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Poverty line income and sheries subsidies in
developing country shing communities
Louise Siok Ling Teh ( l.teh@oceans.ubc.ca )
University of British Columbia
Lydia Chi Ling Teh
University of British Columbia
Ussif Rashid Sumaila
University of British Columbia
Alfredo Giron Nava
Stanford University
Article
Keywords: shing income, sheries subsidies, sh dependency, least developed countries, poverty line
income
Posted Date: March 28th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2731208/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
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Additional Declarations: Competing interest reported. Co-author Rashid Sumaila is one of the Editors-in-
Chief of npj Ocean Sustainability
1
Poverty line income and fisheries subsidies in 1
developing country fishing communities 2
ABSTRACT 3
Eradicating poverty and harmful fisheries subsidies are two pressing challenges frequently 4
addressed in international agendas for sustainable development. Here we investigate a potential 5
solution for addressing both challenges simultaneously by asking the hypothetical question: to 6
what extent can harmful fisheries subsidies provided by a country finance the cost of lifting 7
fishers out of poverty? Focusing on 30 coastal least developed countries, we find that fishers in 8
87% of these countries do not earn sufficient income to satisfy the extreme poverty line income 9
of USD 1.90/person/day, and that it costs an estimated USD 2.2 to 2.6 billion to lift these fishers 10
to different levels of poverty line incomes. Our analysis further suggests that at the country level, 11
redirected harmful fisheries subsidies can cover the entire cost of covering the poverty income 12
gap for between 37 to 43% of assessed countries. Our results provide quantitative evidence that 13
can be used to support simultaneous progress towards achieving several Sustainable 14
Development Goals, including those dealing with poverty reduction, food insecurity, and ocean 15
sustainability. 16
17
Keywords: fishing income; fisheries subsidies; fish dependency; least developed countries; 18
poverty line income 19
20
2
1. INTRODUCTION 21
22
Marine fisheries play a crucial role in supporting global employment, livelihoods, and food 23
security [1-4]. As a major source of food for over 3 billion people worldwide [5], fish provide 24
essential micronutrients that are particularly important for supporting the nutritional needs of 25
rural coastal communities in developing countries [6,7], where they are relatively cheap and 26
accessible [8-11]. Fish is particularly important for the world’s least developed countries 27
(LDCs), where about a quarter (26%) of the world’s 3.2 billion people who acquire 20% of 28
animal protein intake from fish and seafood live [12]. Fisheries and trade play a key economic 29
role in LDCs, as fish and seafood rank among the top 5 merchandise exports in 30% of LDCs, 30
while making up the largest food export for all LDCs as a group [12]. Moreover, with 97% of 31
small-scale fishers living in the Global South [2], fisheries are crucial for supporting coastal 32
livelihoods in LDCs. 33
34
The immense importance of fisheries is, however, threatened by the current trend of ocean 35
unsustainability and overexploited fish stocks, which puts marine biodiversity and the social-36
cultural, economic, food security, and human well-being of millions at risk worldwide [5, 13-16]. 37
The depletion of marine resources and biodiversity is a barrier to sustainable livelihoods for 38
fishers, who are frequently the poorest and most marginalised segment of society [17-20]. 39
Poorest rural households, in particular, rely heavily on fishery income in coastal areas [21,22]. 40
Fishing gains greater significance in LDCs where opportunities for employment are limited, and 41
fisheries are often the only available option for earning a livelihood. At the same time, poverty 42
also motivates excessive fishing pressure, leading to a poverty trap that ensnares fishing 43
3
communities in poverty [23,24], so much so that ‘poverty rhymes with fishery’ is commonly 44
used to describe fisheries [25]. This is exacerbated by the fact that small-scale fishers, who 45
account for 90% of the world’s fishers [2], tend to be excluded from social protection 46
interventions, i.e., programmes and policies that aim to ensure basic income security and other 47
support to address poverty and inequality for the poor and vulnerable [26-28]. 48
49
Poverty among fishing communities has serious impacts on people and marine social-ecological 50
systems [20, 29-30]. The urgency for addressing fisheries poverty vividly came to the fore during 51
the COVID-19 pandemic, which disrupted employment and economies globally [31,32], and 52
plunged fishing communities into further hardship [33-35]. Solving poverty in fishing 53
communities goes hand in hand with improving marine and fisheries management to enable 54
resilient marine social-ecological systems that can support sustainable and socially just fisheries 55
[24,30,36,37]. 56
57
One of the major contributors to overfishing is the provision of harmful fisheries subsidies, 58
which makes fishing more profitable than it otherwise would be [38,39], thereby encouraging 59
excessive fishing effort that can potentially lead to overexploitation of fisheries resources over 60
time [40,41]. Furthermore, subsidies promote an inequitable distribution of societal resources 61
[13], especially since more than 80% of current global fisheries subsidies go to the large-scale 62
