Aquaculture Economics & Management
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Capital structure and firm performance: Agency
theory application to Mediterranean aquaculture
firms
Isaac Ankamah-Yeboah, Rasmus Nielsen & Ignacio Llorente
To cite this article: Isaac Ankamah-Yeboah, Rasmus Nielsen & Ignacio Llorente (2021) Capital
structure and firm performance: Agency theory application to Mediterranean aquaculture firms,
Aquaculture Economics & Management, 25:4, 367-387, DOI: 10.1080/13657305.2021.1976884
To link to this article:  https://doi.org/10.1080/13657305.2021.1976884
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AQUACULTURE ECONOMICS & MANAGEMENT
2021, VOL. 25, NO. 4, 367–387
https://doi.org/10.1080/13657305.2021.1976884
Capital structure and firm performance: Agency theory
application to Mediterranean aquaculture firms
Isaac Ankamah-Yeboaha
 , Rasmus Nielsenb , and Ignacio Llorentec
aDepartment of Agricultural Economics and Agribusiness, College of Basic and Applied Sciences,
University of Ghana, Legon-Accra, Ghana; bDepartment of Food and Resource Economics, Faculty
of Science, University of Copenhagen, Copenhagen, Denmark; cDepartamento de Administraci
on
de Empresas, Facultad de Ciencias Econ
omicas y Empresariales, Universidad de Cantabria,
Santander, Spain
ABSTRACT KEYWORDS
The study uses firm level panel data to determine perform- Agency theory; aquaculture;
ance-leverage relationships among Mediterranean aquaculture capital structure; firm
production firms in Croatia, Italy, Spain, France and Greece. A performance
stochastic frontier production function is used to determine
and define performance through firm level efficiency esti-
mates. The multilevel internal instrument variable approach is
employed to identify the causal relationships between per-
formance and leverage. Our results show that technical effi-
ciency has been increasing across all firms over the period
2008–2016. The agency-cost hypothesis holds such that lever-
age has an inverted U-shaped relationship with performance.
This implies that leverage increases with efficiency, but effi-
ciency begins to decrease at sufficiently higher levels of lever-
age. The reverse relationship confirms the franchise-value
hypothesis, which states that firms with high efficiency will try
to protect the value of their high income by holding more
equity capital. Implications for the results are drawn for the
Mediterranean region.
Introduction
Increased trade and internationalization of seafood markets are fueled by
the continuous growth in aquaculture production, which has increased
competition for firms operating within this sector (Anderson et al., 2018;
Garlock et al., 2020). This development is influencing firm structure due to
mergers to exploit scale and scope economies (Asche et al., 2013; Kvaløy &
Tveterås, 2008) as well as access to input suppliers including access to cap-
ital (Asche, 2008). In addition to traditional studies of technical change and
CONTACT Rasmus Nielsen rn@ifro.ku.dk Department of Food and Resource Economics, University of
C
openhagen, Copenhagen, Denmark.We would like to dedicate this work to our good friend and colleague, Isaac, who passed away much too
soon. Our thoughts go out to his wife and children.

