Socio-economic Indicators are Linked to Ecological Outcomes: Insights into the Lives of Fish and Shrimp Farm Workers in West Bengal

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While most existing research focuses on fish and shrimp farmers, there is a notable gap in understanding the conditions of farm workers. Socio-economic indicators such as income, education, and employment are closely linked to ecological outcomes, influencing resource use, consumption, and environmental sustainability. The study aims to fill this gap by profiling the socio-economic characteristics of workers in two key fisheries districts, Purba Medinipur and South 24 Parganas. Data were collected through interviews with 180 workers (90 fish and 90 shrimp farm workers), focusing on factors such as age, education, religion, caste, residency, marital status, work experience, income, and household facilities. The findings indicated that majority of workers were Hindus, with an average age of 34 years for fish farm workers and 30 years for shrimp farm workers, most having primary education. Notably, 65.55% of fish farm workers were locals, while 71.11% of shrimp farm workers were migrants from other states. Farm related experience varied from 5 to 10 years. About half (52.22%) of fish farm workers lived in pucca (made of brick/concrete) houses and 47.78% shrimp farm workers resided in semi pucca (made of mud/thatch and concrete) houses. The average monthly income was ₹10,655 for fish farm workers and ₹9,888 for shrimp farm workers, with fish farm workers incurring higher personal expenditures (One US $ = 84.07 ₹ i.e., Indian Rupee). Additionally, shrimp farm workers had a lower percentage of personal insurance compared to their fish farm counterparts. While incomes were below the national average, they were slightly above the state average. Statistical tests revealed no significant difference in age of fish and shrimp farm workers. However, fish farm workers had statistically significant higher working experience, monthly income expenditure compared to shrimp farm workers. These findings underscore the need for targeted interventions. The socio-economic conditions of farm workers, are closely tied to resource consumption and environmental effects, functioning as important ecological indicators for assessing sustainability for which more research focus is required. Social Policy Other Economics Agricultural Economics & Policy Socio-economic Fish and shrimp farm workers West Bengal Figures Figure 1 1. Introduction The Ministry of Statistics and Programme Implementation, Government of India attaches considerable importance to coverage and quality aspects of statistics released in the country. In this context, studies on socio-economic status are of prime importance. Many studies have highlighted the importance of socio-economic factors (Reza et al., 2015 ; Dhenuvakonda et al., 2019 ; Kumari and Sharma, 2022 ; Anegla and Sharma, 2023). India, the third largest fish producer and second largest in aquaculture, considers its fisheries sector an important part of its economic growth, strengthened by the Blue Revolution. The fishing industry is vital to India's economy, contributing ₹1,37,716 crores to the Gross Value Added (GVA) in 2022–2023, accounting for 1.09% of total GVA and 6.72% of agricultural GVA (DAHD Annual Report, 2022 -23). India's inland fish production reached 12.12 million metric tonnes, with 50% coming from culture fisheries, which cover 2.36 million hectares of ponds and tanks, contributing 8.5 million metric tonnes to the total output (Dept. of Fisheries, Govt. of India, 2024). India is a leading producer and exporter of seafood, primarily shrimp, which is raised on approximately 1.67 lakh hectares, contributing to 8.43 lakh tonnes of production. The country has about 1.191 million hectares of brackish water suitable for shrimp farming across 10 states, but only 1,20,000 hectares are currently utilized. Since 1970, shrimp production has surged from 20 metric tonnes to 7,47,000 metric tonnes in 2022, significantly boosting India's fisheries export revenue to ₹ 46,662 crores (CIBA, 2022). Besides providing nutrition and ensuring food security, fisheries and aquaculture are crucial for livelihoods. The most recent figures show that around 61.8 million people were employed in the primary sector of fisheries and aquaculture in 2022 (FAO, 2024). Das et al. ( 2024 ) have highlighted the importance of socio-economic studies of fish workers. Some previous research has reported on the conditions of fish and shrimp farmers across various regions of W.B., yet none have specifically focused on the workers in fish and shrimp farms (Abraham et al., 2010 ; Dana et al., 2015 ; Biswas et al., 2018 ; Maity et al., 2020 ). To address this gap, this study presents the socio-economic profile of fish and shrimp farm workers in W.B. 2. Materials and Methods 2.1. Study area The present study was carried out in South 24 Pargana and Purba Medinipur districts, W.B. (Fig. 1 ) which are two key districts in the fisheries sector. W.B. spans from 21° 36' N to 27° 13' N latitude and 85° 50' E to 89° 50' E longitude. Purba Medinipur district holds the third position in inland fish production among W.B.'s districts. Purba Medinipur district also leads in the number of Coastal Aquaculture Authority (CAA) registered shrimp farms across W.B. (CAA, 2024 ). South 24 Parganas district secures the second position in inland fish production among all districts in W.B. South 24 Parganas district also holds the second highest number of CAA registered shrimp farms in W.B. (CAA, 2024 ). 2.2. Data collection Information was collected from 90 fish farm workers and 90 shrimp farm workers. An interview schedule was developed and information was collected on various variables such as age, education, religion, caste, resident status, marital status, working experience, personal insurance, household facilities, monthly income, personal monthly expenditure, savings, and loans taken. 