metadata: authors: 0: author: Peralta Quiros,Tatiana
1: author: Kerzhner,Tamara
2: author: Avner,Paolo
count: Africa
docna: 0: docna: Exploring Accessibility to Employment
            Opportunities in African Cities : A First Benchmark
docty: Policy Research Working Paper
subtopic: Transport Services,Labor Markets,Urban Transport,Transport in Urban Areas,Rural Labor Markets,Poverty Reduction Strategies
teratopic: Social Protections and Labor,Transport,Urban Development,Rural Development,Poverty Reduction
abstracts: cdata!: This paper presents an analysis of
            transit accessibility to employment for 11 African cities.
            The use of identical methodologies and similar data sets
            allows for the creation of the first benchmark to compare
            accessibility across urban areas in Africa through different
            metrics and visuals. The study shows how the spatial pattern
            of land use and transport systems perform in connecting
            people to employment opportunities in these various
            settings. This first comparable benchmark is achieved by
            overcoming two significant data hurdles that are common in
            many developing country cities and in Africa in particular:
            (i) the scarcity of information on the distribution of
            employment and (ii) the lack of information on transit
            routes and travel times. These data gaps are filled through
            a novel methodology to estimate the distribution of
            employment in urban areas (Employment Opportunity Areas) as
            well as a comprehensive mapping of informal transit
            networks. The analysis developed here can be replicated in
            different cities in the future. The computation of these
            baseline accessibility studies also opens up the possibility
            to assess the impacts of future transport investments and/or
            land use changes, through the use of counterfactual
            scenarios, which could assist decision makers in these
            cities. Finally, this analysis can serve as a demonstration
            that the computation of accessibility metrics is achievable,
            including in data scarce environments, and should be
            considered as a progress indicator for Sustainable
            Development Goal 11.2, which focuses on safe and affordable
            transport for all, including public transport.
repnme: repnme: Exploring Accessibility to Employment
            Opportunities in African Cities : A First Benchmark
display_title: Exploring Accessibility to
            Employment Opportunities in African Cities : A First Benchmark
listing_relative_url: /research/2019/08/31323816/exploring-accessibility-employment-opportunities-african-cities-first-benchmark
topicv3: Labor Markets m1326488 855,Transport m1326661 135,Mass Transit m1327372 1726,Income Inequality m1503891 2668,Jobs m1503931 2670
publishtoextweb_dt: 2019-08-08T00:00:00Z
historic_topic: Social Protections and Labor,Transport,Urban Development,Rural Development,Poverty Reduction
no_of_pages: 42
author_keys: 0: author_key: 1271177
1: author_key: 1612296
2: author_key: 1256219
model_extractions: Policy Research Working Paper 8971 Exploring Accessibility to Employment Opportunities in African Cities A First Benchmark _Tatiana Peralta-Quiros Tamara Kerzhner Paolo Avner_ August 2019
Policy Research Working Paper 8971

# **Abstract**

This paper presents an analysis of transit accessibility to employment for 11 African cities. The use of identical methodologies and similar data sets allows for the creation of the first benchmark to compare accessibility across urban areas in Africa through different metrics and visuals. The study shows how the spatial pattern of land use and transport systems perform in connecting people to employment opportunities in these various settings. This first comparable benchmark is achieved by overcoming two significant data hurdles that are common in many developing country cities and in Africa in particular: (i) the scarcity of information on the distribution of employment and (ii) the lack of information on transit routes and travel times. These data gaps are filled through a novel methodology to estimate the distribution of employment in urban areas (Employment Opportunity Areas) as well as a comprehensive mapping of informal transit networks. The analysis developed here can be replicated in different cities in the future. The computation of these baseline accessibility studies also opens up the possibility to assess the impacts of future transport investments and/or land use changes, through the use of counterfactual scenarios, which could assist decision makers in these cities. Finally, this analysis can serve as a demonstration that the computation of accessibility metrics is achievable, including in data scarce environments, and should be considered as a progress indicator for Sustainable Development Goal 11.2, which focuses on safe and affordable transport for all, including public transport.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice, the Transport and Digital Development Global Practice, and the Global Facility for Disaster Reduction and Recovery and the Global Solutions Group for Territorial Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at tperaltaquiros@worldbank.org and pavner@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Exploring Accessibility to Employment Opportunities in African Cities** A First Benchmark<sup>1</sup>

**Tatiana Peralta-Quiros** World Bank

**Tamara Kerzhner** UC Berkeley

**Paolo Avner** World Bank

JEL codes: R14, R49, R52, R58, C63 Keywords: Accessibility, Urban transport, Urban form, Labor market

> 1 This work was financially supported by UKAID/DfID through the Multi-donor Trust Fund on Sustainable Urbanization. The research was performed under the guidance of Somik Lall, Lead Urban Economist and Global Lead for Territorial Development and Shomik Mehndiratta, Practice Manager, Transport. We are grateful to Arturo Ardila Gomez and Mark Roberts for very useful comments which helped improve the previous drafts.

