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Chapter 6
Is There a Relation Between HDI
and Economic Performances?
Efehan Ulas and Burak Keskin
Abstract The aim of this study is to compare the economic performances of 20
selected countries, in terms of their growth rate, for the period 2010–2014. We
include economic performance indicators as growth rate, GDP per capita, youth
unemployment, inflation rate, and account balance. Firstly, AHP is used for the
determination of the weights for indicators. Then, ranking is carried out with
TOPSIS analyses. Results show that the best performance is showed by Germany.
In addition, mean HDI (Human Development Index) scores for the period 2010–
2014 are calculated and countries are ranked. To understand the effect of HDI on
economic performances, we investigated the relationship between these two find-
ings with Spearman’s rank correlation coefficient. It is clear that there is a positive
correlation between HDI and economic performances.
Keywords TOPSIS AHP Economic performance HDI
6.1 Introduction
In last decades, many countries have achieved varying levels of economic devel-
opments. Furthermore, the development rate changes over time. For instance, the
rate is decreased if the country is developed. However, other factors are important
to understand the economic performance in such countries. Main indicators of
improvement play an important role for ranking in economic performance in
countries. In many countries, the conduct of a nation’s economic policy usually has
four goals: unemployment, trade balance, GDP per capita, and inflation. The
Organization for Economic Cooperation and Development (OECD) generally used
E. Ulas (&)
Department of Statistics, Cankiri Karatekin University, Cankiri, Turkey
e-mail: ef_ulas@hotmail.com; efehanulas@karatekin.edu.tr
B. Keskin
Department of Business Administration, Cankiri Karatekin University, Cankiri, Turkey
e-mail: burakkeskiin@gmail.com; burakkeskin@karatekin.edu.tr
©Springer International Publishing AG 2017
D. Procházka (ed.), New Trends in Finance and Accounting,
Springer Proceedings in Business and Economics,
DOI 10.1007/978-3-319-49559-0_6
61
these indicators for calculating economic performances of countries. Economic
growth has impact on nation’s voice on an international scale as well as competes
with the other nations in other fields such as military, politics, international treaty,
and so on.
The aim of this study is to deliver a report for economic performance in 20
European countries between the years 2010–2014. Our study utilises the Okun’s
Misery Index, which comprises of unemployment rate and inflation rate.
Furthermore, we consider OECD’s magic diamond measures which are growth of
GDP, inflation rate, unemployment rate, and the GDP-normalized trade balance.
Five different indicators are used in this study to evaluate economic performance for
each nation. GDP per capita is considered as one of the key indicators which is
reflecting a society’s welfare and improvement of global competitiveness. Youth
unemployment has impact on nation’s economic growth. Inflation as measured by
the consumer price index reflects the annual percentage change in the cost to the
average consumer of acquiring a basket of goods and services that may be fixed or
changed at specified intervals, such as yearly. So that it is one of the important
factors that affect economic performance. We also consider current account balance
and growth rate for economic performance ranking.
In this study, the weights of indicators are determined with analytic hierarchy
process (AHP) between the years 2010–2014. Economic performances are calcu-
lated and ranked using the method called Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) approach. Furthermore, results will be
compared with mean of Human Development Index (HDI) for the years
2010–2014.
6.2 Literature Review
There are many studies in the literature-related economic performances. Some
studies had focused on economic growth rates of developing–developed countries,
and some of them had focused on region of nations. Literature review on economic
performances of different countries is as follows:
Ashourian (2012) used multiple attribute decision making (MADM) method to
study the rank of performance of selected Middle East and North Africa (MANA)
countries. This approach is a management science technique that is popularly used
to rank indicated that the MENA countries achieved higher values of desirable
attributes.
Cam et al. (2015) analyzed financial performances of textile firms publicly traded
in The Borsa Istanbul via TOPSIS method by using financial rates in 2010–2013.
They calculated rates for financial performance and measurements have been con-
verted to one single score through TOPSIS method and firms have been ranked.
After, in the next step, the TOPSIS ranking score is compared with the ranking using
traditional performance indicators. They found that TOPSIS performance scores
62 E. Ulas and B. Keskin
show variability in the term and do not match with rankings which are done using
traditional performance measurement.
