# Factors effecting on gdp intoduction economics essay

## OBJECTIVE:

Our objectiveof the study is to get complete knowledge about the factors effecting on gross domestic product (GDP), and find their relationship with GDP growth rate. There are different types of factors which effect on GDP but in our study we have discussed only two factors like; balance of payment and infrastructure. These three factors show greater effect than other factors. We have also discussed that at what percent these factors contribute to enhance the GDP growth rate, and how we can able to enhance the efficiency of these factors so that these factors can positively contribute to GDP growth rate.

## SIGNIFICANCE OF THE STUDY:

This study has high significance. After study of factors effecting on GDP we get more things to learn. This study produces new knowledge for reader. This study tells us how we can enhance the GDP growth rate of a country. This study infrastructure, balance of payment and infrastructure are the variables which affect the GDP greater than other factors. In future this studies becomes helpful for policy makers, so that they will make policies, which will increase GDP growth rate. If we become able to increase our revenues than our GDP growth rate will enhance in future.

## PURPOSE OF STUDY:

The first purpose of study is to find out relationship between infrastructure, balance of payment, infrastructure and GDP. Second purpose of this study is to learn how these factors positively contribute to enhance the GDP growth rate of country. Third purpose of the study is to provide some key points for policy makers.

## Research question

Our research problem is” What are the factors affecting on GDP”.

## Hypothesis

H1= There is relationship between balance of payment and GDPH0= There is norelationship between balance of payment GDPH1= There is relationship between infrastructure and GDPH0= There is no relationship between infrastructure and GDP

## VARIABLES:

The variables of our study areGross domestic product or income (GDP)Balance of paymentInfrastructure

## GDP:

GDP is our dependent variable. We can define GDP as” Income generated by factors of production in a country during a year is called gross domestic product or income”. GDP includes income generated from; wages and salaries, interest income, rent income, undistributed corporate profit, mixed income and direct taxes. We may write GDP in numeric form asGDP = C+I+G+(X-M)C = Private consumptionI = Investment privateG = Government spendingX = ExportsM = Imports

## BALANCE OF PAYMENT:

This is independent variable. The balance of payment shows the balance of import and exports. If the imports volume are greater than exports volume the balance of payment will be unfavorable. And if the exports volume are greater than imports volume than this balance will be favorable. The balance of payments is said to be balanced when the credit items are exactly equal to the total of debit items. The balance of payment is comprehensive record of a countrywith the rest of the world during a given period of time.

## INFRASTRUCTURE:

This is also independent variable. Infrastructure includes roads, building, hospital, motor vehicles, crude oil, streets etc. Investment in infrastructure thus will tend raise production given the level of private capital and employment. It will also raise the marginal product of private capital and thus raising the incentives to invest.

## Key words: political instability, economic growth, regime type, endogeneity, IV/GMM estimation.

(Nur-tegin, 2012) Corruption: Democracy, Autocracy, and Political Stability The recent empirical literature on corruption has identified a long list of variables that correlate significantly with corruption but only five were distinguished by Leamer’s Extreme Bounds Analysis as robust to various samples, measures of corruption, and regression specifications. Among these five factors that were found to reduce corruption are decades-long tradition of democracy and political stability. In today’s world, however, there are many countries that combine one of these two robust determinants of corruption with the opposite of the other: politically stable autocracies or newly formed and unstable democracies. The central question raised in this paper is: Is it worth, in terms of corruption, for a country to trade stability with autocratic rule for political freedoms but with transitional instability? We find that the answer to this question is in the affirmative – the level of corruption is indeed lower in unstable democracies than in stable dictatorships. Our results are robust to various measures of corruption, alternative regressor indices, and regression specifications.(Giskemo, 2012) exploring the relationship between socioeconomic inequality, political instability and economic growth. The hypothesis that socio-economic inequality has a detrimental effect on economic growth by breeding political instability has been subject to empirical investigation for decades. However, the numerous studies in the field have yielded highly different conclusions, and still no agreement has been reached as to what the relationship between these variables really looks like. This study investigates why empirical studies have given such diverging results. By using several different measures both of socio-economic inequality, political instability and economic development it examines whether differences in methods and measurement can explain the variation in previous findings. It is revealed that the effect of socio-economic inequality upon political instability is dependent on which measures are used, and that the effect of instability upon economic development varies between different analytical models. The study thus shows that conclusions about the relationship between these phenomena are not robust to alternative measurement. A possible explanation of why previous empirical studies have reported such diverging findings is therefore that socio-economic inequality and political instability have been measured in different ways, or that different analytical models have been used.