2.3. Statistical analysis For descriptive statistics (percentage, frequency, mean, and standard deviation), MS Excel was used, and to check for significant differences between various variables among fish and shrimp farm workers, the Z-test was conducted. Difference in monthly income, expenditure and work experience between fish farm workers and shrimp farm workers was tested using Z test. The formula for conducting a Z-test to compare means was as follows. Where: 3. Results and Discussion 3.1. Social status Most of the workers in shrimp farms (32.23%) were between the ages of 20 and 30 years, while the majority of workers in fish farms (33.33%) were in 30 to 45 year age bracket. Prusty and Sharma ( 2023 ) found that 50% of pond farmers in Odisha were classified as middle aged. Additionally, a study by Biswas et al. ( 2018 ) in the North 24 Parganas district of W.B. revealed that a significant majority (88.33%) of fish farmers were middle aged, specifically within the age range of 31 to 60 years. These findings highlight age demographics among fish/shrimp farm workers across different regions in India. The educational background of shrimp and fish farm workers in W.B. revealed that a significant proportion of shrimp farm workers (45.55%) and fish farm workers (38.88%) had only primary education. Notably, fish farm workers exhibited a higher percentage of secondary school education compared to their shrimp farm counterparts, and some shrimp farm workers lacked any formal schooling. Studies such as those conducted by Bhattacharjee et al. ( 2021 ) indicated that fish farmers across the Maldah, Murshidabad, and Birbhum districts in W.B. were literate, with 20.02% able to read and write, while 19.88% had completed primary education and 19.09% had reached middle school, with very few graduates. Similarly, Maity et al. ( 2020 ) found that the majority of farmers (56%) in Purba Medinipur had attained a middle school education, with 20.5% having completed secondary education. These studies underscore the educational disparities among fish/shrimp farm workers. The demographic profile of the workforce revealed that the majority of fish farm workers (84.45%) and shrimp farm workers (76.67%) were Hindus, while rest were Muslims. These findings align with study by Sharma et al. ( 2023 ) who reported similar trends among shrimp farm workers in Andhra Pradesh, Tamil Nadu, Maharashtra, Gujarat and Odisha, where 76.66% were Hindu, 10.41% were Muslim, and 12.93% were Christians. Moreover, a significant proportion of shrimp (38.88%) and fish (50%) farm workers belonged to the General Caste, with a higher percentage of Scheduled Castes and Scheduled Tribes represented among shrimp farm workers compared to those in fish farms. Most of the fish farm (67.78%) and shrimp farm (62.22%) workers were married. The workforce on shrimp farms predominantly consisted of migrants, with 71.12% originating from neighbouring states such as Jharkhand and Odisha. These migrant workers typically arrive during the crop season each year to earn wages. In contrast, a majority of fish farm workers (65.55%) were local residents, highlighting the reliance of shrimp farms on migrant labour while fish farms primarily draw from the surrounding community. A similar finding was reported by Sharma et al. ( 2023 ), indicating that migrant shrimp farm workers in Andhra Pradesh and Tamil Nadu came from states such as Odisha, Karnataka. These studies underscore the significant role of migrant labours in shrimp farming in various regions of India. Workers in shrimp farms typically had less experience compared to those in fish farms. Specifically, 61.12% of shrimp farm workers reported having less than 5 years of experience, while 40% of fish farm workers had 5 to 10 years of experience. A similar finding was noted by Sharma et al. ( 2023 ). Additionally, Prusty and Sharma ( 2023 ) reported that 63% of fish pond farmers in Odisha had farming experience ranging from 10 to 20 years. These findings highlight the varying levels of experience among farm workers in the aquaculture sector. Table 1 summarizes the key findings related to age, education, religion, residence, marital status, and working experience of shrimp and fish farm workers. Table 1 Information on social variables of fish and shrimp farm workers Variable Category Fish farm workers (%) Shrimp farm workers (%) Age group 45 years 17.78 Mean age: 34.08 16.67 Mean age: 30.22 Education status No formal schooling 23.34 25.55 Primary 38.88 45.55 Secondary 34.44 27.78 Higher secondary 3.34 1.12 Religion Hindu 84.45 76.67 Muslim 15.55 23.33 Caste General 50 38.88 SC 35.56 31.12 ST 8.88 21.12 OBC 5.56 8.88 Resident status Local 65.55 28.88 Migrant 34.45 71.12 Marital status Married 67.78 62.22 Unmarried 32.22 37.78 Working experience 1–5 years 34.44 61.12 5–10 years 40 18.88 10–15 years 17.78 12.22 15–20 years 7.78 Mean experience: 6.82 7.78 Mean experience: 5.24 3.2. Economic status The majority of fish farm workers resided in pucca houses (52.22%), while most shrimp farm workers lived in semi-pucca houses (47.78%). In terms of drinking water, a higher proportion of fish farm workers (73.33%) had facilities in their homes compared to shrimp farm workers (68.88%). The primary source of drinking water for both groups was tubewells, supplemented by public taps. Supporting this observation, Bhutti et al. ( 2022 ) reported that 92% of fish and shrimp farm workers in Gujarat used tubewell water for drinking, with only 8% relying on alternative sources. This indicates a significant reliance on tubewell water among aquaculture workers in the region. Furthermore, the majority of workers reported having electricity connections in their homes. Regarding monthly income, the majority of fish farm workers (67.78%) reported earnings between ₹10,000 to ₹15,000 per month, while most shrimp farm workers (52.22%) earned between ₹5,000 to ₹10,000 per month. The average monthly income for fish farm workers was ₹10,655, whereas shrimp farm workers had an average monthly income of ₹9,888. (One US $ = 84.07 ₹ i.e., Indian Rupee) The average incomes for both fish and shrimp farm workers were lower than the national average but slightly exceeded the W.B. state average, according to the Economic Survey of India for 2023-24. This indicates that while these workers earn relatively modest incomes, they are somewhat better off compared to the broader state economic context. The disparity highlights on-going challenges within the aquaculture sector, suggesting the need for targeted interventions to enhance financial stability and improve the livelihoods of those employed in this vital industry. Overall, the income levels underscore the economic realities faced by these workers in W.B. Additionally, fish farm workers reported higher personal expenses each month than shrimp farm workers. Specifically, most shrimp farm workers (47.78%) had