Research Methods and Shipping Project
TABLE OF CONTENTS
Contents
ABSTRACT .................................................................................................................................. 2
INTRODUCTION ......................................................................................................................... 3
1. LITERATURE REVIEW.............................................................................................................. 4
2. METHODOLOGY ..................................................................................................................... 6
3. DATA ANALYSIS ..................................................................................................................... 7
3.1 Graph analysis. .................................................................................................................... 7
3.2 Descriptive statistics. ........................................................................................................... 8
3.3 Correlation. .......................................................................................................................... 8
3.4 Regression. .......................................................................................................................... 8
CONCLUSION ........................................................................................................................... 10
LIMITATIONS & SUGGESTIONS ................................................................................................ 11
LIST OF REFERENCES ................................................................................................................ 12
APPENDIXES............................................................................................................................. 14
ABSTRACT
This project identifies to what extent a flourishing economy like China's affects the
Baltic Dry Index, prior, during and after the financial crisis of 2008. The aims of the
project are analyzed with the use of MS- Excel 2007, through statistical analysis and
regression, where China's GDP annual percentage growth is the independent
variable and the BDI is the independent variable and all the graphs can be found in
the appendixes. The result of the data analysis indicated that there is a strong
positive correlation between China's annual GDP percentage growth and the BDI
equal to 0.78, and with a relatively high accuracy of r2= 61%. The regression model
was significant at 0.00004, which showed that on average China's GDP annual
percentage growth has an average impact between 420 and 965 units on the BDI
with 95% accuracy, provided that the right model is used. The report accepted both
hypotheses that were made in the methodology section since there is indeed a
positive correlation between China's GDP and BDI. Recommendations for future
projects are the employment of multi regression or a different model, and the
conduction of a similar project for the tanker market .Furthermore, a conduction of a
hybrid project with both quantitative and qualitative data, could be more accurate
since, the market psychology and the political stability, are also important factors
that affect the shipping industry. Limitations of the research, was the insufficiency of
data for the Baltic Capesize Index, since the available data from the previous 4 years
would not be sufficient to conduct a regression analysis, the fee required from
various databases to acquire access to their data and the word count since the
project could be more accurate by performing correlation and regression to all trade
routes to China for the bulk sector, specifically the iron ore and coal dry bulk routes.
2
INTRODUCTION
The shipping industry is a volatile market and its volatility is driven by a plethora of
factors, therefore it may be challenging to make any predictions for the future state
of the market with great accuracy. However, by analyzing both internal and external
factors that affect the shipping industry and taking into consideration that the
market develops in cyclical patterns, decision makers may have a more holistic point
of view, allowing them in that manner to understand at which stage of the shipping
cycle they are and make decisions for the near future accordingly.
In addition, like every other market, the laws of supply and demand apply to
the shipping industry, with the only peculiarity being that the shipping market has a
derived demand. Consequently, the global economy is one of the most important
factors that affect the freight market. By monitoring the fluctuations of the global
economy, it can be observed that the shipping industry follows a similar pattern, but
in a more volatile behaviour due to the fact that is not solely affected by the global
economy. However, there is little to none information on how the fluctuation in the
GDP of a single country solely, can affect the shipping industry in general. To conduct
such an experiment, the candidate country must fit two requirements. Firstly it has
to have a high GDP growth and secondly, it should employ heavily the shipping
industry. Therefore a suitable candidate for this project could be China, since it had a
rapid economic development, and is also the biggest importer, as far as the iron ore
and coal are concerned. Another suitable candidate could be the United States since
the U.S has a higher GDP than China. However China has a higher importing and
exporting activity than the U.S, therefore it will be a more suitable candidate for the
nature of the project.
Thus, the Purpose of this project is to examine to what degree China's gross
domestic product (GDP) is related to the Baltic dry index (BDI), since China by being a
developing country has an increasing importing and exporting behaviour especially
in the dry bulk sector.
3
1. LITERATURE REVIEW
The shipping industry is a unique market for two reasons. Firstly, because of
the segmentation that exists, where changes may affect each market differently or
have no effect at all for a specific market and secondly due to the volatile nature of
the shipping market, a phenomenon which is excessively observed in the dry bulk
sector which has the characteristics of a perfectly competitive market (Thorburn
1960 cited in Thien 2005).