 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives
License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction
in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
368 I. ANKAMAH-YEBOAH ET AL.
productivity, this has led to increasing attention to factors influencing
industry structure. This includes agglomeration (Asche et al., 2016;
Tveteras, 2002), learning by doing (Nilsen, 2010) and innovation structure
(Bergesen & Tveterås, 2019). In aquaculture industries where firm size has
increased, financial performance is also receiving more attention (Asche
et al., 2018; Dahl et al., 2021; Misund & Nygård, 2018; Nygård, 2020), while
access to capital is shown to limit firm development in other sectors (Mitra
et al., 2019).
This paper will test two agency-cost hypotheses related to financial leverage
for Mediterranean aquaculture: The franchise-value and efficiency-risk
hypotheses (Berger & Di Patti, 2006). The franchise-value hypothesis indicates
that firms with high efficiency will try to protect the value of income by hold-
ing more equity capital, whereas the efficiency-risk hypothesis says that more
efficient firms will have higher debt ratios than less efficient firms. More spe-
cifically, this paper investigates the development of the Mediterranean aqua-
culture firms’ performance using panel data over the period 2008–2016 to
determine firm efficiency and performance-leverage relationships. The firms
investigated are located in Croatia, Italy, Spain, France and Greece, which are
the main producers of seabream and seabass within the European Union. A
stochastic frontier production function is used to determine and define per-
formance through firm level efficiency estimates. The multilevel internal
instrument variable approach is employed to identify the causal relationships
between performance and leverage.
The presence of a relationship between capital structure and performance
has been widely debated. As a starting point for this debate, Modigliani
and Miller (1959) suggested that firm performance was independent of its
capital structure. However, this only holds within a theoretical context
(only in efficient markets without taxes, bankruptcy costs, agency costs,
and asymmetric information) and has found little empirical support (Le &
Phan, 2017). Empirical studies have revealed that firm’s capital structure
and relationship to performance is highly dependent upon context such as
the sector of the industry, strategy of the firm, growth or country (Berger
& Di Patti, 2006; Degryse et al., 2012; Lindblom et al., 2011; O’Brien,
2003). Thus, capital structure is in most cases an active strategic choice by
firm management and that strategies are changed over time and are not
fixed (O’Brien, 2003).
Hence, the choice of how to supply capital to a firm is an important
decision. The decision is most often based on a cost-benefit assessment to
evaluate the return rate of the borrowed capital (leverage) and the price
that the firm must pay in interest to secure the loan. The capital structure
of a firm then refers to a mix of debt and equity capital that a firm holds,
supplied by different sources of funds, such as short-term and long-term
AQUACULTURE ECONOMICS & MANAGEMENT 369
funds (Margaritis & Psillaki, 2010). Thus, this framework tries to uncover
the optimal balance between equity and debt used to finance firm opera-
tions. Following the efficiency-risk hypothesis, equity capital will be substi-
tuted by the high-expected returns from greater profit efficiency in order to
avoid the costs of bankruptcy and financial distress (Berger & Di Patti,
2006). On another hands, franchise-value hypothesis proposed high effi-
ciency firm to hold more equity capital in order to protect the
expected return.
There is increasing evidence that firm structure is changing in
Mediterranean aquaculture, a sector that is primarily producing gilthead
seabream (Sparus aurata) and European seabass (Dicentrarchus labrax)
(Fern
andez-Polanco et al., 2021; Llorente et al., 2020; Nielsen et al., 2021).
In the 1990s, large-scale aquaculture production of seabream and seabass
was started in several countries around the Mediterranean Sea. At first, the
industry was quite successful showing fast growth in production volume
and turnover. However, during the 2000s, the industry faced serious set-
backs due to falling prices of their product caused by the increased volumes
of supply (Llorente & Luna, 2014; Llorente et al., 2020). This led to bank-
ruptcies of firms and longer periods of market instability, with high volatil-
ity in supply and prices (Fern
andez-Polanco et al., 2021; STECF, 2016,
2018), strongly affecting the firms’ operational margins (Llorente
et al., 2020).
Despite the fact that this industry is the most important within aquacul-
ture production in the Mediterranean area and the second largest in the
EU, surprisingly little attention has been directed toward how these firms
perform and how they cope with the new more competitive business envir-
onment (Nielsen et al., 2021). Only a hand full of papers have examined
the productivity and efficiency among these aquaculture producers focusing
on one major producer, Greece (Karagiannis et al., 2000; Karagiannis &
Katranidis, 2000; Karagiannis et al., 2002) and only two on the overall
Mediterranean aquaculture production (Fern
andez-S
anchez et al., 2020;
Nielsen et al., 2021). One of the main reasons is that seabass and seabream
production takes place in many different countries, and there has not been
a common system for data collection, which has been a limitation to con-
duct research. This limitation has been even greater when research
demands information at the firm level, such as efficiency, productivity and
performance (Nielsen et al., 2021). The review of literature shows that no
one has ever related the aquaculture firm efficiency with firm capital struc-
ture within the aquaculture sector. Thus, the relationship in focus here is
the capital structure as a driver for performance.
Other studies of technical efficiency in aquaculture with a longer time
dimension estimating technical change are (Aponte, 2020; Aponte &
370 I. ANKAMAH-YEBOAH ET AL.
Tveteras, 2019; Asche et al., 2013; Rahman et al., 2021). Furthermore,
recent studies analyzing specific effects on technical efficiency include
agglomeration (Asche et al., 2016; Rahman, Nielsen, & Khan, 2019), effects
of co-management (Hukom et al., 2021) and environmental heterogeneity
(Rahman, Nielsen, Khan, et al., 2019; Mitra et al., 2020). There also exists a
significant literature on technical efficiency using firm level data with a
short time dimension. Sharma and Leung (2003), Iliyasu, et al. (2014),
Long et al. (2020) and See et al. (2021) provide overviews and some recent
examples are Khan et al. (2021) and Hukom et al. (2020).
The paper is structured as follows. After this introduction, we provide a
brief description of the Mediterranean seabream and seabass aquaculture
industry. This is followed by a method and data section before the empir-
ical results are reported. Finally, we discus and conclude on the
main findings.
Overview of seabream and seabass production and markets
The combined total aquaculture production of Gilthead seabream and
European seabass was under 8.000 ton in 1990. It increased to 158.000 ton in
2000 and reaching 464.000 ton in 2018 valued at 2,247 million dollars, what
represented an increase of 71% and 29% in quantities and value, respectively
