MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

https://doi.org/10.47193/mafis.3522022010504 181

Caracterización de los medios de vida de una comunidad de pescadores a

pequeña escala en el Caribe colombiano (Medios de vida de PPE del Caribe

colombiano)

RESUMEN. Las comunidades costeras que dependen de la pesca en pequeña

escala (PPE) son poco conocidas. El diseño de políticas para abordar sus

vulnerabilidades requiere comprender el contexto socioeconómico en el que operan

los PPE. Desafortunadamente, esa información suele ser incompleta en los países

en desarrollo. Este estudio busca cerrar esta brecha examinando la

sociodemografía, los activos, las estrategias de sus medios de vida, la seguridad

alimentaria y los niveles de pobreza de los hogares pescadores y no pescadores en

una comunidad de pescadores en el Caribe colombiano. El análisis sigue el enfoque

de los medios de vida sostenibles. Nuestros resultados muestran que: (i) los PPE

juegan un doble papel en los hogares pesqueros: autoconsumo y generación de

ingresos. (ii) Los PPE juegan un papel esencial en la seguridad alimentaria tanto

para los hogares pescadores como para los que no lo son. (iii). La diversificación de

los medios de vida, incluida la pesca multiespecífica y las actividades de los miem-

bros del hogar, además del cabeza de familia, es clave para diversificar el riesgo y

suavizar el consumo. (iv) Las comunidades pesqueras enfrentan importantes

restricciones en el acceso a los mercados financieros. (v) Aunque los hogares pes-

ABSTRACT. Coastal communities depending on small-scale fisheries (SSFs)

are poorly understood. Designing policies to address their vulnerabilities requires

understanding the socioeconomic context in which SSFs operate. Unfortunately,

that information is usually incomplete in developing countries. This study seeks to

close this gap by examining the socio-demographics, assets, livelihood strategies,

food security, and poverty levels of both fishing and non-fishing households in a

fishing village in the Colombian Caribbean. The analysis follows the sustainable

livelihoods approach. Our results show that: (i) SSFs play a double role in fishing

households: self-consumption and income generation. (ii) SSFs play an essential

role in food security for both fishing and non-fishing households. (iii) Livelihood

diversification, including multispecies fishing and activities by household members

in addition to the head, is key for diversifying risk and smoothing consumption.

(iv) Fishing communities face significant restrictions in access to financial markets.

(v) Although fishing households earn more income than non-fishing ones, they

exhibit lower education and literacy. These results show that SSF is a buffer against

the vulnerability of fishing communities. Strict conservation strategies might be

necessary to sustain SSF, but these must be accompanied by alternative income

sources, such as compensation schemes, social protection, or policies enabling

alternative livelihoods.

JEL Codes: D13, I21, J22, J46, Q22, Q56

Keywords: Sustainable Livelihoods Approach (SLA); small-scale fisheries;

assets; poverty; education gap; food security; Colombia

*Correspondence:

jmaldona@uniandes.edu.co

Received: 21 june 2021

Accepted: 14 December 2021

ISSN 2683-7595 (print)

ISSN 2683-7951 (online)

https://ojs.inidep.edu.ar

Journal of the Instituto Nacional de

Investigación y Desarrollo Pesquero

(INIDEP)

This work is licensed under a Creative

Commons Attribution-

NonComercial-ShareAlike 4.0

International License

ORIGINAL RESEARCH

Livelihoods Characterization of a Small-Scale Fishing Community in

the Colombian Caribbean

JORGE HIGINIO MALDONADO*, ROCÍO DEL PILAR MORENO-SÁNCHEZ, MYRIAM ELIZABETH VARGAS-MORALES,

EMILIO LEGUÍZAMO

Department of Economics, Universidad de los Andes, Calle 19A #1-37E Bloque W Of W-814, Bogotá, Colombia. romoreno@uniandes.edu.co

(RPM-S); me.vargas374@unidades.edu.co (MEV-M); s.leguizamo10@unidades.edu.co (EL). ORCID Jorge Higinio Maldonado

https://orcid.org/0000-0001-5501-6374, Rocío del Pilar Moreno-Sánchez https://orcid.org/0000-0002-2817-7399, Myriam Elizabeth Vargas-Morales

https://orcid.org/ 0000-0002-3380-3542, Emilio Leguízamo https://orcid.org/0000-0002-1272-3543

182 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

cadores obtienen más ingresos que los que no lo son, exhiben una educación y alfabetización más bajas. Estos

resultados muestran que la PPE es un amortiguador contra la vulnerabilidad de las comunidades pesqueras. Es posible

que se necesiten estrategias de conservación estrictas para sostener la pesca artesanal, pero ellas deben ir acompañadas

de fuentes alternativas de ingresos, como esquemas de compensación, protección social o políticas que permitan

medios de vida alternativos. JEL Codes: D13, I21, J22, J46, Q22, Q56.

Palabras clave: Enfoque de medios de vida sostenibles (SLA); pesquerías de pequeña escala; activos; pobreza;

brecha educativa; seguridad alimentaria; Colombia.

INTRODUCTION

Fishing is a key component in the livelihoods

of millions of people (Asiedu 2011).

Approximately 90 % of the almost 40 million

people that the Food and Agricultural Organiza-

tion (FAO) register globally as fishers are

classified as small-scale, and 97 % of them are

located in developing countries (FAO 2021).

