impact of macro economic variables of poverty

8/2/2019 Impact of Macro Economic Variables of Poverty

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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

COPYRIGHT 2012 Institute of Interdisciplinary Business Research788

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VOL 3,NO 10

THEIMPACT OF DIFFERENT

MACROECONOMIC VARIABLES ON POVERTYIN PAKISTAN

*Dr. Hazoor Muhammad Sabir and **Safdar Hussain Tahir

Abstract

The objective of the study was to investigate the impact of different macroeconomic

variables on the welfare of the poor in Pakistan, through annual time series data from1981-2010. In this study, through annual time series data from 1981-2010, the multiple

regression technique was applied to detect the relation between macroeconomic variables

and poverty. Inflation, GDP growth, population growth, major crops, minor crops,

livestock and per capita income were taken as independent variables while poverty (HCI)

as dependent variable. The study results revealed that GDP growth rate per capitaincome, major crops, minor crops and livestock had negative impact while inflation and

population growth rate had positive impact upon poverty. The conclusions drawn fromthe study are that in the long run, the reduction in poverty in Pakistan is to be driven by

the changes in the macroeconomic variables.

Key words: Macroeconomic variables, Poverty, Pakistan

1. Introduction:Poverty is a multidimensional phenomenon. Poverty is pronounced deprivation in well

being, and comprises many dimensions. It includes low income and the inability toacquire the basic goods and services necessary for the survival. Poverty also encompasses

low level of health and education, poor a access to clean water and sanitation, inadequate

physical security, lack of voice and insufficient capacity and opportunity to better oneslife. The definition of poverty may vary from country to country. There are two kinds of

poverty absolute or relative. In Pakistan, both absolute and relative poverty exist.

Absolute poverty refers to the lack of basic needs, education health, clothing shelter etc.

Relative poverty refers to the lack of socially acceptable level of income or otherresources as compared to other countries or societies. Absolute poverty can be alleviated

but relative poverty is a vital concept and exists in all parts of the world. It involves

comparison between groups. The poverty largely measures in monetary terms. The

causes of poverty are also multidimensional. It may be physical, psychological, economic

and socio cultural. Physical factors for poverty are lake of basic physical and economicinfrastructure and unfavorable natural environment and may also relate to poor health and

malnutrition. Psychological factors refer to stress, depression, loss of self esteem, loss ofambition and aspiration. Usually poverty is measured by the poverty line. Poverty line isdefined as the minimum level of income deemed necessary to achieve an adequate

standard of living in a given country. To determine the poverty line there are two

methods per capita income and consumption expenditures and per capita calories

requirements._________________________________________________________

*Associate Professor, Department of Economics, GC University Faisalabad.

(Corresponding Author)**Assistant Professor, Department of Banking & Finance, GC University Faisalabad.


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Per capita income and consumption expenditure refers to the total income that anadult human consume in one year at all the essential resources. Per capita calories

requirements refers to the 2350 calories per adult per day. International caloriesrequirement is 2550 calories per adult per day. The reason of adopting low calories

requirement is to reduce the percentage of the population below the poverty line.

(Economic Survey of Pakistan-2008-9)

Pakistan is a low income developing country. From the last three decadesPakistan has experienced periods of high economic growth accompanied with increase in

poverty, period of low economic growth and reduction in poverty and also the period in

which economic growth has a positive impact on poverty. The differential of economic

growth on poverty is based on basic structural characteristics of the economy and

economic policy administration which were assertive during these different time periods.Economic policies play an important role in changing the economic structure and also the

economic growth process. The main hindrance in achievement of high sustained rate of

growth of per capita income and reduction in poverty is slow progress in educationespecially of women high infant and child mortality rate that resulted in very high rate of

population.

Inflation refers to the increase in general price level of goods and services in aneconomy over time. Inflation is the main indicator of the economy. It provides important

insight on the economic state of a country and sound macroeconomic policies to control

it. Stable prices not only provide a developed environment for the economic growth butalso elevate the poor who are the most accessible in society. In Pakistan inflation has

become a serious problem and a main hurdle in the way of progress. Inflation is a keyvariable that has a significant impact on poverty.