in the period 2011–2018 (FAO, 2020). The seabream and seabass aquaculture
production in the Mediterranean Sea covers 95% of the global production in
2018. In common with other successful aquaculture species (Asche, 2008;
Kumar & Engle, 2016), the rapid production growth has been accompanied
by a steep price decline creating a keen competitive environment where poor
profitability at times is a challenge (Llorente et al, 2020).
Turkey and Greece are the leading producer countries, producing 42%
and 22% of the total volume, respectively. Together with Egypt, Spain, and
Tunisia they cover 88% of production volume in 2018. Most producers
have increased production since 2014 led by Turkey, Egypt and Tunisia,
while production among European producers Greece, Spain and Italy grows
at a lower rate (FAO, 2020). In 1990, the real price per kilo was more than
16 dollars, falling to an all-time low in 2002 of 4 dollars per kilo. This price
reduction initiated a crisis among the aquaculture producers in the sector.
Hereafter, the prices slightly increased level around 5–6 dollars per kilo,
but with cycles. However, increasing production in later years has initiated
a decreasing trend from 2011 and onwards (Figure 1).
The reaction of seabream and seabass producers to the over-supply crises
was price competition, and with portion-sized fish as the main product, it
was difficult to differentiate. This has led to a process of business concen-
tration to enhance economic efficiency (Cidad et al., 2018; Llorente et al.,
AQUACULTURE ECONOMICS & MANAGEMENT 371
5,00,000 18
4,50,000 16
4,00,000 14
3,50,000
12
3,00,000
10
2,50,000
8
2,00,000
6
1,50,000
1,00,000 4
50,000 2
0 0
European Seabass Gilthead Seabream Total Producon Seabass Price Seabream Price
Figure 1. Global aquaculture production of seabream and seabass (ton) and average price per
kilo (USD) 1990–2018. Source: FishStatJ—Software for Fishery and Aquaculture Statistical Time
Series (FAO).
2020; STECF, 2016, 2018). The economic performance of seabream and
seabass firms in the EU was on average negative from 2008 to 2013 and,
after this period, firms returned to positive profitability. Llorente et al.
(2020) observed positive effects of a larger firm size on profitability. The
comparison of performance indicators by firm size shows that very large
firms obtain the highest returns on assets, followed by large firms and the
medium-sized firms. However, the very large firms do not necessarily
obtain the highest return on equity. The most leveraged firms suffer largely
the negative effects of a high degree of indebtedness (Llorente et al., 2020).
In Greece, the concentration process of the sector was mainly financed
by loans. By 2014, many of these firms were unable to repay these loans
and the ownership of the major seabass and seabream aquaculture firms
was transferred to the Greek banks. In 2018, the ownership of the three
largest firms in Greece was transferred to an investment fund (STECF,
2018). Despite the fact that both the financing and the economic perform-
ance of seabream and seabass firms have undergone significant changes in
recent years, there are no studies that have addressed the effects that capital
structure and leverage can have on their competitiveness and profitability.
Method
Application of the agency theory involves determining the relationship
between capital structure and firm performance and the relationship that
exists between them (Berger & Di Patti, 2006).
Several measures have been employed in literature to capture firm per-
formance and encompass variations of financial ratios, mixes of accounting
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
372 I. ANKAMAH-YEBOAH ET AL.
and market values, stock market returns and their volatility, and efficiencies
computed from parametric and nonparametric approaches. In this article,
we use firm level inefficiency estimates as an indicator of firm performance
computed from a stochastic production function. Following Battese and
Coelli (1992), we ex
press the stochastic frontier function as:
0
Yit ¼ exp Xitbþ VitUit , i ¼ 1, :::,N, t ¼ 1, :::,Ti, (1)
where Yit is the output in terms of sales revenue of the ith aquaculture firm
in period t; Xit is the vector of input quantities including cost of labor, cap-
ital and materials; b is the vector of unknown parameters to be estimated;
Vit is a random term assumed to be independent and identically distributed
N  ð0, r2vÞ and independent of Uit; and Uit is random term associated
with non-negative technical inefficiency of production that is also assumed
to be independent and identically distributed with half normal, Nþ 
ð0, r2vÞ, or truncated normal of N  ðu, r2uÞ: Both the truncated normal of
Pitt and Lee (1981) and the half-normal distribution assumptions of Battese
and Coelli (1992) can be tested. Furthermore, time varying effects of tech-
nical efficiency is allowed by Uit ¼ gitUi ¼ expfgðt  TiÞgUi, where g is
an unknown scaler parameter. As such, the non-negative firm effects, tech-
nical efficiency, Uit, decrease, remain constant or increase as t increases, if
g > 0, g ¼ 0 or g < 0, respectively. The case in which g is positive is likely
to be appropriate when firms tend to improve their technical efficiency over
time. The variance components of the likelihood function are estimated in
terms of r2 ¼ r2 þ r2v u and c ¼ r2 2 2u=rv þ ru:
The stochastic frontier estimation requires a specific functional form and
different forms exist in the literature. We use the more flexible widely used
functional form, the translog. A translog can be expressed as:
X3 1X3 X3
lnYit ¼ a0 þ ailnXi þ aijlnX2 ilnXj þ VitUit, (2)i¼1 i¼1 j¼1
where when all aij ¼ 0, the translog function reduces to a Cobb–Douglas
function. In the Cobb-Douglas function, ai represent output elasticities
with respect to inputs and their sum equals the estimated output elasticity,
which measures returns to scale. In the translog function, thPe correspond-
ing elasticity evaluated at sample means is given by e ¼ a þ 3i i k¼1 aijlnXij:
Agency cost and reverse causality
In the heart of corporate governance literature is the effect of capital struc-
ture impact on firm performance. This relationship is described by what is
called the agency cost theory. The agency cost theory deals with the mis-
aligned interests of managers and stakeholders of a firm. In the seminal
AQUACULTURE ECONOMICS & MANAGEMENT 373
paper of Jensen and Meckling (1976), the authors emphasize the import-
ance of agency costs of equity arising from the separation of ownership
and control of firms whereby managers tend to maximize their own utility
rather than the firm’s value. Such conflicts are also observed between debt
and equity investors, which arise when there is risk of default that may
lead to underinvestment or debt overhang. In this case, debt will have a
negative relationship with the value of the firm (Myers, 1977). Beyond
Myers (1977), early researchers such as Stulz (1990) showed that debt
financing can mitigate overinvestment but aggravate underinvestment prob-
lem and conclude that debt can have both negative and positive effects on
firm performance. This has led to the generally tested agency cost hypothesis
that states that higher leverage is expected to lower agency cost, reduce
inefficiency and thereby increase firm’s performance. The regression equa-
tion for testing this hypothesis can be expressed as:
