Economics

Enhancing the enrolment at elementary level is a big challenge to any welfare state. India is not an exception. Different countries have taken different measures to increase this enrolment ratio. At the same time keeping the enrolled students in their respective classes is another big challenge. This work wants to project the scenario of enrolment and dropout on the basis of gender in India over a decade. It is observed that male enrolment ratio is higher than the females. On the other hand the female dropout rate is much higher than the males.

The traditional multi-dimensional measures have failed to properly project  the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to recognise the graduality inside the concept of poverty and the disparities within the household in wealth distribution. So this work wants to develop a measure to estimate the vulnerability of households in becoming poor in a multidimensional perspective through incorporating the intra-household disparities and graduality within the causal factors. Dimensional decomposition of the developed vulnerability measure is also under the purview of this work. To estimate the vulnerability and dimensional influences with the help of artificial intelligence an integrated mathematical framework is developed.

Ensuring a decent livelihood is one of the greatest challenges being faced by the human society. Even after multifaceted efforts, the outcome to sustain livelihood throughout the world is not uniform. This work wants to find the actual scenario of sustainable livelihood in our global village. To that respect an index of under-achievement is constructed here. The concepts of mathematical topology, artificial intelligence and quantum computing have been used for that construction. The created index is tested to find the significant influencing factors. On the basis of the existing literature it appears that the outcomes on delivering sustainable livelihood are distinctly different in different economies. Incidentally the research on sustainable livelihood has failed to deliver desired answers to this diverse result due to the absence of proper research methods. This problem in the academic discourses can well be solved through the ideas of artificial intelligence. Artificial intelligence techniques can properly optimize the decisions about sustainable livelihood in a multi dimensional framework. This decision optimization operation in a multi dimensional framework can best be achieved through the platform of quantum computing. Thus the specific objectives of this study are. • Firstly, to construct an under-achievement index in the sustainable livelihood space with the help of mathematical topology. • Secondly, to cluster the economies on the basis of their under-achievements to find the underlying histro-geographical patterns of economy wise under-achievements. • Thirdly, to locate the direct as well as indirect effects of influencing factors on the composite under achievement with the help of artificial intelligence in the platform of quantum computing. This work is based on secondary data published by World Bank. Sustainable livelihood is determined through the concepts published by Department of Foreign and International Development, UK. The ideas of mathematical topology and artificial intelligence have been used to come to the conclusion. The whole work is executed in the platform of quantum

The traditional multi-dimensional measures have failed to properly estimate  the vulnerability of households towards poverty. The reasons behind this inability are the failure of the existing measures to recognise the graduality inside the concept of poverty and the ex-post consideration of the idea of poverty. So this work wants to develop a measure to estimate the vulnerability in an ex-ante multidimensional perspective with the help of fuzzy logic and artificial intelligence. To estimate and to decompose the vulnerability an integrated mathematical framework is developed. The constructed index is tested and the dimensional influences are compared under different socio-economic clusters.

Like many developing countries in India too joining household informal production is an important livelihood strategy. To maintain competitiveness this informality depends upon non-monetisation of inputs and intra-household division of labour. This division of labour is not horizontal but hierarchical where some members of the family enjoy net benefits and some others face extreme drudgery. This hierarchy helps to shift the burden of competiveness to the unobserved household labour. Informality of production through uncounted labour ensures the sustainability of the profession through family-based exploitation. This work wants to locate the process through which the informality of household production ensures the sustenance of the profession and intra-family exploitation. Current study is purely based on primary data collected through questionnaire based household survey. It is found that in the face of adverse markets and occupational immobility the household entrepreneurs subsidise the cost of production through unpaid family labour. It is mainly the unpaid female labour which helps to sustain the activity. So deprivation of the female workers is a deliberate policy towards a sustainable livelihood. The marginalization of women is a necessity to fight severe poverty.

The traditional multi-dimensional measures have failed to properly project the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to recognise the graduality inside the concept of poverty and the disparities within the household in wealth distribution. So this work wants to develop a measure to estimate the vulnerability of households in becoming poor in a multidimensional perspective through incorporating the intra-household disparities and graduality within the causal factors. Dimensional decomposition of the developed vulnerability measure is also under the purview of this work. To estimate the vulnerability and dimensional influences with the help of artificial intelligence an integrated mathematical framework is developed.

