|
Showing 1 - 8 of
8 matches in All Departments
This book unifies and extends the definition and measurement of
economic efficiency and its use as a real-life benchmarking
technique for actual organizations. Analytically, the book relies
on the economic theory of duality as guiding framework.
Empirically, it shows how the alternative models can be implemented
by way of Data Envelopment Analysis. An accompanying software
programmed in the open-source Julia language is used to solve the
models. The package is a self-contained set of functions that
can be used for individual learning and instruction. The source
code, associated documentation, and replication notebooks are
available online. The book discusses the concept of economic
efficiency at the firm level, comparing observed to optimal
economic performance, and its decomposition according to technical
and allocative criteria. Depending on the underlying technical
efficiency measure, economic efficiency can be decomposed
multiplicatively or additively. Part I of the book deals with the
classic multiplicative approach that decomposes cost and revenue
efficiency based on radial distance functions. Subsequently, the
book examines how these partial approaches can be expanded to the
notion of profitability efficiency, considering both the input and
output dimensions of the firm, and relying on the generalized
distance function for the measurement of technical
efficiency. Part II is devoted to the recent additive
framework related to the decomposition of economic inefficiency
defined in terms of cost, revenue, and profit. The book presents
economic models for the Russell and enhanced graph Russell
measures, the weighted additive distance function, the directional
distance function, the modified directional distance function, and
the Hölder distance function. Each model is presented in a
separate chapter. New approaches that qualify and generalize
previous results are also introduced in the last chapters,
including the reverse directional distance function and the general
direct approach. The book concludes by highlighting the
importance of benchmarking economic efficiency for all business
stakeholders and recalling the main conclusions obtained from many
years of research on this topic. The book offers different
alternatives to measure economic efficiency based on a set of
desirable properties and advises on the choice of specific economic
efficiency models.
This book surveys the state-of-the-art in efficiency and
productivity analysis, examining advances in the analytical
foundations and empirical applications. The analytical techniques
developed in this book for efficiency provide alternative ways of
defining optimum outcome sets, typically as a (technical)
production frontier or as an (economic) cost, revenue or profit
frontier, and alternative ways of measuring efficiency relative to
an appropriate frontier. Simultaneously, the analytical techniques
developed for efficiency analysis extend directly to productivity
analysis, thereby providing alternative methods for estimating
productivity levels, and productivity change through time or
productivity variation across producers. This book includes
chapters using data envelopment analysis (DEA) or stochastic
frontier analysis (SFA) as quantitative techniques capable of
measuring efficiency and productivity. Across the book's 15
chapters, it broadly extends into popular application areas
including agriculture, banking and finance, and municipal
performance, and relatively new application areas including
corporate social responsibility, the value of intangible assets,
land consolidation, and the measurement of economic well-being. The
chapters also cover topics such as permutation tests for production
frontier shifts, new indices of total factor productivity, and also
randomized controlled trials and production frontiers.
This book grows from a conference on the state of the art and
recent advances in Efficiency and Productivity. Papers were
commissioned from leading researchers in the field, and include
eight explorations into the analytical foundations of efficiency
and productivity analysis. Chapters on modeling advances include
reverse directional distance function, a new method for estimating
technological production possibilities, a new distance function
called a loss distance function, an analysis of productivity and
price recovery indices, the relation of technical efficiency
measures to productivity measures, the implications for
benchmarking and target setting of imposing weight restrictions on
DEA models, weight restrictions in a regulatory setting, and the
Principle of Least Action. Chapters on empirical applications
include a study of innovative firms that use innovation inputs to
produce innovation outputs, a study of the impact of potential
"coopetition" or cooperation among competitors on the financial
performance of European automobile plants, using SFA to estimate
the eco-efficiency of dairy farms in Spain, a DEA bankruptcy
prediction model, a combined stochastic cost frontier analysis
model/mixture hazard model, the evolution of energy intensity in
nine Spanish manufacturing industries, and the productivity of US
farmers as they age.
This book grows from a conference on the state of the art and
recent advances in Efficiency and Productivity. Papers were
commissioned from leading researchers in the field, and include
eight explorations into the analytical foundations of efficiency
and productivity analysis. Chapters on modeling advances include
reverse directional distance function, a new method for estimating
technological production possibilities, a new distance function
called a loss distance function, an analysis of productivity and
price recovery indices, the relation of technical efficiency
measures to productivity measures, the implications for
benchmarking and target setting of imposing weight restrictions on
DEA models, weight restrictions in a regulatory setting, and the
Principle of Least Action. Chapters on empirical applications
include a study of innovative firms that use innovation inputs to
produce innovation outputs, a study of the impact of potential
"coopetition" or cooperation among competitors on the financial
performance of European automobile plants, using SFA to estimate
the eco-efficiency of dairy farms in Spain, a DEA bankruptcy
prediction model, a combined stochastic cost frontier analysis
model/mixture hazard model, the evolution of energy intensity in
nine Spanish manufacturing industries, and the productivity of US
farmers as they age.
This book unifies and extends the definition and measurement of
economic efficiency and its use as a real-life benchmarking
technique for actual organizations. Analytically, the book relies
on the economic theory of duality as guiding framework.
