1

Collective catch quotas and ex-vessel price

determination: Bilateral Monopoly Bargaining in

the Chilean Artisanal Austral Hake Fishery*/

Julio Peña-Torresa, Jorge Dresdnerb & Iván Luzardoc

(May 12, 2015)

Abstract

This paper analyses evidence about atomized fishermen collectively bargaining over ex-vessel

prices with a monopsony-like buying sector. Government allocation of collective catch quotas to

fishermen organizations triggered the voluntary formation of cooperative fishermen’s bargaining

associations, while a highly concentrated processing sector started behaving as a countervailing

monopsony. This drove ex-vessel price determination into a sort of region-specific bilateral

monopoly price bargaining. We estimate an empirical model of regional ex-vessel price

determination, taking advantage of between-regions regulatory differences to identify the

differential effects on ex-vessel prices. The hypothesis tested is that the allocation of catch rights to

fishermen organizations could have improved fishermen’s bargaining position when selling their

fresh-chilled (perishable) catch. Our results support the tested hypothesis at only one of the regions

studied. This region is precisely where fishermen were able to achieve more stable and better

organized fishermen associations to deal with the price bargaining issue.

JEL Codes: Q22, Q28, L11

Keywords: Fishery Cooperatives; Small producers’ Associations; Community Fishing Rights;

Concentrated Demand; Bilateral Monopoly bargaining.

*/ We thank S. Tveteras and J. P. Montero for helpful comments and also the very efficient assistance of

Felipe Quezada. We also gratefully acknowledge the support from staff of Subsecretaría de Pesca (A.

Gertosio, P. Rojas, D. Rivas, L. Flores) and E. Palta (staff of the Chilean Fisheries Research Institute, IFOP)

for providing us data and information about this fishery. a Director Ejecutivo (Ph.D.), Peña y Sánchez Consultores Ltda., La Península 11296, Las Condes, Santiago,

Chile (Postal Code: 7601016). (Corresponding author; e-mail: julioalept@gmail.com) b Associate Professor, Departamento de Economía, Universidad de Concepción, Research Nucleus on

Environmental and Natural Resource Economics, and Interdisciplinary Center for Aquaculture Research

(INCAR), Postal Address: Victoria 471, Barrio Universitario, Concepción, Chile. (e-mail: jdresdne@udec.cl) c Escuela Nacional de Administración Pública de Colombia, Calle 44, #53-37, Bogotá, Colombia. (e-

mail: ivanluzardo@gmail.com).

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1. Introduction

Traditional views about common-pool fisheries presume that high atomization in the harvesting

sector increases contracting costs when attempting an efficient enclosure of national marine

fisheries, thus promoting widespread overfishing (Hardin 1968; Peña-Torres 1999). However, there

are two features ignored by the previous statement that might change the predicted result. First, in

many export-oriented fisheries --especially in those specialized on direct human consumption--

atomistic harvesters frequently sell their catches to highly concentrated (and sometimes vertically

integrated) processing and marketing sectors (Crutchfield and Pontecorvo 1969, Platteau 1989,

Geirsson and Trondsen 1991, Doeringer and Terkla 1995, Arnason 1995, Peña-Torres, Bustos and

Pérez 2006).1 Second, atomized harvesters may try to improve coordination among them to

strengthen their bargaining position when trading with concentrated catch buyers (Munro 1982).

These two features can have implications upon the overfishing result but also over the fish

resource’s rent distribution. The former issue has been dealt with in relation with the question of

whether vertical integration between harvesters and processors may lead to harvesting solutions

closer to the first-best outcome (Clark and Munro 1980; Munro 1982; Schworm 1983; Stollery

1987). While the second issue has been discussed in papers analyzing the resulting ex-vessel (catch)

price (Blair and Kaserman 1987; Matulich, Mittelhammer and Greenberg 1995; Matulich,

Mittelhammer and Reberte 1996).

However, scant evidence is available on the second issue and even more so in the case of

developing countries (Deacon 2012). Yet knowing more about how ex-vessel prices are determined

is usually a relevant policy issue. This is especially so when ex-vessel prices can be affected by

policy reforms aimed at improving the enclosure mechanisms for an otherwise common-pool

1 The feature of a concentrated demand sector doing trading with an atomized production sector is common to

many food processing industries, such as in agribusiness (e.g., sugar beet, tomato pulp, tobacco or fruit

growing production; Scott 1984; Glover and Kusterer 1990; Korovkin 1992; Little and Watt 1994), and the

production chains in the beef, pork and poultry meat industries (Barkema et al. 2001; Martinez 2002).

3

resource. Policy-shift driven impacts on fish resource’s rent distribution will surely affect the

political viability of enclosure reforms.

This paper provides empirical analysis about an artisanal (small-scale) fishery at which both

traditionally ignored industry features are present. The case-study is the Chilean Austral Hake

(Merluccius Australis) artisanal fishery, specialized on fresh-chilled production for direct human

consumption. This was the first Chilean fishery that started being managed since the early 2000s

with a system of collective catch quotas, assigned on a voluntary basis to Fishermen Organizations

(FOs). The allocation of collective catch quotas to FOs was thought as an alternative to then

politically-unfeasible individual transferable catch quotas (ITQs) for the artisanal sector (Peña-

Torres, 2002(a,b) and 2015, Gomez-Lobo et al. 2010).

We focus on estimating the effects on ex-vessel prices resulting from regulatory reforms

which, while gradually enhancing fishermen’s collective fishing rights, led to the voluntary

formation of cooperative bargaining associations within an atomized harvesting sector; meanwhile a

highly concentrated processing sector de facto started to behave as a countervailing monopsony.

This drove ex-vessel price determination into a sort of region-specific bilateral monopoly price

bargaining. The allocation of collective catch quotas proceeded through different paces of fishing-

rights consolidation at different regions in this fishery. Our estimations take advantage of between-

regions regulatory differences to identify the effects of regulatory reform on ex-vessel prices.

We test the hypothesis that the allocation of collective fishing rights to FOs may have

improved fishermen’s bargaining position when selling their catch to a quasi-monopsonistic

demand sector. The allocation of collective fishing rights to FOs triggered changes in the way ex-

vessel prices were determined. Before the regulatory reforms ex-vessel prices were set on the basis

of a highly decentralised procedure: numerous small groups of fishermen would sell their labour

(fishing) effort and their catch to oligopolistic catch buyers, who acted as agents of rival

processing/exporting firms. Depending on the fish abundance at different sea areas, catch buyers

could compete among them for contracting with several small artisanal fleets. Nonetheless, the

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supply of fishing effort was often abundant, implying that a fairly elastic fishermen supply would

leave no much room for labor bargaining gains. But even in this case, the changing scarcity of fish,

together with the high perishability of fresh-chilled fishing products, imply that fishermen still

could have some bargaining power when selling the catch, especially if they could sell it in a

coordinated way.

In fact, once collective catch quotas started to be assigned to FOs, the whole system of ex-

vessel price determination evolved into a more centralized procedure: now FOs started to

coordinate among themselves, at different fishing regions, to do a more centralized bargaining over

ex-vessel prices with catch buyers. As a reaction, the latter also started to negotiate more as

regional-consolidated monopsonies, driving the whole ex-vessel price determination system into a

sort of region-specific bilateral monopoly bargaining problem. Had ex-vessel prices increased as the

result of this new price-setting context, having already controlled for other conditioning factors, it

could then be argued that ex-vessel prices would have increased as the result of a regulatory-

induced shift in the balance of market power in fishermen’s favour.

Our case study should be of some interest as evidence on the economic performance of

fishermen organizations in developing countries is scarce (Deacon, 2012). In particular, very little

empirical analysis has been published about the possibility that atomized fishermen may

collectively bargain over ex-vessel prices with monopsony-like processing or marketing sectors.

Our analysis should also serve as evidence on how the benefits from sectorial growth are shared

between big processing/marketing firms and small producers; and also about the scope for small

producers’ associations as a way to achieve more equitable welfare gains for the smaller producers

segment. Lastly, our analysis also provides evidence about the regulatory reforms made in Chile in

the last two decades concerning the management of its artisanal mobile-species fisheries.2 To our

knowledge there is very scant published evidence on the latter topic (Castillo & Dresdner, 2013).

2 This paper will not refer to the regulatory reforms in Chile aimed at introducing Areas de Manejo or

territorial user fishing rights (TURFs) for slowly-mobile marine resources, such as the Chilean abalone --

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The rest of this paper is organized as follows. First we present background evidence about the

fishery studied, followed by a description of the regulatory reforms leading to the allocation of

collective catch quotas to FOs. Afterwards we specify an ex-vessel price determination model,

explain the data and variables considered in the empirical estimations, and analyze the estimation

results. Finally, we offer concluding remarks.

2. The Chilean Austral Hake Fishery

Austral hake catches occur both in inland (fjord) waters and at open seas within the Chilean EEZ, to

the south of 400SL, by industrial and artisanal fleets.

