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Item type:Publication, A novel two-phase approach to computing a regional social progress index(Springer, 2020-01-01)In recent decades, concerns have emerged regarding the fact that standard macroeconomic statistics (such as gross domestic product) do not provide a sufficiently detailed and accurate picture of societal progress and well-being and of people’s true quality of life. This has further translated into concerns regarding the design of related public policies and whether these actually have the intended impact in practice. One of the first steps in bridging the gap between well-being metrics and policy intervention is the development of improved well-being measures. The calculation of a regional Social Progress Index (SPI) has been on the policymakers’ agenda for quite some time, as it is used to assist in the proposal of strategies that would create the conditions for all individuals in a society to reach their full potential, enhancing and sustaining the quality of their lives, while reducing regional inequalities. In this manuscript, we show a novel way to calculate a regional SPI under a two-phase approach. In the first phase, we aggregate the item-level information into subfactor-level indices and the subfactor-level indices into a factor-level index using an objective general index (OGI); in the second phase, we use the factor-level indices to obtain the regional SPI through a pure data envelopment analysis (DEA) approach. We further apply the method developed to analyse a single period of social progress in Peru. The manuscript is a contribution to the practical measurement of social progress. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stochastic data envelopment analysis(Springer Science+Business Media, 2021-12-11)In traditional DEA models, the technologies are developed using the premise that inputs and outputs are precisely measured and are, therefore, deterministic. However, in practical situations, the general production processes are often stochastic. The stochastic production relationship in a DEA setting may arise in different situations, for example, when stochastic variations in inputs and outputs affect the production frontier; when inputs and outputs are faced with stochastic prices while measuring allocative efficiency; when the slacks obtained from the DEA efficiency frontier are analyzed in terms of their statistical distribution; when an economic method is applied to estimate the stochastic production frontier; etc. (Sengupta, 1990). Over the last two decades, many researchers have proposed DEA-based models with stochastic data. Sengupta (2000) applied a stochastic DEA model using mathematical expectations for random inputs and outputs. Banker (1993) added statistical elements to DEA and developed an approach aimed at influencing statistical noise in inference. Many studies (e.g., Cooper et al., 1996, 1998; Land et al., 1993; Olesen & Petersen, 1995, 2016) have introduced chance-constrained programming in DEA to accommodate random changes in data. Banker (1986) proposed a related semi-parametric stochastic frontier analysis (SFA) based on a minimization of the sum of the absolute value of all composed error terms. For parametric and semi-parametric models, see Banker (1989, 1996), Banker and Chang (1995), Banker et al. (1994, 2015), and Banker and Maindiratta (1992). Additional approaches and applications can be found in Charles and Cornillier (2017), Charles and Udhayakumar (2012), Charles et al. (2018), Grosskopf (1996), Horrace and Schmidt (1996), Simar (1996), Simar and Wilson (1998), and Udhayakumar et al. (2011), among others. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An introduction to data envelopment analysis(Springer Science+Business Media, 2021-12-11)Following the seminal work of Farrell (1957), Charnes et al. (1978) introduced DEA as a deterministic and nonparametric efficiency evaluation tool. DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units, DMUs (Charles et al., 2016, 2018; Tsolas et al., 2020). DEA is a data-oriented technique (Zhu, 2020) that is used to construct an empirical production frontier to measure efficiency. Note that the original DEA program of Charnes et al. (1978) is based on the CRS specification of technology and is used to measure the technical and scale efficiency of DMUs. However, Banker et al. (1984) extended this program to the case of VRS to estimate purely technical efficiency. Over the past three decades, DEA has been widely used to evaluate the relative efficiency of production firms, the nature of the returns-to-scale, and the productivity changes. The DEA literature has seen a wide variety of applications across a plethora of domains, having become a powerful management science tool (Charles et al., 2018). In this chapter, we briefly review the fundamental concepts in DEA, along with the basic technologies and programs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A DEA and random forest regression approach to studying bank efficiency and corporate governance(Palgrave Macmillan, 2021-05-10)We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stochastic network data envelopment analysis(Springer Science+Business Media, 2021-12-11)Most real-life production processes are multi-stage in nature. Characterization of such processes via concepts such as technical efficiency is considered important to firm managers for the stage-specific analysis of their business decisions in improving their performance. Therefore, it is imperative to estimate the efficiency of a firm not only for the network production system but also for its sub-processes to locate the sources of inefficiency. In this chapter, we deal with production processes characterized by a two-stage network structure that links their stage-specific processes with intermediate products (measures). In this two-stage production process, the first stage uses input resources to produce intermediate products, which are all, in turn, used as inputs in the second stage to produce final outputs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, School education development index: A meta-frontier range directional measure benefit-of-the-doubt model(Elsevier Ltd, 2024-04-01)While the imperative of gauging school education development across regions is widely acknowledged, a scarcity of methodologically robust measures to quantify it persists. This paper introduces an innovative non-parametric framework for constructing an all-inclusive school education development index (SEDI) across regional entities. The proposed framework, termed “meta-RDM-BoD”, seamlessly integrates three distinct yet interconnected non-parametric efficiency modeling approaches: the range directional measure (RDM) proposed by Portela et al. (2004) [5], the meta-frontier analysis developed by O'Donnell et al. (2008) [6], and the benefit-of-the-doubt (BoD) technique put forth by Melyn and Moesen (1991) [7]. Notably, the proposed framework adeptly handles both desirable and undesirable indicators, accommodates indicators with negative and zero values without compromising the properties of translation and unit invariance, and effectively accounts for underlying heterogeneity across regional entities. To illustrate the efficacy of the SEDI, we provide a compelling example using data on 36 school education indicators for Indian states and union territories in 2021–2022. These indicators cover five crucial dimensions of school education: access to school, school infrastructure and facilities, teacher quality, school outcomes, and equity in education. The results reveal spatial gaps in school education development, offering valuable insights for benchmarking, ranking, and classifying regional entities. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Demonetisation, Financial Inclusion and Bank Efficiency(SAGE Publishing, 2026-01-01)We estimate three efficiencies, namely new profit, new cost and new technical, to examine the impact of demonetisation and financial inclusion on bank performance in India. Bank efficiency for the 2011–2019 period across size and ownership groups is measured using data envelopment analysis (DEA). In the second stage, differences in impact of the event across bank groups and efficiency types, using repeated analysis of variance (ANOVA), are observed. Significant effects across size and ownership, albeit not uniform across groups, events and efficiency types, were found. There was an increase in the number of frontier banks. SBI, the state-owned and largest bank, with the most significant role in those policies, had seen a positive impact on cost and profit efficiency. Our study is perhaps the first of its kind to examine demonetisation and financial inclusion impact on the banking sector and a two-stage estimation that combines DEA with repeated ANOVA. Our study does not lend support to the view that those events have burdened and adversely affected banks. The study carries important managerial and policy implications and provides much-needed scientific evidence to the popular debate on the impact of the events. JEL Codes: G21, G34, D61, M40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A novel inverse data envelopment analysis model with negative ratio data(Springer Science+Business Media, 2025-06-01)Data envelopment analysis (DEA) is a mathematical programming method for evaluating the efficiency of a homogeneous set of decision-making units (DMUs) using multiple inputs and outputs. Inverse DEA estimates a DMU’s input (or output) when some or all DMU outputs (or inputs) are changed. Ratio DEA (DEA-R) combines DEA with ratio analysis to handle ratio data. Real-world DEA-R models often involve negative values for the inputs or outputs. This study presents a novel model for solving inverse DEA problems with negative ratio data for the first time. We present a real-life case study to demonstrate the applicability and efficacy of the DEA models proposed in this study.1