Download - Final Project of QTB
Quantitative Techniques in Business
ASSIGNMENT:
“Dependent variable and independent variables analysis”
SUBMITTED TO:“SIR MOHAMMAD ILYAS”
SUBMITTED BY:
MUHAMMAD UMAIR BUTT ID # 11422WAQAS WAHEED ID # 11434ARSLAN AHMAD ID # 11449CH MUHAMMAD ASHRAF ID # 11410AATISAM NASIR ID # 11411
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Determinate Industrial value added (% of GDP) in Sri Lanka
1. Introduction:
The Industry value added (% of GDP) in Sri Lanka was reported at 29.37 in 2008, according to the World Bank. Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. For VAB countries, gross value added at factor cost is used as the denominator.
A chart with historical data for Machinery and transport equipment (% of value added in manufacturing) in Sri Lanka. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division 3. Machinery and transport equipment comprise ISIC groups 382-84. Sri Lanka is a developing economy off the southern coast of India. In spite of years of civil war, the country has recorded strong growth rates in recent years. The main sectors of the Sri Lanka's economy are tourism, tea export, apparel, and textile and rice production. Remittances also constitute an important part of country's revenue.
Exports to the United States, Sri Lanka's most important market, were valued at $1.8 billion in 2002, or 38% of total exports. For many years, the United States has been Sri Lanka's biggest market for garments, taking more than 63% of the country's total garment exports. India is Sri Lanka's largest supplier, with exports of $835 million in 2002. Japan, traditionally Sri Lanka's largest supplier, was its fourth-largest in 2002 with exports of $355 million. Other leading suppliers include Hong Kong, Singapore, Taiwan, and South Korea. The United States is the 10th-largest supplier to Sri Lanka; U.S. exports amounted to $218 million in 2002, according to Central Bank trade data—U.S. Customs data places U.S. exports to Sri Lanka at $166 million in 2002. Wheat accounted for 14% of U.S. exports to Sri Lanka in 2002, down from the previous year. This table show industrial production growth rate in Sri Lanka.
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Year Industrial production growth rate
Rank Percentage change
Date of information
2003 1.10% 117 2002
2004 5.80% 57 427.27% 2003
2005 7.10% 47 22.41% 2004
2006 8.20% 31 15.49% 2005est.
2007 6.20% 60 -24.39% 2006est.
2008 7.60% 45 22.58% 2007est.
2009 5.90% 41 -22.37 2008est.
2010 4.20% 29 -28.81% 2009est.
2011 6.90% 48 64.29% 2010est.
In 1977, Colombo abandoned statist economic policies and its import substitution trade policy for market-oriented policies and export-oriented trade. Sri Lanka's most dynamic industries now are food processing, textiles and apparel, food and beverages, telecommunications, and insurance and banking. By 1996 plantation crops made up only 20% of exports (compared with 93% in 1970), while textiles and garments accounted for 63%. GDP grew at an annual average rate of 5.5% throughout the 1990s until a drought and a deteriorating security situation lowered growth to 3.8% in 1996. The economy rebounded in 1997-98 with growth of 6.4% and 4.7% - but slowed to 3.7% in 1999. For the next round of reforms, the central bank of Sri Lanka recommends that Colombo expand market mechanisms in no plantation agriculture, dismantle the government's monopoly on wheat imports, and promote more competition in the financial sector. A continuing cloud over the economy is the fighting between the Government of Sri Lanka and the LTTE, which has cost 65,000 lives in the past 15 years.
Government provides employment for 13% of the work force and follows state enterprise oriented policies. Privatization of such enterprises has stopped and reversed, with several new state enterprises launched.
I. Research Question : How does the growth of exports of goods and services (% of GDP), Inflation,
GDP deflator (annual %), final consumption expenditure (% of GDP), and gross domestic saving (% of GDP), affects the industrial worth in Sri Lanka?
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II. Objective of study :
The objective of this study is to investigate the determinants of Industry value added (% of GDP) in Sri Lanka. In this study time series data on five variables would be used to investigate the dependence of industry value added growth on all other four independent variables.III. Scope of industry:
Sri Lanka has traditionally been an agro-based economy. But over a period of time the government in Sri Lanka realized the need to have an industrialization strategy for the development of the economy.