sector, the bulk (64%) of which are harmful subsidies [42]. LDCs stand to benefit from the 63
eradication of fisheries subsidies because the subsidised fleets of major fishing nations are a 64
driving force behind the overexploitation of fisheries resources in the Exclusive Economic Zones 65
of many LDCs in western Africa and the Pacific islands [43,44]. 66
4
Within this context, we see an opportunity for a solution that can potentially overcome the 67
challenges of eliminating fisheries poverty and harmful fisheries subsidies simultaneously. 68
Specifically, we examine a hypothetical scenario in which countries divert harmful fisheries 69
subsidies to help fishers get out of poverty. Doing so would not change the total cost to 70
governments, while at the same time remove the incentive to overfish, thus leading to potential 71
positive impacts on marine resources, fishers, and distribution of wealth among different fishery 72
sectors [42, 45]. We argue that this is a promising solution that can produce both social and 73
biodiversity gains. To assess the economics of doing this, our study asks two questions: 1) How 74
much would it cost to bring fishers out of poverty? and 2) To what extent can harmful fisheries 75
subsidies finance the cost of bringing fishers out of poverty? 76
77
Poverty is multidimensional in that it encompasses a range of deprivations, including poor 78
health, lack of education, disempowerment, discrimination, racism and poor quality of work 79
[25,46,47]. In this paper, however, we focus on income poverty, which is relatively easier to 80
measure compared to other social and institutional aspects of fisheries poverty. A barrier to 81
reducing income poverty in fisheries is incomplete understanding of fishing income levels [30, 82
48]; this hampers poverty alleviation decisions, such as the investment required to close the 83
poverty gap for fishers and fishing households. Recent studies have started to shed light on the 84
extent of fishers living in poverty: [48] found that incomes of fishers in approximately one third 85
of 89 assessed countries were below national poverty lines, while [49] estimated that, even with 86
well-managed fisheries, the average income of up to 70% of fishers worldwide (equivalent to 87
39.9 million fishers) would not meet minimum living wages. 88
5
In this study, we quantify the level of fishing income poverty among LDCs, where falling below 89
the poverty line would have the biggest livelihood, food security, and nutritional impacts. We 90
assume that income poverty occurs when an individual or household falls below a certain 91
poverty threshold, which is defined by the aggregate cost for meeting minimum subsistence 92
needs, such as food, clothing, education, health, and housing. Our specific objectives are to: i) 93
assess the extent of poverty among fishers; ii) estimate the cost of covering the poverty gap for 94
fishers; and iii) evaluate whether harmful fisheries subsidies are sufficient for covering the costs 95
of providing a poverty line income for fishers. 96
97
We report results for: a) the extent of poverty among fishers, as measured by the gap between 98
fishing income and two levels of poverty line income; b) the cost of covering the poverty gap for 99
fishers; c) the magnitude of harmful fisheries subsidies provided by the assessed countries and 100
whether this amount is sufficient for covering the income gap for fishers in each country. We 101
group the 30 LDCs according to their World Bank income classification1 to facilitate 102
comparisons across countries. By bringing together these three elements, this study provides a 103
novel, quantitative output that can guide policy decisions on social welfare and fisheries 104
sustainability. In particular, our results provide a timely option for World Trade Organisation 105
Member countries, as an Agreement on Fisheries Subsidies to end prohibited subsidies was 106
recently adopted in June 2022. 107
108
We recognise that redirecting harmful subsidies requires substantial investment in designing the 109
right delivery mechanisms, but that this is beyond the scope of this paper. Nevertheless, our 110
1 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
6
study has the potential to inform progress towards several Sustainable Development Goals, 111
among them the goals to end poverty (SDG1), hunger (SDG2), ensuring and promoting human 112
well-being (SDG3), as well as directly addressing SDG 14.6 aimed at prohibiting harmful 113
fisheries subsidies by 2020. 114
115
2. METHODS 116
117
2.1 Country coverage 118
As this study focuses on the intersecting issues of fisheries and income poverty, it covers coastal 119
countries that are categorised as ‘least developed’ by the United Nations2 (Table 1). For each of 120
the 30 coastal LDCs, we calculate the national fish dependency, which is the percentage of total 121
animal protein supply obtained from fish and seafood, based on food balance sheet data from the 122
United Nations Food and Agriculture Organization (FAO) Statistics Division 123
(http://faostat3.fao.org) (SI Table 1). FAO’s food balance sheet includes a separate category for 124
freshwater fish, which we did not include in our calculation of fish dependency rate given our 125
focus on marine fisheries. Out of the 30 LDCs, 17 (57%) are considered to be highly fish 126
dependent, meaning that they have a fish dependency rate of more than 30% [9]. 127
128
129
130
131
132
2 Note that the United Nations list of 46 least developed countries includes those that are landlocked, which are not covered in
this analysis.
