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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

The COVID-19 pandemic, starting in early 2020, has significantly compromised global commitment to the 2030 Agenda for Sustainable Development Goals, notably affecting areas like food security (SDG 2) and the economy (SDG 8). Informal economy platform employees have been among the most impacted. In India alone, 7.7 million workers in the informal economy have suffered, with nearly 90% of unskilled and semi-skilled workers experiencing income loss. The widespread income loss among a significant portion of the workforce has led to disruptions in demand and supply mechanisms, thereby worsening food insecurity. This study investigates the determinants of the food consumption score (FCS) to serve as an indicator of food security within informal-economy households. A longitudinal survey of 2,830 unskilled and semi-skilled employees, including drivers, domestic workers, delivery personnel, beauticians, street vendors, small business owners, and self-employed individuals, was conducted. The findings show a significant shift towards borderline household FCS during the pandemic, with a sharp decline in daily consumption of dairy products and non-vegetarian items, indicating reduced protein intake. Consuming two or fewer meals per day increases the likelihood of poor FCS, highlighting the need for systematic interventions to ensure three regular meals per day. Moreover, insufficient government support for adequate food intake in informal economy households calls for redesigned assistance programs. Policymakers should prioritize practical solutions, such as community-based food distribution centers and mobile food vans, to ensure the delivery of nutritious food to vulnerable populations in Bangalore.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This study explores the configurations of management control systems (MCSs) while taking into account entrepreneurial cognition styles (ECSs) in small and medium enterprises (SMEs). The objective is to understand the impact of ECS on deployment and identify the various modes of MCS configurations employed by SMEs. The authors draw on and synthesise two theoretical perspectives relating to cognition and management control packages to understand the associations between ECS and MCS employed by SMEs in managing their business. This study was conducted using a quantitative approach that utilises a questionnaire survey to collect cross-sectional data from 150 SMEs. The authors uncovered three cognitive styles: knowing (e.g. preciseness), planning (e.g. organising), and creativity (e.g. innovativeness). Furthermore, five configurations of MCS utilised by SMEs were identified: customer focus, performance monitoring, administrative focus, strategic focus, and development focus. By combining both analyses, the authors discovered three constellations of significant association between ECS and MCS characterised by Cluster 1’s cohesive integration approach, Cluster 2’s revealing strategic approach, and Cluster 3’s multifaceted exploration. The study is significant because it uncovers the complex relationship between ECS and MCS configurations, highlighting their interdependence within the institutional context. Using a cognitive view, the authors explore how the cognitive styles of entrepreneurs facilitated imprinting institutional context into MCS configurations. These insights enable us to envisage that ECS is not mutually exclusive but forms a continuum that provides more plausible explanations that relax the direct universal relationship between MCS configurations and contextual factors.