personal monthly expenditures ranging from ₹2,000 to ₹3,000, while the majority of fish farm workers (52.22%) spent between ₹3,000 and ₹4,000 per month. Workers in shrimp farms exhibited a lower percentage of personal insurance coverage (10%) compared to their fish farm counterparts (15.55%). However, the overall prevalence of insurance among these workers was low. Those who did have insurance typically relied on personal policies, such as Life Insurance Coverage (LIC), which further emphasizes the need for improved financial security for aquaculture workers. A majority (73.33%) of fish farm workers had savings in the bank which was relatively higher than those of shrimp farm workers as 56.67% had savings in the bank. Additionally, 57.77% of fish farm workers reported monthly savings between ₹3,000 to ₹4,000, whereas 42.22% of shrimp farm workers reported monthly savings of ₹2,000 to ₹3,000. Shrimp farm workers had fewer loans than fish farm workers, with 21.12% of shrimp farm workers reporting loans from the bank, compared to 28.88% of fish farm workers. Table 2 summarizes the key findings related to economic variables. Table 2 Information on economic variables for fish and shrimp farm workers Variable Category Fish farm workers (%) Shrimp farm workers (%) Monthly income ₹5,000- ₹10,000 32.22 52.22 ₹10,000- ₹15,000 67.78 Mean income: ₹10,655 47.78 Mean income: ₹9,888 Personal monthly expenditure ₹2,000- ₹3,000 32.22 47.78 ₹3,000- ₹ 4,000 52.22 40 ₹4,000- ₹5,000 10 8.88 ₹5,000- ₹6,000 5.56 Mean expenditure: ₹3,190 3.34 Mean expenditure: ₹2,884 Savings Yes 73.33 56.67 No 26.67 43.33 Loan Yes 28.88 21.12 No 71.12 78.88 Insurance Yes 15.55 10 No 84.45 90 Type of house Kuchha* 13.34 10 Semi Pucca* 34.44 47.78 Pucca* 52.22 42.22 Drinking water Yes 73.33 68.88 No 26.67 31.12 Electricity facility Yes 85.55 82.22 No 14.45 17.78 *Kuchha house: Often made of mud/thatch, Semi Pucca House: Made of mud/thatch and concrete, Pucca: Made of brick/concrete Table 3 summarizes the statistical (z-test) results to see if there was a significant difference between age, working experience, monthly income, and monthly expenditure between fish and shrimp farm workers. Table 3 Comparison of Age, Experience, Income, and Expenditure between fish and shrimp farm workers Mean (Fish Farm Workers) Mean (Shrimp Farm Workers) p value Significance Age 34.08 30.22 > 0.05 No significant difference Working Experience 6.82 5.24 0.024 Significant difference Monthly Income 10,655.55 9,888.88 0.002 Significant difference Monthly Expenditure 3,190 2,884 0.012 Significant difference Table 3 indicates that there was no significant difference in age of fish and shrimp farm workers. However, fish farm workers have significantly higher working experience, higher monthly income and expenditure compared to shrimp farm workers. In a study by Katre et al. ( 2024 ) it has been reported that there were differences among socio-economics of reservoir fishers. However, more studies will be required to conclude that fish farming may be providing better economic opportunities and benefits, than the shrimp farm workers despite a similar demographic profile. 4. Conclusions The analysis of fish and shrimp farm workers in W.B. state reveals distinct demographics. Workers in shrimp farms are predominantly younger, with many aged 20–30, while fish farm workers are mostly in the 30–45 age group. Education levels show that a significant portion of both groups had primary level education, with fish farmers relatively having better education. The workforce was largely Hindus, with representation of Scheduled Castes and Scheduled Tribes among shrimp farm workers. Migrant labour plays a crucial role in shrimp farming, contrasting with the local focus of fish farms. Additionally, shrimp workers tend to have less experience than their fish farming counterparts. From the study it is clear that fish farm workers had relatively higher monthly income and expenditure. Although both groups earn below the national average, they fare slightly better than the state average. As a result, their income levels can be considered modest. Additionally, the low insurance coverage for both groups underscore the need for enhanced financial security measures within the aquaculture sector. The socio-economic conditions of farm workers are closely linked to resource consumption and environmental impacts, serving as key ecological indicators for evaluating sustainability. This relationship warrants further research and attention. Declarations The study received approval from the Institutional Review Board (IRB) of the Central Institute of Fisheries Education, Mumbai, India, prior to the commencement of data collection. The research was conducted in accordance with institutional ethical guidelines, and informed consent was obtained from all participants. CRediT authorship contribution statement Sayantan Das : Data collection, Analysis, Paper drafting Arpita Sharma: Conceptualisation, Schedule preparation and testing, Guidance in data collection and analysis, Paper writing Amitava Ghosh : Schedule testing, Supervision in data collection, Paper writing Vinod K. Yadav : Statsitical analysis, Paper editing Kripa V : Finalising the Schedule, Paper editing Vijendra Kumar : Data feeding and analysis, Paper editing Nidhi Katre : Data feeding and analysis, Paper editing Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements The authors would like to thank ICAR-Central Institute of Fisheries Education for providing the necessary research facility for this study. They thank all the fish and shrimp farm workers for sharing information and for their cooperation. References Abraham, T.J., Sil, S.K., Vineetha, P., 2010. Comparative study of the aquaculture practices adopted by fish farmers in Andhra Pradesh and West Bengal. Indian Journal of Fisheries. 57, 1-48. Angela, S., Sharma, A., 2023. Analysis of socio-economics and occupational dimensions of shrimp farmers of Tamil Nadu. Aquaculture Research. 1, 1-17. https://doi.org/10.1155/2023/2457208. Bhattacharjee, S., Dhara, K., Ghosh, S., Das, P., Giri, A., Sarkar. B., 2021. Study on socio-economic status and constraints faced by the livestock farmers of the aspirational districts of West Bengal, India. International Journal of Research and Innovation in Social Science (IJRISS). 7, 331-340. http://dx.doi.org/10.47772/IJRISS.2021.5714 Bhutti, J.K., Lende, S., Pargi, N.A., Vasava, R.J., Taral, P.V., 2022. Studies on the socio-economic condition of fish farmer in Sabarkantha district of Gujarat state. 