7
Table 1. United Nations List of Least Developed Countries. * indicates landlocked countries.
Afghanistan*, Angola, Bangladesh, Benin, Bhutan*, Burkina Faso*, Burundi*, Cambodia,
Central African Republic*, Chad*, Comoros, Democratic Republic of the Congo, Djibouti,
Eritrea, Ethiopia*, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Dem.
Republic*, Lesotho*, Liberia, Madagascar, Malawi*, Mali*, Mauritania, Mozambique,
Myanmar, Nepal*, Niger*, Rwanda*, Sao Tome and Principe, Senegal, Sierra Leone,
Solomon Islands, Somalia, South Sudan*, Sudan, Timor-Leste, Togo, Tuvalu, Uganda*,
United Republic of Tanzania, Vanuatu, Yemen, Zambia*
Source: https://www.un.org/development/desa/dpad/least-developed-country-
category/ldcs-at-a-glance.html
133
2.2 Fishing income 134
The basis for evaluating the extent of poverty was an individual fisher’s monthly fishing income. 135
In order to maximise consistency of reported income levels across the 30 assessed countries, we 136
used global or regional studies of fishing income that provided as many data points as possible 137
from a single study. The most recent and complete set of fishers’ income data at the time of this 138
study was a global study of fishers’ income by [48], from which we obtained data for 7 out of the 139
30 countries (SI Table 2). The second source was [50], which provided estimates of fishers’ 140
income for 9 West African countries. For the remaining 14 countries, we obtained fishers’ 141
income data through desk-based research (SI Table 2). We searched the databases of 142
international and regional institutions (e.g., International Labour Organisation, FAO), online 143
repositories such as Mendeley, and primary and secondary literature. We used online and 144
academic search and indexing engines (e.g., Google Scholar, Aquatic Sciences and Fisheries 145
8
Abstracts) to search for the key terms “fishing income”, “fisher income”, “fishermen income”. 146
We also used alternative terms for “income”, such as “wage”, “salary”, “earnings”, and 147
“revenue”. In cases where no data for a country were available, we based fishing income for the 148
particular country on the geographical regional average calculated from all other countries 149
included in this study. This applied only to 1 country – Djibouti. 150
151
Following the methodology of [48], we report all fishing income in terms of per capita monthly 152
rate in 2016 USD. This required first standardising fishing income data that were provided in 153
different units by pro-rating to a monthly rate; this was done for 2 countries (SI Table 3). We 154
used the frequency of fishing reported in the respective studies for pro-rating. A default of 4 155
weeks fishing per month and 20 fishing days per month was used if no fishing frequency was 156
provided. We then converted data reported in local currency to US dollars (USD) using exchange 157
rates supplied by the World Bank [51], and adjusted to 2016 dollars using the Consumer Price 158
Index [52]. 159
160
We assumed that all fishers live within a household; thus, monthly fishing income was divided 161
by average household size to derive per capita daily income in fishing households. This assumed 162
that fishing is the only source of household income; we also considered the case of multiple 163
livelihoods within a fishing household (see ‘Multiple livelihoods’ section below). Our approach 164
also assumed that there was only one fisher per household. We recognise the shortcoming of this 165
assumption, particularly in fishing households where women also contribute to fishing income 166
(e.g., [53, 54]). Nevertheless, the wide geographical scope of this analysis necessitated this 167
assumption because as far as we know, there is no data source which provides a consistent set of 168
9
demographic data specific to fishing households worldwide. As such, our analysis reflects a 169
‘worst case’ scenario since the presence of more than one fisher per household would increase 170
household income and hence increase per capita daily income in fishing households. 171
172
Household size data was taken either from the United Nations database on household size and 173
composition for the most recent available year [55], or from case studies of fishing households. 174
For west African countries where fishers’ income data are based on [50], we followed the 175