3 The latter operate in inland waters and

concentrate most of their activities between regions X and XI (see Table 1). Region XII is where the

southernmost fishing grounds and processing plants of this fishery are located. 4

Region XII

northern limit is more than 800 kms further south than region X northern limit. Thus region XII

inland waters are at great distances from the main austral-hake processing plants that operate in

regions X and XI. Hence region XII’s austral-hake catch market operates in a more independent

way from the other two regional catch markets and has a lesser share in austral hake total artisanal

landings. To estimate a more parsimonious model our estimations are focused on data only referring

to regions X and XI.

Given the closer vicinity between the last two regions’ austral-hake regional markets, we will

test whether there is interdependence between the ex-vessel austral-hake prices that prevail in each

region. Nonetheless, given the inter-region transport costs that exist between regions X’s and XI’s

fishing grounds and processing plants, our empirical model will assume that each region has a

different regional catch market (though possibly interdependent one of another).

Concholepas concholepa. For analysis about these reforms, see Jarvis and Wilen (2015), Gelcich et al.

(2010), Castilla et al. (1998), Castilla (1994). 3 According with Chilean fisheries law, industrial vessels are those with more than 18 meters of length and

weighting more than 50 tons of registered gross tonnage. 4 Chile is divided for administrative purposes into 15 regions. The latitudinal limits of the fjords areas of X,

XI and XII regions are: X (from 410

28,60’ to 430 44,28’), XI (from 43

0 44,28’ to 48° 49,42'), XII (from 48°

49,42' to 550 33,2’). If we make a projection of these three regions’ latitudinal limits to the corresponding arcs

of the earth circumference (surface), region X’s arc would cover an extension of about 244 kms, region XI

about 567 kms, and region XII about 756 kms.

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The austral hake stock is fully exploited within Chilean EEZ. But the stock is not so

sedentary and cannot be managed on a purely spatial basis (e.g. using TURFs). Nonetheless,

regulatory rules in this fishery normally define region-specific catch restrictions.

At 2007 the official registers recorded a total of about 5400 artisanal fishermen and 1757

artisanal vessels operating, as monthly average, during the high fishing season of this fishery.

Here Table 1

Most of this fishery production goes to export markets. The yearly-average exported value

during 2008-2010 was US$86.7 million, of which US$37.5 million was fresh-chilled production

whose catch supply is mostly provided by artisanal boats. Since 2002, the artisanal sector has 50%

of the annual total allowable catch (TAC) for this species in Chile. Most of the Chilean austral hake

production is exported to Spain. In 2011 the Spanish market bought 97% of the exported value by

this fishery (including fresh-chilled and frozen products). A predominant proportion of the Chilean

fresh-chilled exports are sold at Mercamadrid, Madrid main wholesale fish market.

Historically austral hake has been mainly used to produce frozen products. However, since

the mid-1990s an increasing share has gone into fresh-chilled exports. In 2003 it stood for 40% of

the total austral hake exports and 43.4% in 2011. This reorientation of landings was benefited with

the success achieved since the early 2000s in coordinating artisanal fishing efforts. Better

coordination among fishermen’s fishing effort allows for catch buyers’ better planning and control

of catch supplies, thus improving the marketing conditions for selling abroad fresh-chilled products.

In fresh-chilled hake sales, texture and freshness are key conditionals of buyer price. In this context

artisanal fishing provides better quality for doing exports of fresh-chilled products (Proyecto FIP

2006-32).

All along the estimation sample the aggregate of the artisanal fleet can be thought of as the

single supplying sector of austral hake catches for fresh-chilled exports. Industrial vessels also catch

austral hake, but during the study period they were mostly specialized in exporting frozen products.

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Because of these different supply-specialization patterns, our empirical model will not control for

time-series changes in austral hake industrial catches.

In both frozen and fresh-chilled product segments (the predominant exported product

formats), a few wholesale importers control a significant market share. In the fresh-chilled segment,

during our sample period a single wholesale importer regularly traded at Mercamadrid about 70-

80% of the total Chilean fresh hake that entered into Spain (Proyecto FIP 2006-32). This importer

most probably had buyer power when demanding Chilean austral hake catches. However, the

perishability of fresh-chilled products also implies that fishermen could have some bargaining

power when selling their catch, at least in case they could sell it in a coordinated way.

In Spain the Chilean hake competes with other hake species coming from Argentina, New

Zealand, Namibia, South Africa and Spain (the last two are the closest substitutes). Along our

sample period, Chilean hake sales at Mercamadrid show in general an increasing trend. In 2003

there was in total 16,700 tons of fresh-chilled hake that reached Mercamadrid, of which 9,500 tons

came from Chile. In 1996, the fresh-chilled austral hake supplied volume in that market was 4,750

tons. However, since the starting (late 2008) of the economic crisis in Spain Chilean fresh-chilled

hake sales at Mercamadrid started to decline.

3. Allocation of catch quotas to fishermen organizations

This fishery has had de jure entry restrictions since 1992. But for many years they were ineffective

because of enforcement weaknesses. In December 2000 it was estimated that the number of

artisanal fishermen regularly operating in this fishery, but not formally registered by the regulatory

authority (Subpesca), was around 40% of the total number of acting fishermen (Peña-Torres, 2006).

Similarly, annual TACs have been in operation since 1995 but a more effective enforcement of

them only started since the late 1990s. Additionally, all along the 1990s and up to present times

there have been in operation minimum catch-size regulations and seasonal biological closures.

Other regulatory adjustments have been developed to improve compliance with the artisanal TAC:

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The latter has been allocated according to an increasing number of fishing areas in each region; and

differentiated per-area fishing seasons have been implemented to smooth out fishing efforts across

time, to reduce oversupply pressures on ex-vessel prices.

Here Figure 1

Despite entry restrictions and TACs, this fishery faced an acute productivity crisis between

the late-1980s and mid-1990s (Fig. 1). The fall in fishing yields reinforced fishermen’s common-

pool incentives to anticipate others’ catch; e.g. during 1997 monthly catch quotas were typically

fished in just 2 days. This process caused temporal concentration of fish supplies, diminishing ex-

vessel prices. The latter played a key role in encouraging artisanal fishing communities to start

coordinating their fishing efforts.

In 1997 representatives of fishermen and catch buyers approached the Government on the

concern of implementing a more effective enforcement of entry restrictions and quota regulations.

Then it started a process of regulatory reforms which gradually led to the allocation of (increasingly

more complete) collective fishing quotas to FOs. Today a well-developed system of collective catch

quotas exists, in some cases including a de facto operation of (per-boat) transferable catch quotas,

with FOs’ co-management efforts being directed to fill in Government’s enforcement weaknesses

(Peña-Torres et at. 2006).

Initial regulatory priorities were put on creating a new legal instrument to control the number

of boats and fishermen taking part in this fishery: the so-called ‘Pescas de Investigación’ (PI) or

‘Research Fishing Trips’ Programs. Under this management scheme, and the next one that would

follow it, per-area TACs can be allocated to FOs.

Once an agreement among Subpesca and the different participating FOs --each of them

representing different fishing areas -- was reached, each PI-Program defined the boats that would

take part in the catch of the TAC for the participating fishing areas. To do so Subpesca and the

participating FOs decided how to allocate per-area quotas, based on historical catch records. They

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also jointly decided how to divide the use of per-area quotas between intra-year fishing seasons.

And an agreement between Subpesca and each FO was also reached regarding how to divide each

FO’s TAC among its member vessels, thus de facto assigning per-boat catch quotas. These

assignments were made on the basis of different criteria of historical fishing presence. Per-area

quota allocations are only valid for the incoming fishing year. Therefore, the PI-scheme, though it

formally allocated collective (per-FO) TACs, de facto worked as a per-boat (non-transferable5)

quota scheme. However, the fishery regulator had a detailed involvement in the PI-scheme, e.g.

defining who, how and when catch quotas could be used.

There are two distinguishable stages in the development of the PI-scheme. In the first stage,

that formally started at September 2000 in both X and XI regions6, control of quota use was not

fully effective as the official Register of operating fishermen, vessels and landings of the artisanal

sector was not complete neither fully accurate. Thus PI programs’ initial priorities were given to

perfecting the artisanal-sector register issue, essential for achieving an effective enclosure of the

fishery. At this stage, the incorporation of FOs to different PI-Programs was gradual and partial, in

both X and XI regions.