The role of governments, over the years, especially in developing nations like Sri Lanka has changed radically because the world itself saw rapid changes – especially over the last few decades. In Sri Lanka there was a period when the state sector led industrial growth. This gradually gave way to the semi-government sector and corporations. The three main strengths that Sri Lanka offers are:
Cheap Labor that was easily available and accessible Conducive conditions, including infrastructure and tax relief Literate Workforce, both in terms of skill and literacy
Free Trade Zones and Export Processing Zones were set up offering many concessions to foreign (and local) investors. Sri Lanka’s traditional exports have been tea, rubber, coconuts, gems and jeweler, in the recent past the apparel industry has gained prominence.
According to the Central Bank annual report in Sri Lanka the industrial sector comprises of four main categories. Those are:
Mining and Quarrying Manufacturing (i.e. processing of agricultural products, Factory industry, Cottage
Industry) Electricity, Gas & Water Construction
The mining and quarrying, which has the highest growth of the industry sector has been growing with increasing rates in the last few years, while the growth of the electricity, gas and water has been reducing drastically since 2006.
Further, the production of the manufacturing sector in 2007 was Rs.394, 233 million. However, when considering the growth rate, the manufacturing sector was the lowest growing sector under the industrial sector.
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Industry account for 28% of Gross Domestic Product (GDP) in 2009.
Manufacturing is the largest industrial subsector, accounting for 18% of GDP. The construction sector account for 7% of GDP. Mining and quarrying account for 1.5% of GDP. Electricity, gas, and water account for 2% of GDP.
Within the manufacturing sector, food, beverage, and tobacco are the
largest subsector in terms of value addition, accounting for 44%. Textiles, apparel, and leather are the second-largest sector with 20% of value addition. The third-largest sector in value added terms is chemical, petroleum, rubber, and plastic products.
2. Data and methodology: I. Data:
A sample period of 45 years has been selected for this study for the period of 1965-2009 with annual frequency. Depending on the availability of data we have selected the longest possible sample period to avoid the small sample bias. Data on all the variables have been collected from World Development Indicators. Five variables have been selected for this study. Industry, value added (% of GDP) has been used for dependent variable to represent the industrial worth. Whereas, exports of goods and services (% of GDP), inflation, GDP deflator (annual %), final consumption of expenditure (% of GDP), gross domestic savings (% of GDP) has been used an independent variable. The description of variables has been given below.
Dependent variable:
Industry, value added (% of GDP):This variable is used as economy policy & debt. Industry
corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.
Independent variable:
1. Exports of goods and services (% of GDP): Exports of goods and services
represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business,
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personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
2. Inflation, GDP deflator (annual %): Inflation as measured by the annual growth
rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.
3. Final consumption expenditure, etc. (% of GDP):Final consumption expenditure
(formerly total consumption) is the sum of household final consumption expenditure (private consumption) and general government final consumption expenditure (general government consumption). This estimate includes any statistical discrepancy in the use of resources relative to the supply of resources.
4. Gross domestic savings (% of GDP):Gross domestic savings are calculated as GDP
less final consumption expenditure (total consumption).
Quality of Data:It is really tough to comment on quality of the secondary data. However,
the above definitions of the variables show that the variables measure the concepts which we
intended to measure. Given that the data have been collected according to the above definitions
of the variables, the data used in this study is valid for the purpose of analysis. It is important to
note that the above definitions of the variables have been taken from the user guide of the World
development Indicators which is the source of the data used in this study. No data values are
missing from any series. Data on World Development Indicators are drawn from the sources
thought to be most authoritative.
II. Methodology:
To present the overall picture of the variables the descriptive statistics are
used. The scatter-plot is used to view the relationships among the variables used in this study.
The scatter-plot is used to find the linear relationship between variables. A table of correlations
among variables is also a part of the study. This table provides the values and signs of the
coefficients of correlations. This table also provides the P-values of the test of the null
hypothesis which states that the said variables are not correlated to each other. This table is also
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helpful to check the problem of multi-collinearity. The large correlations between the predictor
variables indicate the problem of multi-collinearity.