INEFF ¼ d þ d LEV þ d LEV2it 0 1 it 2 it þ dzZit þ 2it (3)
Where INEFF denotes firm performance measured in terms of ineffi-
ciency estimates (Uit) generated from Equation (1), LEV indicates the lever-
age ratio (debt-asset ratio), and Z denotes vectors of factors other than
capital structure that correlate with leverage. 2 is the stochastic term and di
are unknown parameters to be estimated. Under the specified inefficiency
model in Equation (3), the effect of leverage on inefficiency should be
negative, d1 < 0: However, the literature (Margaritis & Psillaki, 2010)
shows that possibilities exist such that at high leverage levels, the effect of
leverage on inefficiency may be positive, hence, d2 > 0: We allow for quad-
ratic specification of leverage in order to capture this U-shaped
relationship.
Following Berger and Di Patti (2006) and Margaritis and Psillaki (2010),
firm performance may also affect the choice of capital structure, leading to
a reverse causality between capital structure and firm performance. It is
stipulated that under the efficiency-risk hypothesis, more efficient firms
choose higher leverage ratios because higher efficiency is expected to lower
the costs of financial distress. On the other hand, firms which are expected
to sustain high efficiency rates into the future are likely to choose lower
debt to equity ratios as a means of safeguarding economic rents or fran-
chise value generated by these inefficiencies from the threat of liquidation
(Berger & Di Patti, 2006). Hence, under the franchise-value hypothesis
more efficient firms are likely to choose lower leverage ratios to protect
their future income or franchise value. As a result, we empirically estimate
the following leverage equation:
374 I. ANKAMAH-YEBOAH ET AL.
LEVit ¼ h0 þ h1INEFFit þ hzSit þ eit: (4)
Here, S captures all factors that correlate with leverage other than firm
performance while all other terms are as defined in Equation (3). The par-
ameter h1 could be negative, h1 < 0, in which case reflecting the efficiency-
risk hypothesis or positive, h1 > 0, denoting the franchise-value hypothesis.
According to Myers (2001), outcome of the model depends on firms debt-
equity choice and the economic aspect as well as firm’s characteristics
focused on.
The use of ordinary least squares to estimate Equations (3) and (4) can
result in biased estimates for two reasons: first because of simultaneity or
the bidirectional causal nature between capital structure and firm perform-
ance, and secondly, because of unobserved heterogeneity of firms, which
may be correlated with capital structure and performance. This occurs
when the independence assumption between explanatory variables and the
random terms is violated. Omitted variable bias due to unobserved hetero-
geneity can be addressed using the panel data structure. The simultaneity
problem is often addressed using an instrumental variable approach, how-
ever, this comes with the challenge of finding valid and strong instruments
for the endogenous variables. Internal instrumental variable (IIV) models
have now advanced to correct for endogeneity problems when valid instru-
ments are hard to find (see Ebbes et al., 2005; Lewbel, 1997, 2012; Park &
Gupta, 2012; Kim & Frees, 2007).
In this study, we employ Kim and Frees (2007) IIV generalized method
of moment multilevel modeling with correlated effects to identify our
model given the panel data structure. The multilevel model1 can simply be
expressed in stacked form as:
ys ¼ Xsbþ ds, (4)
where y is the response variable, X is composed of regressors that may be
exogenous or exogenous in nature and b is the corresponding unknown
parameter to be estimated. d is a composite term for all random elements
in the model: the structural error term and random components. The
multilevel GMM estimator is such that, the GMM estimator is the usual
GLS estimator (random effect) when all X are assumed exogenous (i.e.,
b ¼ bRE) while fixed effects result when all X are assumed endogenous (i.e.,
b ¼ bFE). The more generalized estimator GMM proposed by Kim and
Frees (2007) allows X to be composed of both endogenous and exogenous
predictors and uses this information to build internal instruments and this
results in b ¼ bGMM: To facilitate the choice of the estimator, a Hausman-
test for panel data (Hausman, 1978) is used to compare a robust estimator
and an estimator that is efficient under the null hypothesis of no omitted
variables, and to compare two robust estimators at different levels.
AQUACULTURE ECONOMICS & MANAGEMENT 375
Table 1. Summary statistics, numbers of firms divided on country and year.
Country/Year 2008 2009 2010 2011 2012 2013 2014 2015 2016
Croatia 5 5 5 5 6 6 7 7 6
France 8 6 6 9 7 6 8 8 6
Greece 2 2 2 2 2 2 2 1
Italy 14 19 19 20 19 20 21 19 19
Spain 38 37 36 40 38 34 32 32 24
Total 65 69 68 76 72 68 70 68 56
Source: Orbis database (Orbis, 2018).
Table 2. Summary statistics of variables included in the models.
Variable Description of variables Mean Std. dev.
SDA Short-term leverage ((current liabilitiesþ other current 0.58 0.59
liabilities)/total assets) ratio
LDA Long-term leverage ((long term debtþ other non-current 0.22 0.42
liabilities)/total assets) ratio
DA Total leverage (total debt/total assets) ratio 0.98 0.44
Growths The percentage change in sales over the year 0.67 7.27
Investment The ratio of capital expenditure to total assets 0.07 0.26
Risk The standard deviation of the ratio of operating income 0.10 0.36
before interest, taxes, and depreciation to total assets
Profit margin Profit margin (%) (Profit before tax/turnover) 2.12 18.80
Liquid The ratio of current assets to current liabilities 1.00 2.11
Tang The ratio of fixed assets to total assets 0.27 0.44
Sales Sales revenue (e1,000) 11,900 28,300
Capital Firms capital value (e1,000) 4,856 15,100
Labor Cost of employees (e1,000) 2,111 6,129
Material Cost of materials (e1,000) 7,986 18,000
Source: Orbis database (Orbis, 2018).
Data
The research is carried out with economic and financial data from the
accounts of aquaculture firms. The data is obtained from the Orbis database
(Orbis, 2018). This database contains firm-level financial accounts (balance
sheets and profit and loss accounts) for more than 300 million firms around
the world. The information is obtained from public balance sheet declara-
tions for the European firms. Firms within the aquaculture sector are identi-
fied by their NACE code,2 which reveals their main business activity.
For our analysis, 91 firms farming seabass and seabream were selected,
covering 612 observations over the years 2008 and 2016. Five countries are
included in the data set France, Croatia, Italy, Spain and Greece.3 To be
included in the analysis, firm must have a minimum of two observations
over the years covered. The number of firms within each country and year
are presented in Table 1.
The output in the efficiency model is represented by the sales revenue,4
whereas the inputs are represented by labor cost, material cost and capital
cost. All variables are expressed in Euros.5 In this analysis, each variable is
deflated by the country-specific producer price index (however, conclusions
from real and nominal values are the same, thus we only present the real
value results here) (Table 2).
376 I. ANKAMAH-YEBOAH ET AL.
Table 3. Results of the stochastic frontier production functions.
TINVARTL TVARTL TVARTRTL TINVARCD TVARCD TVARTRCD
Coeff Coef Coef Coeff Coef Coef
Constant 1.728 1.096 0.778 2.237


