October 20, 2009

Kenya's Stimulating economic growth


Stimulating economic growth



The focus is to have basis towards stimulating economic growth from certain income levels, Kenyan household perspective. Why emphasis should be put on raising levels of income to the low income earners? Why other concentrating on putting ceilings on high income earners as a way of stimulating economic growth?



Whether income inequality reduces economic growth is an issue that has been explored in many empirical studies over the last decade or so. Many studies find that there is negative correlation between income inequality and economic growth. Research will argue that these studies need to be interpreted with a great deal of caution, as they measure inequality in an inconsistent manner. Inequality can be measured using data on gross income, net income or expenditure. In addition, the unit of measurement can be the individual or the household. There would expect to obtain quite different measures of inequality, depending on which of these classifications are used. It follows that in empirical work it is important to use consistently measured data that are not, for example, based on gross income for some countries and based on expenditure for others. Some researchers (eg Barro, 2000) suggest that mixing different classifications of data together does not affect the results. The discussion will bring out the fact that for one of these arguments it is the distribution of gross income that is relevant, but that for the other arguments it is the distribution of net income or expenditure that matters. This is something that should be kept in mind when conducting empirical work, but that has tended to be ignored in the past. The fact that most of the arguments are more likely to apply in the long run, rather than the short run, will also be discussed. Analyse in more detail the problems with the way income inequality data have been used in previous empirical work to assess the sensitivity of the results to how income inequality is measured.



When world trade is costly, country can profitably industrialize only if its domestic markets are large enough, for increasing returns technologies to break even, sales must be high enough to cover fixed setup costs, income generated must be broadly enough distributed that it materializes as demand for broad range of domestic manufactures have importance on economic growth episodes. The decomposable inequality measure is defined as measure such that the total inequality of a population can be broken down into weighted average of the inequality existing within subgroups of the population and the inequality existing between them. Thus, decomposable measures differ only by the weights given to the inequality within the subgroups of the population, which facilitates fruther research, country longitudinal data are easily available than national income distribution data. Next, using larger set of nation and find signigicant increase in world income inequality as measured by Gini and Theil coefficients. The findings are robust after controlling for differential rates of population growth or using alternative sources of data. By indentifying these trends, the research is able to explain past discrepancies and recent shifts in relevant empirical and theoretical literature. The evolution of income inequality during the course of economic development will be investigated. The source of inequality is market luck in obtaining employment in the protected urban `formal sector' versus employment in the unprotected urban `informal sector.' It is shown that with development, inequality tends to follow an `inverted U.' It rises when urbanization is low and consequent pressure on the land keeps rural incomes low, making agents willing to incur high risks of `underemployment' in the urban informal sector. It eventually falls after urbanization and consequently rural incomes have increased sufficiently to allow agents to make better than even bets in the urban-industrial sector.



The exceptional economic growth has beenpp accompanied by more exceptional fall m labor income inequality. Using case methodology, the using of data from Kenya's wage surveys to quantify the importance of various factors that have contributed to the fall in labor income inequality in the region. To find out that one important factor explaining the levelof income inequality could be job tenure, years of education and occupation, while important in explaining the changein income inequality are years of education and potential experience. To have comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality, significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal, find systematic link between growth and changes in earnings. Case survey method in order to find strong positive relationship between economic growth and income of Kenyan household as affected by changes in income levels and other salient domains.



The growth in inequality is considered distributional problem only if it results in a decline in the economic position of persons at the bottom of the distribution. If incomes grow throughout the distribution, but the growth is higher at the top than at the bottom, then inequality increases, but the absolute incomes of those at the bottom improve. Changes in the absolute incomes of those at the bottom are affected by amount of economic growth, changes in inequality and changes in mobility. Without information on mobility it is impossible to tell what proportion of low earners in one cross-section had low earnings in subsequent cross-section. If many low earners in one year have high earnings in other years, then the crosssectional earnings distribution is not very informative. Only longitudinal data can yield that information. Likewise, cross-section data cannot reveal whether people with low earnings in one year are getting poorer, nor for that matter whether the rich are getting richer. Cross-sectional data can only be used to compare the characteristics and number of persons with low earnings in one year with those in another year. Suppose that people have sufficient assets or access to capital so that they can smooth consumption across periods. In this case, the relevant income concept for measuring inequality is average discounted earnings across these periods. A person might have temporarily low earnings in one period, but this would not indicate his or her position in the distribution of permanent earnings. If multiple-period earnings is the relevant concept, then inequality in each subperiod and mobility across subperiods would impact inequality of permanent earnings. In keeping with the notion that economic growth, changes in inequality and changes in mobility are three distinct concepts that describe different aspects of changes in the distribution of income. The rise in the price of skill is a result of both an increase in the real wages paid to more skilled workers and also a sharp decline in the absolute real wages paid to less skilled workers. The increase in inequality reflects an absolute as well as relative decline in the earnings of less skilled workers, the decline in wages for less skilled workers canceled out the impact of rising wages for more skilled worker, no change in mean wages can occur. Research confirms that there is negative correlation between inequality and growth across countries, but only when the focus is on inequality after redistribution has taken place. No evidence is found of significant correlation between gross income and economic growth.


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