Chapter 1

INTRODUCTIONThe distribution of income in Pakistan has been completely unequal. Information happened in the table 1 spectacles that in 1963-64, the gini-coefficient for Pakistan was 0.3666 that reached at the level of 0.4129 in 2001-02. It was the lowest thing one day 0.3379 in 1970-71 and the highest thing one day 0.4197 in 1998-99. It is interesting to notice that the inequality of income in Pakistan has been more perverse in urban areas that this in rural areas. The Gini-coefficient for urban areas in Pakistan was 0.3698 in 1963-64 and 0.4615 in 2001-02. It was the lowest thing one day 0.3589 in 1985-86 and the highest thing one day 0.4615 in 2001-02. The Gini-coefficient for rural areas in Pakistan was 0.3543 in 1963-64 and 0.3762 in 2001-02. It was the lowest thing one day 0.3005 in 1968-69 and the highest thing one day 0.4218 in 1990-91. It is important to notice that the changeability in the distribution of income as measured by the typical deviation of gini-coefficients it has been major in urban areas that this in rural areas of Pakistan during the period of this study.

On the spectacle of figures that the inequality of income in Pakistan is the worsening with the time. This has happened although the government introduced the scheme of special redistribution of Zakat at the beginning of the year 1980 for Pakistan. Since then the government collects funds of Zakat of the rich people and pays them out directly to the poor people of the society. This means that there must be some structural problems in the economy due to which the distribution of income has worsened even after the publication of the system Zakat. Due to such an undesirable change of the inequality of income, the poor segments of the society lose the hope for the best future. One believes commonly that the increasing inequality of income is degenerative for the social, political and economic cohesion of the society. This transfers the step to several social evil as corruption, theft, nepotism and discrimination. This also paves the way for the direct confrontation between 'rich' and poor of the society that is not by any means nice for economic growth and welfare of the society. It is why the manufacturers of politics in every country even Pakistan give the high priority with the eradication of inequality of income and poverty.

There are multiple sociocultural and economic factors that can have a direct or indirect effect in the distribution of income of a country.

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For example, system of social security in the place, the social position of a family and system of mould, political situation in the country and definitively fond worried region, the geographical local value the distribution of income. It is therefore completely difficult to bear all such variables in mind in a model econométrico manageably. Therefore, only few economic variables that the previous authors have identified as probable determinants of the inequality of income are analyzed in this study.

In the literature, three variables that are the financial development, fluctuation in valuation of growth of GNP and level of the GNP per capita were mentioned often like to have the significant influence in the distribution of income of a country or region. Since the target of this investigation is to identify structural variables that have aggravated the inequality of income with the time even after the publication of the redistributive scheme interventionary of Zakat, therefore, we have tried to identify rigorously any relation discernable between the inequality of income with these often it was quoting determinants of the inequality of income.

There are wide tests that a financial system well that works that it is easily accessible for masses of a country or region spurs on the economic growth and levels the distribution of income. It can be understood imagining the economically underdeveloped one and an economically suppressed system where the asymmetries of information are high, the expenses of deal are not economic and the imposition of contract is slack. In such circumstances, the bank loans go especially to rich borrowers because they can put oneself about the problem of the asymmetry of information in high grade allowing him subsidiary valuable guarantees and high net value against bank loans.

On the other hand, the bank officials ration poor businessmen who lack subsidiary guarantees and unions with high place - ups even if they chalk promising projects of investment. As consequent, they remain poor because they were born poor even if they possess commercial excellent capacities and they rise with economic projects that it is worth while. It is supposed therefore that the financial development improves the businessmen's worries that belong to the best full class relatively more and this way stuffed the hollow of income in the society.

The link between distribution of income and changeability of exit is established looking at the economic history of several nations. It has been observed in several countries Latin-American that higher is the changeability of exit of a country, worse it is the distribution of income in that country. Therefore, the proposition that the Changeability in the GNP of a country negatively fond his distribution of income has been also proved empirically for Pakistan.

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The relation between per capita GNP and inequality of income is extracted of the theory ' of trickle below ' proposed by Kuznets (1955). According to this theory, the economic high growth at first benefits rich segments of a society especially industrialists. The benefits of the economic growth ‘ trickle below ’ the working class only after rich industrialists are allowed of manufacture in series and visible consumption. Therefore, it is supposed that an increase of the GNP per capita worsens the inequality of income in the short term.

For the empirical analysis of the inequality of income, most of the previous studies has used information of cross-country probably due to the scarcity of the series of time data for an alone country or region But the fact is that the information of cross-country does not incorporate completely sociopolitical special features of respective countries or regions, which can affect strongly the relation between inequality of income and independent variables. Therefore, the results based on information of cross-country cannot be so reliable. This means that the analysis of the inequality of income on the database of series of time is very imperative. This study is supposed to investigate significant determinants as the statistics of the inequality of income on the database of series of time for Pakistan. Since the released information of series of time consists of only 17 discontinuous observations that are definitively inadequate for the statistical reliability, therefore we have generated information for the simple interpolation for those intermediate years during which the released information is not available.

The rest of the study is organized as they continue. The chapter 2 examines the excellent literature in the subject. The chapter 3 speaks about variables that are used in our model of the inequality of income, mentions his sources of information and emphasizes the methodology of this investigation. The chapter 4 presents the descriptive analysis of the inequality of income in Pakistan. The chapter 5 takes empirical results of equations of retrogression and his discussion. The conclusion of comments and implications of politics is given in the last chapter.

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Chapter 2

REVIEW OF LITERATUREThere are several sociopolitical, financial and economic factors that influence the distribution of income in of one or another form. This is a too big task for this study to identify and to quantify all such factors and then to analyze his affecting a significant way. Therefore, to keep it simply and manageably, few economic and financial variables that the previous studies have found excellent explaining the distribution of income have been selected. These variables are the level of the financial development, fluctuations in GNP and GNP per capita. This chapter is divided in four sections. In the first three sections, especially those studies have been quoted what illustrates the importance of each of three explanatory variables. The fourth section mentions some studies that are interesting to examine, but not to fall down to the category of any of the first three sections of this chapter.

2.1 Financial distribution of Income and DevelopmentThe financial development has the important effect in the distribution of income. There are two schools of the thought about the relation between inequality of income and finance. A school of the thought suggests that not linear one ‘ should invest Or - shaped ’ relation between finance and inequality. With his pioneering work, the green Forest and Javanovic (1990) developed a relation between financial development, growth and distribution of income. They have supported that the financial mediation and the growth are endógenamente certain. The financial development stimulates the growth providing the highest price of the return to be gained in the capital. On the other hand, the growth provides the ways of putting into practice the financial costly structure.

They have developed a model who predicts not linear relation between financial development and inequality of income during the process of the economic development. In the early stages of the economic development, only the rich one can afford to having access to financial markets, which means that in low levels of the economic development, the financial development raises the inequality of income and in higher levels of the economic development, the financial development benefits an increasing proportion of the society and this way it does the most equitable distribution of income.

In contrast with the relation Or - inverted shaped predicted by the green Forest and Javanovic (1990), Galor and Zeira (1993) and Baneerjee and Newman (1993) suggest a negative and linear

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relation between financial development and inequality of income. His argument is that the financial development helps to the poor person to raise his standard of living independently of the level of development. This means that the level of the financial development remains an important determinant of the inequality of income in the whole level developed you economically.

The Galor and Zeira (1993) shape the dynamic evolution of the distribution of income in an economy with the indivisibility in the investment of the human capital, where the alive agents during two periods, and generations are joined by bequests. The agents can work or as the labour not qualified during both periods, or make an indivisible investment in the human capital being a young man in the first period and then work as the labour qualified in the second period. Nevertheless, due to financial imperfection of market, only the agents with the sufficiently big heredity or at levels of high income they can afford to investing in the human capital and after his workpeople do qualified as children and the rich people of the society in the future, while the poor agents cannot allow, due to the absence of funds, give high skills to his children, therefore they remain not qualified and poor in the future. Therefore, the distribution of initial wealth matters much for the distribution of income. of an economy.. Since the inequality in the wealth perpetúa for bequests on generations, therefore, in the long career, there will be a polarization of wealth between workpeople qualified as high income and low income not qualified. Consequently the rich / educated families will converge to the stationary state of high income, whereas the poor / uncultivated ones will converge to the stationary state of low income inside the same economy.

The similar prophecies also can be found in the model of Baneerjee and Newman (1993) that correlates the dynamics of the distribution of wealth with financial imperfection of market. The model shows that the opportunity of the investment in projects back high can be restricted to those individuals who possess the wealth major than a level of threshold. More expressly, under the financial imperfect markets, only the agents with the wealth superior to this level of threshold can tackle the high return projects of super investment while those with the inadequate wealth do not go to. As consequent, a family at first rich will achieve richer and richer for his investment in projects back high while a family at first poor not that it has no access to believe markets will remain poor. Another study for Perotti and Claessense (2005) investigates that inequality of income it might be an obstacle to financial productive reforms and financial development when the powerful lobbies can block or manipulate reforms to capture his benefits for them avoiding his expenses. In other words, if the financial development drives to the extension of already economically economic healthy entities at the cost of weaker, because being discreet the ancient

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ones are capable of socializing the expenses of investment retaining most of his benefits, then the financial reforms really can worsen the distribution of income and wealth.

