Smart cities and urban inequality

ABSTRACT

Smart city technologies are criticized because they might exacerbate income inequalities. Four factors are suggested to explain this phenomenon: the uneven diffusion of information and communication technologies (ICTs); that these ICTs cannot be afforded by low-income citizens; that smart cities could further human capital divides; and the involvement of private actors in the implementation of projects. These critiques are not based on empirical verification. We test whether smart urban characteristics are associated with increases in urban income inequalities, using data on urban smartness and urban income inequality for 106 European cities. Results show that smart cities are associated with lower levels of urban income inequality.

KEYWORDS:

• smart cities
• urban inequality
• digital divide
• income distribution

JEL:

• D63
• J24
• R11

ACKNOWLEDGEMENTS

The authors thank Annie Tubadji, Fabio Mazzola, Ling Liu, Marcello Graziano and Mildred Warner; the participants at both the 66th Annual North American Meetings of the Regional Science Association International, Pittsburgh, 13–16 November 2019, and the XXXII Annual Conference of the Italian Society of Public Economics, Milan, Italy, 17 September 2020; the editor, and two anonymous reviewers for their useful suggestions and comments. We also thank Roberto Patuelli for sending the data used for the 2SLS estimates. All remaining errors are the authors’ own.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. For a more detailed and comprehensive systematization of the general literature on smart cities, see, for example, Komninos and Mora (2018).

2. Because the choice of this geography is non-trivial, for more details on the motivations for this choice, see Appendix A in the supplemental data online.

3. Due to space limitations, the description and empirical analysis in the main text refer only to the Gini index. The general entropy index and the Atkinson index are discussed and analysed in Appendix A in the supplemental data online, along with the results of the corresponding regression analyses.

4. The European Values Study (EVS) is ‘a large-scale, cross-national, repeated cross-sectional survey research programme on basic human values’, providing quantitative assessments of ‘ideas, beliefs, preferences, attitudes, values and opinions of citizens all over Europe’ (EVS, 2020).

5. For a more detailed account of this approach, along with the results of the first stage regressions, see Appendix A in the supplemental data online.

6. We thank the editor for suggesting the controls presented in Table 4, columns 7 and 8.

7. This information was collected for different years to maximize data availability in the sample, and we have considered averages over the years. Poverty exposure data refer to the period 2016–19; environmental exposure data to 2009–13; and energy exposure to 2010–19.

8. For details on the first-stage Mincerian wage regressions and on additional robustness checks, see Appendix A in the supplemental data online.

9. As multicollinearity may potentially represent an issue in these estimates, as implicitly suggested in the works about urban scaling laws (e.g., Bettencourt et al. 2007; Ribeiro et al., 2020), we also verify the extent to which this affects our estimates by means of the standard variance inflation factor (VIF). The results suggest that in none of our baseline model specifications multicollinearity represents an issue (as demonstrated by the largest VIF recorded in the models shown in Table 2, equal to 2.47). The largest VIF for each specification is reported in the penultimate line of Table 2.

10. In the form of capitalized incomes, that is, wealth.

11. In fact, our main results suffer from a minor loss of significance (p = 0.106).

12. For space limitations, the results of this ancillary regression are shown in Appendix A in the supplemental data online.

13. For the spatial distribution of optical fibre in Europe for 2005, see Appendix A in the supplemental data online.

Additional information

Funding

The authors report there is no funding associated with the work featured in this article.