The current study investigates the effect of implementing six development plans on Multidimensional inequality in Iran. To this end, the Multidimensional Gini Index of inequality by Assis Kumar Banerjee (2010) for dimensions such as welfare, education, housing, health, and social welfare (aggregation of other dimensions in household expenditure-income basket) calculated for years 1984-2021 and their performance was evaluated. The results of this study showed that the implementation of the development plans led to an inequality increase. Among these six plans, implementing the first plan had an incremental effect on the inequality value. The third and fifth socio-economic plans have a decremental impact on inequality at a significant level of 5%. Also, there was no difference between implementing and not implementing other plans on the inequality value. Also, the results indicated that given the comprehensiveness and Multidimensionality of the development plans, inequality did not experience a constant trend and had mild fluctuations in the urban and rural areas and the whole country. Moreover, at the end of the sixth development plan, the inequality value (0.825) reached a value higher than the beginning of the first plan (0.771) in 1989.

inequality is a major problem in the developing countries. It is also an acute and critical subject in Iran compared with other developing economies. Besides the existence of inequality, its social effects have made its explanation crucial. The aim of this research is to measure the Multidimensional inequality in urban areas of Iran during three presidential periods: 1989-1997, 1997-2005 and 2005-2013. Income, health and education are the dimensions under study. This research uses household expenditure-income survey data for the selected periods. Gini coefficient is measured for one-dimensional inequalities in terms of each dimension, generalized entropy is computed for the beginning and ending years of each period, and the Multidimensional inequality is measured by using the Bourguignon index. Results indicate that the Gini coefficient decreased in terms of income dimension in all three periods while it increased in terms of health dimension during all periods. In education dimension, Gini index increased in the second period and decreased in other periods. The measured entropy indices are of some fluctuations in all periods for all dimensions. The measured Multidimensional inequality index provides a wide range of results for different substitution and inequality aversion parameters. In general, this index indicates worse conditions in the second period compared with other periods.

In this paper, by the use of the weight coe cients, the transfer formula and the technique of real analysis, an extended Multidimensional Hardy{Hilbert{type inequality with a general homogeneous kernel and a best possible constant factor is given. Moreover, the equivalent forms, the operator expressions and a few examples are considered.

Unidimensional inequality has been estimated many times; but individual well-being is a multi-dimensional concept that depends not only on income but also on other benefits such as education, health, and housing. The main aim in this research has been to evaluate the inequality concerning commodity groups in the household basket and compare the multi-dimensional inequality with that of one-dimensional (total expenditure) inequality for urban, and rural areas as well as the country as a whole. Therefore, the Multidimensional Gini coefficient is estimated according Kumar Banerjee (2010) during the period 1984-2021. Doing so, we have used the household income-expenditure microdata collected in the 9 commodity groups of food, clothing, housing, services, health, recreation and entertainment, education, transportation and communication, and others by Statistical Center of Iran.  The results indicate that the multi-dimensional inequality trend have been increasing for urban and rural so the country as a whole, while the one-dimensional expenditure(unidimensional) inequality shows a decreasing trend and the gap between trends were also increasing. The Multidimensional inequality headways been more than that of unidimensional inequality level as has been expected, which more corresponds with real evidence in Iran.JEL Classification: D3, I14, I24, I3

Regional inequality is a Multidimensional phenomenon and includes different areas of economic, social and cultural. Therefore for any country is necessary to introduce a Multidimensional composite index for the measurement of regional inequality. This study aimed to determine the level of regional inequality of Iranian provinces and to identify the main determinants of the inequality, to introduce a combined index for regional inequality measurement. To this end, this study has selected 25 indicators in 5 different dimensions (economic, knowledge and human capital, infrastructure, social-cultural and environmental) and it has collected data relating to the years 2001 and 2013. Also, by applying innovation, it has used two-step principal component analysis. The results show that regional disparities are declining in period under review. In this period, Provinces of Tehran, Yazd and Semnan have had the highest development level, respectively, and Sistan- Baluchistan province also has been fixed at the end place. The results show that in provinces with higher development level. Economic and human capital indicators were main causes of inequalities in the regional development. Also high population density has forced the government to invest more in infrastructures, health and education in more developed provinces. On the other hand, having knowledge and investment spillovers is an effective factor in development of Tehran’s neighboring provinces.

The main purpose of this paper is to measure the Multidimensional inequality index. To achieve this goal and answer of what trend inequality has gone through during the study period, using the data of the household expenditure income plan of the statistical center of Iran and also using the Bourguignon index, inequality was measured in several dimensions for the period 1984-2018. In addition, it should be noted that in this study, household expenditures were initially adjusted based on age composition and number of household members by calculating the equivalence scale. This adjustment was made possible by estimating the share of expenditures of different commodity groups by considering its functional form in the the quadratic almost ideal demand system (QUAIDS). Then, using data mining techniques and Principal Component Analysis(PCA), the weight of the studied dimensions in the analysis (income, education and health) was calculated and when measuring inequality, the degree of social aversion of inequality was considered in the form of two scenarios, zero and one. the results of this study indicate that the magnitude of Multidimensional inequality per zero value for both the degree of social aversion of inequality parameter and the degree of substitution parameter based on the Bourguignon, index is between 0. 28 and 0. 41 in urban areas and between 0. 26 and 0. 41 in rural areas. fluctuations in the Bourguignon index and the Gini index of income have not necessarily been similar. The findings of this study also showed that the size of Multidimensional inequality in rural areas is lower than urban areas in most of the years studied. There is an approximate similarity between its trend in urban and rural areas. inequality in the 1980s was higher than in other periods (despite higher oil revenues than in the previous and subsequent periods and more government shares transferred than in previous periods), in the early 1390s, declined, and then increased again. Finally, the research findings indicate the failure of the egalitarian goals of development programs and thus emphasize the need for a fundamental review of the forthcoming programs with more attention to distribution by the market institution rather than the government.

Objective: Income inequality affects consumption structure via changes in the composition of expenditure basket. Shocks, fluctuations, disparities, and dispersions of income inequality, is expected to be transmitted to consumption expenditures. These shocks might induce inequalities in consumption expenditures. This paper tries to measure the amount of such consumption expenditures inequalities that are derived from income inequality.Method: To measure the consumption expenditures inequality and income inequality, the variance of logs is employed. For this purpose, Iranian urban households' data of the period of 1982 to 2007 is used. To analyze relations between variables, panel data methodology is applied. Findings: All fluctuations of income distribution do not translate in consumption expenditure distribution. Conclusion: The distribution of households' consumption expenditure in comparison to the distribution of income is less subjected to fluctuations and variations.

Background: We examined whether Multidimensional poverty index (MPI) explained variations in life expec-tancy (LE) better than income poverty; and assessed the relative importance of MPI indicators in influencing LE. Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regres-sions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1. 9 and 3. 1 USD) in explaining LE. Results: Adjusting for controls, both MPI (β =-0. 245, P<0. 001) and IPG at 3. 1 USD (β =-0. 135, P=0. 044) sig-nificantly correlates with LE, but not IPG at 1. 9 USD (β =-0. 147, P=0. 135). MPI explains 12. 1% of the variation in LE compared to only 3. 2% explained by IPG at 3. 1 USD. The effect of MPI on LE is higher on female (β =-0. 210, P<0. 001) than male (β =-0. 177, P<0. 001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity. Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.