Marine Protected Areas in Lower Income Countries: Labor Allocation,
Location Decisions, and Incomplete Enforcement
January 2016
Abstract
Using a spatially-explicit bio-economic model of a fishery, this paper considers the
optimal size, location, and enforcement level of a marine protected area (MPA) that
reflects characteristics of lower-income country settings. In determining a spatial Nash
equilibrium of fishing locations and effort at a steady state fish stock, the analysis
incorporates two aspects of fishing household decisions: labor allocation decisions across
income-generating activities and fishing location decisions as a function of distance costs
and of other fishers’ behavior. Reflecting observations of low budgets in Costa Rica and
Tanzania, this analysis considers how a budget constraint that can prevent complete
enforcement informs optimal MPA decisions for 1-site MPAs and optimally-sized MPAs,
finding interactions between MPA location, distance costs, and fish dispersal. In
addition, exploring a range of goals for MPA managers observed in low-income
countries, this analysis finds important differences between the optimal MPA decisions
for objective functions focused on yield, income, and fish stock.
Key words: Marine protected areas; spatial; Nash equilibria; bio-economic model;
fisheries; development; no-take zones; marine reserves
I.
Introduction
Marine Protected Areas (MPAs) are increasingly popular policy tools for protecting
fisheries, marine biodiversity, and reefs, as reflected by the signatories of the Rio
Convention on Biodiversity’s commitment to protect at least 10% of coastal and marine
areas within MPAs by 2020. Many lower-income countries, including Tanzania and
Costa Rica, have yet to reach this goal. In addition to protecting biodiversity and
particular sites for recreation, most MPAs explicitly recognize the role of the fish
resource in livelihoods (Carter 2003). In a low-income country context such as Tanzania,
MPAs go even further due to legislative requirements for MPAs to address both
biodiversity and poverty alleviation (Béné 2003; Gjertsen 2005; Silva 2006; Albers et al.,
2015).
Although many of the world’s MPAs are located in lower-income countries, little of
the economics literature focuses on the particular characteristics of the developing
country setting that influence the outcome of MPAs (exceptions include Allison and
Ellis, 2001; Béné, 2003; Gjertsen, 2005; Eggert and Lokina, 2007; and Robinson et al.,
2014; and in the policy literature without an economic framework, Levine 2006; and
Mwakubo et al. 2007). Rather, the literature centers on spatial metapopulation models
and the impact of dispersal from perfectly-enforced no-take MPA reserves on fishing
outside the MPA (Sanchirico et al. 2006). Several articles find that dispersal from a
perfectly protected MPA is rarely large enough to completely offset the costs imposed on
fishers of not being able to fish in the no-take MPA patch (Carter 2003; Hannesson 1998;
Sanchirico and Wilen 2001; Smith and Wilen 2003). Building from these spatial
metapopulation models of fisheries with MPAs, and based on observations in Tanzania
and Costa Rica, this framework incorporates household labor allocations decisions across
fishing and wage labor, heterogeneity in distance costs from the village to fishing
locations, interactions among fishers in their labor and location decisions, and incomplete
enforcement of MPA extraction restrictions, which fills gaps in the general MPA
economics literature and the low-income country MPA policy literature. Some of the
earlier fisheries literature recognized that when enforcement is costly, it is rarely perfect
(Sutinen and Andersen, 1985; Milliman, 1986; Nostbakken, 2008). In this paper, we
assume enforcement may be imperfect because of limited management budgets,
reflecting the lower-income country setting.
A relatively small number of economic analyses of MPAs incorporate incomplete
enforcement, despite evidence that illegal fishing is commonplace and thus should be
used to inform marine reserve siting (Kritzer, 2004). Byers and Noonburg (2007)
develop a game theoretic model with illegal harvests in a marine reserve and costly
enforcement to examine how poaching alters the impact of the reserve on fish
populations and yield, and to identify levels of enforcement that induce compliance. That
model’s assumption of constant fishing effort prevents discussion of labor allocation and
fisher’s exit from fishing in response to the marine reserve, which interviews in lowerincome countries suggest is critical to understanding the outcome of marine reserves
(Madrigal, et al., 2015; Albers, et al., 2015). Yamazaki, et al. (2014) develop a
bioeconomic model with illegal fishing that, using a goal of fishery stock size at
maximum economic yield, identifies the level of illegal harvest in both the reserve and
the neighboring regulated fishery, with particular insights about enforcement within and
outside the reserve, the displacement of illegal fishing with increasing reserve size, and
the possibility of lose-lose situations with more illegal activity and lower total fish
biomass with incomplete enforcement. Both of these enforcement papers present a tworegion model – in or out of the MPA – in an implicitly spatial setting, but without
distance as a component of the fishing location choice. Davis, et al. (2014) emphasizes
TURF (territorial user right fishery) enforcement costs as a function of distance in an
optimization that includes some no-take MPA zones but without fish dispersal and
fishing costs, including fisher distance costs, as part of zoning decisions. Expanding on
this limited literature, this paper develops an explicitly spatial framework for the location,
size and enforcement of the MPA. A spatial game theoretic model incorporates
behavioral responses of individual fishers to a (possibly imperfectly enforced) no-take
MPA in terms of both fishing effort and fishing location, thus allowing fishing effort in
the system to increase or decrease through labor allocation decisions in response to the
MPA and its management.
Section 2 develops the general, explicitly spatial bio-economic model of a fishery
with dispersal between fishing locations in a fish metapopulation. Section 3’s
presentation of results explores the no-MPA, open-access spatial Nash equilibria,
discusses the impact of incomplete enforcement of MPA restrictions, considers the
optimal 1-site MPA locations and enforcement levels under budget constraints, and
describes the optimal size, location, and enforcement of multi-site MPAs under budget
constraints. The results description compares the optimal MPAs for achieving goals
centered on fish stocks, yield, and income, in addition to depicting the impact of MPAs
on the dual goals of conservation and rural welfare. Section 4 discusses the implications
of these results for the economics literature on MPAs and for MPA policy in lowerincome countries, particularly Costa Rica and Tanzania. Section 5 concludes.
II.