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Dept. of Fisheries, Govt. of India, 2024. https://dof.gov.in/ (accessed 13 September 2024). Dhenuvakonda, K., Sharma, A., Prasad, K.P., Sharma, R., 2019. Socio-economic profile of fish farmers of Telangana and usage of mobile apps. Asian Journal of Agricultural Extension, Economics and Sociology. 37, 1-9. https://doi.org/10.9734/ajaees/2019/v37i330268. Economic Survey of India, Government of India, 2023-24. https://www.indiabudget.gov.in/economicsurvey. FAO Report, 2024. https://www.fao.org/newsroom/detail/fao-report-global-fisheries-and-aquaculture-production-reaches-a-new-record-high/en. Katre, N., Sharma, A., Ojha, S.N., Tyagi, L., Yadav V.K., 2024. Reservoir fisheries governance quality index: Development and validation. Ecological Indicators . 158, 111562. https://doi.org/10.1016/j.ecolind.2024.111562. Kumari, S., Sharma, A., 2022. Socio-economic status of fishers and fish production trends from cage culture in Chandil reservoir, Jharkhand. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6342305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436169176,"identity":"308cd66c-96e9-46ba-86d3-1128a286d9a9","order_by":0,"name":"Sayantan Das","email":"","orcid":"","institution":"ICAR-Central Institute of Fisheries Education, Mumbai, India","correspondingAuthor":false,"prefix":"","firstName":"Sayantan","middleName":"","lastName":"Das","suffix":""},{"id":436169177,"identity":"934a0ef1-ab40-4af0-a21b-7c68b3e40dbc","order_by":1,"name":"Dr. Arpita Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYJCCA0CcAGZ9YJDgB9ESYIRPywGoFsYZDBKSDcRoYYBpYeZhYIBpwQ34Z589ePhDTV0e/+zTiZ9taiyALmM+eJuHwSIPlxaJc3kJBw4cO1wscS53s3TOMQmgy9iSrXkYJIpxWnOGx+DAAbYDiQ1neDdI5zZI1Bkc4DGTBmpJbMChQx6s5V9d4vwzvJt/WzZISNgf4P+GV4sBSMvBNubEDWd4t0kzArUYMPCw4dVieIYv4cDZvsOJG4FaLHuAfpE4zGZsOccAtxa5M7yHP1R8q0ucB3TYjR81dRL87c0Pb7ypqMOphYGBB12AGexgnOqxaRkFo2AUjIJRgAYA6ahWtsN/SXcAAAAASUVORK5CYII=","orcid":"","institution":"ICAR-Central Institute of Fisheries Education, Mumbai, India","correspondingAuthor":true,"prefix":"Dr.","firstName":"Arpita","middleName":"","lastName":"Sharma","suffix":""},{"id":436169178,"identity":"9de98d98-add9-43e9-a9ab-7cc7f4e4e795","order_by":2,"name":"Dr. Amitava Ghosh","email":"","orcid":"","institution":"College of Fisheries, Central Agricultural University (Imphal), Tripura, India","correspondingAuthor":false,"prefix":"Dr.","firstName":"Amitava","middleName":"","lastName":"Ghosh","suffix":""},{"id":436169179,"identity":"c3cb2ea9-e965-42b6-9d73-3def13e3dd53","order_by":3,"name":"Dr. Vinod Kumar Yadav","email":"","orcid":"","institution":"ICAR-Central Institute of Fisheries Education, Mumbai, India","correspondingAuthor":false,"prefix":"Dr.","firstName":"Vinod","middleName":"Kumar","lastName":"Yadav","suffix":""},{"id":436169180,"identity":"ea80db07-0897-4172-873a-0c9b3927185e","order_by":4,"name":"Dr. Vasant Kripa","email":"","orcid":"","institution":"Coastal Aquaculture Authority, Chennai, India","correspondingAuthor":false,"prefix":"Dr.","firstName":"Vasant","middleName":"","lastName":"Kripa","suffix":""},{"id":436169181,"identity":"7a8dbeae-e34a-437e-bdcc-3a70030c1623","order_by":5,"name":"Vijendra Kumar","email":"","orcid":"","institution":"ICAR-Central Institute of Fisheries Education, Mumbai, India","correspondingAuthor":false,"prefix":"","firstName":"Vijendra","middleName":"","lastName":"Kumar","suffix":""},{"id":436169182,"identity":"20d7a252-476f-41a2-8e4c-a6bd546332f5","order_by":6,"name":"Nidhi Katre","email":"","orcid":"","institution":"ICAR-Central Institute of Fisheries Education, Mumbai, India","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Katre","suffix":""}],"badges":[],"createdAt":"2025-03-31 07:06:05","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6342305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6342305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80511945,"identity":"53253d68-2593-4d57-9972-80b65967c748","added_by":"auto","created_at":"2025-04-14 07:14:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710289,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6342305/v1/3ecc9fd303b178d48c365355.png"},{"id":80512265,"identity":"b602d2ec-85ae-4ccd-b29e-0344c4f0f240","added_by":"auto","created_at":"2025-04-14 07:22:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1389039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6342305/v1/7aa3d946-de6b-4343-af16-a4c0f80cd11e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSocio-economic Indicators are Linked to Ecological Outcomes: Insights into the Lives of Fish and Shrimp Farm Workers in West Bengal\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Ministry of Statistics and Programme Implementation, Government of India attaches considerable importance to coverage and quality aspects of statistics released in the country. In this context, studies on socio-economic status are of prime importance. Many studies have highlighted the importance of socio-economic factors (Reza et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dhenuvakonda et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kumari and Sharma, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anegla and Sharma, 2023).\u003c/p\u003e \u003cp\u003eIndia, the third largest fish producer and second largest in aquaculture, considers its fisheries sector an important part of its economic growth, strengthened by the Blue Revolution. The fishing industry is vital to India's economy, contributing ₹1,37,716 crores to the Gross Value Added (GVA) in 2022\u0026ndash;2023, accounting for 1.09% of total GVA and 6.72% of agricultural GVA (DAHD Annual Report, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e-23). India's inland fish production reached 12.12\u0026nbsp;million metric tonnes, with 50% coming from culture fisheries, which cover 2.36\u0026nbsp;million hectares of ponds and tanks, contributing 8.5\u0026nbsp;million metric tonnes to the total output (Dept. of Fisheries, Govt. of India, 2024). India is a leading producer and exporter of seafood, primarily shrimp, which is raised on approximately 1.67 lakh hectares, contributing to 8.43 lakh tonnes of production. The country has about 1.191\u0026nbsp;million hectares of brackish water suitable for shrimp farming across 10 states, but only 1,20,000 hectares are currently utilized. Since 1970, shrimp production has surged from 20 metric tonnes to 7,47,000 metric tonnes in 2022, significantly boosting India's fisheries export revenue to ₹ 46,662 crores (CIBA, 2022).