Liang (2006) examines empirically the impact of the financial development in the inequality of income in rural China, using provincial Chinese information during the period of 1991-2001 and applies the widespread method of the moment technical (GMM). The proportion of the entire loans to the rural GNP is used as the power for the financial development. He proves the linear hypothesis suggested by Galor and Zahira (1993) and Banerjee and Newman (1993). In addition to the financial variable, he uses the real GNP per capita in rural areas, expense of the government in the agriculture as a proportion of GNP and the level of development of the city and companies of people (TVE) measured by the proportion of the employment TVEs to the entire labour as explanatory variables. The study reveals that the financial development has reduced the inequality of income in China. Nevertheless, when the squared terms of financial variable and GNP per capita are included in empirical evaluations to prove the linear not relation between finance and inequality of income, then it turns out the spectacle that the coefficient of financial variables is always insignificant and it even has the incorrect signs.

Li, the Landowner and Zou (1998) examine the relation between financial depth and inequality of income using dataset of coefficients gini for 40 developed countries. They rise with similar results that the financial markets that work better strongly have to see with the low inequality of income.

The specification of Li and Zou (2002) analyzes at the level of the Gini as the person's variable to charge and controls that include the inflation, the financial development, the government spending and the frankness. His results suggest that, the highest inflation should to lower the inequality, whereas higher government spending, it improved the financial sector, and the best education would lower it. In the same way, in another study of cross-country, Calrke, Xu and Zou (2003) analyze the relation between inequality of income and finance. They also think that the inequality of income is low in countries with financial developed sectors, and that the inequality of income develops additional decreases as economies to his financial intermediaries.

Using the wide Cuba of dyeing of comparison of cross-country, Demirgue-Kunt and Lavine (2004) examine the relation between the financial development, changes of the distribution of income and changes of the level of the poverty. They use two specifications to investigate the relation between finance and distribution of income and two additional specifications to examine the connection of

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eradication of poverty of finance. First, the role examines the impact of the financial development in the valuation of growth of income of the 20 poorest per cent of every economy. The role appraises the effect of finance in the growth of income of the poor person controlling for the average per capita growth of GNP. Second, they examine the consequences distribucionales of the financial development examining the valuation of growth of the coefficient gini. The valuation of growth of the coefficient gini measures the deviations of the perfect equality of income. In his third specification they prove the financial meteorological development growth has any impact in the poverty beyond his impact on average per capita. Finally they use the same experiment using the valuation of growth of the measurement of Hollow of Poverty.

The role thinks that the financial development increases the income of poor person and hence it reduces the inequality of income. The countries with financial intermediaries better developed experience the most rapid decline in measured so much of inequality of income as of poverty. The biggest financial development induces income of poor person to become more rapid than the per capita GNP, inequality of income to fall down more rapidly, and prices of poverty to be diminished in a more rapid price.

2.2 Macroeconomic fluctuations and Distribution of IncomeA study well documented by Caroli, Eva and Penalosa (2004) suggests that the volatile nature in the exit could affect the distribution of income if the agents with different endowments have different attitudes towards the risk. For the illustration of his point of view, they consider an economy with workpeople and businessmen, and suppose that the businessmen are less opposite of risk that workpeople. They also suppose that the businessmen have the access to the industrial technology, which is subject to added arbitrary shocks. This means that due to the arbitrary event of shocks of technology, the marginal product of workpeople also fluctuates from the period to the period. Therefore, the workpeople who are the opposite risk would like to accept fixed day's wages less than his average productiveness in order to avoid the suspense of not fixed day's wages that they must be joined with marginal fluctuating productiveness and shocks of technology. In other words, by virtue of being less opposite of risk, the businessmen can capture the award of risk of fixed day's wages, and to the fall of the increase his part of income. This means that more volatile the technology is, bigger will be the award of risk, which the workpeople would like to pay to have they fix day's wages. This shows that the inequality of income will worsen with the time between businessmen and workpeople due to his different attitudes towards the risk.

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In another study Garcia-Penalosa and Turnovsaky (2005) they examine the effect of the technology of production in average valuations of growth, his volatile nature and the distribution of income when the individuals have an elastic supply of the work. Since it is known on such a real type of commercial cycle of models, a technological progress and the consequent increase of the exit have two conflict it affects, income and effects of substitution, in the supply of the work and this way in earned incomes. For realistic values of the grade of distaste of risk in his sight, the effect of income controls itself. This means that a technological progress reduces the supply of the work. Consequently the relative part of the capital in increases of exit that, for his{your} part, the capital accumulation accelerates. In short the most rapid technological progress and the economic growth raise the part of income to the capital and reduce the part of earned incomes although the price of salary keeps on increasing. An alternative mechanism explored by Checchi and Penalosa (2004), concentrates on effects of the attitude towards the risk on the formation of the human capital. For the illustration, the authors suppose that the exit fluctuates due to shocks of technology and at least a part of this risk or the fluctuation in the exit is spent to day's wages because, in the neoclassic frame, the day's wages must be equal to do an average of the productiveness of the work. In such an ambience, the decision of young individuals, if it is necessary to invest or not to increase his capital humanizes, depends on the quantity of bequests of his parents and the quantity that they take given financial intermediaries.

If the agents have the absolute distaste of risk decreasing, of that time the acts of wealth inherited as a mechanism of insurances, so that only those individuals were tackling the investment risked to construct his human capital those who have the sufficiently high heredity or have sufficiently high net value or subsidiary guarantees to take given financial intermediaries. When the future profit that are narrowly joined with the GNP fluctuates more, the award of risk in the human capital also increases. As consequent, the level of required heredity to increase capital humanizes directly or to allow him bank that takes given on the base of high net value and subsidiary guarantees also rises. This means that the poor who are born with the value of poor heredity become even more poor when they cannot invest in the formation of the human capital. Therefore, an economy with the biggest change in his level of GNP would exhibit less years of the education for the entire population, in particular for his poor people and this way it would finish with the distribution of income one day that worsens.

In an empirical analysis, Breen and Garcia-Penalosa (2005) explored the impact of the macroeconomic volatile nature in the distribution

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of income using the sample of cross section of 80 developing countries and developed of the world. In his evaluation, they have measured the macroeconomic volatile nature of a country for the typical deviation of the valuation of growth of exit. The role reveals that the volatile nature of exit together with rigidity of labour market seems to be the principal determinant of the grade of a country of the inequality. Instead of recognizing the theory of trickle below traditionally, they have suggested the part of enfrente. That is to say the equity distribucional should be given the priority for political redistributive and regulatory that, for his{your} part, they would heighten the growth of GNP removing rigidity of labour market and heightening the macroeconomic stability.

For the empirical evaluation, they have calculated annual valuations of growth of the real GNP during the period from 1960 to 1990. Then they have calculated typical deviations of annual valuations of growth on 30 years. The principal result of his role salt that the biggest volatile nature has to see with the highest grade of the inequality of income.

2.3 GNP Per Capita and Distribution of IncomeIt is known in the economic literature of development that the economic growth is the necessary one, but not a condition sufficient for the economic development. If the GNP per capita of a country increases but the inequality of income worsens in early stages of the development of an economy as predicted by the theory ' of trickle below ' for Kuzznet (1955), growth of that time and level of development of the movement of country in the opposite senses. The economic development needs a real GNP taller per capita and the progress of the inequality of income. According to the theory ' of trickle below ', the benefits of the first economic growth go to the rich one and then in the second round, when the rich ones afford of visible consumption and manufacture in series, go to the poor person. This way, the poor advantage of economic growth only indirectly for a vertical flow of income of the rich one to the poor person. This implies that the proportional benefits of the growth that goes to the poor person will always be less than what they they would be if they pay to the poor workpeople according to his contribution.

The Ali and Tahir (1999) have analyzed the relation of long career between per capita GNP, poverty and inequality of income in Pakistan. They developed consistent estimations of series of time of the rural and urban poverty for 14 years. Since the information of series of time of the poverty consisted of only 14 observations, therefore they assembled rural and urban information to do 28 observations. Using remarks 28 - gathered, of that time they estimated the elasticity of poverty of growth and elasticity of poverty of the inequality of income.

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The authors also analyzed the relation between per capita GNP and poverty, and between per capita GNP and inequality of income and interaction between these three variables. There it turns out to be the spectacle that the inequality of income worsens as increases of valuation of growth of GNP and increases of inequality of income of level increases of poverty. The study concludes that the GNP per capita was always helping at the level of poverty that it reduces but this was always worsening the inequality of income in the national level, in particular more in rural areas. The increase of the inequality of income, keeping the GNP per capita without altering, has done that the poverty rises more in urban areas than in rural areas.

