Title: Are fishers poor? getting to the muddy bottom of marine fisheries income statistics

Running title: Marine fisheries income

Authors: Lydia Teh1,2,*, Yoshitaka Ota1,3, Andrés M. Cisneros-Montemayor1,2, Lucy Harrington4,

Wilf Swartz1,5

1 Nippon Foundation Nereus Program 2 Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, British

Columbia, Canada 3 School of Marine and Environmental Affairs, University of Washington, Seattle, Washington,

USA 4 Northeastern University, Boston, Massachusetts, USA 5 Marine Affairs Program, Dalhousie University, Halifax, Nova Scotia, Canada

* Corresponding author. Mailing address: 2202 Main Mall, Vancouver V6T 1Z4, British

Columbia, Canada. Email: lydia.teh@oceans.ubc.ca

Keywords: Fishing labour statistics, income and poverty; socio-economic indicator; small-scale

fisheries

Abstract

Fishers’ economic status is hard to assess because fisheries socio-economic data, including

earnings, are often not centrally available, standardized, or accessible in a form that allows

scaled up or comparative analyses. The lack of fishing income data impedes sound management

and allows biased perceptions about fishers’ status to persist. We compile data from

intergovernmental and regional datasets, as well as cases studies, on income earned from marine

wild capture fisheries. We explore the level and distribution of fishers’ income across fisheries

sectors and geographical regions, and highlight challenges in data collection and reporting. We

find that fishers generally are not the poorest of the poor based on average fishing income from

89 countries, but income levels vary widely. Fishing income in the large-scale sector is higher

than the small-scale sector by about 2.2 times, and in high-income versus low-income countries

by almost 9 times. Boat owners and captains earned more than double that of crew and owner-

operators, while income from fisheries is greater than that from agricultural work in 63% of

countries in this study. Nonetheless, incomes are below national poverty lines in 34% of the

countries with data. More detailed fishing income statistics is needed for quantitative scientific

research and for supporting socio-economic policies. Key gaps to address include the lack of a

centralized database for fisheries income statistics and the coarse resolution at which economic

statistics are reported internationally. A first step to close the gap is to integrate socio-economic

monitoring and reporting in fisheries management.

Table of Contents

Introduction

Methods

Data collection

Data analysis

Results

Data description

Fishing income trends

Discussion

Conclusion

Acknowledgement

Data Availability Statement

References

1

Introduction

Fisheries sustainability has to address not only the health of fish stocks but also the socio-

economic needs of fishers. The latter is increasingly recognised as being equally important to

ecological health and has fueled interest in tackling economic equity and social justice issues in

fisheries and ocean management (Carr, 2016; Quimby & Levine, 2018). Policy focus on fishers’

socio-economic condition has tended to revolve around questions of the environmental

consequences of being poor (Cinner, 2009; Silva, 2006) such as whether poverty drives the use

of destructive fishing practices. This approach reflects an underlying assumption that fishers are

poor, if not the “poorest of the poor”(Bailey, 1988; Béné, 2003; Nayak, Oliveira, & Berkes,

2014). Yet, there is inconclusive evidence to substantiate this assumption. In a paper exploring

poverty and fisheries, Béné and Friend (2011) conclude that there is “no firm causal

relationshipetween fishing and being poor.” Taking narratives for granted inhibits efforts for

more in-depth understanding of the reality at fishing communities. For example, many agencies

view the provision of alternative employment as a desirable livelihood option for fishers under

the assumption that fishing is an occupation of last resort, when in fact many poor fishers would

resist changing occupation for income reasons alone (Pollnac, Pomeroy, & Harkes, 2001).

Adopting a ‘one size fits all’ approach may be unsuitable when in fact fishers’ socio-economic

conditions may be diverse and influence their susceptibility and response to different drivers of

change and interventions (Pollnac et al., 2001).

Adequately determining fishers’ economic status can be challenging because fisheries socio-

economic data, including earnings (revenue, wages, income), assets and expenditures, are often

not centrally available or accessible in a form that is amenable for scaled up or comparative

analyses. Where they are available, the metrics and definitions of fishing income vary widely and

often are not standardized, making it difficult to apply data to analyses across geographical and

temporal scales. The lack of standardization is a drawback given how fisheries issues are

increasingly raised in international policy frameworks and economic dialogues that recognise a

need to address systemic challenges faced by fishing communities. At these platforms,

standardised indicators are essential to enable countries worldwide to measure and compare

progress towards agreed targets. For instance, Sustainable Development Goal 14.B on healthy

oceans aims to provide benefits to artisanal fisheries in every country; yet practical attainment of

this hinges on having standardised indicators such as fishing income to assess countries’

performance. Likewise, standardised fishing income statistics provide critical benchmarks that

countries need to argue their positions at international negotiations. For instance, at ongoing

debates on fisheries subsidies at the World Trade Organization (World Trade Organization,

2019), countries that maintain their fishers need subsidies to alleviate poverty need to have

comparative fishing income data to give credence to their argument.