Moreover, the volatile nature of the shipping market is enhanced by the
shipping market cycles, where according to Stopford, they have a 'Darwinian
purpose', because they create a nonviable environment through long periods of
trough, which drives the shipping companies with weak cash flows out of the
shipping market, and leaves the winners to prosper during the profitable periods of
the shipping cycle (Stopford 2009). Predicting the stage that will follow in a shipping
market cycle might be challenging, due to the fact that the demand for shipping
services is affected by many different factors. However, bearing in mind the factors
that affect the shipping industry and correspondingly the freight rates, and also by
being able to identify them when they occur is of the essence because the
shipowners will be able to anticipate how the shipping cycle will develop in the near
feature and act accordingly. As it was aforementioned the global economy is one of
the main factors that affect the shipping industry (Komadina et al 2015). However,
there is a limited amount of research, on how a rapidly growing economy like China's
affects on a standalone basis the demand for shipping services, and in the same
extent the shipping freight rates in the dry bulk market.
China had a sharp economic and industrial growth in the past two decades
becoming in that manner one of the driving forces in the global market. By taking
into consideration, that China had to develop over two decades in order to reach to
the leading position that she is today, it is evident that China had to primarily
undergo a transforming stage, to create the needed environment to develop. This
4
phenomenon is known as the 'China effect', where over the past decade China's
export behaviour has increased rapidly, which is attributable to foreign investments
mostly from the United States, and China's economic growth, utilizing in that way
China's production capacity (Britton and Mark 2016).
Furthermore, China's increasing exporting behaviour augments the demand
for raw materials for the production of refined products, but also for the support of
the industrial development. Thus, there is a strong correlation between China's
economic growth, the imports, and exports both in the Asian and the global shipping
industry in general (Alan and Hyungsuk 2007).
From the Previous stated, it is evident that China has a positive impact on the
shipping industry as far as the transportation of goods is concerned since the steadily
increasing imports to and exports from China increase the ton-mile demanded from
the shipping market. However, Zhang and Tong, through the employment of Vector
auto-regression model in their research, found that the prices of the shipping market
have a causal relationship to China's economy but China's economy does not have a
significant impact to the freight rates in general (Zhang and Tong 2017). Moreover,
Mrs. Kampa supports in her research, that China's demand for iron ore would
constitute to a small increase to the freight rates of the iron ore dry bulk market, but
would not provide any significant profits to the shipowners (Kampa 2011). On the
other hand, Mr. Hyung concluded in his research that there is indeed a positive
correlation between the Chinese economic fluctuations and the BDI, and supported
his findings through the employment of Johansen's multivariate co-integration
model, followed by an error correction model (Hyung 2011).
As far as the coal market, is concerned China appears to buy coal heavily in
bulk when the prices are at the right spot mainly from Australia, but when the
domestic price is lower China prefers the domestic coal market, and in the same
manner the domestic shipping market (Morse and He 2010). However it should be
noted that China as it shifts from coal to environmental friendlier sources of energy,
it is expected that the coal imports will decrease dramatically in the future, thus the
demand for the transportation of coal, is going to be affected heavily since China is
the biggest importer of coal for the past 5 years.
Lastly, Mr. Almkvist in his research, he employed the MIDAS model to find
the co -integration between US GDP and BDI, investigating if the BDI could be an
indicator for the economic growth of a country, since demand could be interpreted
as the economic activity of a country, he then further suggested in his conclusion
that the results of his research could be different if a country with high demand like
was used as a parameter (Alkmvist 2016).
In summary, the existing literature confirms that there is a strong relationship
between the fluctuations of China's GDP and Chinas import and export activities
which indicates that there is a direct connection to the shipping market. However,
there is a debate on what scale China's GDP affects the BDI in general, however by
5
taking into consideration the demand for commodities such as iron ore and coal and
China's exporting behaviour, it is reasonable to assume that there is a positive
correlation between China's GDP and BDI. Undoubtedly, the abovementioned 'China
effect' has an impact on the shipping industry, yet the shipping market has a unique
unstable environment, therefore there is no certainty on what extent China's
economic growth solely can affect the dry bulk freight rate market.