Jamal (2004) also has investigated the impact of per capita GNP and inequality of income in the level of poverty. Since the information of series of time continuos in the inequality of income and in the poverty is not available for Pakistan, therefore the author has used the information of series of time interpolated in the inequality of income and in the poverty. To generate the date of series of time, at first, a curve cuadrática is fitted into current remarks taking the trunk of the measurement of poverty and then taking the trunk of coefficients gini on time and variables of square of time. Therefore he obtains the information of series of time in coefficients gini and population under the poverty level. His measurement of poverty is based on inventories under the poverty level> his analysis is extended during the period of 31 years, 1973 2003.The study reveals that the elasticity of the poverty with regard to several measurements of the inequality of income is negative and according to the statistics significant. Also his magnitude is relatively major than the elasticity of the poverty with regard to the growth of GNP.

2.4 Some Other Studies Related to the Topic Humberto and Lopez (2003) I try of appraising of a perspective of cross-country, the impact of the series of political against inequality. Nevertheless it has demonstrated since in the literature of growth these series of political have the empirically significant effect in the growth. Rather that to construct a new model of inequality, this role is added by the model of empirical existing growth and the results of evaluation are based on a dynamic model. This role thinks that the progress of education and infrastructure, and so much it appraises low levels of inflation progress of growth of GNP as the distribution of income and reduces the poverty. The role also concludes that the financial development, the commercial frankness and the size of the governmental budget have the positive impact in valuation of growth of GNP and inequality of income. Also, the role has appraised if the negative indirect impact of political these against the inequality of income for the progress of the growth of GNP is compensated by his positive direct impact in

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the distribution of income. The role thinks that pro the political ones of growth have been proved to be pro poor in the long career, but anti poor in the short term. Using information of series of time, Nath and Mamun (2004), it tries to examine the interrelation between commercial liberalization, growth and inequality of income in Bangladesh. His results of the autoregressive vector (VAR) the model suggests to be no tests what changes the liberalization they have accelerated the growth of GNP. Nevertheless, according to his results, the commercial frankness promotes the investment. The role has not found any strong test what the fond liberalization trades the distribution of income.The Amjad and Kemal (1997) analyze the impact of police officers economic macro in the lightening of poverty. This is the first try in Pakistan to explain the tendency of poverty with the help of economic determinants macro. They provide a few consistent information of series of time in the poverty for the period 1963-64 to 1992-93 so much for rural areas as for urban. The role explores the influence of such factors as economic growth, growth of agriculture, the terms of the commerce for the agriculture, industrial production, the price of inflation, employment, day's wages, remittances and fiscal structure in the poverty. Thinking that the number of remarks was completely limited in his study, the authors use simply an analysis of variable retrogression. Using the double transformation of trunk they step back a variable exogenous simultaneously in the frequency of poverty. They think that the real PNB per capita, real remittances per capita, real day's wages in the manufacture, entire labour as the percentage of demographic and real subsidies entire per capita is determinant according to the statistics significant of poverty and signs of all that this variable of explanatory is waited like. The role concludes that the Program of Structural Adjustment has tended to increase levels of poverty. The role also outlines some strategies for the eradication of poverty. In addition to other protection networks, the promotion of informal companies of sector has been emphasized.

The Ehtisham and Ludlow (1989) examine the impact of the valuation of growth of GNP in inequality of income and poverty. Being a critic of earlier methodologies to estimate poverty in Pakistan principally because the levels of income that are ' too arbitrary ' are chosen to estimate the poverty, and to be a critic of the idea that the level of income, which keeps interrelation badly with the standard of living, the authors argues to estimate the poverty to use some he keeps interrelation of the standard of living. They are also critics of the approach towards estimations of inequality of income, that, they believe, he has been a dear slightly mechanically. The target of the role is to improve the methodology and the estimation of walk of poverty and inequality of income

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systematically. Using a variety of poverty levels to examine the sensibility of values discriminantes and providing some points of the comparison of the frequency of the poverty with the time, the authors present estimations of the Senator's Index.

In addition to the theoretical progress of the evaluation of poverty, another important contribution of this role consists of the fact that this disintegrates information in provincial, urban and rural levels. The authors have focused correctly his attention in the population poor in every province. His results suggest that in national as well as in provincial levels, in general there was an absolute and relative decline in conditions of life of the poor person. His results for the inequality of income measured by coefficients gini show that the progress only marginal of the distribution of income has been observed. Nevertheless, using indicators different from inequality of income and information that disintegrate forward for the level of district, his results show a big change in the inequality of income with the time.

The results in the provincial level for the period 1976 1985, shows that the extreme poverty increased in the majority of the rural and urban areas of NWFP and Balochistan while the inequality of income in urban SIndh and urban Punjab improved during this period. In general the authors conclude that due to the high growth from the year 1960, there was a progress of the standard of living that had a favorable impact in poverty and distribution of income in Pakistan. The Jafri, and To - (1995) he analyzes tendencies in inequality of income and poverty during the period 1979 1991 in Pakistan. The coefficients of Gini and the parts of income for groups of different income are used to show the changes of the inequality of income during this period. The study concludes that the inequality of income diminished during the period 1979-87, but then 1988-91 worsened during the period. The coefficients of Gini in the period 1979-87 they fell down lightly while the proportion of parts of income of the tallest population of 20 per cent to the lowest population of 20 per cent it improved lightly. His results also show that the inequality of income is worse in urban areas than in rural arrears. The results of this study are consistent with those of other studies, where it is seen that the frequency of poverty had increased during this period of time, with a lot of major worsening in urban areas.

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Chapter 3

VARIABLES, DATA AND METHODOLOGY

This chapter describes possible power for variables dependent and independent from this investigation, mentions sources of information for chosen power of respective variables and finally chalk methodology econométrica for the empirical evaluation. It is divided in five sections. The first section speaks about several measurements and the sources of information of the dependent variable that is the inequality of income. The second section proposes a practical measurement of the financial development and his sources of information. The section three rises with an ingenuous measurement of fluctuations in the valuation of growth of GNP. Section four sources of information of mentions of GNP per capita. The final section speaks about several methodologies to identify significant determinants as the statistics of the inequality of income.

3.1 Inequality of incomeMany approaches exist for the measurement of the distribution of income across regions and groups. The measurements more commonly used of the inequality of income include the coefficient gini; proportion of decile or the proportion of entire income gained by the fund 20 %, came up 60 % and 20 the first % of population. The coefficient of Gini is the area between Lorenz's curve and line of perfect equality. The coefficient of Gini for any country can take the value between 0 and 1. When the value of the coefficient Gini is 0, this one means that there is no inequality of income and every individual in the economy has the same level of income. When the value of the coefficient Gini is 1, this one indicates that there is perfect inequality of income and only an individual has the entire income of the economy and each one more has income zero. The coefficient gini satisfies four important properties of a good measurement of the inequality of income: anonymity, scale and independence of size and beginning Pigou-Dalten of transference. The anonymity implies that the identity of the rich one and the poor person should not bring any change in the measurement of inequality. For example, if the movements of Zaid to population fall - inome and movements of Umar, then the measurement of inequality should not change to the population of hig-income. The independence of scale means that the coefficient gini does not change with the size of economy. The level of the inequality of income can be same in a small economy, to say an US$ billion economies, and in a big economy, to say an US$ one trillion of economy. The demographic independence implies that the

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demographic size of a country does not matter. The level of the inequality of income can be same in a small economy, to say one a million of economy of the people, and in a big economy, to say a few billion economies of the people. The beginning of Pigou-Dalton of the transference needs that whenever some income is transferred from a rich person to the poor one but such a transference does not invest the classification of two individuals, the of that time one the measurement of the inequality it should be diminished.

In addition to all these qualities, a principal limitation of the coefficient gini is that, it is more sensitive to the average part of the population than to any of two extreme parts of the population. It is therefore important to use some other measurements of the inequality of income that more weightage give to or both extreme ends of the population. Therefore we have used another two measurements of inequality of income. One is the part of income of the 20 poorest per cent of the population, and other is the proportion of parts of income of 20 per cent richest to the 20 poorest per cent of the population. On measurements of income the inequality is calculated using information happened in several questions of Reviews of Expense and Income of Household it (IS) published by the Federal Office of the Statistician (FBS). The FBS led his first one IT GOES in 1963 and more 17 reviews with unequal intervals until 2006. Several authors have calculated coefficients gini and parts of income of every quintile family income that they use or expenses of household or income per capita, and capture of grouped information or primary information for some years when IT GOES was led. Some authors have improved his measurements of inequality using information of income tax. Therefore, to have a few consistent measurements of the inequality of income, we have adopted the measurement of inequality of Anwar (2005) who has used income of household and has grouped information to calculate measurements of inequality of income for every year when IT GOES the last one was led except in 2006.