However, there is limited information about

the livelihoods of small-scale fishing (SSF)

communities in developing countries (Bailey

and Jentoft 1990; Cinner et al. 2010). Collecting

reliable data is difficult (Pita et al. 2019) and the

sector lacks quantitative studies on

socioeconomic variables (FAO and World Fish

Center 2008). Research on fisheries has

emphasized biological issues (Béné 2003). By

contrast, there is little rigorous data estimating

the poverty level of fishing households

(Willmann 2004). In some cases, fishers’

poverty has been inferred rather than proven

(Thorpe et al. 2007). A review of 202 articles

concluded that fisheries’ role in poverty

alleviation is unclear because good conceptual

models are lacking (Béné et al. 2016).

Moreover, the estimation of poverty indexes and

the measurement of vulnerability depend on

reliable longitudinal data (Béné 2009), which

has not been available. In a special issue of

Marine Policy (Vol. 101, March 2019),

coordinated by Pita et al. (2019), authors not

only discuss the challenges of managing small-

scale fisheries under scenarios of poor data, but

present a variety of innovative approaches for

SSF data collection, including participatory

methods.

Despite this lack of detailed information,

there exist multiple proposals and interventions

to improve the well-being of these communities

and the sustainability of fishing resources. These

solutions have centered on increasing the

efficiency of SSF while implementing mecha-

nisms to conserve fish stocks, through a

combination of management strategies to limit

access (e.g., protected areas, community and

territorial use rights, community-based manage-

ment, closed seasons) and incentives to reduce

fishing effort (e.g., alternative livelihoods,

subsidies, conservation agreements) (Allison and

Ellis 2001; Cinner 2014). As Cinner et al. (2009)

argue, successful interventions to reduce fishing

efforts in overexploited fisheries require under-

standing the socioeconomic context in which

fishers operate.

Research on livelihoods in SSF communities

has increased recently, and shows heterogeneous

results in developing countries. For instance, in

terms of income levels, findings are ambiguous:

while some authors find that fishing communi-

ties are poorer than the national level, as in

Malaysia (Teh and Sumaila 2007), or more

vulnerable than other groups, as in Ghana

(Asiedu 2011), other authors show that fishers

are not always the poorest of the poor and can

even be better off than non-fishing households,

as shown in Malawi, Uganda and Kenya

(Allison 2005), or Philippines, Bangladesh,

India, Senegal and Tanzania (Tietze et al. 2000).

Besides, as Thorpe et al. (2007) assert,

poverty cannot be captured only in monetary

terms: literacy, access to education, health, and

clean water, as well as other factors are

dimensions of wellbeing. Landownership, debt,

financial capital, and marginalization from

political decision-making affect income and

well-being in SSFs (Béné and Friend 2011;

Nayak et al. 2014). Others have highlighted the

importance of SSF interventions to strengthen

tenure and community governance, cover

upfront opportunity costs, reduce vulnerability

to market shocks by supporting a broader liveli-

hood portfolio, and relax credit constraints (Barr

et al. 2019).

In the framework of socio-ecological

systems, some researchers have proposed

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 183

indexes of vulnerability (Béné 2009) or adaptive

capacity (McClanahan et al. 2008, 2009; Cinner

et al. 2012; Moreno-Sánchez and Maldonado

2013; Maldonado and Moreno-Sánchez 2014)

for SSF communities. These indexes have in-

cluded income, occupational diversification,

poverty, material assets, wealth, dependence on

natural resources, and social capital.

Income diversification is a livelihood strategy

for fishing households (Ellis and Allison, 2004;

Thorpe et al. 2007; Béné 2009). Fishing is

generally a part-time activity that is comple-

mented with other sources of income. But

fishing is also an essential component of food

security, not only for fishing households but for

their communities. SSF goes beyond being a

last-resort activity for the poorest of the poor; it

is relevant to other socioeconomic groups

(Garaway 2005). For example, Kawarazuka

(2010) analyzes the role of SSFs in the food and

nutrition security of poor rural households in

developing countries, particularly in Africa,

Asia, and Oceania. The author shows that fish

captured in common-pool resources are used for

self-consumption and traded in local markets

and highlights how those fisheries can

compensate for the shortage of food in poor

households. He also finds that SSFs provide

other income-generation opportunities such as

processing and trading and that (among those

better-off) fishing income is used to purchase

non-staple foods and to invest in agriculture.

Kawarazuka (2010) also describes the impor-

tance of fish in rural poor communities for the

consumption of high-quality nutrients. Confirm-

ing these findings, Kawarazuka and Béné (2010)

identify two pathways between small-scale

fisheries and household nutritional security: (i)

the direct nutritional contribution from fish

consumption and (ii) the increased purchasing

power through the sale of fish. While some

members of SSF households fish as their

primary source of income, and some households

engage in economic activities not related to

fishing at all, fishing shapes the livelihoods and

food security of all households in these commu-

nities.

In general, these studies confirm the

heterogeneity within and among fishing commu-

nities and the relevance of social, economic, and

institutional context in understand poverty levels

and vulnerability of fishing households. In the

same way, the literature discussed above

confirms the role of fishing in the food security

of fishing households and their communities.

In Latin America, however, socioeconomic

studies of SSF are limited and Colombia is not

the exception. According to the Organization for

Economic Co-operation and Development

(OECD 2016), there are no reliable statistics

about Colombia’s SSF activities and commu-

nities. For the case of Colombia, there are only

some cross-sectional surveys, characterizing

some aspects of fishing households (García

2010; Agudelo et al. 2011; Moreno-Sánchez and

Maldonado 2013; Viloria et al. 2014). These

studies have found that fishers are typically

adults who belong to households exhibiting low

education levels and assets ownership, whose

livelihoods depend on more than one source of

income. Others have collected information about

fishing gear, types of boats, captured species,

and levels of effort (Rueda et al. 2011; Viloria et

al. 2014). However, little is known about the

dynamics of the fishing household economy.