Chattopadhyay and Bose (2015) studied composite indicator design to sum-
marize in a single statistic variety of different facets of macroeconomic performance
and assessed relative performances of countries with six different indicators which
are the growth rate of real GDP, real per capita GDP, unemployment rate, fiscal
balance, rate of inflation, and current account balance. They first calculated the
performance scores obtained from TOPSIS method and the country rankings in
each year according to those scores. Then, rankings are summarized in terms of
measures which together depict the degree of stability or disarray in country-wise
rankings over the years. Correspondence analysis (CA) is applied for providing a
graphical display of the status of rankings elegantly which summarizes the
cross-sectional variability in the relative positions of countries.
Mandic et al. (2014) proposed a multi-criteria fuzzy model that eases financial
performance where TOPSIS approach is used to rank the banks and they used the
data collected from Serbian banks between the years 2005 and 2010.
Bao et al. (2010) investigated the application of the classical TOPSIS method,
for which crisp numerical values replace the linguistic assessments that might face
some practical problems because of vague judgment. They use the TOPSIS
approach proposed to make a more rational decision.
Gustav (2004) discussed that human development may be a necessary prereq-
uisite for long-term sustainable growth. It may exhibit threshold effects, in the sense
that nations must attain a certain HD level before future economic growth becomes
sustainable.
6.3 Methodology
Analytic hierarchy process (AHP) and TOPSIS method are applied to the selected
indicators to evaluate economic performance of nations between the years 2010 and
2014. The countries are selected from their growth rate. 20 European countries
which have the highest GDP rates are included in this study. AHP is used to
determine weights of criteria that are subsequently inserted to weighted decision
matrix in TOPSIS approach. The implementation is carried out with R statistical
software and Excel Solver.
6.3.1 Analytic Hierarchical Process (AHP)
In this study, AHP approach is used to determine weighting of the components of
the economic performance. Table 6.1 presents possible classification of the strength
of preferences. This nine fundamental scale is introduced by Saaty and Alexander in
1981. The calculation procedure is clearly explained by Wabalickis (1987). Firstly,
6 Is There a Relation Between HDI and Economic Performances? 63
each element of the pairwise comparison matrix is divided by the sum of the
column. Then, obtained new matrix rows are summed and mean of the rows is
taken. Same process is repeated for each column and final matrix is found as
weighted matrix. This process is done for countries and indicators separately. Then,
final weighted matrix is obtained by multiplying country and indicator matrix.
6.3.2 TOPSIS
After weights are calculated with AHP approach, TOPSIS is used to rank the
economic performances of nations. It is a distance-based approach and TOPSIS is a
method that is used for multi-attribute decision making by ranking the alternatives
according to the closest between the alternative and ideal. TOPSIS has four
assumptions (Opricovic and Tzend 2004);
1. A quantitative value, xi, can be assigned to each criterion, Ki, representing its
importance.
2. For each Ki, a quantitative value, Vij , can be assigned to each alternative, aj,
representing the performance of ajon the basis of Ki.
3. The performance of each alternative relative to each criterion can be evaluated
on the basis of a common scale.
4. The criteria are different and mutually independent.
Table 6.1 The fundamental AHP scale
Intensity of
importance
Definition Explanation
1 Equal importance Two activities contribute equally
3 Moderate importance Experience and judgement slightly
favor
5 Strong importance Experience and judgement strongly
favor
7 Very strong importance An activity is strongly favored
9 Absolute importance Highest possible order
2, 4, 6, 8 Intermediate values When compromise is needed
Reciprocals If activity ihas one of the above
numbers assigned to it when compared
with activity, then jhas the reciprocal
value when compared with i
Rationals Ratios arising from the scale If consistency were to be forced by
obtaining a numerical values to
span the matrix
Source Saaty (1987)
64 E. Ulas and B. Keskin
The computational procedure of TOPSIS method is provided by Mahmoodzadeh
et al. (2007) as follows:
1. An n×qmatrix contains the raw consequence data for all alternatives against
all criteria.