In economics growth means the increasing capacity of the economy to satisfy theneeds and wants of the society. Economic growth can be achieved by increasing the

productivity of the economy. GDP growth means that economy is growing and

developing, technically it means the increase or decrease in the GDP compared with

previous years. It is the most important indicator of the economy. If it is growing then thegrowth rate is positive and if it is decreasing then the growth rate is negative and the

economy is in recession. The component of the GDP growth is retail expenditures,

government spending and imports and exports.

Population growth means the increase in the proportion of the individuals in a

country using per unit time for measurement. In demographics population growth refersto the increase in the number of individuals within a country in a given time period. It is

determined by the four factors birth (B), death (D), immigrant (I) emigrant (E). If thegrowth rate is increasing then it is called positive growth rate and if it is decreasing then

it is called negative growth rate. Population growth and the poverty are correlated.

Agriculture growth is also an important component that affecting the povertytrends in a country. Agriculture sector include the production of major crops, miner crops

and livestock. The major crops of Pakistan are divided in to food crops and non food

crops. The food crops include wheat, maize, grains, grams and other pulses and the cashcrops are cotton, sugarcane, tobacco and mustard. Historically it is proved that agriculture


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growth and production played a very important role in the process of development and

growth and helps to reduce poverty.Per capita income is the mean income of per person within an economic aggregate

such as a city or a country. It does not mean the distribution of income or wealth of acountry. It is also the measure of wealth of the population as compared to other nations.

It is calculated by taking the aggregate income and then dividing it by the total

population. Per capita income also has impact on poverty.

1.2 Objectives of the study:

Following are the main objectives of the study:

a) To see the poverty situation in Pakistan over timeb) To see the impact of different macroeconomic variables growth on povertyc) To work out on the growth rate of different macroeconomic variablesd) Suggestion to improve the situation2. Literature Review:

Richad (2002) in his study Macroeconomic adjustment the poor: analytical

issues and cross country evidence showed with the cross country regression analysis thatthere is a link between poverty and macroeconomic adjustment by using some

macroeconomic and structural variables. The results shows that negative growth rates,

illiteracy, income inequality tends to increase the poverty while the reduction in outputgrowth and real exchange rate tends to reduce the poverty levels.

Jamal (2006) analyzed at the macro level the relation between growth, poverty

and inequality for 1979 to 2002 for Pakistan. He concluded that there was a positiverelation between GDP per capita and the income inequality and found that inflation,

sectoral wage gap and the terms of trade worsen the inequality. He also explained thatlow level of income inequality helped in poverty alleviation and suggested adapted

policies to control and reduce the poverty and inequality.Adeyemi, Ijaiya and Raheem (2009) in their study Determinants of poverty in

Sub Sahara Africa analyzed the determinants of poverty in sub Sahara Africa by using

cross country data of 48 countries and used multiple regression technique. The results

showed that the factor like increase in population, inflation, external debt, lack of safewater, gender discrimination and ethic and religious clash causes increase in the level of

poverty in the sub-region. The results suggested that the measures like debt forgiveness,

use of family planning and stable macroeconomic variables like inflation and exchange

rate are the possible solution for poverty alleviation

Chaudhary, Malik and Hassan (2009) in there study The impact of socioeconomic and demographic variables on poverty: A village study analyzed the impact of

socio economic and demographic variables on poverty. They used two approachespoverty profile and an econometric approach for empirical analysis by using primary

data. The results show that house hold size, landholdings, dependency on household and

number of livestock has a significant impact on the incidence of poverty. They concludedthat there should be the need to improve the socio economic and demographic factors and

land should be allotted to landless households.

Chani (2011) in his study Poverty, inflation and economic growth: empiricalevidence from Pakistan investigated the relationship between the economic growth,

inflation and poverty. With the help of ARDL testing approach there exist long run


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relationship between the variables poverty, economic growth, inflation, investment and

trade openness over the period of 1972-2008. The result showed that inflation hadpositive impact on poverty while the economic growth and investment negatively affect

the poverty. He analyzed that trade openness insignificantly effect the poverty. The shortrun analyses showed that economic growth had negative and inflation had positive impact

on poverty while the effect of investment and trade openness on poverty is not significant

in short run.