Notably, there is scarce literature on the

variability of income throughout the year.

Our objective then is to describe the

demographics, assets, livelihood strategies, food

security, poverty level, and sustainability of a

fishing village in the Colombian Caribbean

(Barú-Cartagena). We hypothesize that fishing

and non-fishing households differ with respect

to characteristics such as education, access to

financial capital, income level and diversifica-

tion, and food security. We collected informa-

tion from fishing and non-fishing households in

the village of Barú, administering monthly

socioeconomic surveys from July 2018 to

September 2019. The data collection started with

a baseline and was followed by monthly surveys

administered to each participating household.

The sample included around 100 fishing

households and 150 non-fishing households. To

analyze the data, we organized the information

following the Sustainable Livelihoods Approach

(SLA).

Our contribution is a comprehensive descrip-

tion and analysis of a fishing community’s

livelihood that involves: (i) the characterization

of fishing and non-fishing households in terms

of capital (human, financial and social), liveli-

hood strategies (diversification of sources of

income, access, use of financial services, and the

role of social capital), and livelihood outcomes

(monetary poverty and food security) and (ii) a

longitudinal study that collects monthly panel-

data information at a household level for a year.

184 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

MATERIALS AND METHODS

Conceptual framework

We follow the conceptual framework of the

sustainable livelihood approach (Chambers and

Conway 1992; DFID 1999). The SLA assumes

that household well-being depends on consump-

tion and production decisions (including

livelihood strategies) in light of its endowment

of assets (human, social, natural, physical, and

financial) in a specific institutional and

geographical framework, and the interact-tions

of these factors (Allison and Ellis 2001). Assets

we consider are as follows. (1) Human capital:

education and employment. (2) Social capital:

participation in organizations and supporting

networks. (3) Physical capital: housing,

appliances, vehicles, livestock, and fishing

assets. (4) Financial capital: savings, credit. The

livelihood strategies considered are labor and

non-labor strategies, fishing, use of financial

services, allocation of expenditures for house-

hold consumption, and food security strategies.

Finally, outcomes include household income,

expenditures, food security, poverty, and in-

equality.

Study site

Barú peninsula is part of the rural area of the

Tourist and Cultural District of Cartagena de

Indias in the department of Bolívar, Colombia. It

covers approximately 7,117 hectares and

consists of three villages: Ararca, Santa Ana,

and Barú. This research project was imple-

mented in Barú village (Figure 1). The number

of residents in Barú village averages 2,700-

2,800 inhabitants in the populated center, mostly

ancestral Afro-descendants (Lizarazo and López

2007; Márquez 2014; Mendoza and Moreno-

Sánchez 2014).

Barú is a major tourist destination with high

demands of ecosystem services such as seafood

and recreation. It is located in the area of

influence of the National Natural Park Corales

del Rosario and San Bernardo (MADS 2012),

where commercial fishing is prohibited.

However, subsistence fishing is allowed. In the

most recent management plan, the park

authorities recognized small-scale fishery

activities as traditional and ancestral practices

(PNN 2020). In practice, longline and diving are

the most frequent fishing arts. The management

plan also identifies some species that currently

are being harvested and are under some level of

threat. There are two zones within the park

clearly defined and managed as no-take zones,

whose characteristics imply that this fishery runs

under a semi-open access regime. At the time of

the survey, there was no official record of

fishers.

In terms of infrastructure, Barú village lacks

an adequate aqueduct and sewer service, and

rainwater is the primary source of water supply

for most households. Drinking water comes

from Cartagena by boats adapted to transport

water – known as bongoductos (Pineda et al.

2006; Rodríguez-Sánchez et al. 2016). There is a

health post in the village which offers first aid,

primary care, and vaccination campaigns

(MADS 2012; Villamil et al. 2015); a health

center is currently under construction and is

expected to provide more services and better

equipment.

Target and sample population

In July of 2018, the population of Barú

village accounted for 801 households: 158

fishing households (F-hh) and 643 non-fishing

households (non-F-hh). We randomly selected a

stratified sample of 255 households (97 F-hh,

and 158 non-F-hh) to carry out the surveys. The

size of the sample included oversampling of

10% to cope with attrition during the informa-

tion gathering process. The sample anticipates a

margin of error of 5 % and a confidence level of

95 %. The baseline survey was conducted

between July and October 2018; follow-up

surveys were administered monthly from

October 2018 to October 2019.

Collection instruments

We ran a baseline survey to (i) register the

participating households, (ii) gather general

information assumed to remain constant

throughout the study period, and (iii) initiate the

collection of socioeconomic information. The

baseline survey consisted of seven sections: (i)

Household characteristics and economic

activities, (ii) Household Expenditures, (iii)

Household assets and income, (iv) Finances, (v)

Fishing, (vi) Food Security, and (vii) Land

Tenure.

Follow-up surveys were conducted once a

month for each household for the following

consecutive 11 months and had the same

structure as the baseline except for the sections

on household characteristics and land tenure.

During the follow-up survey, new household

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 185

members were recorded, as well as those who

left.

Two members of the Barú community were

trained to apply the survey and became inter-

viewers and co-researchers for the project. This

made it easier for the community to accept the

researchers and thus be collaborative in offering

information. Interviewers were trained in topics

related to ethics, survey administration, and the

objectives of the project.

