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Impact of IMF Funding on Pakistans economy

Introduction

The funding by International Monetary Fund (IMF) to developing countries has always raised a debate on its positive and negative impacts on the economy of the creditor country. Pakistan has an extended history of funding from IMF starting from 1958 to 2004 in various time spans and now the current agreement from 2008. This study analyzes the impact of IMF funding on Pakistan. Although there has been criticism regarding both issues of policies and the funding impact but the focus of this research is to study the impacts and not to discuss or criticize the policies of IMF.

The IMF works to foster global growth and economic stability. It provides policy advice and financing to members in economic difficulties and also works with developing nations to help them achieve macroeconomic stability and reduce poverty. It is working to foster global monetary cooperation, secure financial stability, facilitate international trade, promote high employment and sustainable economic growth, and reduce poverty around the world.

Although monetary fund provides financial assistance to the developing countries but its role in economic prosperity has been highly criticized from the last few years due to its strict policies and restrictions imposed on the borrower country. Under current agreement, IMF imposes 11 main conditions on Pakistan which includes: introduction of the Central Excise Duty on service and agricultural sector, reduction in the expenditures on Public Sector Development Program, devaluation of rupee, freezing of non-development expenditure under the defense budget, non-provision of supplementary grants to government departments, ending subsidy on gas and electricity, reduction in non-development expenditure of civil departments and federal ministries, increase in markup rate of banks and on inter-bank transactions, uniformity in the inter-bank and open market dollar exchange rate and stoppage of government financial intervention in stock markets.

The main aim of IMF behind imposition of policies is to increase the revenues of the borrower country. But some studies reveal that it affects the economy both directly and indirectly. Directly it imposes impact in the sense of control of certain variables on which it put restrictions and indirectly with regard to the relationship of these variables with other macroeconomic driving variables that drives the economic growth.

The matter here is not the IMF funding but the policy impositions that could impact the economic growth. IMF provides funds for the three major areas, to reduce deficit of fiscal account and current account and to increase the revenues. The question here arises that whether the increase in taxes, elimination of subsidies and development projects will help boost the economy or causes the real GDP to fall from the expected value through increased inflation.

An extensive research has been done to address the issue of IMF policies and impact on economy of the borrower country but there are conflicting results derived by different researchers due to particular conditions related to that country, the researches that tried to study all countries under IMF program also reveals contradicting results. This study focuses specifically on Pakistan so that particular effects could be revealed that IMF funding is pouring on Pakistan’s economy.

Problem statement

The problem statement of research is “Impact of IMF funding on Pakistan’s economy”. Major variables that are used in this study include IMF funds and macro economic variables that are the indicators of an economy i.e. real GDP, employment rate, current account balance, balance of payments and FDI.

Objectives

The objectives of our study are:

To study how IMF funding is putting its effect on economy of Pakistan.

To reveal that whether there is any significant relationship between IMF funding and economic growth and if there is a relationship then whether it is positive or negative.

To draw conclusion and make recommendations through analysis that whether Pakistan should borrow from IMF or seek other ways of borrowing

Significance

Although a number of studies have addresses the stated issue but these researches mostly carried out aggregate affect taking into account all the countries under IMF program. The Research that we are going to conduct will try to find out impact of IMF funding on economic growth in particular scenario of Pakistan.

Delimitation

Our scope of study will be limited to the impacts on Pakistan economy. More over the variable that we will use for analysis of economic growth will be only major macroeconomic variables which are majorly contributing towards the growth factor. In our study we are not considering the political instability and inconsistency in the prevailing policies and other social & environmental issues that could impact economic growth side by side.

Chapter 2

Review of Related Literature

This chapter includes the work done in the same area by other researchers. It put a glance on studies of some of the researchers along with their proposed conclusions

Literature review

IMF funding has been one of the most debated issues from the last few years in terms of its policies, restrictions and its impact on the economy of countries under IMF programs. A number of studies have been done in this regard. However the results of these studies are contradicting making this issue still debatable. Recent studies have produced mixed and sometimes puzzling results regarding the impact of IMF programs on a nation's balance of payments, current account balance, foreign direct investment, real GDP, per capita income and long-run economic growth.

