Complex evaluation of the economic crisis impact on Lithuanian industries.
Krivka, Algirdas
Introduction
Nowadays economic reality, characterised by growing countries'
and regions' economic integration, globalization of business
relations, free movement of capital and labour force, offers wide
possibilities for the social and economic development of market economy
countries and for increasing the welfare of their citizens. Expansion of
financial markets together with growing banking sector assure the
sources of financing business setting up and further development;
diminishing barriers of international trade provide access to new
markets for companies and satisfaction of growing needs for customers
with a wide variety of goods and services.
Although there is a little doubt about the advantages of
international economic integration, a few recent years have shown in
practice the other side of the coin. In 2007 the crisis, which initially
affected the financial system of the United States, shortly spread all
over the world and stimulated the economic recession, with both business
and ordinary citizens suffering from its consequences (Thao et al. 2013;
Kowalski 2012). In many countries the financial crisis caused a rapid
decrease in tax revenues, while austerity measures in fiscal policy
(raising taxes and cutting public spending) applied by governments even
deepened the economic problems (Adam, Iacob 2012).
The Republic of Lithuania was amongst the countries to experience
the deepest economic downturn: according to GDP data, the economic
crisis, which started in the end of 2008, caused the fall of the annual
GDP by 14.8 % in 2009 (Statistics Lithuania 2013). It has to be admitted
that deep recession was stimulated not only by the global economic
crisis, but also by the internal specifics of the national economy
evolution, and particularly because of the economy overheating and real
estate price bubble caused by irresponsible lending and speculation.
Though the first signs of economic recovery appeared in the 2nd quarter
of 2010, the country's economic growth remained very slow during
the last 3 years, while the GDP of 2012 is still under the pre-crisis
level of 2007.
It has to be mentioned though, that GDP dynamics and other
macroeconomic indicators provide general information only about the
impact of the economic crisis, whereas even with a naked eye one may
indicate the dissimilar effect of the crisis on various industries, also
unequal rates of after-crisis recovery. Possibly uneven development of
Lithuanian industries during the economic crisis of 2008 and afterwards,
in the author's opinion, requires calculation-based evaluation with
its results providing more detailed and scientifically grounded
information about the impact of the recent crisis on business
enterprises.
The problem of this paper is the complex quantitative evaluation of
the economic crisis impact on industries. The aim of the research is to
complexly evaluate the impact of the economic crisis of 2008 on
Lithuanian industries on the basis of the system of quantitative
indicators characterising enterprise's financial state and
performance. Relying on scientific literature the system of industry
research criteria is developed, while relative weights of the criteria
are estimated by involving competent experts. By applying multi-criteria
decision making methods (MCDM) relative positions (ranks) of Lithuanian
industries are determined for every year of the period of 2006-2011. The
ranks and their changes are further analysed distinguishing pre-crisis,
crisis, and post-crisis periods, determining the industries most and
least affected by the economic crisis; also, the industries
characterised by the fastest and the slowest after-crisis recovery.
1. Literature review
Modern quantitative methods of enterprise performance analysis are
based on the company's financial reports: horizontal analysis of
enterprise financial statements studying accounts' dynamics during
several periods; vertical analysis--a study of the structure of
enterprise assets, equity and liabilities, and their changes; analysis
of financial ratios--the indicators, characterising enterprise's
financial state and performance, are calculated, compared through
different accounting periods, between various companies, also with their
recommended values (Hofmann, Lampe 2012; Erdogan 2013; Kotane,
Kuzmina-Merlino 2012; Hegazy, M., Hegazy, S. 2012; Zelgalve, Zaharcenko
2012).
With an enterprise being a complex phenomenon for research,
individual financial ratios are combined into complex (integrated)
indicators in the research on bankrupt probability (Altman 1968; Bhunia,
Sarkar 2011; Yap et al. 2010), complex evaluation of enterprise
financial state and performance by applying multi-criteria evaluation
methods (Ginevi?ius, Podviezko 2013; Hsu 2013; Hosseini et al. 2013). In
strategic management models enterprise's financial indicators are
complemented with qualitative criteria in order to complexly evaluate
enterprise's strategic potential, calculate the results of strategy
application (Ginevi?ius et al. 2012; Ginevi?ius, Krivka 2010;
Punniyamoorthy, Murali 2008; Hegazy, M., Hegazy, S. 2012).
Analysis of enterprise financial indicators is also applicable for
studying economic sectors or industries. The research of that kind deals
with generalised (average) values of financial indicators of a group of
enterprises or the whole industry assessing efficiency of
companies' performance (Li et al. 2011), studying the relation
between enterprise performance and the value of its shares (Balatbat et
al. 2010; Hosseini et al. 2013), performing comparative analysis of
inter-industry performance or inter-state industries' evolution
(Kotane, Kuzmina-Merlino 2012; Claudiu-Marian 2011; Hon, Chu 2011),
implementing the research on the relations between enterprise size,
organization structure, market share or market concentration, and
performance (Hays et al. 2009; Uslayet al. 2010) and other research of
the similar nature.