Model
In common with much of the marine economics literature, a fish metapopulation
structure on a grid with density dispersal defines the biological and spatial setting
explored here. However, in contrast to that literature, here fishers consider explicit
distance costs from the village to the various fishing locations and make explicit labor
allocation decisions in response to off-sea wages and MPA policies. Fish stock changes
in each location occur through growth over time, harvest, and dispersal:
[1]
𝑋𝑡+1 = 𝑋𝑡 + 𝐺(𝑋𝑡 , 𝐾) + 𝐷𝑋𝑡 − 𝐻𝑡
where 𝑋𝑡 is a vector of fish stocks in each location in time 𝑡, 𝐾 is a vector of
carrying capacities, 𝐺(𝑋𝑡 , 𝐾) is the net growth function, 𝐷 is the dispersal matrix, and 𝐻𝑡
is a vector of the sum of all fishers’ harvest in a location at time 𝑡. All vectors are 1 × 𝐼𝐽
(i.e., the dimensions of the closed system are 𝐼 × 𝐽), and each element of 𝑋𝑡 , 𝐾, 𝐻𝑡 refers
𝑋
to a specific 𝑖 × 𝑗 patch. The logistic growth function 𝐺(𝑋𝑡 , 𝐾) = 𝑔𝑋𝑡 (1 − 𝐾𝑡) depicts
the specific per-patch growth with 𝑔 indicating the intrinsic net growth rate.
The 𝐼𝐽 × 𝐼𝐽 dispersal matrix 𝐷 operationalizes the dispersal process as a linear
function of fish stocks and densities of all patches (Sanchirico and Wilen 2001). For
example, in a three-patch system with patches indexed {1,2,3}, the following dispersal
matrix contains the information about dispersal across all possible combinations of the
three patches:
𝑋
𝑑11
𝐷𝑋𝑡 = [𝑑21
𝑑31
𝑑12
𝑑22
𝑑32
𝑎1 ( 𝐾𝑡2 −
𝑋𝑡1
𝑋
) + 𝑎2 ( 𝐾𝑡3 −
𝑋𝑡1
)
𝐾1
𝐾1
2
3
𝑑13 𝑋𝑡1
𝑋
𝑋
𝑋
𝑋
𝑑23 ] [𝑋𝑡2 ] = 𝑎1 ( 𝐾𝑡1 − 𝐾𝑡2 ) + 𝑎3 ( 𝐾𝑡3 − 𝐾𝑡2 )
1
2
3
2
𝑑33 𝑋𝑡3
𝑋𝑡1
𝑋𝑡3
𝑋𝑡2
𝑋𝑡3
[𝑎2 ( 𝐾1 − 𝐾3 ) + 𝑎3 ( 𝐾2 − 𝐾3 )]
[2]
This matrix implies that
𝑑11
𝐷 = [𝑑21
𝑑31
𝑑12
𝑑22
𝑑32
𝑎1
𝑎2
𝐾1
𝑎1
𝐾2
−(𝑎1 +𝑎3 )
𝐾3
𝑎3
𝐾1
𝑎2
𝐾2
𝑎3
𝐾3
−(𝑎2 +𝑎3 )
𝐾1
𝐾2
𝐾3
𝑑13
𝑑23 ] =
𝑑33
−(𝑎1 +𝑎2 )
[
[3]
]
Here, {𝑎1 , 𝑎2 , 𝑎3 } represent pairwise dispersal coefficients for each pair of patches.
For example, 𝑎1 affects dispersal between patches 1 and 2. Each column of 𝐷 sums to
zero, which guarantees (mechanically) zero net dispersal.
For an 𝐼 × 𝐽 grid, we generalize an 𝐼𝐽 × 𝐼𝐽 dispersal matrix, where each element
is 𝑑𝑘𝑙 =
𝑏𝑘𝑙 1
.
𝐾𝑙
In this paper, dispersal occurs between neighbors that share a
boundary through rook contiguity and not across patch corners as in queencontiguity. Numerators 𝑏𝑘𝑙 derive from a system-wide dispersal coefficient 𝑚 ∈
[0,1], where
𝑏𝑘𝑙 = 0, if 𝑘 ≠ 𝑙 and patches 𝑘 and 𝑙 are not neighbors
We use k and l as indices to avoid confusion with the 𝐼 × 𝐽 dimensions of the
system. Here, i and j refer to the row and column of a given patch; k and l index the
patch itself.
1
𝑚
𝑏𝑘𝑙 = 𝜈 , if 𝑘 ≠ 𝑙 and patches 𝑘 and 𝑙 are neighbors, where 𝜈𝑙 is patch 𝑙’s total
𝑙
number of neighbors (i.e., 𝜈𝑙 = 2 for a corner patch)
𝑏𝑙𝑙 = − ∑𝑘≠𝑙 𝑏𝑘𝑙 , constraining each column of 𝐷 to sum to zero.
[4]
These conditions ensure, respectively, that direct dispersal only occurs between
contiguous patches, that the same fish cannot migrate to multiple neighboring patches,
and that the dispersal matrix maintains a constant aggregate fish stock.
Identical villagers maximize income by allocating their labor time across fishing
labor in location j (𝑙𝑓𝑗 ), travel time to location j (𝑙𝑑𝑗 ), and wage labor (𝑙𝑤 ), subject to a
time constraint and a one-location constraint on their fishing location choice:
𝛾
max 𝑉 = 𝑝𝑙𝑓𝑗 𝑥𝑗 𝑞𝑗 (1 − 𝜙𝑗 ) + 𝑤𝑙𝑤 , 𝑠. 𝑡. 𝑙𝑓𝑗 + 𝑙𝑑𝑗 + 𝑙𝑤 ≤ 𝐿
𝑙𝑓𝑗
[5]
where 𝑝 is the price of fish, and fishing harvest is given by 𝑙𝑓𝑗 𝑥𝑗 𝑞𝑗 . Harvest per unit
effort in location j depends on the fish stock in patch j (𝑥𝑗 ) and on that location’s
catchability coefficient (𝑞𝑗 ). Importantly, this rate of harvest does not directly depend on
the number of other fishers in patch j, although the total harvest in a location j is the sum
over all nj fishers’ harvests there. Rather, congestion effects occur indirectly through the
effect of harvest on the state variable 𝑥𝑗 (i.e., the jth element of 𝑋) in the steady state. 𝜙𝑗
is an enforcement parameter that equals 1 if location j is a fully-enforced no-take zone, 0
in the no-enforcement case, and a value in between 0 and 1 for incomplete enforcement.
Finally, 𝑤 represents the onshore wage rate, while 𝛾 ∈ (0,1) allows for diminishing
returns to onshore wage labor to reflect imperfect labor markets.
Spatially, we model a 3 ×2 grid with one fish subpopulation located at the centroid
of each grid square. A single village is centered at the top of the leftmost column,
providing a benchmark seascape with six biologically-identical fish patches that differ
only in their distance from the village (Figure 1). Distance (𝑙𝑑𝑗 ) is simply the Cartesian
distance from the village to the centroid of patch j. A MatLab program solves for all of
the spatial Nash equilibria for identical fishers’ location and labor allocation decisions in
the long-run steady state. Because we do not have full case-specific parameters, we use
the parameter values in Smith et al. (2007) where our models overlap and choose other
parameter values that, through parameter sensitivity analysis, provide the range of
outcomes observed in our settings (Table 1). At these parameter values, no more than 12
villagers choose to fish, which leads us to use 12 as our villager population.
