A meta-analysis of estimates of urban agglomeration economies

Melo, Patrícia C.; Graham, Daniel J.; Noland, Robert B.

doi:10.1016/j.regsciurbeco.2008.12.002

Cited by 646 publications

(531 citation statements)

References 53 publications

Supporting

Mentioning

451

Contrasting

Unclassified

Order By: Relevance

“…The unconditional size elasticity of mean household income with respect to urban population is 0.081 and statistically significant at 1%. This estimate falls within the range usually found in the literature: the estimated elasticity of income or productivity with respect to population (or population density) is between 2% and 10%, depending on the method and the data used (Rosenthal and Strange, 2004;Melo et al, 2009). The right panel in Figure 4.2 depicts the corresponding urban costs ("congestion" for short), with the median gross rent in the MSA as a proxy.…”

Section: Locational Fundamentalssupporting

confidence: 78%

“…What is remarkable in all these models is that, despite having very different underlying microeconomic mechanisms, they generate a reduced-form citywide production function given by (4.2), where only the structural interpretation of E changes. The empirical literature on the estimation of agglomeration economies, surveyed by Rosenthal and Strange (2004) and Melo et al (2009), estimates this parameter to be in the range from 0.02 to 0.1 for a variety of countries and using a variety of econometric techniques. The consensus among urban economists nowadays is that the "true" value of E is closer to the lower bound, especially when unobserved heterogeneity is controlled for using individual data and when different endogeneity concerns are properly addressed (see the chapter by Combes and Gobillon, 2015 in this handbook).…”

Section: Inside Ementioning

confidence: 99%

See 1 more Smart Citation

Agglomeration Theory with Heterogeneous Agents

Behrens

Robert‐Nicoud

2015

Handbook of Regional and Urban Economics

View full text Add to dashboard Cite

Section: Locational Fundamentalssupporting

confidence: 78%

Section: Inside Ementioning

confidence: 99%

Agglomeration Theory with Heterogeneous Agents

Behrens

Robert‐Nicoud

2015

Handbook of Regional and Urban Economics

View full text Add to dashboard Cite

“…Dans cette partie, nous suivons la démarche utilisée dans la plupart des méta-analyses eff ectuées dans le domaine des sciences économiques ou politiques, c'est à dire avec des données non obtenues à l'issue d'expérimen-tations contrôlables (voir par exemple Card et Krueger [1995], sur l'eff et du salaire minimum, Nijkamp et Poot [2004], sur l'eff et des politiques fi scales sr la croissance, Abreu, De Groot et Florax [2005], sur la valeur du coeffi cient de σ-convergence, Melo, Graham et Noland [2009], sur l'eff et des agglomérations urbaines sur la productivité ou Stanley [2005], sur le taux naturel de chômage).…”

Section: Test De La Presence De Biais De Publication Et Estimation Duunclassified

“…On peut citer les exemples suivants d'analyses de méta-régression appliquées aux sciences économiques : Card et Krueger [1995] (sur l'eff et du salaire minimum), Abreu, De Groot et Florax [2005] (sur le coeffi cient de beta-convergence), Melo, Graham et Noland [2009] (sur les eff ets des agglomérations urbaines sur la productivité), Stanley [2005] (sur le niveau du taux naturel de chômage). Etant donné que le CLO constitue un paramètre central dans l'analyse des eff ets des fl uctuations de l'activité sur le taux de chômage, il nous parait particulièrement important de tester la présence d'une valeur fondamentale sous-jacente de ce paramètre après avoir éliminé les biais de publication potentiels.…”

Section: Introductionunclassified

Quelle est la valeur du coefficient d’okun ? une analyse de type meta-regression

Tavéra

Perman

Stephen³

2016

SOEP

View full text Add to dashboard Cite

Résumé :A partir d'un échantillon compose de 522 estimateurs du coeffi cient d'Okun, nous eff ectuons une méta régression pour analyser les origines des diff érences entre les valeurs estimées et publiées de ce coeffi cient. L'analyse révèle la présence de biais de sélection de type I et de type II dans l'échantillon. Après prise en compte de ces biais, nous obtenons un intervalle de confi ance à 95% pour le véritable coeffi cient d'Okun compris entre une valeur minimale de -0,40 et une valeur maximale de -0,12. L'analyse multivariée révèle ensuite que les coeffi cients d'Okun estimés sont signifi cativement plus grands (en valeur absolue) lorsque les analyses économétriques sont basées sur des données régionales ou utilisent une fi ltre de type trend linéaire déterministe. Par contre les coeffi cients d'Okun estimés sont plus faibles (en valeur absolue) lorsque les analyses empiriques sont eff ectuées à partir de données trimestrielles ou semestrielles, ou bien utilisent des données relatives à des pays en développement, ou bien encore utilisent des spécifi cations non linéaires.Mots Clé : macro-économie, taux de chômage, loi d'Okun, méta-régression. JEL Classyfi cation : E24, C49. 108Christophe Tavéra, Roger Perman, Gaëtan Stephan WHAT IS THE VALUE OF THE OKUN COEFFICIENTAbstract: We selected a sample of 522 estimates of the Okun's Law coeffi cient from literature to uncover the reasons for the diff erences in empirical results across studies and to estimate the 'true' OLC with meta-regression methodology. We show that there is evidence of both type I and type II bias amongst the papers studied. Taking into account those biases and then evaluating the 95% confi dence intervals of the true eff ects estimated with various methodologies, we fi nd that the estimated true OLC vary between a minimum value of -0,40 and a maximum value of -0,12. Moreover, the estimated OLCs are signifi cantly larger (in absolute value) with models using regional data or a linear time trend fi ltering procedure. On the other hand we fi nd that lower absolute values of the OLC are obtained with models using quarterly (or semestrial) data bases, data for developing countries, or non-linear models.

show abstract

“…A recent trend in research in this domain is that accessibility is used as a central concept (Melo et al 2009), where accessibility is defined as the potential for spatial interaction that incorporates both population size of destinations and distances. One step further would be the explicit introduction of travel times in such approaches.…”

Section: Agglomeration Advantages Transport and Communicationsmentioning

confidence: 99%

Flying Carpets and Urban Development Trajectories

Rietveld

2015

Appl. Spatial Analysis

View full text Add to dashboard Cite

Generalised costs of transport and communication have decreased in many domains during the past centuries. Some cities have strongly benefitted from these developments. The more recent development is that passenger transport has become the leading driving force, as opposed to freight transport in earlier logistical revolutions. Information and Communication Technology (ICT) is a substitute for some forms of physical transport but certainly not for all; cities continue to depend on high quality transport networks. An underestimated component of transport networks is vertical transport in high rise buildings. This played a key role in urban developments in the 20th century and it may be expected to continue its role in the next century. Probably, the 'flying carpet' notion is most clearly visible in the vertical movements in high-rise buildings.

show abstract

A meta-analysis of estimates of urban agglomeration economies

Cited by 646 publications

References 53 publications

Agglomeration Theory with Heterogeneous Agents

Agglomeration Theory with Heterogeneous Agents

Quelle est la valeur du coefficient d’okun ? une analyse de type meta-regression

Flying Carpets and Urban Development Trajectories

Contact Info

Product

Resources

About