Industry performance analysis in the context of an economic crisis
also deserves economists' attention during the recent few years;
however, most of the researchers are concentrated on the particular
sector of economy, industry or market, e.g. furniture industry (Li et
al. 2011), textile (Abbas et al. 2012), banking sector (Romanova 2012;
Lakstutien? et al. 2011), agriculture (Li et al. 2011), TFT-LCD panel
industry (Hon, Chu 2011), automobile industry (Du 2009; Bok 2009),
tourism (Baleanu et al. 2009), construction (Al-Malkawi 2013). Other
scientists perform research on the economic crisis effect on small and
medium enterprises (Yiannaki 2012; Soininen et al. 2012) or large
publicly listed companies (Dzikowska, Jankowska 2012; Norvaisiene 2012;
Hsu 2013).
Summarizing the literature analysis performed, absence of the
detailed, complex research on the economic crisis effect on industries
is discovered. With regards to the accomplished literature study, the
author indicates a niche for the research on the economic crisis of 2008
impact on Lithuanian economy presented in this paper, which has to
involve all the main industries, be based on quantitative criteria--the
system of financial state and performance indicators--and integrated
approach to industry, as a complex phenomenon, analysis, with support of
widely recognized mathematical instruments applicable for complex
quantitative evaluation.
2. Research scope and methodology
The industries analysed in the paper are identified according to
the 2nd-digit level classification of economic activities (based on
NACE2) published by Statistics Lithuania (official national authority in
the sphere of statistics). With regards to experience of other authors
(Erdogan 2013; Kotane, Kuzmina-Merlino 2012; Balatbat et al. 2010; Hsu
2013; Hosseini et al. 2013; Abbas et al. 2012; Al-Malkawi 2013), the
system of financial state and performance indicators is composed of four
main groups of enterprise financial ratios: profitability, liquidity,
solvency and asset turnover. The indicators selected for the research
and their formulas are presented in Table 1.
The period of the research are the calendar years 2006-2011
including both pre-crisis, crisis and post-crisis years (at the moment
of the research the data of 2012 had not been published yet). The
research involves all the industries (2nd-digit level economic
activities), which data is published by Statistics Lithuania (the list
of the industries under research is provided further with the results of
the research in Table 5), combining for 97.6% of Lithuanian enterprises
(according to their value-added).
The complex quantitative evaluation of the economic crisis impact
on Lithuanian industries is considered to be a mathematical problem of
assessing the industries selected for the research with regards to the
system of enterprise financial indicators as the evaluation criteria. To
solve a problem of that kind, multi-criteria evaluation methods,
developed throughout the recent years and widely applied in construction
(e.g. Zavadskas et al. 2008; Ginevicius et al. 2008; Saparauskas et al.
2011), economics and management (e.g. Ginevicius et al. 2012, 2013;
Ginevicius, Podvezko 2008, 2009; Ginevicius, Podviezko 2011, 2013; Hsu
2013), seem to be an appropriate tool.
The alternatives under evaluation are 68 industries--each of them
is assessed with regards to 10 financial state and performance
indicators (the scheme of evaluation is presented in Table 2); the
evaluation is performed for every year of the research period of
2006-2011. The value [r.sub.ij] of the particular evaluation criterion
(financial indicator) i (i = 1, ..., m) for the assessed alternative
(industry) j (j = 1, ..., n) is taken from the officially published data
by Statistics Lithuania (2013). To estimate weights [[omega].sub.i] of
the financial indicators, the method of expert evaluation is applied,
with respect to the condition [m.summation over (i=1)] [[omega].sub.i] =
1. The experts (financial directors or CEOs) were asked to provide
single set of criteria weights (showing the relative importance of the
particular financial indicator) for the whole period of the research.
The result of multi-criteria evaluation is the ranking of
industries for every year of the period of 2006-2011. The further
analysis is implemented studying the changes of the ranking to compare
pre-crisis year of 2006, the crisis years of 2008-2009, and after-crisis
year of 2011--the dynamics of the ranks reflect the impact of the crisis
on the particular industry, including after-crisis recovery.
