metadata: authors: 0: author: Avner,Paolo
1: author: Lall,Somik V.
count: Kenya
docna: 0: docna: Matchmaking in Nairobi : the role of land use
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Labor Markets,Urban Governance and Management,Urban Housing,Multi Modal Transport,Municipal Management and Reform,Transport Services,Rural Labor Markets
teratopic: Urban Development
abstracts: cdata!: Well-functioning cities reduce the
            economic distance between people and economic opportunities.
            Cities thrive because they enable matchmaking -- among
            people, among firms, and between people and job
            opportunities. This paper examines employment accessibility
            in Nairobi, Kenya and evaluates whether modification of land
            use patterns can contribute to increases in aggregate
            accessibility. The assessment is based on simulation of
            counterfactual scenarios of the location of jobs and
            households throughout the city without new investments in
            housing or transport infrastructure. The analysis finds that
            modifications to the spatial layout of Nairobi that
            encourage land use clustering can increase the share of
            overall opportunities that can be accessed within a given
            time-frame. When commuters travel by foot or using the
            minibus network, the share of accessible economic
            opportunities within an hour doubles from 11 to 21 percent
            and from 20 to 42 percent respectively. The analysis also
            finds that spatial layouts that maximize the number of
            households that have access to a minimum share of jobs,
            through a more even jobs-housing balance, come at the
            expense of average accessibility. This result is interpreted
            as a trade-off between inclusive and efficient labor markets.
geo_reg_key: 517191
repnme: repnme: Matchmaking in Nairobi : the role of land use
display_title: Matchmaking in Nairobi : the role of
            land use
listing_relative_url: /research/2016/12/26997409/matchmaking-nairobi-role-land-use
topicv3: Labor Markets m1326488 855,Transport m1326661 135,Land and Housing m1327648 817,Jobs m1503931 2670,Pedestrians m1583024 4267
publishtoextweb_dt: 2016-12-05T00:00:00Z
historic_topic: Urban Development
no_of_pages: 33
author_keys: 0: author_key: 1256219
1: author_key: 124534
model_extractions: WPS7904 Policy Research Working Paper 7904 Matchmaking in Nairobi The Role of Land Use _Paolo Avner Somik Lall_ Social, Urban, Rural and Resilience Global Practice Group December 2016
Policy Research Working Paper 7904

# **Abstract**

Well-functioning cities reduce the economic distance between people and economic opportunities. Cities thrive because they enable matchmaking—among people, among firms, and between people and job opportunities. This paper examines employment accessibility in Nairobi, Kenya and evaluates whether modification of land use patterns can contribute to increases in aggregate accessibility. The assessment is based on simulation of counterfactual scenarios of the location of jobs and households throughout the city without new investments in housing or transport infrastructure. The analysis finds that modifications to the spatial layout of Nairobi that encourage land use clustering can increase the share of overall opportunities that can be accessed within a given time-frame. When commuters travel by foot or using the minibus network, the share of accessible economic opportunities within an hour doubles from 11 to 21 percent and from 20 to 42 percent respectively. The analysis also finds that spatial layouts that maximize the number of households that have access to a minimum share of jobs, through a more even jobs-housing balance, come at the expense of average accessibility. This result is interpreted as a trade-off between inclusive and efficient labor markets.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group supported by the Africa Chief Economist office and UKAID through the Multi-donor Trust Fund on Sustainable Urbanization. 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://econ.worldbank.org. The authors may be contacted at avner@worldbank.org and slall1@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
# **Matchmaking in Nairobi: The Role of Land Use**

Paolo Avner and Somik Lall JEL codes: R14, R52, R58, C63 Keywords: Matchmaking, Accessibility, Land use patterns, Urban form, Labor market
# **Matchmaking in Nairobi: The Role of Land Use**

Paolo Avner<sup>(a)</sup> and Somik Lall<sup>(a)</sup><sup>_1_</sup>

- (a) The World Bank

## **Abstract**

_Well-functioning cities reduce the economic distance between people and economic opportunities. Cities thrive because they enable matchmaking – among people, among firms, and between people and job opportunities. This paper examines employment accessibility in Nairobi, Kenya and evaluates whether modification of land use patterns can contribute to increases in aggregate accessibility. The assessment is based on simulation of counterfactual scenarios of the location of jobs and households throughout the city without new investments in housing or transport infrastructure. The analysis finds that modifications to the spatial layout of Nairobi that encourage land use clustering can increase the share of overall opportunities that can be accessed within a given time-frame. When commuters travel by foot or using the minibus network, the share of accessible economic opportunities within an hour doubles from 11 to 21 percent and from 20 to 42 percent respectively. The analysis also finds that spatial layouts that maximize the number of households that have access to a minimum share of jobs, through a more even jobs-housing balance, come at the expense of average accessibility. This result is interpreted as a trade-off between inclusive and efficient labor markets._

## **1 Introduction**

Cities bring together people, ideas and capital so as to benefit from agglomeration economies which stem from proximity and exchanges (Glaeser and Kahn 2004; Ciccone and Hall 1996). The most common sources of urban agglomeration economies arise from: 1/ labor market interactions (pooling), 2/ linkages between intermediate and final goods producers or input sharing and 3/ knowledge spillovers (Rosenthal and Strange 2004)<sup>2</sup> . In this paper we are primarily concerned with agglomeration economies that arise from labor-market interactions or matching, sharing Bertaud’s (2014) view that cities can primarily be seen as labor markets.