1
# **1 INTRODUCTION**

Cities can schematically be viewed as labor markets (Bertaud 2014), which excel in matching job seekers and employers because they are able to connect the two efficiently. Access to economic opportunities in cities, and jobs in particular, is central to achieving large and efficient labor markets which can promote agglomeration economies, economic development and social inclusion (Rosenthal and Strange 2004). Conversely, low accessibilities can have adverse consequences whereby (i) households are forced to compromise on living conditions to remain within reasonable travel times of jobs (Gulyani, Talukdar, and Jack 2010), and (ii) fragmented urban labor markets impede the development of large scale, high productivity economic activities (Duranton and Puga 2004; Combes and Gobillon 2015). Beyond these, employment spatial mismatch in developing country cities can reinforce patterns of social segregation with high social exclusion and economic costs in terms of vulnerability, crime, violence and inequality.

The analyses supporting most urban transport projects have been aimed traditionally at enhancing citizens’ ‘mobility’ – i.e. their ability to move freely and swiftly about the city – instead of enhancing a citizen’s access to opportunities through the adoption of a more comprehensive land use and transport lens. This approach has led most cities to develop far reaching and intricate highway networks that have mostly bred more congestion and the perils that come with it (e.g. auto-dependency; longer travel times for the poor vis-à-vis higher income residents; pollution; climate change) (Levine, Grengs, and Shen 2009; Litman 2017). Dysfunctional transport and land markets mean that cities have failed to capture the productivity and agglomeration benefits of integrated labor markets.

Accessibility offers a powerful lens to assess how a mobility system is serving an urban area. We can reframe the efficiency of transport systems in terms of their ability to connect people with opportunities rather than mobility. A city’s accessibility levels is a product of two variables: (i) the land use patterns of a city, and (ii) the efficiency of the transport system. Accessibility emphasizes the interconnected nature of mobility and land-use, and provides a framework to evaluate the city in terms of its ability to connect its citizens to opportunities. It provides a starting point to identify land use planning options and transport investments that can foster more integrated urban labor markets through the use of counterfactual scenarios. While the natural candidate to increase accessibility to employment opportunities has traditionally been investments in the transportation networks, some works have shone a light on the importance of urban planning and coordinating land uses with transport infrastructure (Peralta Quirós and Mehndiratta 2015; Avner and Lall 2016). In particular Avner and Lall (2016), show in Nairobi, Kenya, that alternative land use settings could double average accessibility to employment opportunities, without any investments in the transportation system.

Utilizing new tools to study accessibility offers a lens by which we can better understand and plan transport and land use policies. But whereas the use of accessibility metrics is becoming more common in many countries (most OECD countries, Argentina, Brazil, and Colombia, for example), it hinges on the availability of data such as the distribution of people, jobs and the characteristics of the transport network that can connect them. These data are typically difficult to come by in developing countries and for African cities in particular. As a result, accessibility metrics have seldom been computed or used in these settings and when they have, analysis has 2
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.

3
average accessibility matters in urban settings. In order to attract skilled workers, firms must compensate for their travel costs by offering higher wages. While this will benefit households, it can prevent firms from reaping productivity gains and entering international markets because the average wage needed to compensate workers is higher than competitive international standards.

In terms of individuals’ employment outcomes, evidence of the link with accessibility is more ambiguous, in part because of the methodological difficulty of disentangling the role of accessibility relative to individual characteristics and preferences. In particular, residential location is endogenous – households are not randomly assigned to specific locations, so that a link between low accessibility and bad employment outcome may in fact reflect self-sorting of low productivity households into areas with less good accessibility. There are nonetheless a number of studies that have shown positive impacts of increased accessibility on labor market outcomes. Employment accessibility has been found to have positive impacts on the employment prospects of the youth in Ethiopia (Franklin 2015), through reductions in transport costs, and in Accra (Chen et al. 2017), both in terms of the probability of being employed and in more formal and better paying positions. In France, an experiment of subsidizing driving lessons for young disadvantaged adults, thereby lowering their lifecycle commuting costs, proved to increase their access to temporary (but not permanent) jobs (Le Gallo, L’Horty, and Petit 2017). On the other hand, the introduction of a commuter train in Sweden, on a preexisting rail-track was shown to have no impact on the earnings and unemployment rates of affected individuals (Åslund, Blind, and Dahlberg 2017). Jin and Paulsen (2017) find that job accessibility plays a strong role in explaining employment rates and household income in Chicago. Increased accessibility to jobs improved both, especially for low-income households. Norman et al. (2017) find similar results in Sweden with a negative relationship between employment accessibility and unemployment rates which is stronger for low-educated workers. Aslund et al. (2010) using a natural experiment in Sweden again – a refugee dispersal policy – that allowed them to overcome the endogeneity challenge inherent to this type of study, also find that the proximity to jobs positively impacts employment prospects even in the long term.