The relative obscurity of fishing income statistics is surprising considering the wide applicability

of these data. Income is usually a primary input to poverty measurements, which typically

involve using a money metric (i.e., income) to establish a poverty line to distinguish poor from

non-poor households (Carter & Barrett, 2006). Income data are also important in integrated

ecological models to solve the ‘poverty trap’ problem (Ngonghala et al., 2017), as well as in

bioeconomic models to assess impacts of potential policies that affect fishers’ compensation or

access to resources. Fisheries rebuilding (Sumaila et al., 2012), marine spatial planning(Janßen et

al., 2018), and climate change adaptation (Lam, Cheung, & Sumaila, 2016) are examples of

issues that benefit from insight provided by fishing income. The absence of these data is a barrier

to identifying trends and making projections about the future, while also allowing biased

perceptions about fishers’ status to abound.

That said, fishing income data do exist – notably the income of workers in major economic

sectors, including fishing, are reported by the International Labour Organisation’s (ILO) labour

statistics, although the application of ‘Fishing’ as a statistical category based on the International

Standard Industrial Classification of All Economic Activities (ISIC) is limited both temporally

and regionally. Some countries capture economic indicators including income in primary

industry surveys (e.g., China Bureau of Fisheries, Ministry of Agriculture; Japan Ministry of

Agriculture, Forestry and Fisheries), while fishing income data may also be collected by

statistics departments at the federal or provincial level (e.g. Canada (BC Stats, 2018)). Aside

from these relatively standardized information sources, finding data on fishing incomes requires

searching through case studies from muliple disciplines. Provision of baseline fishing incomes

will not only supply quantitative data for scientific research, but also serve as an entry point for

influencing policies aimed at improving the socio-economic status of fishing communities

(Pascoe, 2006).

We compile data on income earned from marine wild capture fisheries around the world. The

goals of this are twofold: First, to explore the level and distribution of fishers’ income across

fisheries sectors and geographical regions. Second, to highlight the challenges in data collection,

standardization, and reporting of fishing income, and provide recommendations for its

improvement to best aid policy development.

Methods

Data collection

Income in this paper is inclusive of the wages earned by salaried fisheries workers and revenue

received by individuals and/or owner-operators from the sale of fish. In the specific case of boat

owners, income also includes a portion generated from capital investment, that is, boat owners’

income include a share of the revenue from the sale of fish and a share for the use of the boat.

Data were collected in intervals from February to May 2017 and updated until October 2019.

Fishing income data were obtained through the web based research data repository Mendeley,

and an online search of international and regional institutional databases, public websites, and

the primary and grey literature. We began by searching for existing compilations or databases of

fishing income. The primary term entered in the internet search engine ‘Google’ was ‘fishing

income’, and additional searches were done using the words ‘wage’, ‘salary’, ‘earning’, and

‘revenue’ sequentially in place of ‘income’. We also used the terms ‘fish production’, ‘fish

harvesting’, ‘fish farming’, and ‘fisheries’ in place of ‘fishing’ to cover the range of potential

terminology that relate to ‘fishing’.

At this stage we encountered only one database - ILOSTAT, ILO’s comprehensive database of

international labour statistics - containing income from fishing. We extracted this dataset, from

here on referred to as ‘ILO dataset’, excluding records labelled as ‘unreliable’ or ‘not

significant’, as well as two obvious outliers (Ghana and Estonia). The retained ILO dataset

contained fishing income by gender for 53 countries. After exhausting all search terms and

relatively confident that there was no other publicly available database of fishing incomes we

moved on to the second stage of the search.

The second stage was a targeted search of country specific fishing income data. We concentrated

first on the countries that were in the ILO dataset. This was not to duplicate effort, but to enable

cross scale comparisons of national level statistics in the ILO dataset with local empirical data, in

order to gauge the degree to which higher-level indicators of fishers’ socio-economic status

reflect their actual conditions. Individual country searches were not exhaustive of the literature,

relying only on the most relevant items returned by Google Scholar search engine. The search

terms that were used were ‘[country] fishing income’ and ‘[country] fisheries income’. When a

case study was encountered it was recorded, then we moved on to search for the next country.

