Measure Of Inefficiency Research Articles

Measuring environmental inefficiency through machine learning: An approach based on efficiency analysis trees and by-production technology

The main objective of this study is to introduce machine learning-type extensions for the measurement of environmental inefficiency based on regression trees under shape constraints. The new methods developed are implemented using a by-production approach that distinguishes two technologies, one related to the generation of pollution and the other to the production of good outputs. In particular, we define two alternative approaches to measuring environmental inefficiency: by-production Efficiency Analysis Trees (by-production EAT) and by-production Convexified Efficiency Analysis Trees (by-production CEAT). The main advantage of the methods developed is that they do not suffer from the typical statistical problem of overfitting connected to Free Disposal Hull (FDH) and Data Envelopment Analysis (DEA). The performance of the new models is evaluated through a simulation study which shows that the new approaches outperform FDH and DEA in terms of mean squared error and bias. We also illustrate the practical usefulness of the new techniques through empirical application to 43 developing and developed countries over a fifteen-year period - from 2000 to 2014. Our empirical findings using real data clearly indicate the higher discriminating power of the by-production EAT and CEAT models as compared respectively to FDH and DEA.

Numeraire choice, shadow profit, and inefficiency measurement

We study a programming approach to inducing inefficiency measures for convex technologies. It takes the technology and the numeraire as given and uses variational arguments to isolate shadow prices that make a decision maker’s observed behavior as efficient as possible. The focus is on how the numeraire determines the element of the efficient frontier to which a decision maker’s performance is compared, the resulting technical inefficiency measure, and whether that measure offers a cardinal representation of the technology. We use the results to study an inefficiency measure, the polyhedral measure, that generalizes an array of existing measures.

The flexible reverse approach for decomposing economic inefficiency: With an application to Taiwanese banks

Profit inefficiency is conventionally decomposed into two mutually exclusive components representing profit loss due to technical inefficiency, and, through duality theory, a residual interpreted as allocative inefficiency. Although conventional models solve technical inefficiencies by reducing inputs and increasing outputs, achieving profit efficiency may require larger than observed input quantities and/or smaller than observed output quantities. However, overcoming the restrictions in the direction of the technical adjustments in input and output quantities demands flexibility that existing models do not offer. Thus, to achieve this flexibility, we introduce an endogenous profit inefficiency measure that reverses the subordinate role played by allocative inefficiency. The new measure is based on a monetized version of the weighted additive model seeking maximum feasible profit gains without restricting inputs and output adjustments. This prevents the conflicting prescriptions that the conventional model may offer in the form of non-monotonic input and output changes, thereby reducing adjustment costs. We apply the proposed model to real data from financial institutions. The differences in the managerial and policy recommendations for optimal resource allocation are relevant, with the conventional model wrongly recommending reductions in inputs in terms of the amounts and scale required to maximize profit.

Accounting for technology heterogeneity in the measurement of persistent and transient inefficiency

This article tackles the problem of ignoring firm technology heterogeneity when measuring persistent and transient inefficiencies, which can lead to distorted estimates. Given that firms’ technologies exert a greater influence on long-run rather than short-run management, accounting for technology heterogeneity should prevent distortions in persistent inefficiencies and, to a lesser extent, in transient inefficiencies, which has been overlooked in the literature. Employing data from Dutch dairy farms spanning the years 2009 to 2016, I estimate random and fixed coefficients generalized true random effects models in a Bayesian framework, subsequently comparing the derived inefficiency scores. The findings indicate that average persistent inefficiency and its dispersion are inflated when technology heterogeneity is ignored, whilst transient inefficiency remains virtually unaffected. This finding is crucial to prevent firms and policy-makers from receiving misleading information about long-run performance metrics, whilst highlighting the need for future studies to account for technology heterogeneity when analyzing firms’ decisions.

Activity Theory Study of China’s Rural Book House Project

This study explores why China’s rural information projects tend to be inefficient by identifying the dynamic mechanisms driving the construction and development of China’s Rural Book House Project (RBHP). A qualitative survey was used to collect data from 35 participants in the RBHP and to analyze the data through the lens of activity theory. Two dynamic mechanisms were found, namely power coordination and benefit protection, with the former characterized by compulsion, self-interest, and inefficient punitive measures and the latter by the participants’ differing objectives when pursuing benefits. The interaction of these mechanisms drove the RBHP forward. The findings of the study suggest the development process of the RBHP and other similar projects need to be reanalyzed from an integrative, dynamic and connected perspective. By way of concluding, insights concerning the measures the Chinese government could take to resolve the issues confronted in rural information service development as well as the need for information professionals to take a critical stance toward their community practice are offered.