## 3. 0 Introduction

The purpose of this chapter is to explain the research methodology used for collecting and analyzing data used to test the research model, the factors that affect on GDP of in Pakistan. In this chapter data collection method, population and sampling, measures, reliability and validity and data analysis are discussed.

Three main paradigms are used in research which is positivism, interpretivism, and pragmatism. In positivism quantitative research approach is used. In positivism existing theory is verified. To verify the existing theory assumptions are made. In positivism the numeric data is collected. The collected data is analyzed through SPSS. In interpretivism qualitative research approach is used. In interpretivism a new theory is generated. The data is collected through observations and interviews are used to collect the data. The collected data is analyzed through images and words in interpretivism approach. The third approach is pragmatism approach which is the mixture of both positivism and interpretivism. The present study used the positivism research approach because present study employed the quantitative research method to verify the existing theories. The present study collected the secondary data through financial statements. Secondary data collection is one of the data collection technique used in quantitative research approach. The study collected the numeric data and analyzed through SPSS.

## 3. 2 Data Collection

In order to find the data on the factors effect on GDP in Pakistan, the data was collected on four variables namely infrastructure, balance of payment and GDP the present study used the positivism research approach which is a quantitative research approach. The present study used the numeric the data on the variables because quantitative method vastly used by social sciences. The data on the variables was collected from the WDI.

## Research design:

The data for this study has been collected from the software called world development indicator (WDI). In which we found frequency of our related research data that what were the fact and figures of this inflation in last five years 2007 or 2011 year in Pakistan. This study conduct on the two independent variables to check the relationship between independent variables or dependent variables, and our dependent variables is GDP and the independent variables are infrastructure and balance of payment. According to the (WDI) Software the data for these variables are already available in the world development indicator so that’s why the secondary source tool for the information. For this study is the world development indicators software the frequency or all data already has been posted in the world development indicators software.

## Measures

Secondary data obtained thorough web site web site. From data collected through 2007 to 2011. we are not used data before 2007. Face validity is the validity where measure apparently reflects the content of the concept in question. For this study I used the face validity to measure the study it is right. Asked to the expert and relevant person whether extrinsically the measure seems to reflect the concerned concept or not. For this research I check the reliability through Cronbach’s Alpha

## Research site:

Research site means that where we want to conduct this research in Pakistan. Pakistan our main focus is that we want to conduct research in the Pakistan to check the GDP condition which is increased day by day. We will complete our research with the help of the economic survey for Pakistan.

## GDP Growth (Economic growth):

The data for this variable is selected from the predefined software which is called (WDI) World Development Indicators and the sample year is ……..

## BALANCE OF PAYMENT:

This is independent variable. The balance of payment shows the balance of import and exports. If the imports volume are greater than exports volume the balance of payment will be unfavorable. And if the exports volume are greater than imports volume than this balance will be favorable. The balance of payments is said to be balanced when the credit items are exactly equal to the total of debit items. The balance of payment is comprehensive record of a countrywith the rest of the world during a given period of time.

## INFRASTRUCTURE:

This is also independent variable. Infrastructure includes roads, building, hospital, motor vehicles, crude oil, streets etc. Investment in infrastructure thus will tend raise production given the level of private capital and employment. It will also raise the marginal product of private capital and thus raising the incentives to invest.

## Data Analysis

The collected data of the study was analyzed by using SPSS 16. 0 software. The data was analyzed by using descriptive statistics, correlation, and regression analysis.

## Descriptive Statistics

The objective of the descriptive statistics was to find the frequency of the data. Descriptive statistics also tells the minimum and maximum range of the data.

## Histograms

Histograms were applied to check the graphical representation of the entire data whether it is normally distributed or not. The bell curved shapes of the data in histogram verify that the data is normally distributed.

## Scatter Plots

Scatter plots were applied to the data to check the relationship between variables. Scatter plots also confirm the linear or non linear relationship between two variables.

## Correlation

Correlation is used to check the inter relationship among variables. For checking the relationship we will make two hypotheses: null (H0) and alternative (H1). We interpret the findings on the acceptance or rejection of the hypothesis. We used correlation matrix to check the mutual relationship of different variables.