Where available we documented all types of fishing incomes from wild capture marine fisheries,

regardless of the type of fishery, fishing sector, gear type, study year, job position, or unit of

measure (individual, group). Aquaculture and freshwater fisheries were not included, although

for comprehensiveness ‘fish farming’ was initially searched. All publications, including reports,

working papers, and newsletters, were considered for inclusion in the study as long as the data

source provided clear units of reporting for the fishing income, a description of the nature of

fishing activity, the basis of the reported income, and an indication of time. Studies older than

1990 were excluded. We also excluded studies that estimated fishers’ income using methods that

were not fully explained (see for e.g. (Belhabib, Sumaila, & Pauly, 2015). For each income case

we recorded the source country, geographical region and national income group (high, upper-

middle, lower-middle, or low income)[1], as defined in the World Bank List of Economies (World

Bank Group, 2019c), to which the country belonged, fishing sector, and job position of the

fisher.

When the search for countries covered by the ILO dataset was completed, we then followed up

on case studies that had been returned by the search engine during the course of the search (but

had no associated data from the ILO dataset), and searched opportunistically for other case

studies based on the authors’ knowledge of socio-economic research in fisheries.

Data analysis

Fishing incomes came from four main data source groups (SI 1). Two regional sources, one of

the European Union (EU) and the other of Caribbean Regional Fisheries Mechanism (CRFM)

member states, supplemented the national level dataset from ILO. CRFM data consisted of

fishing operations data in the small-scale and large-scale sector that were collected from field

surveys (Beltrán, 2017), while EU data consisted of fishing fleet economics statistics that are

collected from member states and reported annually (STECF, 2017). The final source was case

studies that were primarily of the small-scale sector with varied geographical coverage. We

conducted an analysis of variance (ANOVA) to evaluate if fishing incomes from the ILO dataset

differed from other data sources.

Fishing income in this study is expressed as a per capita monthly rate as this was the most

frequently used data reporting unit we encountered in the literature search. All fishing incomes

that were reported in different units such as daily or annual rates were pro-rated to the monthly

rate. Fishing income statistics sourced from the ILO were inclusive of aquaculture and used as

reported, as we did not have sufficient information to determine the relative weight of

aquaculture in total reported fishing income.

Fishing incomes were standardized for analysis to account for differences in time and foreign

exchange. Compiled data were standardized to 2016 prices in US dollars (USD). Raw data were

adjusted for inflation using the Consumer Price Index (World Bank Group, 2019a), then

converted to USD equivalent (if reported in other currencies) using the Purchasing Power Parity

conversion factor (World Bank Group, 2019b) that also provides a relative measure of the

standard of living in each country. We searched for and assigned national poverty line income to

each country (SI 2). If national poverty lines were not available, we assigned a poverty line

income that corresponds to the national income group that each country belongs to as defined by

the World Bank. These national income group-specific poverty lines account for differences in

the cost of living among countries that is not reflected in the International Poverty line of USD

1.90 per day (Ferreira & Sánchez-Páramo, 2017). The median poverty line for high-income

countries is USD 21.70; upper-middle income USD 5.48; lower-middle income USD 3.21; and

low-income countries USD 1.91 (Ferreira & Sánchez-Páramo, 2017; Jolliffe & Prydz, 2016).

The analysis consisted of three parts. First, we examined income distribution within fisheries by

looking at disparities in standardized fishing incomes across national income groups, job

positions, gender, geographical regions, and fishing sector. Second, we assessed the opportunity

cost of fisheries by comparing fishing income with income from agriculture, another primary

sector that fishers may turn to as an alternative income source (Martin, Lorenzen, & Bunnefeld,

2013; Rahman, Tazim, Dey, Azam, & Islam, 2012). We extracted gross income from the data

series ‘mean nominal monthly earnings of employees’ in the sectors of ‘Fishing’ and

‘Agriculture, hunting and forestry’ from ILOSTAT (International Labour Organization, 2019)

(Agriculture was not available alone as an economic sector). In total, 51 countries had earnings

data in both economic sectors, from which we calculated a fishing to agriculture income ratio.

Third, we evaluated fishing income sufficiency in all countries by computing the ratio of

monthly fishing income to national average incomes (gross values) and poverty lines, all in

adjusted 2016 values.

Results

Data description

We collected fishing incomes from 89 maritime countries (Table 1). About 48% of income

records pertain to the small-scale fishing sector, 11% to the large-scale sector, while the distant

water sector receives least coverage at 1.8% (Table 2). Sector breakdown was most

comprehensive in the EU dataset, whereas CRFM data were on the small-scale sector only and

not specified in the ILO dataset. Cases which lacked sufficient data to discern their appropriate

fishing sector were classified broadly as belonging to the ‘commercial’ sector (40%) (Table 2).

‘Owner-operator’ was the most common job description of fishers in the dataset, with least

income coverage of captains and boat owners. ‘Production workers’ referred to workers involved

in the capture of marine organisms (excluding processing) but for which we were unable to

attribute a job position (Table 2). Fisheries employees in the ILO dataset fell under this category.

