terms of complete costs, accounting both for time opportunity costs and monetary costs. Lack of data prevents the team at this stage from performing this more complete investigation. In particular, matatu fares vary depending on the time of the day, the level of congestion, the length of the trip, weather conditions and so on. It is therefore difficult to ascertain with reasonable confidence the monetary costs of a matatu trip.

# **Methodological limitations:**

- <u>Representation of economic forces in location decision behaviors:</u> In the model presented and discussed above there is no representation of household and formal employers’/firms’ behavior when deciding where to locate. As a result of this omission there is no possibility of capturing the real estate or land market which is both a result of and a factor in household and economic activity location decisions. High land prices which can result from high demand might prevent some activities from locating in some areas and instead push them towards less desirable (and hence less accessible) locations. In this respect, the model and results presented above overlook an important location decision factor and can be tagged as a-economic, thereby representing an informative yet schematic and maybe idealized outcome. A critical avenue for policy oriented research, which is beyond the scope of the present effort, would be to build an economically grounded location decision model for both firms and households which could represent optimal land use occupation with respect to economic opportunities.

- <u>Representation of local characteristics: the desirability of certain locations for their inherent</u> qualities or conversely the disamenities associated with proximity to certain areas (industrial locations for example) are also disregarded and could play a role in local sorting of households and firms. Ideally, the economically grounded location model for firms and households advocated for above would capture the role of amenities. In its absence however, future work along the lines of this study could integrate the inherent desirability or undesirability of certain locations through simple coefficients encouraging or discouraging certain land use patterns.

- <u>Addressing congestion: Although congestion is accounted for in this study in the baseline situation</u> – the current spatial layout of Nairobi – through realistic transportation speeds, the impact of changing the location of households and formal economic opportunities on these congestion levels is not represented. However, changing the spatial pattern of Nairobi will also modify the direction and intensity of commuting flows, possibly saturating some road links in some areas whilst relieving congestion in some others. The aggregate accessibility improving scenarios both display a higher concentration of households and formal economic opportunities in the vicinity of the city center which is likely to overall increase congestion in these areas. The model, however, falls short of integrating this feedback loop when calculating accessibilities. A future improvement would be to endogenously estimate congestion levels based on commuting flows on the basis of Bureau of Public Roads (BPR) speed-flow curve or a Macroscopic Fundamental Diagram (Geroliminis and Daganzo 2008).

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