From the perspective of households, accessibility constraints in developing country cities may be manageable in the short-run, as long as local employment within low-wage, non-tradeable (and often informal) service sector occupations dominates; but as the nature of employment shifts from non-tradable services to manufacturing and tradable services, and from informal to formal, requiring higher firm density, the demands for metropolitan-wide accessibility can be expected to increase. Indeed, there are already indications that in order to remain within reasonable commuting times of their jobs, households are ready to compromise on living conditions. In Nairobi, most residents of informal settlements have jobs and comparatively high levels of education relative to those living in formal housing, yet their living conditions remain basic (Gulyani, Talukdar, and Jack 2010). This situation probably reflects a premium already placed on accessibility.

The empirical literature is still growing and there remains some disagreement between scholars on the impact of accessibility to employment opportunities. In particular studies investigating the link between employment accessibility and employment rates have come to contradictory conclusions, ranging from no impact to a strong and positive effect. We nevertheless consider that the volume of positive evidence linking employment accessibility to urban productivity and employment outcomes is convincing (with some early day indications that it matters for 4
livability too). And that based on these premises, pursuing increased urban accessibility is a valid and important policy objective.

# **3 METHODOLOGY**

# **3.1 Analysis of Accessibility**

In urban transportation, and loosely following Koenig (Koenig 1980), ‘accessibility’ refers to the ease with which an individual can reach opportunities (e.g. employment, health or education services), given the spatial distribution of land uses in the city, the transportation infrastructure and services available (transportation supply), the temporal constraints of individuals and activities, and the individual characteristics of people.

To calculate accessibility in urban areas we need information on: (i) the travel times for a set of Origin-Destination pairs, (ii) the spatial distribution of opportunities within the urban area and (iii) the spatial distribution of the population.

For the purpose of this research we focus on access to employment opportunities using nonprivate transport modes – specifically public transportation and non-motorized transport. One would expect auto accessibility to be higher, primarily because motorization in the region is low. If the entire population were to travel by car however, then congestions would hurt all users, hindering the access to opportunities.

In order to calculate access, first, we utilize the transport and transit network, to calculate the travel times for every origin-destination pair in the city. Second, we then utilize the estimated travel times for each origin-destination to calculate the number, or the share, of opportunities that are within a given travel time threshold. Thus, we are able to calculate for every point in the city the number of opportunities (e.g., employment opportunities) that are within a given travel time (e.g., 60-min) using a specific transport mode. And finally, accounting for the spatial distribution of people, we can compute the average accessibility in the urban area.

Building on traditional accessibility measures (Pooler 1995; Cervero, Rood, and Appleyard 1999; Geurs and van Wee 2004; Busby 2004), the Isochrone model (Hansen 1959) takes into account the total number of opportunities that can be reached within a given time, distance or cost threshold. This model requires the least data calculation since it uses a standard binary (0 or 1) threshold to determine the accessible opportunities: Where A� is the accessibility indicator for location i, within a given urban area, O� represents the number of opportunities in location j, again of a given urban area, and W� is a parameter which takes the value of 1 if C �C<sup>∗</sup> and the value of 0 if C �C<sup>∗</sup> . C is the travel time or �� �� �� cost between locations i and j and C<sup>∗</sup> is a travel time (or cost) threshold in a given city. When W� �1, opportunities in location j can be reached and are added to the total of reachable opportunities. In the other case they are assigned the value of 0.

The use of a binary threshold considers any opportunity within that threshold equally accessible, although there might be a larger impedance to travel a longer distance. This model is the easiest to compute and interpret, among the accessibility family, compared to alternatives that assume exponentially decreasing attraction of opportunities with costs C�� between 5
locations i and j. But it does not include the temporal and user components of accessibility (e.g. mode preference or subset of opportunity preferences).