authors in using an average household size of 6 (SI Table 2). 176
177
Since we compiled data from diverse sources, fishers’ incomes were reported as gross income in 178
7 countries, net income in 19 countries, and not specified in 4 countries (SI Table 4). Comparing 179
gross income to the poverty line measures may result in an overly optimistic assessment given 180
that it does not consider fishing costs; however, in all cases reported gross fishing income was 181
already below the minimum USD 1.90/person/day extreme poverty line income. This implies 182
that our estimates for the 7 countries reporting gross income reflect the minimal cost for covering 183
the poverty gap. 184
185
2.3 Multiple livelihoods 186
While many fishers rely on fishing as their only source of income [56], in many localities, 187
fishing is part of a multiple livelihood strategy [11,57], i.e., fishers may also participate in 188
alternative food producing activities, such as farming or aquaculture, or engage in non-fishing 189
work (e.g., construction, tourism). This multi-livelihood approach is a means of coping with 190
fluctuating resource levels and minimising income risk, and means that fishing income by itself 191
10
cannot determine whether a fisher is above or below the poverty line. To account for multiple 192
income sources, we searched the literature to find out the contribution that fishing makes to total 193
household income. 194
195
While numerous studies on fishing livelihoods provide a descriptive breakdown of the income 196
generating activities fishers engage in (e.g., [58-60]), very few quantify the proportion of total 197
household income derived from each livelihood activity. For those that did, the contribution of 198
fishing to total household income varied widely, even for cases where fishing is the primary 199
source of income. For example, fishing accounted for 82-100% of total household income in the 200
Philippines [61], 93% in Vietnam [62], 84% in Kenya [63], 63% in Myanmar [64], and 55% in 201
Brazil [65]. Based on these studies, we conducted a sensitivity analysis involving multiple 202
livelihoods – a low fishing contribution scenario in which fishing contributes 50% to total 203
household income, and a high scenario, in which fishing contributes 85% to total household 204
income. Due to the limited data points, we applied these percentages to fishers’ fishing income 205
across all countries to derive total income from all livelihoods. This total income was then 206
compared to two different poverty line levels, described below. 207
208
2.4 Cost of providing poverty line income 209
Per capita daily fishing income was compared to two different poverty line incomes (PLI): an 210
absolute poverty line defined by the World Bank, and one based on national poverty lines. 211
Absolute, as opposed to relative poverty lines (i.e., poverty lines set at a certain percentage of a 212
country’s national median income), provide a consistent way to compare across countries [66]. 213
The absolute PLI we used was the international poverty line income of USD 1.90/person/day, 214
11
which is also the level used to define extreme poverty [67]. The second PLI was based on the 215
national minimum living wage (MLW) of each country, and was used to assess the state of 216
fishers’ income within countries. Country specific MLW data were obtained from a recent global 217
study by [49]. These MLW were provided in 2018 USD; to be consistent with the income data 218
taken from [48], which was provided in 2016 dollars, we adjusted the MLW data to 2016 real 219
USD using the Consumer Price Index. 220
221
For those countries where per capita daily fishing income did not meet any one of the two PLIs, 222
we estimated the cost of providing all fishers with a PLI by multiplying the difference between 223
PLI and fishing income by the number of fishers in each country. This assumed that the average 224
fisher income estimated for each country applied to all fishers in the country, and was a 225
necessary assumption as we did not have information about the distribution of income among 226
fishers across countries. Data for the number of fishers was taken either from [3], [68], or 227
national fishery statistics. For each country, we assumed that the number of fishers reported 228
from these sources were those who would benefit from being provided with a PLI. The gap 229
