North America

2016
Gomez-Lievano, A., Patterson-Lomba, O. & Hausmann, R., 2016. Explaining the Prevalence, Scaling and Variance of Urban Phenomena.Abstract

The prevalence of many urban phenomena changes systematically with population size1. We propose a theory that unifies models of economic complexity2, 3 and cultural evolution4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.

urban_phenomena_cidwp329.pdf

This paper is published in the journal, Nature: Human Behavior.

Explaining the prevalence, scaling and variance of urban phenomena
Gomez-Lievano, A., Patterson-Lomba, O. & Hausmann, R., 2016. Explaining the prevalence, scaling and variance of urban phenomena. Nature Human Behavior. Publisher's VersionAbstract

The prevalence of many urban phenomena changes systematically with population size1 . We propose a theory that unifies models of economic complexity2,3 and cultural evolution4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.

Russell, S., Barrios, D. & Andrews, M., 2016. Getting the Ball Rolling: Basis for Assessing the Sports Economy.Abstract

Data on the sports economy is often difficult to interpret, far from transparent, or simply unavailable. Data fraught with weaknesses causes observers of the sports economy to account for the sector differently, rendering their analyses difficult to compare or causing them to simply disagree. Such disagreement means that claims regarding the economic spillovers of the industry can be easily manipulated or exaggerated. Thoroughly accounting for the industry is therefore an important initial step in assessing the economic importance of sports-related activities. For instance, what do policymakers mean when they discuss sports-related economic activities? What activities are considered part of the "sports economy?" What are the difficulties associated with accounting for these activities? Answering these basic questions allows governments to improve their policies.

The paper below assesses existing attempts to understand the sports economy and proposes a more nuanced way to consider the industry. Section 1 provides a brief overview of existing accounts of the sports economy. We first differentiate between three types of assessments: market research accounts conducted by consulting groups, academic accounts written by scholars, and structural accounts initiated primarily by national statistical agencies. We then discuss the European Union’s (EU) recent work to better account for and understand the sports economy. Section 2 describes the challenges constraining existing accounts of the sports economy. We describe two major constraints - measurement challenges and definition challenges - and highlight how the EU's work has attempted to address them. We conclude that, although the Vilnius Definition improves upon previous accounts, it still features areas for improvement.

Section 3 therefore proposes a paradigm shift with respect to how we understand the sports economy. Instead of primarily inquiring about the size of the sports economy, the approach recognizes the diversity of sports-related economic activities and of relevant dimensions of analysis. It therefore warns against attempts at aggregation before there are better data and more widely agreed upon definitions of the sports economy. It asks the following questions: How different are sports-related sectors? Are fitness facilities, for instance, comparable to professional sports clubs in terms of their production scheme and type of employment? Should they be understood together or treated separately? We briefly explore difference in sports-related industry classifications using data from the Netherlands, Mexico, and the United States. Finally, in a short conclusion, we discuss how these differences could be more fully explored in the future, especially if improvements are made with respect to data disaggregation and standardization.

cidwp_321_assessing_sports_economy.pdf