Egbe and Clement (2011) in their study The Impact of Macroeconomic Policiesand Programs on Poverty Problems discussed the impact of some macroeconomic

policies on poverty in Nigeria during 1980-2002. They analyzed the determinants of

poverty in the country instead of the measures taken by the government to conquer the

incidence of poverty. For the analysis of data they used two regression equation model

based on poverty and GDP and concluded that in Nigeria the policies and programs thatbased on macroeconomic variables have not transmit the upward trends of poverty in the

country.

Zaman et al. (2011) used poverty growth model for five selected SAARCcountries (Bangladesh, Nepal, Pakistan, India and Sri-Lanka) for the year 1988-09. He

used head count ratio as proxy for poverty as dependent variable and GDP as economic

growth, GINI co-efficient as income inequality, health and educational expenditures andforeign direct investment and (export + imports) as proxy for trade openness as

independent variables. All variables were measured in log form. For empirical analyses

pooled OLS and least squares dummy variable (LSDV) methods were used. The resultsshowed that 1 percent increase in income inequality reduce poverty by .67 percent and 1

percent increase in economic growth reduce poverty by 0.1 percent while trade opennessand health expenditures were insignificant in poverty reduction in SAARC countries.

3.0 Methodology:

This paper explains the various tools and techniques for the econometric analyses

related to the research problem to be investigated.

It concentrates on to analyze the impact of inflation rate, GDP growth rate,

population growth rate, major crops, minor crops, livestock and per capita income onpoverty in Pakistan during the period 1981 to 2010. To find out the impact of these

macroeconomic variables on poverty, multiple regression technique was applied. To

check the problem of autocorrelation-Durbin Watson d and Breusch-Godfary tests, for

Hetroscadasticity-Whites test, for structural breaks Chow test and Jarque-Bera tests for

the normality in residuals were used. In order to check intra variables correlation matrixwas also applied. In this study time series data from the year 1981 to 2010 were used on

annual basis. The main source of these data was statistical hand book and economicsurveys of Pakistan.

3.1 Variables of the model

In this study in total eight variables were included in the model.HCI was taken asmeasures of poverty as dependent variable where as inflation rate, GDP growth rate,

population growth rate, major crops, minor crops, livestock and per capita income as

explanatory variables in the model. All the variables were taken in percentage form(growth rate).


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The functional form of the model:

Y = 0 + 1INF+2GDP+3PGR+4MAJC+5MINC+6LS+7PCI+ i

Where

Y = PovertyINF = Inflation rate

GDP = GDP growth rate

POP = Population growth rate

MAJC = Major crops growth rate

MINC = Minor crops growth rateLS = Livestock growth rate

PCI = Per capita income growth rate

i = Error term

3.2 Hypothesis making:

Following hypothesis are tested to check the impact of different variables on poverty

a. Inflation has a significant impact on povertyb. There is a significant impact of GDP growth on the povertyc. Population growth has significant impact to reduce povertyd. Major crops causes significant effect on poverty reductione. Miner crops helps in poverty reductionf.

Livestock significantly affect povertyg. Per capita income causes change in the incidence of poverty

3.3 Test of Autocorrelation:

Autocorrelation means correlation among the member of the series of observation

ordered in time or in space. To check the problem of autocorrelation in the error term

Durbin-Watson dand Breusch-Godfary tests are used.

3.4 Durbin-Watsondtest:This test is used to detect the autocorrelation between the residuals or error term in

regression analysis which is defined as:

=

=

=

=

=

nt

t t

nt

t tt

u

uud

1

2

2

2 1)(

This is the ratio of the sum of squared differences in successive residuals to the

RSS.

3.5 Breusch-Godfrey test:

This test is used to check the autocorrelation in error term in a regression

model that is being considered for regression analysis.

H0 =there is no autocorrelation in the error term

H1=there exists autocorrelation in the error term


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3.7 White Test:

the White test is a statistical test that establishes whether the residual variance of avariable in a regression model is constant: that is for Homoscadasticity.

Where

LM is Lagrange Multiplier and n is sample size

3.8 Chow Breakpoint test

In econometrics, the Chow test is most commonly used in time series analysis totest for the presence of a structural break. In program evaluation, the Chow test is often

used to determine whether the independent variables have different impacts on different

subgroups of the population or not.