Since the beginning of the PI-scheme, private consultants started to be in charge of

implementing the PI-Programs. Consultants were responsible of registering active artisanal

fishermen, boats and quota use in each PI Program.7 They also started to support Subpesca in

monitoring and controlling the use of allocated catch quotas. However, the perfection of the register

issue was fully consolidated only since September 2002. Only from then on per-boat catch quotas

5 Despite the law defined per-FO (and per-boat) quotas as non-transferable, gradually in time the use of

quotas started to be traded, in an informal rental (less than 1 year trading) market, between different FOs and

between the latter ones and industrial fishing companies (Proyecto FIP 2006-32). 6 During the period 1997-2000, a few PI programs de facto started to operate, first in region XI and later in

region X. However, their functioning was informal (there was still no law authorizing this new management

scheme) and typically with short or interrupted durations. 7 Since 2001 region X was divided into 4 areas for the purposes of implementing PI Programs. And these

Programs became in charge of 2 consulting firms (one of them in charge of 3 areas; and the other firm in

charge of a larger area). Region XI was divided into two areas and at both --and all along the PI-period in this

region-- only 1 consulting firm was in charge of PI Programs. The founder and head of this company was

originally a fisherman himself that had previously operated at region XI. Later on, under the RAE regime, a

second consulting firm also started operating at region XI.

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started to be fully enforced. This marks the beginning of a second stage in the functioning of PI

Programs. From then on the consultants became fully in charge of registering, monitoring and

controlling per-boat quota use. Thus, detailed regulatory controls became responsibility of private

consultants, while the regulatory enforcement public agency (Sernapesca) started to prioritize more

of a supervision/auditing-type of control role.

How the consultants were chosen and funded? They were chosen jointly by the PI-

participating FOs, Subpesca and the catch-buying (exporting) firms. And they were funded by

charging a fixed value per kilogram of catch landed in the areas under their control. This value was

ex-ante fixed by negotiation between the consultant, the FO’s representatives and the catch-buying

firms. Both FOs and buying firms shared the consultant bill. Each FO also received a fixed payment

for providing intermediation/supporting services. At catch landing both payments were deduced

from the total catch value. Consultants’ contract lasted one year. Contract renewal required

agreement from FO’s representatives, catch buyers and Subpesca. Despite consultants’ work at PI

Programs has not been out of troubles8, as time has gone by, contract renewal with the same

consultant has become the norm.

Quota allocation rules, within each FO, differed depending on the region of fishing

operation. In region X, FOs’ catch quotas were allocated only on the basis of boat-owners’

historical catch records. Vessels’ crew members resented this allocation criterion, as in this fishery

for long time had been in operation a criterion of dividing catch yields in ‘thirds’ (1/3 of the trip’s

catch for paying the trip’s fishing costs, 1/3 for the vessel’s crew and the other 1/3 for the boat

owner). In region XI quotas were assigned on the basis of both boat-owners’ and crew members’

records of ‘historical fishing presence’, somehow following the historical ‘one third’ division rule

of artisanal share contracts. The latter contributed to produce, among region XI fishermen, a more

consensual and (overall) better assessment of the PI scheme (Proyecto FIP 2006-32, 2008).

8 For example, at one of the initial PI Programs a consulting company was sanctioned with its early

replacement, due to allegations of corruption (more details at Proyecto FIP 2006-32 and Peña-Torres et al.

2006).

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Starting from January 2005, 19 FOs of region XI (representing 633 fishermen and having

45.2% of region XI’s artisanal austral hake TAC)9 decided to join another new type of fishery

management scheme called Artisanal Extraction Regime (RAE, in its Spanish acronym). This

regime applied to closed-entry artisanal fisheries and FOs could voluntarily apply for joining it.

Once accepted an application (based on administrative requirements such as certification of

fishermen membership), Subpesca can allocate annual TACs, in a given administrative region,

according to fishing area, FO’s membership, fleet’s size or even on a per-boat or per-fishermen

basis. Since its beginnings, most RAE Programs have allocated collective catch quotas according to

fishing areas and FOs’ (per-boat) membership. Quota allocation is based on historical catch records

and other historical fishing presence criteria. Along the period 2005-2006 all artisanal FOs of region

XI finally joined the (austral hake) RAE scheme and, until now, have remained in it.10

Under the

RAE scheme, private consultants are in charge of registering and controlling tasks.

The crucial difference between the PI and RAE schemes is that under the latter one FOs have

much greater autonomy for deciding operational matters related to quota use: e.g., how, when and

where to use the allocated quotas. Also, each FO decides by itself how to allocate the collective

quota assigned to it among its members. In some RAE Programs collective quotas have been

divided on a per-fishermen basis, allowing for quota transferability within the corresponding FO

(Proyecto FIP 2006-32, 2008).

Regarding quota transferability between different FOs operating under PI- or RAE-Programs,

originally the Law did not allow it, because of strong political opposition to it. Nonetheless,

informal quota (short-term/rental) trading, among FOs and also between FOs and industrial fishing

companies, gradually started to emerge. As a result of it, along the period 2010-2012 the regulator

Subpesca formally authorized (initially on a case-by-case basis) quota-rental short-term trading (i.e.,

9 These 19 FOs were authorized to participate in an 8-year RAE program. Other 26 FOs (having 49.2% of

region XI’s TAC) remained under PI Programs, while other fishermen (having 5.5% of the regional TAC)

remained operating under TAC but subject to Olimpic Race fishing conditions. 10

Starting from January 2012, most of the FOs of regions X and XII also joined the RAE regime.

12

the transfer of quota use rights, at most for one calendar year) between different FOs and also with

the industrial fishing sector.

In the next section we describe how the process of ex-vessel price determination changed

once collective catch quotas started to be assigned to FOs.11

4. Ex-vessel price determination: facts and theoretical model

Before the PI-programs, ex-vessel prices were ex-ante agreed (before starting the fishing season)

between a given fleet of artisanal vessels and catch buyers. When settling these price agreements,

small artisanal fleets operated in a highly atomized and decentralized fashion, while catch buyers

represented a highly concentrated sector. Thus, all along this period artisanal fishermen most

probably were price-takers (Proyecto FIP 2006-32).

The starting of PI Programs triggered the strengthening of FO’s leaderships. As a result,

FOs became more numerous and smaller but also more local in their representation and more

focalized on improving operational productivity and catch marketing issues: e.g., fishermen union

leaders started to negotiate directly with catch buyers when selling the catch (before the PI

programs these negotiations were done through independent intermediaries, Proyecto FIP 2006-32).

Afterwards, and coinciding with the consolidation of the consultants’ work of registering

and monitoring fishermen participation at PI-Programs, it did start a new scheme of periodic

(usually monthly) assemblies, occurring before starting the next incoming program of fishing trips,

to determine a unique ex-vessel price for all catch landings under a given PI-Program. In each

assembly it was agreed the ex-vessel price and the catch volumes to be bought from the different

vessels that were participants of that PI-Program. Each side of the market (buyers and sellers)

tended to negotiate as a coordinated single block. Fishermen union leaders acted as the fishermen’s

representatives. The consultants acted as arbiters and legal witnesses of the agreements achieved.

11

There is no scope in this paper for description of other observed changes in FO’s collective actions, e.g.

FOs’ increasing involvement in fishery co-management tasks, as PI and RAE reforms were evolving. For

more details about this, see Peña-Torres (2005, 2006) and Proyecto FIP 2006-32 (2008).

13

By year 2003 the scheme of periodic assemblies was of general use at the different areas under PI

programs, both in regions X and XI. However, as we argue later, region XI artisanal FOs were able

to organize themselves as a more stable and more consolidated regional (collective) negotiation

block than the (more numerous and more heterogeneous) artisanal FOs that were operating at the X

region.

Theoretical Model

Let us propose a simple theoretical model about the assemblies’ process of ex-vessel price

determination. Assume that negotiations about ex-vessel prices are region-specific. Think of fishing

effort as immobile across regions, because of the significant distances between fishing grounds and

on-land processing plants located at different regions. Because catch freshness is a basic quality

aspect of fresh-chilled products, transport costs tend to produce regional segmentation in spot catch

markets.

Assume ex-vessel price negotiations occur before starting each fishing season and define each

variable in the following model by its incoming-season average value. Suppose for the moment

there is no uncertainty. Assume a stationary fishery with identical fishing seasons and where prices

and costs are expressed in present value terms with respect to the start of each season (with all

agents using a common discount rate). To simplify even further, assume that both fishermen and

catch buyers maximize in a per-season (static) fashion their corresponding profits. Given this

fishery’s specialization on fresh-chilled products, suppose (i) the timing of fishers’ harvest is

identical to the timing of processors’ sales of fresh-chilled products, and (ii) there is no possibility

to hold output stocks.

Let Q denote the output of fresh-chilled products and X the catch volume. Assume fixed

proportions in the production of Q; thus X may also be interpreted as a composed input basket. To

simplify, suppose Q=X. CX(X) is the average unit cost of producing X and CP(Q) is the average unit

cost of producing Q, excluding the cost of raw fish supply. Let us allow for the possibility of scale-

14

dependent average costs, given the payment of a per-season fixed cost F (e.g. related to processing

plants’ and fleet’s installed capacities and fixed wage payments). P(Q) is the fresh-chilled output

demand function and PX(X) denotes the ex-vessel price.

Assume the regional sale of X is monopolized by a centralized block of different FOs

operating in a given region, while catch purchase is monopsonized by a centralized block of

regional catch buyers. When these two blocks confront each other, both realize that each have some

market power, given catch perishability. Suppose these two monopolies do not wish to vertically

integrate. This may be so as in the fresh-chilled austral hake business it is efficient to perform

fishing efforts in a highly atomized fashion, thus resulting in costly monitoring of fishing efforts.