between each PLI measure and per capita daily fishing income was multiplied by 365 days to 230
estimate the annual cost of providing PLI. 231
232
2.5 Potential source of revenue for closing the PLI gap 233
Harmful fisheries subsidies were extracted from a global dataset consisting of 13 fisheries 234
subsidy types from across 152 maritime countries [69]. Annual subsidy amounts for each country 235
in the dataset were either based on reported or modelled data; harmful subsidies were inclusive 236
of payments for boat construction, renovation and modernisation, fisheries development 237
12
programmes, fishing port development, marketing and storage infrastructure, tax exemptions, 238
fuel, and fishing access agreements. In total, global fisheries subsidies amounted to USD 35.4 239
billion in 2018 dollars, of which USD 22.2 billion was spent on harmful subsidies [70]. We 240
adjusted the subsidy amounts to 2016 real USD using the Consumer Price Index. For each 241
country, we compared the magnitude of harmful fisheries subsidies to the cost of providing 242
fishers with a PLI. 243
244
3. RESULTS 245
246
3.1 Extent of poverty among fishers 247
3.1.1 Fishers’ income 248
There was a wide range in fishers’ monthly income, ranging from a low of USD 6 in Democratic 249
Republic of Congo to a high of USD 290 in Gambia, with a mean (± standard error) of USD 250
111.1 ± 13.3 per fisher per month (SI Table 5). This was equal to an average per capita daily 251
fishing income of USD 0.64 ± 0.07, which is less than 50% of the extreme poverty line income 252
of USD 1.90/person/day. 253
254
3.1.2 Gap between fishing income and poverty line income measures. 255
Extreme Poverty Line (USD 1.90/person/day) 256
Fishers’ income did not meet the USD 1.90 PLI in all the assessed countries (Fig. 1). The 257
average poverty gap was USD 1.26 ± 0.01, and in 77% of the countries that did not meet the PLI, 258
fishers’ income was less than half the PLI level (i.e., USD 0.95 or less per day). Three of the top 259
5 countries with the biggest gap between fishers’ income and the PLI were in Africa, with Congo 260
13
(Democratic Republic) having the largest gap (Fig. 1). As expected, the average poverty gap (in 261
USD/person/day) was highest in low income countries, followed by lower middle and upper 262
middle income countries (Table 2). 263
264
Table 2. Average fishing poverty gap (USD/person/day) under each poverty line
income measure for different country income groups. Countries are arranged with the
largest poverty gap at the top.
Poverty line measure
Country income group1
Average poverty Gap
(USD/person/day)
USD 1.9/person/day
Low
1.3
Lower & upper middle
1.2
Minimum Living Wage
Lower & upper middle
1.6
Low
0.9
1 The number of countries in each group is: Low = 16, Lower & upper middle = 14 ( 13 Lower middle
and 1 upper middle).
265
266
14
267
Figure 1. Countries where daily per capita income falls below the extreme poverty line income 268
(USD 1.90/person/day), indicated by the red horizontal line. 269
270
National Minimum Living Wage (MLW) 271
On average, per capita daily fishing income in 90% of assessed countries were below the 272
national MLW. Fishing income exceeded the MLW in only 3 countries – Angola, Gambia, and 273
Tanzania (Fig. 2). In contrast to the USD 1.90 PLI, the largest poverty gap occurred in lower and 274
upper-middle income countries; this gap (average of USD 1.6/person/day) was about 77% higher 275
than that of the low income country group (Table 1). 276
277
278
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Tuvalu
Timor Leste
Angola
Cambodia
Myanmar
Solomon Is
Bangladesh
Sudan
Sao Tome Prn
Mauritania
Vanuatu
Kiribati
Djibouti
Yemen
Haiti
Madagascar
Liberia
Mozambique
Sierra Leone
Tanzania
Togo
Gambia
Senegal
Benin
Comoros
Congo Dem Rep
Eritrea
Guinea
Guinea-Bissau
Somalia
Lower & upper middle Low
Per capita income per day (USD)
Gap between
current income
and poverty line
income
15
279
Figure 2. Amount (USD/person/day) required to close the gap between fishing income and the 280
Minimum Living Wage (MLW) PLI. Negative gaps indicate that fishing income exceeds the 281
MLW. 282
283
3.1.3 Number of people affected by fishing poverty 284
Based on average household sizes (SI Table 2), the estimated 6.98 million fishers across all the 285
30 assessed countries represented 33.20 million fishing household members supported by fishing 286
