To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Vaccination is a significant preventive measure to contain the coronavirus disease-2019 (COVID-19) pandemic. Vaccination rates can provide useful information on the potential spread of infection in a given population. In this study, vaccination rates and attitudes towards vaccination in cultural sectors, specifically the music sector, have been investigated. In total, 4341 persons in four different areas, including visitors to performances of classical music and musicals, as well as professional and amateur musicians, have participated in this survey. Results show rates of 86% recovered from the COVID-19 virus or vaccinated at least once, with 54.5% fully vaccinated. These vaccination rates were considerably higher compared to the general population. An attitude of hesitation towards vaccination found in 6.4% of those sampled was half that of the general population. These findings drawn from a large sample indicate that in the field of music a high vaccination rate is to be found, as well as a low rejection rate of vaccination on the part of the audience and performers. The results can be used to provide insights into the vaccination status to be found at cultural events and, importantly, to assist in consideration of whether cultural events should be permitted to continue under pandemic circumstances.
Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an exact computational treatment is impossible. To circumvent this difficulty, the replica-mean-field approach focuses on randomly interacting replicas of the networks of interest. In the limit of an infinite number of replicas, these networks become analytically tractable under the so-called ‘Poisson hypothesis’. However, in most applications this hypothesis is only conjectured. In this paper we establish the Poisson hypothesis for a general class of discrete-time, point-process-based dynamics that we propose to call fragmentation-interaction-aggregation processes, and which are introduced here. These processes feature a network of nodes, each endowed with a state governing their random activation. Each activation triggers the fragmentation of the activated node state and the transmission of interaction signals to downstream nodes. In turn, the signals received by nodes are aggregated to their state. Our main contribution is a proof of the Poisson hypothesis for the replica-mean-field version of any network in this class. The proof is obtained by establishing the propagation of asymptotic independence for state variables in the limit of an infinite number of replicas. Discrete-time Galves–Löcherbach neural networks are used as a basic instance and illustration of our analysis.
We investigate vertex levels of containment in a random hypergraph grown in the spirit of a recursive tree. We consider a local profile tracking the evolution of the containment of a particular vertex over time, and a global profile concerned with counts of the number of vertices of a particular containment level.
For the local containment profile, we obtain the exact mean, variance, and probability distribution in terms of standard combinatorial quantities such as generalized harmonic numbers and Stirling numbers of the first kind. Asymptotically, we observe phases: the early vertices have an asymptotically normal distribution, intermediate vertices have a Poisson distribution, and late vertices have a degenerate distribution.
As for the global containment profile, we establish an asymptotically normal distribution for the number of vertices at the smallest containment level as well as their covariances with the number of vertices at the second smallest containment level and the variances of these numbers. The orders in the variance–covariance matrix establish concentration laws.
We introduce a notion of finite sampling consistency for phylogenetic trees and show that the set of finitely sampling-consistent and exchangeable distributions on n-leaf phylogenetic trees is a polytope. We use this polytope to show that the set of all exchangeable and sampling-consistent distributions on four-leaf phylogenetic trees is exactly Aldous’ beta-splitting model, and give a description of some of the vertices for the polytope of distributions on five leaves. We also introduce a new semialgebraic set of exchangeable and sampling consistent models we call the multinomial model and use it to characterize the set of exchangeable and sampling-consistent distributions. Using this new model, we obtain a finite de Finetti-type theorem for rooted binary trees in the style of Diaconis’ theorem on finite exchangeable sequences.
This paper studies estimation of stochastic block models with Rissanen’s minimum description length (MDL) principle in the dense graph asymptotics. We focus on the problem of model specification, i.e., identification of the number of blocks. Refinements of the true partition always decrease the code part corresponding to the edge placement, and thus a respective increase of the code part specifying the model should overweight that gain in order to yield a minimum at the true partition. The balance between these effects turns out to be delicate. We show that the MDL principle identifies the true partition among models whose relative block sizes are bounded away from zero. The results are extended to models with Poisson-distributed edge weights.
Chapter 10 surveys a range of partial survey and non-survey estimation approaches for creating input–output tables at the regional level. Variants of the commonly used class of estimating procedures using location quotients are reviewed; these presume a regional estimate of input–output data can be derived using some information about a target region. Cross-hauling is discussed and approaches to address it are presented. The RAS technique developed in Chapter 9 is applied using a base national table or a table for another region and some available data for the target region. Techniques for partial survey estimation of commodity flows between regions are also presented along with discussions of several real-world multinational applications, including the China–Japan Transnational Interregional Model and Leontief’s World Model.
Chapter 11 expands the input–output framework to a broader class of economic analysis tools known as social accounting matrices (SAM) and other so-called extended input–output models to capture activities of income distribution in the economy in a more comprehensive and integrated way, including especially employment and social welfare features of an economy. The basic concepts of SAMs are explored and derived from the SNA introduced in Chapters 4 and 5, and the relationships between SAMs and input–output accounts are presented. The concept of SAM multipliers as well as the decomposition of SAM multipliers into components with specific economic interpretations are introduced and illustrated. Finally, techniques for balancing SAM accounts for internal accounting consistency are discussed and several illustrative applications of the use of SAMs are presented.
