Adrien Bilal, Esteban Rossi‐Hansberg,2021.”Location as an asset.” Econometrica, 89(5):2459-2495.(DOI:10.3982/ECTA16699）

● Abstract

The location of individuals determines their job and schooling opportunities, amenities, and housing costs. We conceptualize the location choice of individuals as a decision to invest in a “location asset.” This asset has a current cost equal to the location's rent, and a future payoff through better job and schooling opportunities. As with any asset, savers in the location asset transfer resources into the future by going to expensive locations with high future returns. In contrast, borrowers transfer resources to the present by going to cheap locations that offer few other advantages. Holdings of the location asset depend on its comparison to other assets, with the distinction that the location asset is not subject to borrowing constraints. We propose a dynamic location model and derive an agent's mobility choices after experiencing income shocks. We document the investment dimension of location and confirm the core predictions of our theory using French individual panel data from tax returns.

03

Pierre-Philippe Combes, Gilles Duranton, Laurent Gobillon, 2021. The production function of housing: Evidence from France. Journal of Political Economy, 129(10):2766-2816.(DOI: 10.1086/715076)

● Abstract

We propose a new nonparametric approach to estimate the production function for housing. Our estimation treats output as a latent variable and relies on a first-order condition for profit maximization combined with a zero-profit condition. More desirable locations command higher land prices and, in turn, more capital to build houses. For parcels of a given size, we compute housing production by summing across the marginal products of capital. For newly built single-family homes in France, the production function for housing is close to constant returns and is well, though not perfectly, approximated by a Cobb-Douglas function with a capital elasticity of 0.65.

04

Jessie Handbury, 2021. Are poor cities cheap for everyone? Non-homotheticity and the cost of living across U.S. cities. Econometrica, 89(6):2679-2715.(DOI:10.3982/ECTA11738)

●Abstract

This paper shows that the products and prices offered in markets are correlated with local income‐specific tastes. To quantify the welfare impact of this variation, I calculate local price indexes micro‐founded by a model of non‐homothetic demand over thousands of grocery products. These indexes reveal large differences in how wealthy and poor households perceive the choice sets available in wealthy and poor cities. Relative to low‐income households, high‐income households enjoy 40 percent higher utility per dollar expenditure in wealthy cities, relative to poor cities. Similar patterns are observed across stores in different neighborhoods. Most of this variation is explained by differences in the product assortment offered, rather than the relative prices charged, by chains that operate in different markets.

05

Stephan Heblich, Alex Trew,  Yanos Zylberberg, 2021. East-side story: Historical pollution and persistent neighborhood sorting.  Journal of Political Economy, 129(5):1508-1552.( DOI:10.1086/713101)

●Abstract

Why are the east sides of formerly industrial cities more deprived? To answer this question, we use individual-level census data and create historical pollution patterns derived from the locations of 5,000 industrial chimneys and an atmospheric model. We show that this observation results from path-dependent neighborhood sorting that began during the Industrial Revolution, as prevailing winds blew pollution eastward. Past pollution explains up to 20% of observed neighborhood segregation in 2011, even though coal pollution stopped in the 1970s. We develop a quantitative model to identify the role of neighborhood effects and relocation rigidities underlying this persistence.

06

J Vernon Henderson, Tanner Regan, Anthony J Venables, 2021. Building the city: From slums to modern metropolis. The Review of Economic Studies, 88(3):11571192. (DOI:10.1093/restud/rdaa042)

● Abstract

We model the building of a city, estimate parameters of the model, and calculate welfare losses from institutional frictions encountered in changing land-use. We distinguish formal and slum construction technologies; in contrast to slums, formal structures can be built tall, are durable, and non-malleable. As the city grows areas are initially developed informally, then formally, and then redeveloped periodically. Slums are modelled as a technology choice; however, institutional frictions in land markets may hinder their conversion to formal usage that requires secure property rights. Using unique data on Nairobi for 2003 and 2015, we develop a novel set of facts that support assumptions of the model, estimate all parameters of the model, and calculate welfare losses of conversion frictions. We track the dynamic evolution of the city and compare it with model predictions. In the core city formal sector, about a third of buildings were torn down over 12 years and replaced by buildings on average three times higher. For slums in older areas near the centre, even after buying out slumlords, overcoming institutional frictions would yield gains amounting to about $18,000 per slum household, thirty times typical annual slum rent payments.

07

Guy Michaels, Dzhamilya Nigmatulina, Ferdinand Rauch, Tanner Regan, Neeraj Baruah, Amanda Dahlstrand, 2021. Planning ahead for better neighborhoods: Long-run evidence from Tanzania, Journal of Political Economy, 129(7): 2112-2156.(DOI:10.1086/714119) 

● Abstract

Africa’s demand for urban housing is soaring, even as it faces a proliferation of slums. In this setting, can modest infrastructure investments in greenfield areas where people subsequently build their own houses facilitate long-run neighborhood development? We study Sites and Services projects implemented in seven Tanzanian cities during the 1970s and 1980s, and we use a spatial regression discontinuity design to compare greenfield areas that were treated (de novo) with nearby greenfield areas that were not. We find that by the 2010s, de novo areas developed into neighborhoods with larger, more regularly laid-out buildings and better-quality housing.

08

Moretti, Enrico. 2021. The Effect of High-Tech Clusters on the Productivity of Top Inventors. American Economic Review, 111 (10): 3328-75.(DOI: 10.1257/aer.20191277)

● Abstract

The high-tech sector is concentrated in a small number of cities. The ten largest clusters in computer science, semiconductors, and biology account for 69 percent, 77 percent, and 59 percent of all US inventors, respectively. Using longitudinal data on 109,846 inventors, I find that geographical agglomeration results in significant productivity gains. When an inventor moves to a city with a large cluster of inventors in the same field, she experiences a sizable increase in the number and quality of patents produced. The presence of significant productivity externalities implies that the agglomeration of inventors generates large gains in the aggregate amount of innovation produced in the United States.

09

Wei You, 2021. The Economics of Speed: The Electrification of the Streetcar System and the Decline of Mom-and-Pop Stores in Boston, 1885–1905.American Economic Journal: Applied Economics, 13 (4): 285-324.(DOI: 10.1257/app.20180795)

●Abstract

Small firms dominated the American economy in the nineteenth century, and they still dominate in many developing economies today. This paper tests whether geographic market segmentation due to underdeveloped intracity transportation technology precludes the emergence of large retail/wholesale stores. I exploit the natural experiment of Boston's rapid electrification from its previous horse-drawn streetcar system, which occurred between 1889 and 1896. Analyzing newly digitized data, I find that rail-connected locations experienced a sharp decline in the share of sole proprietorships among food retail/wholesale establishments after the electrification relative to off-rail locations. Changes in market access due to streetcar electrification can explain this effect.

10

Hongyu Xiao, Andy Wu, Jaeho Kim, 2021. Commuting and innovation: Are closerinventors more productive? Journal of Urban Economics,121: 103300.(DOI: 10.1016/j.jue.2020.103300)

● Abstract

We estimate the causal effect of workplace–home commuting distance on inventor productivity. We construct a novel panel of U.S. inventors with precisely measured workplace–home distances and inventor-level productivity. Our identification strategy exploits firm office relocations as exogenous variation in the commuting distance of inventors at the firms. We find a significant negative effect from commuting distance on inventor productivity: every 10 km increase in distance is associated with a 5% decrease in patents per inventor–firm pair per year and an even greater 7% decrease in patent quality. The highest-performing inventors suffer more from increased commuting distance. We discuss the implications of our findings in the light of recent trends around telecommuting and remote work during the COVID-19 pandemic.