Indian society is characterized by multiple forms of exclusion associated with group identities. This work wants to examine the nature and dimensions of social exclusion from development programmes in the Indian state of West Bengal on the basis of various castes and to analyze the factors behind. It is tested here whether there exists any relationship between different castes and level of social exclusion, and that between intra-group heterogeneity and group social exclusion values. Both secondary and primary data have been used. Sample of 320 households was chosen through multi-stage stratified random sampling. In a three dimension exclusion space of health, education and income the household level social exclusion is measured by Normalized Euclidean Distance. Calculated value is regressed on caste characteristics of the sample households. Tukey Post Hoc study is undertaken to judge the inter-group differences. Generalized Entropy Index is used to study the intra-group concentrati...

This work aims to develop a data driven multi-horizon incidence forecasting model considering the inter-country variability in static socioeconomic factors. The specific objectives of this study are to predict the future country-wise COVID 19 incidences, to locate the influences of individual socioeconomic factors on the predictions, to analyze the clusters of countries on the basis of influential explanatory variables and thus to search for intra-cluster and inter-cluster characteristics. To that respect this study has used the deep neural network based temporal fusion transformer for the predictions, Pearson correlation to understand the influence of socioeconomic variables on incidence and hierarchical clustering for cluster-analysis. The findings conclude that the inter-country infection related predictions vary widely over spatio-temporal variability and different socioeconomic variables have different influences over this inter-country variability. It is observed that greater the population size, stronger the global connectedness, larger the social cohesion, higher the population density and meaningful the gender based discrimination higher will be the future spread. On the other hand greater the development level, higher the nutritional status, greater the access to quality health services, greater the urban population and greater the material poverty lesser will be the future spread. Definite spatial pattern of influence of the explanatory variables emerged from cluster analysis. To minimize the vulnerability towards unforeseen biological calamities modern and sustainable development policies are needed; affluence may not guarantee less infection. But these policies should vary between economies due to the variation in socioeconomic status of the countries worldwide. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Srinka Basu and Sugata Sen. The first draft of the manuscript was written by both of them. All authors read and approved the final manuscript. The authors would like to thank Mr. Soumya Sengupta, Research Scholar, Panskura Banamali College (Autonomous), India for his contribution in data collection and his inputs through discussions.

UNDP Rio +20 summit in 2012 evolved a set of indicators to realise the targets of SDGs within a deadline. Measurement of the performances under these goals has followed the methodology as developed by UNDP which is nothing but the simple average of performances of the indicators under different domains. This work concludes that this methodology to measure the goal-wise as well as the composite performances is suffering from major shortcomings and proposes an alternative using the ideas of artificial intelligence. Here it is accepted that the indicators under different goals are inter-related and hence constructing index through simple average is misleading. Moreover the methodologies under the existing indices have failed to assign weights to different indicators. This work is based on secondary data and the goal-wise indices have been determined through normalised sigmoid functions. These goal-wise indices are plotted on a radar and the area of the radar is treated as measure under composite SDG performance. The whole work is presented through an artificial neural network. Observed that the goal-wise index as developed and tested here has shown that the UNDP as well as NITI Aayog index has delivered exaggerated values of goal-wise as well as composite performances.

UNDP Rio +20 summit in 2012 evolved a set of indicators to realise the targets of SDGs within a deadline. Measurement of the performances under these goals has followed the methodology as developed by UNDP which is nothing but the simple average of performances of the indicators under different domains. This work concludes that this methodology to measure the goal-wise as well as the composite performances is suffering from major shortcomings and proposes an alternative using the ideas of artificial intelligence. Here it is accepted that the indicators under different goals are interrelated and hence constructing index through simple average is misleading. Moreover the methodologies under the existing indices have failed to assign weights to different indicators. This work is based on secondary data and the goal-wise indices have been determined through normalised sigmoid functions. These goal-wise indices are plotted on a radar and the area of the radar is treated as measure under composite SDG performance. The whole work is presented through an artificial neural network. Observed that the goal-wise index as developed and tested here has shown that the UNDP as well as NITI Aayog index has delivered exaggerated values of goal-wise as well as composite performances.

