Deprivation of Education in Urban Areas: A Basic Profile of Slum Children in Delhi, India

This paper showed the basic educational status of slum children between 5 and 14 years old. The attendance ratio of slum children is much lower than that of children in Delhi as a whole. Parental perception of education and financing education are the major constraints. Even if children are attending schools, the majority of them are over-aged. There are both demand and supply side reasons for discouraging slum children from attending schooling. As opposed to school-based surveys in previous literature, children in slums are more likely to go to government schools rather than low-fee paying private schools. Some policies are suggested.

Influence of Social Institutions on Inequality in China

This study analyses the impact of changes in social institutions, i.e. in the informal and formal social security system, on income inequality in China. This study uses an inequality decomposition analysis approach comparing household survey data for 1988 with 1995.Three main results emerge from the analysis: first, it findsthat the family based social security is losing its importance mainly through the changes in employment pattern in a household. This change contributes to rising income inequality. Second, thestudy shows that the introduction of new formal social security system helped to equalise the distribution of retired household members' income in urban areas in 1995. Third, however, these changes have only benefited a restricted number of persons. Benefits for rural migrants are low and most of the rural population has still no access to the new system.

Economic Reforms and Income Inequality in Urban China

This paper reports the results of an analysis of changes in income inequality, and in its determinants, in urban China since the economic reforms that began in 1978. The intention is to identify new characteristics of economic inequality. It first shows that income differentials acrossand in provinces widened and that their economic rankings were becoming fixed during the period from 1988 to 1995. Second, age was the major factor in inequality in 1988, while education became the important factor in 1995. Third, education significantly contributed to increasing inequality during the period. Fourth, the higher education-level groups had less within-group inequality. These changes reflect the penetration of the market mechanism into China after the reforms. However, this will be problematic without equality of opportunity.

Poverty Analysis of Ethiopian Females in the Amhara Region: Utilizing BMI as an Indicator of Poverty

This paper analyzes poverty-affected females in the Amhara region of Ethiopia. As the measurement of poverty, the paper uses body mass index (BMI) because it is one of the effective tools for measuring individual poverty level. The results of the BMI analysis show that the most poverty-affected female group is the female household heads in urban areas. The results, however, should be treated carefully considering the different social and economic structure of urban and rural areas, and the interdependent relationship between these two areas. In rural areas, access to land is the biggest issue affecting the BMI, while in urban areas, the occupation of husbands or partners is more important. These differences by area do not mean that there is no intersection between the urban and rural female groups because the majority of females in urban areas migrated from rural areas to urban areas due to various reasons such as divorce, marriage, and job opportunities.

Changes in the causes of earnings inequality in urban China from 1988 to 2002

This paper analyzes the causes of earnings inequality in urban China from 1988 to 2002. Earnings inequality in urban China continuously increased, even when adjusting for regional price differences. This paper reveals how the causes of earnings inequality changed between the periods 1988-1995 and 1995-2002 by reflecting labor-related institutional reform in China. Contrary to the situation from 1988 to 1995, between 1995 and 2002, employment status became the largest disequalizer, and the decline of inter-provincial inequality contributed to a reduction in entire earnings inequality. Individual ability, represented by education and occupation, received much greater rewards. Throughout the period from 1988 to 2002, a large part of the explained inequality increase was due to change in price (valuation of each individual's attributes) and not due to change in quantity (composition of individual attributes).

The contribution of supply and demand shifts to earnings inequality in urban China

This paper examines the degree to which supply and demand shift across skill groups contributed to the earnings inequality increase in urban China from 1988 to 2002. Product demand shift contributed to an equalizing of earnings distribution in urban China from 1988 to 1995 by increasing the relative product for the low educated. However, it contributed to enlarging inequality from 1995 to 2002 by increasing the relative demand for the highly educated. Relative demand was continuously higher for workers in the coastal region and contributed to a raising of interregional inequality. Supply shift contributed essentially nothing or contributed only slightly to a reduction in inequality. Remaining factors, the largest disequalizer, may contain skill-biased technological and institutional changes, and unobserved supply shift effects due to increasing numbers of migrant workers.

Agricultural efficiency of rice farmers in Myanmar : a case study in selected areas

This paper try to analyze unique data set for rice producing agricultural households in some selected areas of Bago and Yangon divisions to examine the households' profit efficiency and the relationship between farm and household attributes and profit inefficiency using a Cobb-Douglas production frontier function. The frequency distribution reveals that the mean technical inefficiency is 0.1627 with a minimum of 3 percent and maximum of 73 percent which indicates that, on average, about 16% of potential maximum output is lost owing to technical inefficiency in both studied areas. While 85% of the sample farms exhibit profit inefficiency of 20% or less, about 40% of the sample farms is found to exhibit technical inefficiency of 20% or less, indicating that among the sample farms technical inefficiency is much lower than profit inefficiency.