Table 1: Parameter Values
Description
Parameter
Value
coast)
𝐼
3
No. of rows (moving out to sea)
𝐽
2
Width of each column
-
4
Width of each row
-
3.5
Position of village by column
-
1
Intrinsic growth rate
𝑔
0.4
Fish dispersal constant
𝑚
0.4
Price of fish
𝑝
1
Wage rate for non-fishing labor
𝑤
1.25
time)
𝛾
0.6
Total time available per person
𝐿
24
𝑞𝑗 , ∀ 𝑗
0.007
No. of columns (moving along the
Wage parameter (incr opp cost of
Catchability coefficient
Carrying capacity
III.
𝐾𝑗 , ∀ 𝑗
100
Results
The model’s solution finds all the spatial Nash equilibria in fishing locations and
effort at a long-run fish stock steady state. The equilibrium comprises the location of
each villager, including those who undertake no fishing (located in the village); the
number of villagers per location; the individual and total time allocated to fishing in each
location; travel time to fishing locations; wage labor; individual and total yield per
location; total income including fishing and wage; and the steady state fish stocks per
location.
A. Open Access without MPA policy
As a starting point to which to compare MPA policy cases, a no-MPA setting of an
open-access fishery at the basecase parameters produces the spatial allocation of fishers
depicted in Figure 1. In this benchmark case, tradeoffs between within-location
competition among fishers and the distance costs incurred to reach more distant fishing
locations produce an equilibrium with the highest concentration of fishers (5) in the
fishing site closest to the village (1,1) and no fishers in the most distant site (3,2) despite
the high equilibrium fish stock there (Figure X). In this open access situation, distance
alone keeps fishers from the most distant patch (3,2), just as distance protects the interior
of forests surrounded by encroaching/extracting villagers (Albers 2010; Robinson et al.
2011). Distance costs incurred traveling to a fishing site reduce the labor time available
for wage work or fishing, and congestion in a fishing site reduces the returns to marginal
fishing labor. The interaction of these two effects leads fishers in the closest location to
the village (1,1) to allocate the least time to fishing and the most to wage labor of all
fishers, as we observe in Costa Rica (Madrigal, et al., 2015). In addition to distance costs
and congestion, the pattern of fisher locations reflects fish dispersal as a function of the
number of neighbors per location and the relative densities of fish in those neighboring
locations. For example, more fishers locate in (2,1) than in (1,2) despite the slightly
longer distance from the village to (2,1) because the column 2 locations can support more
fishers due to having dispersal relationships with three neighbors rather than two.
Figure 1: Equilibrium Spatial Pattern of Fishing Location Decisions without MPAs.
Village: 0
5
3
1
1
2
0
The number in each of the six fishing locations in this figure depicts the number of
villagers who fish in that location in equilibrium, while the number above the first
column is the number of villagers who undertake no fishing and work for wage full-time.
Reducing the dispersal to zero permits a comparison to forest protected area analysis
and equating distance costs across fishing locations permits comparisons to most
fishery/MPA analyses. Zero dispersal of fish mimics the behavior of sedentary marine
species and the lack of movement of trees in a forest, and focuses the spatial location
decisions on distance costs. In contrast to the benchmark parameter fishing locations,
zero dispersal leads to two households becoming wage-labor specializers, fewer fishers in
the center column and near the village, and a fisher in the most distant location (Figure
2). Dispersal allows fishers to reduce distance costs and to fish in the same location with
many fishers because fish disperse from more distant and less fished locations. Similarly,
with equal distance costs across fishing locations, fishers spread out across locations to
limit congestion costs but locate in the center column to receive dispersal benefits from 3
rather than 2 neighboring locations (Figure 3).
Figure 2: Equilibrium Spatial Pattern of Fishing Location Decisions without MPAs:
No Dispersal.
Village: 2
3
2
1
2
1
1
The number in each of the six fishing locations in this figure depicts the number of
villagers who fish in that location in equilibrium, while the number above the first
column is the number of villagers who undertake no fishing and work for wage full-time.
Figure 3: Equilibrium Spatial Pattern of Fishing Location Decisions without MPAs:
Equal Distance from Village to All Fishing Locations.
Village has
1
2
2
2
2
1
2
1
2
1
3
1
2 wage specializers in all 4 spatial equilibria.
1
3
2
2
1
2
2
2
1
1
2
2
The number in each of the six fishing locations in this figure depicts the number of
villagers who fish in that location in equilibrium, while the number above the first
column is the number of villagers who undertake no fishing and work for wage full-time.
Without heterogenous distance costs, four equally valued spatial equilibria exist due, in
part, to the lack of “fractional” fishers in any location.
Representing the low-income country setting requires explicit acknowledgement of
the household labor tradeoffs between wage work and fishing, which depends on the
wage level. A parametric variation of the on-shore wage from zero to 2.5 demonstrates
wages’ importance in determining steady-state equilibrium stocks, incomes, and location
decisions (Figure 4). Setting the on-shore wage to zero permits a comparison with many
other fishery/MPA models, may represent some parts of countries like Tanzania, and
isolates the spatial location decisions. With a zero wage, all villagers fish and they are
evenly distributed across the six fishing locations. Even at a zero wage rate, the distance
costs lead to less labor available for fishing in the distant locations and higher steadystate fishing stocks at locations far from the village. As the wage is parametrically
increased, both the pattern and extent of fishing changes. Higher wages imply a higher
opportunity cost of fishing in distant patches as distance costs are a function of distance
and the on-shore wage rate, which discourages fishing far from the village and produces a
more agglomerated pattern of fishers close to the village. In addition to marginal declines
in fishing effort at higher wages, a sufficiently high wage induces some villagers to
specialize in wage labor and refrain from fishing altogether, with a positive impact on
fish stocks and incomes.
Figure 4: Sensitivity to On-shore Wage.
B. Incomplete Enforcement in 1-Location MPAs
Observations in many low-income countries suggest that budgets for enforcing
extraction restrictions in protected areas remain low compared to the levels that deter
illegal extraction. In this framework, varying the enforcement level reveals how that
level interacts with distance costs and with dispersal to determine its impact. With
single-location MPAs, parametric variation of the enforcement probability from zero to
one leads to the expected monotonic increase in the MPA fish stock and to a monotonic
decrease in illegal fishing in the MPA. Despite that monotonic relationship for the MPA
location itself, the impact of an increase in the enforcement level on system-wide
characteristics often demonstrates non-monotonic relationships that reflect marginal
changes in effort, non-marginal shifts in fishing location, exit from fishing, dispersal, and
distance costs.