2. Vector normalization: Each ajis assigned a quantitative value, each Kirepre-
senting the performance of aj:
V
_
IJ ¼Vij
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pn
i¼1V2
ij
qð6:1Þ
3. Assign weights to criteria:
X
n
i¼1
xi¼1:ð6:2Þ
4. Define the ideal and anti-ideal point: Set the ideal point, aþ, and anti-ideal
point, a. For a benefit criterion cj,vjðaþÞ¼max vi
jand vjðaÞ= min vi
j; for a
cost criterion, ck,vjðaÞ= max vi
jand vjðaþÞ= min vi
j.
5. Calculate the separation measures using Euclidean distance, where UðvijÞis a
weighted value:
dþ
j¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
n
1
Uv
ij
Uþ
i
Uv
ij
Uþ
i
sð6:3Þ
d
j¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
n
1
Uv
ij
U
i
Uv
ij
U
i
sð6:4Þ
6. Calculate the relative closeness to the ideal solution. The overall value, UðajÞ,of
each ajis calculated using the following:
UðajÞ¼
dþ
j
dþ
jd
j
ð6:5Þ
6.4 Implementation and Results
The dataset is obtained directly from World Bank Web site for the period 2010–
2014. So that dataset which is used in this study is reliable. Some countries are
eliminated from the dataset due to their growth rate because first 20 countries with
the highest growth rate are included in this study. The analysis was carried out with
the statistical software R and Excel. The weights are calculated with AHP approach.
6 Is There a Relation Between HDI and Economic Performances? 65
Then, ranking is done with TOPSIS method. Five important indicators that are
affecting the economic performances are selected. The selected indicators are GDP
per capita, youth unemployment, inflation, account balance, and growth rate.
Indicators were selected based on the survey literature mentioned at the previous
section.
The weights and descriptive statistics for indicators can be seen in the Table 6.2.
Table 6.3 gives the mean scores of countries for the years 2010–2014. The
analysis was carried out with these mean scores. The first 20 countries with the
highest growth rate in Europe are included in this study. Countries included in this
paper can be seen in the table below. AHP approach is used to determine weights
for indicators. Calculated weights for each indicator are given in Table 6.1. These
Table 6.2 Descriptive statistics
Growth
rate
Youth
unemployment
Inflation GDP per
capita
Account
balance
Weights 0.2833 0.1806 0.2024 0.1826 0.1511
Mean 0.2 21.88 1.74 47.80 16.51
S.D 1.7278 12.3130 0.7924 19.2604 63.3008
Max 3.16 51.78 4.10 97.09 239
Min −4.89 8.26 0 21.90 −99.62
Table 6.3 Mean scores of countries between 2010 and 2014
Country Growth rate Youth
unemployment
Inflation GDP per
capita
Current
balance
1. England 1.96 19.62 2.92 41.43 −99.62
2. France 1.01 23.18 1.39 43.51 −32.48
3. Germany 2.01 8.32 1.52 46.57 239.0
4. Luxembourg 3.16 17.00 2.14 75.70 3.30
5. Netherland 0.51 9.58 1.91 52.90 84.53
6. Spain −0.78 51.78 1.74 30.42 −20.42
7. Italy −0.54 35.18 1.76 36.21 −18.06
8. Slovenia 0.23 18.92 1.64 23.80 1.37
9. Denmark 0.54 13.58 1.76 61.21 20.51
10. Switzerland 1.91 8.26 0.00 82.34 63.40
11. Norway 1.45 8.80 1.71 97.09 57.81
12. Austria 1.23 8.80 2.23 49.95 6.34
13. Sweden 2.39 23.84 0.96 58.32 38.52
14. Belgium 1.20 21.50 2.00 47.26 1.25
15. Ireland 1.95 28.00 0.80 44.20 7.20
16. Portugal −0.84 32.90 1.57 21.90 −7.82
17. Finland 0.52 19.18 1.99 49.08 −0.33
18. Greece −4.89 48.62 1.46 24.80 −12.28
19. Cyprus 0.96 18.32 1.68 19.08 −3.06
20. Iceland 2.97 25.30 2.33 13.22 −19.62
21. World 2.81 13.86 3.45 10.21 15.71
66 E. Ulas and B. Keskin
scores are 0.2833, 0.1806, 0.2024, 0.1826, and 0.1511 for growth rate, youth
unemployment, inflation, GDP per capita, and account balance, respectively. The
mean score for “world”is added to data for the purpose of comparison.
