Socio-demographic status of fisherwomen from the Konkan Coast of Maharashtra

Introduction

Women play an important role in the development of the household, society, and country. Mostly, the contribution of women is not valued and given lesser attention than they deserve. Presently, women are excelling in many fields, and the government also supports the development of women by providing various schemes and support (Kumar et al., 2003) [1] Globally, women in the fishing communities fulfill diverse roles, encompassing livelihood, productive, and reproductive. Women are actively involved in post-harvest activities such as marketing, drying and curing, processing, etc. comparatively less women are involved in capture fisheries. In comparison to the contribution by fisherwomen, their role is often underrecognized. (Gopal et al., 2012) [2] According to FAO, 2024 globally, 65 million people are involved in fisheries for their livelihood. Women play a crucial role in the aquatic food system, accounting for 27% of primary production and 56% in the processing sector. As per FAO, 2024 in capture fisheries, 28% of women and 72% of men are involved, while in inland fisheries, women contribute 26% of total employment (FAO, 2026) [3] . India is the second largest fish producer of the world with a vast coastline of 11,098.81 km. Konkan region of Maharashtra state has a long coastline of 877.97 Km extended in seven districts: Thane, Palghar, Mumbai city, Mumbai suburban, Raigad, Ratnagiri, and Sindhudurg. Maharashtra had 11.87% of the landing centers of the coastal states and UTs in India. There are 526 fishing villages, and the total fisher population of the country is recorded as 15,18,228. Fisheries is an important source of livelihood for fisherwomen. The total fisherwomen population along the Konkan region is recorded as 1,88,499. (Handbook of Fisheries Statistics, 2025) [4] Socio-economic refers to the measurement of a person's or family's economic and social position in relation to factors like occupation, family affluence, physical assets, social position, participation, caste, political influence, etc (Biswal et al., 2024) [5] . The present study focuses on the socio-demographic profile of fisherwomen from the Konkan coast of Maharashtra state. The study details personal information like age, education, religion, caste, category, family type, membership in the organization, family income, information regarding the main income earner and occupation, card holdings (Aadhaar card and ration card), etc.

Materials and methods

The study was conducted in the Konkan region of Maharashtra. A total of 420 women from the fisher community were personally interviewed; the women involved in fishing, drying, selling, and other fisheries-related activities were surveyed during their leisure time. The data were collected from June 2025 to Jan 2026. Before finalizing the interview schedule, a pre-test was conducted on 15 fisherwomen, and the schedule was modified.

Image 1: Data collection from fisherwomen in their leisure time

Data analysis

The sample size was determined using Cochran's (1977) formula for estimating proportions in large populations (Hasamoh et al., 2024) [6] .

where n is the required sample size, Z is the standard normal variate at the desired confidence level (1.96 at 95% confidence), p is the estimated proportion (0.50), and d is the allowable error (0.05). The calculated minimum sample size was 384 respondents. To improve representation across the study area and facilitate district-wise comparisons, 420 artisanal fisherwomen were surveyed, with 60 respondents selected from each of the seven coastal districts of Maharashtra.

Percentage analysis

The percentage analysis is used to make simple comparisons. (Nasre et al., 2024) [7] . Frequency and percentage were calculated for sociodemographic factors.

Spearman rank correlation

Spearman's rank correlation coefficient (Spearman's ρ) is a non-parametric measure of the strength and direction of association between two variables based on their ranks. It is particularly suitable for ordinal data or when the assumptions of normality required for Pearson's correlation are not satisfied. Spearman's coefficient measures a monotonic association and ranges from -1 to +1. (Julie, 2020) [8] 𝑟 𝑠 = 1 -6Σ𝕕 𝑖 2 𝜂 ( 𝜂 2 -1 )

Here, rs = Spearman's rank correlation coefficient di = difference between the ranks of the two variables for the ith observation n = total number of paired observations ∑d i (Gopal et al., 2012) [2] = sum of the squared differences between corresponding ranks

Chi-square test of association

Chi-square (𝑥 (Gopal et al., 2012) [2] ) test of association, also referred to as Pearson χ-square test of independence. It is a non-parametric test and is used to find the significant association between the two categorical variables. The chi-square test was used to find the association between selected demographic parameters and WASH variables (Tripathi et al., 2017) [9] .

