Performance assessment of major global cities by DEA and Malmquist index analysis

Abstract Global cities play an important role in economic and sustainable development of the world. Assessment of their performance is critical for understanding their position in globalization and provides useful information to policymakers, urban planners and general public. In this paper we assess the performance of 40 major global cities during 2012–2018. The assessment utilizes data from the Global Power City Index (GPCI) and involves the cities' performance in six dimensions, including Economy, R&D, Cultural Interaction, Livability, Environment, and Accessibility. Using the data envelopment analysis (DEA) approach, this paper takes all six dimensions into account to generate a unified measure of the cities' performance by benchmarking them against the efficiency frontier. Then we derive the Malmquist indexes to investigate the evolution of global cities' performance from 2012 to 2018, and decompose them to trace the cause of performance change to change of individual city and/or group-wide frontier shift effect. We find that one quarter of the cities are able to achieve perfect efficiency in transforming resources into outputs, including three cities from developing countries: Beijing, Cairo and Mumbai. Among all the global cities under study, Southern Europe cities (Barcelona, Madrid and Milan) display the worst efficiency levels, although they have greatly improved their performance during 2012–2018. Overall, 20% of the cities maintain a status quo between 2012 and 2018, 40% of the cities improve their performance, and 40% experience efficiency loss. Sao Paulo, Moscow and Istanbul have experienced the biggest efficiency loss in 2012–2018, and the loss can be attributed to different reasons.

[1]  Joe Ravetz,et al.  Integrated assessment for sustainability appraisal in cities and regions , 2000 .

[2]  M. Feldman,et al.  Innovation in Cities: Science-Based Diversity, Specialization and Localized Competition , 1999 .

[3]  Lennart Hjalmarsson,et al.  Productivity in multiple output public service: A quadratic frontier function and Malmquist index approach , 1995 .

[4]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[5]  B. Yeoh.  Global/globalizing cities , 1999, Progress in human geography.

[6]  P. W. Wilson,et al.  Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models , 1998 .

[7]  Tan Yigitcanlar,et al.  Benchmarking City Performance , 2019, Geographies of Disruption.

[8]  C. Lovell,et al.  The Decomposition of Malmquist Productivity Indexes , 2003 .

[9]  Daniel M. Rees,et al.  Same Spain, less pain? , 2018, European Economic Review.

[10]  Sven Casteleyn,et al.  The Lisbon ranking for smart sustainable cities in Europe , 2019, Sustainable Cities and Society.

[11]  Denise Pumain,et al.  Firm linkages, innovation and the evolution of urban systems , 2007 .

[12]  Peter Nijkamp,et al.  A Multi-Actor Multi-Criteria Analysis of the Performance of Global Cities , 2013 .

[13]  Mario Arturo Ruiz Estrada,et al.  The impact of terrorism on economic performance: The case of Turkey , 2018, Economic Analysis and Policy.

[14]  Toshiyuki Sueyoshi,et al.  Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces , 2017 .

[15]  Arnab Jana,et al.  Mumbai slums since independence: Evaluating the policy outcomes , 2015 .

[16]  Yannis A. Phillis,et al.  Urban sustainability assessment and ranking of cities , 2017, Comput. Environ. Urban Syst..

[17]  P. Mendi,et al.  Demand drops and innovation investments: Evidence from the Great Recession in Spain , 2018, Research Policy.

[18]  M. Sweet.  Do firms flee traffic congestion , 2014 .

[19]  R. Färe,et al.  Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach , 1994 .

[20]  Jasmina Berbegal-Mirabent,et al.  The pursuit of knowledge transfer activities: An efficiency analysis of Spanish universities , 2013 .

[21]  Peter Nijkamp,et al.  Winners in the urban champions league – A performance assessment of Japanese cities by means of dynamic and super-efficient DEA , 2018 .

[22]  Xiaojian Li,et al.  China urban competitiveness in industrialization: Based on the panel data of 25 cities in China from 1990 to 2009 , 2014 .

[23]  Toshiyuki Sueyoshi,et al.  Measuring the industrial performance of Chinese cities by data envelopment analysis , 1992 .

[24]  T. Storme,et al.  Introducing cluster heatmaps to explore city/firm interactions in world cities , 2019, Comput. Environ. Urban Syst..

[25]  N. Munier,et al.  Methodology to select a set of urban sustainability indicators to measure the state of the city, and performance assessment , 2011 .

[26]  Ben Derudder,et al.  Mapping the evolution of hierarchical and regional tendencies in the world city network, 2000-2010 , 2014, Comput. Environ. Urban Syst..

[27]  B. Nielsen,et al.  Global cities and multinational enterprise location strategy , 2013 .

[28]  Abraham Charnes,et al.  Using data envelopment analysis to evaluate efficiency in the economic performance of Chinese cities , 1989 .

[29]  P. Knox.  Globalization and the world city hypothesis , 1996 .

[30]  C. Lovell,et al.  A note on the Malmquist productivity index , 1995 .

[31]  Peter Nijkamp,et al.  The Rat Race between World Cities: In Search of Exceptional Places by Means of Super-Efficient Data Development Analysis , 2013, Comput. Environ. Urban Syst..

[32]  C. Rozenblat,et al.  Analyzing Diversity, Strength and Centrality of Cities Using Networks of Multinational Firms , 2018, Networks and Spatial Economics.

[33]  S. Malmquist.  Index numbers and indifference surfaces , 1953 .

[34]  Leo Sveikauskas.  The Productivity of Cities , 1975 .

[35]  Rolf Färe,et al.  Productivity and Undesirable Outputs: A Directional Distance Function Approach , 1995 .

[36]  R. Färe,et al.  Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries , 1994 .

[37]  E. Glaeser.  Cities, Productivity, and Quality of Life , 2011, Science.

[38]  Toshiyuki Sueyoshi,et al.  DEA Environmental Assessment on U.S. Industrial Sectors , 2014 .

[39]  Sotiris Vardoulakis,et al.  Taking Action on Air Pollution Control in the Beijing-Tianjin-Hebei (BTH) Region: Progress, Challenges and Opportunities , 2018, International journal of environmental research and public health.

[40]  F. J. C. Carvalho.  Looking into the abyss? Brazil at the mid-2010s , 2016 .

[41]  James E. Rauch,et al.  Productivity Gains from Geographic Concentration of Human Capital: Evidence from the Cities , 1991 .

[42]  Clemens Deilmann,et al.  Data envelopment analysis of cities – Investigation of the ecological and economic efficiency of cities using a benchmarking concept from production management , 2016 .

[43]  Jennifer Robinson,et al.  Global and world cities: a view from off the map , 2002 .

[44]  A. Alderson,et al.  Power and Position in the World City System1 , 2004, American Journal of Sociology.

[45]  Bo Li,et al.  Efficiency differences among China's resource-based cities and their determinants , 2017 .

[46]  D. Rybski,et al.  Benchmarking urban eco-efficiency and urbanites' perception , 2017 .

[47]  B. Derudder,et al.  Can the straw man speak? An engagement with postcolonial critiques of ‘global cities research’ , 2016 .

[48]  Yelena Tuzova,et al.  Global oil glut and sanctions: The impact on Putin’s Russia , 2016 .

[49]  Urban Competitiveness and US Metropolitan Centres , 2012 .

[50]  P. Zhao,et al.  Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing , 2019, Cities.

[51]  Ana S. Camanho,et al.  Evaluation of performance of European cities with the aim to promote quality of life improvements , 2011 .