Martin Feldstein (1998) argues that the IMF required excessively large reductions in government deficits and restrictions on monetary policy. These restrictions resulted in substantial increases in tax rates, interest rates and increase in current account deficit. Feldstein argues that Asian economies have experienced a recession that worsened their economic problems as a result of these policy changes. Feldstein argues that many of the mandated reforms involve unjustified interference with national autonomy and have little or no relationship to the goal of resolving the payment problem. He notes that it would have been better to allow more time for negotiations between borrowers and lenders before providing IMF loans to a country experiencing payment problems.

Ho: There is no significant Impact on the current account deficit by increasing Government Expenditure through IMF Funding.

H5: There is significant Impact on the current account deficit by increasing Government Expenditure through IMF Funding.

Doug Bandow (1999) argues that the existence of IMF bailouts creates a moral hazard problem that encourages countries to not solve their fundamental problems. He suggests that all nations would benefit if healthy economies "quarantined" sick economies instead of providing economic assistance. Bandow argues that IMF assistance programs increase risk for healthy economies and do not provide long-term benefits for troubled economies. He notes that most IMF borrowers have received aid for a decade or more.

Jensen (2004) suggests that international capital markets perceive IMF intervention as a negative development. Regardless of factors driving their decisions, Jensen's research provides strong evidence that developing countries pay a serious price when they take advantage of IMF assistance. His research strongly reveals a negative relationship between IMF funding and foreign direct investment in the country. According to him investors don’t perceive this funding in a positive way that why reducing net investment level in the country and as a result hindering economic growth. For impact of IMF on FDI following hypothesis is developed:

Ho: There is no significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

H1: There is significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

On the other hand there are a number of researchers like Dicks Mireaux (2000), who have found strongly positive economic growth effects of IMF funding. These researches found that there is appositive impact of IMF funding on the economy. While there are also studies which concluded that are no significant effects of IMF on the economy of a country under IMF agreement like, Hardoy(2003) and Hutchison (2004), who argue that IMF funding does not pour any significant impact on the economy of the borrower country. Mireaux argue that economy grows due to the increased tax revenues. Following hypothesis has been developed between tax revenue and IMF funding.

Ho: There is no significant Impact on the Tax Revenue by increasing Government Expenditure through IMF Funding.

H3: There is significant Impact on the Tax Revenue by increasing Government Expenditure through IMF Funding.

Nunnenkamp(1999) in his article discussed that IMF is under serious attack as critics blame that IMF lending lead to financial crisis and suggests to stop IMF funding also the researcher discussed the consequences of ending the lending O’Driscoll (1997) in his article has conducted the descriptive research about the IMF policies towards developing countries by keeping the focus on USA economy. The Policy making of IMF for the developing countries are without any backing of historical decisions taken by the developing countries in past. Thus the financial crises and current account deficit crises is mainly attributed to such policy making. The researcher has give example of Asia in which case the above discussion is particularly true which roots in 1995. The IMF’s handling of the Mexico crisis firmly established moral hazard in international lending and sowed the seeds for the Asian crisis. Thus far, IMF policy in Asia largely repeats the policy mistakes in Mexico. 

Gina (2007) indicates in his article that the reforms enacted by Congress in USA are an important first step toward reforming the IMF. Even more important than the reforms, however, was the congressional debate over IMF funding. That debate focused attention on the process and Substance of IMF policymaking and even questioned the need for that organization in the post–Bretton Woods world.

Przeworski and Vreeland (2000) Using a bivariate, dynamic version of the Heckman selection model, we estimate the effect of participation in International Monetary Fund IMF programs on economic growth. We find evidence that governments enter into agreements with the IMF under the pressures of a foreign reserves crisis but they also bring in the Fund to shield themselves from the political costs of adjustment policies. Program participation lowers growth rates for as long as countries remain under a program. Once countries leave the program, they grow faster than if they had remained, but not faster than they would have without participation. So for the relation between IMF and GDP following hypothesis are developed:

Ho: There is no significant Impact on the GDP by increasing Government Expenditure through IMF Funding.