Since the objective of this study is to check the dependence of the industry, value added (% of
GDP) on different factors as stated above, in this study ordinary least square (OLS) method of
multiple-regression is used to estimate the effects of those factors on the economic growth. The
objective of the regression in this study is to find such an equation which could be used to find
the predicted value of the industry worth for a given set of values of exports of goods and
services as percentage of GDP (EXR), and inflation, GDP deflator as percentage of annual
(INF), final consumption expenditure as percentage of GDP (FCE), and gross domestic savings
as percentage of GDP (GDS). The specified multiple regression equation takes the following
form:
IVAt = 0 + 1EXRt + 2INFt + 3FCEt + 4GDSt + Ut (1)
As specified in the above equation IVAt is the dependent variable and
other four variables are independent. Since all the variables are time series’, subscript t denotes
the time period. 0 is the constant term. 1, 2, 3, and 4 are the partial regression coefficients of
the independent variables. A partial regression coefficient represents the change in dependent
variable, ceteris paribus, due to one unit change in independent variable. Ut is the error term. To
test the significance of the individual coefficients t-test is also employed in this study. Overall
goodness of fit of the model is checked through F-test and the adjusted coefficient of
determination (adj. R2). To test the problem of autocorrelation Durbin Watson (DW) test is also
conducted.
Justification of the Method:
This study has used the descriptive statistics to present the overall picture
of the variables. For the initial look on the relationship between different variables the scatter-
plot is used. The scatter-plot is used to find the linear relationship between variables. Magnitudes
and signs of the correlation coefficients are provided in the table of correlations. This table is
used to view the strength and direction of the relationship between the variables. This table also
provides the P-values of the test of the null hypothesis that states that there is no correlation
between two variables. This table is used to indicate the problem of multi-collinearity as well.
The method of multiple-regression is used to estimate the effect of multiple predictors on
the predicted. Considering the objective of this study the multiple-regression analysis is used in
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this study to estimate the partial regression coefficients of the independent variables and their
statistical significance. We have used the method of multiple-regression because there are four
independent variables in this study and all of them are scale variables.
3. Empirical findings:
In this part of the study empirical findings have been shown
and interpreted. Table 3.1 presents the descriptive statistics which show the overall picture of the
variables.
Table 3.1Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Industry, value added (% of
GDP)45 19.70 30.64 26.4165 2.49716
Exports of goods and
services (% of GDP)45 18.39 39.02 29.3189 5.27889
Inflation, GDP deflator
(annual %)45 -1.80 24.38 10.2700 5.83487
Final consumption
expenditure, etc. (% of GDP)45 80.11 91.89 85.4998 2.71969
Gross domestic savings (%
of GDP)45 8.11 19.89 14.5002 2.71969
Valid N (list wise) 45
Explanation:
In the above table the minimum values, maximum values, mean values and the values of
standard deviation of all the five variables have been shown. Mean value provides the idea about
the central tendency of the values of a variable. Number of observations of each variable is 45.
Standard deviation and the extreme values (minimum in comparison to maximum value) give the
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idea about the dispersion of the values of a variable from its mean value. Since different units of
measure have been used for different variables the dispersion of a variable using standard
deviation can’t be compared to that of other variable unless both the variables have the same unit
of measure. But still these statistics are helpful to have an idea about the central tendency and the
dispersion of a variable in absolute terms rather than relative terms.
Scatter Diagrams:
Figure 1 Figure 2
Figure 3 Figure 4
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Explanation:
In this scatter plot diagrams we intend to have some idea about the relationship between
industries, value added (% of GDP) and other variables. In figure 1 shows the positive
relationship between industry values (% of GDP) added and grosses domestic savings (% of
GDP) and also shows the linear relationship between both variables because R value(R sq
quadratic-R sq linear) less then P-value (0.005 > 0.05). In figure 2 also show positive
relationship between industry value added and inflation, GDP deflator and shows the linear
relationship between both variables because R value(R sq quadratic-R sq linear) less then P-
value (0.03 > 0.05). They are negative relationship between industry value added and final
consumption in figure 3. They are linear relationship between both variables because R value(R
sq quadratic-R sq linear) less then P-value (0.005 > 0.05). They are also positive relationship
between industry value added and exports of goods and services in figure 4. But they are not
linear relationship between both variables because R value (R sq quadratic-R sq linear) is
equal to P-value (0.05 = 0.05). These results have been confirmed by the table of correlations.