𝑥 (Gopal et al., 2012) [2] = Chi-square value ∑ =Summation Oij= Observed value/frequency for row i and column j E ij = Expected value/Frequency for row i and column j P= Total numbers of rows C = Total number of columns

Results and discussion

Results of the study are evaluated based on the responses from 420 women. The details of variables under study are presented in Table 1.

Age

Age is categorized into three groups: young, middle, and old age. The majority of fisherwomen belong to the old age (≥ 50 years) group (50.48%), followed by middle-aged (25-49 years) 46.90% only few 2.62% belongs to young (15-24 years) age group. Similar results were obtained by other researchers; (Gadde et al., 2024) [10] reported that fisherwomen of Uttar Kannada, the majority (50%) were young (up to 40 years), and only 5% were old. (Bhargavi et al., 2020) [11] reported that fisherwomen in Vizianagaram district, Andhra Pradesh, were predominantly middle-aged (61.6%) belonging to the age group 31-56 years. (Rao et al., 2022) [12] likewise recorded the highest concentration of Srikakulam fisherwomen in the 31-40 year bracket (49.3%).) also reported a 18% are in 20-30 years age group and 17.2% are in 41-50 years age group and 15.5% are in above 50 years age group. (Gunakar et al., 2016) [13] found that in two major fish markets of Mangalore and Malpe in coastal Karnataka, the majority of women retailers belonged to the middle-aged group (46-55 yrs), while male two-wheeler retailers were comparatively younger (average 38 yrs). (Habib et al., 2025) [14] , while studying the fisherwomen of Wular Lake, Kashmir, reported that 50% of fisherwomen belonged to the age group (35-40 yrs), followed by 24% above 40 yrs. In all the stud ies, we found that the fisherwoman age categorization varies according to location; fisherwomen belong to the age group 35 to 55 yrs, suggesting that the younger generation is involved in education or other work.

Educational status

The majority (40.71%) of fisherwomen completed secondary education, followed by fisherwomen with primary education (31.19%). A notable percentage (16.90%) falls under the illiterate category, followed by 10% of higher secondary, and only 1.19% of respondents were educated up to graduation level. Similar results were obtained by (Kumar et al., 2003) [1] in their multi-state level study along Andhra Pradesh, Karnataka, Kerala and Tamil Nadu, reported an overall literacy rate of 70.49%, with Andhra Pradesh recording the lowest literacy (37.56%) among the four states. Conversely, (Bhargavi et al., 2020) [11] in Vizianagaram reported low literacy, as 88.1% were illiterate. (Harsha et al., 2024) [15] in Karnataka reported majority 36.67%) were illiterate, and (Rao et al., 2022) [12] in Srikakulam found 48.8% illiterate. (Habib et al., 2025) [14] found that 80% of respondents were illiterate and only 20% literate. By comparing the education of other states, we found that Konkani fisherwomen are comparatively more educated.

Family type

The distribution of respondents according to their family structure indicated that 67.62% of fisherwomen live in nuclear families, followed by 32.38% belongs to joint families. Similar findings were reported by (Bhargavi et al., 2020) [11] , where the majority 64.9% live in nuclear families. (Ahila et al., 2024) [16] reported that the majority of seaweed farmers of Ramanathapuram (81.67%) had nuclear families. Conversely, (Rao et al., 2022) [12] found that among Srikakulam fisherwomen, joint families were dominant at 55.75%. (Habib et al., 2025) [14] found that 80% of Hanji fisherwomen households in Kashmir live in joint families.