The majority of income data referred to ‘gross’ values, i.e., the value before cost deductions,

with about 19% of records indicating ‘net’ income. All fishing income data from the ILO, EU

and CRFM sources were reported in gross values. The CRFM study provides fishery cost

structure information that would enable users to estimate net fishing income, but we report gross

values as documented in the source mterial (Beltrán, 2017). Gender breakdown came from the

ILO dataset only, with females receiving 45% coverage of total records (23 out of 51 countries)

and males 82% (42 out of 51 countries).

The definition and derivation of fishing income varied across different sources. National level

statistics from the ILO dataset referred to mean nominal monthly earnings of workers employed

in fishing, which includes commercial fishing in marine, coastal or inland waters, as well as fish

farming and aquaculture. Earnings are “gross remuneration in cash and in kind paid to

employees”, and are collected through various instruments including labour force, income, or

employment surveys, household surveys, population and housing census, and social insurance

organisation records. Fishing income from EU countries were the monthly average crew wage

per full time equivalent (FTE)(STECF, 2017) unit (1920 hours per year, from 48 weeks of 40

hours a week working time), while those from CRFM countries were values based on daily

fishing effort and the market price of fish. Fishing income from case studies fall into two general

categories – i) values calculated from fish catch and the price of fish (52%); and ii) direct

responses from fishers either verbally or recorded on paper (48%). The majority of fishing

incomes reported in case studies were obtained through primary data collection (83%), but in a

small number of cases (17%) income values or calculation parameters were extracted from

secondary materials.

Fishing income trends

Gross fishing income, inclusive of all fishing sectors and positions, and adjusted for inflation and

standard of living averaged USD 1,580·fisher-1·month-1, while average net fishing income came to

USD 800·fisher-1·month-1. There was no significant difference in fishing income between the ILO

dataset and other sources, in which the median difference in 29 country pairs was 25%.

However, there was considerable variation in between dataset differences in some countries, with

ILO incomes ranging from being about 5.5 times less (e.g. Colombia) to almost 15 times more

(e.g. Croatia) than data from other sources in this study. Nominal gross fishing incomes, i.e.,

absolute non-PPP adjusted amounts were highest in countries classified as ‘High income’ (HI)

by the World Bank and lowest in ‘Low income’ (LO) countries. This trend also held true when

fishing incomes were adjusted by a PPP conversion factor that accounts for differences in

worldwide standard of living, with the difference between HI and LO countries being 23 times

and 8.8 times in nominal and PPP adjusted values respectively (Fig. 1). There was uneven and

limited coverage of fishing incomes by job position, with comparable within country data

covering boat owners, captain, and crew being most comprehensive for CRFM member states.

Fishing cost surveys of CRFM members showed that boat owners grossed on average USD

7,600·person-1, double the monthly income of captains (USD 3,700·person-1) and 5.8 times that of

crew (USD 1,300·person-1). Data from the ILO dataset showed that female fisheries employees

on average earned USD 480·person-1 compared to USD 951·person-1 earned by males (Fig. 2).

When compared across geographical regions, average monthly gross fishing income was highest

in North America and Europe at USD 2,520∙person-1 and USD 2,170∙person-1 respectively.and

lowest in Sub-Sahara Africa (Fig. 3). Gross monthly fishing income in Latin America and

Caribbean decreased by about 36% when the income of boat owners was excluded. There was an

income gap between sectors, whereby the gross income of workers in the distant water and large-

scale sectors was about twice that of small-scale fishers. At USD 1,250∙person-1, the gross

income of commercial workers was comparable to those in the small-scale sector, while across

all sectors net fishing income was lower than gross income by at least 75%. When compared

with earnings from ‘agriculture, hunting, and forestry’, fishing provided higher returns in all

geographical regions except North America, and was highest in Africa followed by South Asia

and East Asia and the Pacific (Table 3).

Gross fishing income appeared to be sufficient in most regions of the world, whereby the average

ratio of fishing to national income was more than 1 in all regions except North America (Fig. 4).

At the same time, the spread in fishers’ income is wide and there were fishers who earn less than

the poverty line income in all regions covered by the study (Table 3). When broken down by job

positions, captains and boat owners did not fall below the poverty line until cost increased to

55% and 85% of total income respectively, whereas about a quarter of crew and owner-operators

were at the poverty line before costs are considered.

Discussion

The widespread understanding of fishing as a last resort occupation often equates fishers with

poverty (Béné, 2003; Islam, 2011; Macfadyen & Corcoran, 2002), particularly fishers in

developing countries or those engaged in small-scale fisheries (Bailey, 1988; Béné et al., 2003;

Townsley, 1998). To date, it has been difficult to verify or refute this because socio-economic

data, in particular fishing income, is usually location specific and there is limited scope for

broader comparisons across geographical scales.

