North America

What explains contemporary developed-world populism? A largely-overlooked hypothesis, advanced herein, is economic unfairness. This idea holds that humans do not simply care about the magnitudes of final outcomes such as losses or inequalities. They care deeply about whether each individual’s economic outcomes occur for fair reasons. Thus citizens turn to populism when they do not get the economic opportunities and outcomes they think they fairly deserve. A series of cross-sectional regressions show that low social mobility – an important type of economic unfairness – consistently correlates with the geography of populism, both within and across developed countries. Conversely, income and wealth inequality do not; and neither do the prominent cultural hypotheses of immigrant stocks, social media use, nor the share of seniors in the population. Collectively, this evidence underlines the importance of economic fairness, and suggests that academics and policymakers should pay greater attention to normative, moral questions about the economy.

Governments in modern societies undertake an array of complex functions that shape politics and economics, individual and group behavior, and the natural, social, and built environment. How are governments structured to execute these diverse responsibilities? How do those structures vary, and what explains the differences? To examine these longstanding questions, we develop a technique for mapping Internet “footprint” of government with network science methods. We use this approach to describe and analyze the diversity in functional scale and structure among the 50 US state governments reflected in the webpages and links they have created online: 32.5 million webpages and 110 million hyperlinks among 47,631 agencies. We first verify that this extensive online footprint systematically reflects known characteristics: 50 hierarchically organized networks of state agencies that scale with population and are specialized around easily identifiable functions in accordance with legal mandates. We also find that the footprint reflects extensive diversity among these state functional hierarchies. We hypothesize that this variation should reflect, among other factors, state income, economic structure, ideology, and location. We find that government structures are most strongly associated with state economic structures, with location and income playing more limited roles. Voters’ recent ideological preferences about the proper roles and extent of government are not significantly associated with the scale and structure of their state governments as reflected online. We conclude that the online footprint of governments offers a broad and comprehensive window on how they are structured that can help deepen understanding of those structures.

Visualizations and datasets available on project website >>

The prevalence of many urban phenomena changes systematically with population size ¹ . We propose a theory that unifies models of economic complexity ^2,3 and cultural evolution ⁴ 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.

Related Content: The Urban Theory of Everything

Harvard Magazine: Recipes for Thriving Cities

The prevalence of many urban phenomena changes systematically with population size¹. We propose a theory that unifies models of economic complexity^{2, 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.

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.

The comparative advantage of a location shapes its industrial structure. Current theoretical models based on this principle do not take a stance on how comparative advantages in different industries or locations are related with each other, or what such patterns of relatedness might imply about the underlying process that governs the evolution of comparative advantage. We build a simple Ricardian-inspired model and show this hidden information on inter-industry and inter-location relatedness can be captured by simple correlations between the observed patterns of industries across locations or locations across industries. Using the information from related industries or related locations, we calculate the implied comparative advantage and show that this measure explains much of the location’s current industrial structure. We give evidence that these patterns are present in a wide variety of contexts, namely the export of goods (internationally) and the employment, payroll and number of establishments across the industries of subnational regions (in the US, Chile and India). The deviations between the observed and implied comparative advantage measures tend to be highly predictive of future industry growth, especially at horizons of a decade or more; this explanatory power holds at both the intensive as well as the extensive margin. These results suggest that a component of the long-term evolution of comparative advantage is already implied in today’s patterns of production.

This paper evaluates if, after ten years of implementation, the conditional cash transfer program Progresa/Oportunidades has had an effect on labor market outcomes among young beneficiaries in rural Mexico. We use a specific module for the young aged 14 to 24 in the 2007 wave of the Rural Households Evaluation Survey and apply a multi-treatment methodology for different time exposition to the program to identify effects on employment probability, wages, migration and intergenerational occupational mobility. Our results show very little evidence of program impacts on employment, wages or inter-generational occupational mobility among the cohort of beneficiaries under study. This suggests that, despite well documented effects on human capital accumulation of the beneficiaries, labor market prospects in the localities under the program remain sparse.

The large economies have each, in sequence, offered "models" that once seemed attractive to others but that eventually gave way to disillusionment. Small countries may have some answers. They are often better able to experiment with innovative policies and institutions and some of the results are worthy of emulation. This article gives an array of examples. Some of them come from small advanced countries: New Zealand’s Inflation Targeting, Estonia’s flat tax, Switzerland’s debt brake, Ireland’s FDI policy, Canada’s banking structure, Sweden’s Nordic model, and the Netherlands’ labor market reforms. Some examples come from countries that were considered "developing" 40 years ago, but have since industrialized. Korea stands for education; among Singapore’s innovative polices were forced saving and traffic congestion pricing; Costa Rica and Mauritius outperformed their respective regions by, among other policies, foreswearing standing armies; and Mexico experimented successfully with the original Conditional Cash Transfers. A final set of examples come from countries that export mineral and agricultural commodities -- historically vulnerable to the "resource curse" -- but that have learned how to avoid the pitfalls: Chile’s structural budget rules, Mexico’s oil option hedging, and Botswana’s "Pula Fund."