First, the level of enforcement that leads to complete deterrence of illegal fishing
differs across single site MPA locations. In terrestrial protected areas with no species
dispersal, the level of enforcement required to achieve complete compliance varies with
the distance to the MPA from the village because higher distance costs reduce the net
benefits to harvesting, which implies lower levels of enforcement to make extraction
unprofitable (Albers, 2010; Robinson, et al. 2011). Similarly in the MPA setting for a
particular wage, an MPA close to the village in location (1,1) always requires a higher
enforcement level than that of an MPA in a more distant location to completely deter
illegal harvest (Figure 5). In the MPA setting, however, this general distance cost
characteristic interacts with the fish dispersal patterns, fisher congestion, and other
parameters to determine the level of enforcement required for complete deterrence. For
example, at a zero wage, an MPA in the location (2,1) requires less enforcement to deter
extraction than required in more distant locations due to the impact of dispersal from that
location to the nearest-village location and other fished locations. In addition, for each
possible 1-location MPA, the level of enforcement to achieve perfect deterrence follows a
generally declining but non-monotonically declining pattern with increases in wage
(Figure 5, same as earlier in paragraph). The lack of monotonicity in this relationship
stems from the fact that higher wages imply higher opportunity costs of time spent in
transit to fishing locations and that households make both marginal decisions about labor
allocation and non-marginal decisions about fishing location and exiting fishing.
Although distance costs don’t dominate location and fishing effort decisions as much in
the marine setting as in the terrestrial setting, ignoring distance cost heterogeneity
misrepresents the tradeoffs between MPA location and enforcement levels at any wage,
thereby missing opportunities to achieve compliance with MPAs at relatively low
enforcement levels due to the MPA’s location.
Figure 5: Minimum Enforcement for Complete Deterrence.
Figure 5. For each 1-location MPA patch in the 3X2 marinescape, this figure
demonstrates how the minimum enforcement level required to completely deter harvest
in that patch varies with onshore wage.
Second, the goals of MPA policy including aggregate fish stock, total income, and
total yield also respond non-monotonically to increases in the enforcement probability,
with differences in those relationships across 1-location MPAs (Figures 6, 7, and 8). For
all single-unit MPA locations except (2,2), complete enforcement leads to higher fish
stocks across the marine area but parametric variation of enforcement from zero to
complete deterrence demonstrates regions in which increasing enforcement decreases
aggregate fish stocks, with some MPA locations generating the highest fish stock overall
at levels of enforcement well below complete deterrence (see Figure 6, MPA in 2,1 for
example). This pattern arises from fishers responding to low levels of enforcement by
decreasing their fishing effort within the MPA but reacting to higher levels of
enforcement by relocating to fish in non-MPA areas. With some MPA locations, high
enough enforcement levels not only deter harvest within the MPA but also induce some
fishers to exit fishing, which leads to increases in the aggregate fish stock. In contrast,
an in MPA (3,1) with complete deterrence induces fishers to relocate to (3,2), which
offsets the impact of reduced fishing in the MPA and leads to no change in the aggregate
fish stock from that MPA. In a more extreme example of such offsetting reactions, an
MPA in (2,2) with complete enforcement induces fishers to relocate to locations with
lower distance costs, which frees time for fishing and where they receive dispersal
benefits from the MPA, which lowers aggregate fish stocks. Using an MPA to increase
fish stocks throughout the marine area requires understanding the marginal and nonmarginal reactions of fishers to different combinations of MPA location and enforcement
level.
Figure 6: Relationship between Fish Stock and Enforcement in 1-Site MPAs
Establishing the location of a 1-unit MPA and the appropriate enforcement level to
achieve the goals of increasing fish yield or increasing household incomes requires this
same type of understanding due to interactions of distance costs, congestion, and
dispersal. For MPAs in the locations of (1,1), (2,1), (2,2) and (3,1), some levels of
enforcement lead to higher aggregate yield while other levels lead to lower yields than a
no-MPA setting (Figure 7). Any enforcement of an MPA in (1,2) reduces aggregate yield
because low levels of enforcement reduce harvests at the margin by fishers in that
location and higher levels of enforcement induce fishers to move to the highly congested
location near the village (1,1) where dispersal does not offset the lost harvests from no
longer fishing in (2,1). The economics literature recognizes that only rare situations
produce the outcome of yield increases outside of MPAs offsetting foregone harvest in
MPAs but this analysis further elucidates the degree to which dispersal, relocation
decisions, distance costs, and enforcement levels interact to determine the impact of
MPAs on aggregate yield.
Figure 7: Relationship between Aggregate Yield and Enforcement for each 1-site
MPA.
These interactions also determine at what levels of enforcement an MPA in a given
location increases household incomes, yet only when wage is zero do the MPA policies
induce the same response for income as they do for yield. At the basecase wage, the
response of household income to increases in enforcement in an MPA location nearly
mirror – in the opposite direction – the response of yield (compare Figures 7 and 8). In
locations near the village, low levels of MPA enforcement decrease fishing enough to
partially solve the open access situation’s congestion and overfishing problem in those
locations, which leads to higher incomes. Enforcement levels high enough to induce
complete deterrence, however, lead to lower incomes for MPAs in (2,1), (2,2), and (3,1),
with only very small increases in incomes for completely enforced MPAs in (1,1) and
(1,2).
Figure 8: Relationship between Aggregate Income and Enforcement for each 1-site
MPA.
C. Optimal Location and Enforcement for 1-Location MPAs
Low-income countries display a range of goals for their MPAs and face tight budget
constraints for enforcing access restrictions. Here, we explore the differences between
optimal MPA siting and enforcement decisions across the MPA goals of maximizing
aggregate fish stocks, maximizing fish yield (both legally caught outside the MPA and
illegally caught in the MPA, see Milliman 1986), and maximizing total household
income.
With the basecase parameter values, the optimal location for a one-unit MPA for
both the yield and income maximizers lies in the area closest to the village, (1,1).