The experience of the recent research (e.g. Ginevicius, Podvezko
2009; Ginevicius et al. 2008, 2012; Ginevicius, Krivka 2010; Ginevicius,
Podviezko 2011, 2013) suggests that the phenomenon under analysis has to
be assessed by applying several multi-criteria methods seeking for
higher reliability of results; moreover, in order to minimize the
subjectivity of the specific method, average ranks are accepted to be
the ultimate result. To efficiently combine several multi-criteria
evaluation methods, it is important to form a "bunch" of
correlating methods (Ginevicius, Podvezko 2008). SAW, TOPSIS and VIKOR
methods are selected for multi-criteria assessment of Lithuanian
industries.
SAW method calculates the sum of normalized weighted values
[S.sub.j] of all criteria for each j-th alternative (Ginevicius et al.
2008, 2012, 2013; Podvezko 2011):
[S.sub.j] = [m.summation over (i=1)] [[omega].sub.i] [[??].sub.ij],
(1)
while initial values are normalized using the formula (Ginevicius
et al. 2008, 2012; Podvezko 2011):
[[??].sub.ij] = [r.sub.ij]/[n.summation over (j=1)] [r.sub.ij]. (2)
TOPSIS indicates the best ([V.sup.*]) and the worst ([V.sup.-])
solutions with regards to each criterion (Opricovic, Tzeng 2004;
Ginevicius et al. 2008):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (3)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (4)
where: [I.sub.1] is a set of maximizing criteria, [I.sub.2] is a
set of minimizing criteria. The distance of each alternative to the best
and the worst solutions is calculated:
[D.sup.*.sub.j] = [square root of [m.summation over (i=1)]
[([[omega].sub.i] [[??].sub.ij] - [V.sup.*.sub.i]).sup.2]], (5)
[D.sup.-.sub.j] = [square root of [m.summation over (i=1)]
[([[omega].sub.i] [[??].sub.ij] - [V.sup.-.sub.i]).sup.2]], (6)
followed by the TOPSIS criterion, which maximum value (i.e. the
value which is closest to 1) corresponds to the best alternative:
[C.sup.*.sub.j] = [D.sup.-.sub.j]/[D.sup.*.sub.j] +
[D.sup.-.sub.j]. (7)
The initial values [r.sub.ij] are normalized by applying the vector
normalization formula (Ginevicius et al. 2008, 2012):
[[??].sub.ij] = [r.sub.ij]/[square root of [n.summation over (j=1)]
[r.sup.2.sub.ij]]. (8)
VIKOR is based on the three evaluation criteria [S.sub.j],
[R.sub.j] and [Q.sub.j], calculated by the following formulas
(Opricovic, Tzeng 2004; Ginevi?ius et al. 2008):
[S.sub.j] = [m.summation over (i=1)] [[omega].sub.i] [[??].sub.ij],
(9)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (10)
[Q.sub.j] = v [S.sub.j] - [S.sup.*]/[S.sup.-] - [S.sup.*] + (1-v)
[R.sub.j] - [R.sup.*]/[R.sup.-] - [R.sup.*], (11)
where: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], v is
the majority criterion, equalled to 0.5 in empiric research (e.g.
Ginevicius, Krivka 2010). The lowest values of [Q.sub.j] indicate the
best alternatives.
Normalization of maximizing criteria values is performed by
applying the formula:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (12)
Where negative values are involved in multi-criteria assessment,
they are transformed into positive by adding the shifting constant
[b.sub.i] to each value [r.sub.ij] of the i-th criterion having at least
one negative value (Podvezko 2011):
[[bar.sub.ij]] = [r.sub.ij] + [b.sub.i]. (13)
For the shifting procedure to have the least possible effect on
evaluation results, minimum values of the shifting constant are
considered, calculated as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (14)
3. Research procedure and results
The questionnaires for estimating weights of the selected financial
state and performance indicators (evaluation criteria) were submitted to
80 enterprises. The experts (financial directors or CEOs) were asked to
evaluate weights of the financial indicators in two steps: first the
weights of the indicators inside every particular group (see Table 1)
were estimated; then the weights of the groups (profitability,
liquidity, solvency and asset turnover) in the integrated criterion were
determined. The ultimate weight [[omega].sub.i] of the i-th indicator
was calculated by multiplying its weight [[omega].sup.g.sub.i] inside
the group by the weight [[omega].sub.g] of the group in the integrated
criterion:
[[omega].sub.i] = [[omega].sup.g.sub.i] x [[omega].sub.g], (15)
with respect to the conditions: [summation] [[omega].sup.g.sub.i] =
1 (for every group of indicators) and [summation] [[omega].sub.g] = 1
(for the integrated criterion).
Such practice was addressed in order to simplify evaluation
procedure and to avoid unintentional overweighting of profitability
indicators, which could occur in case of direct evaluation just because
of the number of indicators in profitability group (4 indicators)
compared to other groups consisting of 2 indicators.
Nine answers with fully and accurately filled questionnaires were
received to provide data for calculating the ultimate criteria weights
(Table 3).