 2.352


 2.278



(1.921) (1.825) (1.562) (0.325) (0.308) (0.326)
Capital 0.301

 0.178 0.146 0.002 0.001 0.003
(0.129
) 

 (0.119)


 (0.115)


 (0.015) (0.014) (0.012)Material 1.184 1.182 1.136 0.513


 0.515


 0.516



(0.19) (0.182) (0.166) (0.022) (0.021) (0.022)
Labor 0.097 0.067 0.038 0.435


 0.424


 0.425



(0.225
) 
 (0.218)

 (0.200)

 (0.031) (0.03) (0.029)Capital2 0.008 0.008 0.007
(0.004
) 

 (0.004) (0.004)Material2 0.033 0.033


 0.035



(0.008
) 

 (0.008)


 (0.008)Labour2 0.103 0.095 0.095



(0.022) (0.021) (0.019)
(Material Labor) 0.130


 0.126


 0.130



(0.029) (0.028) (0.025)
(Material Capital) 0.008 0.007 0.008
(0.011) (0.011) (0.011)
(Labor Capital) 0.018 0.008 0.007
(0.016
) 

 (0.014)


 (0.013)r2 0.440 0.271 0.451


 0.529


 0.329


 0.549



(0.059
) 

 (0.035)


 (0.096)


 (0.075) (0.048)c 0.606 0.400 0.642 0.653


 0.479



(0.121)
0.685



(0.057) (0.078) (0.088)


 (0.053) (0.079) (0.085)l 1.077 1.226




 (0.365) (0.718)g 0.095 0.109


 0.083


 0.103



(0.018) (0.020) (0.016) (0.017)
Loglik 395.887 378.355 374.778 418.195 400.307 398.689
# of Obs. 612 612 612 612 612 612
# of Panels 91 91 91 91 91 91
Mean (Effic.) 0.706 0.721 0.769 0.664 0.678 0.740