\u003c/p\u003e \u003cp\u003eBesides providing nutrition and ensuring food security, fisheries and aquaculture are crucial for livelihoods. The most recent figures show that around 61.8\u0026nbsp;million people were employed in the primary sector of fisheries and aquaculture in 2022 (FAO, 2024).\u003c/p\u003e \u003cp\u003eDas et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have highlighted the importance of socio-economic studies of fish workers. Some previous research has reported on the conditions of fish and shrimp farmers across various regions of W.B., yet none have specifically focused on the workers in fish and shrimp farms (Abraham et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dana et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Biswas et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maity et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address this gap, this study presents the socio-economic profile of fish and shrimp farm workers in W.B.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study area\u003c/h2\u003e\n \u003cp\u003eThe present study was carried out in South 24 Pargana and Purba Medinipur districts, W.B. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) which are two key districts in the fisheries sector. W.B. spans from 21\u0026deg; 36\u0026apos; N to 27\u0026deg; 13\u0026apos; N latitude and 85\u0026deg; 50\u0026apos; E to 89\u0026deg; 50\u0026apos; E longitude. Purba Medinipur district holds the third position in inland fish production among W.B.\u0026apos;s districts. Purba Medinipur district also leads in the number of Coastal Aquaculture Authority (CAA) registered shrimp farms across W.B. (CAA, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). South 24 Parganas district secures the second position in inland fish production among all districts in W.B. South 24 Parganas district also holds the second highest number of CAA registered shrimp farms in W.B. (CAA, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data collection\u003c/h2\u003e\n \u003cp\u003eInformation was collected from 90 fish farm workers and 90 shrimp farm workers. An interview schedule was developed and information was collected on various variables such as age, education, religion, caste, resident status, marital status, working experience, personal insurance, household facilities, monthly income, personal monthly expenditure, savings, and loans taken.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eFor descriptive statistics (percentage, frequency, mean, and standard deviation), MS Excel was used, and to check for significant differences between various variables among fish and shrimp farm workers, the Z-test was conducted. Difference in monthly income, expenditure and work experience between fish farm workers and shrimp farm workers was tested using Z test.\u003c/p\u003e\n \u003cp\u003eThe formula for conducting a Z-test to compare means was as follows.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Social status\u003c/h2\u003e \u003cp\u003eMost of the workers in shrimp farms (32.23%) were between the ages of 20 and 30 years, while the majority of workers in fish farms (33.33%) were in 30 to 45 year age bracket. Prusty and Sharma (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that 50% of pond farmers in Odisha were classified as middle aged. Additionally, a study by Biswas et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in the North 24 Parganas district of W.B. revealed that a significant majority (88.33%) of fish farmers were middle aged, specifically within the age range of 31 to 60 years. These findings highlight age demographics among fish/shrimp farm workers across different regions in India.\u003c/p\u003e \u003cp\u003eThe educational background of shrimp and fish farm workers in W.B. revealed that a significant proportion of shrimp farm workers (45.55%) and fish farm workers (38.88%) had only primary education. Notably, fish farm workers exhibited a higher percentage of secondary school education compared to their shrimp farm counterparts, and some shrimp farm workers lacked any formal schooling. Studies such as those conducted by Bhattacharjee et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) indicated that fish farmers across the Maldah, Murshidabad, and Birbhum districts in W.B. were literate, with 20.02% able to read and write, while 19.88% had completed primary education and 19.09% had reached middle school, with very few graduates. Similarly, Maity et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the majority of farmers (56%) in Purba Medinipur had attained a middle school education, with 20.5% having completed secondary education. These studies underscore the educational disparities among fish/shrimp farm workers.\u003c/p\u003e \u003cp\u003eThe demographic profile of the workforce revealed that the majority of fish farm workers (84.45%) and shrimp farm workers (76.67%) were Hindus, while rest were Muslims. These findings align with study by Sharma et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who reported similar trends among shrimp farm workers in Andhra Pradesh, Tamil Nadu, Maharashtra, Gujarat and Odisha, where 76.66% were Hindu, 10.41% were Muslim, and 12.93% were Christians. Moreover, a significant proportion of shrimp (38.88%) and fish (50%) farm workers belonged to the General Caste, with a higher percentage of Scheduled Castes and Scheduled Tribes represented among shrimp farm workers compared to those in fish farms.\u003c/p\u003e \u003cp\u003eMost of the fish farm (67.78%) and shrimp farm (62.22%) workers were married. The workforce on shrimp farms predominantly consisted of migrants, with 71.12% originating from neighbouring states such as Jharkhand and Odisha. These migrant workers typically arrive during the crop season each year to earn wages. In contrast, a majority of fish farm workers (65.55%) were local residents, highlighting the reliance of shrimp farms on migrant labour while fish farms primarily draw from the surrounding community. A similar finding was reported by Sharma et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), indicating that migrant shrimp farm workers in Andhra Pradesh and Tamil Nadu came from states such as Odisha, Karnataka. These studies underscore the significant role of migrant labours in shrimp farming in various regions of India.