4,736
Views
46
CrossRef citations to date
0
Altmetric
Articles

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ORCID Icon, ORCID Icon & ORCID Icon
Pages 607-628 | Received 18 Apr 2019, Accepted 21 Mar 2020, Published online: 07 Apr 2020
 

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.

This article is part of the following collections:
Moshe Givoni Prize

Acknowledgements

The authors thank the anonymous reviewers for their useful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Qualitative studies could provide important insights into perceived transport barriers to employment, however, these were outside the scope of our study because they do not quantify this relationship.

2 Transport keywords: transport*, infrastructure, travel*, commut*, road, highway, motorway, car, transit, rail, tram, bus, metro, subway, bicycle, walk; Relationship keywords: relation*, impact, caus*, eval*, experiment, affect*, effect*, link*, case*; and Employment keywords: employment, job, labour, productivity, economic activity. Keywords ‘access’ and ‘work’ were not used as being found too general.

3 In total 2392 studies from online searches; 189 studies from WWC report; and 377 studies from own libraries.

4 The citation database is available through: https://doi.org/10.5518/762

5 Following Borenstein et al. (Citation2009), under the random-effects model the weight assigned to each study is Wi = 1/Vi, where Vi is the within-study variance for study i plus the between-studies variance (sum squared deviations of each study from the combined mean), tau-squared.

6 An IV-approach uses a third variable (Z) correlated with employment only through the applied measure of transport access to control for endogeneity between employment probability outcomes and transport access.

7 Abbreviations are reported in the appendix

8 The comparator for ‘youth’ is other (age) groups and for ‘women’ these are studies that used pooled samples of men and women.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.