Although the level of enforcement falls as budgets decline, the MPA location remains
consistent across budgets. The yield maximizer employs an enforcement level high
enough to cause one fisher to become a wage specializer in the case of a moderate budget
constraint, but the income maximizing MPA does not drive exit from fishing at any
budget level. For yield and income maximizers, the location of the MPA causes enough
reduction of fishing in the most-fished portion of the marinescape to partially solve the
open access overharvesting problem there, thus maximizing incomes and yields. The
stock-maximizing MPA location, however, occurs in the center column closest to the
village (1,2) at all of these budget levels, with higher enforcement levels than in the
income and yield cases, which leads to one household exiting fishing to become a wage
specializer. The stock maximizer focuses on the ability to deter fishing, including
through exit, in the optimal MPA location choice.
In contrast, the optimal 1-unit MPAs occur in different locations for each manager
goal type if enforcement costs do not constrain decisions. Without the budget constraint,
the stock maximizer now places the MPA in the area closest to the village and enforces at
a high enough level to deter all fishing in that location and cause 2 households to exit
fishing. Three fishers relocate to the areas that border the MPA to take advantage of
dispersal from the MPA to those locations. The yield-maximizing manager locates the
MPA in (2,2) at a high enough enforcement level to deter harvests in the MPA, which
leads to fishers relocating to the MPA-neighboring locations including the most distant
location that is unfished without an MPA, but the MPA does not cause exit from fishing.
The income-maximizing manager locates the MPA in (1,2) at a high enough enforcement
level to cause one household to exit fishing but leaving 2 fishers to fish illegally in the
MPA. For all MPA goals, the optimal MPA location varies with the budget for
enforcement, which demonstrates that ignoring enforcement costs and budgets leads to
MPA siting mistakes.
As in Tanzania, low-income countries often seek to achieve two goals with their
MPAs, such as increasing stock size while maintaining or increasing incomes or yields.
For the budget-constrained cases, all of the optimal yield and optimal income MPA
locations/enforcement levels also increase the fish stock above its no-MPA level and
produce minor increases in the income or yield, respectively, to produce win-win-win
scenarios. The optimal stock MPAs for the budget-constrained cases all produce “winwin” situations with increases in stock and income but produce “win-lose” situations with
increases in stock and decreases in yield at all budget levels. In contrast, the optimal 1location MPAs for the unconstrained budget case for both the optimal income optimal
stock MPA increases both stock and income but decreases yield. The unconstrained
optimal yield MPA leads to decreases in both stock and income. No budgetunconstrained optimal MPAs produce increases in all 3 targets: stock, income, and yield.
The optimal, budget-constrained MPAs for the equi-distance cost scenario and the
no-dispersal scenario reflect those economic and ecological characteristics. Without
heterogeneity in distance costs across fishing locations, 2 households become wage
specializers, leaving 10 fishers to allocate themselves across 6 fishing locations.
Although the equal distance costs encourages an even distribution of fishers without an
MPA, because we do not consider fractional fishers in a location, the equi-distance
setting leads to multiple equally valued equilibria in terms of the spatial distribution of
fishers. For all optimal MPAs regardless of MPA goal, all the equilibrium distributions of
fishers have 5 fishers per row, 3 fishers in the outer columns, and 4 fishers in the center
column. The optimal MPA location for maximizing income occurs in any location
because the location does not enter fishers’ location decisions differently, 4 equilibrium
spatial distributions of fishers per MPA location. Similarly, although no MPA is preferred
to a single location MPA for the yield-maximizer, no aggregate yield value differences
arise between 1-location MPAs and each MPA location leads to 4 possible equilibrium
distributions of fishers. The optimal stock MPAs occur in either of the center column
locations at moderate budgets but in the side columns at more restrictive budgets. For the
no-dispersal case, both yield and income maximizing MPAs locate in (1,1) near the
village but lead to no changes in the location or number of fishers as compared to the noMPA case. The optimal stock MPA, however, deters one fisher from fishing in the MPA,
who becomes a wage-specializer, whether at high budgets with the MPA in (1,2) or lower
budgets with the MPA in (2,1). In this case, because the lower budgets force a lower
level of enforcement, the optimal MPA locates in the corner location where that lower
level of enforcement causes exit from fishing. At still lower budget levels and the related
lower level of enforcement, the optimal MPA occurs in (1,2) because that enforcement
level cannot cause exit in any location.
D. Optimal MPA Size, Location, and Enforcement
In addition to choosing the location and enforcement level for an MPA, managers
can select the size and configuration of MPAs as well. Given the limited budgets facing
MPA managers in low-income countries, such decisions should reflect tradeoffs between
higher levels of protection in smaller MPAs and low levels of enforcement across larger
MPA areas. Here, managers select the number and locations of MPA sites within the 6
unit marinescape to maximize their objective – fish stock, yield, or income – subject to a
budget constraint on enforcement costs and assigning the same enforcement to all units of
the MPA (Figure 9).
Figure 9. Optimal Size and Configuration of MPAs by Goal and Budget
Budget
35
25
0
15
0
5
0
0
5
1
2
5
1
2
5
1
2
4
4
1
1
3
0
1
3
0
1
3
0
2
0
1
Yield Max MPA
0
0
5
1
2
5
1
2
5
1
2
1
3
0
1
3
0
1
3
0
2
Income Max
MPA
2
1
0
4
2
1
4
2
1
5
2
1
4
4
1
2
0
1
2
0
1
1
2
0
2
0
1
2
Stock Max MPA
0
1
1
0
7
2
0
8
0
2
7
0
0
4
4
1
0
1
0
0
0
1
0
4
0
2
0
1
Figure 9. Each row of this table contains the optimal MPA configuration for that row’s
MPA goal at each column’s budget constraint. The numbers in each location depict the
number of fishers in that location while the number in the upper left of the marinescapes
represents the number of wage-specializers located in the village (who do no fishing). In
some cases, more than one MPA configuration or fisher location allocation proves
optimal.
An MPA seeking to maximize the aggregate fish stock in the marinescape for a
given budget optimally creates large MPAs (Figure 9, row 3). Without a budget
constraint, fish stock is maximized with an MPA covering all locations and enforcement
high enough to deter all fishing. At a high budget level of 35, the manager optimally
includes all locations in the MPA except for the location nearest the village. Despite
equal enforcement across the MPA, illegal fishing occurs in the center column, although
1 fisher is deterred from harvest in each of that column’s locations compared to the noMPA setting, while enforcement deters fishing in (1,2) and (3,1). Two of the deterred
fishers relocate to the one legal fishing location near the village (1,1) and two fishers exit
fishing to become wage specializers. For a smaller budget of 25, the optimal MPA
structure still avoids the nearest-village location but now permits fishing in the most
distant column, with both of those locations containing an extra fisher compared to the
no-MPA case. The farthest location that is protected by distance alone in the no-MPA
setting now has fishing because dispersal from the MPA to the third column increases the
marginal value of fishing there. In contrast to the higher budget case, no illegal harvesting
occurs in the MPA due to the higher enforcement level, but only 1 person becomes a
wage specializer and the near-village location has more congestion but also more
dispersal from the surrounding unfished MPA. A further reduction in the budget also
leads to a smaller MPA with a high enough enforcement level to deter illegal harvests
and produces one wage-specializer. Only at an extremely low budget (5) does the
optimal MPA include the near-village location, where it deters 1 fisher, but that MPA
structure and enforcement level leads to an increase in the number of fishers in the other
unit of the MPA, leaving a high level of illegal fishing and no fishers exiting. That MPA
structure does, however, lead to complete deterrence of fishing in (2,2), which permits
more fishing/fishers in all its neighboring location due to the dispersal it generates.