3.2 Financial development To measure the financial development, the previous studies have used the private domestic credit to the proportion of GNP, liquid responsibility to the proportion of GNP, bank credit to the proportion of GNP and M2 to the proportion of GNP. Between these measurements of the financial development, the domestic credit deprived to the proportion of GNP has been used more often. Therefore also we have used this measurement as a power for the financial development. The private domestic credit equals the credit value for financial intermediaries to the private sector split into the GNP. This measurement excludes credits published by banks of development and the central bank. Also, this excludes the credit to national

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companies and angry claims of a group of intermediaries in other. This way, the private credit shows the quantity of canalized credit of private savers, for financial intermediaries, to private signatures. The information in the private domestic credit has been collected of several questions of the Financial International Statistics (IFS), several questions of World Indicators of Development (WDI) and several questions of the Economic Review of Pakistan.

3.3 Macroeconomic fluctuations In his analysis of multicountry, Richard and Penalosa (2005) have used the typical deviation and the disagreement of valuations of growth of the real GNP as an indicator of macroeconomic fluctuations. Since we want to examine significant determinants as the statistics of the inequality of income only for Pakistan for which the annual valuations of growth of the real GNP give the observation only alone of the typical deviation, therefore we have used the square of the difference between the valuation of annual growth of the respective year of real GNP and compound valuation of growth annual average of the real GNP for the entire period of this study, 1963-2001, like a power for macroeconomic fluctuations. This variable it was he himself who has been calculated by the investigator on the base of information happened in several questions of the Economic Review of Pakistan.

3.4 GNP Per capita The real GNP per capita is one of the important determinants for the economic development. It is defined as the Gross National Product split into the population of middle of the year and then deflated by the corresponding rate of inflation. We have taken information in this variable of World Indicators of Development (2006).

3.5 Methodology econometricalWe have proved the following model to determine significant arguments as the statistics of the inequality of income in PakistanG = f (FD, MEF, Y) Where G means a measurement of the inequality of income, FD represents a measurement of the financial development, MEF indicates macroeconomic fluctuations or change in valuations of growth of the real GNP and Y represents the GNP per capita.We have estimated the linear form of this relation as given abajo:-G = α FD MEF Y � � � μ (1)Where the term to intercept, α, captures the effect of all the theoretically excellent variables for this model, but lost in this specification, is the coefficient of the variable FD, is the coefficient � �of the variable MEF, is the coefficient of the variable Y and or it is �the term of error with the standard assumption of the zero he waited for the value, the constant disagreement and no consecutive interrelation.

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On the base of results of previous investigations, it is hoped that the sign of should be negative the one that means that the financial �development reduces the inequality of income. On the other hand, it is hoped that the signs of and it should be positive that indicate � �that the volatile nature of exit and the GNP per capita forward they expand the existing level of the inequality of income. The model has been a dear three times; at first taking gini coefficients as the measurement of inequality of income, part of that time of income of the poorest population of 20 % and finally the proportion of parts of income of 20 % richest to the poorest population of 20 %. Taking every measurement of the inequality of income, the retrogression is adapted for entire Pakistan as well as for his rural and urban areas

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Chapter 4

Profile of income inequality in Pakistan

Before examining determinants of the inequality of income, it can be interesting to have a superficial look of the state of the inequality of income in Pakistan. After independence in 1947, the first income of household and review of expense IT (GOES) what provided the basic information to appraise distribution of income in the country they were led in 1963. Since then this has continued, but with irregular intervals. Till now, 18 such reviews have been realized.

Several authors have measured the inequality of income using different criteria and during different years on the base of information principally contained in these reviews, but occasionally complemented by information of income tax and other sources. The list informs of such studies and a brief description of each of them is given in Anwar (2005). Therefore, to assure consequence, this investigation has adopted measurements of inequality that are coefficients gini and parts of income for quintile of Anwar (2005). The summary following tables are ready on the base of detailed information happened in that study. These tables show three alternative measurements of the inequality of income; coefficients of gini, part of income of the population of 20 % more poor person and the proportion of parts of income of the population of 20 % richest to the poorest population of 20 %. I mediate, the maximum and minimal values of each of these measurements of the inequality of income are reported for entire Pakistan and for his rural and urban areas in table1. The table also shows the typical deviation of every measurement about his average value during the entire period of this study, 1963-2001. Table 2 repetitions the same exercise as in table 1 during each of four decades of the study. Table 3 gifts simple interrelation of each of three measurements of inequality of income with each of the explanatory variable for entire Pakistan and for his rural and urban areas.

Table 1: Profile of Income Inequality over 1963 – 2001 in PakistanInequality measures Mean Maximum Minimum Standard

deviation

17

Pakistan

Gini 0.3738 0. 4187 0.3379 0.0252

PR 7.22 8.04 6.07 0.56

RTP 6.28 7.83 5.25 0.76

Rural area

Gini 0.3483 0.4218 0.3005 0.0293

PR 7.61 8.54 6.00 0.72

RTP 5.70 8.10 4.56 0.88

Urban area

Gini 0.3899 0.4615 0.3589 0.0299

PR 7.12 7.72 6.59 0.35

RTP 6.55 7.80 5.80 0.63Source: Calculations by the researcher on the basis of data given in Anwar (2005)

The following abbreviations are used in all tables of this research.Gini is the gini coefficient PR is the income share of poorest 20 percent populationRTP is the ratio of income shares of richest 20 percent to poorest 20 percent populationFD is the private domestic credit to GDP ratioGDPP is the real per capita GDPGR is the GDP growth rate MEF is the variable for macroeconomic fluctuations

Table 1 spectacles three measurements of inequality of income for entire Pakistan and for his rural and urban areas during the entire period of this study 1963-2001. If we look at the average value of the coefficient gini, it has been major in the urban area that this in the rural area of Pakistan. The last column of the table that shows the typical deviation of every measurement of inequality of income about his average value indicates that the change in the coefficient gini has been worse in the urban area that this in the rural area. When we examine the average part of income of the population of the 20 poorest per cent, we think that it has been smaller in the urban area that this in the rural area. His typical deviation also has been smaller in the urban area that this in the rural area. The proportion of parts of income of the population of 20 per cent richest to the population of the 20 poorest per cent has been smaller in the rural area that this in the urban area. Nevertheless, his typical deviation has been smaller in the urban area that this in the rural area.

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Three measurements of the inequality of income show that the inequality of income generally is higher in the urban area that this in the rural area. It can stem from the fact that the urban labour is more diversified in terms of skill, education, revenue of union and minimal law of salary. This way, the income of salary in urban areas is distributed more unequally than this in rural areas. Equally, the earned incomes by proper account are more concentrated on urban areas that this in rural areas because urban me the employment spreads from rich businessmen to poor workpeople whereas the bundle of the rural one me used is almost homogeneous in informal sectors.

Table 2: Decade-Wise Profile of Income Inequality in Pakistan

Pakistan Rural area Urban areaGini PR RTP Gini PR RTP Gini PR RTP FD GR

1960s

Mean

Max

Min

St.dev

0.355

0.367

0.339

0.014

7.59

7.91

7.28

0.32

6.71

6.80

6.55

0.12

0.327

0.342

0.301

0.025

7.99

8.52

7.35

0.07

5.17

5.87

4.56

0.61

0.386

0.407

0.369

0.019

7.31

7.72

6.84

0.05

6.37

7.03

5.84

0.53

25.4

26.3

23.1

1.32

7.61

11.4

5.4

2.63

1970s

Mean

Max

Min

St.dev

0.364

0.395

0.338

0.029

7.67

8.04

7.19

0.44

6.60

6.79

6.47

0.17

0.335

0.355

0.306

0.026

7.74

8.54

6.53

1.06

5.46

6.49

4.62

0.95

0.390

0.412

0.369

0.022

7.19

7.53

6.80

0.37

6.51

7.13

5.96

0.59

23.6

29.1

19.2

2.92

4.72

10.2

0.47

3.04

1980s

Mean0.366

7.46

6.68

0.336

8.07

5.22

0.373

7.25

6.21

26.0

6.29

19

Max

Min

St.dev

0.380

0.358

0.010

7.67

7.10

0.27

6.91

6.54

0.16

0.353

0.323

0.013

8.39

7.66

0.33

5.65

4.89

0.34

0.384

0.359

0.013

7.44

6.90

0.23

6.67

5.96

0.33

29.8

24.0

1.94

7.92

4.46

1.23

1990s

Mean

Max

Min

St.dev

0.397

0.417

0.360

0.022

6.60

7.11

6.07

0.33

7.30

7.47

7.03

0.18

0.377

0.422

0.352

0.024

6.98

7.24

6.00

0.48

6.54

8.11

5.84

0.90

0.404

0.462

0.362

0.042

6.88

7.04

6.59

0.35

6.76

7.80

5.94

0.39

23.9

25.5

22.0

1.17

3.95

7.71

1.01

1.91

Source: Calculations by the researcher on the basis of data given in Anwar (2005)

The table 2 gives the wise comparison of decade of all the measurements of the inequality of income because an alone measurement for the entire period hides the process of transition that is essential to understand the frequency of the inequality of income and can be useful for targets of politics. Therefore, the entire period of the study 1963-2001 is divided in four decades; the year 1960, the year 1970, the year 1980 and the year 1990. Last decade of the year 1990 also they include the year 2001. The information for every decade in the table 2 exactly seems to that in the table 1 for the entire period.