H1: There is significant Impact on the GDP by increasing Government Expenditure through IMF Funding.

The estimates of Barroa & Lee (2005) shows that a higher IMF loan-participation rate reduces economic growth. IMF lending does not have significant effects on investment, inflation, employment, government consumption, and international openness. However, IMF loan participation has small negative effects on democracy and the rule of law.

Ho: There is no significant Impact on the Employment by increasing Government Expenditure through IMF Funding.

H2: There is significant Impact on the Employment by increasing Government Expenditure through IMF Funding.

Chapter 3

Research Methodology

This chapter includes the theoretical model, data collection technique and methodology approach used for the analysis

Theoretical model

Economic

Growth

Real GDP

IMF Funding

Employment

FDI RRevenue

Current Account Balance

Data collection

Secondary source for the data collection has been used in this research. For this purpose most of the data will be collected from the Economic Survey of Pakistan, international monetary fund web site and state bank of Pakistan website.

The dependant variables that have been used to analyze the economic growth include: balance of payment, current account balance, real GDP, rate of employment and foreign direct investment. These are the major variables that are the determinants of economic growth of a country. The independent variable is the amount of funding by IMF.

Data analysis

Regression analysis is used for analyzing the impact of IMF funding on Pakistan’s economy. Data is analyzed using SPSS. Data for the IMF funding in Pakistan is in detail below:

 YEARS

IMF FUNDING

1973-1974

527

1974-75

1990

1975-76

1987

1976-77

2497

1977-78

232

1978-79

3406

1979-80

644

1980-81

3789

1981-82

6079

1982-83

7266

1983-84

2812

2000-01

35400

2001-02

65460

Chapter 4

Data Presentation and Findings

This chapter includes the data which has been used for the analysis, analyzed results and the findings that follow through the analysis

Data presentation and findings

Following is the detailed data used for the analysis and the findings of the regression analysis. The data is presented separately for each variable used as the measure of economic growth of the country.

IMF funding and GDP

Data Analysis for First Hypothesis

Ho: There is no significant Impact on the GDP by increasing Government Expenditure through IMF Funding.

H1: There is significant Impact on the GDP by increasing Government Expenditure through IMF Funding.

 

IMF FUNDING

GDP

1974

527

38439

1974-75

1990

39930

1975-76

1987

41229

1976-77

2497

42401

1977-78

232

45679

1978-79

3406

48204

1979-80

644

51736

1980-81

3789

55048

1981-82

6079

59012

1982-83

7266

62975

1983-84

2812

65968

2000-01

35400

180500

2001-02

65460

212200

Findings:

To test the hypothesis, linear regression analysis used. The results of regression of One independent Variable (IMF Funding) against GDP (dependent variable) can be seen in the following output.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.971

.943

.937

14025.2195

a Predictors: (Constant), IMF FUNDING

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

35481741865.974

1

35481741865.974

180.379

.000

Residual

2163774597.718

11

196706781.611

Total

37645516463.692

12

a Predictors: (Constant), IMF FUNDING

b Dependent Variable: GDP

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Model

B

Std. Error

Beta

1

(Constant)

43492.601

4451.563

9.770

.000

IMF

2.861

.213

.971

13.431

.000

a Dependent Variable: GDP

Interpretation of analysis

The ANOVA table shows that the F value of 180.379 is significant at the .000 levels. Degree of Freedom column in the table, the first number represent the number of Independent Variable (1) the second number (13) is the data collected for total number of years (N), minus the number of Independent Variable (K) minus 1 or 11=(N-K-1) or (13-1-1)= 12. The F statistics produce (F= 180.379) is significant at the .000 levels. Which shows that Model validity is significant at 0.000 level of significance.

