Dataset on Marketing Gaps, Infrastructure Deficits, and Welfare Challenges in Artisanal Fishing Communities: Survey Data from Coastal Tamil Nadu, India
Description
This article presents a structured primary survey dataset capturing the marketing realities, logistical constraints, occupational health experiences, and welfare perceptions of artisanal fishing communities across the coastal belt of Tamil Nadu, India. A total of 280 respondents were surveyed between April 16 and 21, 2025, using a pre-tested, 23-variable questionnaire translated into Tamil and administered on paper with field-assisted completion. The instrument spans five thematic domains: socio-demographic and occupational characteristics; seafood distribution channels and marketing challenges; physical and logistical infrastructure requirements; healthcare access and occupational health burden; and food safety practices alongside welfare improvement priorities. Respondents encompassed active fishermen engaged across traditional, mechanized, and aquaculture modalities, though artisanal practitioners constitute the dominant share. The dataset reveals pronounced middleman dependency, acute cold chain infrastructure deficits, limited food safety training coverage, and substantial barriers to healthcare access among coastal fishing households. These data are amenable to quantitative analysis involving logistic regression, structural equation modelling, and value chain mapping, and are intended to support researchers, policymakers, and development practitioners working at the intersection of aquaculture economics, rural livelihoods, and sustainable supply chain management.
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Data Collection Methodology This dataset was generated through a structured primary survey administered to active artisanal fishermen across the coastal belt of Tamil Nadu, India. The study employed a cross-sectional survey design targeting respondents engaged in marine capture fishing across traditional, mechanized, and aquaculture modalities. Survey Instrument: A pre-tested questionnaire comprising 23 variables was developed after a systematic review of literature on small-scale fisheries marketing, rural value chains, and coastal community welfare in South Asia. The instrument was organised into five thematic domains: (1) socio-demographic and occupational characteristics; (2) seafood distribution channels and marketing challenges; (3) physical and logistical infrastructure requirements; (4) healthcare access and occupational health burden; and (5) food safety practices and welfare improvement priorities. Question formats included single-select categorical items, multi-select categorical items, and one open-ended qualitative item. Translation and Back-Translation: The original English questionnaire was translated into Tamil, the local language of the respondent communities, to ensure linguistic accessibility and cultural appropriateness. Following data collection, all responses were translated back into English prior to data entry and archiving. This forward-backward translation protocol is consistent with established standards for cross-cultural survey research and minimises meaning distortion across language transitions. Data Collection Mode: The survey was administered on paper using the Tamil-language version of the instrument. Respondents with sufficient literacy read and answered the questionnaire independently. Respondents with limited literacy were assisted by trained field collaborators who read each question aloud in Tamil and recorded responses on their behalf, ensuring systematic inclusion across literacy levels within the fishing communities. Sampling: Purposive convenience sampling was employed, targeting active fishermen without geographic sub-district stratification. Data collection occurred between April 16–21, 2025, yielding 280 completed responses. Data Processing: Translated responses were entered into Microsoft Excel (.xlsx) format. Records were examined for internal consistency and conditional logic compliance. Personally identifiable information (name and contact fields) was removed prior to archiving. No imputation was performed on missing values; high missingness on conditional follow-up items reflects legitimate skip-logic, not data loss. Software: Microsoft Excel (data storage and cleaning), Python 3.12 with pandas library (data audit and variable renaming).
Institutions
- Sri Ramachandra Institute of Higher Education and ResearchTamil Nadu, Porur