Religion

The religious affiliation demonstrated that the majority of fisherwomen were Hindu 84.29%, followed by 13.33% of Muslims, 1.67% belonged to others (Buddhist), and only 3 respondents (0.71%) belonged to the Christian religion. (Rao et al., 2022) [12] found that 81.8% of Srikakulam fisherwomen were Hindu, followed by 18.2% non-Hindu (Kumar et al., 2003) [1] found that Hindus constituted 66.09% of the pooled sample but Christians accounted for a substantial 31.02%, largely driven by high Christian representation in Kerala (62.00%) and Tamil Nadu (52.23%); the near-absence of Christian respondents in the present study reflects the specific regional and community composition of the study area.

Caste and category

Based on caste-wise distribution, it was found that the largest group belongs to the Koli community, representing 56.19% of the total respondents, followed by (25%) belonging to other castes (individuals from the Buddhist and Christian community), and very few (1.90%) belonging to the Bhandari community. According to category, the majority (43.33%) belong to OBC (Other Backward Category), followed by (33.57%) belonging to SBC (Special Backward Classes), and only a few (0.24%) belong to NT (Nomadic tribes). (Gadde et al., 2024) [10] reported that 100% of Karnataka fisherwomen belong to backward communities (Hindu Karvi and Harikantra groups). Suggesting a broad pattern of caste-based occupation among the coastal communities.

Mobile phone ownership

The substantial majority, i.e., (75.71%), own mobile phones, whereas (24.49%) do not have mobile phones. In contrast, (Ahila et al., 2024) [16] reported that only 9% use mobile phones among Tamil Nadu seaweed fisherwomen.

Housing and electricity

Evaluation of housing structure among the respondents indicates that the majority of respondents (48.81%) reside in pukka houses, followed by (36.67%) living in semi-pucca houses. A smaller proportion (14.52%) lives in kutcha houses. And among these, the majority (82.38%) have access to electricity from the government (MECB); only a few (1.67%) of respondents don't have electricity in their homes. Similarly, (Ahila et al., 2024) [16] reported majority 63.34% lived in pucca houses. (Kumar et al., 2003) [1] In their multi-state findings reported that 51.95% of fisher households across Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu lived in pucca structures. (Rao et al., 2022) [12] reported that 22.3 percent are living in Pacca houses, 29.2 percent are living in Semi-pacca houses, 18.8% are living in Katcha houses and 29.7% are living in slab houses, and 90% reported having electrified homes.

Primary source of income and main income earner

The highest percentage (77.14%) of respondents primarily depend on fishing for their income, followed by (16.90%) who depend on other jobs (like government jobs, private jobs, driving cabs), etc. The main income earner of the family was the spouse for (39.29%) of families, followed by (31.19%) of fisherwomen themselves, while in (29.52%) other members served as the main income earner. (85.95%) of respondents reported that the seasonal availability of catch affects the income, indicating that the main income earner in 1/3 of families was women.

Nature of work

Based on the nature of work as detailed in Fig. 1, the majority (59.05%) of fisherwomen reported selling fish at market, followed by 12.86% of shellfish collectors. Similar results were obtained by (Bhargavi et al., 2020) [11] that 84.9% of Vizianagaram fisherwomen were engaged in fish marketing as their primary occupation, while (Gadde et al., 2024) [10] reported that fisheries were the sole family occupation for 95.00% of Karnataka households. Similarly, (Habib et al., 2025) [14] reported that the majority of fisherwomen (78%) were engaged in selling fish, while 22% were involved in salting, curing, and drying.

Monthly household income

The monthly household income of fisherwomen families varies depending on various situations. The majority (55.95%) of households had more than ₹15,000 income, and only 6.19% had income below ₹5000. (Habib et al., 2025) [14] reported monthly household income of fisherwomen of Wular Lake, Kashmir, varies; the majority (60%) had income of ₹1,500 to ₹3000. While 40% had income between ₹3001 and ₹4000. The variation in income observed is due to the number of members involved from the family in fishing, and the work as sellers was observed to have high income compared to shellfish collectors and others (helpers).