The wise comparison of decade shows that the coefficient gini for entire Pakistan in the year 1990 turns out to be the the highest and in the year 1960 to be the lowest. This means that the inequality of income has worsened consistently with the time. It seems in disagreement with the fact that the government of Pakistan has been making the system of Zakat work in the economy from beginning of the year 1980. The system Zakat is categorically destined for the lightening of poverty. From poverty and movement of inequality of income generally in the same direction, therefore it would be necessary to hope correctly that the distribution of income in the country would have improved with the time in particular after

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the year 1980. The typical deviation of coefficients gini had been the highest in the year 1970 and the lowest in the year 1980 the one that means that the changes of the distribution of income had been more in the year 1970 and less in the year 1980.

Looking at rural and urban figures for coefficients gini and his typical deviations, it is interesting to notice that the model of the inequality of income in the rural area exactly seems to that for entire Pakistan. Nevertheless, for the urban area, the coefficient gini higher is during the year 1990 as for entire Pakistan and his rural area, but the coefficient gini more down had been during the year 1980 in contrast to for entire Pakistan and his rural area where it was the lowest thing in the year 1960. It can stem from the best achievement of the system Zakat in urban areas in the initial years of his publication. Looking at the change in coefficients gini, it was the highest thing in the urban area in the year 1990.

The part of income of the population of the 20 poorest per cent for entire Pakistan had been the highest in the year 1970 that any other decade. In contrast with this one, the part of income of the 20 poorest per cent for entire Pakistan and his rural and urban areas it was the lowest in the year 1990. The typical deviations for especially the Pakistan and for his rural and urban area show that the part of income of the population of the 20 poorest per cent changed more in the year 1970 than in any other decade.In the national level, the year 1970 they turned out to be the best decade for the population of 20 per cent poorest in Pakistan when his part of income was the highest during that decade.

Nevertheless, the same one is not real for rural and urban areas of the country. In the rural area, the part of income of the population of the 20 poorest per cent had been the highest in the year 1980 whereas in the urban area, it had been the highest thing in the year 1960. The typical deviation of parts of income of the population of the 20 poorest per cent had been the highest in the year 1970 for entire Pakistan and for his both rural and urban areas, that shows that the part of income of the 20 poorest per cent changed more in that decade. It might be attributed to political in favor of poor of the Party of the Peoples of Pakistan that had been in the power from beginning of the year 1970 up to 1977.

In entire Pakistan and it rural and urban areas, the proportion of parts of income of the 20 poorest per cent to 20 per cent richest population had been the highest in the year 1990. This one suggests that the hollow between the rich one and the poor person should be the highest in the year 1990 for entire Pakistan and it rural and urban areas. The typical deviation of this variable had been more in the year 1990 for entire Pakistan, but for rural and urban areas it had been the highest thing in the year 1970.

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The worsening of the inequality of income during the year 1990 can be because the reforms of politics of adjustment financed notably by International Monetary Fund and World Bank were aimed to reduce principally current and fiscal deficits and remove the distortion of politics without having properly consequences distribucionales adverse in account. Therefore, such political macro inflicted an adverse effect not only in the frequency of poverty but also in the grade of the inequality of income.

It is important to notice of the table 2 that all the measurements of inequality of income increased from the year 1960 to the year 1970 while the domestic credit deprived to the proportion of GNP diminished during that period. The inequality of income financier remained almost same from 1970 until 1980 while develpment increased considerably during that period. Also, the inequality of income increased considerably from the year 1980 to the year 1990 but the financial development was diminished during that period. This shows that a high level of the inequality of income has been principally accompanied at the low level of the financial development. Also it can be seen of the table that the inequality of income had been the lowest in the year 1960 when the average valuations of growth of GNP had been the highest. Also, the inequality of income increased from the year 1960 to the year 1970 while the average valuation of growth of GNP diminished during this period. Nevertheless, the inequality of income remained almost same from the year 1970 to the year 1980 but the valuation of growth of GNP was increased considerably. Finally, the inequality of income increased considerably from the year 1980 to the year 1990 while the valuation of growth of GNP was diminished considerably during that period.

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Table 3: Simple Correlation of Various Measures of Income Inequality with Independent Variables

Inequality measures FD GDPP MEF

Pakistan

GINI -0.46 0.64 0.21

PR 0.48 -0.71 -0.14

RTP -0.52 0.71 0.06

Rural area

GINI -0.47 0.59 0.15

PR 0.51 -0.43 -0.09

RTP -0.48 0.48 0.03

Urban area

GINI -0.47 0.28 0.03

PR 0.42 -0.42 -0.03

RTP -0.48 0.35 0.07Source: Calculations by the researcher on the basis of data given in

Anwar (2005) and various issues of IFS, WDI and Economic Survey of Pakistan.

Table 3 gifts simple interrelation of several measurements of inequality of income with each of the explanatory variables. The coefficients of interrelation in the column 1 and 2 suggest to be negative relation between coefficients gini for entire Pakistan and his rural and urban areas, and financial development in Pakistan. This means that the inequality of income reduces with the development of the financial sector. Nevertheless, the coefficient of interrelation of the part of income of the population of 20 per cent poorest in entire Pakistan and in his rural and urban areas with the financial development had been a positive but this implies the same one that the financial development helps to the poor person to raise his part of income. In the same way, a negative relation between the proportion of parts of income of the population of 20 per cent richest to the population of 20 per cent poorest in entire Pakistan and in his rural and urban areas with the financial development indicates that the financial development had contributed decisively to reduce the hollow of income between the rich one and the poor person. So on the base of simple coefficients of interrelation, a negative relation between inequality of income and financial development in entire Pakistan and in his rural and urban areas is supposed.

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The column 3 of this table show that there is a positive interrelation of coefficients gini for entire Pakistan and his rural and urban areas with the GNP per capita. Whereas, there is a negative coefficient of interrelation between the part of income of the population of 20 per cent poorest in entire Pakistan and in his rural and urban area and GNP per capita. In a similar way the coefficient of interrelation of the proportion of parts of income of the population of 20 per cent of 20 per cent richest demographic and poorer in entire Pakistan and in his rural and urban areas with the GNP per capita has been a positive. The coefficients of interrelation of every measurement of the inequality with per capita the GNP lead to the same conclusion that per capita the GNP has a negative impact in the inequality of income.

The last column of table 3 spectacles the coefficient of interrelation of every measurement of inequality of income in entire Pakistan and in his rural and urban areas with fluctuations in valuation of growth of GNP or with macroeconomic fluctuations. The signs of all the coefficients of interrelation are exactly the same one like in the column 3, although the magnitude of coefficients of interrelation in the last column has been completely smaller than those in the column 3. This means that the macroeconomic fluctuations worsen the inequality of income as it does the GNP per capita but the adverse consequences of macroeconomic fluctuations it is much less than those than the GNP per capita.

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Chapter 5

EMPERICAL RESULTS AND THEIR DISCUSSION

One of the principal problems with information of inequality of income is that it is inadequate for the statistical confidence and is also discontinuous. The income of household and the reviews of expense (GO) they were begun in 1963 that they have been continued till now. Nevertheless, they have been led by irregular intervals of time. As consequent, only 17 reviews had been completed until 2001. From almost all the measurements of the use of inequality of income IT GOES information of one or another form, therefore only 17 observations of the inequality of income can be obtained of released information, which are, of course, insufficient of drawing causal relations as the reliable statistics. To assure the statistical confidence, the information can be generated by several methods of the interpolation but a game of information so generated implies the tendency of interpolation.

Therefore, the evaluation of the causal relation between inequality of income and three independent variables, financial development, per capita GNP and macroeconomic fluctuations has been subdivided in two parts, evaluation using released information and evaluation using so much published information as interpolated. First the part designated as 5.1 contains the evaluation on the base of released information only that cannot be according to the reliable statistics but they are free of the tendency of interpolation. The empirical results of this part are showed in tables 4 to 6. On the other hand, the section 5.2 contains the evaluation in the base released and autogenerated information. The empirical results of this section are showed in tables 7 to 10. These results are serious according to the statistics, but they can be subject to some error due to the particular method used for the interpolation. 5.1 Empirical evaluation using Released Information Although the released information contains only 17 observations that are insufficient to draw any reliable inference as the statistics, they are still beyond the tendency of interpolation. Therefore, to avoid tendency of interpolation, the equations of retrogression of this study are estimated on the base of released information only. Three powers for inequality of income; the coefficient of gini, the part of income of the population of the 20 poorest per cent and the

25

proportion of parts of income of 20 per cent richest to the population of the 20 poorest per cent has been used. Then for every power of inequality of income; three equations; one for entire Pakistan and others two for his rural and urban areas, have been estimated. In whole, 9 equations of retrogression have been estimated in this section.