Lack of cohesion among human beings is a major problem in modern India. This absence of cohesion appeared due to many historical, geographical and economic factors. It has ultimately culminated in differentiated development outcomes in India. This work wants to evaluate the effect of social cohesion on the development achievements. To that respect Graph theory, Markov chain analysis and Artificial Intelligence have been used. Here society has been conceived as a network of n agents. To initiate the Markov process the idea of Genetic Algorithm is used. On the other hand this work tries to measure the Cohesion Density Index for the concerned network and correlation with the time and apace complexity of that very network. This work actually compares the stationary states through Artificial Intelligence to find the most acceptable or optimum. It is concluded that the level of cohesion among different social groups can largely influence the expected outcome of any inclusionary development programme. This lack of cohesiveness within the Indian society due to some strong religio-philosophical reasons is one of the greatest challenges to India in achieving desired inclusive growth. In this paper it is shown that increasing the connectedness of the society can improve the efficiency of the development programs.

Rural-urban structural duality is one of the main reasons behind the systematic backwardness of the rural areas in West Bengal. One of the major mechanisms to address the problem of dual economy is the bottom-up decentralised development planning. But decentralised planning has failed to deliver desired result due to the existence of centralised components inside the method and incentive incompatibility. This failure is compared here with the non-existence of abstract relationship between the urban and rural. Abstract relationship as described here fails to deliver desired outcome when the methods declared at the urban class fail to find proper agent for execution at the derived rural class. This mismatch between the method and the agent is described here as the failure of the development program or failure of runtime polymorphism. Thus, this work wants to explain this structural phenomenon through Object Oriented Paradigm. It is concluded here that this backwardness arises due to the non-existence of abstract relationship between the urban and rural areas. This impediment can be corrected through development of interface using the concept of successive approximation. Ultimately this technique is tested over West Bengal for statistical significance of the model on policy planning.

Ensuring a sustainable livelihood to the larger counts of the population is the most challenging agenda of any developing economy. So this work wants to quantify the level of under achievements of different economies in the field of sustainable livelihood. The contribution of influencing variables of underachievement and contributing variables to achievement in the construction of under achievement livelihood space is also determined under this work. The interaction between these two types of variables and their juxtaposed effects are evaluated through the use of artificial neural network. Finally this method of artificial intelligence is used to achieve a self-sustained monotonic high rate of development. The whole work is presented through a set theoretic approach which is followed by the testing of the same. It is expected that the application of neural network in the process of self-sustained growth of sustainable livelihood is unique in academic discourses.

Social exclusion is one of the greatest threats of our contemporary world. It is a process that revolves around societal institutions to exclude, discriminate against, isolate and deprive groups or individuals on the basis of identities such as caste, ethnicity, religion, gender, location of residence, academic status, income etc. in various spheres of society, polity, and economy. This work evolves a measure to quantify the extent of social exclusion in the sphere of development programmes and develops an index of social exclusion from development programmes. Constructed scale is tested over a sample of households to examine its reliability and to find the nature of exclusion. Different statistical and mathematical tools have been used to construct the measure. The constructed measure is tested on a sample of 320 households chosen through multi-stage stratified random sampling. Sample data are used to find the level of exclusion of each household within the sample on the basis of this measure. With the help of different statistical and econometric tools the scale is also used to examine the nature of social exclusion from development programmes in India. This scale can be used in a greater spectrum to quantify the qualitative concept of social exclusion.

What is risk? Everyone is exposed to some type of risk every day-whether it's from driving, walking down the street, investing, capital planning, or something else. Risk is defined in financial terms as the chance that an outcome or investment's actual gains will differ from an expected outcome. But an exact definition for risk is hard to find and its measurement is controversial as well. Most of the definitions of risk are focused on the probability or likelihood of the event. For example, the OECD defines risk as the probability that the actual outcome will deviate from the expected outcome.

The well accepted multi-dimensional measures have failed to properly project the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to consider the graduality within the concept of poverty and the disparities within the household in wealth distribution. So, this work wants to develop a measure to estimate the vulnerability of households in becoming poor through incorporating the intra-household disparities through the factors which suffer from graduality. The decomposition of the grade of vulnerability on the causal factors is also under the purview of this work. To that respect the idea of fuzzy logic and decomposition through artificial intelligence has been used to develop a mathematical framework. So, the idea of Shapley Value Decomposition method has been used extensively. This decomposition is implemented here with the help of Shapley Machine Learning. This decomposition will help the planners to locate the role of different dimensions behind the vulnerability of human beings to become poor more efficiently.