After weights are calculated, the decision matrix is created and normalized
decision matrix is obtained from formula (6.1). Then, the normalized values are
multiplied by weights and weighted normalized decision matrix is obtained which
is shown in Table 6.3. Furthermore, positive ideal and negative ideal scores are
calculated. Maximum value in each column of matrix is selected for positive ideal
set and minimum value in each column of matrix is selected for negative ideal set.
Positive Ideal: {0.0962, 0.0147, 0.000, 0.0632, 0.1225}
Negative Ideal: {−0.1489, 0.0922, 0.1197, 0.0143, −0.0511} (Table 6.4).
Separation measures from the ideal positive and ideal negative for each country
are calculated. Formula (6.5) is used for calculating relative closeness. Ranking of
countries in terms of their economic performance can be seen in Table 6.5.
Germany is found as the country that shows the best economic performance.
Table 6.4 Weighted normalized decision matrix of countries
Country Growth rate Youth unemployment Inflation GDP per capita Current balance
1. England 0.0597 0.0349 0.0853 0.0270 −0.0511
2. France 0.0308 0.0413 0.0406 0.0283 −0.0167
3. Germany 0.0612 0.0148 0.0444 0.0303 0.1225
4. Luxembourg 0.0962 0.0303 0.0625 0.0493 0.0017
5. Netherland 0.0155 0.0171 0.0558 0.0344 0.0433
6. Spain −0.0238 0.0922 0.0508 0.0198 −0.0105
7. Italy −0.0164 0.0627 0.0514 0.0236 −0.0095
8. Slovenia 0.0070 0.0337 0.0479 0.0155 0.0007
9. Denmark 0.0164 0.0242 0.0514 0.0398 0.0105
10. Switzerland 0.0582 0.0147 0.0000 0.0536 0.0325
11. Norway 0.0442 0.0157 0.0499 0.0632 0.0296
12. Austria 0.0375 0.0157 0.0651 0.0325 0.0033
13. Sweden 0.0728 0.0425 0.0280 0.0380 0.0197
14. Belgium 0.0365 0.0383 0.0584 0.0308 0.0006
15. Ireland 0.0594 0.0499 0.0234 0.0288 0.0037
16. Portugal −0.0256 0.0586 0.0458 0.0143 −0.0040
17. Finland 0.0158 0.0342 0.0581 0.0319 −0.0002
18. Greece −0.1489 0.0866 0.0426 0.0161 −0.0063
19. Cyprus −0.0509 0.0487 0.0368 0.0189 −0.0007
20. Iceland 0.0323 0.0235 0.1197 0.0262 0.0000
21. World 0.0856 0.0247 0.0604 0.0066 0.0081
6 Is There a Relation Between HDI and Economic Performances? 67
Switzerland, Sweden, Norway, and Luxembourg are followed, respectively. If we
include world as a country in this study, it takes the rank of 4th place. In order to
make comparison for economic performances, Human Development Index values
are ranked and compared with economic performance ranking which is found in
this study.
Human development has important effects on economic growth. More specifi-
cally, each of the components of human development is likely to have a distinct
impact on economic growth. Thus, to understand its effect on economic perfor-
mances, we compared mean HDI scores with economic performances found using
TOPSIS analysis. Mean scores of HDI values for 2010–2014 can be seen in
Table 6.6. The first 20 countries in ranking of economic performances and the first
20 countries which get the highest HDI scores are compared. To see the correlation
between HDI and economic performances, Spearman’s rank correlation is calcu-
lated. Correlation coefficient is found as 0.804 and p-value is 1.09e−05. It is clear
that there is a strong correlation between HDI and economic performances. Also,
we observed that 8 countries which are Germany, Switzerland, Sweden, Norway,
Ireland, Netherland, Belgium, and Denmark appeared in the first 10 countries for
both HDI ranking and economic performance ranking.