What the result mean is that 94.3 percent of variance (R square) in increase in GDP has been significantly explained by increasing Government Expenditure by way of IMF Funding (Independent variable) with standard error of estimate of 14025.2195. Standard error of estimate shows amount falls outside the regression line or shows standard deviation from mean. There is .000 percent or less chance of this is not holding true. There is correlation of 0.971 (denoted as r=0.971) between IMF Funding (Independent variable) and GDP (dependent variable) with level of significance 0.000. so there is positive relationship between the two variables and probability of this is not true is zero percent or less. That is 100 percent of time we would expect that this correlation to exist. There is a beta value of 0.971, which shows that 97.1 percent chance of making TYPE II error if null hypothesis is accepted when it is false. At the same time Un standardized coefficient B= 2.861 indicates that the value of GDP increase by 2.861 unit for a one unit increase in Government Expenditure by IMF Funding. What the result mean is that t value 13.431 significant at 0.000. Thus hypothesis 1 is substantiated.

IMF funding and employment rate

Data Analysis for Second Hypothesis

Ho: There is no significant Impact on the Employment by increasing Government Expenditure through IMF Funding.

H2: There is significant Impact on the Employment by increasing Government Expenditure through IMF Funding.

YEARS

IMF FUNDING

EMPLOYMENT

1973-1974

527

19.76

1974-75

1990

20.3

1975-76

1987

21.08

1976-77

2497

21.89

1977-78

232

22.73

1978-79

3406

23.62

1979-80

644

24.15

1980-81

3789

24.7

1981-82

6079

25.27

1982-83

7266

25.85

1983-84

2812

26.4

2000-01

35400

37.51

2001-02

65460

38.29

Findings:

To test the hypothesis, linear regression analysis used. The results of regression of One independent Variable (IMF Funding) against Employment (dependent variable) can be seen in the following output.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.912

.832

.817

2.5175

a Predictors: (Constant), IMF Funding

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

345.318

1

345.318

54.485

.000

Residual

69.717

11

6.338

Total

415.035

12

a Predictors: (Constant), IMF Funding

b Dependent Variable: EMPLOYMENT

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Model

B

Std. Error

Beta

1

(Constant)

22.636

.799

28.328

.000

IMF

2.823E-04

.000

.912

7.381

.000

a Dependent Variable: EMPLOYMENT

Interpretation of analysis

The ANOVA table shows that the F value of 54.485 is significant at the .000 levels. Degree of Freedom column in the table, the first number represent the number of Independent Variable (1) the second number (13) is the data collected for total number of years (N), minus the number of Independent Variable (K) minus 1 or 11=(N-K-1) or (13-1-1)= 12. The F statistics produce (F= 54.485) is significant at the .000 levels. Which shows that Model validity is significant at 0.000 level of significance.

What the result mean is that 83.2 percent of variance (R square) in increase in Employment has been significantly explained by increasing Government Expenditure by way of IMF Funding (Independent variable) with standard error of estimate of 2.5175. Standard error of estimate shows amount falls outside the regression line or shows standard deviation from mean. There is .000 percent or less chance of this is not holding true. There is correlation of 0.912 (denoted as r=0.912) between IMF Funding (Independent variable) and Employment (dependent variable) with level of significance 0.000. So there is positive relationship between the two variables and probability of this is not true is zero percent or less. That is 100 percent of time we would expect that this correlation to exist. There is a beta value of 0.912, which shows that 91.2 percent chance of making TYPE II error if null hypothesis is accepted when it is false. At the same time Un standardized coefficient B= 0.00283 indicates that the value of Employment increase by 0.00283 unit for a one unit increase in Government Expenditure by IMF Funding.

What the result mean is that t value 7.381 significant at 0.000. Thus hypothesis 2 is substantiated.

IMF funding and tax revenue

Data Analysis for Third Hypothesis

Ho: There is no significant Impact on the Tax Revenue by increasing Government Expenditure through IMF Funding.

H3: There is significant Impact on the Tax Revenue by increasing Government Expenditure through IMF Funding.