To our knowledge this study is the first compilation of standardised fishing incomes across

geographical regions, containing data for more than half of all maritime countries and territories

in the world. The analysis is intended to be illustrative rather than exhaustive of the level of

fishers’ income and of fishing income reporting. More fishing income data lie outside the public

domain with researchers or institutions, and are not always in the economics or social science

field. For instance, coral reef scientists in Kenya who have monitored how much fishers earn for

more than 20 years are likely the best source of fishing income data in that country (Mcclanahan

& Abunge, 2014).

Data coverage and issues

Regionally, the European Union is well represented with coverage of fisheries transversal and

economic data that are also reported by fishing fleet segments (STECF, 2017). Although fishing

incomes within countries can vary considerably, data from the ILO dataset did not differ

significantly from case studies and regional datasets, suggesting that data reported at the national

level can reflect conditions at different scales. However, as only about half of the countries in the

ILO dataset had complementary data from other sources, this requires further investigation. The

ILO reports employee earnings sourced from national population census, labour force surveys,

and household surveys in a range of economic activities, including capture fisheries. However,

the ILO dataset omits the earnings of self-employed owner-operators, which is the category that

most small-scale fishers fall under, and for our purpose, does not disaggregate marine fisheries

from aquaculture and freshwater fisheries. Depending on the fishery, aquaculture may carry a

heavier weighting on the aggregated income value, as in the case of Canada where aquaculture

workers earned about 11% more than wage earning fish harvesters and up to 60% more than self-

employed fish harvesters (Fisheries and Oceans Canada, 2011). Even where fishing income data

are available, they are from different yers and in local currencies which makes comparisons

across time and space challenging. We address this issue by adjusting for the effects of inflation

and foreign exchange.

The distinction between gross and net values is not always made clear in source materials, but

the difference between the two can be substantial, with the former being the value before fishing

costs (or income tax in the case of employee earnings) are deducted, and the latter the actual take

home amount. This is evident by the increase in records that fall below the poverty line across all

regions, with the exception of Sub-Sahara Africa, when net income rather than gross income is

considered (Table 3). It implies that fishing income may not provide sufficient buffer against

unexpected events such as illness or natural disasters that can potentially push fishers into

financial hardship (Islam & Chuenpagdee, 2013). The opposite trend observed in Sub-Sahara

Africa is likely driven by relatively high extrapolated net income from one case study

(Grandcourt & Cesar, 2003).The exception may be boat owners and captains, who, according to

our results, are able to absorb cost increases of up to 85% and 55% respectively before their

incomes fall to the poverty level.

Omitting to account for the discrepancy between gross and net values can make fishers’ incomes

appear higher than they actually are, and this should be kept in mind when interpreting results

presented herein, which are in gross rather than net values as the former is the metric most data

are reported in. A distorted perception of fishers’ well-being can be misleading and have serious

repercussions if for example it withholds needed social assistance to fishing communities. In

Bahrain, fishers’ total earnings turned out to be 76%-442% lower once costs such as fuel, bait,

and depreciation were deducted (Abdulqader, 2007). The impact of cost accounting means that

complementary data such as local scale fishing costs data are also crucial to produce more

accurate estimates of fishers’ actual financial status (Beltrán, 2017; Lam, Sumaila, Dyck, Pauly,

& Watson, 2011).

Fishing income in context

Our results show that fishing incomes fall in line with the economic status of their respective

countries in absolute terms, whereby the gap between high income (HI) and low income (LO)

countries is about 8.8 times. Small-scale fishers’ incomes fall behind those in the distant water

and large-scale sectors, lending support to concerns about the socio-economic plight of, and

equality in small-scale fisheries (Béné & Friend, 2011; Islam, 2012; Jentoft & Midré, 2011).

That the majority of small-scale fishers live in developing countries, often with limited access to

government welfare services and below the poverty threshold (Ardales & David, 1985; Smith,

Pauly, & Mines, 1983) compounds the challenge of improving this group’s socio-economic

status. Even as recently as 2015, 34% of fishermen in the Philippines were poor, and fishermen

had higher poverty incidence than the general population (Philippines Statistics Authority, 2016).

High income countries are not exempt from financial pressures and the effects of poverty in

fisheries. When fishing incomes were compared to national poverty lines, 28 out of 60 records in

which fishers earned below the poverty line income were from HI countries. At 22%, HI

countries had the second highest proportion of records in which fishing income fell below the

poverty line after LO countries (27%), followed by 17% and 15% for LM and UM countries

respectively. Therefore, although fishers in HI countries earn more than those from lower income

country groups, the higher cost of living negates the actual purchasing power of that income.