The hypothesis about the test is as

H0 =there is no structural breakH1 = there exists structural breaks

3.9 Jarque- Bera(JB) Test

JB test is used to check that whether the data is drawn from a normal

distribution or not or whether the data have skewness and kurtosis equal to normaldistribution. Skewness and kurtosis of the OLS residuals are computed through following

expression.

+=

24

)3(

6nJB

22 KS

Where n=sample size, S = Skewness coefficient, and K=kurtosis coefficient3.10 Correlation Matrix:

Correlation is a statistical technique that can show whether and how strongly pairs

of variables are related. The main result of a correlation is called the correlation

coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the moreclosely the two variables are related.

4.0 Result and discussion:4.1 Breusch-Godfery serial correlation LM test:Table 4.1 Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.631 Probability 0.542

Obs*R-squared 1.806 Probability 0.405

The results shown in the table 4.1 illustrate that by applying the Berusch Godfery test on the model the value of Obs* R-squared which is the value of Chi-square

is greater then at 1%, 5% and 10% level of significance so the null hypothesis is

accepted that there is no auto correlation in the error term.

4.2 White Hetroscadasticity test:

Table 4.2 White Heteroscedasticity Test:

F-statistic 2.170 Probability 0.079

Obs*R-squared 19.851 Probability 0.134


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By applying the white Hetroscadasticity test on the regression model the resultshave been shown in the table 4.2 that the value of the probability is greater then , at 1%,

5% and 10% level of significance, so that the null hypothesis is accepted as the error termis homoscedastic and there is no problem of Hetroscadasticity in the model.

4.3 Chow test:

Table 4.3 Chow break point test:

Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

F-statistic 0.62 0.71 0.77 3.01 2.94 3.48 10.14 9.23 9.77 5.16 5.47 4.92

Probability 0.75 0.68 0.64 0.04 0.04 0.02 0.00 0.00 0.00 0.00 0.00 0.01

In the Table 4.3, data results show that by applying the chow test on the variables

from the years 1991-1993 the value of the probability of F-statistics is greater than , at

1%, 5% and 10%, so the null hypothesis is accepted and concluded that there is nostructural break occurs in these years. In the year 1994-95 the probability of F-statistic is

less than at 1% and 5% level of significance so the null hypothesis is rejected at 1% and

5% and concluded that there exist structural break during these years. The value of

probability of F-statistics of the year 1996 is less than , at 1% level of significance sothe null hypothesis is rejected at 1% level of significance. When we apply the test on the

variables from the year 1997-2002 the derived values of probability of F-statistics is less

than , at 1%, 5% and 10% level of significance so we accept the alternative hypothesisthat there is structural break occurs in these years.

In 1995 WTO regime was introduced. It had a great impact on Pakistan economy.

During this time period the political situation of our country also remained uncertain. 1n1996 Benazir government was dismissed and Nawaz Sharif comes into power in 1997.

Again in 1999 Pervez Musharaf coming into power and our government changed from

democratic government to Military government. In the year 2002 9/11 incidents occurred

and a war on terror begins. So due to the above mentioned factors a structural breakoccurs in the variables from the year 1994-2002.


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4.4 Jarque-Bera test:Table 4.4 Jarque-Bera test of normality:

The results of J.B statistics are shown in the table 4.4. The value of the Jarque-

Bera probability is lesser than the at 1%, 5% and 10% level of significance, so the null

hypothesis is accepted that the residuals are normally distributed.

4.5 CorrelationTable 4.5 Correlation matrix

Correlation INFLATION GDP PGR MAJOR MINOR LS PCI AGR

INFLATION 1

GDP 0.01 1

PGR -0.06 0.21 1

MAJOR -0.02 0.55 0.01 1

MINER 0.14 0.21 0.34 -0.09 1

LS 0.09 0.14 0.05 -0.09 0.09 1

PCI -0.21 -0.10 -0.09 0.30 -0.48 -0.09 1

AGR 0.03 0.59 0.09 0.84 0.13 0.37 0.15 1

Table 4.5 shows the results of the degree of the relationship among the variables

and to check the problem of Multicollinearity. Population growth rate, major crops andper capita income are negatively correlated with inflation while GDP growth rate, minor

crops and livestock are positively correlated. Per capita income negatively correlated with

all the variables. The results show that there is a high correlation between major crops

and agriculture growth rate and there exist problem of Multicollinearity in the model. Soto remove the problem of Multicollinearity the study exclude the variable of agriculturegrowth from the model and take only its sub sectors such as major crops, miner crops and

livestock.