\u003c/p\u003e \u003cp\u003eWorkers in shrimp farms typically had less experience compared to those in fish farms. Specifically, 61.12% of shrimp farm workers reported having less than 5 years of experience, while 40% of fish farm workers had 5 to 10 years of experience. A similar finding was noted by Sharma et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, Prusty and Sharma (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that 63% of fish pond farmers in Odisha had farming experience ranging from 10 to 20 years. These findings highlight the varying levels of experience among farm workers in the aquaculture sector.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the key findings related to age, education, religion, residence, marital status, and working experience of shrimp and fish farm workers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on social variables of fish and shrimp farm workers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFish farm workers (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShrimp farm workers (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003cp\u003eMean age: 34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003cp\u003eMean age: 30.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResident status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMigrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWorking experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003cp\u003eMean experience: 6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003cp\u003eMean experience: 5.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Economic status\u003c/h2\u003e \u003cp\u003eThe majority of fish farm workers resided in pucca houses (52.22%), while most shrimp farm workers lived in semi-pucca houses (47.78%). In terms of drinking water, a higher proportion of fish farm workers (73.33%) had facilities in their homes compared to shrimp farm workers (68.88%). The primary source of drinking water for both groups was tubewells, supplemented by public taps. Supporting this observation, Bhutti et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that 92% of fish and shrimp farm workers in Gujarat used tubewell water for drinking, with only 8% relying on alternative sources. This indicates a significant reliance on tubewell water among aquaculture workers in the region. Furthermore, the majority of workers reported having electricity connections in their homes.\u003c/p\u003e \u003cp\u003eRegarding monthly income, the majority of fish farm workers (67.78%) reported earnings between ₹10,000 to ₹15,000 per month, while most shrimp farm workers (52.22%) earned between ₹5,000 to ₹10,000 per month. The average monthly income for fish farm workers was ₹10,655, whereas shrimp farm workers had an average monthly income of ₹9,888. (One US \u003cspan\u003e$\u003c/span\u003e = 84.07 ₹ i.e., Indian Rupee)\u003c/p\u003e \u003cp\u003eThe average incomes for both fish and shrimp farm workers were lower than the national average but slightly exceeded the W.B. state average, according to the Economic Survey of India for 2023-24. This indicates that while these workers earn relatively modest incomes, they are somewhat better off compared to the broader state economic context. The disparity highlights on-going challenges within the aquaculture sector, suggesting the need for targeted interventions to enhance financial stability and improve the livelihoods of those employed in this vital industry. Overall, the income levels underscore the economic realities faced by these workers in W.B.\u003c/p\u003e \u003cp\u003eAdditionally, fish farm workers reported higher personal expenses each month than shrimp farm workers. Specifically, most shrimp farm workers (47.78%) had personal monthly expenditures ranging from ₹2,000 to ₹3,000, while the majority of fish farm workers (52.22%) spent between ₹3,000 and ₹4,000 per month.\u003c/p\u003e \u003cp\u003eWorkers in shrimp farms exhibited a lower percentage of personal insurance coverage (10%) compared to their fish farm counterparts (15.55%). However, the overall prevalence of insurance among these workers was low. Those who did have insurance typically relied on personal policies, such as Life Insurance Coverage (LIC), which further emphasizes the need for improved financial security for aquaculture workers.\u003c/p\u003e \u003cp\u003eA majority (73.33%) of fish farm workers had savings in the bank which was relatively higher than those of shrimp farm workers as 56.67% had savings in the bank. Additionally, 57.77% of fish farm workers reported monthly savings between ₹3,000 to ₹4,000, whereas 42.22% of shrimp farm workers reported monthly savings of ₹2,000 to ₹3,000. Shrimp farm workers had fewer loans than fish farm workers, with 21.12% of shrimp farm workers reporting loans from the bank, compared to 28.88% of fish farm workers.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the key findings related to economic variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on economic variables for fish and shrimp farm workers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFish farm workers (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShrimp farm workers (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹5,000- ₹10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹10,000- ₹15,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.78\u003c/p\u003e \u003cp\u003eMean income: ₹10,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003cp\u003eMean income:\u003c/p\u003e \u003cp\u003e₹9,888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePersonal monthly expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹2,000- ₹3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹3,000- ₹ 4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹4,000- ₹5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₹5,000- ₹6,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003cp\u003eMean expenditure: ₹3,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003cp\u003eMean expenditure: ₹2,884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSavings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLoan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuchha*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemi Pucca*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePucca*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrinking water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElectricity facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Kuchha house: Often made of mud/thatch, Semi Pucca House: Made of mud/thatch and concrete, Pucca: Made of brick/concrete\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the statistical (z-test) results to see if there was a significant difference between age, working experience, monthly income, and monthly expenditure between fish and shrimp farm workers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Age, Experience, Income, and Expenditure between fish and shrimp farm workers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (Fish Farm Workers)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (Shrimp Farm Workers)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo significant difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,655.