Purpose – Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks. Design/methodology/approach – The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010. Findings – The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability. Research limitations/implications – The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature. Practical implications – The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis. Originality/value – The goal programming model used here is invariant to the type of bank and year of consideration.

Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.

PurposeEnergy-efficiency leads to productivity gains as it can lower operating and maintenance costs, increase production yields per unit of manufacturing input and improve staff accountability. Implementation of energy-efficient technologies amongst industries, the factors influencing them and the barriers to their adoption have been the subject of several studies during the past three to four decades. Though energy-use behaviours of individuals or households are sufficiently explored, industrial energy conservation behaviour is scarcely studied. This study identifies the relationship between the different behavioural elements to open up a door for behaviourally informed intervention research.Design/methodology/approachTotal interpretive structural modelling technique was used to determine the relationship between different elements of the behaviour of energy managers. Expert responses were collected to understand the relationship between the behavioural elements, through telephone interviews.FindingsThe study identified the relationship between the behavioural elements and found imperfect evaluation as the key element with the highest driving power to influence other elements.Research limitations/implicationsThe authors postulate that a behaviourally informed intervention strategy that looks into the elements with high driving power such as imperfect evaluation, lack of focus on energy-saving measures and the lack of sharing energy-saving objectives can lead to: an increase in the adoption of energy efficiency measures and thereby a reduction in the energy efficiency gap; greater productivity gains and reduced greenhouse gas (GHG) emissions; Preparation of M&V protocol that incorporates behavioural, organisational and informational barriers.Social implicationsVarious policy level interventions and regulatory measures in the energy field which did not address the behavioural barriers are found unsuccessful in narrowing the energy-efficiency gap, reducing the GHG gas emissions and global warming. Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement.Originality/valueUnderstanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement. This study argues that through the systematic analysis of the imperfect evaluation of energy audit recommendations, it is possible to increase the adoption of energy efficiency measures that can lead to greater productivity gains and reduced GHG emissions.

While earlier studies have focused excessively on bankruptcy prediction of banks, this study classifies banks based on their financial strength from the perspective of retail depositors who currently do not have an authentic guiding framework that helps them identify banks with higher risk profiles. Using machine learning techniques, we classify 44 Indian banks into distinct categories of financial health based on 12-year data from 2005 to 2017. We first use unsupervised learning to identify a pattern leading to logical groups in terms of financial health and then move to supervised learning for prediction. Using linear discriminant analysis (LDA), Classification and Regression Tree (CART) and Random Forest methods, we predict the cluster membership with the associated explanatory power alongside. We also compare our classification with the credit ratings awarded by rating agencies and highlight certain discrepancies that exist between what is predicted by our models and the credit rating awards. JEL Codes: C53; M10

Mental health is a neglected health issue in developing countries. We test if mental health issues are particularly likely to occur among some of the most vulnerable children in developing countries: those that work. Despite falling in recent decades, child labor still engages 168 million children across the world. While the negative impacts of child labor on physical health are well documented, the effect of child labor on a child's psychosocial wellbeing has been neglected. We investigate this issue with a new dataset of 947 children aged 12–18 years from 750 households in 20 villages across five districts of Tamil Nadu, India. Our purpose‐built survey allows for a holistic approach to the analysis of child wellbeing by accounting for levels of happiness, hope, emotional wellbeing, self‐efficacy, fear and stress. We use a variety of econometric approaches, some of which utilize household‐level fixed effects and account for differences between working and nonworking siblings. We document a robust, large and negative association between child labor and most measures of psychosocial wellbeing. The results are robust to a battery of exercises, including tests for selection on unobservables, randomization inference, instrumental variable techniques, and falsification exercises.

The impact of COVID-19 on the United Nations Sustainable Development Goals (SDGs) continues to be researched. Initial signals warn of significant setbacks in achieving SDG targets by 2030. The achievement of SDGs could abet improved protection from future pandemics. This article suggests reprioritizing SDGs to facilitate a more robust global response to future pandemics. Specifically, we recommend that SDGs 3, 6, 5 and 4 (in that order) are prioritized in order to optimize efforts at a more inclusive and resilient socio-economic recovery post-pandemic. This paper suggests that mandatory CSR regimes enable governments, in combination with corporate fiscal resources, to influence the selection and progress of these SDGs. The case of India’s mandatory CSR regime is employed to illustrate our position. This study extends the debate on SDGs by raising the possibility of universal concentration on a few critical SDGs.

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Small and marginal farmers contribute significantly to agricultural production and livelihoods all over the world. The small size of operational holdings, however, makes them highly susceptible to market risks leading to low levels of farm income. The farmer producer organizations (FPOs) are considered as effective mechanisms as they give voice to the small farmers, help overcome the challenges, by reducing the transaction costs and improving market access. However, in India, farmer collectives suffer from several institutional and structural impediments that affects their performance and thereby not resulting in empowerment and wellbeing of the farmers. In this regard, this article discusses the role of FPOs based on an empirical case study of Sahyadri Farmers Producer Company Ltd (SFPCL) from Maharashtra. The case study analyses the specificities of a private initiative such as Sahyadri, which focuses on making farming viable for farmers with small holdings in particular. The Sahyadri model contributes building the social capital of the farmers and improving the farm income and sustainable livelihoods. The article uses logistic regression to determine the factors influencing collective action and the Cobb–Douglas (CD) Production function to highlight the economic benefits realized by the farmers from being members of the Farmers Producer Company in case of Sahyadri in Maharashtra.