generally focused on one or two cities (Avner and Lall 2016; Chen et al. 2017; Campbell et al. 2019).

This paper is a first attempt to fill this gap. It brings together a number of data sets on the distribution of economic opportunities,<sup>2</sup> people and the characteristics of the public transport in 11 African cities to compute baseline public transport accessibility metrics in a consistent manner. In doing so it creates a benchmark for accessibility that allows for comparisons between cities and produces visuals and descriptive statistics for each city. In producing this baseline, the paper also sets the basis for further analysis that could investigate the impacts of transport or land use modifications.<sup>3</sup>

# **2 WHY FOCUS ON ACCESSIBILITY? A SHORT LITERATURE REVIEW**

Starting with Kain’s spatial mismatch hypothesis (Kain 1968), there has been a vast literature investigating how accessibility to employment opportunities<sup>4</sup> could affect employment, wages, productivity and well-being in urban areas. Initially focused on African-Americans’ employment outcomes in US urban areas, the research has broadened to investigate the role of space and accessibility first for all minorities and then for all individuals’ outcomes in general. While the literature is still massively geared toward developed country cities, some studies focusing on urban areas in developing countries are emerging.

From a theoretical standpoint, there are several reasons why increased accessibility could reduce unemployment or increase the quality of individuals’ employment (for a detailed review of theoretical explanations for the spatial mismatch, see Gobillon et al. (2007)). In this paper the rationale for focusing on accessibility is underpinned by the following theoretical framework: higher accessibility implies lower job search costs which i) incentivizes a more intensive job search and improves the probability of finding a job and/or ii) for a given job search intensity will improve the probability of finding a job that matches a worker’s skills and aspirations – the quality of the matching.

On the productivity side, empirical evidence indicates that accessibility, measured as the number of opportunities an individual can access within a given amount of time, matters. Prud’homme and Lee (1999) demonstrate that the elasticity of productivity to the share of jobs accessible within 30 minutes for the average worker in 23 French cities is 0.15. From an employer’s point of view, Cervero (2001) shows that in the San Francisco Bay area there is weaker yet positive evidence that labor-marketshed<sup>5</sup> positively influences worker productivity levels. Melo et al. (2013) show for a sample of US cities that increasing accessibility to jobs results in increased productivity as measured by real wages. The authors report that a doubling of the number of jobs accessible per worker within a 20-minute thresholds results in an average increase in real wages of 6.5%. Venables (2017) identifies that commuting costs and thus

> 2 In this paper we use ‘employment opportunities’ understood as a proxy for jobs. Because we are using a proxy for job distribution, we prefer not to call them jobs. Employment opportunities should also not be understood as vacant jobs to be filled.

> 3 Although this paper does not go as far as computing counterfactuals, such work can easily piggy-back on this effort.

> 4 Accessibility is defined differently depending on the studies considered (bee-line distance to jobs, network distance, costs of commuting,...). All these metrics share that accessibility goes up decreasing costs to reach jobs (time and/or money).

> 5 Number of workers that can access work within a defined peak period ranging from 30 to 60 minutes.

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