Measuring inequality of poverty: theory and an application to India

The commonly used poverty indices measure the overall level of poverty in a society but fail to capture the differential intensity across different socioeconomic groups. This paper proposes a new measure, Inequality of Poverty Index (similar to dissimilarity index in the literature on inequality of opportunity) which captures inequality in distribution of poverty across different subgroups. It can be used to determine the major socioeconomic factors/characteristics/circumstances causing between-group disparity in poverty and effect of a specific factor on poverty relative to other factors and time. The paper also provides an application of the index and potential policy implications.Poverty; Inequality; Inequality of Poverty Index

Does returns to farming depend on Caste? New evidence from India

This paper analyses the relationship between net farm income per unit of land cultivated and caste divisions in India using a micro unit recorded and nationally representative survey conducted in 2004-05. Findings suggest that the groups that are generally considered disadvantaged (Scheduled Castes/Scheduled Tribes) have, after controlling for other factors, substantially lower farm returns compared to the advantaged (Others) castes, whereas the ‘Other Backward Castes’ occupy position in between. Decomposition of overall net farm income inequality using mean-log deviation indicates that caste based inequality forms a substantial part of it. Results call for policies for neutralizing the impact of caste on agricultural returns in addition to the general policy of land redistribution.Caste, returns to farming, farm income inequality, Caste based inequality, India

Gender based intra-household inequality of opportunity in academic skills among Indian children

Using households with a pair of male-female siblings (aged 8-11 years) from a nationally representative survey, the paper estimates gender based intra-household inequality of opportunity in academic skills by comparing test scores of the siblings in reading and mathematics skills within each household. The study finds substantial level of gender based intra-household inequality in both the skills. The paper also estimates household fixed-effects models for reading and mathematics skills, and finds significant difference between male and female children with female children at a disadvantaged position. Further support for gender differential (bias against female children) is provided by the analysis of the expenses incurred by households on the education of their children, which shows that the educational expenditure on female children is substantially lower than that on male children.Gender, Intra-household, Inequality of Opportunity, Academic Skills

Inequality of opportunity in India

The paper associates inequality of opportunities with outcome differences that can be accounted by pre-determined circumstances which lie beyond the control of an individual, such as parental education, parental occupation, caste, religion and place of birth. The overall opportunity share estimates (parametric) of total consumption expenditure inequality due to a set of circumstances comprising of caste, religion, geographical region, parental education and parental occupation vary from 16% to 25% in urban India. The corresponding figures for rural areas stand at 20% and 23%. The analysis further reveals that, parental education is the major contributor to the opportunity share of consumption expenditure inequality in urban India, but caste and geographical region also play an equally important role when rural India is considered.Inequality of opportunity; consumption expenditure inequality; India

Family background, academic ability and associated inequality of opportunity in India

The paper adds to the existing work on Inequality of Opportunity in India and using data and techniques proposed in earlier studies, estimates inequality of opportunity in academic ability of Indian males. Taking data from India Human Development Survey (2004-05) and mean log deviation as the inequality measure, the overall observed inequality in academic ability has been decomposed into two components. One of which can be associated to family background (inequality of opportunity) of individuals and another one due to all other factors. The paper finds substantial level of inequality of opportunity in academic ability in India, with the figures relatively higher for the urban regions compared to the rural ones.Inequality of Opportunity, Academic Ability, India

Dual automorphism-invariant modules

A module $M$ is called an automorphism-invariant module if every isomorphism between two essential submodules of $M$ extends to an automorphism of $M$. This paper introduces the notion of dual of such modules. We call a module $M$ to be a dual automorphism-invariant module if whenever $K_1$ and $K_2$ are small submodules of $M$, then any epimorphism $\eta:M/K_1\rightarrow M/K_2$ with small kernel lifts to an endomorphism $\varphi$ of $M$. In this paper we give various examples of dual automorphism-invariant module and study its properties. In particular, we study abelian groups and prove that dual automorphism-invariant abelian groups must be reduced. It is shown that over a right perfect ring $R$, a lifting right $R$-module $M$ is dual automorphism-invariant if and only if $M$ is quasi-projective.Comment: To appear in Journal of Algebr

Rings of Invariant Module Type and Automorphism-Invariant Modules

A module is called automorphism-invariant if it is invariant under any automorphism of its injective hull. In [Algebras for which every indecomposable right module is invariant in its injective envelope, Pacific J. Math., vol. 31, no. 3 (1969), 655-658] Dickson and Fuller had shown that if $R$ is a finite-dimensional algebra over a field $\mathbb F$ with more than two elements then an indecomposable automorphism-invariant right $R$-module must be quasi-injective. In this paper we show that this result fails to hold if $\mathbb F$ is a field with two elements. Dickson and Fuller had further shown that if $R$ is a finite-dimensional algebra over a field $\mathbb F$ with more than two elements, then $R$ is of right invariant module type if and only if every indecomposable right $R$-module is automorphism-invariant. We extend the result of Dickson and Fuller to any right artinian ring. A ring $R$ is said to be of right automorphism-invariant type (in short, RAI-type) if every finitely generated indecomposable right $R$-module is automorphism-invariant. In this paper we completely characterize an indecomposable right artinian ring of RAI-type.Comment: To appear in Contemporary Mathematics, Amer. Math. So

Biometric Identification using Phonocardiogram

Phonocardiogram (PCG) signals as a biometric is a new and novel method for user identification. Use of PCG signals for user recognition is a highly reliable method because heart sounds are produced by internal organs and cannot be forged easily as compared to other recognition systems such as fingerprint, iris, DNA etc. PCG signals have been recorded using an electronic stethoscope. Database of heart sound is made using the electronic stethoscope. In the beginning, heart sounds for different classes is observed in time as well as frequency for their uniqueness for each class. The first step performed is to extract features from the recorded heart signals. We have implemented LFBC algorithm as a feature extraction algorithm to get the cepstral component of heart sound. The next objective is to classify these feature vectors to recognize a person. A classification algorithm is first trained using a training sequence for each user to generate unique features for each user. During the testing period, the classifier uses the stored training attributes for each user and uses them to match or identify the testing sequence. We have used LBG-VQ and GMM for the classification of user classes. Both the algorithms are iterative, robust and well established methods for user identification. We have implemented the normalization at two places; first, before feature extraction; then just after the feature extraction in case of GMM classifier which is not proposed in earlier literature