, 

, and 


 indicate significance at 10%, 5%, and 1% levels.
Results
The estimation results are presented in two parts: first the measurement of
firm performance and subsequently the capital structure—firm performance
relationship.
Measurement of firm performance results
Table 3 presents the parameter estimates of the stochastic frontier model
comparing different specifications time varying and time invariant assump-
tions of the (in)efficiency estimates. Under the time varying assumption,
we estimate both half normal and truncated normal models. These variants
of models are explored as a search process to identify which properly fits
the data at hand. Columns 2, 3 and 4 of Table 3 presents the time invariant
(TINVARTL), the variant—half normal (TVARTL) and time variant—trun-
cated normal (TVARTRTL) variants of the translog production function
respectively. The truncated normal time varying production function
AQUACULTURE ECONOMICS & MANAGEMENT 377
Table 4. Parameter restrictions of inefficiency distribution and elasticity estimates.
v2  Statistic ðTranslogÞ
Restrictions TVTRTL
g ¼ l ¼ 0 42.22



l ¼ 0 7.15



g ¼ l ¼ 0 35.06



Translog (TVARTRTL) function output elasticities
Inputs Scale elasticities (at means) Confidence Interval
Capital 0.068 (0.036) (0.003, 0.138)
Material 0.007 (0.109) (0.207, 0.221)
Labor 0.767 (0.252) (1.261, 0.274)
*** indicate significance at 1% level.
Figure 2. (A) Firm level inefficiency trends. (B) Firm level efficiency trends.
appear to perform better than the time invariant and half-normal time
varying functions. Hence, we choose the time varying translog production
function under the truncated-normal assumption. This is confirmed by a
likelihood ratio test of hypotheses of parameters of the distribution of the
firm effects, the efficiency estimates shown in Table 4. All restrictions
rejected at the 1 percent significance level indicating that the truncated nor-
mal translog function has the best representation of the data.
To assess the input effects the marginal effects of each input is estimated,
where the marginal product is equal to the elasticity of scale for each input.
These are presented at the lower part of Table 4 where labor shows signifi-
cantly negative estimate while capital and material have confidence interval
estimates that include zero. The parameter, g is significantly positive even
across models indicating that technical efficiency has been improving over
time. Trends in (in)efficiency estimates of individual firms are presented in
Figure 2 where each firm’s technical efficiency improves over time. The
individual firm level technical efficiencies are our estimates of interest and
our measure of firm performance to be used in determining the capital
structure and performance relationship in the subsequent section.
378 I. ANKAMAH-YEBOAH ET AL.
Table 5. Hausman test of representative model for capital structure and perform-
ance relations.
1 2 3 4
CAP bFE,2 ¼ bRE bFE,3 ¼ bRE bRE ¼ bGMM,2 bFE,3 ¼ bGMM,2 bGMM,2 ¼ bGMM,3 Decision
SDA 9.00 (0.44) 8.48 (0.58) 17.54 (0.00) 0.17 (1.00) bGMM,2
LDA 8.03 (0.63) 9.04 (0.53) 4.27 (0.04) 5.46 (0.07) bGMM,2
DA 10.38 (0.41) 33.12 (0.00) 81.95 (0.00) 5.18 (0.02) bGMM,3
EFF
EFFS 9.00 (0.44) 9.00 (0.44) 7.86 (0.02) 7.86 (0.02) bGMM,3
EFFL 9.00 (0.44) 9.00 (0.44) 6.58 (0.04) 6.58 (0.04) bGMM,3
EFFD 9.00 (0.44) 9.00 (0.44) 8.35 (0.02) 8.35 (0.02) bGMM,3
EFFC 9.00 (0.44) 9.00 (0.44) 8.08 (0.09) 8.08 (0.09) bRE
X2—Statistic (p-Value).
Capital structure-firm performance model identification results
In the following, the multilevel IIV approach by Kim and Frees (2007) is
applied to identify the appropriate model that identifies the equations of
interest. The performance variable is the technical efficiency estimates
derived from the previous section. In this subsection, it is represented as
inefficiency estimates and hence has values greater than one to infinity.
The capital structure or leverage ratio is defined as the debt-asset ratio for
which we distinct between short-term (SDA), long-term (LDA) and total
(DA) debt-asset ratios. Therefore, we estimate three capital structure equa-
tions and four performance equations where each of the SDA, LDA, DA
and SDAþ LDA are used as explanatory variables. Table 5 presents
Hausman (1978) tests to facilitate the choice of estimator that best repre-
sents each equation. The test compares a random effect, fixed effect and
general method of moments estimator.
Column labeled 1 compares fixed effect estimator at levels 2 and 3 with
random effect estimator where lack of statistical significance indicates that
the random effect estimator is the most suitable. With the exception of the
SDA model, all models show that the random effect model should be
chosen. Following this, the deciding estimator is tested against level 2 and
3 GMM estimator where a significant model indicates that a GMM estima-
tor should be used. This implies explanatory variables are composed of a
mix of exogenous and endogenous variables. This conclusion applies to all
models except the inefficiency model with SDAþ LDA as explanatory vari-
ables. For the indicative GMM estimator, level 2 is tested against level 3 to
decide the most appropriate estimator. In all models except one, the test
concludes using a GMM estimator for which internal instruments are con-
structed to alleviate endogeneity problems.
The agency cost model results
We now turn to evaluating the role of leverage on inefficiency and in turn,
whether differences in inefficiency are related to leverage. We evaluate in the
AQUACULTURE ECONOMICS & MANAGEMENT 379
Table 6. Agency cost equations.
Model A Model B Model C Model D
Coef SError Coef SError Coef SError Coef SError
Constant 1.690