Table 6.5 Topsis S+, S−,C*
scores Country S+ S- C* Ranking
1. England 0.2012 0.2194 0.5217 14
2. France 0.1650 0.2062 0.5555 11
3. Germany 0.0654 0.2936 0.8178 1
4. Luxembourg 0.1376 0.2668 0.6597 5
5. Netherland 0.1293 0.2147 0.6241 7
6. Spain 0.2063 0.1486 0.4188 18
7. Italy 0.1914 0.1578 0.4518 16
8. Slovenia 0.1665 0.1886 0.5311 13
9. Denmark 0.1490 0.2027 0.5764 10
10. Switzerland 0.0982 0.2678 0.7317 2
11. Norway 0.1176 0.2386 0.6698 4
12. Austria 0.1512 0.2165 0.5887 8
13. Sweden 0.1154 0.2561 0.6895 3
14. Belgium 0.1531 0.2098 0.5781 9
15. Ireland 0.1358 0.2401 0.6387 6
16. Portugal 0.1931 0.1550 0.4453 17
17. Finland 0.1620 0.1929 0.5435 12
18. Greece 0.2930 0.0893 0.2336 20
19. Cyprus 0.2032 0.1447 0.4160 19
20. Iceland 0.1868 0.2007 0.5180 15
68 E. Ulas and B. Keskin
6.5 Conclusion
In this study, economic performances of 20 European countries for the period
2010–2014 are analyzed. Indicators used to measure the economic performances of
those countries have been identified. Indicators that are included in these analyses
are growth rate, youth unemployment, inflation, GDP per capita, and account
balance. The weights are determined with the criteria by using AHP. Weights for
growth rate, youth unemployment, inflation, GDP per capita, and account balance
are found as 0.2833, 0.1806, 0.2024, 0.1826, and 0.1511, respectively.
Furthermore, TOPSIS is used to evaluate ranking of 20 European countries. Highest
and lowest economic performance rank is highlighted.
After mean HDI scores are calculated and ranked for the years 2010–2014,
ranking of economic performances and HDI scores are compared. We found
association between HDI and economic performance measured by Spearman’s rank
correlation coefficient (value of 0.804). Furthermore, 8 countries appeared in the top
10 countries for both HDI ranking and economic performance ranking. For the
following studies, different indicators and more countries will be analyzed.
Table 6.6 Mean HDI values and rankings for 2010–2014
Country 2010 2011 2012 2013 2014 Mean Ranking
1. England 0.906 0.901 0.901 0.902 0.907 0.903 8
2. France 0.881 0.884 0.886 0.887 0.888 0.885 12
3. Germany 0.906 0.911 0.915 0.915 0.916 0.913 5
4. Luxembourg 0.738 0.751 0.756 0.759 0.761 0.889 11
5. Netherland 0.909 0.919 0.92 0.920 0.922 0.918 4
6. Spain 0.867 0.870 0.874 0.874 0.876 0.8722 16
7. Italy 0.869 0.873 0.872 0.873 0.873 0.872 17
8. Slovenia 0.783 0.79 0.795 0.797 0.798 0.878 15
9. Denmark 0.908 0.920 0.921 0.923 0.923 0.92 3
10. Switzerland 0.924 0.925 0.927 0.928 0.93 0.927 2
11. Norway 0.94 0.941 0.942 0.942 0.944 0.942 1
12. Austria 0.879 0.881 0.884 0.884 0.885 0.882 13
13. Sweden 0.901 0.903 0.904 0.905 0.907 0.904 7
14. Belgium 0.883 0.886 0.899 0.888 0.89 0.889 10
15. Ireland 0.908 0.909 0.91 0.912 0.916 0.911 6
16. Portugal 0.819 0.825 0.827 0.828 0.830 0.826 20
17. Finland 0.878 0.881 0.882 0.882 0.883 0.881 14
18. Greece 0.866 0.864 0.865 0.863 0.865 0.865 18
19. Cyprus 0.863 0.866 0.867 0.868 0.87 0.85 19
20. Iceland 0.829 0.833 0.838 0.84 0.843 0.897 9
6 Is There a Relation Between HDI and Economic Performances? 69
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