## Regression Analysis

Regression analysis is a statistic technique used to investigate the relationships between a dependent variable and one or more independent variables. In regression analysis technique the strength of the relationship among the variables is checked. T-test is applied in regression analysis to check the significance of the relationship and R-test tells about the dependence of variables on each other. F-test is also applied to check the influence of the independent variable on the dependent variable in regression analysis.

## Descriptive Statistics

NMinimumMaximumMeanStd. DeviationPolitical instability5. 501. 00. 7800. 25884Balance of payment5-9. 55-. 77-3. 93003. 72275infrastructure53. 704. 103. 9080. 18349GDP56. 228. 847. 59001. 17934Valid N (listwise)5

## Interpretation:

Table 4. 1 presents the descriptive statistics that show the overall picture of all the four variables. There were scales of 5 responses observe the above output to assess the average response rate or the respondent then we come to know the mean of different variables political instability (mean: . 78), balance of payments (mean:-3. 93), infrastructure (mean: 3. 70) and GDP (mean: 7. 59). If we observe then for all variables (infrastructure, balance of payment, political instability and GDP) the average response rate of responded is lie within the option 3-7 (and for GDP the average response rate of GDP is lie in the option 7. 5 which is high value of all variables.. So, if we observe then in the response rate for the variable of infrastructure is value of standard deviation is (S. D. 183) which is the lowest value as compare to other variable values. Which shows that most we observe then for balance of payment the value of standard deviation is (S. D 3. 70) which is quite high as compare to other variables which clearly shows that the response regarding balance of payment of not consistence.

## Histogram :

4. 2. 14. 2. 24. 2. 34. 2. 4

## Interpretation:

This above figures shows the graphical representation of the variables with the curve to check the normality of the response rate. This above histogram shows variables with the curve to check data is normally distributed or not according to response rate. Above figure 4. 2. 1 show response of GDP, Mostly data lie on option 8-9. Similarly small numbers of data were lie on very low. Data shows data is not normally distributed bell curve also shows data normality. The figures 4. 2. 2 show the graphical representation of the bars that is showing the response of the respondent’s infrastructure. Most of the data lies in the option 3 – 4. The bars in the histogram from a distribution (pattern or curve) that is similar to not normal, bell shaped curve. Thus, frequency distribution of the infrastructure is normal. The figures 4. 2. 3 show the graphical representation of the bars that is showing the response of the respondents regarding balance of payment. Most of the data lies in the option -6to -2. Similarly small numbers of data were lie very low. The bars in the histogram from a distribution (pattern or curve) that is similar to not normal, bell shaped curve. Thus, frequency distribution of the balance of payment is approximately not normally distributed. The figures 4. 2. 4 show the graphical representation of the bars that is showing the response of the respondents regarding political instability. Most of the data lies in the option . 90to 1 Similarly small numbers of data were lie very low. The bars in the histogram from a distribution (pattern or curve) that is similar to not normal, bell shaped curve. Thus, frequency distribution of the political instability is approximately not normally distributed.

## Interpretation:

The output shows a scatter plot matrix shows the four scale variables i. e. infrastructure, balance of payment and political instability the overall pattern of dots show that it is diagonal upward straight regression line showing positive relationship between all variables infrastructure, balance of payment and political instability a positive relationship.

## Correlations

Political instabilityBalance of paymentinfrastructurePolitical instabilityPearson Correlation1. 934*. 988**Sig. (2-tailed). 020. 001N555Balance of paymentPearson Correlation. 934*1. 943*Sig. (2-tailed). 020. 016N555InfrastructurePearson Correlation. 988**. 943*1Sig. (2-tailed). 001. 016N555*. Correlation is significant at the 0. 05 level (2-tailed). To verify if there was a statistically significant association between and infrastructure, balance of payment and political instability a correlation matrix was computed all the variables were approximately normal there is linear relationship between them hence fulfilling the assumption for Pearson correlation. r calculated of balance of payment and political instability is r=. 934, p value is less than 0. 05 that’s shows there is positive relationship balance of payment and political instability according to Cohen’s (1988) the effect size is large that’s shows there is strong relationship. Similarly the infrastructure and political instability value of r=. 988, p=. 001 is shows the highly significant and strong relationship in the same way the relationship between infrastructure and balance of payment is strong and highly significant the value of between two variable is r=. 943, p=. 000. First hypothesis accept there is relationship between balance of payment and political instability its means H1 accept and H0 is rejected in the same way second hypothesis there is relationship between infrastructure and political instability H1 is accept and H0 is rejected and last also there is relationship between infrastructure and balance of payment means H1 is accepted and H0 is rejected.

## Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate1. 827a. 684. 579. 76504a. Predictors: (Constant), infrastructure

## ANOVAb

ModelSum of SquaresdfMean SquareFSig. 1Regression3. 80813. 8086. 505. 084aResidual1. 7563. 585Total5. 5634a. Predictors: (Constant), infrastructureb. Dependent Variable: GDPThe value of the coefficient of determination (R2) is . 57. This shows that the relation between the observed values of infrastructure and the fitted value of the GDP 57% percent. The adjusted coefficient of determination (adj. R2) shows is adjusted for the degrees of freedom. The value of F-statistic is statistically significant at less than five percent that exhibits that in the estimated model at least one of the partial regressions coefficients is not different from zero.

## Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientsTSig. BStd. ErrorBeta1(Constant)28. 3698. 1543. 479. 040infrastructure-5. 3172. 085-. 827-2. 551. 084a. Dependent Variable: gdpThe coefficient table presents the results of the regression analysis. The objective of the regression in this study is to find such an equation that could be used to find the factor that affect on GDP of Pakistan . The specified regression equation takes the following form: GDP = C + infrastructure X1GDP= 28. 36 -5. 31 X1The results show the relationship among the independent and dependent variables, infrastructure not significantly affect the GDP. Alternative hypothesis in GDP and infrastructure is rejected as the significance level is greater than o. o5 which means that there is no significant relationship between GDP and infrastructure.

## Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate1. 723a. 522. 363. 94115a. Predictors: (Constant), balanceofpayment

## ANOVAb

ModelSum of SquaresdfMean SquareFSig. 1Regression2. 90612. 9063. 281. 168aResidual2. 6573. 886Total5. 5634a. Predictors: (Constant), balanceofpaymentb. Dependent Variable: gdpThe value of the coefficient of determination (R2) is . 36. This shows that the relation between the observed values of balance of payment and the fitted value of the GDP 36% percent. The adjusted coefficient of determination (adj. R2) shows is adjusted for the degrees of freedom. The value of F-statistic is statistically significant at less than five percent that exhibits that in the estimated model at least one of the partial regressions coefficients is not different from zero.

## Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientsTSig. BStd. ErrorBeta1(Constant)6. 690. 65110. 275. 002Balanceofpayment-. 229. 126-. 723-1. 811. 168a. Dependent Variable: gdpThe coefficient table presents the results of the regression analysis. The objective of the regression in this study is to find such an equation that could be used to find the factor that affect on GDP of Pakistan . The specified regression equation takes the following form: GDP = C + balance of payment X1GDP= 6. 69 -. 229 X1The results show the relationship among the independent and dependent variables, balance of payment not significantly affect the GDP. Alternative hypothesis in GDP and balance of payment is rejected as the significance level is greater than o. o5 which means that there is no significant relationship between GDP and infrastructure.

## Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate1. 826a. 683. 577. 76694a. Predictors: (Constant), politicalinstability

## ANOVAb

ModelSum of SquaresDfMean SquareFSig. 1Regression3. 79913. 7996. 458. 085aResidual1. 7653. 588Total5. 5634a. Predictors: (Constant), politicalinstabilityb. Dependent Variable: gdpThe value of the coefficient of determination (R2) is . 57. This shows that the relation between the observed values of political instability and the fitted value of the GDP 57% percent. The adjusted coefficient of determination (adj. R2) shows is adjusted for the degrees of freedom. The value of F-statistic is statistically significant at less than five percent that exhibits that in the estimated model at least one of the partial regressions coefficients is not different from zero.

## Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig. BStd. ErrorBeta1(Constant)10. 5271. 2058. 733. 003Politicalinstability-3. 7651. 481-. 826-2. 541. 085a. Dependent Variable: gdpThe coefficient table presents the results of the regression analysis. The objective of the regression in this study is to find such an equation that could be used to find. the factor that affect on GDP of Pakistan. The specified regression equation takes the following form: GDP = C + political instability X1GDP= 10. 52-3. 765 X1The results show the relationship among the independent and dependent variables, political instability not significantly affect the GDP. Alternative hypothesis in GDP and balance of payment is rejected as the significance level is greater than o. o5 which means that there is no significant relationship between GDP and infrastructure.

## Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate1. 848a. 718. 711. 25196a. Predictors: (Constant), infrastructure, balance of payment, political instability

## ANOVAb

ModelSum of SquaresDfMean SquareFSig. 1Regression3. 99631. 332. 850. 003aResidual1. 56711. 567Total5. 5634a. Predictors: (Constant), infrastructure, balance of payment, political instabilityb. Dependent Variable: GDP

## Interpretation:

The value of the coefficient of determination (R2) is . 71. This shows that the relation between the observed values of political instability and the fitted value of the GDP 71% percent. The adjusted coefficient of determination (adj. R2) shows is adjusted for the degrees of freedom. The value of F-statistic is statistically significant at less than five percent that exhibits that in the estimated model at least one of the partial regressions coefficients is different from zero anova value show the model is good fit because its less than 0. 05.

## Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig. BStd. ErrorBeta1(Constant)32. 93984. 844. 388. 764Political instability-1. 84015. 997-. 404-. 115. 927Balance of payment. 167. 507. 528. 330. 797Infrastructure-5. 95124. 338-. 926-. 245. 847a. Dependent Variable: GDPThe coefficient table presents the results of the regression analysis. The objective of the regression in this study is to find such an equation that could be used to find. The factor that affect on GDP of Pakistan. The specified regression equation takes the following form: GDP = C + political instability X1+balance of payment X2+ infrastructureX3+E1FDI= 32. 97-1. 84 X1+. 167X2-5. 95X3+E1The results show the relationship among the independent and dependent variables political instability, balance of payment and infrastructure not significantly affect on GDP. Alternative hypothesis in instability, balance of payment and infrastructure and GDP is rejected because significance level greater than 0. 05.

## Discussion and conclusion

The present study was done to check The factor that affect on GDP of Pakistan. For this purpose the present study used quantitative research method to check the proposed model in the context of Pakistan and the present study used the last five years data from WDI. The present study collected the data by using WDI on the variables of the study from the sample of the study. The study selected the infrastructure, political instability and balance of payment as the independent variables while GDP as the dependent variables of the study. The literature review of the present study has reported the theoretical evidence from past studies which confirmed the factor that affect on GDP of Pakistan. The literature also provided evidences from previous studies which have been done to check this relationship but in Pakistan specifically in GDP, this linkage still has a gap which was filled by the present study. So the present study attempted to enhance the literature on the GDP. In the present study descriptive analysis used the descriptive statistics to find the maximum and minimum range of data and also to find the mean and standard deviation of data through frequency tables as well as histogram to check normal distribution curve. To check the acceptance or rejection of hypothesis and check the relationship between variables the present study used inferential analysis. In inferential analysis the present study tested the relationship among the variables of the study. After observing the descriptive analysis, histograms, scatter plot matrix, and the correlations, the regression has been used for further analysis. The factor that affect on GDP of PakistanThe empirical evidences showed that infrastructure has strong correlation with the political instability and balance of payment. The results of correlation also confirmed that balance of payment has strong correlation with the political instability. The results of regression analysis showed that the infrastructure has significant and positive relationship with the balance of payment. Since Pakistan is a developing country and the topic of research has little evidence from developing countries so this study helped to enhance the evidence from the developing countries context. The present study helps the finance managers to build strategies keeping in mind infrastructure, political instability and balance of payment which can result in GDP in Pakistan. Despite of the many benefits of the study, there are several limitations of the study. First, the study is only limited to the on three variable who effect of Pakistan because it collected the data only from the five years, thus the results may not be valid to the other country. The results of the present study may not be valid to the other developing countries as the present study has evidence from the developing country of Pakistan.

## 5. 2 Conclusion

The present study concluded that infrastructure, political instability and balance of payment resulted in better performance of the country by positively effecting its GDP of Pakistan. So the economists and higher authorities of Pakistani government sector must use infrastructure, political instability and balance of payment to take maximum output from the country.

## 5. 3 Suggestion and Recommendation:

The existing literature suggests that there are links between the infrastructure, political instability and balance of payment with GDP. But this study denied the result of previous researches because regression analysis shows there is not positive relationship between infrastructure, political instability and balance of payment with GDP of Pakistan.