Metro-induced gentrification: A 17-year experience in Taipei
Introduction
After World War II, many emerging cities have started developing their metro systems to enable efficient and environment-friendly urban progress. For instance, the metro systems in Eastern Asia have started their services sequentially in Beijing (1969), Pyongyang (1973), Seoul (1974), Hong Kong (1979), Singapore (1987), Shanghai (1993), Kuala Lumpur (1996), Taipei (1996), Guangzhou (1997), and Bangkok (2004). Empirical evidence and the literature generally agree that metro systems are associated with improved transportation efficiency (e.g., Cervero, 1994), reduced air pollution and energy consumption (e.g., Poudenx, 2008), elevated accessibility (e.g., Lewis-Workman & Brod, 1997) and land value (e.g., Lin & Hwang, 2004), and relocated populations and industries (e.g., Cervero & Landies, 1997) along metro corridors. However, the above changes also increase living expenses because they usually increase the livability and commercialization of a metro corridor. These changes can attract people and businesses who can afford land costs to move in the corridor and displace pre-existing low-income families and small businesses. This class-upward process along metro corridors can be considered metro-induced gentrification. Developing a metro system welcomes capital investment, and it is regarded as one of the major causes of gentrification (Zheng & Kahn, 2013); however, its effects on gentrification have not convincingly been recorded in the literature.
Previous research on the effects of capital investment on gentrification have mostly focused on urban redevelopment (e.g., the Shanghai study of Wang and Lau (2009s)) and housing renewal (e.g., the Seoul study of Ha (2004)); however, studies that have explored the relationships between metro system and gentrification are rare. The research by LeRoy and Sonstelie (1983) is the first in the literature to study the influence of transportation innovation on gentrification. The Alonso–Muth model (Alonso, 1964, Muth, 1969) was extended and used to explain how declining car costs influenced the residential locations of the rich and the poor. Their major argument is that the affluent resided at the city center before the era when cars were used as commuting modes, moved to suburbs when cars became affordable for only the rich, and returned to city center as cars were affordable to both the rich and the poor. They used socioeconomic descriptive statistics and commuting mode attributes between 1850 and 1977 in the US to support the above argument. Lin (2002) used the model developed by LeRoy and Sonstelie (1983) and hypothesized that metro station access spurs gentrification and empirically confirmed the hypothesis using the changes in residential property values between 1975 and 1991 in Chicago. Both of the above studies used a single indicator of gentrification (house price) while ignoring many of its other features (Hamnett, 1991, Lees, 1994, Zukin, 1987). The study by Kahn (2007) on 14 major cities in the US in 1970–2000 is a start of using multiple indicators to represent gentrification in transit and gentrification literature. He found that gentrification (denoted by house price and college graduates) is greater in communities with easy access to “Walk and Ride” stations compared to communities close to “Park and Ride” stations. From then, further research on transit and gentrification mostly used multiple indicators to represent gentrification and distance to station to represent transit access. These studies included those of Young (2007), Plevak (2010), Pollack, Bluestone, and Billingham (2011), Feinstein and Allen (2011), and Saldana and Wykowski (2012) in the USA, Pagliara and Papa (2011) in Italy, and Zheng and Kahn (2013) in China. However, none of the above studies determined whether their samples were from gentrifiable areas. A gentrifiable area reveals a below-average social status that could be measured through income, education, or percentage of residents in professional occupations. For an area to be susceptible to gentrification, it must be considered gentrifiable at the beginning of the analysis period according to the arguments by Freeman (2005), Hammel and Wyly (1996), and Walks and Maaranen (2008). Considering the above requirement, Grube-Cavers and Patterson (2015) analyzed a sample of gentrifiable census tracts in three major Canadian cities and reported that proximity to metro stations has a statistically significant effect on gentrification in Toronto and Montreal.
The above metro-induced gentrification research obtained interesting conclusions but left notable questions unanswered. The first question is whether metro-induced gentrification occurs in both inner and outer city areas. If so, then the next question is: what are the differences between metro-induced gentrification in the inner and outer city areas. Hackworth (2002) argued that gentrification extended from city centers to suburban areas and even rural areas by the 1990s; however, previous metro-induced gentrification research neglected this sprawling and provided very limited information about the outer areas of a city. The last question is whether metro-induced gentrification also occurs in regions other than North America in view of long-term experiences. The existing evidence of metro-induced gentrification outside North America are all short-term changes, which are three-year changes in China (Zheng & Kahn, 2013) and seven-year changes in Italy (Pagliara & Papa, 2011), and these changes were not confirmed that they were in gentrifiable areas. Gentrification is related to land development and household migration; thus, the changes that occurred over a period longer than a decade should be more convincing than those that occurred within a few years.
To answer these questions, the present study used data collected during a 17-year period in order to explore how access to metro stations is related to gentrification in Taipei, Taiwan. Sample data were from seven gentrifiable districts in both inner and outer areas in Taipei City between 1996 and 2013. Panel data and traditional linear regression analyses were used to examine the relationships between proximity to metro stations and gentrification. The present study contributes two novel arguments to the literature. First, in addition to the developed cities in North America, metro-induced gentrification could also occur in emerging cities worldwide. Second, metro-induced gentrification could happen in both inner and outer city areas, as well as reveal the different features between them. Analytical results imply that governments should adopt sufficient means to deal with metro-induced gentrification when upgrading public transport systems.
Section snippets
Method
This section specifies the gentrification outcomes and the factors that can potentially affect such outcomes. Furthermore, it describes the hypotheses and model specifications of the present study.
Data
The number of stations in the Taipei metro system increased from 12 stations in 1996 to 108 stations by the end of 2015. Fig. 1 shows that by the end of 2015, the network included5lines and was 131.1 km in length. The system currently serves 5million residents in the 12 Districts of Taipei City and 10 of the 29 Districts in New Taipei City. On average, the system has two million passengers per day. The Taipei metropolitan area is located in a basin, and the metro network covers the bottom of the
Results
Two regression methods were used to analyze the four gentrification outcomes: panel data and ordinary least-squares regressions. Hausman tests were used to determine whether the fixed effects model or the random effects model should be selected in the panel data analyses. Explanatory variables with a coefficient significance below the confidence level of 1-α = 90% were withdrawn. Table 3, Table 4, Table 5, Table 6 present the estimation results. For each outcome, models were estimated for the
Discussion
Surprisingly, population migration declined with decreasing distance to metro stations among the sample Lis in the inner city areas. Based on the sample data, we infer two possible reasons causing the results. The first reason is from an extremely outlier observation in the inner city sample. The distribution of the Migrateit and MetroDistit distributions among the inner city sample clearly reveals an outlier, i.e., Chengkong Li. This observation denotes a migration ratio of 0.42 and a travel
Limitations
Future studies should more closely examine the following issues, which represent the possible limitations of the present study, for a more thorough exploration of how metro systems affect gentrification. First, given that gentrification is related to multiple features, multiple outcomes must be analyzed to obtain reliable conclusions. Owing to data availability, the present study analyzed the four gentrification outcomes associated with neoclassical and Marxist perspectives. Study outcomes are
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