Nonetheless, it would be inaccurate to generalize fishers as being the ‘poorest of the poor’. When

compared to agriculture, another primary sector that also has high participation of poor people

who rely on farming for food security and livelihood (Adato & Meinzen-Dick, 2002), our study

showed that fishing provided higher income across all regions except North America. This

outcome is consistent with literature that finds fishers’ earnings are comparable to crop farmers,

minimum wage labourers, and rural occupations in low to upper-medium income countries

(Macedo Lopes, Francisco, & Begossi, 2009; Mkama et al., 2010; Smith et a., 1983; Wijayaratne

& Maldeniya, 2003). The higher income attainable in fishing compared to the next best

alternative may be a contributing factor to why poorer fishers tend to be reluctant to exit

declining fisheries (Cinner, Daw, & McClanahan, 2009; Muallil et al., 2011). Furthermore,

contrary to common views of fishing as an occupation of last resort, in places like Timor-Leste

fishing was in fact associated with more secure livelihoods (Mills et al., 2017).

Drivers of fishing income differences

While fishing income statistics supplied by intergovernmental agencies such as the ILO tend to

reveal little beyond a quantitative measure of fishers’ financial status, case studies can offer more

insight on what factors belie the incomes that fishers earn. The income gap between HI and LO

countries can be attributed to differences in standard of living, whereby an absolute (nominal)

amount of money is able to purchase more local goods in LO countries relative to HI countries.

Nonetheless, a number of other social, economic and market factors are also likely driving the

difference in fishing incomes observed between sectors, geographical regions, and position.

Drawing from the case studies, fisheries that target high value species for export tend to gross

higher earnings than others (Gill, Oxenford, & Schuhmann, 2019; L. Teh, Cabanban, & Sumaila,

2005). Part of the additional income that boat owners earn can be attributed to compensation for

exposure to financial risk boat owners assume in capital investment and operating cost

fluctuations (Beltrán, 2017). Social marginalization also impacts fishing income. The lower

income earned by females compared to males at the national level supports observations at local

fisheries (Ardales & David, 1985; Islam & Chuenpagdee, 2013; Kraan, 2011; Purcell,

Lalavanua, Cullis, & Cocks, 2018). While ethnic diversity was not addressed in the fishing

income dataset, studies suggest that social exclusion based on ethnicity or caste can lead to

poverty (Jentoft & Eide, 2011; Nayak et al., 2014).

Fishing incomes can also be subject to imbalanced power dynamics, a situation not uncommon

in fisheries client-patron relationships that is often to the disadvantage of fishers who obtain low

prices for their fish catch and become indebted to the fish buyers (Islam, 2012; Miñarro,

Navarrete Forero, Reuter, & van Putten, 2016; Muldoon & Johnston, 2006). On the other hand,

profit-sharing systems in some small-scale fisheries demonstrate fairness. This is the case in

traditional motorized boats in Sri Lanka where the crew obtain half and up to one-third of the

share of income from a fishing trip (Wijayaratne & Maldeniya, 2003), or in Tanzania where local

custom dictates that all crew obtain an equal share of the revenue earned from the catch of fish

(Mkama et al., 2010).

Even within the same fishing community, there are fishers who are poor and those who are not

poor (Béné et al., 2003; Jani & Aziz, 2014). A range of ecological, socio-cultural and governance

factors can affect fish catch and hence fishing income derived from the sale of fish. Factors

include what gear fishers use, their fishing knowledge, access to fishing grounds and markets, as

well as the type of species they target and fishing ground habitat (Busilacchi et al., 2015; Dacks,

Ticktin, Jupiter, & Friedlander, 2018; Gill et al., 2019; Purcell et al., 2018; L. C. L. Teh, Teh, &

Meitner, 2012; L. Teh et al., 2005). Although the data used in this study are not stratified to

reveal such finer resolution insights, making the data points available is valuable in itself as it

can draw out the contextual nuances behind the numbers, which would otherwise remain

obscured in qualitative studies, to a larger policy oriented audience.

Attempting to classify fishers as poor or not based on income alone overlooks several key

elements that affect fishers’ “poorness”, that is, their ability to meet short and long-term

livelihood needs. First, the subsistence component of fishing mitigates low income by offsetting

the need for cash to buy protein food. Catch that is kept by fishers to eat at home or to share with

neighbours and relatives also contributes directly to local food security (Bell et al., 2009; Cruz-

Trinidad, Aliño, Geronimo, & Cabral, 2014; Gillett, 2016; Kraan, 2011), and has monetary value

equivalent to the market price of the take-home fish catch (Cruz-Trinidad et al., 2014; Gill et al.,

2019; Smith et al., 1983). These ‘shadow’ values can be highly significant in the national

economy even if they are not officially recorded. In Tanzania, about 30% of the catch- not an

insignificant amount- is shared among the crew to provide for their families’ consumption

(Mkama et al., 2010). Second, social networks support fishermen financially through functions

such as provision of food and shelter during crisis/span> (Béné et al., 2003; Grant, Berkes, &

Brierley, 2007), sharing boat use and fishing knowledge, and family support (child care,

sickness). These intangible elements compensate for services that fishers would otherwise have

to pay or assume risk for, thus help to ameliorate the level of poorness in fishing communities.