income. The number of fishers potentially living below the USD 1.90 and MLW poverty lines 287
were 6.96 million and 6.63 million, respectively. This was equivalent to at least 95% of total 288
estimated fishers across all assessed countries. When accounting for fishing household members, 289
the estimated number of people living below the USD 1.90 and MLW poverty lines rose to 33.20 290
million and 31.52 million people, respectively. 291
292
-1
0
1
2
3
4
5
6
Benin
Comoros
Congo Dem Rep
Gambia
Guinea
Sierra Leone
Togo
Eritrea
Guinea-Bissau
Haiti
Liberia
Madagascar
Mozambique
Senegal
Somalia
Tanzania
Bangladesh
Cambodia
Kiribati
Myanmar
Sao Tome Prn
Solomon Is
Vanuatu
Angola
Djibouti
Mauritania
Sudan
Timor Leste
Yemen
Tuvalu
Low Lower and upper middle
Gap to reach MLW (USD)
16
3.2 Cost of covering the poverty line income gap 293
The estimated cost of covering the USD 1.90 PLI gap for fishers was USD 2.65 billion per year 294
(Table 3). Bangladesh, Sierra Leone, Congo Democratic Republic, Cambodia, and Myanmar 295
were among the top 5 countries with the highest estimated total annual cost for all fishers to 296
attain the USD 1.90 PLI. Cumulatively, the top 5 countries made up 74% of the total cost (SI 297
Table 6). The total estimated cost for closing the national MLW gap for fishers was slightly 298
lower at USD 2.24 billion per year, with Myanmar, Congo Democratic Republic, and Guinea-299
Bissau having the highest costs (SI Table 6). Across country income groups, lower middle 300
income countries incurred the highest cost for meeting both PLIs for fishers (Table 3). 301
302
Adjusting for the number of countries per income group resulted in lower middle income 303
countries accounting for the biggest cost per country (USD 110 million /country) to meet the 304
USD 1.9 PLI, followed by low income (USD 76 million/country) and upper middle income 305
(USD 1 million/country) countries. For closing the MLW gap, lower middle income countries 306
again incurred the largest cost per country (USD 111 million/country), followed by low income 307
(USD 50 million/country), and upper middle income (USD 2 million/country) countries. 308
309
Table 3. Cost of covering the gap between fishing income and the USD 1.90 and
MLW poverty line incomes (millions real 2016 USD) per year. Results are grouped
by country income group.
Country income group
Poverty line
1.90 (million USD)
MLW (million USD)
Fishers
Low
1,214
797
17
Lower middle
1,430
1,443
Upper middle*
0.91
2.45
Total
2,645
2,240
*Upper middle income group consists of only one country (Tanzania)
310
3.3 Financing the poverty income line gap 311
Harmful fisheries subsidies provided by each of the 30 assessed countries ranged from a low of 312
USD 91,000 to a high of USD 246 million per year (SI Table 7). Eleven countries provided 313
sufficient harmful subsidies for covering the entire USD 1.90 poverty gap, while 13 countries 314
could cover the MLW poverty gap (Fig. 3). This could help 402,756 and 744,685 fishers reach 315
the USD 1.90 and MLW poverty lines, respectively. For countries where harmful subsidies were 316
insufficient for covering the poverty gap, the magnitude of subsidies provided by each country 317
could still contribute anywhere from a low of less than 1% up to 92% towards covering the 318
different PLI gaps (Table 4). 319
320
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0-25% 26-50% 51-99% 100
Proportion of countries
% of income gap covered by harmful subsidies
1.9 MLW
18
Figure 3. The extent to which harmful subsidies can cover the cost of closing the poverty 321
income gap for assessed countries under the USD 1.90/day (1.9) and Minimum Living Wage 322
(MLW) poverty income line measures. 323
Table 4. The percentage of each PLI poverty gap covered by harmful
subsidies provided by each country.
% of poverty gap covered by harmful
subsidies
Country
USD 1.90
MLW
Angola
100
100
Bangladesh
11
48
Benin
4
6
Cambodia
4
11
Comoros
100
100
Congo Dem Rep
<1
< 1
Djibouti
100
100
Eritrea
100
100
Gambia
100
100
Guinea
25
32
Guinea-Bissau
<1
<1
Haiti
4
4
Kiribati
100
92
Liberia
51
100
Madagascar
35
100
Mauritania
100
100
Mozambique
19
55
19
Myanmar
35
9
Sao Tome Prn
74
91
Senegal
100
100
Sierra Leone
2
6
Solomon Is
100
100
Somalia
3
5
Sudan
13
19
Tanzania
5
100
Timor-Leste*
0
0
Togo
49
55
Tuvalu
43
16
Vanuatu
100
100
Yemen
100
100
*No harmful subsidies recorded for Timor-Leste.