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Table 4: Regression Results on the Basis of Published Data Only; Dependent Variable is Gini Coefficient

Explanatory variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

3.7082

-0.4644

0.0354

0.1337

5.9051

-2.9221

1.4139

3.6036

0.0001

0.0119

0.1809

0.0032

R-square 0.63 F-statistic 7.4989Adjusted R-

square 0.55 Prob. of F-statistic 0.0037

Rural area

Constant

FD

MEF

GDPP

3.6964

-0.5338

0.1310

0.1563

4.0831

-2.3299

0.3618

2.9251

0.0013

0.0366

0.7233

0.0119

R-square 0.53 F-statistic 4.9226Adjusted R-

square 0.42 Prob. of F-statistic 0.0168

Urban area

Constant

FD

MEF

GDPP

4.6763

-0.4625

0.0006

0.0483

4.9041

-1.9167

0.0153

0.8581

0.0003

0.0775

0.9880

0.4064

R-square 0.27 F-statistic 1.6619Adjusted R-

square 0.11 Prob. of F-statistic 0.2238

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Table 5: Regression Results on the Basis of Published Data Only; Dependent Variable is Income Share of Poorest 20 %

PopulationExplanatory

variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

1.9228

0.6056

-0.0565

-0.1781

3.2483

4.0425

-2.3982

-5.0931

0.0063

0.0014

0.0322

0.0002

R-square 0.78 F-statistic 14.9585Adjusted R-

square 0.72 Prob. of F-statistic 0.0002

Rural area

Constant

FD

MEF

GDPP

1.2633

0.5819

0.0262

-0.1205

1.0588

1.9269

0.5505

-1.7096

0.3090

0.0761

0.5913

0.1111

R-square 0.40 F-statistic 2.8588Adjusted R-

square 0.26 Prob. of F-statistic 0.0778

Urban area

Constant

FD

MEF

GDPP

1.5519

0.3664

-0.0468

-0.0659

2.8874

2.6939

-2.1837

-2.0751

0.0127

0.0184

0.0479

0.0584

R-square 0.51 F-statistic 4.5812Adjusted R-

square 0.40 Prob. of F-statistic 0.0212

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Table 6: Regression Results on the Basis of Published Data only; Dependent Variable is Ratio of Income Shares of

Richest 20% to Poorest 20% PopulationExplanatory

variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

2.0745

-0.3924

0.0369

0.1040

6.5889

-4.9251

2.9377

5.5897

0.0000

0.0003

0.0115

0.0001

R-square 0.82 F-statistic 19.7338Adjusted R-

square 0.78 Prob. of F-statistic 0.0000

Rural area

Constant

FD

MEF

GDPP

2.5122

-0.9204

0.0086

0.2246

1.4746

-2.1349

0.1269

2.2320

0.1641

0.0524

0.9010

0.0438

R-square 0.45 F-statistic 3.5979Adjusted R-

square 0.33 Prob. of F-statistic 0.0432

Urban area

Constant

FD

MEF

GDPP

3.1070

-0.6864

0.0417

0.0887

2.6800

-2.3394

0.9034

1.2962

0.0189

0.0359

0.3827

0.2175

R-square 0.36 F-statistic 2.4688Adjusted R-

square 0.22 Prob. of F-statistic 0.1082

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The table 4 spectacles the results of three retrogressions estimated in which the inequality of income in entire Pakistan and in his rural and urban areas respectively has been stepped back in three independent variables, financial development, per capita GNP and macroeconomic fluctuations. In these equations of retrogression, the inequality of income has been brought over by gini coefficients, financial development by the credit of financial intermediaries to the private sector to proportion of GNP and macroeconomic fluctuations by the square of the difference between the valuation of annual growth of the respective year of real GNP and compound valuation of growth annual average of the real GNP for the entire period of this study, 1963-2001. All the equations of retrogression have been estimated in the linear form of trunk. This means that the values of trunk of all the variables rather that his values of reference have been used. Therefore, the coefficients of retrogression express respective elasticities that are interpreted as the change of percentage of the dependent variable due to an increase of one per cent of the independent variable.

The empirical results show that the elasticity of the coefficient gini for entire Pakistan with regard to the domestic credit deprived as the percentage of the GNP leaves - 0.4644, which means that an increase of one per cent of the domestic credit deprived to the proportion of GNP causes a decrease of 0.4644 per cent in the coefficient gini. The elasticity of the coefficient gini for entire Pakistan with regard to the GNP per capita is 0.1337. This implies that an increase of one per cent of the GNP per capita drives to an increase of 0.1337 per cent of the coefficient gini. The coefficient for fluctuations of growth is 0.0354, which can be interpreted when the change of one per cent of the valuation of growth of GNP of his average drives to an increase of 0.0354 per cent of the coefficient gini for Pakistan.

The coefficients for financial development and GNP per capita are significant while the coefficient for fluctuations of growth is insignificant for entire Pakistan. The results reveal that the financial helps of development reduce the inequality of income in Pakistan while the GNP per capita does that the inequality of income increases forward. The variable for the macroeconomic fluctuations has the negative impact in the distribution of income but it is not significant. These results are consistent with those of Thorsten Asli and Levine (2004). His study concludes that the financial development within a period of the developing supply of the domestic credit does the most equitable distribution of income while the GNP per capita has a negative impact. The variable for the macroeconomic fluctuations has the sign waited according to the theory of Breen and Garcia-Penalosa (2005). In his sight, more fluctuations in the national exit drive to the highest inequality of income.

30

Our results are also consistent with those of Jamal (2005) that the GNP per capita has the positive relation with the inequality of income. Nevertheless, Jamal and some other studies of the inequality of income as the Cuba of dyeing, Demirgue-Kunt and Lavine (2004), Breen and Garcia-Penalosa (2005) have used coefficients gini for the entire country without disintegrating them for rural and urban components as variable dependent. On the other hand, this study uses measurements of the inequality of income for rural and urban areas also.

In the second equation, gini coefficients for the rural area of Pakistan have been stepped back in the financial development, per capita GNP and fluctuations of growth. All the coefficients of this equation of retrogression have the same signs that those in the first equation without Sequestration, magnitudes and levels of meaning of two equations are slightly different. In the same way, the whole coefficient of the third equation, which is for the urban area, has the same signs as the coefficients of another two equations they have. Nevertheless, in the third equation, the development only financial has right to a significant determinant as the statistics in the level of 8 per cent of the meaning meanwhile the fluctuations of growth and the GNP per capita are insignificant although with awaited signs.

In general, our results show that the financial development is contributed towards smoothening of the distribution of income. Nevertheless, the impact of the financial development is more in rural areas than in urban areas. The same one is real with regard to per capita GNP and macroeconomic fluctuations although in the opposite sense. These results confirm that simple results of interrelation happened in the table 3 of the previous chapter as the coefficient of interrelation between inequality of income and financial development are positive and coefficients of interrelation between inequality of income and GNP per capita and the macroeconomic fluctuations are both prints.

The table 5 gifts the results of three following equations in which the part of income of the population of 20 per cent poorest in entire Pakistan and in his rural and urban areas respectively has been used as an income measured of inequality of second. It is stepped back in financial development, fluctuation of growth and GNP per capita for entire Pakistan and his rural and urban areas separately. All the equations of retrogression also have been estimated in the linear form of trunk.For entire Pakistan the elasticity of the part of income of the 20 poorest populations with regard to the financial development is 0.6056 and it is very significant, that indicates that an increase of one per cent of the domestic credit deprived to the proportion of GNP drives to an increase of the part of income of the population of 20 per cent poorest in 0.6056 per cent. The elasticity of the part of

31

income of the population of the 20 poorest per cent with regard to macroeconomic fluctuations is - 0.0565 and significant in the level of 3 per cent of the meaning that it indicates that a change of one per cent of the growth of GNP of his average value drives to the decrease of 0.0565 per cent in the part of income of the population of the 20 poorest per cent for entire Pakistan. The elasticity of the part of income of the population of the 20 poorest per cent with regard to the real GNP per capita is - 0.1781 and significant in the level of one per cent of the meaning, that it shows that an increase of one per cent of the real GNP per capita does that the part of income of the population of the 20 poorest per cent is diminished in 0.1781 per cent.

For the rural area of Pakistan, the coefficients of retrogression of financial development and GNP per capita have waited for signs that are positive and negative respectively meanwhile the coefficient of macroeconomic fluctuations has the positive sign that is in contrast with previous conclusions quoted in the chapter two. Also, only the coefficient of the financial development is significant in the level of 10 per cent of the meaning meanwhile others two coefficients are insignificant. This means that in the rural area of Pakistan, the financial helps of development increase the part of income of the population of the 20 poorest per cent considerably meanwhile the fluctuations in valuation of growth of GNP and GNP per capita they do not aggravate the inequality of income considerably.

For the urban area of Pakistan, all the coefficients of retrogression are of signs waited as those from the equation of retrogression for entire Pakistan. That is to say the financial development has two positive variables and others, macroeconomic fluctuations and GNP per capita has negative signs. Also, first two coefficients of retrogression are significant in 5 per cent and the last ones in the level of 10 per cent of the meaning. This has an interesting implication that the fluctuations in valuation of growth of GNP and GNP per capita have the depressing significant effect as the statistics in the part of income of the population of 20 per cent poorest in the urban area only and not in the rural area of Pakistan.