Thus vertical integration may not be the most efficient contractual solution (Peña-Torres et al.

2006). Instead, assume they agree on signing a binding contract that maximizes the combined

profits T = (F+B), where F is the seller block’s (fishermen) profits and B is the buyer block’s

profits (Blair & Kaserman 1987). Assume no side payments are received by fishermen.12

The

combined profits T are given by:

Π𝑇 = 𝑃(𝑄)𝑄 − 𝐶𝑋(𝑋)𝑋 − 𝐶𝑃(𝑄)𝑄 (1)

In this model, a solution that maximizes T is to agree on assigning (constant per-season)

shares of the combined profits, equal to and (1-), to the seller and buyer, respectively (01).

Such an assignment of profit shares will occur through the ex-vessel price determined by the

contract (Blair & Kaserman 1987). Thus, the two parties must agree on a start-of-season ex-vessel

price, before fishing effort occurs. Setting F=T, hence B=(1-)T, and maximizing T by

solving for PX, we get:

𝑃𝑋 = 𝐶𝑋 + 𝛼 ⋅ [𝑃(𝑄) − 𝐶𝑃(𝑄) − 𝐶𝑋(𝑋)] (2)

12

In practice, side-payments to fishermen do occur in this fishery, but they are due to interlinked contracting

aimed at solving agency problems related to performance, funding and insurance coverage of costly-to

monitor fishing efforts (Peña-Torres et al. 2006). In this paper, these issues are excluded from the analysis.

15

Hence, while each party is driven to produce the joint profit-maximizing level of output (the PX

solution in (2) uniquely determines supply volume X=Q), both negotiating blocks only need to

agree on mutually acceptable shares of T, through their choice of PX.13

Equation (2) can be made to accommodate random shocks to demand and supply terms.

Following Matulich et al. (1995), assume both fishermen and catch buyers are risk-neutral, thus

they maximize their (per-season) expected joint total profits Π̅𝑇, through their agreement on the

constant -share on Π̅𝑇 . This agreement leads to the following average (per season) ex-vessel price

equation:

𝑃𝑋 = 𝐶𝑋(𝑋) + 𝛼 ⋅ [�̅�(𝑄) − 𝐶�̅�(𝑄) − 𝐶𝑋(𝑋)] (2’)

where variable �̅� denotes the average expected value of and the remaining variables in (2’) are

expressed in terms of their average (per-season) values. Therefore, given (2’), per-region ex-vessel

prices will depend on the price of the processed product �̅�(𝑄) and the average (per-unit) processing

and fishing costs, all of which may be landing-scale dependent.

5. Data

We consider monthly data for most of the variables in the empirical model. Our focus is on price

determination. But it is possible that prices and quantities are simultaneously determined.14

Hence

we define as endogenous variables the ex-vessel prices (expressed as monthly regional averages)

and the regional total artisanal landings (expressed in tons). We model regions X and XI as different

geographical markets, given inter-regional transport costs between fishing grounds and processing

plants’ locations. Thus we have four endogenous variables in our model: 2 regional ex-vessel price

13

In our case study, different fleets participating in a given PI (or RAE) Program could have different fishing

costs. Given that the regional assembly’s price agreement is about a single ex-vessel price for all landings in

that (PI or RAE) fishing program, cost differences between fleets would be accommodated throughout

different levels of contracted catch. 14

This approach is confirmed by our estimation results. Among other possible reasons for this, regional TACs

will not always be necessarily binding for at least three reasons: (i) stock-assessment based TACs could

overestimate stock abundance; (ii) TAC determination is not only dependent on stock-assessment

considerations; other issues –socio-economic factors—are also taken into account (Leal et al. 2010; Peña-

Torres 2015); (iii) under certain circumstances (weather, market conditions, fish availability) it may be

rational for fishing companies not always to fully catch available TACs.

16

equations and 2 regional landing equations. Denote the regional ex-vessel prices by P_ex-vesselX

and P_ex-vesselXI, and the corresponding regional artisanal landings by LandingX

and LandingXI, for

X and XI regions respectively.

Nonetheless, given market arbitrage and non-observable biological/environmental effects,

which may be common to both regions, the temporal variations of ex-vessel prices and artisanal

landings in both regions are expected to be interdependent of each other.15

This is incorporated in

our estimation strategy.

Our estimation sample covers from January 2000 to December 2011, excluding August each

year. During this month there is regularly in force an absolute ban (biological closure) on fishing

which coincides with the reproductive period of this species. Landing data were provided by

Sernapesca (the sectorial enforcement agency) and ex-vessel prices by Subpesca (the regulatory

agency).

All (unit) price series are expressed in the domestic currency (CLP/kg) and in nominal terms

(see Fig. 2). In our estimations we assume that both fishermen and catch buyers negotiate on ex-

vessel prices while thinking of nominal domestic values. Our justification for this is twofold: First,

interviews made to fishermen and exporters operating at this fishery (Proyecto FIP 2006-32, 2008)

did not support the idea that ex-vessel price negotiations might focus on domestic inflation-adjusted

values. Second, as a double-checking procedure, we initially included in our estimations the

domestic CPI index as another exogenous control variable. But it systematically got non-significant

results.

Here Fig. 2

The control variables in the empirical model are the following: First, the monthly average of

the unit (fob) export price, expressed in (nominal) domestic currency values (CLP/kg). Denote it by

15

The simple correlation between the ex-vessel price series for regions X and XI is 80% during our sampling

period. The corresponding correlation coefficient for both series of regional artisanal landings is 20%.

17

Pfob. It results from multiplying the monthly average international export price (expressed in

Euros/kg) by the monthly average of the nominal (CLP/Euros) currency rate. Both values were

obtained from the Chilean Central Bank. The unit export price is a proxy for the demand price of

austral-hake catch buyers. Given this fishery strong orientation to export markets, it is a priori

expected a non-negative (possibly positive) relationship between ex-vessel prices and export prices;

the magnitude of its estimated coefficient is expected to depend on the relative bargaining power of

both (supply and demand) negotiating sides. Export data was provided by the Chilean Fisheries

Research Institute (IFOP).

Given the existence of high industrial concentration in the Chilean exports of fresh-chilled

austral hake, non-observables might affect both the endogenous variables and the export price. This

might happen if Chilean exporters of fresh-chilled hake had enough market power in Mercamadrid,

the predominant wholesale market destiny of the Chilean exports of fresh-chilled austral hake.16

Nonetheless, and despite that high industrial concentration has prevailed all along our sample period

in the imports of Chilean fresh-chilled hake to the Spanish market, other countries’ supplies of fresh

hake species --also arriving at Mercamadrid-- could introduce enough contestability so that to

effectively counteract the possibility of Chilean exporters’ market power in that market. During

2004-2010 the Chilean exported volumes of fresh-chilled austral hake sold at Mercamadrid

represented (in different years) between 45-55% of the total volume of traded fresh hake species at

this market. We will test the possibility of an endogeneity bias when using the export price as a

control variable.

A second control variable is a proxy for industrial concentration at the demand side. We

expect that higher concentration on the demand side implies, ceteris paribus, a greater bargaining

power in favor of catch buyers and a lower ex-vessel price. For this control we consider two proxy

variables: the (monthly) percentage market share of the main five Chilean exporters of fresh-chilled

16

It was not possible to use the monthly-average price of austral hake transactions at MercaMadrid as a

control variable in our model because there was not long enough time-series data about this price.

18

austral hake (denoted by Share5); and the Herfindahl-Hirschman Index (HHI) of the monthly

Chilean exported value of fresh-chilled austral hake. In terms of time-series trends, both variables

show first a declining phase (until the end of 2002), then a period with short-run variability but

without clear trend, and afterwards (since early 2008 and until the end of our sample) an increasing

trend which tends to overlap with the starting of the economic crisis in Spain. We made alternative

estimations with these two control variables. The HHI variable showed slightly more significant

results and it is the control included in the preferred estimation model (Table 3).

A third control variable is the domestic (monthly average) diesel price (expressed in

nominal CLP/lt.). Denote it by Pdiesel. Diesel costs represent about 50-60% of total operational per

trip costs in this fishery (Proyecto FIP 2006-32). Thus it is a proxy for variations in variable unit

fishing costs. This price shows an increasing trend along the sampling period. However,

interpretations about the estimates to be obtained for this control should be careful. First, there are

sources of fishing cost variability which are non-observables in our model. For example, there is

evidence about fishermen’s perceptions that fishing costs on average increased along the estimation

period, given an increasing scarcity of austral hake shoals (especially at regions X). This would

imply longer fishing trips and higher fishing costs (Proyecto FIP 2006-32; pp. 138, 141). But we do

not have data to control for variability in fishing trips’ duration. We try to control for increased

costs from greater fish scarcity by using the regional (annual)17

artisanal TACs as additional

controls (see below).