0

2

4

6

8

-3 -2 -1 0 1 2 3 4 5

Series: Residuals

Sample 1982 2010

Observations 29

Mean 5.82E-16

Median -0.094261

Maximum 4.611411

Minimum -2.841783

Std. Dev . 1.832499

Skewness 0.621981Kurtosis 2.957110

Jarque-Bera 1.872046

Probability 0.392185


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4.6 Application of OLS:

Y = 0 + 1INF+2GDP+3PGR+4MAJC+5MINC+6LS+7PCI+ i

Table 4.6 Parameter Esimates Through OLSVariable Coefficient Std. Error t-Statistic Prob.

INFLATION 0.246 0.104 2.352 0.028

GDP -0.753 0.214 -3.518 0.002

PGR 4.412 0.994 4.435 0.000

MAJC(-1) -0.310 0.051 -6.090 0.054

MINC(-1) -0.093 0.093 -0.992 0.332

LIFESTOCK -0.470 0.091 -5.240 0.060

PERCAPITA -0.012 0.005 -2.462 0.022

C 22.138 2.755 8.034 7.685

R-squared 0.6880 Mean dependent var 27.051

Adjusted R-squared 0.584 S.D. dependent var 3.281

Durbin-Watson stat 2.015 Prob(F-statistic) 0.0003

The table 4.6 shows the regression results. The variable Y is dependent variable

which is poverty and the macroeconomic variables such as inflation, GDP growth,population growth, major crops, miner crops, livestock and per capita income were all

explanatory variables. The coefficient of determination R2

shows that 68% variation

caused by the explanatory variables on the explained variable. By applying the DurbinWatson test, (d) value was 2.01 which implies that there was no problem of auto

correlation in the error term.The co efficient of inflation was positive implying that there was a direct

relationship between poverty and inflation. Statistically it was highly significant .So, we

can reject the null hypothesis and accept the alternative that inflation had significant

impact on poverty. The reason behind was that as the general level of prices of agri-commodities rise, the purchasing power of the people decline and hence low

consumption level leading to higher levels of poverty. The estimation results stay against

the findings of Agenor (1998) who concluded that inflation has negative impact on

poverty.The co efficient of GDP growth had highly significant negative impact on

poverty. So, we the null hypothesis was rejected and concluded that there was a

significant impact of GDP growth on poverty. However the GDP growth rate isnegatively related with poverty and verifies our theoretical findings that when GDP

growth rate is increasing poverty is reduced. Our results also match with findings ofRomar and Gugerty (1997) who believed that an increase in GDP growth directly

increases the incomes of the poor.

The co efficient of population growth was highly significant on poverty and hadexpected theoretical sign implies a positive relation with poverty. Our results also match

with the findings of Chaudhary (2005), who concluded that there was a need in the

improvements in socio and demographic variables to reduce poverty in the southern areasof Pakistan.


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The coefficients of major, minor crops and livestock had negative relationship

with the incidence of poverty. Econometric analysis given in table-4.6 revealed that threevariables had significant impact on poverty. So, we cannot reject the alternative

hypotheses and concluded that the reason behind was that the share of major crops, minorcrops and livestock in GDP was about 25% of the total.

The co efficient of per capita income postulated an inverse relation with poverty

and significant impacts the incidence of poverty. So we can reject the null hypothesis and

conclude that per capita income causes change in the incidence of poverty. Our estimatedresults also match with findings of Chaudhary, Malik and Ashraf (2006) who explained

the macroeconomic determinants of rural poverty in Pakistan. Their results also

concluded that per capita income has negative relation with poverty but it has

insignificant impact on poverty which is not match with our results. The reason behind is

that they took nominal per capita income which has no impact on the incidence ofpoverty while we take real per capita income which defiantly has significant impact on

poverty.