The concordance coefficient, calculated as the ratio of actual (S)
and ideal ([S.sub.max]) dispersions, is applied to check the degree of
agreement of expert estimates (Kendall 1970; Ginevicius et al. 2008):
W = S/[S.sub.max] = 12S/[r.sup.2]m([m.sup.2]-1), (16)
while the actual dispersion is calculated by the formula:
S = [m.summation over (i=1)] [([c.sub.i] - [bar.c]).sup.2], (17)
where: [c.sub.i] is the sum of ranks of all r experts'
criterion i estimates, [bar.c] is the mean value of sums of all criteria
(i = 1, ..., m) ranks. The consistency of estimates is tested by [chi
square] distribution with v = m - 1 degrees of freedom:
[chi square] = Wr(m - 1) = 12S/rm(m + 1). (18)
Whereas the calculated value of [chi square] = 21.01 is larger than
the critical value of [X.sup.2.sub.cr] = 16.92 (with the significance
level of [alpha] = 0.05 and 9 degrees of freedom), the expert estimates
are considered to be in agreement, while the average weights are
employed for multicriteria assessment of Lithuanian industries.
For every year of the research (2006-2011) the ranks of the
industries are calculated by applying the three chosen MCDM methods:
SAW, TOPSIS and VIKOR. The test for correlation of the results obtained
(Table 4) discloses diverging results of VIKOR, with the correlation
coefficient (modulus value) with SAW being less than 0.8. Thus, only SAW
and TOPSIS methods are considered for ultimate ranking of the
industries.
The ultimate ranks of Lithuanian industries, presented in Table 5,
are the average results obtained by SAW and TOPSIS. Absolute changes of
the rank compared to pre-crisis year of 2006 are further calculated: a
positive change discloses the improvement of the relative position of
the industry, while a negative change corresponds to the fall of the
rank.
The changes of the ranks in the years 2008-2009 compared to
pre-crisis year of 2006 are supposed to indicate the industries most and
least affected by the economic crisis. The further dynamics of the
ranks, particularly in 2011, allow determining the industries
characterised by the fastest and the slowest after-crisis recovery, also
indicate the changes of the ranking during the whole period of the
research (2006-2011).
The most affected by the economic crisis industries are considered
to be L68 Real estate activities (significant fall of the rank from the
6th in 2006 to the 66th in 2008-2009); G45 Wholesale and retail trade
and repair of motor vehicles and motorcycles, H53 Postal and courier
activities, H49 Land transport and transport via pipelines - three
industries falling by 20 or more positions in the ranking during the
crisis; F43 Specialised construction activities, F41 Construction of
buildings, N82 Office administrative, office support and other business
support activities, M69 Legal and accounting activities, B08 Other
mining and quarrying, C23 Manufacture of other non-metallic mineral
products, J61 Telecommunications - all falling by 15-19 positions in the
industries' ranking in 2008-2009 compared to 2006.
The least affected by the crisis industries are H51 Air transport,
R93 Sports activities and amusement and recreation activities, C26
Manufacture of computer, electronic and optical products, C20
Manufacture of chemicals and chemical products, C31 Manufacture of
furniture, C10 Manufacture of food products--all experiencing the rise
of the rank by at least 20 positions during the crisis compared to 2006;
also, Q86 Human health activities, M74 Other professional, scientific
and technical activities, A03 Fishing and aquaculture, N78 Employment
activities, C30 Manufacture of other transport equipment, M72 Scientific
research and development--rising by 10 or more positions in the ranking.
By comparing the ranks of 2011 (post-crisis period) to 2008-2009
(the years of the deepest crisis) industries' after-crisis recovery
is analysed. The fastest recovery, considering the industries
significantly affected by the crisis, appeared in I56 Food and beverage
service activities, G45 Wholesale and retail trade and repair of motor
vehicles and motorcycles, M69 Legal and accounting activities, N82
Office administrative, office support and other business support
activities, L68 Real estate activities and H49 Land transport and
transport via pipelines. On the other hand the list of crisis-affected
industries, which even worsened their relative position comparing 2011
to 2008-2009, includes C33 Repair and installation of machinery and
equipment, C23 Manufacture of other non-metallic mineral products, C15
Manufacture of leather and related products and F41 Construction of
buildings.