324
3.4 Sensitivity Analysis 325
The estimated cost of closing the fishing poverty gap was fairly sensitive to the presence of 326
multiple livelihoods. If fishing only contributed 50% to household income, the estimated cost of 327
closing the USD 1.90 PLI and MLW income gaps fell by 42% and 73%, respectively (SI Table 328
6). This would result in harmful fisheries subsidies in 16 and 22 countries being sufficient to 329
cover the USD 1.90 and MLW PLI income gaps, respectively. On the other hand, if fishing 330
contributed to 85% of household income, the overall cost of closing both the USD 1.90 PLI and 331
MLW income gaps decreased by 15% and 29%, respectively (SI Table 6). This would enable 332
harmful fisheries subsidies in 13 and 14 countries to cover the entire USD 1.90 and MLW PLI 333
income gaps, respectively. 334
20
335
4. DISCUSSION 336
337
Overcoming the world’s biodiversity and humanitarian challenges requires win-win solutions 338
that can provide both ecological and social gains [71]. Our study examines a potential solution 339
that can contribute to achieving both poverty and ocean sustainability goals that rank high on 340
international agendas. Specifically, diverting the USD 850 million that the 30 least developed 341
countries provide annually in harmful fisheries subsidies can, at the minimum, cover the USD 342
1.90 and Minimum Living Wage income gap for fishers in 11 and 13 countries, respectively. 343
344
Closing the poverty gap can potentially benefit around 7 million fishers and up to 33 million 345
people if accounting for fishers and their household members. Indeed, it is concerning that the 346
average fisher in all the assessed countries do not earn sufficient income to satisfy the extreme 347
poverty line of USD 1.90/person/day when fishing is the only income source. Further, the 348
average income of fishers in 90% of the assessed countries fall below their national minimum 349
living wage. These findings are consistent with earlier narratives about poverty among fishers 350
[30, 72], and the consequent social, health, and economic repercussions fishers and their 351
households face [20,73]. 352
353
The immense social and economic benefits fisheries provide in LDCs underlines the importance 354
of ensuring the sustainability of their fishery resources. In particular, 57% of the assessed 355
countries are highly fish dependent, which reinforces the urgency for ensuring sustainable 356
fisheries in the assessed LDCs. Within this context, we see an opportunity for countries to use 357
21
poverty alleviation as a vehicle to redirect harmful fishery subsidy funds, which are a major 358
driver of overfishing. 359
360
While we have not come across a real-world example where harmful fisheries subsidies have 361
been redirected to poverty alleviation, some countries have removed environmentally damaging 362
subsidies when implementing social protection measures for the poor in general. For instance, 363
Ghana targeted social spending programmes at lower-income households when it eliminated fuel 364
subsidies in 2005 [74]. Similarly, the Egyptian government targeted a social assistance 365
programme that guaranteed a minimum income to the poor when it reduced electricity and fuel 366
subsidies in the mid-2010s [27]. Some countries, such as Mexico and Ghana, have already 367
considered applying fuel subsidies to pay for income supplements or insurance and pension 368
schemes for fishers instead [75,76]. Thus, there is a strong potential for uptake of our proposed 369
redirection of harmful fisheries subsidies. 370
371
Having said that, a feasible policy for redirecting harmful subsidies will have to consider how the 372
cash transfers will be distributed, and the effect of these transfers on long-term poverty 373
alleviation. Although this is beyond the scope of this paper to address, channels do exist for 374
distributing funds across countries, e.g., development aid, such as that provided by governments 375
to the United Nations World Food Programme. At the country level, digital technology is 376
making it easier to deliver cash transfers to rural recipients, which typify a large proportion of 377
fishers (e.g., [77]). In terms of alleviating poverty, studies have shown that other social 378
assistance initiatives, such as universal basic income (i.e., unconditional monetary transfers from 379
the government to every individual in a society) can help stave off poverty for households at the 380
22
margins of society [78] and address existing economic and social inequalities [79,80]. An 381
important point here is that for any subsidy reform, there is a need to carefully consider trade-382
offs, especially the impact on vulnerable groups [45]. 383
384
Our results suggest that fishing alone is not sufficient for keeping people out of income poverty 385
in countries that are most vulnerable in terms of low income and high fish dependency. We 386
acknowledge that we cannot ascertain what proportion of fishers may already be receiving social 387
assistance, and who may thus either have income sources that put them above the poverty line, or 388