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Table 3.2 Correlations
Industry, value added (% of GDP)
Inflation, GDP deflator (annual %)
Final consumption expenditure,
etc. (% of GDP)
Gross domestic
savings (% of GDP)
Industry, value added (% of GDP)
Pearson Correlation 1 .405** -.387** .387**
Sig. (2-tailed) .006 .009 .009
N 45 45 45 45
Inflation, GDP deflator (annual %)
Pearson Correlation .405** 1 .004 -.004
Sig. (2-tailed) .006 .981 .981
N 45 45 45 45
Final consumption expenditure, etc. (% of GDP)
Pearson Correlation -.387** .004 1 -1.000**
Sig. (2-tailed) .009 .981 .000
N 45 45 45 45
Gross domestic savings (% of GDP)
Pearson Correlation .387** -.004 -1.000** 1
Sig. (2-tailed) .009 .981 .000
N 45 45 45 45
Explanation:
Table 3.2 represents the table of correlations. They are linear relationship between
variables and all assumption fulfills the Pearson correlation but one variable is not relationship.
This table reflects two variables – inflation, GDP deflator (% of GDP) and gross domestic
savings – are positively correlated to industrial worth (r= .405, p = .006, and r= .387, p= .009,
respectively). Final consumption is negatively correlated to the industrial worth (r= -.387, p
= .009). They are made the hypothesis and reject the null hypothesis because they are
relationship between the variables. The magnitudes of the above discussed correlations are
greater than 0.4 in the absolute terms, which shows the moderate correlations between the said
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pairs of the variables. All the above correlations are statistically significant at less than five
percent level of significant.
Table 3.2.1Correlations
Industry, value
added (% of
GDP)
Exports of goods
and services (%
of GDP)
Spearman's rho Industry, value added (% of
GDP)
Correlation Coefficient 1.000 .505**
Sig. (2-tailed) . .000
N 45 45
Exports of goods and
services (% of GDP)
Correlation Coefficient .505** 1.000
Sig. (2-tailed) .000 .
N 45 45
Explanation:
Table 3.2.1 also represents the table of correlations. One variable is not fulfilling the
assumptions of Pearson correlation. So he apply spearman test to check the significant level of
variable and strength of variable. This table reflects the two variables – industry value added and
exports of goods and services are positively correlating the industrial worth. They are made the
hypothesis and reject the null hypothesis. They are relationship between the variables because
significant value is less than P value (0.00>0.05). The magnitudes of the above discussed
correlation value is 0.51 in the absolute term which show the moderate relationship between the
variables. The correlation of the variables is statistically significant at less than five level of
significant.
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Table 3.3Regression
Dependent variable: industry value added (% of GDP)
Variables Coefficients Std. Error t-testSig.
Level
Constant 17.258 1.955 8.827 .000
Exports of goods and services (%of GDP)
.163 .068 2.395 .021
Inflation, GDP deflator (annual % )
.139 .054 2.575 .014
Gross domestic savings (%of GDP)
.205 .128 1.597 .118
Regression equation:
IVAt = 0 + 1EXRt + 2INFt + 3FCEt + 4GDSt + Ut
IVAt = 17.258 + 0.1631 + 0.1392 + 03 + 0.2054
Explanation:
Table 3.3 presents the results of the regression analysis. The results show that all of the
independent variables except final consumption as shown by the values of the t-statistic and the
corresponding P-values. T-test is used to test the significance of the individual partial regression
coefficients. Null hypothesis in this test is set as the partial regression coefficient is zero. They
are one excluded independent variable that final consumption. Final consumption result in SPSS
not shows that show the excluded variable in multiple regression result. This test shows that the
coefficients of all the predictors except gross domestic savings and final consumption are
statistically significant at less than five percent level of significance. All of the significant
coefficients have the positive signs. The magnitude of the partial regression coefficient of the
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exports of goods and services is 0.163, reflects the change of 0.163 units in industrial worth due
to one unit change in the growth rate of exports. The partial regression coefficient of inflation on
industrial worth is 0.139 which represents that, given no change in other factors; an increase of
one unit in inflation would reduce the industrial growth by 0.139 units. Though it is interpret the
value of the constant term in our regression analysis, its value is positive which shows that in the
absence of all the predictors used in this study the industrial worth would be positive.