Considering the whole period of the research (2006-2011), which
includes pre-crisis, crisis and post-crisis years, the main changes in
the ranking of Lithuanian industries due to the recent economic cycles
are further indicated. The most appreciable improvement of the rank is
noticed to be in R93 Sports activities and amusement and recreation
activities (+63 positions), C20 Manufacture of chemicals and chemical
products (+42), C13 Manufacture of textiles (+33), C26 Manufacture of
computer, electronic and optical products (+31), C31 Manufacture of
furniture (+28), C14 Manufacture of wearing apparel (+27) and M74 Other
professional, scientific and technical activities (+26); while a
significant fall of the rank is determined in H50 Water transport (-60),
L68 Real estate activities (-44), C33 Repair and installation of
machinery and equipment (-36), C23 Manufacture of other non-metallic
mineral products (-33), J60 Programming and broadcasting activities
(-30) and F42 Civil engineering (-26).
Finally, the average ranks of the industries in the period of
2006-2011 are compared, identifying the best and worst performing
industries during the recent economic cycles. The top industries
according to their average ranks are N81 Services to buildings
and-landscape activities, B06 Extraction of crude petroleum and natural
gas, M70 Activities of head offices; management consultancy activities,
A02 Forestry and logging, E36 Water collection, treatment and supply,
M75 Veterinary activities, G47 Retail trade, except of motor vehicles
and motorcycles and N79 Travel agency, tour operator reservation service
and related activities, J63 Information service activities and N78
Employment activities; while the worst performing industries are
supposed to be F41 Construction of buildings, C16 Manufacture of wood
and of products of wood and cork, except furniture; manufacture of
articles of straw and plaiting materials, S96 Other personal service
activities, C15 Manufacture of leather and related products, C13
Manufacture of textiles, J59 Motion picture, video and television
programme production, sound recording and music publishing activities,
C27 Manufacture of electrical equipment, C17 Manufacture of paper and
paper products, N77 Rental and leasing activities and C22 Manufacture of
rubber and plastic products.
Conclusions
The paper presents the empiric research on the impact of the
economic crisis of 2008 on Lithuanian industries. The research has
involved 68 industries, while the crisis effect has been evaluated on
the basis of the system of 10 financial state and performance indicators
belonging to four main groups of enterprise financial ratios:
profitability, liquidity, solvency and asset turnover.
According to the research methodology, considering the integrated
approach to industry as a complex phenomenon, the problem of complex
evaluation of the economic crisis impact has been formalised as the
comparative quantitative assessment of the industries (alternatives for
evaluation) with regards to the chosen financial state and performance
indicators (evaluation criteria). Multi-criteria decision making methods
SAW, TOPSIS and VIKOR, widely applied in the recent research for
evaluating complex economic phenomena, have been chosen as the tool for
evaluation. Considering low correlation of the results between SAW and
VIKOR, the latter MCDM method has been rejected, with ultimate ranks
being the average of SAW and TOPSIS.
By analysing the changes of the ranks in 2008-2009 compared to
pre-crisis year of 2006, the industries most and least affected by the
economic crisis have been indicated. Furthermore, the ranks of
post-crisis year of 2011 have been compared to 2008-2009, and the
industries characterised by the fastest and the slowest after-crisis
recovery have been identified.
Considering the whole period of the research (2006-2011), which
includes pre-crisis, crisis and post-crisis years, the most improved
industries, as well as the ones with the deepest fall of the rank, have
been determined. Finally, the average ranks of the industries during the
period of 2006-2011 have been compared identifying the industries being
on the top and in the bottom of the list according to their performance
indicators.
The results of the research from the practical point of view might
be useful for potential investors while choosing the particular
industries or enterprises for long-term investment, also for government
authorities involved in forming and implementing economic policy. For
other researchers the approach and methodology of the research might
seem interesting, as well as the results obtained.
doi: 10.3846/16111699.2013.867277
Received 13 July 2013; accepted 05 November 2013
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Algirdas Krivka
Department of Economics and Management of Enterprises, Vilnius
Gediminas Technical University, Sauletekio
al. 11, LT-10223 Vilnius, Lithuania
E-mail: algirdas.krivka@vgtu.lt
Algirdas KRIVKA was born in 1982 in Lithuania. In 2004 he received
a Bachelor of Economics, in 2006--Master of Economics (Finance
specialization), in 2010--Doctor of Social Sciences (Economics).
Associate Professor at the Department of Economics and Management of
Enterprises, Faculty of Business Management, Vilnius Gediminas Technical
University since 2011. Research interests: market structures, industry
analysis, oligopoly, competitive strategies.