have access to food resources that mitigates the food insecurity impact of being below the 389
poverty line. Costa Rica, Brazil, and Peru, for example, have nationally funded insurance that 390
provide payments to small-scale fishers during closed fishing seasons [28], while a food 391
compensation scheme provided wheat to the ‘poorest and most vulnerable’ fishers affected by 392
fishing bans in Bangladesh [81]. Overall however, the proportion of fishers receiving social 393
assistance is unlikely to be high, given that social protection coverage of fishing communities 394
remains sparse [26-28]. 395
396
A limitation of this study is that, due to the general paucity of data on fishers’ income [48], the 397
fishing incomes we present here are not consistent across countries in terms of scale (small vs. 398
large-scale fisheries), fishery or gear type, or type of fisher (crew or owner-operator). These data 399
gaps mean that our best available option was to apply a national average income to all fishers in 400
a country, while acknowledging that income levels and distribution are usually variable, even 401
within a country or fishing community [82]. We found that across the assessed countries, the 402
variation in fishers’ income ranged from 1.3 to 2.5 fold, with an average of 1.9 fold variation (SI 403
23
Table 8). This is in line with a study by [83], which found a 2 to 3 fold variation in sea cucumber 404
fishers’ incomes among regions in Fiji. 405
406
Our use of a national average fisher income may not properly reflect wealth distribution among 407
fishers. For instance, [49] demonstrated that the proportion of global fishers under the minimum 408
living wage could increase by 5-10% when accounting for uneven wealth distribution. 409
Meanwhile, [84] showed that income distribution among sea cucumber fishers was much more 410
equal than that of octopus fishers within the same site in Kenya. Indeed, out of the 10 assessed 411
countries with lowest fishers’ income, six had a national Gini index of more than 40 (SI Table 9), 412
which indicates a high disparity in income distribution3. This suggests that income for a large 413
proportion of fishers may be below the average level, thereby suggesting that our estimated cost 414
of closing the poverty gap is likely on the conservative side. 415
416
5. CONCLUSION 417
418
We find that diverting harmful fisheries subsidies to finance the cost of lifting fishers out of 419
poverty can provide economic benefits that translate to social, human health and environmental 420
gains associated with getting people out of poverty [22,85], while ending the unsustainable 421
fishing practices arising from harmful subsidies [38]. This not only contributes directly to 422
achieving SDG 14.6, aimed at eliminating harmful fisheries subsidies, but can also contribute to 423
3 A Gini index of <0.2 is generally considered to correspond with perfect income equality, 0.2-0.3 with relative equality, 0.3-0.4
with a relatively reasonable income gap, 0.4-0.5 with high income disparity, and > 0.5 with severe income disparity
(https://www.unicef.cn/en/figure-27-national-gini-index-20032017). We assume that the national Gini index is applicable to
fishing communities.
_
24
other SDGs, particularly SDGs 1 (zero poverty) and SDG 2 (zero hunger), due to the linkages of 424
SDG14–life underwater–, to all other SDGs [86]. 425
426
This study provides the quantitative data policy makers need for targeting financial and other 427
resources towards poverty alleviation interventions. Our main message is that there is a need, and 428
also a potential revenue source, for mobilising resources towards alleviating poverty for the 429
world’s most vulnerable fishers. This is particularly relevant given that the recent World Trade 430
Organisation agreement on fisheries subsidies reached in June 2022 may pave the way for 431
practical policies to tackle the removal of harmful fisheries subsidies. 432
433
ACKNOWLEDGEMENTS 434
LSLT and URS acknowledge financial support from the OceanCanada and the Solving FCB 435
Partnerships funded by the Social Science and Humanities Research Council of Canada. URS 436
also acknowledges support from the Killam Awards Committee, Pew Charitable Trusts and 437
Oceana. 438
439
DATA AVAILABILITY STATEMENT 440
The data that support the findings of this study are available from the corresponding author upon 441
reasonable request. 442
443
444
445
446
25
COMPETING INTERESTS 447
URS is one of the Editors-in-Chief of npj Ocean Sustainability and all other authors declare no 448
Competing Financial or Non-Financial interest. URS is not involved in the journal’s review of 449
the manuscript. 450
451
AUTHOR CONTRIBUTIONS 452
LSLT conceptualized the framework for this paper, analysed data, wrote, reviewed and revised 453
the manuscript. LCLT contributed data and analysis of fishers’ income, reviewed, and revised 454
the manuscript. URS contributed to conceptualizing the idea for this paper, reviewed, and revised 455
the manuscript. AGN contributed data on fishers’ income and reviewed the manuscript. 456
457
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