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,888.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly Expenditure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that there was no significant difference in age of fish and shrimp farm workers. However, fish farm workers have significantly higher working experience, higher monthly income and expenditure compared to shrimp farm workers. In a study by Katre et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) it has been reported that there were differences among socio-economics of reservoir fishers. However, more studies will be required to conclude that fish farming may be providing better economic opportunities and benefits, than the shrimp farm workers despite a similar demographic profile.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe analysis of fish and shrimp farm workers in W.B. state reveals distinct demographics. Workers in shrimp farms are predominantly younger, with many aged 20\u0026ndash;30, while fish farm workers are mostly in the 30\u0026ndash;45 age group. Education levels show that a significant portion of both groups had primary level education, with fish farmers relatively having better education. The workforce was largely Hindus, with representation of Scheduled Castes and Scheduled Tribes among shrimp farm workers. Migrant labour plays a crucial role in shrimp farming, contrasting with the local focus of fish farms. Additionally, shrimp workers tend to have less experience than their fish farming counterparts.\u003c/p\u003e \u003cp\u003eFrom the study it is clear that fish farm workers had relatively higher monthly income and expenditure. Although both groups earn below the national average, they fare slightly better than the state average. As a result, their income levels can be considered modest. Additionally, the low insurance coverage for both groups underscore the need for enhanced financial security measures within the aquaculture sector. The socio-economic conditions of farm workers are closely linked to resource consumption and environmental impacts, serving as key ecological indicators for evaluating sustainability. This relationship warrants further research and attention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study received approval from the Institutional Review Board (IRB) of the Central Institute of Fisheries Education, Mumbai, India, prior to the commencement of data collection. The research was conducted in accordance with institutional ethical guidelines, and informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSayantan Das\u003c/strong\u003e: Data collection, Analysis, Paper drafting\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArpita Sharma:\u003c/strong\u003e Conceptualisation, Schedule preparation and testing, Guidance in data collection and analysis, Paper writing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmitava Ghosh\u003c/strong\u003e: Schedule testing, Supervision in data collection, Paper writing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVinod K. Yadav\u003c/strong\u003e: Statsitical analysis, Paper editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKripa V\u003c/strong\u003e: Finalising the Schedule, Paper editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVijendra Kumar\u003c/strong\u003e: Data feeding and analysis, Paper editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNidhi Katre\u003c/strong\u003e: Data feeding and analysis, Paper editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors would like to thank ICAR-Central Institute of Fisheries Education for providing the necessary research facility for this study. They thank all the fish and shrimp farm workers for sharing information and for their cooperation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraham, T.J., Sil, S.K., Vineetha, P., 2010. Comparative study of the aquaculture practices adopted by fish farmers in Andhra Pradesh and West Bengal. Indian Journal of Fisheries. 57, 1-48.\u003c/li\u003e\n\u003cli\u003eAngela, S., Sharma, A., 2023. Analysis of socio-economics and occupational dimensions of shrimp farmers of Tamil Nadu. Aquaculture Research. 1, 1-17. https://doi.org/10.1155/2023/2457208.\u003c/li\u003e\n\u003cli\u003eBhattacharjee, S., Dhara, K., Ghosh, S., Das, P., Giri, A., Sarkar. B., 2021. Study on socio-economic status and constraints faced by the livestock farmers of the aspirational districts of West Bengal, India. International Journal of Research and Innovation in Social Science (IJRISS). \u003cstrong\u003e7,\u003c/strong\u003e 331-340. http://dx.doi.org/10.47772/IJRISS.2021.5714\u003c/li\u003e\n\u003cli\u003eBhutti, J.K., Lende, S., Pargi, N.A., Vasava, R.J., Taral, P.V., 2022. Studies on the socio-economic condition of fish farmer in Sabarkantha district of Gujarat state. The Pharma Innovation Journal. 11, 970-974.\u003c/li\u003e\n\u003cli\u003eBiswas, B., Das, S.K., Mandal, A., 2018. Socio-economic dimensions and their impacts upon productivity of composite fish farming in North 24 parganas district, West Bengal. Nature. 25, 41-67.\u003c/li\u003e\n\u003cli\u003eCAA, GoI, 2024. https://caa.gov.in/CAA/Farm/Hatcheries. (accessed 20 September, 2024).\u003c/li\u003e\n\u003cli\u003eCIBA Annual Report, 2022. https://ciba.icar.gov.in/wp-content/uploads/AnnualReports/2022.pdf. \u003c/li\u003e\n\u003cli\u003eDAHD Annual Report, 2022-23. https://www.dahd.gov.in/node/48. \u003c/li\u003e\n\u003cli\u003eDana, S.S., Ghosh, A., Bandyopadhyay, U.K., 2015. Socio-economic profile and problems of mud-crab farmers of South 24-Parganas, West Bengal: An explorative study. Journal of Crop and weed. 11, 120-123.