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

Friendly boards and capital allocation efficiency

AbstractThis study examines the effect of friendly boards on capital allocation efficiency. We provide evidence that firms with friendly boards have a positive and statistically significant effect on capital allocation inefficiency. We find our results robust to different measures of friendly boards and capital allocation inefficiency, alternative model specifications, omitted variable bias, self‐selection bias and other endogeneity concerns. We also show that the positive association between friendly boards and capital allocation inefficiency is lower in firms with high external corporate governance quality but higher in firms with high financial constraints. The findings imply that poor board monitoring and high agency conflicts in firms with friendly boards lead to high capital allocation inefficiency.

An Eco-friendly Mangifera indica Leaves Extract Corrosion Inhibitor for Stainless Steel in Acidic Medium

Corrosion of metals and alloys is one of the most frequent problems encountered in chemical and process industries. Inefficient corrosion control measures typically lead to an increased risk of unplanned downtime, huge economic loss, environmental damage, and health and safety hazards. Hence, it is essential to develop environment-friendly and cost-effective corrosion inhibitors over existing toxic anticorrosive agents. The main objective of this work is to examine the efficacy of eco-friendly ethanolic extract of Mangifera indica leaves (MIL) in different concentrations as a green corrosion inhibitor for stainless steel (SS-316L) under an acidic environment. The inhibition efficiency of Mangifera indica leaves extract in 1 M hydrochloric acid (HCl) was evaluated by conventional weight loss method along with adsorption isotherm analysis. Chemical compounds present in leaf extract and changes in surface morphology of SS-316L samples were assessed using Fourier Transform Infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscopy (FE-SEM) provided with elemental analysis. The results of the weight loss method revealed that the inhibition efficiency increases with increasing MIL extract concentration due to higher surface coverage. The highest inhibition efficiency of almost 63.43% in 14 days and minimum corrosion rate of 0.433 mm per year was obtained for SS-316 L in 1.0 M HCl with 1000 ppm concentration. The adsorption of MIL extract on SS-316L surface followed Freundlich adsorption isotherm, and the obtained value of free Energy of adsorption (ΔG˚ads = – 9.20 kJ.mol-1) indicates the physical adsorption mechanism. The developed regression-based models can predict the corrosion rate as a function of inhibitor concentration and exposure time with good accuracy (>80%). Thus, the present findings demonstrate that Mangifera indica L. leaves extract can suitably be applied as an inexpensive, non-toxic, biodegradable, efficient green corrosion inhibitor for the protection of stainless steel in acidic media.

Multi-View consistency-based point cloud registration method with low overlap rate

Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.

A unified framework for nonperforming loan modeling in bank production: An application of data envelopment analysis

The aim of this paper is to conduct a comparative analysis of three environmental approaches in the context of a bank production framework, considering the presence of nonperforming loans (NPLs). Specifically, we examine banks' inefficiency levels using the "by-production technology," "joint-weak disposable technology," and "material balanced technology." To ensure comparability within a directional slack inefficiency framework, we propose a two-step procedure. The study is based on a sample of 379 prominent banks operating in the United States from 2003 to 2017. Our findings reveal that the material balance and by-production technologies result in estimated inefficiency measures with higher sensitivity compared to the estimator utilizing the joint-weak disposable technology. Additionally, we identify distinct properties among the estimators, emphasizing their unique characteristics for modeling nonperforming loans. Finally, our paper sheds light on the differences between the three estimators in relation to banks' inefficiency levels, considering the incorporation of nonperforming loans in the production process.

Input inefficiencies in the hotel industry. A non-radial directional performance measurement

This paper investigates the inefficiencies of inputs in the hotel industry. To do this, we apply the weighted Russell directional distance model (Barros et al., 2012; Chen et al., 2014) considering an input-oriented approach. This model allows to obtain not only aggregate inefficiency measures, but also their components. In a second stage, we analyze the individual and overall efficiency measures considering several factors in a truncated Bayesian Tobit regression. The empirical analysis is based on the hotel sector of two mature Spanish destinations (the Canary and Balearic Islands). Results show that although hotels located in both regions obtain high efficiency levels, they are notably inefficient in terms of capital costs management. Furthermore, overall results show that Canarian hotels are less efficient than Balearic hotels in the management of all inputs. Finally, the type of chain and location variables appear to affect the overall and individual efficiency scores obtained by Canarian hotels.