2. METHODOLOGY
For the purpose of this project we are going to use the deductive approach
and therefore, we will generate two hypotheses, which will later accept or reject
based on our findings.
a) Hypothesis 1: There is a causal relationship between China's GDP annual
percentage growth and the BDI
b) Hypothesis 2: The relationship between China's GDP annual percentage
growth and the BDI is positive
In order to examine the abovementioned hypotheses, we will collect data on
an annual base for the years between 1999 and 2018 in order to investigate if the
data correlate prior, during and after the financial crisis of 2008 which affected
greatly the shipping market, and accordingly accept or reject the aforementioned
hypotheses. Furthermore, through the employment of descriptive statistics and the
regression analysis, the relationship of the data will be examined, where the annual %
growth of China's GDP will be the independent variable and will be depicted on the X
axes, whereas the BDI will be the depended variable and will be depicted on the Y
axes.
The purpose of this project is exploratory and explanatory. By exploring how
China's GDP affects the dry bulk freight market and will generate necessary
information to understand if a flourishing economy like China's, has an impact on the
shipping market.
The research strategy of the project is experimental, due to the fact that the
causal relationship of the variables will be examined, and if there is a causal link, on
what degree a change to the independent variable will affect the dependent variable.
Moreover, we are going to employ the mono method in this project, since we are
going to use numerical quantitative data, which will provide more reliable results
due to the nature of the project. The research will be based solely on secondary data,
acquired mostly from the internet. In addition, the numerical data will be gathered
from reliable organizations, such as the World Bank and Clarksons. Clarksons, is a
reliable shipping intelligence network, which monitors the shipping industry for
6
years, providing various information about every segment of the shipping industry
and publishes forecasts with great accuracy, which indicates that the data found on
their site are reliable for our project. Lastly, the World Bank organization, with a vast
knowledge and experience that assists countries in different developing stages, by
providing financing and various other services, is also a reliable source like the
Clarksons. Lastly, for the processing of the data the MS - Excel 2007 was used.
3. DATA ANALYSIS
3.1 Graph analysis.
Firstly the fluctuations of China's GDP and BDI will be depicted separately on graphs
for the years between 1999 and 2018, to understand the pattern that both sets of
data fluctuate. As it can be observed in appendix 1 China's GDP growth had a
constant increase from 1999, which peaked at approximately 14% GDP growth in the
middle of 2008. Afterward a constant decrease in the annual percentage growth of
China's GDP can be observed up to 2018. Furthermore, even though the decrease is
continuous since 2008, with only exception the period between the 2009 and 2010
where there was a small increase followed by a continuous decrease, a phenomenon
known as the dead cat bounce. Lastly, since 2012 it can be observed that the
decrease in China's annual percentage growth is less rapid which indicates that the
Chinese economy starts to stabilize after the financial crisis of 2008.
As far as the Baltic Dry Index is concerned, it can be observed in appendix 2,
that the movement of the BDI since 1999 up to 2018 is similar to the fluctuations of
China's annual percentage changes in the GDP but more volatile. Between 1999 and
2002 it can be observed that the BDI is at the trough stage. From the middle of 2002
up to 2004, a constant increase can be observed, followed by a small decrease in the
following two years. Between 2006 and 2008 a rapid increase can be observed,
where the BDI had approximately a 100% increase compared to its previous peak.
After 2008 a constant decrease can be observed where the BDI follows the same
pattern as China's annual GDP percentage growth but in a more volatile manner.
The constant decrease, in both variables after 2008, is attributable to the
financial crisis of 2008, whereas China's GDP growth started to stabilize after 2012,
where on the other hand the BDI had a more unstable fluctuation. Nevertheless, by
taking into consideration the data in appendix 1 and 2 it is evident that both
variables move in a similar pattern and are also influenced similarly by external
factors.
7
3.2 Descriptive statistics.
Moreover, as it can be observed, appendix 4 illustrates the descriptive statistics of
the data for a 20 year period, where the average percentage growth of China's GDP
is 9%, with min 6.56% and max 14.23%. As far as the BDI is concerned, it has an
average rate of 2315, with min 673.12 and max 7071. By examining the annual data
in appendix 8, we can observe that the min and max values of both variables occur at
the same time, which further supports the positive correlation of the variables, and
it is going to be examined in the following section.