Figure 1. Location of Barú Peninsula adjacent to the marine protected area Corales del Rosario and San Bernardo

National Park. (https://runap.parquesnacionales.gov.co).

Variables

In order to capture the different dimensions

of the SLA, we use information to construct

statistics related with several variables (Table 1).

For most of these indicators, reported statis-

tics compare average values for fishing and non-

fishing households. In some other cases, particu-

lar indicators are proposed.

For income diversification, we use the

Simpson Diversity Index (SDI; Etea et al. 2019).

According to this approach, if a household has

only one activity the index will be zero. To the

extent that the household participates in more

activities and the income it receives from these

activities is similar, the index tends to one.

Therefore, the greater the diversification of

activities and the distribution of income deriving

from them, the greater the SDI.

The indicator for food security was calcula-

ted using an adaptation of the Latin American

and Caribbean Food Security Scale (ELCSA)

(FAO 2012). Insecurity levels were estimated

using the answers to the following questions,

with reference to the week previous to the

survey:

 Did you want to vary the household nutrition

and could not?

 Did you have to reduce the food portion of a

household member?

 Did someone in this household go to bed

hungry?

 Did someone in this household have to skip

breakfast, lunch, or dinner due to lack of

food?

If the answer to all four questions is yes, the

household is considered in severe insecurity. If

the answer is yes to two or three questions, the

household is in moderate insecurity, and if the

answer is yes to one of the questions the

household is in slight insecurity. Finally, if the

answer to all questions is no, the household is

considered to have food security.

To measure the inequality of income

distribution and household expenditure in the

Barú village, we estimated the Gini coefficient

for labor income, non-labor income, total

income, household per capita income, and total

expenditure.

186 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

Table 1. Summary of variables to be evaluated in the SLA approach

Dimension

Variable

Indicator

Endowments

Human capital

Household size, Age and sex distribution, Literacy and schooling

Social capital

Organizations, Fish sharing

Physical capital

Housing, Household appliances, Vehicles, Livestock, Fishing assets

Financial capital

Savings, Credit

Strategies

Labor

Labor force participation, Labor activities, Income diversification

Fishing

Profile of fishers, Catches, Fishing techniques, Species

Non-labor

Remittances, Transfers

Use of financial capital

Savings and Credit, Consumption, Food and protein consumption

Food security

Shortage responses

Outcomes

Income and expenditures

Income, Expenditures, Income sources

Food security

Scale of food security

Poverty

Headcount poverty index

Inequality

Gini coefficient

Sustainability

Fishing sustainability

To approach the potential effects of fishing

on the ecological system, we analyzed the

degree to which fishing gear affects the eco-

system, and the conservation status of the main

species caught.

To do this, the approach proposed by Bjordal

(2005) was used, which considers seven catego-

ries of effects on coastal marine ecosystems: size

selection, species selection, incidental mortality,

ghost fishing, habitat effects, energy efficiency

and catch quality. A score of favorability

(unfavorable = 1 to favorable = 10) of the gear

with respect to the ecosystem is assigned to each

of these catego-ries, which are then averaged

arithmetically, resulting in an overall index of

the average effect of each gear on the ecosystem.

Weighted scores were calculated for the average

use of each gear type, measured as the

percentage of fishers who used each gear type in

each month during the survey period.

RESULTS

Household’s endowments

Human capital

Households in Barú were composed of four

persons on average. Barú had a predominantly

young community, with about 71% of the

population aged under 40; the median age was

26 years. Although the gender distribution was

even (52.7% men and 48.3% women), 24.8% of

non-F-hh were headed by women, while among

F-hh this percentage was only 4.9%.

Households showed important differences in

literacy rate and schooling. The percentage of

people (15 years and older) who can read and

write was higher for non-F-hh than for fishing

ones (Table 2). This difference was greater when

heads of household were considered: 95% of

household heads in non-F-hh can read and write,

whereas 78% of those in F-hh can do so. Both

differences were statistically significant.

Educational achievement by household mem-

bers older than 24 years in non-F-hh was signi-

fycantly higher than that of F-hh (7.7 vs 6.1

years). When considering only the heads of

household, the difference in education level was

accentuated. Non-F-hh heads were more educa-

ted (6.3 years) than those from F-hh (4.7 years).

Twenty-seven percent of the population over

18 years of age completed secondary education

(34% in non-F-hh and 18% in F-hh). In both

types of households, the percentage of women

who have completed secondary education was

higher than that of men (Table 2). Regarding

school attendance rate among 5 to 18 year old

household members, there were no significant

differences: 75% and 80% for non-fishing and

fishing households, respectively.

Finally, 53% of individuals aged between 18

and 28 were considered NEET (Not in Educa-

tion, Employment or Training). For women, this

rate rose up to 72%; i.e., 7 out of 10 women in

this age range were neither working nor stu-

dying. We believe this was related to child-

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 187

bearing and child-rearing by women in this age

group, as well as limited job opportunities for

both men and women. For men, this rate was

28%. When comparing types of households, the

NEET rate was higher for non-F-hh than for F-

hh, although this difference was not significant.

The estimated rate for Barú was double that

reported at the national level.

Social capital

The participation of Barú's households in

community fisheries organizations is part of

their structural social capital. The village had

four formally constituted fishing organizations.

The organizations’ main objectives were to

stabilize their members’ income and to promote

marketing and fishing control practices.

According to the baseline survey, 5% of non-F-

hh and 39% of F-hh were linked to one of these

organizations.