Second, during the pre-reforms (Olimpic Race) period it was predominant that catch buyers

funded in an ex-ante way the diesel costs of incoming fishing trips (Peña-Torres et al. 2006). In this

context an increase in diesel prices could have two effects: on the one hand, to increase fishing costs

and ceteris paribus the equilibrium ex-vessel price. On the other, to reduce fishermen’s bargaining

17

Annual TACs in this fishery formally consider different fishing seasons within each year. But in practice

seasonal TACs only play a reference role, as seasonal limits are not binding. In fact, fishermen organizations

have ample freedom to adjust the use of their TACs between seasons, as long as they comply with the TAC

annual maximum.

19

power –given their financial dependence on catch buyers’ ex-ante funding--, and thereby producing

lower ex-vessel prices. There is also evidence suggesting that the allocation of catch quotas to

artisanal vessels gave them (especially to the biggest artisanal operators) some access to formal

(banking) credit (Proyecto FIP 2006-32), thus helping to reduce fishermen dependence on catch

buyers’ ex-ante funding. This change might have increased fishermen’s bargaining power.

Unfortunately, we have no data to control for time-series changes in the financial structure of

fishing operations.

Hence, we do not have a priori an expected sign for the overall impact of changes in diesel

prices upon the endogenous variables. Information about diesel prices was obtained from the

Chilean Energy Commission. In search of parsimony, our estimations use the monthly regional

average of diesel prices in region X as the common control for diesel prices in both (X and XI)

regions, as diesel prices in both regions are highly collinear.

We also include the regional artisanal annual TACs as additional controls. Denote these

controls by QuotaX and QuotaXI

. To the extent that TAC determination is based, to a certain degree,

on scientific stock assessment (Leal et al. 2010; Peña-Torres 2015), TACs should be useful

estimates of fish stock abundance; the latter presumably having an effect on fishing costs and thus

on ex-vessel prices. Hence, we expect a priory that P_ex-vessel should be negatively related to the

corresponding regional TAC.18

How binding should be the regional TACs on the corresponding landings? Among other

things, it depends on how accurately regional TACs tracks down changes in fish abundance and its

catchability. It also depends on how effective is TAC enforcement. We later argue that the fishery

regulator has some suspicious about excessive (illegal) fishing occurring at this fishery, especially

18

In our estimations we restricted regional Quota controls to have direct effects only on the corresponding

region’s landings and ex-vessel prices, as in this fishery it is illegal to obtain catches in a given region and

then land them in another region. Staff of the fisheries regulating Agency told us that this specific prohibition

is regularly obeyed. Nonetheless, our regional quota variables do account for (a few) quota transfers that

occurred during the period 2009-2011, all of which involving quota selling from region XI’s artisanal

fishermen to the benefit of either region X’s artisanal fishermen (year 2009) or industrial vessels (years 2010

and 2011).

20

at region X. Related to these issues, during the sample period TAC official use in region X was

fairly constant and very close to 100%. In region XI, TAC official use was more fluctuating and, in

at least 5 years of the sample period, no greater than 80-90%.

With the purpose of controlling for the regulatory changes that occurred during the sample

period we consider the use of dummy variables: PIR (with R=X or XI region) is a variable that takes

the value of 1 in months when the Pescas de Investigación (PI) regime was consolidated and fully

in force at region R, and zero otherwise. This variable starts to be operative, in both X and XI

regions, from September 2002. The PI Programs had been formally in operation at both regions

since the beginning of our sample period. But fully effective quota enforcement only started since

September 2002. Thus the base period in our estimations corresponds to the first two years of

operation of imperfectly enforced PI-Programs; PIX remains operative until the end of our sample

period.

In region XI the RAE Program started to be operative since January 2005. Soon afterwards

(during 2005-2006) all artisanal FOs in this region joined the RAE regime and have remained in it

until the end of our sample period. Thus, a second dummy regulatory variable (denoted by RAE)

takes the value of 1, in the case of region XI, since January 2005 and until the end of our sample

period (and zero otherwise). Hence, in region XI the overall estimated effect of the RAE regime

(relative to the base period), upon the endogenous variables in the model, will be given by the sum

of the estimated parameters for the PIX and RAE dummies.

19 Our estimations also attempted to

control for the possibility of interactive effects between the regulatory dummies and the control

variables HHI and Pfob. The conjecture was that ex-vessel price effects from these two controls

could be affected by the bargaining power of each negotiating side, which in turn may be affected

by the regulatory shifts. However, we did not obtain significant results for these interactions and

hence they were excluded from the preferred (parsimonious) estimation model.

19

To avoid perfect collinearity among the three regulatory dummies (PIX, RAE and PI

XI), in our estimations

we dropped off the latter dummy.

21

Finally, our estimations also consider two other sets of dummy controls: First, eleven

region-specific monthly (centered) dummies to control for seasonal effects (the base month is

January).20

And second, three annual dummies (denoted as 2009, 2010 and 2011)21

to control for

effects related to the economic crisis in Spain; effects which may not been totally captured by

variations in the Pfob variable.22

Table 2 shows the summary statistics of the data.

Here Table 2

6. Estimation model

Due to the likely existence of market arbitrage between regions and common non-observable

shocks to both regions, our estimation strategy allows for the possibility of interdependent regional

markets. We use cointegration analysis to identify the effect of the regulatory shifts on ex-vessel

prices.

The potential problem of non-stationary time-series in a multivariate context suggests that

the vector autoregressive (VAR) model with cointegration analysis (Johansen and Juselius, 1990)

can be an adequate estimation approach. It can deal with non-stationary series, allowing for lagged

effects, while also solving the simultaneous determination of different variables. Our model

considers four endogenous variables. This model was estimated conditioned on a series of

exogenous variables, including the regulatory controls, which we treated as deterministic for

estimation purposes. The exception to the latter rule was Pfob: we allowed for this variable to have

20

Given the absolute fishing ban that prevails on August each year, our estimation sample contains 11

monthly observations per year. Thus we calculated centered seasonal dummies on an eleven-month basis so

that the total impact on the annual series was zero. 21

The domestic economic crisis in Spain did start toward the late 2008. Looking for parsimony, we control

for possible effects of this phenomenon by considering three annual dummies. 22

For example, with the deepening of the economic crisis in Spain the dominant wholesale importer of

Chilean austral hake into Spain gradually started to sell more fresh-chilled Chilean hake directly to the

supermarket segment, which has a more stable demand than other hake buyers. However, supermarkets’

demand favors the buying of bigger-size fish. Hence, this change in marketing channels meant a reduction in

the dominant Spanish wholesale importer’s demand for Chilean artisanal hake catches, as some Chilean

industrial fishing firms started to supply more regularly the new market niche for bigger-size fresh-chilled

austral hake (personal communication with entrepreneurs working at this industry).

22

contemporaneous and lagged effects on the endogenous variables. We also made an exogeneity test

for Pfob. We run the VECM model including Pfob as a fifth endogenous variable and then tested

whether it could be excluded from the cointegration relations. The null hypothesis of zero-

coefficient could not be rejected. We also tested for weak exogeneity of Pfob in the complete model

and this hypothesis could not be rejected either. We therefore conclude that for estimation purposes

it is safe to consider Pfob as an exogenous variable.

The estimation model can be established as:

1 1

k l

t t t i t t j t

i j

Z A Z B X D

(3a)

or in its error correction form (VECM) as:

1

1 1

k l

t i t i t k t t j t

i j

Z Z Z B X D

(3b)

where Zt is a (nx1) vector of endogenous variables, Xj is a (mx1) vector of continuous deterministic

variables, and t denotes the time period. At is a matrix of coefficients of dimension (nxn), Bj is

matrix of coefficients of dimension (mxm), D is a matrix of binary deterministic variables, is the

corresponding parameter vector, tis a column vector of (nx1) random errors, and k and l are the

number of lags included in the endogenous and exogenous components of the VAR model. is the

difference operator, ( 1 2 .....i iI A A A ), with I as an identity matrix of order n and

)....( 21 kAAAI .

In our case, vector tZ is the following:

'

t X ,t XI ,t X ,t XI ,tZ p , p ,q ,q (4)

where pX,t is the ex-vessel price for austral hake in region X, pXI,t the corresponding price in region

XI, qX,t the artisanal landings of austral hake at region X and qXI,t the corresponding landings at

region XI. All these variables are expressed in levels. The model in (3b) includes an error correction

23

mechanism (ECM), which allows us to distinguish between short-run and long-run analysis. Short-

run relations are associated with the i matrices, while the long run relations to the matrix.