Features of bottomhole zone treatment and formation stimulation by large volumes of hydrochloric solutions

The study confirms the hypotheses on the impact of the injection rate and the specific flow rate of the acid composition on the efficiency of treatments. Factors that increase the risk of inefficient measures were identified: fracture pressure excess during the implementation of the technology significantly increases the risk of water flooding of a well after treatment; energy state of the formation – level of formation pressure at targeted development is below 40% of the initial state; presence of active water-saturated feed zones that support formation energy during development, but increase the risk of water breakthrough. The following changes to the technology were proposed to increase the efficiency of carbonate reservoir treatment: when calculating the planned volumes of the acid composition to use specific parameters depending on the length of the treated interval or perforated thickness; acid composition is injected at the maximum possible specific flow rate (at least 1.8 m3/h per meter) without exceeding the fracture pressure; to reduce the minimum permissible level for large-volume acid treatments of formation pressure from 60 to 40% of the initial level.

CAPACITY BUILDING PLANNING FOR FISHERMEN COMMUNITY’S EMPOWERMENT

Efforts to promote fishing community empowerment frequently face obstacles owing to inefficient measures carried out by multiple parties that do not result in substantial improvements in the fishing communities. The persistence of this problem, however, can be linked to a lack of capacity building activities, particularly in the context of sustainability, which is critical for long-term development among fishermen. This research intends to investigate the reality of fishermen's capacity building in Terengganu, Malaysia, using the aspects of economic sustainability, institutional environment, and fishermen's technology. The study included 220 coastal fishermen who were sampled in stages. Questionnaires were used to collect data, which was then analysed using descriptive statistics and Partial Least Square-Structural Equation Modelling (SEM-PLS). According to the study's findings, the development of sustainability capabilities (technological, economic, institutional, and technological) influences the empowerment of fishing communities. As a result, community empowerment programmes should focus on capacity building in order to foster a well-empowered community.

Productive efficiency analysis with unobserved inputs: An application to endogenous automation in railway traffic management

Productive efficiency analytics are commonly used in managerial decision making, but are vulnerable to an omitted variable bias issue when there is incomplete information on the used production factors. In this paper, we relax the standard assumption in productive efficiency analysis that all input quantities are observed, and we propose a nonparametric methodology for cost inefficiency measurement that accounts for the presence of unobserved inputs. Our main contribution is that we bridge the nonparametric OR/MS and the economic Industrial Organization literature by addressing the general critique of Stigler (1976) on the concept of inefficiency (Leibenstein, 1966), which states that found inefficiencies reflect unobserved inputs rather than waste. Our methodology explicitly differentiates between cost inefficiency (i.e., waste; deviations from optimizing behavior) and unobserved input usage (i.e., optimally chosen input factors that are unobserved to the empirical analyst). We apply our novel method to a purpose-built dataset on Belgian railway traffic management control rooms. Our findings show the existence of meaningful inefficiencies that cannot be attributed to use of unobserved inputs or environmental factors. In addition, we document how the omitted variable bias impacts cost efficiencies of individual observations in a dissimilar way in case the use of unobserved inputs is not controlled for.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

The Efficiency of European Universities and the Role of Exogenous Factors

The evaluation of Higher Education represents a worldwide interesting topic of discussion. Many quantitative studies regard efficiency and inefficiency measures of universities’ performance in terms of traditional Data Envelopment Analysis (DEA) or of their variants: the efficiency of a specific university is given by a relation between supplied inputs such as costs, students and faculty, and produced outputs such as graduates and profit, while external influences are taken for granted. The purpose of this analysis is to measure the efficiency of public European Higher Education Institutions (HEIs) through non-parametric approaches and to consider the Conditional Frontier treatment of Daraio and Simar (2005) to test how the introduction of some potential exogenous efficiency determinants might affect the university’s productivity, namely, the proportion of women as faculty staff members, the Gross Domestic Product and the institution foundation year. We analyze a sample of 457 HEIs from 15 countries by measuring their performance during 2013. Our results suggest the proportion of women as a substitutive input that has a positive effect on the production process.

