We study the impact of the Covid-19 pandemic on Euro Area inflation and how it compares to the experiences of other countries, such as the United States, over the two-year period 2020-21. Our model-based calibration exercises deliver four key results: 1) Compositional effects – the switch from services to goods consumption – are amplified through global input-output linkages, affecting both trade and inflation. 2) Inflation can be higher under sector-specific labor shortages relative to a scenario with no such supply shocks. 3) Foreign shocks and global supply chain bottlenecks played an outsized role relative to domestic aggregate demand shocks in explaining Euro Area inflation over 2020-21. 4) International trade did not respond to changes in GDP as strongly as it did during the 2008-09 crisis despite strong demand for goods. These lower trade elasticities in part reflect supply chain bottlenecks. These four results imply that policies aimed at stimulating aggregate demand would not have produced as high an inflation as the one observed in the data without the negative sectoral supply shocks.

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 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.