Empirically, it shows how the alternative models can be implemented
by way of Data Envelopment Analysis. An accompanying software
programmed in the open-source Julia language is used to solve the
models. The package is a self-contained set of functions that can
be used for individual learning and instruction. The source code,
associated documentation, and replication notebooks are available
online. The book discusses the concept of economic efficiency at
the firm level, comparing observed to optimal economic performance,
and its decomposition according to technical and allocative
criteria. Depending on the underlying technical efficiency measure,
economic efficiency can be decomposed multiplicatively or
additively. Part I of the book deals with the classic
multiplicative approach that decomposes cost and revenue efficiency
based on radial distance functions. Subsequently, the book examines
how these partial approaches can be expanded to the notion of
profitability efficiency, considering both the input and output
dimensions of the firm, and relying on the generalized distance
function for the measurement of technical efficiency. Part II is
devoted to the recent additive framework related to the
decomposition of economic inefficiency defined in terms of cost,
revenue, and profit. The book presents economic models for the
Russell and enhanced graph Russell measures, the weighted additive
distance function, the directional distance function, the modified
directional distance function, and the Hoelder distance function.
Each model is presented in a separate chapter. New approaches that
qualify and generalize previous results are also introduced in the
last chapters, including the reverse directional distance function
and the general direct approach. The book concludes by highlighting
the importance of benchmarking economic efficiency for all business
stakeholders and recalling the main conclusions obtained from many
years of research on this topic. The book offers different
alternatives to measure economic efficiency based on a set of
desirable properties and advises on the choice of specific economic
efficiency models.
This book includes a spectrum of concepts, such as performance,
productivity, operations research, econometrics, and data science,
for the practically and theoretically important areas of
'productivity analysis/data envelopment analysis' and 'data
science/big data'. Data science is defined as the collection of
scientific methods, processes, and systems dedicated to extracting
knowledge or insights from data and it develops on concepts from
various domains, containing mathematics and statistical methods,
operations research, machine learning, computer programming,
pattern recognition, and data visualisation, among others. Examples
of data science techniques include linear and logistic regressions,
decision trees, Naive Bayesian classifier, principal component
analysis, neural networks, predictive modelling, deep learning,
text analysis, survival analysis, and so on, all of which allow
using the data to make more intelligent decisions. On the other
hand, it is without a doubt that nowadays the amount of data is
exponentially increasing, and analysing large data sets has become
a key basis of competition and innovation, underpinning new waves
of productivity growth. This book aims to bring a fresh look onto
the various ways that data science techniques could unleash value
and drive productivity from these mountains of data. Researchers
working in productivity analysis/data envelopment analysis will
benefit from learning about the tools available in data science/big
data that can be used in their current research analyses and
endeavours. The data scientists, on the other hand, will also get
benefit from learning about the plethora of applications available
in productivity analysis/data envelopment analysis.
This book includes a spectrum of concepts, such as performance,
productivity, operations research, econometrics, and data science,
for the practically and theoretically important areas of
'productivity analysis/data envelopment analysis' and 'data
science/big data'. Data science is defined as the collection of
scientific methods, processes, and systems dedicated to extracting
knowledge or insights from data and it develops on concepts from
various domains, containing mathematics and statistical methods,
operations research, machine learning, computer programming,
pattern recognition, and data visualisation, among others. Examples
of data science techniques include linear and logistic regressions,
decision trees, Naive Bayesian classifier, principal component
analysis, neural networks, predictive modelling, deep learning,
text analysis, survival analysis, and so on, all of which allow
using the data to make more intelligent decisions. On the other
hand, it is without a doubt that nowadays the amount of data is
exponentially increasing, and analysing large data sets has become
a key basis of competition and innovation, underpinning new waves
of productivity growth. This book aims to bring a fresh look onto
the various ways that data science techniques could unleash value
and drive productivity from these mountains of data. Researchers
working in productivity analysis/data envelopment analysis will
benefit from learning about the tools available in data science/big
data that can be used in their current research analyses and
endeavours. The data scientists, on the other hand, will also get
benefit from learning about the plethora of applications available
in productivity analysis/data envelopment analysis.
This book surveys the state-of-the-art in efficiency and
productivity analysis, examining advances in the analytical
foundations and empirical applications. The analytical techniques
developed in this book for efficiency provide alternative ways of
defining optimum outcome sets, typically as a (technical)
production frontier or as an (economic) cost, revenue or profit
frontier, and alternative ways of measuring efficiency relative to
an appropriate frontier. Simultaneously, the analytical techniques
developed for efficiency analysis extend directly to productivity
analysis, thereby providing alternative methods for estimating
productivity levels, and productivity change through time or
productivity variation across producers. This book includes
chapters using data envelopment analysis (DEA) or stochastic
frontier analysis (SFA) as quantitative techniques capable of
measuring efficiency and productivity. Across the book's 15
chapters, it broadly extends into popular application areas
including agriculture, banking and finance, and municipal
performance, and relatively new application areas including
corporate social responsibility, the value of intangible assets,
land consolidation, and the measurement of economic well-being. The
chapters also cover topics such as permutation tests for production
frontier shifts, new indices of total factor productivity, and also
randomized controlled trials and production frontiers.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|