Table 3.4Necessary statistics
Coefficient of
Determination (R2)
Adjusted Coefficient of
Determination (Adj. R2)
Durbin-Watson
StatisticF-Statistic Sig. (F-Stat)
.399 .355 .644 9.067 .000
Explanation:
Necessary statistics have been shown in table 3.4. The value of the coefficient of
determination (R2) is 0.399. This shows that the correlation between the observed values of
industrial growth and the fitted values of the industrial growth is thirty seven percent. The
adjusted coefficient of determination (adj. R2) shows is adjusted for the degrees of freedom. The
value of the adjusted coefficient of determination (adj. R2) is not affected by the inclusion of the
irrelevant variables. The value of the adjusted coefficient of determination (adj. R2) is 0.255,
which shows that twenty six percent variations in industrial growth are explained by the
variations in independent variables. The value of F-statistic is statistically significant at less than
five percent which exhibits that in the estimated model at least one of the partial regression
coefficients is different from zero. The value of Durbin-Watson statistic is 0.644 which is very
much supportive and reveals that there is no serial correlation in the error term. Independent
variables are jointly effect on industrial worth. Model of regression is good fit while null
hypothesis is rejected.
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4. Summary and Conclusion:
This study has investigated the determinants of industrial worth for the period 1965-2009
in the case of Sri Lanka. After observing the scatter plot and the correlations ordinary least
square method of multiple-regression has been used for this purpose. The industrial value added
as percentage of GDP has been used as dependent variable as the representative of industrial
worth. The study could not find any impact of gross domestic savings on industrial worth. The
impacts of exports of goods and services as a percentage of GDP and inflation, GDP deflator as
percentage of annual are found to be positive and statistically significant.
The transition to a free market economy based on a liberalized trade and exchange rate
regime has brought benefits to the Sri Lankan economy. Unemployment, a problem for decades,
has reduced significantly, and remains at historically low levels (8 percent in 2000). Nonetheless,
the high levels of inflation , fueled by the sharp deterioration of the Sri Lankan currency,
combined with the mounting cost of civil war has raised the cost of living to very high levels.
The soaring cost of living has made many Sri Lankans struggle to satisfy their basic needs. Over
45 percent of the population depends on benefits under the income supplement programs
initiated by the government. The balance of payments problem remains unresolved. The
persistent trade deficit has led to increased reliance on foreign aid to meet the country's import
requirements, leading to an inevitably mounting foreign debt. Foreign debt as a percentage of the
gross domestic product, which accounted for 21 percent in 1975, grew to 75 percent in 1994, and
amounted to 59 percent in 1999.
The coefficients of all the other two statistically significant coefficients are positive as
they were expected. The impact of gross domestic savings on industrial worth of Sri Lanka is not
statistically significant. This shows that on average gross domestic savings has been not a
problem in Sri Lanka during the period under study.
Positive and significant impact of exports of goods and services on industrial worth
suggests that Sri Lanka should focus on export expansion. Partial regression coefficients of
inflation on industrial worth are statistically significant suggested that Sri Lanka should worked
and make low price of the products. They are low imports of the products and increase the
exports of goods.
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Although this study has included many important determinants in the analysis on the
basis of theoretical narrations, yet in future studies it would be useful to include some other
variables in the analysis as well. Inclusion of other variables e.g. technical change and human
efforts, latest machinery etc may improve the value of the coefficient of determination.
5. References:
http://databank.worldbank.org/ddp/home.do?
Step=2&id=4&hActiveDimensionId=WDI_Series
http://www.tradechakra.com/economy/sri-lanka/industry-in-sri-lanka-
350.php
http://en.wikipedia.org/wiki/Economy_of_Sri_Lanka
http://www.nationsencyclopedia.com/Asia-and-Oceania/Sri-Lanka-
INDUSTRY.html#ixzz1JmX61j85
http://en.wikipedia.org/wiki/Economy_of_Sri_Lanka.
Sri Lanka Overview of economy, Information about Overview of economy in
Sri Lanka http://www.nationsencyclopedia.com/economies/Asia-and-the-
Pacific/Sri-Lanka-OVERVIEW-OF-ECONOMY.html#ixzz1QXx2Mvoo