Table 1. Financial state and performance indicators selected
for the research and their formulas
No Indicators Formulas
Group A. Profitability indicators
1 Gross margin ratio Gross profit/Sales revenues
2 Return on sales (ROS) Net profit/Sales revenues
3 Return on assets (ROA) Net profit/Total assets
4 Return on equity (ROE) Net profit/Equity
Group B. Liquidity indicators
5 Current ratio Current assets/Current
liabilities
6 Quick ratio (Current assets--Inventory)/
Group C. Solvency indicators Current liabilities
7 Equity-to-debt ratio Equity / Total liabilities
8 Debt ratio Total liabilities / Total assets
Group D. Asset turnover indicators
9 Total asset turnover Sales revenues / Average total
assets
10 Accounts receivable turnover Sales revenues / Average
accounts receivable
Table 2. The scheme of multi-criteria assessment of Lithuanian
industries with regards to financial state and performance
indicators
Criteria
No Description Max (+)/ Weight Industry
Min (-) 1
1 Gross margin ratio + [[omega].sub.1] [r.sub.1,1]
2 Return on sales + ... ...
(ROS)
3 Return on assets + ... ...
(ROA)
4 Return on equity + ... ...
(ROE)
5 Current ratio + ... ...
6 Quick ratio + [[omega].sub.1] [r.sub.i,1]
7 Equity-to-debt + ... ...
ratio
8 Debt ratio - ... ...
9 Total asset + ... ...
turnover
10 Accounts + [[omega].sub.10] [r.sub.10,1]
receivable
turnover
Criteria Criteria values
No Description ... Industry ... Industry
j 68
1 Gross margin ratio ... [r.sub.1,j] ... [r.sub.1,68]
2 Return on sales ... ...
(ROS)
3 Return on assets ... ...
(ROA)
4 Return on equity ... ...
(ROE)
5 Current ratio ... ...
6 Quick ratio ... [r.sub.ij] ... [r.sub.1,68]
7 Equity-to-debt ... ...
ratio
8 Debt ratio ... ...
9 Total asset ... ...
turnover
10 Accounts ... [r.sub.10,j] ... [r.sub.10,68]
receivable
turnover
Table 3. Evaluation criteria weights based on expert estimates
Evaluation criteria Experts and criteria weights
No Description 1 2 3 4 5
1 Gross margin ratio 0.053 0.060 0.080 0.063 0.060
2 Return on sales
(ROS) 0.140 0.090 0.160 0.088 0.150
3 Return on assets
(ROA) 0.018 0.075 0.040 0.038 0.030
4 Return on equity
(ROE) 0.140 0.075 0.120 0.063 0.060
5 Current ratio 0.060 0.120 0.060 0.100 0.080
6 Quick ratio 0.090 0.180 0.090 0.150 0.120
7 Equity-to-debt
ratio 0.090 0.100 0.150 0.210 0.110
8 Debt ratio 0.210 0.100 0.150 0.140 0.090
9 Total asset
turnover 0.140 0.080 0.105 0.090 0.240
10 Accounts
receivable
turnover 0.060 0.120 0.045 0.060 0.060
Totals 1.000 1.000 1.000 1.000 1.000
Evaluation criteria Experts and criteria weights
No Description 6 7 8 9 Average
1 Gross margin ratio 0.160 0.098 0.075 0.140 0.081
2 Return on sales
(ROS) 0.040 0.338 0.105 0.420 0.139
3 Return on assets
(ROA) 0.100 0.005 0.045 0.035 0.044
4 Return on equity
(ROE) 0.100 0.049 0.075 0.105 0.085
5 Current ratio 0.060 0.004 0.140 0.128 0.078
6 Quick ratio 0.090 0.006 0.210 0.023 0.117
7 Equity-to-debt
ratio 0.090 0.050 0.098 0.050 0.112
8 Debt ratio 0.060 0.050 0.053 0.050 0.107
9 Total asset
turnover 0.180 0.320 0.140 0.035 0.162
10 Accounts
receivable
turnover 0.120 0.080 0.060 0.015 0.076
Totals 1.000 1.000 1.000 1.000 1.000
Table 4. Correlation of the results of multi-criteria
evaluation
TOPSIS VIKOR
SAW 0.923 -0.618
Table 5. The ultimate ranks of the industries and their changes
compared to 2006
Ranking
Industries Ultimate ranks
2006 2007 2008 2009 2010 2011
1 2 3 4 5 6 7
A02 Forestry and logging 6 4 10 5 4 5
A03 Fishing and 41 60 20 35 61 62
aquaculture
B06 Extraction of crude 2 5 2 2 2 4
petroleum and natural gas
B08 Other mining and 15 14 15 45 48 32
quarrying
C10 Manufacture of food 62 44 52 28 34 50
products
C11 Manufacture of 25 26 24 24 47 44
beverages
C13 Manufacture of textiles 65 65 65 54 53 32
C14 Manufacture of 48 50 48 38 36 21
wearing apparel
C15 Manufacture of leather 43 67 53 58 58 65
and related products
C16 Manufacture of wood 63 53 68 62 55 59
and of products of wood
and cork, except furniture;
manufacture of articles of
straw and plaiting
materials
C17 Manufacture of paper 55 58 62 55 42 55
and paper products
C18 Printing and 39 59 52 43 44 30
reproduction of recorded
media
C20 Manufacture of 51 38 28 20 12 9
chemicals and chemical
products
C21 Manufacture of basic 35 16 36 24 11 25
pharmaceutical products and
pharmaceutical preparations
C22 Manufacture of rubber 52 52 59 54 51 54
and plastic products
C23 Manufacture of other 28 28 36 49 56 61
non-metallic mineral
products
C24 Manufacture of basic 39 59 33 52 63 44
metals
C25 Manufacture of 50 46 51 45 51 46
fabricated metal products,
except machinery and
equipment
C26 Manufacture of 68 66 28 38 28 37
computer, electronic and
optical products
C27 Manufacture of 63 60 58 62 46 39
electrical equipment
C28 Manufacture of 36 46 32 27 26 34
machinery and equipment
n.e.c.