YEARS

IMF FUNDING

TAX REVENUE

1973-1974

527

9,444.00

1974-75

1990

11,428.70

1975-76

1987

13,914.80

1976-77

2497

16,112.50

1977-78

232

20,041.10

1978-79

3406

23,475.70

1979-80

644

30,720.40

1980-81

3789

36,509.30

1981-82

6079

40,367.60

1982-83

7266

46,475.00

1983-84

2812

55,360.00

2000-01

35400

444,800.00

2001-02

65460

486,000.00

Findings :

To test the hypothesis, linear regression analysis used. The results of regression of One independent Variable (IMF Funding) against Tax Revenue (dependent variable) can be seen in the following output.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.958

.917

.910

49565.7061

a Predictors: (Constant), IMF FUNDING

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

300517013184.665

1

300517013184.665

122.323

.000

Residual

27024351475.824

11

2456759225.075

Total

327541364660.489

12

a Predictors: (Constant), IMF

b Dependent Variable: TAX REVENUE

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Model

B

Std. Error

Beta

1

(Constant)

10370.086

15732.008

.659

.523

IMF

8.326

.753

.958

11.060

.000

a Dependent Variable: TAX REVENUE

Interpretation of analysis

The ANOVA table shows that the F value of 122.323 is significant at the .000 levels. Degree of Freedom column in the table, the first number represent the number of Independent Variable (1) the second number (13) is the data collected for total number of years (N), minus the number of Independent Variable (K) minus 1 or 11=(N-K-1) or (13-1-1)= 12. The F statistics produce (F= 122.323) is significant at the .000 levels. Which shows that Model validity is significant at 0.000 level of significance.

What the result mean is that 91.7 percent of variance (R square) in increase in Tax Revenue has been significantly explained by increasing Government Expenditure by way of IMF Funding (Independent variable) with standard error of estimate of 49565.7061. Standard error of estimate shows amount falls outside the regression line or shows standard deviation from mean. There is .000 percent or less chance of this is not holding true. There is correlation of 0.958 (denoted as r=0.958) between IMF Funding (Independent variable) and Tax Revenue (dependent variable) with level of significance 0.000. So there is positive relationship between the two variables and probability of this is not true is zero percent or less. That is 100 percent of time we would expect that this correlation to exist. There is a beta value of .958, which shows that 95.8 percent chance of making TYPE II error if null hypothesis is accepted when it is false. At the same time Un standardized coefficient B= 8.362 indicates that the value of Tax Revenue increase by 8.326 unit for a one unit increase in Government Expenditure by IMF Funding.

What the result mean is that t value 11.060 significant at 0.000. Thus hypothesis 3 is substantiated.

IMF funding and FDI

Ho: There is no significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

H1: There is significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

YEARS

IMF FUNDING

FDI

1973-1974

527

-189

1974-75

1990

447

1975-76

1987

675

1976-77

2497

321

1977-78

232

1065

1978-79

3406

1080

1979-80

644

840

1980-81

3789

1225

1981-82

6079

3430

1982-83

7266

1473.5

1983-84

2812

1680

2000-01

35400

19995

2001-02

65460

30051.4

Findings :

To test the hypothesis, linear regression analysis used. The results of regression of One independent Variable (IMF Funding) against FDI (dependent variable) can be seen in the following output.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.991

.982

.981

1290.1947

a Predictors: (Constant), IMF

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1010046942.200

1

1010046942.200

606.780

.000

Residual

18310625.532

11

1664602.321

Total

1028357567.732

12

a Predictors: (Constant), IMF

b Dependent Variable: FDI

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Model

B

Std. Error

Beta

1

(Constant)

-128.350

409.504

-.313

.760

IMF

.483

.020

.991

24.633

.000

a Dependent Variable: FDI

Interpretation of analysis

The ANOVA table shows that the F value of 606.780 is significant at the .000 levels. Degree of Freedom column in the table, the first number represent the number of Independent Variable (1) the second number (13) is the data collected for total number of years (N), minus the number of Independent Variable (K) minus 1 or 11=(N-K-1) or (13-1-1)= 12. The F statistics produce (F= 606.780) is significant at the .000 levels. Which shows that Model validity is significant at 0.000 level of significance.