Third, income from other activities such as farming or formal employment often supplement

fishing income at the household level (Mills et al., 2017) and help to fill the financial gap left by

relying on fishing only.

Although this paper focusses on fishing income, it is important to acknowledge that income

alone does not capture the multi-dimensions of poverty. Fishing households that earn above the

poverty line may be lacking in other assets or property, or have difficulty meeting basic health

and nutrition needs (Béné et al., 2003; Gabrielsson, 2017; Islam, 2012; Nayak et al., 2014).

Focussing on income alone without simultaneously addressing the other dimensions of poverty

may not be enough to make a significant improvement in fishers’ socio-economic condition. In

addition, there is a transitory nature to poverty that is not captured by the data. For instance, we

cannot tell if the fishing income is earned by a fisher who is and remains chronically poor, or by

one who is temporarily poor while accumulating assets to transition out of being poor (Carter &

Barrett, 2006). Finally, being ‘poor’ is in itself a subjective judgement that is liable to change as

conditions demand (Jani & Aziz, 2014). It is essential to consider these complexities in any

application of fishing income to policy decisions so that interventions deliver their intended

outcomes.

Data limitations

Even though standardised national-level datasets are important for comparative analyses such as

the one carried out here, there is an associated loss of context that can make it difficult to

interpret trends or draw nation-specific insights. For example, the available data analysed in this

study did not allow us to control for the effects of fishing gears, targeted fishery, and fishing

effort, which are among variables that influence fishers’ incomes (Al Jabri, Collins, Sun,

Omezzine, & Belwal, 2013; Sherif & Gheblawi, 2009). Additionally, fishing income data are at

the scale of individuals not of vessels or fishery sector, and keeping to this unit of reporting rules

out data from countries and studies that do report statistics at the latter scales (ABARES, 2019;

Kasperski & Holland, 2013). Income at the level of fishing households were not included in this

study when the income had a non-fishing component (Branch, May, Roberts, Russell, & Clark,

2002; Sowman, 2006). Fishing incomes are derived in different ways and and different studies

have their own definitions of income that may not overlap. The quality of income data across

studies may be subject to varying methodological rigour, with some studies relying on recall

surveys and self-reporting while others adopt independent data based calculations. On the other

hand, ILO labour statistics are flagged if they appear unreliable, thus fishing incomes in the ILO

dataset are expected to have some degree of quality control. Lastly, fishing is a seasonal activity

and fishing incomes are liable to fluctuate throughout the year. For this reason, extrapolation of

monthly incomes should be guided by the seasonality of local fisheries. Any income

comparisons should be made with these data limitations in mind.

Improvements to fishing income data collection

Improvements are required to make fisheries socio-economic statistics more accessible and

amenable for scientific and policy use. Although dispersed, fishing income data do exist, and the

first step is to assemble these disparate sources which we have initiated with this study. National

statistics are often reported at aggregated industrial levels, as is the case for fisheries which tends

to be aggregated with other primary industries, and this can prevent clear discernment of

individual sectors’ performance. Changing the reporting resolution to provide data at

disaggregated levels can increase the number of fishing income data points from existing

sources. However, confidentiality concerns may inhibit some national agencies from doing so.

Data on fishing income can be increased by incorporating simple socio-economic questions in

fisheries surveys that are conducted at landing sites. We thus recommend national fisheries

and/or environmental management agencies to become more active in monitoring socio-

economic indicators rather than relying on social or economic agencies to do so. This will

augment income statistics that are typically collected and reported in periodical national

household or labour force surveys. Finally, generating awareness of the need to track and report

socio-economic data among fisheries managers and researchers can broaden the pool of data

contributors.

Conclusion

We found that the perception of fishers as the ‘poorest of the poor’ is not accurate based on

average fishing incomes from 89 countries. However, income disparities, sometimes large, exist

among fishers. The disparities highlight areas such as small-scale fisheries that require special

attention to ensure socio-economic needs are being met. At the same time, income data on small-

scale fisheries warrant further research as this sector is under-represented in international labour

statistics that are reported by the ILO. Providing a centralized database for fishing income,

increasing the resolution at which economic statistics are reported at country level, and

integrating socio-economic questions in fisheries surveys can leader to greater availability and

accessibility of fisheries socio-economic data. We caution that fishing income provides only a

simplistic view of fishers’ overall socio-economic status and should not be taken at face value

without being complemented by understanding of the multi-dimensional facets of poverty.