The table 6 contains the results of three following equations in which the proportion of parts of income of 20 per cent richest to the population of 20 per cent poorest in entire Pakistan and in his rural and urban areas respectively has been used as a third power for the inequality of income. As others two powers of the spoken inequality of income above, it is stepped back in financial development, fluctuation of growth and GNP per capita for entire Pakistan and his rural and urban areas separately. These equations of retrogression also have been estimated in the linear form of trunk.

32

The results of our evaluation for entire Pakistan and for his rural and urban areas have algebraic signs as waited. That is to say the financial development seems to help to limit decisively the proportion of parts of income of 20 per cent richest to the population of the 20 poorest per cent while the fluctuations in valuation of growth of GNP and GNP per capita seem to contribute decisively to the worsening of the proportion. The coefficients of retrogression of three variables are according to the statistics significant for entire Pakistan. Nevertheless, the coefficient of retrogression of macroeconomic fluctuations is not significant for the rural area of Pakistan and the coefficients of retrogression so much of macroeconomic fluctuations as of GNP per capita are insignificant for the urban area of Pakistan. This means that the development only financial has waited and significant sign as the statistics both in rural and urban areas of Pakistan while the macroeconomic fluctuations and the GNP per capita have waited for sign, but they are insignificant so much in rural as areas of Pakistan. It is interesting to notice that the macroeconomic fluctuations, being insignificant so much for rural areas as for urban of Pakistan, seem to be significant for entire Pakistan.

To sum up, our results suggest that the financial development should be profitable in reducing the inequality of income in entire Pakistan and in his rural and urban areas independently of the fact as the inequality of income it is measured. The coefficients of retrogression of this variable have waited for the sign and they are according to the statistics significant in all the cases. The macroeconomic fluctuations, almost in all the cases, have waited for the sign that indicates an impact of distortion in the inequality of income. Nevertheless, this variable especially has an insignificant impact as the statistics in the distribution of income. The same one is real for the GNP per capita as for macroeconomic fluctuations except the difference that the results as the insignificant statistics in case of the GNP per capita are less than those in case of macroeconomic fluctuations.

The financial development affects the inequality of income of several ways. To enter of financial markets heightens the financial mediation that facilitates the accumulation of the capital in a more rapid step. The accumulation of the intensive capital, for his{your} part, it raises the productiveness of work and the real price of salary that helps in smoothening to the inequality of income. The accumulation of the intensive capital also opens opportunities of work of unemployed workpeople and underemployed that especially belong to the poor class of the society. This way, the part of income of classes of low income improve relatively more. The employment and the increase of day's wages and wages also influence family decisions participation of labour, fertility and education especially of

33

such ways that are conducive for the development of the human capital.

In economically suppressed societies, the credit is generally limited by the entire need of the society but it is in particular inaccessible to individuals of low income and small businessmen because they rarely have any subsidiary valuable guarantee to be promised against bank loans and personal unions to be used for the bank loan. This way, in the context of an economically underdeveloped system, being a poor person of birth it reinforces consequences distribucionales adverse for the entire life because this limits the capacity of to small scale and producers of low income to obtain the credit of financial intermediaries.

As for macroeconomic fluctuations, the real theory of commercial cycle implies that when the individuals have an elastic supply of the work, a major suspense in savings of increases of exit and accelerate the growth. Consequently, the most rapid growth implies higher future day's wages, and higher consumption hence for any extra-time dedicated to the work. This therefore increases the offer of labour, raising the return to the capital and lowering day's wages of the work. Since the capital endowments are distributed more unequally than the time of work, this change of the inequality of income of increases of prices of factor as suggested by Garcia-Penalosa and Turnovsaky (2005).

The coefficients of determination remain low in our analysis. It might stem to not paying attention many other sociopolitical variables, which affect the inequality of income, but are not included in the model due to the absence of information, time and resources. Nevertheless, comparing with previous studies of the inequality of income for Liang (2006), Ali and tahir (1999), Breen and Gracia-Penalosa (2005), Deemirgue-Kunt and Lavine (2004), in this investigation it is not too bad.

To avoid any possibility of multicollinearity in our evaluation, we calculate simple coefficients of interrelation for every pair of explanatory variables that have not been reported in any table for the targets of briefness. Nevertheless, all the deliberate coefficients of interrelation were found too low to give rise to the problem multicollinearity in our dear equations of retrogression.

34

5.2 Use of empirical Evaluation Released and Generated Information

In the section 5.1, the empirical evaluation has been realized using the (released) insufficient but authentic number of remarks in order to avoid the tendency of interpolation. On the contrary, in this section, the number of remarks has been increased from 17 to 39 on the base of the simple interpolation of absent remarks. The simple interpolation means that the calculation of the combined annual average index of the change between discontinuous remarks at first places, and then working of values during absent years adding the annual average valuation to the value of the immediately previous year. Since there are many other methods of the interpolation, therefore our interpolated information can be questionable.

Stationarity of Variables of Series of Time To identify any causal relation discernable in a game of information of series of time, stationarity of information in every variable it is a requisite previous. Therefore, stationarity or the order of the integration has been verified before the evaluation of the model. In order to verify stationarity of all the variables we have applied the root of unit or have increased the Fullest Birdie (ADF) it tries. The test results of ADF are presented in the table 7. It is clear of these results that all our variables are not immobile in the level but they are immobile at first the difference when the value calculated for every variable is major than the critical value (-2.94) in the level of 5 % of the meaning.

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Table 7: Unit-Root EstimationInequalityMeasures

ADF TestAt Level

ADF TestAt First Difference

P-Gini

P-PR

P-RTP

R-Gini

U-Gini

R-PR

U-PR

R-RTP

U-RTP

FD

GDPP

MEF

-2.6948

-2.3331

-2.1649

-2.8822

-1.8515

-2.9946

-4.0217

-3.1602

-2.9136

-3.6141

-0.1210

-4.1131

-4.2311

-4.3807

-4.1915

-4.1024

-4.1024

-3.9855

-4.4686

-4.2474

-4.3841

-5.2081

-3.5664

-5.8919

Critical value at 5% level of significance is -2.9422.

As in section 5.1, three proxies for income inequality; gini

coefficient, income share of poorest 20 percent population and the

ratio of income shares of richest 20 percent to poorest 20 percent

population have been used. Then for each proxy of income

inequality; three equations; one for overall Pakistan and the other

two for its rural and urban areas, have been estimated. In total, 9

regression equations have been estimated in this section on the

36

basis of 39 observations. Empirical results are shown in tables 8 to

10.

Table 8: Regression Results on the Basis of Published & Interpolated Data; Dependent Variable is Gini Coefficient

Explanatory variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

AR(1)

AR(2)

2.8273

-0.0624

0.0024

0.1022

1.2961

-0.6731

7.9863

-2.2998

2.9740

3.0802

9.5521

-4.9131

0.0000

0.0278

0.0056

0.0043

0.0000

0.0000R-Square 0.87 F-Statistic 41.7907

Adjusted R-Square

0.85 Prob. of F-statistic

0.0000

D.W. Statistic 1.91

Rural area

Constant

FD

MEF

GDPP

MA(1)

2.9554

-0.1968

0.0057

0.1247

0.6117

7.5253

-2.4018

2.7805

3.7788

4.5200

0.0000

0.0219

0.0088

0.0006

0.0001R-Square 0.71 F-Statistic 20.6765

Adjusted R-Square

0.67 Prob. of F-statistic

0.0000

D.W. Statistic 1.60

Urban area

Constant

FD

MEF

GDPP

MA(1)

3.7079

-0.1571

3.78E-05

0.0479

0.9349

7.6224

-1.8616

0.0731

1.1531

12.4323

0.0000

0.0713

0.9422

0.2569

0.0000R-Square 0.65 F-Statistic 15.9094

37

Adjusted R-Square

0.61 Prob. of F-statistic

0.0000

D.W. Statistic 1.52

38

Table 9: Regression Results on the Basis of Published & Interpolated Data; Dependent Variable is Income Share of Poorest 20 % Population

Explanatory variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

MA(1)

3.1723

0.1455

-0.0011

-0.1696

0.9695

9.4405

2.3598

-1.3824

-5.7880

24.0600

0.0000

0.0242

0.1759

0.0000

0.0000R-Square 0.85 F-Statistic 49.5418

Adjusted R-Square

0.54 Prob. of F-statistic

0.0000

D.W. Statistic 1.75

Rural area

Constant

FD

MEF

GDPP

MA(1)

2.2556

0.2119

0.0011

-0.0945

0.6285

3.5474

1.6154

0.9407

-1.7754

4.6632

0.0012

0.1155

0.3535

0.0848

0.0000R-Square 0.50 F-Statistic 8.7426

Adjusted R-Square

0.45 Prob. of F-statistic

0.0001

D.W. Statistic 1.65

Urban area

Constant

FD

MEF

GDPP

AR(1)

MA(2)