\u003c/li\u003e\n\u003cli\u003eDas, S., Naik, B., Kumar, V., Soni, S., Prakash, S., Yadav, V.K., Kurmi, R., Sharma, A., 2024. Socio-economic profile of women fish workers in Mumbai. International Journal of Agriculture Extension and Social Development. \u003cstrong\u003e7\u003c/strong\u003e, 201-205. https://doi.org/10.33545/26180723.2024.v7.i5c.621.\u003c/li\u003e\n\u003cli\u003eDept. of Fisheries, Govt. of India, 2024. https://dof.gov.in/ (accessed 13 September 2024).\u003c/li\u003e\n\u003cli\u003eDhenuvakonda, K., Sharma, A., Prasad, K.P., Sharma, R., 2019. Socio-economic profile of fish farmers of Telangana and usage of mobile apps. Asian Journal of Agricultural Extension, Economics and Sociology. 37, 1-9. https://doi.org/10.9734/ajaees/2019/v37i330268.\u003c/li\u003e\n\u003cli\u003eEconomic Survey of India, Government of India, 2023-24. https://www.indiabudget.gov.in/economicsurvey.\u003c/li\u003e\n\u003cli\u003eFAO Report, 2024. https://www.fao.org/newsroom/detail/fao-report-global-fisheries-and-aquaculture-production-reaches-a-new-record-high/en.\u003c/li\u003e\n\u003cli\u003eKatre, N., Sharma, A., Ojha, S.N., Tyagi, L., Yadav V.K., 2024. Reservoir fisheries governance quality index: Development and validation. Ecological Indicators\u003cem\u003e.\u003c/em\u003e 158, 111562. https://doi.org/10.1016/j.ecolind.2024.111562.\u003c/li\u003e\n\u003cli\u003eKumari, S., Sharma, A., 2022. Socio-economic status of fishers and fish production trends from cage culture in Chandil reservoir, Jharkhand. Journal of Krishi Vigyan. 10, 290-294. http://dx.doi.org/10.5958/2349-4433.2022.00051.4.\u003c/li\u003e\n\u003cli\u003eMaity, A., Saha, B., Dana, S.S., Gupta, R., Bandyopadhyay, U.K., 2020. Socio-economics of the \u003cem\u003ePenaeus vannamei\u003c/em\u003e (Boone, 1931) farmers in West Bengal, India: a descriptive study. Journal of Crop and Weed. 16, 104-109. https://doi.org/10.22271/09746315.2020.v16.i3.1373\u003c/li\u003e\n\u003cli\u003ePrusty, S., Sharma, A., 2023. Occupational hazards faced by inland fishers of Odisha state, India. Journal of Agromedicine\u003cem\u003e.\u003c/em\u003e 28, 425-432. https://doi.org/10.1080/1059924X.2023.2178572.\u003c/li\u003e\n\u003cli\u003eReza, S., Hossain, M.S., Hossain, U., Zafar, M.A., 2015. Socio-economic and livelihood status of fishermen around the Atrai and Kankra Rivers of Chirirbandar Upazila under Dinajpur District\u003cem\u003e. \u003c/em\u003eInternational Journal of Fisheries and Aquatic Studies. 2, 402-408.\u003c/li\u003e\n\u003cli\u003eSharma, A., Prusty, S., Rathod, R., Arthi, R., Watterson, A., Cavalli, L., 2023. Occupational hazards of Indian shrimp farm workers. All Life. 16,1-15. https://doi.org/10.1080/26895293.2023.2225762.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ICAR- Central Institute of Fisheries Education, Mumbai, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Socio-economic, Fish and shrimp farm workers, West Bengal","lastPublishedDoi":"10.21203/rs.3.rs-6342305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6342305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the socio-economic status of fish and shrimp farm workers in West Bengal (W.B.), a state with a significant role in fisheries due to its extensive coastline and diverse aquatic ecosystems. While most existing research focuses on fish and shrimp farmers, there is a notable gap in understanding the conditions of farm workers. Socio-economic indicators such as income, education, and employment are closely linked to ecological outcomes, influencing resource use, consumption, and environmental sustainability. The study aims to fill this gap by profiling the socio-economic characteristics of workers in two key fisheries districts, Purba Medinipur and South 24 Parganas. Data were collected through interviews with 180 workers (90 fish and 90 shrimp farm workers), focusing on factors such as age, education, religion, caste, residency, marital status, work experience, income, and household facilities. The findings indicated that majority of workers were Hindus, with an average age of 34 years for fish farm workers and 30 years for shrimp farm workers, most having primary education. Notably, 65.55% of fish farm workers were locals, while 71.11% of shrimp farm workers were migrants from other states. Farm related experience varied from 5 to 10 years. About half (52.22%) of fish farm workers lived in pucca (made of brick/concrete) houses and 47.78% shrimp farm workers resided in semi pucca (made of mud/thatch and concrete) houses. The average monthly income was ₹10,655 for fish farm workers and ₹9,888 for shrimp farm workers, with fish farm workers incurring higher personal expenditures (One US \u003cspan\u003e$\u003c/span\u003e = 84.07 ₹ i.e., Indian Rupee). Additionally, shrimp farm workers had a lower percentage of personal insurance compared to their fish farm counterparts. While incomes were below the national average, they were slightly above the state average. Statistical tests revealed no significant difference in age of fish and shrimp farm workers. However, fish farm workers had statistically significant higher working experience, monthly income expenditure compared to shrimp farm workers. These findings underscore the need for targeted interventions. The socio-economic conditions of farm workers, are closely tied to resource consumption and environmental effects, functioning as important ecological indicators for assessing sustainability for which more research focus is required.\u003c/p\u003e","manuscriptTitle":"Socio-economic Indicators are Linked to Ecological Outcomes: Insights into the Lives of Fish and Shrimp Farm Workers in West Bengal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 07:13:57","doi":"10.21203/rs.3.rs-6342305/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cc9549b-4978-471c-aee6-606aa5963def","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46435255,"name":"Social Policy"},{"id":46435256,"name":"Other Economics"},{"id":46435257,"name":"Agricultural Economics \u0026 Policy"}],"tags":[],"updatedAt":"2025-04-14T07:13:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 07:13:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6342305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6342305","identity":"rs-6342305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}