Marine managers that seek to maximize household income through the use of MPA
optimally employ smaller MPAs than those for a stock-maximizer and prioritize different
locations. In contrast to the stock-maximizing MPA, the income-maximizing MPA
includes the site closest to the village (1,1) at all budget levels. Even rather small
amounts of enforcement in that location helps to solve the open access over-harvesting
problem there, which leads to higher incomes (Figure 9, row 2). Without a budget
constraint and at large budgets, the optimal MPA size contains 3 locations including the
entire second – or central – column of the marinescape. That MPA configuration and
enforcement level deters 4 fishers from fishing in the MPA but 2 of the 3 MPA locations
face illegal harvest, with complete deterrence only in the most distant location of the
MPA, site (2,2). Of the deterred fishers, two exit fishing altogether to become wagespecializers while two relocate their fishing, including one fisher locating in the
previously un-fished location farthest from the village, site (3,1). Fishing occurs in that
distant location because the dispersal from the MPA, with its lack of fishing in (2,2),
offsets the distance costs. Two optimal MPA configurations occur at the moderate
budget of 15 with one MPA including the 2 locations closest to the village in the first row
while the other MPA contains those 2 locations and the most distant location. Both of
these MPAs lead to the same distribution of fishers, including 7 fishers illegally fishing in
the MPA and 1 household exiting from fishing. At still lower budget levels, the optimal
MPA and enforcement level protects the locations in the first row near the village but the
enforcement level is not high enough to cause households to exit fishing and 8 fishers
remain in the MPA. Although this lowest budget MPA does not lead to exit from fishing
nor a reduction in the number of fishers in the two MPA locations as compared to the noMPA case, one fisher moves from the near-village location to its neighbor, which lessens
the congestion problem there.
The yield-maximizer optimally creates smaller MPAs at all budgets than the income
and stock maximizing MPAs (Figure 9, row 1). At moderate to high and unlimited
budget levels, the MPA that maximizes yield includes the locations (1,1) and (2,1) – the
two locations in the row closest to the village – with higher levels of enforcement at
higher budgets. That configuration and the optimal enforcement level do not alter the
congestion/over-harvesting problem in the nearest-village location but fishers move from
the second MPA location to a more distant location, leaving 6 illegal fishers in the MPA.
As expected for the yield-maximizing MPA, no fishers exit fishing to become wage
specializers at any level of enforcement. In contrast to the income and stock-maximizing
MPAs at a low budget level, the lowest budget MPA for the yield maximize includes
only one MPA site, and that site is not near the village but is instead in the distant
location of the central column. In this budget case, the MPA deters all fishing from that
site, which creates large enough dispersal to lead fishers to relocate to the MPA’s
neighboring locations, including the most distant location that supports no fishing in the
no-MPA case.
Low-income country managers may consider the impact of MPAs on both ecological
and economic outcomes. The optimal MPAs for the yield maximization goal with
binding budget constraints always produce win-win situations with both higher yield and
higher stock but always lead to decreases in household incomes. Similarly, the budget-
constrained income-maximizing MPAs produce increases in stock and income but
decreases in yield. The budget-constrained stock-maximizing MPAs lead to higher fish
stocks in the marinescape but always lead to lower yields and only avoid lower incomes
in the lowest budget case. To address the dual goals of stocks and incomes jointly, low
income country protected area managers may seek to maximize the ecological stock
while addressing income needs through a minimum income level constraint. At high
budget levels and across a range of income minimum constraints, the stock-optimizer
places several locations in the MPA and enforces to deter completely harvest within the
MPA, with exceptions for the location closest to the village where some illegal extraction
remains. At lower budgets, either the number of MPA sites or the level of enforcement
falls. For moderate budgets (35) and a relatively low minimum income constraint (350),
the optimal MPA covers the 4 locations closest to the village at a moderate enforcement
level that causes two households to exit fishing, deters fishing in the MPA’s most distant
location but moves a fisher to the previously unfished most distant location due to
dispersal from the MPA. At higher income minimum constraints, however, the optimal
MPA contains only 2 locations (1,1) and (1,2) but enforces at a high enough level to
reduce congestion and fishing in (1,1) from 5 to 2 fishers, causing 3 households to
become wage specializers, and completely deterring harvest from the other half of the
MPA. These cases demonstrate a tradeoff between larger MPAs and higher enforcement
levels, with high enforcement levels in small near-village MPAs dominating when
budgets and minimum income constraints bind tightly. The location aspect of the optimal
MPA choice derives from the income gains associated with solving the open access
harvest congestion problem in the near shore locations, which also generates stock
conservation as households exit fishing.
IV.
Discussion
Building off of spatial bio-economic models of MPAs in the economics literature, this
paper incorporates several key characteristics of the low-income country context that
influence the optimal location and enforcement of MPAs and their impact on stocks,
income, and yield. As observed in Costa Rica and Tanzania, the lower-income country
aspects of fisher decisions include explicitly spatial fishing location choices that include
distance costs and competition with other fishers, off-sea wage opportunities in imperfect
labor markets, and the probability of being caught fishing illegally. For MPA managers,
those characteristics include small budgets that limit enforcement of MPA regulations,
concern for local welfare in addition to biodiversity or fish stocks, and choices over MPA
location, size, and enforcement levels. The following discussion focuses on the role of
distance costs, wage labor opportunities, MPA goals, and incomplete enforcement in
determining the outcomes from MPAs. Then, using the modeling results as a lens, we
discuss the MPA policies and outcomes observed in Tanzania and Costa Rica.
4.1 Interactions between Distance Costs, Dispersal, and MPA Locations
Empirical fishery economic analysis and our interviews and surveys in Costa Rica
and Tanzania emphasize the importance of distance costs in fisher location decisions both
with and without MPAs. In contrast to most MPA economic models, the results here
identify patterns of fisher locations and effort as a function of the distance to fishing
locations from the artisanal fishing village. Distance costs drive a pattern of less fishing
effort far from the village, which results in higher fish stocks in those locations, and more
congestion near the village. Where distance represents a significant cost of extraction,
some areas remain unharvested even in the absence of MPAs, just as in the terrestrial
park economics literature, where distance itself provides protection (Albers, 2010;
Robinson, et al. 2011).
