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Growing Skylines: The Economic Determinants of Skyscrapers in China

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Abstract

Since 1978, when China instituted economic reforms, cities throughout the country have embraced skyscraper construction. Despite their importance to the domestic economy, little is understood about what has been driving skyscraper heights and frequencies in China. This work explores the degree to which skyscraper construction patterns are the result of economic fundamentals, versus political factors and intercity competition. We find a strong economic rational across China, but we also find evidence of noneconomic factors. We show that incentives for political officials, such as career promotion, are helping to contribute to the growth in China’s skylines. We also find that small cities tend to overbuild skyscrapers. Spatial autoregression results further suggest some intercity competition, especially for those within the same tier.

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Notes

  1. Unfortunately, the sources that provide information on skyscrapers do not provide year of completion for about 10% of the buildings. Also, if a building is not reported on one of the several websites that we used to collect data, then we do not know of its existence.

  2. This report is written in Mandarin.

  3. For example, in 1890, the world’s tallest building was 90 m; today it is 828 m (Barr et al. 2015).

  4. Note that in several cases different websites gave different years for completion. For this paper, we use year of completion as the average of the 2 yrs. Our investigations show that using the latest year, for example, does not appreciably affect the results. In addition, we do not have data of building use and year of building start.

  5. We choose 600 ft (183 m) as a comprise height—one that is sufficiently tall to be unusual, but not so unusual as to severely reduce the number of ones in the dependent variable.

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Authors and Affiliations

  1. Department of Economics, Rutgers University-Newark, Newark, NJ, USA

    Jason Barr

  2. Madden School of Business, Le Moyne College, Syracuse, NY, USA

    Jingshu Luo

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  1. Jason Barr
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  2. Jingshu Luo
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Correspondence to Jason Barr.

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Appendices

Appendix 1: Data Sources and Preparation

  1. 1.

    Skyscraper data

  2. 2.

    City-level economic data

    • Population in the municipal area, Year-end Population of the municipal district of one city (Population in rural area and in counties that belongs to the city is not included.).

    • GDP, Gross Domestic Product (2010 constant US$).

    • GDP per capita, Gross Domestic Product divided by population (2010 constant US$)

    • CPI, Consumer Price Index each city, each year.

    • Government expenditure, Local Government Expenditure (2010 constant US$).

    • Budget Deficit: dummy variable, which is one if expenditures are greater than revenues in a city-year, zero otherwise.

    • Government revenue, Local Government Revenue (2010 constant US$).

    • Employment in the service industry, Total Employment in the service industry.

    • The rate of contribution of the service industry to total GDP, The proportion of value-added of the service industry to GDP (municipal district).

    • Land area: The land area in the municipal area of a city.

All of the city-level economic data are panel data collected from the Chinese Statistical Year Book.

  1. 3.

    Corruption Data

The number of corruption cases brought against city officials data is collected by Guang Zhang, Xiamen University. The data set covers all the corruption cases disclosed by major Chinese newspapers between 1995 and 2007. The data has been discussed in Zhu (2012). We appreciate Zhu and Zhang for sharing the dataset.

  1. 4.

    Age of City Leader

The original dataset of the age of city leaders is from Yu et al. (2016). They include the age and the year of taking and leaving the office for mayors and Secretaries of CPC Municipal Committees of the included cities between 2000 to 2005. We extend the dataset from 1978 to 2015. The Chinese officials’ data were collected manually from several related websites and the major source is www.baike.com, a large database that includes the biographies of Chinese government officials. We appreciate Yu, Zhou, and Zhu for sharing their dataset.

Table 9 Strategic Interaction Between Skyscraper Cities in China (1987–2013). The Dependent Variable: The height of new tallest skyscraper of city i in year t
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Appendix 2: Additional Regression Results

Table 11 Determinants of the Height of the Tallest New Skyscrapers (First Stage of Heckman Model)
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Table 12 Cities Fixed Effects for Tallest Completed Building Each Year
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Table 13 Strategic Interaction Between Skyscraper Cities in China (1987–2013). Dependent Variable: Log (1+ skyscraper completions in city i in year t)
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Table 14 Strategic Interaction Between Skyscraper Cities in China (1987–2013). Dependent Variable: The height of the tallest new skyscraper of city i in year t
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Table 15 Average Direct Effect, Indirect Effect and Total Effect (SAR). Dependent Variable: Ln(1 + Skyscraper Completions)
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Table 16. Average Direct Effect, Indirect Effect and Total Effect (SAR). Dependent Variable: The Height of the Tallest New Skyscrapers
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Barr, J., Luo, J. Growing Skylines: The Economic Determinants of Skyscrapers in China. J Real Estate Finan Econ 63, 210–248 (2021). https://doi.org/10.1007/s11146-020-09764-7

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