Nonetheless, investigating fishing income data can help to identify areas of high inequality or

extreme poverty, or opportunities to transition communities out of poverty. These insights have

wide policy application in the areas of artisanal fisheries management, fisheries benefits

allocation, climate change adaptation and social development in coastal communities, and

ultimately will contribute to meeting countries’ sustainable development goal targets.

Acknowledgement

This research is supported by the Nippon Foundation Nereus Program. We thank S. Jardin and C.

Anderson for their input on earlier versions of this paper. The authors confirm that there is no

conflict of interest to declare in this paper.

Data Availability Statement

The data that support the findings of this study are available upon request from the

corresponding author.

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Table 1. The number of fishing income records grouped by region and country

income status according to data source. Brackets indicate number of records

reported in net value, all others are gross value.

National Regional Local

ILO CRFM STECF Case studies

East Asia & Pacific

High income 6 3

Lower middle income 7 15

Upper middle income 3 9 (2)

Europe & Central Asia

High income 19 37 1

Lower middle income 6

Upper middle income 11 6

Latin America &

Caribbean

High income 9 6 5 (5)

Lower middle income 9 (1)

Upper middle income 26 19 8 (10)

Middle East & North

Africa

High income 5 2 12 (7)

Lower middle income 5 3

North America

High income 6 2

South Asia

Lower middle income 5 8 (13)

Upper middle income (1)

Sub-Saharan Africa

High income 1 (4)

Low income 8 4 (3)

Lower middle income 1 4 (2)

Upper middle income 5

Table 2. Composition of income records (%) by sector and job

position from different data sources.

National

ILO

Regional Local

Case studies CRFM STECF

Sector

Distant water - - 11.1 0.8

Large scale - 28.0 46.7 4.2

Small-scale - 72.0 42.2 90.4

Commercial 100 - - 4.2

Job position

Boat owner - 32.0 - 8.3

Captain - 28.0 - -

Crew - 32.0 100 10.8

Owner-operator - 8.0 - 78.3

Production worker 100 - - 2.5

Table 3. Benchmark comparisons of fishing income by region: 1) fishing to agriculture

income ratio, where values >1 indicate fishing income is higher relative to agriculture

income; 2) fishing income to poverty line, where values >1 indicate fishing income is lower

than the poverty line; and 3) the percentage of records in which fishers' gross and net incomes

are less than poverty line income.

Region

Fish:Agri

ratio

Poverty

ratio

% below poverty

Gross

income Net income

East Asia & Pacific 1.64 0.53 18 53

Europe & Central Asia 1.08 0.61 17 -

Latin America & Caribbean 1.49 0.33 5 13

Middle East & North Africa 1.08 1.45 40 50

North America 0.82 0.60 29 -

South Asia 1.74 0.34 15 18

Sub-Saharan Africa 2.28 0.73 22 11

Figure legends

Figure 1. Gross fishing incomes by countries grouped according to the World Bank’s income

group classification. The ‘nominal’ series shows non-adjusted absolute amounts. The ‘PPP’

series is adjusted for differences in standard of living among countries. HI=High income

(n=124); UM=Upper middle income (n=96); LM=Lower middle income (n=89); LO=Low

income (n=11).

Figure 2. Percentage contribution to total fishing income by gender and (a) country income

status: HI=High income (n=28); UM=Upper middle income (n=28); LM=Lower middle income

(n=20); LO=Low income (n=5); and (b) geographical region: NA= North America (n=4); EU=

Europe (n=21); CARIB= Latin America & Caribbean (n=29); SA= South Asia (n=3); MENA=

Middle East and North Africa (n=6); EAP= East Asia and Pacific (n=10); AFR= Sub Sahara

Africa (n=8).

Figure 3. Gross fishing incomes by geographical region. ‘X’ marks the mean income value. The

top and bottom box lines show the first and third quartiles, the middle line is the median, and

whiskers are minimum and maximum values. Outliers are not shown. Abbreviations: NA= North

America (n=7); EU= Europe (n=76); CARIB= Latin America & Caribbean (n=76); SA= South

Asia (n=13); MENA= Middle East and North Africa (n=26); EAP= East Asia and Pacific

(n=40); AFR= Sub Sahara Africa (n=18).

Figure 4. Gross fishing income relative to average national income in different regions of the

world. X’ marks the mean income value. The top and bottom box lines show the first and third

quartiles, the middle line is the median, and whiskers are minimum and maximum values.

Outliers are not shown.Abbreviations: NA= North America (n=7); EU= Europe (n=76); CARIB=

Latin America & Caribbean (n=76); SA= South Asia (n=13); MENA= Middle East and North

Africa (n=26); EAP= East Asia and Pacific (n=40); AFR= Sub Sahara Africa (n=18).