What the result mean is that 98.2 percent of variance (R square) in increase in FDI has been significantly explained by increasing Government Expenditure by way of IMF Funding (Independent variable) with standard error of estimate of 1290.1947. Standard error of estimate shows amount falls outside the regression line or shows standard deviation from mean. There is .000 percent or less chance of this is not holding true. There is correlation of 0.991 (denoted as r=0.991) between IMF Funding (Independent variable) and FDI (dependent variable) with level of significance 0.000. So there is positive relationship between the two variables and probability of this is not true is zero percent or less. That is 100 percent of time we would expect that this correlation to exist. There is a beta value of 0.991, which shows that 99.1 percent chance of making TYPE II error if null hypothesis is accepted when it is false. At the same time Un standardized coefficient B= .483 indicates that the value of FDI increase by .483 unit for a one unit increase in Government Expenditure by IMF Funding.

What the result mean is that t value 24.633 significant at 0.000. Thus hypothesis 4 is substantiated.

IMF funding and current account deficit

Data Analysis for Fifth Hypothesis

Ho: There is no significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

H5: There is significant Impact on the FDI by increasing Government Expenditure through IMF Funding.

YEARS

IMF FUNDING

CURRENT ACCOUNT DEFICIT

1973-1974

527

3318

1974-75

1990

10639

1975-76

1987

9212

1976-77

2497

11718

1977-78

232

14835

1978-79

3406

19463

1979-80

644

23519

1980-81

3789

24216

1981-82

6079

33212

1982-83

7266

33709

1983-84

2812

39368

2000-01

35400

87930

2001-02

65460

56629

Findings:

To test the hypothesis, linear regression analysis used. The results of regression of One independent Variable (IMF Funding) against FDI (dependent variable) can be seen in the following output.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.738

.544

.503

16297.8554

a Predictors: (Constant), IMF

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3486593978.160

1

3486593978.160

13.126

.004

Residual

2921820995.533

11

265620090.503

Total

6408414973.692

12

a Predictors: (Constant), IMF

b Dependent Variable: current account deficit

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Model

B

Std. Error

Beta

1

(Constant)

19177.053

5172.891

3.707

.003

IMF

.897

.248

.738

3.623

.004

a Dependent Variable: current account deficit

Interpretation of analysis

The ANOVA table shows that the F value of 13.126 is significant at the .004 levels. Degree of Freedom column in the table, the first number represent the number of Independent Variable (1) the second number (13) is the data collected for total number of years (N), minus the number of Independent Variable (K) minus 1 or 11=(N-K-1) or (13-1-1)= 12. The F statistics produce (F= 13.126) is significant at the .000 levels. Which shows that Model validity is significant at 0.000 level of significance.

What the result mean is that 54.4 percent of variance (R square) in increase in Current Account Deficit has been significantly explained by increasing Government Expenditure by way of IMF Funding (Independent variable) with standard error of estimate of 16297.855. Standard error of estimate shows amount falls outside the regression line or shows standard deviation from mean. There is .004 percent or less chance of this is not holding true. There is correlation of 0.738 (denoted as r=0.738) between IMF Funding (Independent variable) and Current Account Deficit (dependent variable) with level of significance 0.004. So there is positive relationship between the two variables and probability of this is not true is zero percent or less. That is 99.60 percent of time we would expect that this correlation to exist. There is a beta value of .738, which shows that 73.8 percent chance of making TYPE II error if null hypothesis is accepted when it is false. At the same time Un standardized coefficient B= .897 indicates that the value of Current Account Deficit increase by .897 unit for a one unit increase in Government Expenditure by IMF Funding. What the result mean is that t value 3.623 significant at 0.004. Thus hypothesis 5 is substantiated.

Summarized Results

Hypothesis

R

R square

S.E.E.

B

BETA

F

T

SIG

Decision

H1

.971

.943

14025.2

2.861

.971

180.37

13.43

.000

SUBSTATIATE

H2

.912

.832

2.5175

.0028

.912

54.485

7.381

.000

SUBSTATIATE

H3

.958

.917

49565.7

8.326

.958

122.323

11.06

.000

SUBSTATIATE

H4

.991

.982

1290.19

.483

.991

606.78

24.633

.000

SUBSTATIATE

H5

.738

.544

16297.85

.897

.738

13.126

3.623

.000

SUBSTATIATE

Interpretations of Summarized Results

In line with the objectives, the interpretation focuses on the Impact of IMF funding on GDP, Employment, tax revenue, FDI and current account deficit.