5.0 Conclusions

Based on the empirical analysis obtained through the econometric analyses of the

time series data, it was concluded that the macro economic variables like GDP growth,

per capita income, major and minor crops had the inverse relationship with poverty, thereason behind was that said macro variables contributed towards the enhancement of per

capita income of the people leading to poverty reduction. On the other hand the inflation

and population growth both were positive correlated with poverty. The increase inpopulation number will reduce the available per capita as denominator and inflation due

persistent rising level of prices will reduce the purchasing power of the people leading torise in poverty level.

5.1 Suggestions and policy implications

Following suggestions are given to improve the situation

1. GDP growth rate should be improved on consistent basis. Government has optedgrowth oriented policies to promote sustained economic growth with the help of

fiscal, monetary and trade incentives.2. The main characteristic of the poor is their large size of family. The people are

convinced that increasing number of children is responsible for their poverty.

Rather increasing the children they should increase the power to work: they

should improve their skill efficiency and knowledge. All this will have the effect

of increasing their incomes which may result in the reduction of poverty.Accordingly, government must stimulate efforts at curtailing the growth rate of

population.3. In the agriculture sector the production of major crops miner crops and livestock

should be encouraged. If the role of middle man and land tenure system

eliminated then the farmers will get better prices of their crops which increasetheir income and enable them to get ride from the vicious circle of poverty.

4. Real per capita income should be increased and would not be accompanied withworsening the distribution of income. Real per capita income should be increasedrather than the nominal income. If the increase in real per capita income is greater


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than the increase in inflation, this will lead to increase the standard of living of the

people and poverty will come down.5. Persistent rising level of prices hurt the consumers; lessen the purchasing power

leading to poverty. In this regard, the strict price monitoring system should beestablished in economy .

So in the light of the findings of the study government should

bring stability in macroeconomic variables and implement such growth oriented and

stabilization policies especially at macro level which will helpful for povertyalleviation


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References

Adeyemi, S.L., Gafar T Ijayia and Usman A Raheem (2009), Determinants of Poverty inSub Sahara Africa, International Multi Disciplinary Journal Ethopia, Vol. 3, No. 2

Agenor, P.R (1998), Poverty Income distribution and Labor market Issues in SubSahara Africa, Collaborative Research Project, CR-2-3

Chani, M.I., Zahid Pervaiz, Sajjad Ahmed Jan, Amjad Ali and Amanat R Chaudhary

(2011), Poverty Inflation and Economic Growth: Evidence from Pakistan, World

Applied Science Journal, 14(7), 1058-1063Chaudhary, I.M., Shahnawaz Malik and Muhammad Ashraf (Winter 2006),Rural

Poveerty in Pakistan some related concepts and issues an Empirical Analysis, Pakistan

Economic and Social Review, 44(2): 259-276

Chaudhry, I. S., Malik and Hassan (2009), The Impact of Socioeconomic and

Demographic Variables on Poverty: A Village Study, The Lahore Journal ofEconomics, 14(1), pp. 30 68

Egbe, G and Awogbemi Clement (2011), The Impact of Macroeconomic Policies And

Programs on Poverty Problems, Journal of Economics and Sustainable Development,Vol 2, No. 9

Govt of Pakistan (2002), Pakistan Economic Survey 2002-03, Ministry of FinanceGovt of Pakistan

Govt of Pakistan (2008), Pakistan Economic Survey 2008-09, Ministry of Finance

Govt of Pakistan

Gujarati, D. N (2003), Basic Econometrics, McGraw Hill Education, fourth edition,pp. 300 560

Jamal, Haroon (2006), Does Inequality Matter for Poverty Reduction? Evidence fromPakistan Poverty Trends , The Pakistan development Review, Vol 45, No.3 (Autumn),pp. 439 459

Richard, A.P (2002), Macro Economic Adjustment and the poor: Analytical issues and

Cross country evidence, Policy research working paper, Report No. WPS2788

Vol. 1Romar, M and Mary Kay Gugerty (March 1997), Does Economic Growth Reduce

Poverty?, Harvard Institute for International Development

Zeman, K., Rashid, K, Khan, M.M and Ahmad, M (2011), Panel Analysis of Growth,

Inequality and Poverty: Evidence from SAARC countries, Journal of Yasar University,

Vol. 21, No. 6, pp. 3525 3537

impact of macro economic variables of poverty

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