Cities are engines of economic development, but the world economy's ability to reap the benefits of urbanization is limited by the costs of urban scale. Urban costs depend on commuting costs and the city's capacity to expand up and out. These three dimensions of cities' urban costs combine to form a city's urban cost elasticity, which measures how urban costs scale with population size. Using a structural model and geospatial data on over 10,000 cities, I measure urban costs globally. Cities in developing countries grow by building out, rather than up, even though residents face higher transportation costs. Compared to cities in the developed world, developing nations' cities have urban cost elasticities that are 35% higher. Embedding my estimates of urban costs in a quantitative spatial model featuring a system of cities and rural-to-urban migration, I find that high urban costs have large implications. Lowering urban cost elasticities to the average level observed in the United States would raise welfare in developing nations by 66%, six times the gains in the rich world. One third of the gains in the developing world are driven by general equilibrium responses, as workers both move out of agriculture and reallocate to more productive cities. To examine how policy might achieve these gains, I focus on urban road paving, which I find to be a cost-effective way to reduce urban costs in developing economies. Additionally, high urban costs not only affect economic development, but also hinder the efficacy of urbanization as a climate change adaptation strategy.

One rationale for place-based policy is that local development produces positive productivity spillovers. We examine the employment spillovers from a large local development project: opening a casino. Comparing employment in neighborhoods that won a casino license to runner-up neighborhoods that narrowly lost, we find casinos create jobs in their immediate vicinity. However, in the broader neighborhood, we estimate net job losses. The employment boom concentrates in the leisure and hospitality industry, suggesting spillovers are industry-specific or are driven by demand-side forces like trip-chaining. We develop theory to show that our estimates imply a rapid spatial decay of productivity spillovers.

A firm’s residual demand elasticity takes into account both the household's demand elasticity and the equilibrium response of competitors, and therefore measures its market power. We measure over 9 million of these residual demand elasticities for over 100,000 products in different regions and years using retail scanner data. We find the distribution of these elasticities is stationary over time, suggesting any conclusions that markups are rising in retail markets must be driven by assumptions on conduct. We document substantial spatial heterogeneity in residual demand elasticity estimates, implying the toughness of competition varies considerably more across markets than across time.

Quantifying and projecting climate migration is a critical challenge for policymakers and researchers. While causal inference, agent-based, gravity, and general equilibrium models have been employed to estimate the climate-migration relationship, their results often diverge, and they are typically used in isolation and at different spatio-temporal scales. This paper aims to bridge this gap by introducing a unified conceptual framework, designed to rigorously compare and evaluate the assumptions and outputs of these diverse models at a common scale. We illustrate our comparison using an empirically relevant case study: estimating and projecting the relationship between annual average temperature and internal state-to-state migration in the United States. We show that model results can coincide under certain conditions and illustrate mechanisms through which they can diverge. We conclude by combining insights from each approach, with the aim of improving the quantitative modelling of the climate migration relationship and facilitating future interdisciplinary collaboration.

Uncertainty regarding the rise in global temperatures increases the expected welfare cost of climate change if the welfare function is convex in temperature. We focus on how the geography of the climate shock interacts with geographic adaptation forces (trade and migration) to determine the shape of welfare response to climate change. We demonstrate theoretically that adaptation forces dampen convexity in damage functions, but the strength of these adaptation forces depends on the spatial distribution of the climate shock. Integrating over different climate scenarios, uncertainty amplifies the welfare loss from climate change. The geography of welfare loss, incorporating climate uncertainty, concentrates previously unaccounted for excess losses in the developing world, alongside the United States and some regions in Europe. These excess damages are highly spatially correlated, because adaptation forces through trade and migration display a strong gravity relationship in the data.

How do local housing markets interact across space and time? To answer this question, this paper develops a tractable dynamic spatial equilibrium model with continuous space and time and computable transition dynamics. Our model features fixed housing developers that build housing subject to adjustment costs, and interact in a spatial equilibrium generated by freely mobile households. The spatial equilibrium is key, because it reduces the dimensionality of the problem: rather than solving for the price at every location at every instant (an N x T dimensional problem), we only need to solve for the common household utility at each instant (a T dimensional problem). We show numerically that following a demand shock, housing adjustment paths may be nonmonotonic, as short-run demand increases may induce some developers to overshoot their long-run housing supply. In ongoing work, we apply our model to study how zoning restrictions affect the dynamics following local housing demand shocks in San Francisco. We infer de facto zoning restrictions using bunching in the building height distribution over different zoning classifications, and use our estimates of latent zoning parameters to quantify the model.

Ideas that challenge the status quo either evaporate or dominate. The study of opinion dynamics in the socio-physics literature treats space as uniform and considers individuals in an isolated community, using ordinary differential equation (ODE) models. We extend these ODE models to include multiple communities and their interactions. These extended ODE models can be thought of as being ODEs on directed graphs. We study in detail these models to determine conditions under which there will be consensus and pluralism within the system. Most of the consensus/pluralism analysis is done for the case of one and two cities. However, we numerically show for the case of a symmetric cycle graph that an elementary bifurcation analysis provides insight into the phenomena of clustering. Moreover, for the case of a cycle graph with a hub, we discuss how having a sufficient proportion of zealots in the hub leads to the entire network sharing the opinion of the zealots. This article is part of the theme issue ‘Stability of nonlinear waves and patterns and related topics’.