The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl

We undertake a systematic comparison of existing models measuring and decomposing the economic efficiency of organizations. For this purpose we introduce the package BenchmarkingEconomicEfficiency.jl for the open-source Julia language including a set of functions to be used by scholars and professionals working in the fields of economics, management science, engineering, and operations research. Using mathematical programming methods known as Data Envelopment Analysis, the software develops code to decompose economic efficiency considering alternative definitions: profit, profitability, cost and revenue. Economic efficiency can be decomposed, multiplicative or additively, into a technical (productive) efficiency term and a residual term representing allocative (or price) efficiency. We include traditional decompositions like the radial efficiency measures associated with the input (cost) and output (revenue) approaches, as well as new ones corresponding to the Russell measures, the directional distance function, DDF (including novel extensions like the reverse DDF, modified DDF, or generalizations based on Hölder norms), the generalized distance function, and additive measures like the slack based measure, their weighted variants, etc. Moreover, regardless the underlying economic efficiency model, many of these technical inefficiency measures are available for calculation in a computer software for the first time. This article details the theoretical methods and the empirical implementation of the functions, comparing the obtained results using a common dataset on Taiwanese Banks.

Are incentives excessive or insufficient? The impact of R&D fiscal policies on R&D inefficiency in China

The extant literature on evaluating the influence of R&D fiscal policies primarily focused on the advantages of the input or output additionality, neglecting that excessive incentives caused by R&D fiscal policies may lead to R&D inefficiency. From the perspective of resource misallocation, this paper divides R&D inefficiency into input redundancy and output insufficiency, then further measures the impact of three typical R&D fiscal policies on R&D inefficiency based on the firm-level sample of Zhongguancun Science Park in China. A two-step empirical analytical framework is constructed in this paper, namely, the inefficiency measurement step and the influence assessment step. The results prove that R&D fiscal policies may lead to resource misallocation and aggravate R&D inefficiency, and the heterogeneous conclusions cross three policies are found. Specifically, the subsidy policy significantly promotes the increase of R&D inefficiency, which primarily reflects in the rise of input redundancy. The pre-tax additional deduction policy does not show a significant impact on the R&D inefficiency but increases the input redundancy. On the contrary, the preferential tax rate for high-tech enterprises policy effectively inhibits the inefficiency. This paper not only attempts to reveal the formation of R&D inefficiency theoretically from the perspective of resource misallocation, but also takes China as an empirical sample to provide evidence for guiding the design of more effective R&D fiscal policies.

On why we lack confidence in some signal-detection-based analyses of confidence

Signal-detection theory (SDT) is one of the most popular frameworks for analyzing data from studies of human behavior – including investigations of confidence. SDT-based analyses of confidence deliver both standard estimates of sensitivity (d’), and a second estimate informed by high-confidence decisions – meta d’. The extent to which meta d’ estimates fall short of d’ estimates is regarded as a measure of metacognitive inefficiency, quantifying the contamination of confidence by additional noise. These analyses rely on a key but questionable assumption – that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences (the normality assumption). Here we show, via analyses inspired by an experiment and modelling, that when distributions of experience do not conform with the normality assumption, meta d’ can be systematically underestimated relative to d'. Our data highlight that SDT-based analyses of confidence do not provide a ground truth measure of human metacognitive inefficiency. We explain why deviance from the normality assumption is especially a problem for some popular SDT-based analyses of confidence, in contrast to other analyses inspired by the SDT framework, which are more robust to violations of the normality assumption.

A general direct approach for decomposing profit inefficiency

Considering any graph technical inefficiency measure, we show that the so-called standard or traditional approach for decomposing profit inefficiency relying on Fenchel-Mahler inequalities obtained from duality theory, establishes that profit inefficiency is greater than or equal to the product of technical inefficiency times a positive factor expressed in monetary units. This product is identified as the technical profit inefficiency and its difference with respect to the profit inefficiency as the allocative profit inefficiency. Dividing profit inefficiency by the mentioned positive factor one obtains the normalized (units’ invariant) profit inefficiency of the firm, which is a pure number, and can be decomposed into the sum of technical inefficiency and the normalized allocative profit inefficiency, usually called allocative inefficiency. We propose a new decomposition based on equalities that starts from the input and output slacks connecting the firm with the frontier benchmark, obtained through the pre-specified technical inefficiency measure. Profit inefficiency is then decomposed into the value of the technological gap and the profit inefficiency of the frontier benchmark. Expressing the value of the technological gap as the product of the technical inefficiency times a certain normalizing factor, we deduce a new normalized profit inefficiency decomposition. Our decomposition ensures that the allocative efficiency of a firm corresponds to that of its benchmark on the frontier and therefore avoids the possibility of overestimating it. We compare the traditional and the general direct approach and show that the new decomposition is conceptually sound and more accurate, with the only exception of the family of directional distance functions, for which both decompositions are equivalent.