3.3 Correlation.
By taking the abovementioned data and literature into consideration, we will analyze
the relationship of the data, by performing a correlation analysis, where the
independent variable X will be China's GDP annual percentage growth and the
dependent variable will be the BDI. As it can be observed in appendix 3, the
correlation between the two variables is depicted through the employment of
scatter plot. By examining the abovementioned scatter plot, it is evident that there is
a strong positive correlation between the two variables, and the correlation
coefficient r= 0.78 it is illustrated in the regression statistics table in appendix 5,
which indicates a strong positive correlation between the aforementioned variables.
The data points follow a linear pattern and four extreme values are detected. Lastly,
the density of the values is located to the bottom left of the scatter plot between the
6% and 10% values.
3.4 Regression.
With the employment of the simple linear regression model, to illustrate the
connection between the variables by setting as an independent variable x the annual
growth of China's GDP growth in percentage, and as depended variable y the BDI
rate. Furthermore, the equation for the simple linear regression analysis is
Y=b0+b1*X, whereas b0 is the population of y-intercept and b1 is the population
slope. By consolidating the data in appendix 5 in the coefficients section, the first
value represents the b0 and the second value represents the b1. Thus our regression
equation will be following; Y=-3956+692*X. As it was mentioned before, the y value
8
is the BDI and the X value China's GDP growth in percentage, so the equation by
including the dependent and the independent variable will be like this;
BDI=-3956+692*China's GDP growth %
Therefore, when China's GDP growth is equal to 0 (X), the BDI (Y) will be 3956 units. Furthermore, if China's GDP growth increases by 1% (x), accordingly BDI
(Y) will increase by 692 units (b1). After the regression equation we will examine the
accuracy of the model through the regression statistics table in appendix 5, the value
of r2 depicts the accuracy of the model, we want the value to be between 0 and 1
where zero means that the model is no accurate and 1 that the model is 100%
accurate. In our case r2= 0.61, which translates to a 61% accuracy meaning that the
variation of China's annual GDP growth explains 61% of the variations in the BDI the
remaining 39% of the variations in the BDI is attributable to other factors.
To further support the linear regression of the data, as it can be observed in
the residual plot, in appendix 6, the linear model is unbiased but a heteroscedasticity
can be observed which means there are no constant variances, therefore the linear
model might not be appropriate. In addition, by examining the normal probability
plot in appendix 7, we can observe that the data do not follow a straight line after
one point, which evidences a right skewed curvature.
Moreover, we are going to examine the significance of the model, since it
confirms if our dependent value (x) linearly affects our independent variable (y),
whereas x stands for China's annual GDP growth in % and y for the BDI. For our
model to be accurate the significance has to be lower than 0.05. Therefore as it can
be observed in the ANOVA table in appendix 6, the significance F is 0.00004 which is
lower than 0.05, thus our model is significant with predictive capabilities.
Lastly, since the model is significant with predictive capabilities due to the
abovementioned inequality F < a => 0.00004 < 0.05, the average impact of China's
annual % GDP growth on the BDI will be between 420 and 965 units with 95%
accuracy if the appropriate model is used.
9
CONCLUSION
In conclusion, in the literature review, we discussed the impact that China had on
the shipping industry due to her rapid economic growth and industrial development
over the past two decades, which was translated to an also rapid increase in China's
importing and exporting behavior. Furthermore, it was not debatable that as far as
the volume of commodities is concerned, China had a significant impact on the
shipping industry, also known as the 'China effect'. However, the important subject
matter was, whether a country like China, could on a standalone bases, affect the
BDI in a positive way and at this point, the opinions presented in the literature
review were divided. It was fascinating to confirm in the project that indeed a
growing country like China had a positive correlation with the BDI, however the
accuracy of the regression model, stood at 61% which even though it is relatively
good by taking into consideration the magnitude of the shipping dry bulk sector, and
the plethora of factors that affect the shipping market in general, it is still small in
order to be used for making accurate prediction. The main factor aside from curiosity
to conduct this project was the opinion that Martin Stopford supported in his book,
maritime economics that the maritime economy will shift from China to India in the
future, based on his west line theory. Therefore by identifying, the correlation
between China's GDP and the shipping market might prove useful in the future, since
it is expected that the maritime economy will shift towards India, and by being able
to identify when it will start and how the variables, specifically the Indian GDP and
the BDI, interact together.
Lastly, by identifying such a connection might be important for various stake
holders in the shipping market, for the near future where the maritime economy will
continue to revolve around Asia, and for the moment that the maritime economy
will start to shift towards India.