We utilize an urban accessibility tool, which utilizes the road network (in this case OpenStreetMap), and transit attributes (specifically, the cities’ General Transit Feed Specification (GTFS) files and/or a transit network GIS layer), to calculate the travel times between origin and destination pair in the city. The tool then combines the estimated travel times and location data for employment opportunities (or other opportunity inputs) to calculate the accessibility value for every point in the city.

This flexible open source tool provides, with ease and detailed granularity, the basis for the accessibility analysis of the city. It is developed by Conveyal,<sup>6</sup> and uses a simple web-based interface to generate detailed raster accessibility maps. Raster maps offer a representation of the area divided into a regular, standardized grid of cells and are useful for storing high resolution data that vary continuously. This output can be processed and analyzed in greater depth and collated with other sources of data with other GIS software.

When averaged over the whole urban area, localized accessibilities are weighted by population n�, to account for population distribution within the city. This produces an average accessibility for the urban area, A̅.

While the time threshold can be set to any value, this analysis takes 60 minutes as the threshold, which has been recognized as meaningful to capture labor market access in urban areas (Bertaud 2014).

A second set of metrics and visuals complement the average accessibility indicator. They provide information on the distribution of accessibility – spatially through maps and in aggregate through Lorenz curves and the calculation of Gini coefficients. The distribution of accessibility is an important component of the overall picture, in tandem with the average accessibility, which brings to light the equity distribution of access within a city.

Similar to the way income inequality is often captured, we compute Lorenz curves and Gini coefficients to represent how equally accessibility is distributed for each urban area in our sample. When looking at income inequality, it is common to sum the total incomes of a country and explore what proportion of that total “income pot” goes to a given share of the country’s population. The higher the inequality the lower the share of the total incomes that goes to the poorest.

In the framework of this paper, and following the methodology proposed in Avner and Lall (2016), we replace the “income pot” by the “accessibility pot”, which is the total of accessibility to opportunity levels when summed over the entire population of a given urban area. And the Lorenz curves will capture what share of the population has access to what share of the “accessibility pot”, sorting the population within a city on the x-axis by increasing accessibility levels.

> 6 <u>https://www.conveyal.com/</u>

6
The Gini coefficient is a metric that captures the extent to which the situation in a given urban area departs from the perfect equality situation (where every individual has an accessibility level equal to the average of the urban area). Mathematically it is equal to the area between the 45 degree line and the Lorenz curve for accessibility (see section 5 for a graphical illustration). The higher the Gini coefficient the more unequally distributed the level of accessibility within the urban area.

The Gini coefficients for accessibility Gare calculated with the following formula: Where the localized accessibility indicators are sorted in increasing order so that A� �A���. The first part of the numerator displays the cumulative sum of total accessibilities opportunities, where k∈�1, ... , I� and I represents the index of the pixel with the highest localized accessibility.

# **3.2 Geographic Frame of Analysis**

In order to have a comparable frame of analysis between metropolitan areas, regardless of different definitions of administrative boundaries, we established a uniform boundary for the metropolitan area. The boundaries of the analysis for each region were defined as all areas with an accessibility of above zero – that is, locations that fall inside a 1-hour travel time access to at least a single employment opportunity or proxy thereof. This defined the study area of our analysis.

As opposed to relying on administrative areas, and extending beyond the geographic distribution of opportunities/jobs within the analysis, peri-urban areas are included in the analysis based only on access to employment opportunities identified within the urban area, and not in outlying areas themselves. Thus, areas at the outer edges of the analysis may not account for access to nearby employment opportunities that fall outside the core area analyzed.

# **4 DATA**

Based on the methodology previously described, this work required three main sources of data inputs: (i) the public transportation network in order to calculate travel times between OD pairs, (ii) the spatial distribution of employment opportunities and (iii) the spatial distribution of population. Below is a description of the data sources used.

# **4.1 Public Transportation Networks**

Transport data were based on available GTFS feeds and shapefiles. In order to calculate accessibility, first a digital representation of the public transport network is created - either the General Transit Feed Specifications (GTFS) which is the standard data used for planning routes or a Geographic Information System (GIS) shapefile with the information on public transportation travel speeds and frequencies. General Transit Feed Specification (GTFS) is a free and open template for entering data related to basic transit system services, such as routes, stops, fares, and schedules. The information on routes may also be digitally recorded as a GIS shapefile. This shapefile must include each route that is served in the city. Additional to the routes the file must contain information about the frequency and speed, at different times of day, particularly the am peak.

