 60.0 2 No 2 40.0 Source: Field Survey, February 2014. Table 9: Utilization of compensation Code Category Frequency Percent- Options > 1 age * A Compensation utilized for 1 Purchase of Land 12 24.0 7 (2) 2 Construction of house 4 8.0 4 (1), 6 (1) 3 Repair of House 7 14.0 4 (1), 5 (1), & 8 (1) 4 Kept for 6 12.0 8 (1) children marriage 5 Luxurious bought 7 14.0 4 (1), 8 (1) 6 FDs/Savings 10 20.0 7 (1), 8 (2) for children 7 O/S Loan cleared 12 24.0 5 (1), 6 (1) 8 Invested on 7 14.0 children education 9 If > 1 option 15 30.0 Source: Field Survey, February 2014. Multiple responses reported regarding utilization of compensation amount. Table 10: Change in Farmer Category after Award of Compensation Code Category of After After Land Owner Acquisition Utilization of Compensation 1 Marginal Farmer 25 22 (< 2.5 acre) 2 Small Farmer 7 9 (2.5 to 5 acre) 3 Semi-medium 5 9 (5 to 10 acre) 4 Medium -- 1 (10 to 25 acre) 5 Large -- -- (25 acre & above) Total 37 41 Code Category of Increase Decrease Land Owner 1 Marginal Farmer -- 3 (< 2.5 acre) 2 Small Farmer 2 -- (2.5 to 5 acre) 3 Semi-medium 4 -- (5 to 10 acre) 4 Medium 1 -- (10 to 25 acre) 5 Large -- -- (25 acre & above) Total 7 3 Source: Field Survey, February 2014. * There is fall of percentage in the category of Marginal Farmers as compared to table 7 (from 16.7 to 12.0 percent) after utilization of compensation for land. However, rise in percent has come up in the categories of small, semi-medium and medium. Table 11: Broad Picture from Land Acquisition to Land Purchase Category of Before After land owner acquisition acquisition Marginal Farmer 30 25 (< 2.5 acre) Small farmer 6 7 (2.5 to 5 acre) Semi-medium 5 5 (5 to 10 acre) Medium 1 -- (10 to 25 acre) Large (25 acre -- -- and above) Total 42 37 Category of After Net land owner utilization of change compensation Marginal Farmer 22 -8 (< 2.5 acre) Small farmer 9 +3 (2.5 to 5 acre) Semi-medium 9 +4 (5 to 10 acre) Medium 1 -1/+1 (10 to 25 acre) Large (25 acre -- -- and above) Total 41 3 Source: Field Survey, February 2014. Table 12: Awareness about Land acquisition Acts, Policies and schemes Code Category Frequency Percentage A Land Acquisition Act, 1894 (3) 1 Yes 33 66.0 2 No 17 34.0 B LARR Act, 2013 (4) 1 Yes 23 46.0 2 No 27 54.0 C Land Acquisition Process 1 Yes 27 54.0 2 No 23 46.0 D Land Pooling Policy (5) 1 Yes 15 30.0 2 No 35 70.0 E Rehabilitation Act and Policies (6) 1 Yes 27 46.0 2 No 23 54.0 Source: Field Survey, February 2014. Table 13: Economic Status of the Households Code Category Frequency Percentage A Before Acquisition 1 Better 30 60.0 2 Worst 10 20.0 3 No change 10 20.0 B After Acquisition 1 Better 3 6.0 2 Worst 27 54.0 3 No change 20 40.0 Source: Field Survey, February 2014. Table 14: Relationship between Household's Economic Status Before and After Acquisition Correlations Economic Economic status status before after acquisition acquisition Economic status Pearson correlation 1 .332 * before acquisition Sig. (2-tailed) .018 Economic status Pearson correlation .332 * 1 after acquisition Sig. (2-tailed) .018 * Correlation is significant at the 0.05 level (2-tailed). Table 15: Change in Primary Occupation after Land Acquisition Primary Occupation Before After Frequency Percentage Frequency Parentage Cultivation 35 12.7 34 12.3 Agricultural Labour 13 4.7 14 5.1 Govt./Pvt. Job 30 10.9 30 10.9 Others 14 5.1 14 5.1 If 1 and 4 5 1.8 5 1.8 If 2 and 4 2 0.7 2 0.7 Student 60 21.7 60 21.7 No work 117 42.4 115 41.7 In search of job/ -- -- 2 0.7 business prospects Source: Field Survey, February 