An empirical analytical framework for agglomeration economies

Graham, Daniel J.; Kim, H. Youn

doi:10.1007/s00168-007-0156-0

Cited by 75 publications

(60 citation statements)

References 30 publications

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“…Examples include studies using firm/worker level data to estimate production/wage functions (e.g. Wheeler, 2001;Henderson, 2003;Mion and Naticchioni, 2005;Graham, 2007a;Graham, 2007b;Graham, 2007c;Graham and Kim, 2008;Combes et al, 2008a;Combes et al, 2008b;Rosenthal and Strange, 2008). The advantages of micro level data are that they can better represent firm optimizing behavior that we assume from economic theory, provide greater data variability, and can reduce multicollinearity and aggregation bias resulting from unobserved heterogeneity (Griliches and Mairesse, 1995).…”

Section: Overview Of the Literaturementioning

confidence: 99%

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A meta-analysis of estimates of urban agglomeration economies

Melo

Graham

Noland

2009

Regional Science and Urban Economics

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638

451

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Section: Overview Of the Literaturementioning

confidence: 99%

“…While some studies adopt a distance-unrestricted market potential measure (e.g. Graham, 2007a;Graham, 2007c;Graham and Kim, 2008), others separate the distance-unrestricted market potential function into a set of successive distance-band measures (e.g. Rice et al, 2006;Rosenthal and Strange, 2008).…”

Section: Overview Of the Literaturementioning

confidence: 99%

A meta-analysis of estimates of urban agglomeration economies

Melo

Graham

Noland

2009

Regional Science and Urban Economics

Self Cite

638

451

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“…Turning to the current evidence, there has been many studies investigating urbanization economies and its impact at the industry-and country-level [29][30][31][32]. Concerning the analysis of urbanization economies-firm productivity nexus, most current evidence is from developed countries.…”

Section: Literature Reviewmentioning

confidence: 99%

How Urbanization Economies Impact TFP of R&D Performers: Evidence from China

Dai

2017

Sustainability

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There have been extensive studies exploring the relationship between agglomeration economies and economic growth. However, far less attention is paid to the nonlinear relationship of urbanization economies-firm productivity nexus, especially in developing countries. This paper aims to explore how urbanization economies impact the total factor productivity (TFP) of research and development (R&D) performers using a large sample of Chinese manufacturing firms. The generalized propensity score matching methodology is employed to not only address the selection-bias and endogenous issues, but also quantitatively investigate how firm TFP responds to urbanization economies at each urbanization level. Based on the full data sample, our results show that there exists an S-shaped relationship between urbanization economies and TFP of Chinese R&D performers. Importantly, there is an optimal interval in which urbanization economies' impact on firm TFP can be maximized. There is also a threshold value, only beyond which a further increase in urbanization economies improves firm TFP, and a saturation point, beyond which a further increase in urbanization economies does not increase and even decrease firm TFP. The relationship between urbanization economies and TFP of Chinese R&D performers presents a consistent pattern for firms in high-and medium-low-technology industries, as well as in Eastern and North East China. However, for firms in Central and Western China, there is a much smaller and even no range of optimal interval of urbanization economies, respectively. Our research helps provide insights into policy makers to maximize urbanization economies' positive impact on firm TFP.

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“…A description of the translog model used is given in appendix 1. A full demonstration of the model for the estimation of agglomeration economies is provided by Graham and Kim (2007).…”

Section: Estimating the Link Between Agglomeration And Productivitymentioning

confidence: 99%

“…The generalised translog production function system based on an inverse input demand framework was first proposed by Kim (1992) and has been applied in full for the estimation of agglomeration economies by Graham and Kim (2007).…”

Section: Appendix 1: the Translog Production Inverse Input Demand Modelmentioning

confidence: 99%

Agglomeration Economies and Transport Investment

Graham

2007

OECD/ITF Joint Transport Research Centre Discussion Papers

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This paper is concerned with the links between agglomeration, productivity and transport investment. If improvements in transport systems give rise to changes in the mass of economic activity accessible to firms, for instance by reducing travel times or the costs of travel, then they can induce positive benefits via agglomeration economies. The paper presents empirical results from an econometric analysis of the relationship between productivity and accessibility to economic activity for different sectors of the UK economy. The results show that agglomeration economies do exist and that they can be substantial, particularly for services. Furthermore, the effect of agglomeration externalities is not trivial when considered in the context of transport appraisal. Initial calculations typically indicate additions to conventional user benefits of 10%-20% arising from increasing returns to economic mass.

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An empirical analytical framework for agglomeration economies

Cited by 75 publications

References 30 publications

A meta-analysis of estimates of urban agglomeration economies

A meta-analysis of estimates of urban agglomeration economies

How Urbanization Economies Impact TFP of R&D Performers: Evidence from China

Agglomeration Economies and Transport Investment

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