The introductory chapter recaps the genesis of the field of input–output or interindustry analysis as a widely utilized framework to analyze the interdependence of industries in an economy. The introduction chronicles how the input–output framework, conceived originally by Wassily Leontief in the 1930s, has matured over the last seven decades to become a key component of many types of economic analysis and one of the most widely applied methods in economics. This book presents the framework set forth by Leontief and explores the many extensions that have been developed, and the introduction concludes by summarizing the key features of the succeeding chapters, appendices, and related online resources chronicling those developments.
The Preface recaps the history of the development of the 1985, 2009, and current editions of the text and how the content has evolved with the discipline of input–output analysis. It also includes a summary of the principal updated and added material in this new edition.
Chapter 15 briefly describes some additional extensions to input–output analysis for which space does not permit a detailed treatment in this text, including measuring total factor productivity, modeling economic impacts of disasters, the inoperatbility input–output model, accounting for alternative technologies, and linkages to econometric or computable general equilibrium models.
Chapter 9 introduces approaches designed to deal with the major challenge in input–output analysis that the kinds of information-gathering surveys needed to collect input–output data for an economy can be expensive and very time consuming, resulting in tables of input–output coefficients that are outdated before they are produced. These techniques, known as partial survey and nonsurvey approaches to input–output table construction, are central to modern applications of input–output analysis. The chapter begins by reviewing the basic factors contributing to the stability of input–output data over time, such as changing technology, prices, and the scale and scope of business enterprises. Several techniques for updating input–output data are developed and the economic implications of each described. The bulk of the chapter is concerned with the widely utilized biproportional scaling (or RAS) technique and some related “hybrid model” variants.
Chapter 6 examines a number of key summary analytical measures known as multipliers that can be derived from input–output models to estimate the effects of exogenous changes on (1) new outputs of economic sectors, (2) income earned by households resulting from new outputs, and (3) employment generated from new outputs, (4) value-added generated by production, or (5) energy and environmental effects. The general structure of multiplier analysis and special considerations associated with regional, IRIO, and MRIO models are developed. Extensions to capture the effects of income generation for various household groups are explored, as well as additional multiplier variants. Chapter appendices expand on mathematical formulations of household and income multipliers.
Chapter 5 explores variations to the commodity-by-industry input–output framework introduced in Chapter 4, expanding the basic input–output framework to include distinguishing between commodities and industries, i.e., the supply of specific commodities in the economy and the use of those commodities by collections of businesses defined as industries. The chapter introduces the fundamental commodity-by-industry accounting relationships and how they relate to the basic input–output framework. Alternative assumptions are defined for handling the common accounting issue of secondary production, and economic interpretations of those alternative assumptions are presented. The formulations of commodity-driven and industry-driven models are also presented along with illustrations of variants on combining alternative assumptions for secondary production. Finally, the chapter illustrates the problem encountered with commodity-by-industry models, such as non-square commodity–industry systems, mixed technology options, or the interpretation of negative elements.
Chapter 2 introduces Leontief’s conceptual input–output framework and explains how to develop the fundamental mathematical relationships from the interindustry transactions table. The key assumptions associated with the basic Leontief model and implications of those assumptions are recounted and the economic interpretation of the basic framework is explored. The basic framework is illustrated with a highly aggregated model of the US economy. In addition, the “price model” formulation of the input–output framework is introduced to explore the role of prices in input–output models. Appendices to this chapter include a fundamental set of mathematical conditions for input–output models, known as the Hawkins–Simon conditions.
Chapter 13 reviews the extensions of the input–output framework to incorporate activities of environmental pollution and elimination associated with economic activities as well as the linkages of input–output to models of ecosystems. The chapter begins with the augmented Leontief model for incorporating pollution generation and elimination, from which many subsequent approaches have been developed. The chapter then describes the now widespread application of input–output analysis to environmental lifecycle assessment and establishing a “pollution footprint” for industrial activity. Environmental input–output is also now widely used to evaluate global environmental issues. The special case of analyzing the relationship between global climate change and industrial activity with a carbon footprint is then explored along with using input–output to attribute pollution generation to the demands driving consumption compared with the more traditional attribution of pollution generation to the sectors of industrial production necessary to meet that demand.
This paper is concerned with developing low variance simulation estimators of probabilities related to the sum of Bernoulli random variables. It shows how to utilize an identity used in the Chen-Stein approach to bounding Poisson approximations to obtain low variance estimators. Applications and numerical examples in such areas as pattern occurrences, generalized coupon collecting, system reliability, and multivariate normals are presented. We also consider the problem of estimating the probability that a positive linear combination of Bernoulli random variables is greater than some specified value, and present a simulation estimator that is always less than the Markov inequality bound on that probability.