C29 Manufacture of motor 8 35 15 8 8 10
vehicles, trailers and
semitrailers
C30 Manufacture of other 32 27 25 18 6 26
transport equipment
C31 Manufacture of 63 35 46 33 36 35
furniture
C32 Other manufacturing 30 49 36 35 30 51
C33 Repair and installation 22 40 36 33 39 58
of machinery and equipment
D35 Electricity, gas, steam 20 18 20 16 23 43
and air conditioning supply
E36 Water collection, 7 8 9 5 5 11
treatment and supply
E38 Waste collection, 53 62 52 42 24 31
treatment and disposal
activities; materials
recovery
F41 Construction of 49 57 65 68 67 68
buildings
F42 Civil engineering 40 47 43 49 42 66
F43 Specialised 37 36 50 61 60 58
construction activities
G45 Wholesale and retail 24 15 50 52 44 29
trade and repair of motor
vehicles and motorcycles
G46 Wholesale trade, except 47 37 45 44 41 48
of motor vehicles and
motorcycles
G47 Retail trade, except of 17 18 17 6 10 11
motor vehicles and
motorcycles
H49 Land transport and 30 37 52 50 42 40
transport via pipelines
H50 Water transport 6 10 8 13 25 66
H51 Air transport 61 28 5 28 64 50
H52 Warehousing and support 37 40 31 28 25 24
activities for
transportation
H53 Postal and courier 22 36 41 49 54 37
activities
I55 Accommodation 48 32 68 38 65 45
I56 Food and beverage 39 34 63 36 42 17
service activities
J58 Publishing activities 48 46 48 36 46 54
J59 Motion picture, video 60 57 56 65 39 55
and television programme
production, sound recording
and music publishing
activities
J60 Programming and 13 15 6 24 23 43
broadcasting activities
J61 Telecommunications 4 21 20 17 18 18
J62 Computer programming, 29 25 23 46 26 22
consultancy and related
activities
J63 Information service 11 10 11 17 17 16
activities
L68 Real estate activities 6 28 66 66 64 50
M69 Legal and accounting 20 32 40 31 33 17
activities
M70 Activities of head 8 1 5 4 4 2
offices; management
consultancy activities
M71 Architectural and 33 36 30 29 27 29
engineering activities;
technical testing and
analysis
M72 Scientific research and 20 14 10 11 17 36
development
M73 Advertising and market 42 27 41 50 52 42
research
M74 Other professional, 62 54 49 48 57 36
scientific and technical
activities
M75 Veterinary activities 11 13 13 12 9 6
N77 Rental and leasing 54 47 49 68 67 39
activities
N78 Employment activities 25 11 15 10 10 12
N79 Travel agency, tour 14 10 21 9 16 11
operator reservation
service and related
activities
N80 Security and 16 23 17 13 19 36
investigation activities
N81 Services to buildings 2 2 3 1 1 1
and landscape activities
N82 Office administrative, 25 32 37 47 26 24
office support and other
business support activities
P85 Education 17 18 20 13 13 15
Q86 Human health activities 35 17 21 21 14 16
R90 Creative, arts and 59 32 60 55 38 46
entertainment activities
R93 Sports activities and 67 68 2 57 59 4
amusement and recreation
activities
S95 Repair of computers and 49 45 38 29 28 36
personal and household
goods
S96 Other personal service 57 62 52 53 63 66
activities
Ranking
Industries Rank absolute changes
compared to 2006
2007 2008 2009 2010 2011
1 8 9 10 11 12
A02 Forestry and logging 2 -4 1 2 1
A03 Fishing and -19 21 6 -20 -21
aquaculture
B06 Extraction of crude -3 0 0 0 -2
petroleum and natural gas
B08 Other mining and 1 0 -30 -33 -17
quarrying
C10 Manufacture of food 18 10 34 28 12
products
C11 Manufacture of -1 1 1 -22 -19
beverages
C13 Manufacture of textiles 0 0 11 12 33
C14 Manufacture of -2 0 10 12 27
wearing apparel
C15 Manufacture of leather -24 -10 -15 -15 -22
and related products
C16 Manufacture of wood 10 -5 1 8 4
and of products of wood
and cork, except furniture;
manufacture of articles of
straw and plaiting
materials
C17 Manufacture of paper -3 -7 0 13 0
and paper products
C18 Printing and -20 -13 -4 -5 9
reproduction of recorded
media
C20 Manufacture of 13 23 31 39 42
chemicals and chemical
products
C21 Manufacture of basic 19 -1 11 24 10
pharmaceutical products and
pharmaceutical preparations
C22 Manufacture of rubber 0 -7 -2 1 -2
and plastic products
C23 Manufacture of other 0 -8 -21 -28 -33
non-metallic mineral
products
C24 Manufacture of basic -20 6 -13 -24 -5
metals
C25 Manufacture of 4 -1 5 -1 4
fabricated metal products,
except machinery and
equipment
C26 Manufacture of 2 40 30 40 31
computer, electronic and
optical products
C27 Manufacture of 3 5 1 17 24
electrical equipment
C28 Manufacture of -10 4 9 10 2
machinery and equipment
n.e.c.
