residential areas (25 percent), reflecting income gaps between those areas (Table 1).<sup>8</sup> In addition, even getting to a bus stop itself takes more time in informal residential areas. A larger proportion of housing is located more than 500 meters away from the nearest bus stop in informal settlements (34 percent) than in formal residential areas (only 14 percent).

To understand the trade-off faced by households in their residential choices, a detailed spatial diagnostic is necessary. Job accessibility for a housing depends on the distance from job clusters and the availability of transport to reach there. If housing rents are determined in the market by not only the dwelling and neighborhood characteristics but also the level of job accessibility, households face a trade-off over them in their residential choices. The existing stock of housing with good job accessibility may be scarce in a not-well-connected city like Nairobi, and thus it can be expensive to live in such housing. Some may have to sacrifice living conditions to live in proximity to job opportunities, given the importance of job accessibility in their labor performance. Low-income households could particularly face a challenge to access job opportunities if low-rent residences in informal settlements do not really offer reasonable job accessibility. Therefore, it is critically important to investigate the spatial distribution of job accessibility in the city and its relationship to spatial distribution of poverty (or households in different income groups) and housing rents.

# **3. Methodology**

## **3-1. Data**

_Transport_ To calculate job accessibility in Nairobi, it is necessary to account for various transportation modes: walking, driving and using the semi-formal minibus (matatus) network. Data for the ‘minibus network’ travel times are available online in the General Transit Feed Specification (GTFS) format (see Figure 2 for the minibus network).<sup>9</sup> These data were compiled by the Digital Matatus consortium comprising University of Nairobi, Columbia University, Massachusetts Institute of Technology, and Groupshot (Klopp et al. 2015; Williams et al. 2015). The GTFS data were processed using a Dijkstra algorithm, which provided times to reach any minibus station from any other in the urban area.<sup>10</sup> Travel times to reach the closest minibus station were computed based on a straight-line distance or ‘as the crow flies’ from the center of each grid cell.

> 8 The distributions of commuting time do not substantially differ between informal and formal residential areas. In Nairobi, most households spend less than 1 hour for commuting (one way). Half of the household heads in Nairobi spend 30 minutes or less getting to their workplaces. No substantial difference is observed in the mean commuting time among households with different consumption quartiles and within/outside informal residential areas.

> 9 GTFS data are a common format for public transportation schedules and associated geographic information. They are a collection of multiple comma-separated values (csv) files that describe transit routes, stops (including geographic coordinates), travel times between stops, and a number of other public transit properties.

> 10 Dijkstra’s algorithm is an algorithm for finding the shortest path between two nodes in a graph that may represent, for example, road networks.

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