The X vector is the following:

𝑋 = [𝑃𝑓𝑜𝑏, 𝑃𝑑𝑖𝑒𝑠𝑒𝑙 , 𝑃𝐼, 𝑅𝐴𝐸, 𝐻𝐻𝐼, 𝑄𝑢𝑜𝑡𝑎𝑗] (5)

where Pfob is the export price of fresh-chilled austral hake (measured in levels but incorporated in

the estimations in first differences); HHI is a measure of hake buyers’ industrial concentration;

Pdiesel is the diesel price (measured in levels); Quotaj is the artisanal fraction of the TAC for region j

(j= X, XI); PIj is the dummy for the period of consolidated (consultants-led) PI-Programs at region

j; and RAE is the dummy for the period under RAE Programs in region XI.

To estimate this model, we first tested for the number (r) of cointegration vectors associated

with the long-run relations. To determine r we used the reduced-rank method (Johansen, 1988).

Once the number of cointegration vectors was obtained, we tested if the obtained cointegration

relations corresponded with the expected theoretical values (over-identifying linear restrictions

tests, Lutkepohl and Kratzig 2004). To accomplish this we first made tests to identify the

integration order of the variables. This information was used to specify the way in which the

different variables should be included in the model (first differences or levels). Then we made tests

to specify the lag order k and the introduction of non-stochastic components in the VAR model.

Once a proper specification was obtained we applied the restricted rank tests to identify the number

of cointegration vectors supported by the data.

When 0<r<n, r cointegrating vectors exist. In our case, at most three cointegration vectors

may exist (long-run relations). When this happens, one can factor such that ’where both

and are (n x r) matrices. Matrix contains the cointegrating vectors or the long-run

relationships and encompasses the adjustment parameters. Once the number of vectors is

identified, we can test whether ex-vessel price determination should be treated as separate processes

between regions. If the latter were the case in the long-run, the data should be consistent with the

24

existence of two cointegration vectors, one for each region, where the variables of the other region

should not participate in the other region’s vector.

The estimation model also allows for short-run analysis. Once the long-run relations are

identified, eq. (3a) can be estimated in the following manner:

1'

1 1

ˆk l

t i t i t k j t j t

i j

Z Z Z B X D

(6)

where 'ˆt kZ

corresponds to the estimated cointegration vectors, previously identified.

All variables included in eq. (6) are stationary and hence classic econometric tests (t- and F-

distributions) can be used. Thus, once eq. (6) is estimated we can test for the significance of the

parameters associated with the lagged dependent variables ( itZ ) and the deterministic variables

(Xt-j). This allows for the presentation of a parsimonious VAR model, where all non-significant

parameters are excluded. Within this framework we can test for the effects that the regulatory shifts

had on the ex-vessel price formation process. The regulatory dummy variables are included as

deterministic variables in the vector Xt-j. Thus their effects can be tested through the error-correction

model.

7. Estimation results

We estimated the model using Johansen´s cointegration method (Johansen, 1988). The Appendix

provides details on the estimation procedure and results. Two cointegration vectors were identified.

Solving each cointegration relation as a per-region (long-run) equilibrium {price, quantity} reduced

equation, that includes the price of the other region as a right-hand variable, we obtain the following

results:

927 0 7829 0 7074X XI XP _exvessel . P _exvessel . Landing (7)

4116 4 1731 5 0764XI X XIP _exvessel . P _exvessel . Landing (8)

25

These results show a positive long-run relation between the ex-vessel prices of both regions,

with P_ex-vesselX having a greater direct quantitative impact on P_ex-vesselXI

than the other way

around (which may be related to region X’s larger scale of production). The resulting sign of the

reduced-equation relation between prices and landings is different in each region. This is a

reflection of different signs of impact, at each region, of the exogenous controls on prices and

landings (more on this in the analysis that follows).

The existence of two (region-specific) cointegration vectors, where the price of the other

region participates in this relation, indicates that there is long-run interdependence between these

two regional markets, which seems reasonable given the vicinity of both markets and the trading of

a fairly homogenous good (except for the transport costs between regions).

Table 3 reports the results obtained for a parsimonious version of the VECM model. Let us

start reviewing the estimation results for variables other than the regulatory dummies. First, there

are significant lagged effects for all four endogenous variables, up to the second lag. Thus, there are

between-region interactions and short-run adjustments occur within a two-month period.

Here Table 3

The Pfob variable only showed significant but lagged (and always positive) effects on the ex-

vessel price equations. In the final parsimonious model the single highly significant effect (at the

99% confidence level) was a two-month lagged effect on P_ex-vesselX. This result signals a positive

but relatively small pass-through rate, in the short run, between the export price and ex-vessel

prices. This is consistent with the hypothesis of low bargaining power on fishermen’s side.

The dummy variables for the economic downturn that affected Spain in years 2009, 2010,

and 2011 all showed a highly significant (negative) impact on ex-vessel prices in the XI region and

a positive (also highly significant) effect on landings in region X (years 2010-2011). What factors

could explain these results (which were robust to several variations in the estimation model)?

26

On the one hand, the negative effects of the economic downturn in Spain on ex-vessel

prices in region XI seem reasonable, given the importance of Mercamadrid as the main export

destiny for Chilean fresh-chilled austral hake. However, these dummy variables could be

controlling for more than just the direct effect from the external demand shock. Other important

changes did occur in the domestic market for Chilean austral hake as a consequence of the 2009-

2011demand shock from Spain.

First, there was a an export demand shift in favor of bigger catch sizes, which reduced the

demand for artisanal catches while favoring industrial catches. Second, since then (particularly

since 2010) a greater proportion of artisanal austral hake catches started to be sold at the domestic

(Chilean) market. And artisanal fishermen from the X region have comparative advantages as

suppliers for the domestic market (relative to region XI’s fishermen): region X is closer and better

connected to the northern (which also coincide with the more urban and richer) parts of the country;

region X also has more numerous, better placed and not-all-officially-controlled landing spots than

region XI. As a result of all this, some sources23

have argued that (difficult-to-detect) illegal

landings are more significant at region X than at region XI. Hence, (legal and illegal) domestic

trading could have played a stronger countervailing effect on ex-vessel prices in the X region (than

in the XI region) during the 2009-2011 years. The latter feature could also help to explain the

estimation results showing that, on average, ex-vessel prices at the X region were insensitive to the

period of economic downturn in Spain, while at the same time artisanal landings at region X were

facing, ceteris paribus, upward pressures (from domestic demand).

There is another (possibly complementary) conjecture about our estimation results for the

2009-2011 dummy controls. Notice first two striking features of the 2008-2011 evolution of

regional ex-vessel prices (see Fig. 2): (i) prices in the XI region tend to converge to the price levels

of region X; this is not an obvious outcome, as both catch sizes and transport costs (to supply

23

Personal communication: with staff from Subpesca.

27

international markets24

) on average are higher at region XI than at region X; and (ii) the time-series

variability of both regional prices is clearly reduced.

A possible conjecture about features (i) and (ii) is related to the issue that the Spanish

economic crisis probably brought about downward adjustments in the profit margins at different

segments of the austral hake exporting supply chain, including downward pressures on ex-vessel

prices. While facing this scenario, as we later argue, fishermen organizations at the X region

probably already had, as it indeed happened all along the sample period, a more disadvantageous

bargaining position vis-à-vis catch buyers, relative to region XI fishermen. If this hypothesis is true,

the previous features (i) and (ii), as well as the insensitivity of P_ex-vesselX to the Spanish demand

shock, could then be interpreted as a signal of a convergence towards fishermen’s reserve supply

prices at region X. In this case there would be no much scope for further reductions in region X ex-

vessel prices.

The HHI variable systematically showed significant effects only in the XI region and

always with negative effects on both price and landing variables; though in the final parsimonious

model (Table 3) the effect on P_ex-vesselXI was not significant at the 90% confidence level. It

should be noticed that industrial concentration at the processing plants segment in the XI region is

clearly higher than at the X region. To illustrate (considering 2000-2011 average shares in each

regional market): at region X the main 5 processors’ (combined) share in the total processed tons of

fresh-chilled austral hake was 89% (and 66% for the main 3 processors). In region XI, by contrast,

the combined share of the main 2 processors was 89%, while the biggest processor by its own had

63%. Hence, the estimation results for the HHI variable could be interpreted as a signal that, in a

buying sector highly concentrated (as it is the case in region XI), as buyers become even more

concentrated they may find beneficial to reduce their volume of purchases so that to possibly

24

Most fresh-chilled austral hake exports are sent to their destiny markets via airplanes. And the main airport

facility in regions X-XI is located at Puerto Montt city (region X). Also, a greater proportion (about 60%) of

the total installed capacity of fresh-chilled austral hake processing plants is located at region X (Peña-Torres,

Bustos and Pérez 2005).

28

exploit in a better way their bargaining power when negotiating about the fish stock’s rent. Recall

that our estimation results systematically showed a negative effect of HHI on P_ex-vesselXI. Notice

that this argument would still be valid for the case of oligopolistic catch buyers who were vertically

integrated to export businesses and had no choice but behaving as price takers at their export

markets (e.g., because of a weakly exogenous export price).