Structural capital also included receiving fish

as a gift and receiving support when a household

needs a loan or was experiencing food shortages;

these examples show the existence of support

networks. In the baseline survey, 35% of non-F-

hh and 27% of F-hh reported having received

fish as a gift; these percentages were significantly

lower during the follow-up survey, averaging 24%

and 19%, respectively (Figure 2).

It should be noted that the practice of gifting

fish can also be related to cognitive capital, as it

expresses values of solidarity. On average, about

28 % of F-hh reported giving away part of their

catch to other households (Figure 2). Note that,

in the month of September, both at the baseline

and in the follow-up survey, the percentage of F-

hh that gifted fish to others was as high as nearly

50%.

Physical capital

Five categories of physical assets were

examined in Barú households: housing and other

real estate (farms and lots), household appli-

ances, vehicles, livestock, and fishing assets

(Figure 3).

Households in Barú exhibited a high level

of ownership of housing and household

appliances. For instance, the percentage of

households that own their residence was 70%

and 81% for non-fishing and fishing households,

respectively. However, the ownership of other

properties such as lots and parcels was low; only

19% report owning lots and 1% rural parcels. No

differences were found in terms of vehicle

ownership (mainly motorbikes) or livestock. As

expected, F-hh report greater ownership of

boats, boat engines and productive assets for

fishing, such as nets, handlines, fish traps, and

coolers.

Table 2. Summary of human capital indicators in Barú (baseline survey).

F-hh

Non-F-hh

Colombia

Dependency ratio (a)

0.52

0.51

0.64 (b)

Literacy rate

(>14 years old)

Total

85.9% ***

92.8%

Total:

95% (b)

Household head

77.5%***

94.8%

Schooling

(Years of attending, older

than 24 years)

Women

6.2**

7.1

8.4

Men

4.6***

6.7

8.6

Total

5.4***

6.9

8.5 (c)

Household head

4.7***

6.3

Complete high school

education

(>18 years old)

Women

26%**

38%

21.9%

Men

11%***

29%

22.2%

Total

18%***

34%

22.1% (c)

Household head

7.1%***

19.6%

% NEET

(18-28 years)

Women

72.3

71.43

Men

28.3

36.8

14.8

Total

55.9

26.1

(a) The age dependency ratio is “the ratio of dependents (people younger than 15 or older than 64) to the working-age

population (those ages 15-64)” (https://data.worldbank.org/indicator/SP.POP.DPND); (b) World Bank (2020); (c) Barro and Lee

(2013). * p<0.10, ** p<0.05, *** p<0.01.

188 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

% Households

Non-fishing Fishing

Baseline

Oct-18

Nov-18

Dec-18

Jan-19

Feb-19

Mar-19

Apr-19

May-19

Jun-19

Jul-19

Aug-19

Sep-19

Exit

Baseline

Oct-18

Nov-18

Dec-18

Jan-19

Feb-19

Mar-19

Apr-19

May-19

Jun-19

Jul-19

Aug-19

Sep-19

Exit

Receive Give

Figure 2. Proportion of households who received and gave fish as a gift.

Among fishing assets, handlines and free-diving

equipment were the most common gear among

F-hh. Likewise, of these households, 43%

owned boats or canoes, 35% boat engines, and

36% refrigerators or coolers. Almost a quarter of

non-F-hh had freezers, and around 10% owned

fishing gear such as handlines and trolling

equipment. On average, the total value of assets

was almost the same for both types of

households (Table 3). However, when classified

in categories, there were some differences

mainly related to the value of fishing assets that,

as expected, was higher for F-hh. There were

other differences in the value of boats and of

housing, but they were not statistically signifi-

cant.

Table 3. Estimated value of physical assets owned by fishing and non-F-hh (in US dollars and proportions) adjusted

by the purchasing power parity of 2018 (US$-PPP).

Variable

Non-fishing

Fishing

Difference

Obs.

Mean (SD)

Obs.

Mean (SD)

Housing and real estate

158

49,142 (95,759)

48,288

(80,353)

854

Appliances and electronics

158

1,462 (3,083)

1,410

(1,786)

Vehicles

158

365 (991)

248 (476)

117

Boats and boat engines

158

1,162 (5442)

1,607 (3349)

-445

Fishing assets

158

150 (610)

756 (1358)

-603***

Livestock

158

131 (869)

107 (540)

Total physical assets

158

52,412 (96,228)

52,412

(81,952)

Proportion of households with

fishing assets

158

0.31 (0.46)

0.89 (0.32)

-0.58***

Proportion of households with

boats or boat engines

158

0.11 (0.31)

0.54

(0.50)

-0.43***

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 189

Figure 3. Percentage of non-fishing and F-hh which own physical assets (* p<0.10, ** p<0.05, *** p<0.01).

The distribution of the value of assets by

quintiles shows assets relatively evenly distrib-

uted across the population. However, in F-hh,

inequality is a little more marked, as the first

two quintiles of this group account for only 27%

of the value of assets. Ownership of fishing-

related assets is distributed evenly among the

quintiles, although this is not the case for boats

and engines, which are more statistically

frequent among households in the 4th and 5th

quintiles

Financial capital

During the period of analysis, 93% of F-hh

on average reported having informal savings,

while this proportion was only 28% for non-F-

hh. Non-F-hh save informally, mostly through

piggy banks (29%), building materials (17%)

and animals (21%). F-hh do so mainly through

piggy banks (34%), animals (22%) and cash

(15%).

In the baseline survey and throughout the

follow-up surveys, on average, 10% of

households reported having formal savings,

from which 81% of the non-fishing and 60% of

F-hh reported depositing these savings in banks.

