Urban distances and labour market participation of the poor: The case of urban Ghana

Over the last three decades, Ghana’s urban population has more than tripled, rising from 4 million in the mid-1980s to nearly 14 million people in 2015. The Greater Accra Metropolitan Area (GAMA) accounts for almost 4 million dwellers, making it one of the largest metropolitan areas in West Africa. Rapid urbanization has led to the emergence of several social problems related to efficiency and inclusion, with large unplanned spatial expansions often lacking appropriate levels of public service provision.

In particular, limited connectivity both within and across Ghana’s cities results in longer than necessary commuting time, and in turn, limits job accessibility and lowers productivity. It also disproportionately affects the poor, who face complex trade-offs between their residential location, travel-to-work distances, and choices of commuting modes.

According to the National Household Transport Survey (2012), walking remained the main mode of commuting across urban areas (50%), followed by trotro (20%), and shared taxis (12%). In the GAMA, high‐capacity mass transport is virtually non-existent. As a consequence, urban residents have to either rely on private cars, often unaffordable, or resort to informal solutions, which is subject to severe cost and safety concerns and contribute to road congestion. Only 0.3% of commuters are estimated to use public buses, with 70% relying on trotro and 11% walking (Ghana Urbanization Review, World Bank 2015). Half of commuting workers cite bad roads as the main limitation to access the workplace, and a quarter cite heavy congestion as the main barrier. Among those actively looking for jobs, more than 50% report inaccessibility to the workplace as their main challenge.

This project aims at investigating the relationship between urban commuting patterns and labour market outcomes within Accra and its surrounding areas. For this, researchers will assemble commuting choices, employment location patterns, and transport networks at the local level using various innovative datasets.

An important contribution of this research is the inclusion of informal transport networks. Understanding these mechanisms is key to informing urban transportation policies as well as more general infrastructure, housing, and planning policies in a rapidly urbanizing context.

Outputs