 





 0.100 1.622 0.096 1.581



 0.134 1.618


 0.125
SDA 0.675 



SDA2 0.554



0.166 0.569
0.088 0.522



0.183
0.092
LDA 0.541

 0.240 0.132 0.248
LDA2 0.678

 0.280 
 0.397 0.273DA 0.364
DA2 0.365



0.192
0.086
Growths 0.006

 0.003 0.005
 0.003 0.005 0.003 0.005
 0.003
Investment 0.026 0.085 0.057 0.090 0.027 0.087 0.013
 0.088
Risk 0.003


 0.070 0.072


 0.074 0.023


 0.072 0.008 0.072Profit margin 0.006

 0.001 0.010

 0.001 0.006 0.001 0.006



 0.001
Liquidity 0.021 0.010 0.023 0.010 0.011 0.010 0.023

 0.011
France 0.209

 0.093 0.239

 0.100 0.237

 0.095 0.180
 0.096
Greece 0.075


 0.147 0.147


 0.157 0.138


 0.149 0.049 0.151Italy 0.234


 0.080 0.235


 0.085 0.281


 0.080 0.224



 0.082
Spain 0.223 0.074 0.212 0.079 0.248 0.076 0.220


 0.076

, 

, and 


 indicate significance at 10%, 5%, and 1% levels.
presence of other moderating factors. Table 6 presents the results of the effi-
ciency model for which we evaluate short-term, long-term and total leverage
effects on inefficiency. Our aim here is to explore the agency cost hypothesis.
The hypothesis states that an increase in leverage increases efficiency and at
sufficient leverage levels, efficiency can be negative. From our results, the col-
umn labeled Model A shows short-term leverage (SDA) has a significant nega-
tive effect on inefficiency where at higher levels of SDA, inefficiency begins to
increase given the significant quadratic leverage term. Such a relationship con-
firms the inverted U shape relationship between efficiency and leverage.
Long-term and total leverage effects (LDA and DA) shown in columns
labeled Model B and C show similar effects. However, when SDA and LDA
are included separately in a single model, only the SDA effect becomes
prominent, indicating a high correlation between SDA and LDA. Hence,
the agency cost hypothesis is confirmed in the Mediterranean aquaculture
industry. Using Croatia as the reference level, we observe that Greece has
the same level of inefficiency as Croatia but France, Italy and Spain have
significantly lower levels of inefficiency.
Regarding the moderating factors, growth in sales has a significant posi-
tive effect on inefficiency only when the short-term leverage is included in
the model. Factors such as profit margin and liquidity generally have a sig-
nificant and negative effect on inefficiency. Risk and investment have no
significant effect on inefficiency.
The leverage model results
The leverage model evaluates the relationship between firm performance
and leverage with a focus on two hypotheses: the franchise-value and
380 I. ANKAMAH-YEBOAH ET AL.
Table 7. Capital structure equations.
SDA LDA DA
Coef Std. error Coef Std. error Coef Std. error
Constant 0.212