The main reasons for not saving formally were

lack of money (69%), unwillingness (14%), high

transaction costs or low returns (5%), not

knowing how to access formal services (4%),

not trusting financial institutions (3%), financial

offices are too far away (2%), or having other

types of savings (0.4%).

During the period of analysis, on average,

24% of non-F-hh and 26% of F-hh received

informal loans. Loan sharking – known in

Colombia as gota a gota or pagadiario – was the

most representative source of informal loans for

both types of households (39.5% for non-F-hh

and 43.8% for F-hh). Food and supplies bought

on credit (21.5% and 22.9%) and loans from

lenders other than usury (16.3% and 12.4%)

were also noteworthy. Traditionally, access to

formal credit has been scarce in this community.

During the period of analysis, only 0.5% of non-

F-hh and 1.9% of F-hh requested loans from the

formal sector.

The level of total indebtedness averaged US$

613 US$-PPP (US dollars and purchasing power

parity) for non-F-hh and US$ 523 US$-PPP for

F-hh, the difference being statistically signifi-

cant. F-hh presented a lower level of indebt-

edness, a higher level of savings, and greater

receipt of formal loans.

Livelihood strategies

Labor strategies

On average, the economically active

population was 40 years old with 6.8 years of

education. Labor force participation, estimated

as the number of people aged 15 and older who

were working out of the total population in this

age range, reached 52.4% in Barú, which was

190 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

lower than the national figure for the same year

(68.4%) (World Bank 2020). However, employ-

ment in the village was seasonal and can

therefore fluctuate over time. In non-F-hh, labor

force participation was 49.5%, while in F-hh it

was significantly higher at 56.3%. In other

words, F-hh tended to have more people

working than did non-F-hh: an average of 1.75

economically active people per household versus

1.37, respectively. F-hh had higher labor partici-

pation in the younger strata (15-19 years) and in

the older population (60-79 years and above).

More than half (56%) of adults over 60 in F-hh

continued to provide income to the household,

while only a third of this population participated

in some economic activity in non-F-hh.

There were more men working in both types

of households than women: of the total number

of people who were working, 34.4% were

women. The labor participation of women in

non-F-hh was higher than in F-hh: 38.2% versus

33.6%. Labor participation of the head of

households in both groups reaches 51%.

The ratio between the theoretically inactive

or dependent population (under 15 years and

over 65 years) and the labor force (15-65 years)

in Barú was 52 % in non-F-hh and 51 % in F-hh.

In other words, for every two persons of poten-

tial working age, there was one economically

dependent person in both F-hh and non-F-hh.

Between 4 and 14 % of people older than 15

reported having a second economic activity

(Figure 4).

Workers who reported carrying out only one

economic activity allocate between 40 (F-hh)

and 47 (non-F-hh) hours per week to that

activity. When workers carry out two income-

generating activities, they spent up to 52 hours

per week, but reduced the average hours

engaged in the primary activity. The share of F-

hh that has an activity was significantly higher

when compared to that in non-F-hh (Figure 5).

Having a second occupation seemed to be

related to the tourism seasons in the case of F-

hh.

Main activities among heads of non-F-hh

were tourism, the production and sale of

handicrafts, construction, and sale of food. For

heads of F-hh, these activities included fishing,

transport, watch keeping (security) and fishing-

related activities (consisting mainly of trading

fish and rental of fishing equipment). Most

frequent secondary economic activities for the

heads of non-F-hh were food sale and handi-

crafts, while for F-hh were fishing, construction

and food sales.

Figure 4. Percentage of household heads and other members (15 years old and older) working in zero, one, or two

economic activities (for all survey months). Note: 8% of this population was studying, of whom 90% only

studied while the remaining percentage worked and studied at the same time.

When analyzing working members other than

the household head, for both types of house-

holds, most important sectors were food sales

and tourism, with tourism being the most

important in non-F-hh and food sales predomi-

nating in F-hh.

In addition, in F-hh, about 8% of non-head,

working household members were engaged in

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 191

fishing as their main activity. In terms of

secondary activities, the predominant economic

sector in both types of households was food

sales, followed by mixed and other activities for

non-F-hh and F-hh and construction for F-hh.

Participation in the formal labor market,

under contract and with social benefits, included

only 1.5% of workers, with no significant

differences between non-fishing and F-hh. This

implies that 98.5% of the workers in Barú were

in the informal sector. People with formal jobs

reported significantly higher incomes than those

with informal jobs: US$ 862 USD PPP versus

U$S 631 USD PPP per month per worker.

Relative to income diversification, we found that

households carry out on average 1.4 different

activities from which they derive income, and F-

hh diversify significantly more than non-F-hh:

1.71 versus 1.16 economic activities. About 72%

of non-F-hh had one activity at most, while

about 58% of F-hh had two or more activities

(Figure 6).

Figure 5. Percentage of the population (15 years and older) with primary economic activity (left) and secondary

economic activity (right).

The proportion of labor income derived from

the primary economic activity for all households

and for households with more than one

economic activity was higher for non-F-hh who,

on average, derived 90% of their labor income

from the main activity, while F-hh derived 78%

of their labor income from this activity (Table

4). According to the Simpson Diversity Index,

F-hh diversified their income significantly more

than non-F-hh. Households did not exhibit large

diversity of income in the main economic

activity; however, secondary economic activity

tended to be more diverse within households.