7
All the analyses for cities in the report were run with the following identical variables:

- 4km/h walking speed for pedestrians accessing transit

- 7AM to 9AM on a weekday – peak travel time

- Within 60 minutes travel time between departure and arrival Where GTFS feeds with specific stops were unavailable, 400 meters was assumed as the distance between stops.

Data produced by WhereIsMyTransport<sup>7</sup> were used for five cities – Lusaka, Kampala, Harare, Douala and Cape Town. WhereIsMyTtransport is a South African company which collects and digitizes data from formal and informal public transport networks to produce GTFS feeds. Data collected include information on fares, stops, routes, frequencies and travel times and speeds. This mapping is carried out with local partners and identification of all available modes of transport within the city and local teams of data collectors. Routes and data are validated in the field by the local team during daily feedback sessions with data collectors.

The transport data for Nairobi are based on the Digital Matatus Project. This was carried out in 2012-2014 by the DigitalMatatus consortium comprising the Universities of Nairobi, Columbia, MIT and Groupshot, to produce a map of the Matatu minibus network in Nairobi. University of Nairobi students collected information on bus routes, schedules and stops with GPS technology based on their phones. Students and the Civic Data Design lab at MIT analyzed the data, and created a public transit map and a GTFS feed (Williams et al. 2015; Klopp et al. 2015).

The transport data for Dar es Salaam were a combination of sources, collected and aggregated by the World Bank office. The transport system includes the DalaDala routes, mapped as an initiative of the Ramani Huria team, the Tanzania Open Data Initiative (TODI) team from the World Bank, and Ally.<sup>8</sup> The transit information for the operating BRT routes where provided the by DART transit agency.

For Conakry, the route map is based on maps produced for the ongoing Plan de Déplacement Urbain (PDU) de Conakry, produced by the European Union and a 2006 study produced by SYSTRA. For Bamako, the maps of the Sotrama minibus taxi network were produced and provided by the “Cellule de Préfiguration de l’Agence Urbaine de Bamako” (CPAUB) housed in the mayor of Bamako’s office. The data for Kigali were collected by IGC for a study on accessibility performed in 2016.

# **4.2 Employment Opportunities**

Data on the location of jobs were available for this study only for Kigali, Bamako and Dar es Salaam.<sup>9</sup> These data sets showed the location and number of firms at the district level, and in the case of Kigali, also the number of employees. For the other cities, an extrapolation of job locations was created by analyzing available information and creating a relative index of job-

> 7 https://www.whereismytransport.com/about/

> 8 _http://ramanihuria.org/putting-dar-es-salaam-dala-dalas-map/_

> 9 Although it exists also for Nairobi, Dakar and Kampala through travel surveys. Because we could not cover the whole sample of 11 cities with official data sources, we looked for proxies for employment data for the whole sample.

8
likeliness throughout each metropolitan area by identifying ‘Employment Opportunity Areas’ (EOAs) of different intensity.

These were determined by scoring different indicators on 500-meter cells in a grid covering each city. The scores include the count of employment-related amenities extracted from Google Maps and Open Streets Map, within each cell and its adjacent cells. This count included the number of retail financial establishments, the proximity to major intersections, and the proximity to taxi ranks or bus stops. A full description of this methodology, as well as its validation, is available in a companion paper (Avner et al. forthcoming). The validation performed on Dakar, Kampala and Nairobi shows that, despite widely varying contexts, correlation coefficients linking urban form attributes to the relative distribution of jobs within cities are consistent.

Thus, EOA scores are comparable both within and between cities, and offer an approximation of the distribution of jobs (but not a conversion into a number of jobs, which may differ between cities). This, in combination with the transport networks, generates an analysis of access to the proportion of ‘EOA points’ in the city from any given point, as a proxy for employment opportunities.

For Kigali, the employment data came from the establishment census, finalized in 2017, which gives a number of employees for each city sector. For Bamako, employment data are based on a business registry carried out in 2015, which locates jobs to city neighborhoods.

# **4.3 Population**

The population data for all the cities in the report were taken from WorldPop,<sup>10</sup> an open repository of population density data based at the University of Southampton, using 100x100 meter population density mapping (Linard et al. 2012). The WorldPop population estimates are based on a combination of available census data, land-cover satellite imagery and mathematical modeling. This allows higher resolution estimates than are available from other sources, and on a nation-wide basis, without regard for administrative units, which can be inconsistent, difficult to compare and do not necessarily match the analysis in the paper. Furthermore, the WorldPop data are freely usable and do not require requesting census and administrative data.