The location of an MPA interacts with the fisher location decisions and the dispersal
of fish to alter patterns of fisher effort and resource stocks, which determine the net
impact of the MPA. An MPA located next to a previously distance-protected area can
create enough dispersal to its neighbors to induce fishers to incur the distance costs and
harvest in that previously unharvested location. In less extreme examples, fishers tend to
locate in dispersed-to locations neighboring MPAs to capture the dispersal from the
MPA, which resembles the phenomenon of “fishing the line.” Some locations of the
MPA, such as those close to the village, induce a subset of fishers to stop fishing and
become wage-specializers. In many cases, the conservation success of the MPA stems
from this exit from fishing, which signals that empirical analysis of MPA impact that
ignores former fishers and modeling analysis that maintain constant effort both fail to
address the true impact of MPAs.
The specific location of an MPA also determines its impact on total fish stocks, total
fish yield, and total income for the community. Our model illustrates explicitly that,
whenever distance costs matter for fishers’ location and effort decisions, the effectiveness
and burden of the MPA depends critically upon its location with respect to the village.
Neither Costa Rica nor Tanzania have sited MPAs, or considered expanding existing
MPAs, with full recognition of the interaction of fisher decisions and the MPA
location/size. Instead, both countries have based their initial MPA siting decisions on the
ecological characteristics of the fishery. Even the ecological outcome of an MPA depends
on the reaction of people to that MPA, with the results here characterizing that response
in terms of patterns of effort and impact on stocks, yields, and incomes.
4.2 Wage Labor and Conservation
Rather than making the classic assumption of some fixed cost and a zero-profit entry/exit
condition for the fishery or assuming constant fishing effort, this analysis explicitly
models villager decisions over labor allocation to fishing or to on-shore wage work.
When villagers have a non-zero opportunity cost of labor, some optimally choose to
forgo fishing, while others allocate some labor to on-shore wage work. Having the
opportunity to work on-shore leads to different patterns of resource extraction, levels of
fish harvest and stocks, and total income than the the no-wage case. With these large
qualitative and quantitative differences between a no-wage and a wage setting, it could
prove difficult to draw insight from the economics and policy literature that doesn’t
incorporate the labor allocation decisions relevant in many lower-income countries. In
addition, although MPAs induce marginal changes in the amount of fishing effort, here,
conservation benefits from MPAs often occur through non-marginal changes in effort
caused by exit from fishing. Given this relationship between onshore wage and
conservation benefits due to exit, countries like Tanzania that require alternative incomegenerating projects near MPAs can effectively augment their limited enforcement
budgets by using such projects and wages to induce exit from fishing.
4.3 MPA Goals: Yield, Income, Stocks
Early MPA initiatives predicted win-win scenarios for improved total fish stocks and
greater total yield due to increased dispersal from the MPA, while economic analyses,
including this one, describe the somewhat rare conditions under which that outcome
occurs. In contrast, much of the terrestrial park literature emphasizes the burden on local
villagers associated with establishing parks on land that had traditionally been a source of
fuel and food. Even if both fish stocks and yields do increase, our paper demonstrates that
total village income may still decline. Income and yield respond differently to MPA
locations, with budget-unconstrained and budget-constrained optimal MPAs rarely
generating a win-win-win in stock, yield, and income. Because the win-wins in stock and
yield typically occur from different MPA locations than win-wins in stock and income,
generalizing about the likely impact of an MPA from the literature that focuses on yield
rather than on income will create problems if the burden or benefit of the MPA on
villagers derives from its impact on income.
4.4 Optimal MPAs and Incomplete Enforcement
The goal of the MPA also determines the optimal size, location, and configuration of
the MPA, with stock-maximizing MPAs typically being larger than those for maximum
income or maximum yield MPAs. The optimal location/configuration of MPAs also
reflects the size of the budget for enforcement, with smaller budgets leading to smaller
MPAs and/or lower enforcement levels. At most budget levels, the income and yieldmaximizing optimal MPAs include the location closest to the village where the MPA can
improve incomes by solving some of the open access overharvesting and congestion
problem. In contrast, the stock-maximizing MPA avoids that near-village location at all
but the lowest budgets, which encourages even more congestion in that location but low
levels of fishing and high stocks elsewhere. At very low budget levels, optimal stockmaximizing MPAs move close to the village to deter some harvest there while optimal
yield-maximizing MPAs move away from the village to create dispersal to nearer-village
fishing locations. The income-maximizing optimal MPAs also demonstrate the role of
dispersal from the MPA to other fishing locations in their emphasis on the center column,
when budgets permit 3-unit MPAs. Overall, the optimal MPA size and configuration
differs across MPA goals and, within each goal, results from complex interactions
between enforcement budgets, distance costs, congestion, and dispersal characteristics.
In addition to their impact on the size, location, and configuration of optimal MPAs,
budget constraints also lead managers to evaluate tradeoffs between higher deterrence of
fishing—achieved through placing MPAs at a distance from the village or by making
MPAs small—and incomplete enforcement with illegal fishing in the MPAs in
equilibrium. In the optimizations considered here, illegal fishing does not enter decisions
other than how that fishing influences the outcomes of interest. In many countries,
however, illegal extraction leads to conflict between park guards and local extractors,
which can create dangerous situations for both groups and undermine other aspects of
people-park interactions. Using the number of illegal fishers as a measure of potential
conflict, the budget constrained optimal MPAs for yield, income, and stock demonstrate
that conflict does not follow a monotonic path with changing budget levels due to
changes in the location and size of the MPAs and in the number of wage-specializers.
Decreasing the budget from 35 to 25 to 15 to 5 leads to illegal fishers numbering 6, 6, 6,
and zero for optimal yield MPAs; 6, 6, 7, and 8 for optimal income MPAs; and 3, 0, 0,
and 8 for optimal stock MPAs (Figure 9). If PA managers are also interested in reducing
conflict, whether with their patrollers or among the local people, this analysis emphasizes
that the size of the conflict results from interactions of the goal and budget constraint in
non-trivial ways.