 0.015 0.084


 0.010 0.612


 0.072
Inefficiency 0.201*** 0.038 0.188*** 0.026 0.186*** 0.030
Tangibility 0.036
Investment 0.196



0.078 0.045 0.053 0.067 0.069
0.062 0.017 0.042 0.227


 0.056
Risk 0.204





 0.046 0.050 0.031 0.162



 0.041
Profit margin 0.004


 0.001 0.000


 0.001 0.006



 0.001
Liquidity 0.048 0.008 0.019 0.005 0.033


 0.007
France 0.167

 0.065 0.114

 0.044 0.000 0.065
Greece 0.070


 0.111 0.030


 0.075 0.009 0.102Italy 0.321

 0.055 0.132 0.037 0.146



 0.056
Spain 0.130 0.052 0.014 0.035 0.074 0.053
** and *** indicate significance at 5% and 1% levels
efficiency-risk hypotheses. Our results indicate that inefficiency has a signifi-
cantly positive effect on all levels of leverage with a somewhat stronger
effect is observed in the short-term leverage model. This relationship is
consistent to that of the franchise-value hypothesis where firms with high
efficiency will try to protect the value of their high income by holding
more equity capital.
The levels of leverage for Greece seems to be the same as that of Croatia
given that there is no significant effect for the Greece leverage coefficients,
which most likely is a consequence of the low number of firms represent-
ing Greece in the dataset. However, differences exist between France, Italy
and Spain across the leverage levels. For instance, we observe that France,
Italy and Spain have significantly higher short-term leverage than Croatia.
For long-term leverage, only France and Italy have lower leverage values
than Croatia, while total leverage values show equal levels across all coun-
tries with the exception of Italy which has higher values than Croatia. The
positive and negative effects of France coefficients in short- and long-term
leverage models, respectively, appear to be nullified in the total lever-
age model.
Regarding the moderating factors, asset tangibility has no effect on any
of the leverage levels. However, investment, profit margin and liquidity
have negative effect on short-term leverage implying that higher values of
these variables tend to decrease firm debt-asset ratios. Risk on the other
hand increases leverage given the significantly positive relationship.
Turning to the long-term leverage equation, the only variable identified to
have an effect is liquidity, with a negative effect. The total leverage model
mimics that of the short-term leverage model. Therefore, it can be con-
cluded that the effects observed in the total leverage model are driven by
the effects in the short-term equation regarding the moderating factors
(Table 7).
AQUACULTURE ECONOMICS & MANAGEMENT 381
Conclusions
This study investigates the relationship between performance in terms of
firm efficiency and firm leverage levels for Mediterranean aquaculture pro-
duction firms. Firm level efficiency is determined using a stochastic frontier
production function. The multilevel internal instrument variable approach
is then employed to identify the causal relationships between performance
and leverage.
Overall, the results from our analysis show that technical efficiency has
been increasing across all firms over the period 2008–2016. After the crises
in the sector at the beginning of 2000 due to increased supply and falling
prices and the financial crises in 2008, Mediterranean aquaculture firms
seems to be on a path to improving their economic performance (Llorente
et al., 2020; Nielsen et al., 2021; STECF, 2018). One of the reasons for this
improvement is that firms are increasing farm size, engaging in mergers
and vertical integrations to exploit economies of scale (Fern
andez-Polanco
et al., 2021; Guillen et al., 2019; Llorente et al., 2020; Rad & Ko€ksal, 2000;
Rad, 2007; STECF, 2014; Wagner & Young, 2009).
Despite improved efficiency the results have shown that there is still room
for improved technical efficiency in the production of seabream and seabass.
The optimization of the operational scale is one driver to increase efficiency,
but requiring new investments (Nielsen et al., 2021). Therefore, it is important
to extend the knowledge about how the financing structure affects the effi-
ciency and performance of companies, as has been done successfully in other
industries such as salmon.
Our results confirm that the agency-cost hypothesis holds in
Mediterranean aquaculture sector, such that leverage has an inverted U-
shaped relationship with performance. This implies that leverage increase
with efficiency, but efficiency begins to decrease at sufficiently high levels
of leverage. This is in line with the results in Llorente et al. (2020), who
observed that firms with the highest leverage did not get the highest return
on equity. Increasing profit margin and liquidity significantly improve
aquaculture firm’s efficiency, whereas risk and investments show no signifi-
cant effects on firm performance. In aquaculture, a large proportion of cap-
ital is bound within the standing biomass compared to other physical
equipment (Asche & Bjørndal, 2011). If firms have a high level of leverage
it might increase the pressure on delivering short-term economic results to
leverage holders. This in turn forces these firms to harvest before the fish
reaches the economically optimal size (receive lower prices). This pressure
to obtain liquidity in the short term, generated by debt, can be partly
explanation of oversupply situations that cause price drops scenarios.
Despite declining prices, companies may be forced to market the product
to obtain liquidity. Instead of keeping biomass in cages expecting more
382 I. ANKAMAH-YEBOAH ET AL.
favorable market conditions, they may need to sell the product, even when
prices threaten the profitability of the activity. Firms with lower level of
leverage may experience less pressure and have more flexibility to wait for
the optimal harvest time. Thus, an optimal balancing of leverage levels
could in turn reflect positively on profit margins and liquidity, which
would improve firm efficiency according to our results.
The markets for fish products are highly competitive (Anderson et al.,
2018) and the supply of aquaculture products are increasing continuously
(FAO, 2020), especially from low cost countries outside of the EU (FAO,
2020). The knowledge that the balancing of leverage can help reduce firm
inefficiency could increase profitability in the European aquaculture sector.
Using this new tool, the sector could also obtain more robustness to with-
stand future economic shocks.
Notes
1. Multilevel models are also called random effects, mixed effects, hierarchical models.
2. NACE Rev.2 is the Statistical classification of economic activities in the European
Community. Section A contains the economic activities related to agriculture, forestry
and fishing. Group 03.2 corresponds to “Aquaculture”, i.e., the production process
involving the culturing or farming of aquatic organisms.
3. In the Orbis database, Turkish and Greek company accounts do not contain the
variables “material cost” (Material) and “cost of employees” (Labour), except for two
Greek firms. Thus, only firms reporting these data are included in the analysis.
4. Using revenue as output is common in economic modelling (Malikov & Lien, 2021)
and in addition a number of studies shows that the law of one price (LOP) holds at
the producer and trade level, implying that aggregation can be conducted (Asche
et al., 1999)
5. When estimating efficiency, using variables on cost and revenue, economic efficiency is
estimated. This is different from estimating technical efficiency using physical inputs
and outputs.
Acknowledgments
The authors would like to thank the MedAID (Mediterranean Aquaculture Integrated
Development) project, under which this research was conducted.
Funding
The MedAID project has received funding from the European Union’s Horizon 2020
Research and Innovation Programme under grant agreement [no. 727315] (http://www.me-
daid-h2020.eu/).
ORCID
Isaac Ankamah-Yeboah http://orcid.org/0000-0003-4587-8311
AQUACULTURE ECONOMICS & MANAGEMENT 383
Rasmus Nielsen http://orcid.org/0000-0001-7357-6965
Ignacio Llorente http://orcid.org/0000-0001-9426-0601
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