As mentioned in section 3.2, in order to provide a comparable geographic unit among cities, we relied on a uniform study area of the metropolitan region based on access patterns, rather than administrative units - which are not comparable between cities. TABLE 1 summarizes the geographic and population scope of each study area – as defined by the uniform metropolitan boundary described previously. This is in comparison with the UN 2015 World Urbanization Prospects (UN DESA 2015) estimated metro population in the next column – which utilize administrative boundaries to define the urban area. Cities whose study area populations fall far below the UN Population estimates may have underperforming transport systems in terms of reach, while those that are above it have more of their peripheral population with access to the city.

||**Area in km sq**|**Population Within**<br>**Study Area**|**UN Pop Estimate**<br>**2015**|
|---|---|---|---|
|**Nairobi**|822|5,685,117|3,914,000|

10 www.worldpop.org. Note that supplementary work could investigate how sensitive our results are to using other global data sources such as Landscan or GHS-Pop.

9
|**Conakry**|196|1,608,238|1,714,000|
|---|---|---|---|
|**Lusaka**|837|1,489,053|2,187,000|
|**Bamako**|478|2,967,235|2,219,000|
|**Kigali**|454|929,723|951,000|
|**Kampala**|834|3,101,666|2,577,000|
|**Cape Town**|2467|2,699,422|4,100,000|
|**Dar es Salaam**|1000|3,673,173|6,048,000|
|**Dakar**|2325|3,065,622|2,978,000|
|**Douala**|288|2,802,368|3,259,000|
|**Harare**|1454|2,273,485|1,500,000|

TABLE 1: FRAMES OF ANALYSIS The data sources used are summarized in the table below.

|**City**|**Population**<br>**Data**|**Opportunity Data**|**Transport Data**|
|---|---|---|---|
|Nairobi|WorldPop|EOA Analysis|Digital Matatus|
|Lusaka|WorldPop|EOA Analysis|WheresMyTransport|
|Kampala|WorldPop|EOA Analysis|WheresMyTransport|
|Cape Town|WorldPop|EOA Analysis|WheresMyTransport|
|Kigali|WorldPop|Rwanda Census|IGC|
|Bamako|WorldPop|Bamako Business<br>Registry|Bamako Mayor’s Office|
|Conakry|WorldPop|EOA Analysis|Plan de Déplacement<br>Urbain (EU, Min.<br>Transports)|
|Dakar|WorldPop|EOA Analysis|SAFEGE|
|Dar es Salaam|WorldPop|Dar Es Salaam Firm<br>Census|Collected by World Bank|
|Harare|WorldPop|EOA Analysis|WheresMyTransport|
|Doula|WorldPop|EOA Analysis|WheresMyTransport|

TABLE 2: DATA SOURCES

# **5 RESULTS**

The table below summarizes the results for each city. Average accessibility is the percentage of estimated employment opportunities throughout the city accessible by the average individual within 60 minutes. The best access level is the highest percentage of jobs accessible from the single best-positioned location.

||**Average Accessibility**<br>**Best access**<br>**level**|**Distribution of**<br>**Accessibility**<br>**‘Gini’**|
|---|---|---|
|**Nairobi**|28.5%<br>67.4%|0.36|
|**Conakry**|29.4%<br>65.1%|0.41|
|**Lusaka**|34.1%<br>86.0%|0.42|
|**Bamako**|34.3%<br>97.5%|0.37|
|**Kigali**|15.2%<br>23.0%|0.26|

10
|**Kampala**|31.7%|71.0%|0.36|
|---|---|---|---|
|**Cape Town**|6.5%|42.2%|0.63|
|**Dar es Salaam**|12.2%|69.7%|0.60|
|**Dakar**|46.7%|70.7%|0.18|
|**Douala**|39.0%|69.3%|0.30|
|**Harare**|20.8%|83.0%|0.42|

TABLE 3: ACCESSIBILITY METRICS The distribution of access shows the percentage of jobs accessible by each percentage of the population. These results are summarized below in Table 4 below which shows cumulative accessibility - the percentage of jobs accessible by each percentage of the population, at 5% intervals. The left column describes the level of accessibility and each column describes the percentage of residents that has that level of accessibility. For example, we can see that in Nairobi, 51% of the population has access to at least 30% of the job opportunities. As can be seen there, no city has 100% access to all job opportunities for 100% of all residents – in fact, no city has 100% access to all job opportunities for any residents: this is represented by the bottom line of the table. But as the different colors of the columns show, some cities come closer to this goal than others – for example, Bamako is best at delivering good accessibility, but only to a relatively small percentage of the population. Dakar, on the other hand, is best at delivering relatively equitable accessibility – over 50% of job opportunities (left columns) are accessible to over 50% of the population (green).