C29 Manufacture of motor -27 -7 0 0 -2
vehicles, trailers and
semitrailers
C30 Manufacture of other 5 7 14 26 6
transport equipment
C31 Manufacture of 28 17 30 27 28
furniture
C32 Other manufacturing -19 -6 -5 0 -21
C33 Repair and installation -18 -14 -11 -17 -36
of machinery and equipment
D35 Electricity, gas, steam 2 0 4 -3 -23
and air conditioning supply
E36 Water collection, -1 -2 2 2 -4
treatment and supply
E38 Waste collection, -9 1 11 29 22
treatment and disposal
activities; materials
recovery
F41 Construction of -8 -16 -19 -18 -19
buildings
F42 Civil engineering -7 -3 -9 -2 -26
F43 Specialised 1 -13 -24 -23 -21
construction activities
G45 Wholesale and retail 9 -26 -28 -20 -5
trade and repair of motor
vehicles and motorcycles
G46 Wholesale trade, except 10 2 3 6 -1
of motor vehicles and
motorcycles
G47 Retail trade, except of -1 0 11 7 6
motor vehicles and
motorcycles
H49 Land transport and -7 -22 -20 -12 -10
transport via pipelines
H50 Water transport -4 -2 -7 -19 -60
H51 Air transport 33 56 33 -3 11
H52 Warehousing and support -3 6 9 12 13
activities for
transportation
H53 Postal and courier -14 -19 -27 -32 -15
activities
I55 Accommodation 16 -20 10 -17 3
I56 Food and beverage 5 -24 3 -3 22
service activities
J58 Publishing activities 2 0 12 2 -6
J59 Motion picture, video 3 4 -5 21 5
and television programme
production, sound recording
and music publishing
activities
J60 Programming and -2 7 -11 -10 -30
broadcasting activities
J61 Telecommunications -17 -16 -13 -14 -14
J62 Computer programming, 4 6 -17 3 7
consultancy and related
activities
J63 Information service 1 0 -6 -6 -5
activities
L68 Real estate activities -22 -60 -60 -58 -44
M69 Legal and accounting -12 -20 -11 -13 3
activities
M70 Activities of head 7 3 4 4 6
offices; management
consultancy activities
M71 Architectural and -3 3 4 6 4
engineering activities;
technical testing and
analysis
M72 Scientific research and 6 10 9 3 -16
development
M73 Advertising and market 15 1 -8 -10 0
research
M74 Other professional, 8 13 14 5 26
scientific and technical
activities
M75 Veterinary activities -2 -2 -1 2 5
N77 Rental and leasing 7 5 -14 -13 15
activities
N78 Employment activities 14 10 15 15 13
N79 Travel agency, tour 4 -7 5 -2 3
operator reservation
service and related
activities
N80 Security and -7 -1 3 -3 -20
investigation activities
N81 Services to buildings 0 -1 1 1 1
and landscape activities
N82 Office administrative, -7 -12 -22 -1 1
office support and other
business support activities
P85 Education -1 -3 4 4 2
Q86 Human health activities 18 14 14 21 19
R90 Creative, arts and 27 -1 4 21 13
entertainment activities
R93 Sports activities and -1 65 10 8 63
amusement and recreation
activities
S95 Repair of computers and 4 11 20 21 13
personal and household
goods
S96 Other personal service -5 5 4 -6 -9
activities