Seasonal variability of income

The majority of fisherwomen (85.95%) reported that seasonal availability of catch affects the household income, reflecting that, based on season, income changes. Similar results reported by (Habib et al., 2025) [14] found that "lack of work in the lean season" ranked as the most serious work-related issue. (Gunakar et al., 2016) [13] also reported that women head loaders are highly affected by poor catch reflected the high retail price. (Galib et al., 2016) [17] also reported that seasonal and climate-related declines in river catch directly affect the livelihood.

Membership in cooperatives, SHG and other organizations

Fisherwomen were found to have membership in various organizations. 39.76% of respondents belonged to a cooperative society, 23.81% to a Self-Help Group, and 13.57% had membership in a women's group. Very few 1.43% had FFPO membership. Similarly, (Ahila et al., 2024) [16] reported that 95% of seaweed farmers had membership in various SHGs. (Gopal et al., 2012) [2] also reported that strengthening FFPOs and SHGs had improved the bargaining power of fisherwomen. Conversely, (Galib et al., 2016) [17] found that the fishing community in Bangladesh had greater access to Non-Governmental Organizations (52-66%), for the purpose of taking loans and saving.

Access to financial service

Regarding access to financial services as detailed in Table 3, the majority of fisherwomen had bank account ownership (88.33%), followed by 14.76% who had access to formal saving groups, 3.33% SHG loans, and 1.90% government subsidies. Similarly, (Gopal et al., 2012) [2] found limited access to credit, technology.

Satisfaction with living conditions

Regarding satisfaction with living conditions, (46.90%) of respondents reported that they are satisfied with the existing living conditions, while (0.71%) reported being very dissatisfied. Similarly, (Rao et al., 2022) [12] reported that fisherwomen of Srikakulam district of Andhra Pradesh reported being satisfied (47.5%), fully satisfied (42.3%), and 10.2% indifferent based on their income. The Spearman's rank correlation analysis revealed that monthly household income was positively and significantly associated with satisfaction with living conditions (rₛ = 0.323, p < 0.001), indicating that respondents with higher household income tended to report greater satisfaction with their living conditions. A similar study was conducted by (Khader, 2013; Kumar et al., 2003) [18,1] in their study also concluded that income is the most consistent determinant of living standards across the study. A weak but statistically significant positive association was also observed between monthly household income and work experience (rₛ = 0.129, p = 0.008). Education showed a weak negative and significant association with work experience (rₛ = -0.173, p < 0.001), suggesting that respondents with higher educational attainment tended to have comparatively lower work experience. Similar findings were reported by (Ahila et al., 2024) [16] and (Rao et al., 2022) [12] , where education had a weak association with work experience, as better-educated women were more likely to be involved in occupations other than fishing. However, education was not significantly correlated with either monthly household income (rₛ = 0.091, p = 0.064) or satisfaction with living conditions (rₛ = 0.078, p = 0.109). Similarly, no significant relationship was found between work experience and satisfaction with living conditions (rₛ = 0.021, p = 0.666). Overall, the findings indicate that household income was the variable most consistently associated with socioeconomic and living-condition outcomes in the study population. [2] = 4.716, df = 3, p = 0.194). Similarly, no statistically significant association was observed between family type and monthly household income (χ (Gopal et al., 2012) [2] = 1.878, df = 4, p = 0.758) or between family type and satisfaction with living conditions (χ (Gopal et al., 2012) [2] = 6.295, df = 4, p = 0.178). Thus, the findings suggest that the respondents' family type did not have a statistically significant relationship with their educational attainment, monthly household income, or satisfaction with living conditions.

Conclusion

The study concludes that in the Konkan region of Maharashtra, the majority of women involved in fisheries activities are old aged having age above 51 years, and most were educated up to the secondary level and mostly preferred to live in nuclear families. About 60% are involved in selling fish at the market. Most had Aadhaar cards (98.10%) and bank accounts (88.33%). All of these factors indicate that the social condition of fisherwomen along Konkan coast is good. 77.14% reported fishing as the income source for their family. And (31.19%) of the women are the main income earners of their families.