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|||**Percenta**|**ge of the p**|**opulatio**|**n (cells) wit**|<br>**h access**|<br>**to x% of jo**|**bs (rows)**|**in each ur**|**ban area**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||**Cape**|**Dar es**|||||
||**Nairobi**|**Lusaka**|**Bamako**|**Kigali**|**Kampala**|<br>**Town**|<br>**Salaam**|**Dakar**|**Harare**|**Douala**|**Conakry**|
|**>0%**|100%|100%|100%|100%|100%|100%|100%|100%|100%|100%|100%|
|**>5%**|85%|85%|89%|84%|86%|39%|62%|97%|85%|88%|82%|
|**>10%**|78%|76%|81%|77%|80%|25%|42%|95%|71%|86%|78%|
|**>15%**|71%|69%|75%|63%|74%|16%|30%|93%|56%|82%|73%|
|**>20%**|64%|62%|69%|39%|68%|9%|20%|90%|43%|76%|65%|
|**>25%**|58%|56%|62%|0%|61%|4%|14%|86%|32%|72%|57%|
|**>30%**|51%|51%|58%|0%|55%|2%|10%|82%|24%|67%|49%|
|**>35%**|43%|46%|49%|0%|48%|0%|7%|79%|18%|63%|44%|
|**>40%**|35%|41%|39%|0%|40%|0%|5%|77%|14%|57%|37%|
|**>45%**|22%|37%|29%|0%|31%|0%|3%|70%|10%|52%|29%|
|**>50%**|11%|32%|27%|0%|23%|0%|2%|56%|6%|43%|20%|
|**>55%**|4%|26%|21%|0%|12%|0%|1%|39%|4%|31%|8%|
|**>60%**|1%|21%|13%|0%|6%|0%|0%|17%|2%|15%|1%|
|**>65%**|0%|15%|7%|0%|2%|0%|0%|4%|1%|4%|0%|
|**>70%**|0%|10%|6%|0%|0%|0%|0%|0%|1%|0%|0%|
|**>75%**|0%|5%|4%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>80%**|0%|2%|3%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>85%**|0%|0%|2%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>90%**|0%|0%|2%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>95%**|0%|0%|1%|0%|0%|0%|0%|0%|0%|0%|0%|
|**100%**|0%|0%|0%|0%|0|0%|0%|0%|0%|0%|0%|

TABLE 4: PERCENTAGE OF THE POPULATION (COLOR/CELL) WITH ACCESS TO X% OF OPPORTUNITIES (LEFT COLUMN/ROWS) Table 3 and Table 4 provide some information on how equally distributed are the access levels to employment opportunities. Cape Town’s best accessibility from a single point, for example, is 42%. This is higher than Kigali, where it is 23% (Table 3). However, in Cape Town the higher levels of access are available to only a small proportion of the population and the average is 7%, while in Kigali the average is 15% as a relatively larger proportion of the population is located within higher-access areas.

Another, maybe more common or intuitive, way of understanding how (un)equally distributed levels of accessibility are, is through the use of Lorenz curves and Gini coefficients of accessibility. Section 3.1 provided some details as to how Lorenz curves for accessibility relate to the more common metric for income inequality. Lorenz curves and Gini coefficients have the advantage of focusing solely on how equally distributed accessibility levels are, irrespective of the absolute accessibility levels in the city.

Similar to the way income inequality is often captured, we compute Lorenz curves and Gini coefficients to represent how equally accessibility is distributed for each urban area in our sample. When looking at income inequality, it is common to sum the total incomes of a country and explore what proportion of that total “income pot” goes to a given share of the country’s population. Graphically, the x-axis represents the increasing population share, ordered from poorest to richest, and the y-axis the increasing share of the total “income pot”. When there is perfect equality in a given economy, the Lorenz curve drawn from joining the pairs of population and “income pot” shares, is the 45% degree line. It means that x% of the population has access to exactly x% of the total wealth in the economy, with x being exactly the average.

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‘star’ pattern of accessibility in the city and its low levels of average accessibility and high inequality – but also high maximum access in the center.