As the IMF attaches conditions with loan such as increase in Tax rates to increase government revenue. The data analysis supporting the hypothesis, that due to increase in Government Expenditure through IMF funding will bring significant increase in tax revenue. The hypothesis is substantiated as value of t = 11.06 is significant at the level of .000. Just one unit increase in IMF funding will bring 8.326 units increase in tax revenue.

The hypothesis substantiated that the increase in government Expenditure through IMF funding will bring significant increase in GDP. Just one unit increase in IMF funding will bring 2.861 units increase in GDP. The hypothesis is substantiated at .000 level of significance with t value of 13.431.

The hypothesis substantiated that the increase in government Expenditure through IMF funding will bring significant increase in Employment. But the one unit increase in IMF funding will increase 0.00283 units increase in level of employment. There is 91.2% correlation between IMF funding and Employment. Where as 83.2% variance (R square) in increase in Employment is significantly explained by increase in Government expenditure by IMF funding with S.E.E. is 2.5175. The hypothesis is substantiated at .000 level of significance with t value 7.381.

The Hypothesis substantiated that the increase in Government expenditure through IMF funding will bring significant increase in FDI and Current Account Deficit at .000 level of significance with t values 24.633 and 3.623 respectively. Just one unit increase in IMF funding will bring .483 units increase in FDI and .897 unit increase in Current Account Deficit. The positive Impact of the IMF funding on the Balance of Payment is less than the negative Impact.

Chapter 5

Conclusion

This chapter includes the discussion on the results derived through regression analysis, conclusion, limitation and recommendations

Discussion

The analysis of the data shows that the Positive Impact of the IMF funding on the economy is more of the increase in the tax revenue. The tax revenue will increase Government revenue. Just one unit increase in IMF funding will bring 8.326 units increase in the Tax Revenue. Where as the Impact of IMF funding on GDP is 2.861 units increase as against one unit of IMF funding. There is minimal increase in the Employment level as one unit increase in the IMF funding will bring 0.002823 units increase in the Employment level. When the Impact of IMF funding on the Employment is considered then it clearly shows that the cost associated with one unit of IMF funding is greater than the 0.002823 units increase in Employment. Thus it can be concluded that as the one of the condition of IMF is to increase tax rates to increase Government revenue is clearly indicated by the Data analysis and its interpretation.

The data analysis shows that the Impact of IMF funding on the Current account deficit is greater than the FDI. The B value shows that the one unit increase in IMF funding will bring .897 units increase in Current Account deficit and .483 units increase in FDI. When the positive and negative factor both are considered as consequences of IMF funding then the Impact on Current Account Deficit is greater than the FDI. The current account deficit has negative effect on the economy of Pakistan. From this we can conclude that the Negative Impact of IMF funding on Pakistan economy is greater than the positive Impact through IMF funding.

Conclusion

The study finds out that there is a negative impact on the current account balance due to the IMF funding, despite of the fact that IMF funding is mainly aimed to cover the current account deficit, deficit in balance of payment and increasing revenues. Tax revenues are increased with IMF funding. There is minimal increase in the level of employment with the IMF funding. As far as FDI is concerned, a positive relationship has been revealed between FDI and IMF funding, but the negative impact of IMF on current account deficit is greater than the positive impact of IMF on FDI, real GDP and employment level.

Limitations and recommendations

The data that has been used for the analysis is taken from the economic survey and the website of state bank of Pakistan. The data that is available on the IMF website and ADB is somewhat different. Moreover the rate of inflation reported by the SBP is very low. So there is a possibility that the relationship derived between real GDP and IMF funding would be different if the inflation rate is available for the same period from other sources like World Bank or Asian Development Bank.

Chapter 6

Bibliography

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