1.8003

0.1418

-0.00021

-0.0309

0.7056

-0.9457

6.9818

2.4885

-0.5761

-1.1700

5.6679

-38.6778

0.0000

0.0182

0.5686

0.0915

0.0000

0.0000R-Square 0.62 F-Statistic 10.3343

Adjusted R-Square

0.56 Prob. of F-statistic

0.0000

D.W. Statistic 1.77

39

40

Table 10: Regression Results on the Basis of Published & Interpolated Data; Dependent Variable is Ratio of Income

Shares of Richest 20% to Poorest 20% PopulationExplanatory

variables Coefficient t-statistic Probability

Pakistan

Constant

FD

MEF

GDPP

AR(1)

0.9909

-0.0527

0.0004

0.1125

0.5335

3.2337

-2.3365

1.3575

3.7583

3.6327

0.0028

0.0259

0.1841

0.0007

0.0010R-Square 0.92 F-Statistic 71.5771

Adjusted R-Square

0.91 Prob. of F-statistic

0.0000

D.W. Statistic 2.10

Rural area

Constant

FD

MEF

GDPP

MA(1)

1.0348

-0.3310

-0.0016

0.1817

0.6250

1.2018

-1.8609

-1.0554

2.5210

4.6296

0.2377

0.0714

0.2986

0.0166

0.0001R-Square 0.57 F-Statistic 11.2232

Adjusted R-Square

0.52 Prob. of F-statistic

0.0000

D.W.Statistic 1.62

Urban area

Constant

FD

MEF

GDPP

AR(2)

2.2196

-0.2154

0.0002

0.0388

0.3797

2.1220

-1.8126

0.2036

0.4106

2.1228

0.0419

0.0796

0.8400

0.6842

0.0419R-Square 0.66 F-Statistic 12.1366

Adjusted R-Square

0.60 Prob. of F-statistic

0.0000

D.W. Statistic 1.74

41

The results in the table 8 show the impact of financial development, fluctuations of growth and GNP per capita in the coefficient gini for entire Pakistan and for his rural and urban areas. The elasticity of the coefficient gini with regard to the financial development is - 0.0624, which means that an increase of one per cent of the domestic credit deprived to the proportion of GNP causes a few decreases of 0.0624 per cent in the coefficient gini of Pakistan. The coefficient for the financial variable is significant and it has the negative sign, which shows that the financial development is profitable for the poor person in the increase of his part of income. This one emphasizes the importance of the credit allocation; this effectively helps to reduce the inequality of income or increase the parts of profit of the poor person. Access to believe bought by the poor person, positively reduces the inequality of income because the poor person can take given funds of the investment in productive channels as the education of his children and establishment of small industrial units or small business.

The coefficient for fluctuations of growth is 0.0024, which can be interpreted when the change of one per cent of the valuation of growth of GNP of his average drives to an increase of 0.0024 per cent of the coefficient gini for Pakistan. The coefficient of the coefficient gini with regard to the GNP per capita is 0.1022. This shows that an increase of one per cent of the GNP per capita drives to an increase of 0.1022 per cent of the coefficient gini of entire Pakistan. This means that the benefits of the economic growth have not reached to the class poor in the country proportionally. This one emphasizes the importance of political from redistribution that there can stimulate the part of income of the poor class. For rural areas, our results are same within a period of his meaning and signs as for entire Pakistan. This one shows that the financial development reduces the inequality of income in the rural area while the macroeconomic fluctuations and the GNP per capita they make it more unequal. In the urban area the variable only financial is significant.

For information of series of time, also we have used the second power of the inequality of income, which is the part of income of the population of the 20 poorest per cent, for entire Pakistan and for his rural and urban areas. The empirical results of three equations of retrogression are presented in tables 9. The results confirm that the financial development positively fond the part of income of the population of 20 per cent poorest in entire Pakistan and in his rural and urban areas while the GNP per capita negatively affects it. Nevertheless, the coefficient of macroeconomic fluctuations turns out to be according to the statistics insignificantly. In case of the third power of the inequality of income, which is the proportion of parts of income of the population of 20 per cent richest to the population of the 20 poorest per cent, the results for

42

entire Pakistan are waited like. It is the financial development he leads to a reduction of the inequality of income while the macroeconomic fluctuations and the GNP per capita do the most perverse inequality of income. The results for the rural area show that the coefficient of macroeconomic fluctuations has the incorrect sign and is according to the statistics insignificantly. The results for the urban area show that the sign of all the coefficients of retrogression is waited like but the macroeconomic fluctuations and the GNP per capita they are according to the insignificant statistics. Also we have verified the possibility of multicollinearity examining in partners the coefficient of interrelation between explanatory variables. The deliberate coefficients of interrelation (it did not do a report) were found so under that there should not be any problem of multicollinearity in the model. Nevertheless, in our initial results of retrogression (it did not do a report), a serious problem of the autointerrelation was found, that might be because 22 of 39 observations used for this evaluation have been simply interpolated. Anyhow, to take care of the problem of autointerrelation, we try several autoregressive (AR) and moving the average (MOM) processes and choose these to report that minimized autointerrelation.

To conclude, the results of the analysis of series of time also indicate a negative and linear relation between the financial development and all the measurements of the inequality of income in entire Pakistan as well as in his rural and urban areas. This means that our empirical results support the hypothesis of the linear relation between inequality of income and development financial as suggested by Galor and Zeira (1993) and Baneerjee and Newman (1993). Nevertheless, the fluctuations of growth do not seem to be a discouraging variable to worsen the inequality of income because the coefficients of retrogression of this variable are especially insignificant. The GNP per capita, on the other hand, seems to be more excellent in the worsening of the inequality of income in Pakistan.

43

Chapter 6

Conclusions and Policy Implications

The inequality of income in Pakistan has been completely perverse from the beginning. This expanded forward with the time. The wise comparison of decade of the inequality of income in the chapter 3 shows that the inequality of income remained almost without altering during the year 1970 and the year 1980 but this aggravated in the year 1990. It happened although the redistributive optional system of Zakat that was introduced in the whole country at the beginning of the year 1980. This shows that there are some endogenous forces that have overturned the impact of the system Zakat and have done the worst distribution of income with the time. Since one believes unanimously that the inequality of income is harmful to the sociopolitical and economic cohesion of a country, it is therefore very desirable to identify endogenous factors that worsen the inequality.

In addition to many sociopolitical factors in which the information is almost nonexistent, few economic variables as the financial development, fluctuations in valuation of growth of GNP and level of the GNP per capita are quoted often in the literature that as powerful determinants of the inequality of income in any country. Therefore, the target of this study was to investigate any relation discernable between the inequality of income and these three independent variables in the context of Pakistan. Since there is no only measurement of the inequality of income, we have used three alternative measurements; coefficient of gini, part of income of the population of the 20 poorest per cent and the proportion of parts of income of population of 20 per cent richest to the population of the 20 poorest per cent. These measurements of the inequality of income have been calculated for entire Pakistan as well as for his rural and urban areas separately.

The previous studies in the topic especially use information of multicountry of cross section to identify significant determinants as the statistics of the inequality of income probably due to the absence of information of series of time in the inequality of income. Therefore such results incorporate the impact of sociopolitical factors that are generally completely different in every country. For the targets of this study, the information of series of time in the inequality of income in Pakistan is also insufficient when there are

44

only 17 available observations. Therefore, we have estimated equations of retrogression first on the base of information published nearly to have an idea of the direction of relation between the inequality of income and three independent variables. Then we have generated information for the simple interpolation to have results as the reliable statistics. Our results show that the financial development has been proved completely nicely to reduce the inequality of income with the time both in rural and urban areas of Pakistan equally. This variable has been significant for every measurement of the inequality of income. To enter of financial markets reduces the inequality of income relieving credit coercions. This way the poor class can take given funds of financial intermediaries and invest them to increase his human capital, to start productive business and to tackle the promising investment. Finally they are capable of increasing and of improving his mobility and economic perspectives, and hence breaking the cycle of the inequality of income.

On the other hand, the GNP per capita has been proved a source of the inequality of income ensanchadora both in rural and urban areas of Pakistan. This means that the effect of trickle below from the increasing GNP per capita still does not seem to descend to poor segments of the society. In the same way, the fluctuations in the valuation of growth of GNP have done that the inequality of income expands forward. Nevertheless, this variable has not been so significant in most of the cases. This means that the fluctuations in the valuation of growth of GNP do not have any discernable and the persistent impact in the inequality of income.

The implication of principal politics of this study is that the financial development should be tied top priority in order to level the inequality of income in the country. The financial development seems to the supply of the poor person with the ground of game levelled. Having the access to the bank loan, the poor seem to do their own fortune without needing further the help for redistributive optional schemes. Another implication of this investigation is that the trickle below theory has not been working like waited in case of Pakistan. From the trickle below the theory impliedly emphasizes an investment strategy in favor of rich to begin the process of the economic development, this thought therefore has to be reconsidered in favor of one in favor of poor person or at least a balanced strategy of throwing a process of guessed right development.

45

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