Fishing activity

One hundred percent of the respondents who

fish were men, with an average age of 45.6 years

and an average of 4.28 years of education. Of

the heads of F-hh, 58.3% were engaged in

fishing. Fishing was the primary economic

activity for 31% of the people working in F-hh,

while 4% engaged in it as a secondary activity.

Fishing households allocated their catch to three

uses: sale (85%), self-consumption (13%), or

giving it as a gift to other households (2%). The

latter two categories were part of the house-

holds’non-monetary income derived from fish-

ing activity.

For the households surveyed, the total catch

of fish resources averaged 9,000 kg of fish per

month. February and July 2019 were the months

with the highest catch, and January and June the

lowest - the latter coinciding with important

holiday seasons.

This catch was around 97 kg per month per

household, which was equivalent to around 23

kg per week. The monthly catch per fisherman

was around 88 kg, while the catch per day

averaged 4.8 kg.

192 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

Figure 6. Number of economic activities by type of household during the study period (left), and distribution of

households by number of economic activities (right)

Table 4. Labor income diversity measures by type of household.

Variables

Non-fishing

Mean (SD)

Fishing

Mean (SD)

Means

difference

Number of economic activities

1.160 (0.021)

1.709 (0.022)

-0.549 (***)

Proportion of income from the main activity

0.899 (0.005)

0.783 (0.006)

0.115 (***)

Proportion of income from the main activity when

household has more than one activity

0.714 (0.009)

0.627 (0.005)

0.088 (***)

Simpson Diversity Index (for the main activity of

household members)

0.131 (0.007)

0.267 (0.007)

-0.136 (***)

Simpson Diversity Index (for household members

with more than one economic activity)

0.370 (0.009)

0.459 (0.005)

-0.089 (***)

* p<0.10, ** p<0.05, *** p<0.01

When fishing was the main activity, most

popular techniques were handlining (44%) and

diving (38%). When fishing was considered a

secondary activity, diving was the preferred

fishing technique (76% versus 13% of

handlining). During the period of study, we

did not find variation in the use of fishing

gear. On average, only 4% of F-hh diversified

their gear for the whole period of the survey,

combining handlining with diving, nets, pots,

throw nets or longlines. Handlining was the

gear with the highest catch per unit of effort

(CPUE) during most of the period under study

(131 kg fisherman-1 month-1), while fish traps

were the lowest (32 kg fisherman-1 month-1)

(Figure 7).

Most fishers reported lobster as the most

important species caught, followed by octopus

and snapper. These species were associated with

the coral-reef ecosystem, one of the most

important ecosystems for fishing in Barú, as

well as with the predominant fishing gear types

among the F-hh, which were handlining and

diving. There were at least other 15 species

reported as captured but in lower proportions.

Three aspects to highlight: (i) fish traps and

free-diving are fishing gear that target lobster;

(ii) a great diversity of species is captured with

handlining, notably snapper (Lutjanus),

yellowtail snapper (Ocyurus chrysurus), great

barracuda (Sphyraena barracuda), and bar jack

(Carangidae); and (iii) nets are mainly used for

bar jack and horse-eye jack (Caranx hipos,

Caranx latus) (Figure 8).

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 193

Figure 7. Catch per unit of effort (CPUE, kg fisherman-1 month-1) and total average by type of fishing gear.

Non-labor strategies

Non-labor strategies include income received

from remittances and transfers from the state.

On average, 7.8% of non-F-hh and 14.8% of F-

hh received subsidies. For these households, and

respectively for non-F-hh and F-hh, these

subsidies were associated with the conditional

cash transfer program Familias en Acción

(42.2% and 24.8%), third-age subsidies (45.8%

and 75.9%) and compensation funds (15.7% and

3.8%). On the other hand, on average, 17.8% of

F-hh and 7.8% of non-F-hh received income

from remittances.

Strategies for the use of financial capital

The main use of informal and formal savings

in the past, for both types of households, was to

deal with unexpected or unforeseen events.

Other uses were related to home improvements,

education, property purchase, payments for

boats or engines, and businesses, which account

for 57% and 60% of the uses reported by non-F-

hh and F-hh, respectively. Other reported uses

included covering household expenses for food

and health, general expenses, and debt

repayment. Thirty six percent of households

(38% non-fishing and 33% fishing) reported

having no savings in the past.

In terms of use of savings during this study,

on average, 18% of non-F-hh and 49% of F-hh

reported using savings (formal and informal) in

the month prior to the visit. The most common

uses, for both types of households, were buying

food and debt repayment. Moreover, about 95%

of households would like to allocate their

savings to future investments such as education,

home improvements, house purchasing, boats

and vehicles acquisition, and independent

business. On the other hand, the use of savings

to cover contingencies was also considered

important by 32% of non-F-hh and 39% of F-hh.

Formal loans acquired in the past were

mainly used for home improvements, business

investment, and contingencies. Informal loans

were used in the year prior to the survey by non-

fishing and fishing households to cover

immediate needs such as food (28% and 26%),

payment of debts (25% and 15%), and

contingencies (18% and 22%). Informal loans

were also used to invest in businesses (12% and

6%), to purchase household items (7% and 9%)

or to make home improvements (7% and 6%).

During the period of the study, households

continued asking for informal loans, used mainly

to buy food (33% in non-F-hh and 45% in F-hh),

pay other debts (30% in non-fishing and 15% in

F-hh) and cope with extraordinary events (13

and 7% for non-fishing and F-hh).

194 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

Figure 8. Main catch fish species by fishing gear.