10
LIMITATIONS & SUGGESTIONS
There were several obstacles during the drafting of the project. One of the most
important obstacles was the change on the Baltic cape size index calculation,
specifically the method of calculating the Baltic Cape size Index (BCI) changed in
2014 thus there were no compatible data prior that stage with the current data. This
was a limitation because the BCI includes only Cape size vessels, which are used for
the transportation of iron ore and coal to China, where both commodities are
heavily demanded, thus it would probably provide a more accurate model, which
could be used for future predictions. In relation with the previous limitation, another
limitation was the word count, which even though it was sufficient, because of the
previous limitation, the alternative would be to examine the correlation between
China's GDP and every route for iron ore and coal towards China, which would
significantly increase the word count of the project. Lastly, most databases require a
fee in order to get access, thus there was a limitation as well to the accessible data.
As far as the suggestions are concerned, a better fit for this project could be
another model or at least a multi regression model, which would include multiple
other variables such as bunker prices, volumes of imports and exports. Furthermore,
it would be appropriate to incorporate on a similar quantitative research, qualitative
data as well, since the market psychology and the political stability are also
important factors that affect the shipping industry. Therefore a hybrid type of
research might prove a lot more accurate than a solely quantitative research since
there are a plethora of cases where political instability is a determinant the factor for
the freight rates to plunge. Lastly, a project should be made investigating the
relationship between China's GDP and the tanker market, since China shifts from
coal to more environmentally friendly sources of energy to relieve the air pollution
and the enforcement of such policy could affect the tanker industry positively.
11
LIST OF REFERENCES
Stopford, M. (2009) Maritime Economics 3d edn London: Routledge
Komadina, N. et al (2015) Factors influencing the formation of freight rates on
maritime shipping markets
https://www.researchgate.net/publication/284170614_Factors_influencing_the_for
mation_of_freight_rates_on_maritime_shipping_markets [4 April 2018]
Thien, D. (2005) Forecasting the dry bulk freight market
https://commons.wmu.se/cgi/viewcontent.cgi?article=1241&context=all_dissertatio
ns [4 April 2018]
Britton, E. and Mark, C. (2006) The China effect: Assessing the impact of the US
Economy of Trade and investment with China
https://www.oxfordeconomics.com/publication/open/222576 [4 April 2018]
Alan, T. and Hyungsuk, L. (2007) An exploratory study examining the impact of
China's rapid economic growth on the Asian shipping industry
http://www.freepatentsonline.com/article/Journal-International-BusinessEconomics/178900114.html [4 April 2018]
Zhang, J. and Tong, Z. (2017) The relationship between the prices of the shipping
market and China's economy
https://pdfs.semanticscholar.org/5897/37756251fc3a8e86d4270b5d64c8c96e382f.p
df [4 April 2018]
Kampa, E. (2011) The Chinese demand for iron ore and its effect on freight rates
https://thesis.eur.nl/pub/33094/Kampa-E.-The-Chinese-Demand-for-Iron-Ore-andits-Effect-on-Freight-Rate [4 April 2018]
12
Hyung, K. (2011) Study about how Chinese economic status affects to the Baltic dry
index
http://www.ccsenet.org/journal/index.php/ijbm/article/viewFile/9698/6952 [4 April
2018]
Morse, R. and He, G. (2010) The World's greatest coal arbitrage: China's Coal Import
Behavior and Implications for the global Coal Market
http://www.thedalles.us/sites/default/files/imported/agendas/city_council/PDFs/co
altrainInfo.pdf [4 April 2018]
Almkvist, M. (2016) Nowcasting US GDP with Baltic Dry Index
