Unravelling urban advantages - A meta-analysis of agglomeration economies

Abstract

A large body of literature considers the productive advantages of cities, or "agglomeration economies". Most empirical studies report positive agglomeration economies, although large variation exists in the magnitude of estimates. We use a meta-analysis to explore this variation, drawing on 6,684 estimates from 295 studies that cover 54 countries and span six decades. Using rich data and robust methods, we unify and extend earlier reviews. For our preferred combination of study attributes, we find agglomeration elasticities are likely to lie in the range 2.7-6.4%. Our findings confirm the controls enabled by detailed data give rise to smaller estimates. We also document several trends, with overall estimates rising from 1980-2000 and then falling. Estimates for manufacturing sectors, in contrast, fell for the entire six decades covered by our data. We speculate on possible causes of these trends, such as urban congestion, technological shocks, freight costs, and regulatory settings.

Cities with Forking Paths? Agglomeration Economies in New Zealand 1976-2018

“…2006, agglomeration economies in production and consumption fell by approximately 0.7% and 1.4%, respectively, but have since been relatively stable. Although the magnitude of this decline is similar to that in Donovan et al (2021), which draws on data for 54 countries, the peak in the latter study occurs approximately one decade after that which we find here for New Zealand. Informally, we observe less temporal variation for the three most recent Censuses compared to those in the period 1976-2001.…”

“…Second, we find evidence of subtle temporal variation in estimates of agglomeration economies, which arises due to the effect of agglomeration on rent. Specifically, from a nadir in 1981, estimates peak in 1991, and then decline by approximately 1%-loosely corroborating the trend in Donovan et al (2021). 1 Since 2006, however, our estimates of agglomeration economies have remained broadly constant; contrary to claims, the world does not appear to be getting "flatter".…”

“…And, unlike restricted maximum likelihood estimators, Bayesian methods directly estimate 4 Unlike Combes, Duranton and Gobillon (2008), our data do not follow workers or dwellings over time, preventing us from including individual effects in the first-stage regressions. Donovan et al (2021) find that including such effects reduces estimates of agglomeration economies in production by 1.1% whereas Ahlfeldt and Pietrostefani (2019) conclude controlling for "sorting" reduces estimates by 2-4%. 5 For workers, we control for gender, age (polynomial by gender), qualification (n = 10, except in 1976 when n = 4), two-digit industry sector (n = 54), ethnicity (n = 15), religion (n = 11), and birthplace (n = 12).…”

“…In terms of temporal stability, Martínez-Galarraga et al (2008) analyse agglomeration economies in production for Spain and find estimates decline in the period 1860-1999. Similarly, the recent metaanalysis by Donovan et al (2021) draws on estimates for the period 1960-2020 and finds evidence that, since 2000, agglomeration economies in production have fallen by 1-2%. More attention has been paid to spatial variation.…”

“…More attention has been paid to spatial variation. Several meta-analysessee, for example, Ahlfeldt and Pietrostefani (2019) and Donovan et al (2021)-and primary studies-see, for example, Ahrend et al (2017) and Chauvin et al (2017)allow for and report differences between countries, although the factors that cause these differences are typically left unexplored. Fewer studies, moreover, consider the potential for spatial variation within countries, which is likely to be more relevant to domestic policy settings.…”