Allocation of consumption expenditures

The monthly monetary expenditure of

households in Barú was US$ 785 USD PPP per

month and was significantly higher for F-hh

(US$ 836) than for non-F-hh (US$738). When

the expenditure was calculated in per capita

terms, this difference was no longer significant

(US$ 236 vs US$ 222). When household size

was scaled by the square root of number of

members, the expenditure per capita was US$

430 for F-hh and US$ 393 for non-F-hh.

As expected, there were some months in

which expenses change. This pattern was similar

for F-hh and non-F-hh (Figure 9). Particularly,

in January expenditures increased significantly,

probably due to the start of the school season

and/or indebtedness during the holiday season

and its associated expenses.

In terms of expenses composition, on

average, 60 % of household expenditure was

allocated to food, including water, which repre-

sents 8.2 % of total expenditure. Leisure and

entertainment accounted for about 15-20 % of

expenses.

With regard to animal protein consumption,

about 40% of expenses were used for white meat

such as chicken and fish. However, for F-hh

most of these expenses were aimed at chicken.

The low figure related to the expenses on fish by

F-hh did not mean that they consume less fish

than non-fishing ones, as self-consumption plays

an important role in terms of consumption. F-hh

also consumed more milk than non-F-hh.

The frequency of consumption by type of

animal protein (fish, seafood, chicken, beef,

pork, or canned protein) was significantly higher

in F-hh than in non-F-hh, although the

proportion of monetary expenditure on protein

was relatively equal for both types of households

(Table 5). On average, F-hh consumed animal

protein 10.3 times a week, while non-F-hh

consumed it 7.6 times a week. This difference

was statistically significant and was mainly

defined by the higher consumption of fish and

other seafood by F-hh. In a community such as

Barú, households obtain fish for consumption

not only from the market but also by catching it

or receiving it as a gift. This consumption does

not need a monetary exchange. The value of

non-monetary consumption by non-F-hh –

estimated at market prices – was similar to the

value of fish they bought. For F-hh, the value of

non-monetary consumption was up to eight to

ten times the value of fish they bought.

Food security strategies

Strategies used by F-hh included going

fishing (45.5 %), followed by asking family

members for help, and reducing food

consumption (27.3 %). In the case of non-F-hh,

MALDONADO, MORENO-SÁNCHEZ, VARGAS-MORALES Y LEGUÍZAMO: COLOMBIAN CARIBBEAN SSF LIVELIHOODS 195

predominant strategies were reducing food

consumption (47.1 %), asking relatives for help

(35.3 %), or informal loans in shops (35.3 %)

(Figure 10). However, 27 % of F-hh and 47 % of

non-F-hh facing a food shortage reported having

to reduce the food of at least one member of the

household; this difference between households

was significant. Note that non-F-hh were the

only ones that turned to moneylenders to solve

food crises.

Figure 9. Household total and per capita monetary monthly expenses.

Table 5. Frequency of weekly animal protein consumption.

Type of protein

Non-F-hh

F-hh

Difference (t-test)

Obs

Mean (SD)

Obs

Mean (SD)

Fish

1,454

2.47 (3.06)

1,307

4.38 (2.67)

-1.91***

Other Seafood

1,454

0.09 (0.46)

1,307

0.22 (0.50)

-0.13***

Chicken

1,454

2.29 (2.12)

1,307

3.17 (1.91)

-0.88***

Beef

1,454

1.26 (1.52)

1,307

0.95 (0.95)

0.31***

Pork

1,454

1.32 (1.60)

1,307

1.31 (1.07)

0.01

Canned protein

1,454

0.18 (0.56)

1,307

0.26 (0.54)

-0.08***

Total protein

1,454

7.63 (0.10)

1,307

10.30 (0.06)

-2.67***

* p<0.10, ** p<0.05, *** p<0.01

When some members of the household must

reduce their food intake, in non-F-hh, it was

either mainly women who did so or all members

of the household equally, and, to a lesser extent,

the head of the household. It was remarkable

that in F-hh the main strategy was to reduce food

for all members of the household equally,

followed by the heads of household. In general,

in the event of shocks affecting the availability

of food, the most vulnerable groups in the

household, i.e., children, were protected.

LIVELIHOOD OUTCOMES

Household income and expenditure

Monthly monetary income, including labor

and non-labor sources of F-hh was higher (US$

1,095 US$-PPP) and relatively more stable over

time compared to that of non-F-hh (US$ 833

US$-PPP). Non-monetary income, estimated as

the value of fish self-consumed at market prices,

amounted to US$ 50.47 US$-PPP for F-hh and

US$ 0.68 US$-PPP for Non-F-hh (Table 6).

196 MARINE AND FISHERY SCIENCES 35 (2): 181-207 (2022)

On average, F-hh were better off than non-f-

hh: US$ 1,145 vs. US$ 834 US$-PPP. Per-

capita monthly income corresponded to US$

333 for F-hh and US$ 255 for non-f-hh. When

household size was scaled by the square root of

total number of members, the monthly per

capita income were US$ 612 and US$ 447 for

fishing and non-fishing households, respective-

ly (Figure 11). For F-hh, 37% of the income

corresponded to income from fishing (monetary

and non-monetary). Fish trade and gear rental

generated an additional 6% of income for F-hh.

For non-F-hh, fishing-related activities contrib-

uted with about 4% of income. The fishing

sector contributed to about 20% of Barú's

economy.

Figure 10. Household strategies to tackle food scarcity.

Figure 11. Total monthly household income (left) and total monthly per capita income (right). Reported values

cover total monthly income including plus non-labor and labor, monetary and non-monetary.