http://www.diva-portal.org/smash/get/diva2:944910/FULLTEXT01.pdf [4 April 2018]
13
APPENDIXES
Appendix 1
China's GDP Growth In %
16,00
GDP Growth Rate %
14,00
12,00
10,00
8,00
6,00
GDP % growth
4,00
2,00
0,00
1999 2001 2003 2005 2007 2009 2011 2013 2015 2017
Year
Source: Worldbank.org
Appendix 2
14
8 000,00
7 000,00
6 000,00
5 000,00
4 000,00
3 000,00
2 000,00
1 000,00
0,00
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
BDI
1999
BDI
BDI
Year
Source: Clarksons.net
Appendix 3
Correlation between China's GDP & BDI
BDI
8 000,00
6 000,00
y = 692,48x - 3956,8
4 000,00
2 000,00
0,00
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
16,00
China's % GDP Growth
Source: Worldbank.org and Clarksons.net
Appendix 4
GDP % growth
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample Variance
Kurtosis
Baltic Exchange Dry Index
9,058
0,461910563
8,81
#N/A
Mean
Standard Error
Median
Mode
2,065726838 Standard Deviation
4,267227368 Sample Variance
0,687558955 Kurtosis
2315,653
408,6525
1443,343
#N/A
1827,549
3339937
1,91219
15
Skewness
Range
Minimum
Maximum
Sum
Count
0,953510484
7,67
6,56
14,23
181,16
20
Skewness
Range
Minimum
Maximum
Sum
Count
1,586551
6398,084
673,12
7071,204
46313,06
20
Source: Worldbank.org and Clarksons.net
Appendix 5
16
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0,78273062
0,612667224
0,591148736
1168,562092
20
ANOVA
df
Regression
Residual
Total
1
18
19
SS
38879125,76
24579672,53
63458798,3
MS
F
38879125,76 28,4716675
1365537,363
Significance F
4,51392E-05
-3956,849775
692,482102
Standard Error
1204,222616
129,7783061
t Stat
P-value
-3,285812542 0,004107927
5,335884885 4,51392E-05
Lower 95%
-6486,827606
419,8279988
Coefficients
Intercept
GDP % growth
RESIDUAL OUTPUT
Upper 95%
Lower 95,0% Upper 95,0%
-1426,871944 -6486,827606 -1426,871944
965,1362053 419,8279988 965,1362053
PROBABILITY OUTPUT
Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Predicted Baltic Exchange Dry Index
1347,563126
1922,323271
1811,526135
2365,511816
2967,971245
3044,144276
3930,521367
4844,597742
5897,170537
2725,602509
2545,557163
3404,234969
2642,504657
1479,134726
1409,886516
1091,344749
821,2767288
668,9306664
807,4270868
585,8328141
Residuals
Standard Residuals
-9,563126367
-0,008407922
-314,509781
-0,276517684
-594,9245447
-0,523058954
-1228,025876
-1,079683022
-350,5392451
-0,308194867
1465,815884
1,288748514
-559,6498769
-0,492045389
-1664,894932
-1,463779246
1174,033463
1,032212775
3664,696291
3,222008952
70,95083713
0,06238013
-646,1989694
-0,56813954
-1093,819177
-0,9616882
-558,7813158
-0,491281749
-204,0225155
-0,17937704
13,94725138
0,012262454
-103,0767288
-0,090625284
4,18933362
0,003683271
337,8058432
0,296999632
596,5671859
0,524503168
Percentile
2,5
7,5
12,5
17,5
22,5
27,5
32,5
37,5
42,5
47,5
52,5
57,5
62,5
67,5
72,5
77,5
82,5
87,5
92,5
97,5
Baltic Exchange Dry Index
673,12
718,2
920,35341
1105,292
1137,48594
1145,23293
1182,4
1205,864
1216,60159
1338
1548,68548
1607,81349
2616,508
2617,432
2758,036
3179,70281
3370,87149
4509,96016
6390,2988
7071,204
Source: Worldbank.org and clarksons.net
Appendix 6
17
GDP % growth Residual Plot
4000
Residuals
3000
2000
1000
0
-1000
0,00
2,00
4,00
6,00
-2000
8,00
10,00
12,00
14,00
16,00
GDP % growth
Source: Worldbank.org and Clarksons.net
Appendix 7
Baltic Exchange Dry Index
Normal Probability Plot
10000
5000
0
0
-5000
20
40
60
80
100
120
Sample Percentile
Source: Worldbank.org and Clarksons.net
18
Appendix 8
Date
GDP % growth
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
BDI
1.338,00
1.607,81
1.216,60
1.137,49
2.617,43
4.509,96
3.370,87
3.179,70
7.071,20
6.390,30
2.616,51
2.758,04
1.548,69
920,35
1.205,86
1.105,29
718,20
673,12
1.145,23
1.182,40
7,66
8,49
8,33
9,13
10,00
10,11
11,39
12,71
14,23
9,65
9,39
10,63
9,53
7,85
7,75
7,29
6,90
6,68
6,88
6,56
Source: Worldbank.org and clarksons.net
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