References

1. Kumar RN, Khader V, Sathiadhas R, Kasim HM, Sudhakara NS, Dhanpal K, Lakshmi HM. Socio•economic status of fisherwomen. ICAR. 2003.
2. Gopal N, Ashok A, Jeyanthi P, Gopal TKS, Meenakumari B, editors. Gender in Fisheries, a Future Roadmap. Gender in Fisheries: A Future Roadmap Workshop Report. Cochin: Central Institute of Fisheries Technology, Indian Council of Agricultural Research; 2012.
3. FAO. The State of World Fisheries and Aquaculture 2026. Blue Transformation: turning vision into impact. Rome, Italy: FAO; 2026.
4. Handbook of Fisheries Statistics 2025 | PDF | Aquaculture | Fishery. 2025.
5. Biswal T, Wasave SM, Chaudhari KJ, Patil SV, Wasave SS, Yadav BM, et al. Socio-economic status of gillnet operators from Ratnagiri block of Maharashtra state. Journal of Krishi Vigyan. 2024;12(3):683-90.
6. Hasamoh A, Wichaidit W. Hasamoh & Wichaidit, 2024. International Journal of Public Health Science. 2024;13(1):191-9.
7. Nasre MN, Wasave SM, Chaudhari KJ, Yadav BM, Desai AS, Patil SV, et al. Study on social profile of trawler operators from Ratnagiri block of Maharashtra. Journal of Krishi Vigyan. 2024;12(2):405-13.
8. Julie P. SPSS Survival Manual | A step by step guide to data analysis using IBM. London; 2020.
9. Tripathi P, Kamath R, Tiwari R. Occupational health and role of gender: A study in informal sector fisheries of Udupi, India. Indian Journal of Occupational and Environmental Medicine. 2017;21(2):45.
10. Gadde H, Sankangoudar S, Basavakumar KV, Rajeshwari N, Patil M. Socio economic profile of fisherwomen involved in marine fisheries in Karnataka. International Journal of Agriculture Extension and Social Development. 2024;7(8):188-90.
11. Bhargavi K, Chirwatkar BB, Das A, Behera S, Bhakta D. Socioeconomic status of fisherwomen community in coastal Vizianagaram district of Andhra Pradesh. Journal of Fisheries. 2020;8(1):741-5.
12. Rao ICS, Sobhasri T. Socio-Economic Status of Fisher Women in Srikakulam District, AP. International Journal of Creative Research and Thoughts. 2022;10(6).
13. Gunakar S, Bhatta R. Socioeconomic Status of Fisher-Women in Segmented Fish Markets of Coastal Karnataka. Agriculture Economics Research Review. 2016;(2):253-66.
14. Habib N, Rather MS. Socio-Economic Status of Fisherwomen in Kashmir: A Study on Hanji Community of Wular Lake. The Academic International Journal of Multidisciplinary. 2025;3(2).
15. Harsha G, Sankangoudar S, Kv B, N R, Patil M. Socio economic profile of fisherwomen involved in marine fisheries in Karnataka. International Journal of Agriculture Extension and Social Development. 2024;7(8):188-90.
16. Ahila MS, Qureshi NW, Argade SD, Ananthan PS, Johnson B. Women of tides: Gender analysis of wild seaweed harvesting in Tamil Nadu, south India. Indian Journal of Fisheries. 2024;71(4):141275-18.
17. Galib SM, Hoque N, Akter S, Chaki N, Mohsin A. Livelihood, Climate Change and Fisheries: A Case Study of Three Fishing Communities of Northwestern Bangladesh. International Research Journal of Social Science. 2016;5(8).
18. Khader V. Socio-economic Empowerment of Fisherwomen in Southern States of India. Fishery Technology. 2013;50:258-64.