2014. Table 16: Change in Secondary Occupation after Land Acquisition Secondary Before After Occupation Frequency Percentage Frequency Percentage Cultivation 5 1.8 1 0.4 Agricultural 3 1.1 4 1.4 Labour Dairying 3 1.1 3 1.1 Pension 6 2.2 6 2.2 Rent from 6 2.2 4 1.4 (Land/House) Others 25 9.1 25 9.1 If > 1 option 3 1.1 4 1.4 No work 225 81.5 222 80.4 In search of job/ -- -- 7 2.5 business prospects Source: Field Survey, February 2014. Table 17: Relation between Annual Income Primary Source before and after land Acquisition Correlations Primary income Before After Acquisition Acquisition Annual Income Primary Pearson 1 .970 * Source Before correlation Acquisition Sign. .000 (2-tailed) Annual Income Primary Pearson .970 * 1 Source After Acquisition correlation Sign. .000 (2-tailed) * Correlation is significant at the 0.01 level (2-tailed) Note: Value less than 0.0005 are shown as 0.000 in SPSS outputs. Table 18: Relationship between Resident Status before Acquisition and Economic Status after Acquisition Correlations Resident Resident status status before After acquisition Acquisition Resident status Pearson correlation 1 .393 * Before Acquisition Sign. (2-tailed) .005 Economic Status Pearson correlation .393 * 1 after acquisition Sign. (2-tailed) .005 * Correlation is significant at the 0.01 level (2-tailed). # Resident Status before Acquisition refers that living place is Native place (i.e. belongs to ancestral property), Migrated or Rehabilitated. Table 19: Acquisition of Agricultural Land Household Primary income Before LA After LA 1 Rs.1,20,000 Rs.0 2 Rs.1,00,000 Rs.0 3 Rs.1,44,000 Rs.72,000 4 Rs.8,80,000 Rs.8,40,000 5 Rs.5,00,000 Rs.4,50,000 6 Rs.4,25,000 Rs.3,75,000 7 Rs.4,50,000 Rs.4,00,000 8 Rs.2,50,000 Rs.2,25,000 9 Rs.4,00,000 Rs.3,80,000 10 Rs.2,00,000 Rs.1,75,000 11 Rs.4,20,000 Rs.3,60,000 Source: Field Survey, February 2014. Down fall in Secondary Source of Income Household Secondary Before LA After LA income 1 Rs.1,65,000 Rs.65,000 2 Rs.1,50,000 Rs.1,20,000 3 Rs.1,50,000 Rs.25,000 4 Rs.1,00,000 Rs.0 Source; Field Survey, February 2014. Figure 1: People Displaced by Development Projects Category 1 4.3 Category 2 2.5 Category 3 3.5 Category 4 4.5 Note: Table made from bar graph. Figure 2: Size of the households Household Size Small 34.0% Medium 56.0% Large 10.0% Note: Table made from pie chart. Figure 3: Age Group Categories of Household Members Age Group: Household Members Middle aged (40-60 years) 25.0% Old (Senior Citizens 60+ years) 9.4% Children (0-7 Years) 11.6% Teenager (8-18 years) 15.2% Adolescence (19-25 years) 15.2% Young (25-40 years) 23.6% Note: Table made from pie chart. Figure 4 Change in Farmers Category Before LA, After La, and After Compensation Utilisation Marginal Small Semi- Medium Large Farmer Farmer medium Before Acquisition 30 6 5 1 0 After Acquisition 25 7 5 0 0 After Utilization 22 9 9 1 0 of Compensation Note: Table made from line graph. Figure 5 Acquisition of Agricultural Land: Source of Primary Income Before Land After Land Acquisition Acquisition 0.12 0 0.144 0.1 0.88 0.072 0.5 0.84 0.425 0.45 0.45 0.375 0.25 0.4 0.4 0.225 0.2 0.175 0.42 0.36 Note: Table made from line graph. Figure 6 Acquisition of Agricultural Land: Source of Secondary Income Before After Acquisition Acquisition 0.165 0.065 0.15 0.12 0.15 0.025 0.1 0
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|Author:||Shorey, Brij Mohan Krishan; Farooquee, Nehal A.|
|Publication:||Political Economy Journal of India|
|Date:||Jul 1, 2014|
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