We also tested for the effect of the diesel price. This price showed no significant effect on

ex-vessel prices or landings in none of the regions. One possible interpretation for this result might

be that fishermen’s bargaining power is weak enough so that unit cost increases are not properly

transmitted to ex-vessel prices. But recall as well that in this fishery artisanal fishermen are

financially dependent on catch buyers’ ex-ante payments for fuel costs. What might then happen is

that fuel cost increases might reduce fishermen’s bargaining power when negotiating ex-vessel

prices, creating a countervailing effect versus the more traditional (cost-plus) effect. Unfortunately

we have no data to test this hypothesis. Nonetheless, we can still argue that our estimation results do

not contradict the hypothesis of fishermen’s rather weak bargaining power.

Both Quota variables showed significant (positive) effects on the corresponding landing

equations, as expected. However, only QuotaXI showed a significant effect (with the expected sign)

on the corresponding ex-vessel price. Regarding the insensitivity of P_ex-vesselX with respect to

variations in QuotaX (which was a robust result),

the only explanation we can think of is that this

result may be conditioned by the occurrence of (probably more important) illegal landings at region

X, so that Quota in this region would be less of a binding constraint.

Let us now focus on the ex-vessel price effects of the regulatory dummies.25

These variables

showed significant effects on both regional prices. But the results differ between regions. In region

X the impact of the consolidated PI-scheme (controlled by the PIX dummy) is (marginally)

25

These dummies also show significant effects on regional landings. The variable PIX shows negative effects

on both regional landing variables, possibly as the result of better-coordinated (less rent-dissipating) decisions

about fishing operations. However, the RAE dummy shows a positive impact on region XI landings (this was

a robust result). We have no explanation for the latter result.

29

significant but negative, while in region XI the impact is highly significant and positive. Why do we

get these differences between regions?

From the beginning of the regulatory reforms, fishermen in the XI region were able to

achieve more stable and more efficient regional FOs than fishermen in the X region (Peña-Torres et

al. 2006). Better coordination among within-region FOs should help fishermen to have a better

bargaining position when negotiating about ex-vessel prices. By contrast, at the X region artisanal

FOs are far more numerous (see Table 1) and more heterogeneous (in terms of their economic

dependence on austral hake fishing). Region X fishermen hence faced greater difficulties for

building and maintaining more compact and stable negotiation blocks. At the same time, post-

reforms greater coordination was also occurring among catch buyers, at both regional markets, to

strengthen their own bargaining position. Thus, the estimated negative effect of PIX on P_ex-vesselX

may reflect a net worsening effect on the price bargaining position of fishermen at region X.

In the XI region the estimated positive effect of PIX on P_ex-vesselXI

captures the combined

(averaged) effect, relative to the base period, of years under consolidated-PI or RAE Programs. It

thus shows, ceteris paribus, that during the years under one or the other (PI or RAE) management

scheme ex-vessel prices at the XI region were on average higher than in the pre-reforms period.

This result signals an important difference with respect to the estimated price effect of the

regulatory reform at region X.

In the case of the RAE dummy, the estimated effects on prices and landings of region XI are

both significant but with unexpected signs (this result was robust). As the RAE scheme can be seen

as a process aiming to consolidate more complete fishing rights in favor of Fishermen

Organizations, we expected to obtain a positive effect on regional ex-vessel prices. In practice, the

estimated parameters for the RAE dummy capture the differential effect, on the dependent variables,

between ‘RAE-alone years’ and the combined (‘PI and RAE’) period controlled by the PIX dummy.

Thus, our estimations show that ‘RAE-alone years’ are associated to a lower effect on P_ex-vesselXI,

relative to the effect associated to the PIX dummy. If we calculate the joint effect (sum of

30

coefficients) of both PIX and RAE dummies on P_ex-vesselXI

, the net effect is not significantly

different from zero.26

Hence, the RAE-period shows no net differential effects on P_ex-vesselXI,

relative to the base period.

A possible explanation for this unexpected result is that the RAE-dummy may be capturing

the averaged effect of other changes, unrelated to the regulatory reform, also occurring along the

‘RAE years’ but that we were unable to control for in our model. For instance, not all the relevant

impacts related to the economic crisis in Spain are necessarily captured by our annual dummies. We

know that some contemporaneous changes did occur in the domestic structure of the buying market

for artisanal austral hake catches. We also know that along the first decade of the 2000s changes in

average catch sizes (per fish caught) did happen in this fishery, and that the dynamics of these

changes was not monotonic and neither the same at regions X and XI (IFOP, 2014, p. 31).27

There is no further space in this paper for a thorough discussion about underlying reasons

for the more successful cooperative organizational environment achieved post-reforms by fishermen

at the XI region, versus the case of region X. Peña-Torres, Bustos and Pérez (2006) have argued this

outcome was mostly due to factors exogenous to the policy-shifts studied at this paper.28

However,

one of the probable causal factors was indeed a direct consequence of how catch quotas were

initially allocated in each region.

26

We used a generalized t-test procedure. The resulting point estimate is 28.5 but it is not significantly

different from zero (at the 90% confidence level). 27

During the first half of our sample period, (per fish-caught) catch sizes showed, as annual (sampling)

regional averages, a declining trend at both regions X and XI. During the second half of our sample period,

the same declining trend continued at region X. but at region XI it did show a recovery, which could have led

to a relative increase in the demand for this region’s austral hake catches. Still, available sampling data shows

high within-year dispersion in (per-fish-caught) catch sizes, so regional annual averages of this variable might

be noisy signals. It is also possible that this variable would need to be treated as endogenous in our estimation

model. A proper treatment of these issues is beyond the scope of this paper. 28

For example: (relative to the XI region) at region X the scale of fishing operations is greater, while artisanal

FOs are also far more numerous, more fragmented and more heterogeneous. Along the sample period there

were in the X region 6 artisanal fishermen federations (i.e., regional organizations of more atomised

fishermen unions) with direct participation in the austral hake fishery. In the XI region, by contrast, there was

only one artisanal fishermen federation that enjoyed high cohesion and coordination power over its associated

unions.

31

At the XI region the Government’s initial quota allocation respected the way how the catch

outcome was historically divided among crew members and boat owners (both types of agents did

receive quota participations). In the X region (collective) catch quotas were allocated only on the

basis of boat owners’ landing records and thus only they received quota participations (Proyecto

FIP 2006-32). As a result of this, greater resentment and distributive disputes quickly arose at

region X, somehow eroding fishermen’s perceptions about the fairness of the new management

schemes. This undoubtedly affected the different organizational success that fishermen were able to

achieve at each region.

8. Conclusions

Our results suggest that at only one of the regions studied (region XI) the allocation of collective

catch quotas to Fishermen Organizations may have improved fishermen’s bargaining position when

selling their catch. This was the region where fishermen were able to achieve more stable and better

organized fishermen associations to deal with the price bargaining issue. At the other region studied

fishermen associations were more unstable, more fragmented and less homogeneous in their

economic interest regarding the catch under bargaining. As a result, in this second region different

artisanal fleets acted more as competitors rather than as coordinated bargaining players.

Our analysis has shown evidence of ex-vessel prices being affected by policy reforms

aimed at improving the enclosure mechanisms for an otherwise common-pool resource. As a result

of these reforms, atomized fishermen started to collectively bargain over ex-vessel prices with a

monopsony-like processing sector. Notwithstanding the regional specificities affecting the price

bargaining results achieved by different fishermen associations, our analysis provides evidence

about the scope for small producers’ associations as a way to reconcile enclosure reforms and more

equitable welfare gains for the smaller producers segment.

32

The introduction of enforceable production rights at common-pool fisheries is an important

issue. In the last decades several countries have been introducing right-based fisheries management

schemes (Wilen 2006, Arnason 2012, Orensanz and Seijo 2013). In the case of small-scale and

atomized fisheries that exploit local fish resources, the allocation of collective catch quotas to

fishermen associations may provide a politically more feasible enclosure option than direct

allocation of individual catch quotas.

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Table 1: Austral Hake Artisanal Fishery Statistics

(1) (2) (3) (4)

Region

# of Fishermen

Organizations

(Oct. 2006)

# of registered fishermen (2007) # boats in

operation

(monthly average)

2004-2006

Annual Landings

(tons)

Average 2000-2010 # boat

owners

# crew

members Total

X 180 1952 1654 3606 1535 7157,5

XI 60 631 758 1389 177 4250,8

XII 4 120 322 442 45 1862,9

TOTAL 244 2703 2734 5437 1757 13271,2

Source: Proyecto FIP 2006-32 (2008) and data from Government agencies Sernapesca & Subpesca.

Notes: (1): includes Federations, Unions, Cooperatives and Fishermen Guilds; (3) monthly average for the high fishing

season, period 2004-2006.