# **5.3 In-between Cities**

# _5.3.1 Bamako_

The transport data are based on the SOTRAMA network, which is composed of privatelyowned minibuses traveling on different routes. The maps of the Sotrama network were produced and provided by the “Cellule de Préfiguration de l’Agence Urbaine de Bamako” (CPAUB) housed in the mayor of Bamako’s office. The original data, in Autocad format, were converted into a shapefile for the purpose of the accessibility analysis. While the routes are identified in the shapefile, public transportation speeds were not determined. So, the analysis makes some assumptions on average speeds that were discussed and validated with local experts. The Sotrama speeds are assumed to be linked to the road class they circulate on as follows:

||**SOTRAMA- rush hour speeds**|
|---|---|
|**Primary**|21 km/h|
|**Secondary**|11.2 km/h|
|**Tertiary paved**|9.8 km/h|
|**Tertiary unpaved**|7 km/h|

TABLE 6: SPEED ASSUMPTIONS DURING RUSH HOUR BASED ON ROAD CATEGORY IN BAMAKO The employment data for Bamako are based on a business registry carried out in 2015, listing 8,808 firms for the Bamako urban area. The registries include all formal firms, and the wage bill is reported at the level of the firm (and not the plant). To account for this, the top 5% of firms with the biggest wage bills were dropped from the analysis, as these would run the greatest risk of reporting firm (and not plant) level data. In addition, large mining, extractive and security firms were dropped from the analysis, since it can be safely assumed that a large proportion of their operations take place outside the city.

The firms are geo-referenced at the neighborhood level. The data are somewhat limited, and only allow computation of the total number of firms and the total wage bill per neighborhood. Unlike the situations in Kigali (see above) Bamako’s central neighborhoods are small, few firms are tracked in the outlying areas, and most businesses are therefore tightly clustered in the analysis, which may also play a role in the high levels of accessibility found in the analysis. As well, the population distribution in the city is notably even, without a significant downtown peak.

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# TF
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# eer
When comparing across these groups, the five In-between Cities follow a very similar pattern of employment access per population, despite very different geographies, sizes and processes of economic development. They only begin to show differentiation when reaching the highest levels of accessibility, which serve only the top (in access terms) 30% of their populations. This may suggest, in comparison to the other two types of cities, that in fact the public transit system is relatively poor at affecting spatial mismatch for a large proportion of the population, and follows closely the existing residential and job patterns rather than being able to disrupt.

In terms of population distribution, while not universal, several cities show a distinct access hollow’, where the densest populated districts cluster around, but not at, areas of maximum employment access. As accessibility is calculated for all employment opportunities, for locations with multiple clusters, and even multiple parallel transit routes (as in the case of Conakry) the best access levels may fall not on, but between them, in locations with good access to multiple transport routes and multiple employment centers, highlighting currently low-density areas with high development potential.

This is the first study applying a similar methodology across different cities and based on similar, if not identical, data, to produce comparable accessibility metrics in data-poor environments. As such, it allows an analysis of patterns and trends in spatial data, establishing both a relevant benchmarking process and the integration of individual geographic circumstances. This data set can be used, and further built on, to understand the interactions between distributions of jobs, people and transport and mapping out the dynamics and challenges of urban agglomeration in Africa. It can potentially serve as a component for transport planning, transit-oriented development, land use planning, and investments in increasing labor markets and employment opportunities in African cities.

Specifically, this initial benchmarking opens up a number of avenues for further policy oriented research and operational engagements. First, the simulation of counterfactual scenarios, by comparison to the baseline, can provide some orders of magnitude as to the employment accessibility impacts of urban plans and/or transport investments. This can be useful for local decision makers which aim to compare different projects or assess the benefits of a specific project.

Second, in follow-up work, we will seek to link urban form indicators (such as job-housing balance, land use patterns and others) to the performance of urban areas in terms of employment accessibility. We could equally attempt to quantify the requirements in terms of transportation infrastructure investments needed to offset the impacts of spatial structures that are less conducive to employment accessibility. This work would be meaningful for policy makers as it could provide some estimates of the benefits of acting on urban forms and of the costs of failing to do so.

Third, this work provides a solid basis for thinking about how access to employment opportunities impacts employment outcomes (status of employment, formal or informal job, wages...). Some recent works are going in this direction (Franklin 2015) and the constitution of this accessibility database could constitute the first step in expanding this growing body of literature over several African cities.

Last, but not least, in the context of defining metrics to inform the Sustainable Development Goals (SDG), in particular, SDG 11.2, we hope that this analysis can serve as a demonstration 29
of the possibility (and relative ease) of computing accessibility to employment metrics, including in data scarce environments. SDG 11.2 reads “by 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons”. One of the current proposed indicators to measure progress along SDG 11.2 is the percentage of people within 0.5km of public transit, running at least every 20 minutes. We argue that focusing on accessibility to jobs would be more meaningful as the goal of transit systems is ultimately to connect people to opportunities, including economic. While the calculation of employment accessibility is more difficult than that of the distance to transit stops, it is not out of reach, including in urban areas of the developing world.

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