4.5 Costa Rica and Tanzania
Interviews and surveys in Tortuguero National Park in Costa Rica and Mafia Marine
Island Marine Park and Mnazi Bay – Ruvuma Estuary Marine Park in Tanzania, in
addition to policy literature review, suggest the importance of the labor allocation
decision, distance costs, and limited management budgets in determining MPA decisions
and impact. Despite these commonalities, Costa Rica´s and Tanzania´s MPAs differ in
important ways. First, Tanzania’s MPAs must consider the impact of MPA decisions on
rural poverty while Costa Rica’s MPAs have no such goals or restrictions. Second,
Tanzania’s MPAs range in their ability to draw tourists but even the most touristed MPA
creates few onshore wage opportunities and such jobs offer low wages. In contrast,
Tortuguero National Park in Costa Rica receives many tourist visitors annually, which
provides on-shore wage opportunities in the tourism sector in addition to jobs in
agriculture and manufacturing.
Using these stylized facts to determine the wage setting and the policy goal, the
optimal MPAs for Costa Rica differ from those for Tanzania. Because Costa Rica values
marine stocks without consideration of rural incomes but has a relatively low
enforcement budget, optimal MPAs there are large but with fairly low enforcement
levels. Despite no direct policy to preserve incomes, these large but weakly enforced
MPAs do not generate large income burdens to local people because Costa Ricans who
limit or exit fishing replace their fishing labor time with high hourly wage jobs. In
contrast, optimal MPAs in Tanzania reflect both stock and income goals but face serious
budget constraints, which implies large sized MPAs located away from villages and
enforced at low levels. The MPAs are constrained away from placing a large burden on
local people by virtue of the income minimum constraint, without which fishers would
bear costs of the MPA due to an inability to replace fishing labor with well-paid wage
labor. In addition, the low levels of enforcement possible in Tanzanian MPAs paired
with the low wage opportunities leads to widespread illegal fishing in MPAs and lower
conservation benefits than in situations with more enforcement or better onshore jobs.
These results based on stylized facts mimic observations in both countries. In Costa
Rica, many fishers report that they now fish less and others have exited fishing in
response to the good wage opportunities in the tourism industry (Madrigal et al., 2015).
Also following the results here, maps of fisher locations near Tortuguero National Park
reveal a pattern of highly congested near-shore fishing by people that allocate a large
fraction of their time to onshore labor rather than fishing (Madrigal, et al., 2015). In
Tanzania, fishers report that their location decisions reflect a desire to fish in particular
locations as opposed to distance cost tradeoffs, as is predicted by the evenly distributed
fishers in settings with low distance costs, which stem from low opportunity costs of time
due to low wages. Few households exit fishing in response to the low wages and rare
opportunities. As required by law, MPAs invest in alternative income-generating
projects within villages, but such projects include relatively few villagers and relatively
small fractions of villager time, which limits their impact on fishing decisions. In
addition, Tanzania’s income-generating projects and payments rarely incorporate any
conditionality on fishing levels to create incentives to reduce fishing in exchange for
benefits-sharing (Albers, et al., 2015). Viewed through the lens of the results here, the
Tanzanian low-wage scenario implies that well-enforced MPAs can be a large burden for
local people because they cannot allocate their effort away from fishing. Based on these
results and the Costa Rican experience, expansions of alternative income-generating
programs that create opportunities with higher returns than those in fishing could be
powerful marine conservation tools through their impact on labor allocation decisions. As
a caveat, however, no economic analysis exists to describe the potential tourism response,
and thus the labor opportunity response, to increasing the size or number of MPAs in
Costa Rica or in Tanzania.
The lack of funding for enforcement limits the impact of Costa Rica’s and
Tanzania’s MPAs on conservation outcomes but also limits the burden on local people.
Across all the MPAs analyzed in Costa Rica and in Tanzania, managers and villagers
alike report that the MPA undertakes very few patrols to enforce the no-take zones. The
lack of enforcement of MPA rules is partially offset by the draw of the on-shore labor
opportunities near Costa Rica’s Tortuguero National Park, which, as in our modeling
results, encourages people to reduce or forgo fishing, thereby reducing fishing pressure in
the MPA, increasing fish stocks, and increasing local incomes despite low levels of
enforcement. Fishers depict the resources in Tortuguero to be of higher quality than
those in unprotected waters, which drives some ongoing illegal fishing there, but fishers
also contend that enforcement generates some deterrence and that the MPA restrictions
have been generally positive for the community (Madrigal, et al., 2015). Costa Rica’s
relatively high off-sea wage reduces the amount of enforcement required to deter or
reduce illegal fishing while Tanzania’s off-sea opportunities have a limited impact on
fishing decisions and do little to augment the impact of the limited enforcement of MPA
restrictions. Neither country has explored opportunities to locate MPAs far enough from
villages to increase the impact of low enforcement levels, as explored in the results here.
VI. Conclusion
This paper uses a spatially explicit bio-economic model of artisanal fishers’ location
and fishing effort decisions in a spatial Nash equilibrium to examine the impact of the
location, configuration, and enforcement level of MPAs on conservation of fish stocks,
yield, and rural incomes. Using observations from Costa Rica and Tanzania to ground the
analysis, the framework characterizes lower-income country settings by incorporating the
distance costs associated with artisanal fishing across space, explicitly modeling labor
allocation choices between fishing and on-shore opportunities, and examining incomplete
enforcement. Using this framework and several MPA goals, the analysis determines the
optimal 1-location MPAs and the optimal size and configuration of MPAs for various
budget levels. Given the rapid expansion of various types of MPAs in lower-income
countries, this modeling effort, informed by fisher and manager interviews, identifies
important aspects of the interaction of MPA siting decisions and fishing decisions that
determine both the success of MPAs in protecting fish stock and the impact of MPAs on
rural communities.
The optimal MPAs to maximize yield, stock, or income all reflect the interaction of
distance costs, dispersal and geometry, onshore wages, enforcement levels, and the MPA
location. Distance costs deter some extraction in more distant locations, which can
augment limited enforcement budgets, but also interact with fish dispersal in determining
fishing locations. Optimal MPAs with budget constraints differ in size and configuration
across MPA goals and optimal MPAs for one goal rarely create positive changes in the
other two outcomes. In a context with a positive onshore wage, MPAs aimed at yield
often lead to lower incomes while MPAs aimed at incomes often lead to lower yields,
particularly by generating exit from fishing. Analyzing incomplete enforcement proves
particularly important in defining the optimal MPA and predicting its impact on
conservation and development goals. Without consideration of these characteristics of
artisanal fisheries in low-income countries, MPA policies may impose larger than
expected burdens on local people, site MPAs in locations to which the reaction of fishers
limits conservation value, and overestimate the conservation and community benefits due
to misrepresenting the fishers’ reactions in terms of illegal fishing, fishing locations, and
fishery exit.
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