Tabla 2: Data summary (Estimation sample) Variable (unit) N Mean Std. Dev. Minimum Maximum

P_ex-vesselX (CLP/kg) 132 818.6 129.1 550 1200

P_ex-vesselXI

(CLP/kg) 132 946.3 246.1 425 1512

LandingX (tons) 132 660 315.5 1 1587.3

LandingXI

(tons) 132 348.9 220.4 0.34 1632.4

Pdiesel (CLP/lt) 132 415.9 134.4 174.3 732.3

Pfob (CLP/kg) 132 1677.3 458.3 682.2 2674.7

Share5 (%) 132 74.3 7.7 57.2 94.3

HHI (Index) 132 1479.1 519.45 964.2 5120

QuotaX (tons) 12 7252.8 754.6 5451.8 8266.2

QuotaXI

(tons) 12 4589.6 335 3857 5083.4

PIX 132 0.78030 0.41562 0 1

PIXI

132 0.19697 0.39922 0 1

RAE 132 0.58333 0.49488 0 1

Note: N = Number of (monthly) observations

36

Table 3: Estimation Results (Parsimonious VECM Model)

Variable

(1)

d(P_ex-vesselX)

(2)

d(P_ex-vesselXI

)

(3)

d(LandingX)

(4)

d(LandingXI

)

Endogenous lagged

d(P_ex-vesselX

t-1) -0.434

{0.000}

-0.215

{0.021} ---

0.268

{0.077}

d(P_ex-vesselXI

t-1) --- --- -0.594

{0.000} ---

d(LandingX

t-1) -0.04

{0.021}

-0.129

{0.000}

-0.204

{0.002} ---

d(LandingXI

t-1) 0.039

{0.136} ---

-0.231

{0.058} ---

d(P_ex-vesselX

t-2) -0.097

{0.124} --- --- ---

d(P_ex-vesselXI

t-2) --- --- -0.518

{0.001}

-0.392

{0.000}

d(LandingX

t-2) --- --- --- -0.056

{0.155}

d(LandingXI

t-2) --- --- -0.133

{0.141} ---

Exogenous lagged

d(Pfob) --- --- --- ---

d(Pfob t-1) --- 0.056

{0.224}

--- ---

d(Pfob t-2) 0.089

{0.009}

--- --- ---

Deterministic variables

2009 --- -84.853

{0.003} --- ---

2010 --- -111.033

{0.000}

169.821

{0.009} ---

2011 --- -126.082

{0.000}

257.324

{0.000} ---

QuotaX ---

0.204

{0.000}

QuotaXI

-0.084

{0.000}

0.054

{0.001}

HHI --- --- --- -0.068

{0.006}

Regulatory variables

PIX

-26.746

{0.087}

68.365

{0.007}

-333.855

{0.000}

-87.98

{0.029}

RAE -39.86

{0.043}

141.756

{0.000}

Notes: (A) Notations: variable without t subscript denote its contemporaneous value; ---: means that

the variable was not significant at the 90% of confidence; d: is the first-difference operator; Value

between {.}: p-value. (B) The model also includes seasonal dummies and impulse dummies that are

not presented in the Table for parsimony. However, the results for the seasonal dummies indicate

that landings on average show higher seasonal values during the second semester of each year; while

ex-vessel prices show more seasonality at region XI, with on average higher prices during the 2nd

and 4th quarter of each year. (Details about these results can be requested to the authors).

37

Fig. 1: Artisanal Fleet: Annual (official) landings and TAC (tons)

Source: Own elaboration based on official data from Subpesca

Fig. 2: Regional ex-vessel and export (fob) prices (monthly averages) (Nominal values; all expressed in CLP/kg.)

Source: Own elaboration, based on data provided by Subpesca.

0

5000

10000

15000

20000

25000

30000

Tons.

Total Landings (X, XI & XII) Landings X Landings XI Landings XII TAC

0

500

1.000

1.500

2.000

2.500

3.000

Ch

ilean

pes

os

($/k

g.)

Ex-vessel price (X)

Ex-vessel price (XI)

Export (fob) price

38

Appendix: Estimation of the VECM model

First we present the unitary root tests. Then we look at the optimal number of lags for the VAR model.

Afterwards we show the results for the Johansen’s restricted rank test for identifying cointegration vectors.

Finally, we present the uni- and multi-equation specification tests.

We made several unit root tests for all variables in the model (in levels, logs, first differences, and first log

differences). We also checked different specifications with deterministic terms (without constant, with

constant, with trend, with seasonal centered dummies, and combinations). This helped to specify the VECM

model. Table A1 reports selected results for the endogenous variables.

Table A1. Augmented Dickey Fuller Unit Root Tests for Endogenous Variables

(in a model with a constant as deterministic term)

Variable Deterministic terms Optimal lags Test value 5% critical value

P_ex-vesselX

constant 5 -2.67 -2.86

P_ex-vesselXI

constant 0 -2.95 -2.86

LandingX constant 0 -7.45 -2.86

LandingXI

constant 1 -5.96 -2.86

d(P_ex-vesselX

) constant 4 -7.21 -2.86

d(P_ex-vesselXI

) constant 3 -8.35 -2.86

d(LandingX

t-1) constant 5 -7.23 -2.86

d(LandingXI

t-1) constant 5 -7.04 -2.86

The results suggest that the price variables should be I(1) while the landings variables could show a I(0)

behavior. However, the tests for cointegration vectors (reported later) show that there should exist a common

stochastic trend for these variables.

To specify the VAR model we first tested for the optimal number of lags to be included. Table A2 repots the

results. Since the number of optimal lags differs between alternative standard tests we decided to select the

Hannan-Quinn Criterion, as it is known that the Akaike Information Criterion asymptotically overestimates

the optimum order with positive probability and that the Hannan Quinn and the Schwartz Criteria estimate

the order consistently under quite general conditions (Lűtkepohl and Krätzig, 2004). Since we did not have

strong arguments to choose between these two last criteria, we chose the one with more possibilities for

dynamic adjustments.

Table A2. Optimal number of lags for the multivariate model Test Optimal number

of lags

Akaike Info Criterion 2

Final Prediction Error 2

Hannan-Quinn Criterion 1

Schwarz Criterion 0

39

We estimated different versions of the model and controlled for their statistical specifications. We made

specification tests for normality, autocorrelation, and autoregressive conditional heteroskedasticity which we

discuss later. Finally, we selected a model that included two lags in the VECM, a constant term restricted to

the cointegration space, two cointegration vectors, and centered seasonals dummies. Table A3 reports the

results for the Johansen’s test over the cointegration rank.

Table A3. Johansen’s trace test over the cointegration rank

Critical values at different levels of confidence

r LR p-value 90% 95% 99%

0 139.09 0.0000 50.50 53.94 60.81

1 76.69 0.0000 32.25 35.07 40.78

2 23.19 0.0174 17.98 20.16 24.69

3 4.43 0.3641 7.60 9.14 12.53

r: cointegration rank tested; LR: Likelihood ratio statistic value; p-value: probability value

The tests suggest at the 99% confidence level that two cointegration vectors exist. In the main text we

interpret these vectors as {price, quantity} reduced-equations for each region. Table A4 reports the estimated

coefficients for the cointegration vectors and their precision. We use these results to derive the regional long-

run price equations presented in the main text.

Table A4. Estimated cointegration vectors

P_ex-vesselX P_ex-vessel

XI Landings

X Landings

XI Constant

Vector 1:

Coefficient 1.000 0.000 -0.321 1.753 -1830.675

(p-value) (0.000) (0.000) (0.049) (0.000) (0.005)

Vector 2:

Coefficient 0.000 1.000 -1.302 2.235 -3522.843

(p-value) (0.000) (0.000) (0.000) (0.000) (0.000)

Table 3 (main text) reports the results obtained for the parsimonious VECM model. To obtain this model we

tested sequentially with F-tests for the exclusion of non-significant variables. Tables A5 and A6 report the

specification tests obtained for the parsimonious model.

Table A5. Univariate tests with 12 lags

Residuals from

equation:

Portmanteau1/

ARCH-

LM2/

p-value p-value

P_ex-vesselX

0.000 0.804

P_ex-vesselXI

0.003 0.303

LandingsX 0.004 0.812

Landings XI

0.005 0.934 1/: Autocorrelation test; 2/: Non-autoregressive heterocedasticity test

40

Table A6. Multivariable tests

Test Test statistic p-value

Autocorrelation

LM 78.84 0.1002

Normality

Doornik & Hansen 45.57 0.0000

Autoregressive Heterocedasticity

ARCH-LM 498.156 0.006

Both the univariate and multivariate tests suggest that the residuals do not suffer from autocorrelation or

heterocedasticity. However, we do have evidence of non-normality in the results. This non-normality is

generated by the existence of some outliers in different months. We corrected for normality by introducing

impulse dummies to control for outliers. In the resulting parsimonious model, the normality null hypothesis

could not be rejected. However, in the corrected version we obtained slightly significant results for the

heterocedasticity test. We compared the results with both procedures and they did not change qualitatively.

The central results for the regulatory variables are all the same for practical purposes. Following Gonzalo

(1994), who states that the VAR model’s estimations are robust when non-normality is due to kurtosis rather

than skewness, which is our case, we hence continued with the estimations. Thus, we report and discuss the

results for the most parsimonious model version.