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