The analysis presented in this paper shows that in Nairobi County, Kenya, employment accessibility tends to be limited for a large share of the population that cannot afford to purchase cars. This situation is problematic both for productivity and for livability. On the productive side, it limits the effective size of the labor markets, thereby preventing workforce/employer sorting which hampers the

> 1 The authors are grateful to Sarah Elizabeth Antos for performing the network analysis which provided the automobile transport times matrix and to Annie Bidgood for GIS assistance. Joel Hamann ensured the processing of the matatu GTFS data through a Dijkstra algorithm that he also coded. Dean Cira and Sheila Kamunyori’s guidance as well as efforts to put us in contact with counterparts in Nairobi and locate datasets are highly appreciated. The authors would also like to thank Nancy Lozano Gracia, Adrien Vogt-Schilb, Gunnar Eskeland, Vernon Henderson, Roger Gorham, Tony Venables, Paul Collier, Daniel Rodriguez, Marianne Fay and Stephane Hallegatte for comments on this work and crucial suggestions.

> 2 A more comprehensive list differentiating between up to 12 types of economies of scale can be found in the 2009 World Development Report (World Bank 2009, p 128).

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potential for agglomeration economies (Duranton and Puga 2004). Empirical evidence indicates that accessibility, measured as the number of opportunities an individual can access within a given amount of time, matters for productivity in cities. 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>3</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 (2015) identifies that commuting costs and thus 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.

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 nontradable 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 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 Spatial Mismatch Hypothesis literature (Kain 1968), although primarily focusing on Black minorities in the US, also links labor market outcomes to measures of job accessibility. There is evidence that physical segregation of unskilled workers away from job opportunities leads to high commuting and job search costs which can partially explain higher unemployment rates and lower average wages<sup>4</sup> .

From a research and policy design perspective it is important to understand whether and how to increase metro-wide accessibility and reduce commuting costs to enable labor market pooling, one of the main sources of agglomeration economies. A large and growing body of literature on “excess commuting” is strongly related to the topic of accessibility and provides insights on how to reduce commuting distances within cities. Excess commuting literature (Hamilton and Röell 1982; White 1988; Small and Song 1992) is primarily concerned with documenting the ‘jobs – housing balance’ and measuring how much commuting is “wasteful” or in excess of a theoretical minimum (the minimum commute indicator). The definition of excess commuting has triggered a number of contributions in particular aimed at i) understanding the reasons for the existence and magnitude of excess commuting and ii) understanding how to craft land use and transport policies to reduce both the level of the minimum commute and the associated excess commuting (Ma and Banister 2006). However, the excess commuting literature ignores the benefits of large labor markets, which by pooling large

> 3 Number of workers that can access work within a defined peak period ranging from 30 to 60 minutes.

> 4 see Gobillon et al. (2007) for a detailed review of theoretical explanations and empirical evidence of spatial mismatch.

3
numbers of workers and job opportunities, allows for sorting and matching between employers and employees<sup>5</sup> .

For these reasons, in this paper, we focus on metrics that capture accessibility to all employment opportunities in Nairobi as in Peralta Quiróz and Mehndiratta (2015). The purpose of the study is first to provide a diagnostic of the current situation in terms of access to jobs in Nairobi but also to initiate a discussion on ways in which residents’ accessibility can be improved. There are two main ways of enhancing accessibility: (a) build more or better transportation infrastructure, (b) help land uses evolve and adjust so as to allow for smooth connections between residential areas and opportunities. While the first possibility has triggered both a vast amount of literature and many investment projects, the second has received less attention. Transport projects are often necessary to ‘de-crowd’ cities, but improving the coordination of land uses throughout the urban area can probably achieve some of this goal without the extra costs of financing transport projects. There are typically two ways of changing land use patterns. The first one is to change the residential building stock for example by investing in more floor space in close proximity to opportunities or to transit stations. Advocates of compact city or Transit Oriented Development have focused on these options. The second is to consider the current building stock as fixed and to spatially allocate this capital so as to improve overall accessibility to opportunities. In this paper we investigate the second option and whether accessibility can be enhanced without additional investments. Developing economies are capital constrained so there is value in looking at whether or not it is possible to increase accessibility without additional financing. The assessment is based on the simulation of counterfactual scenarios of the location of jobs and households throughout the city without new investments in housing or transport infrastructure.

We derive a number of results from our analysis. The main result is that modifications to the spatial layout of Nairobi can increase the share of overall opportunities which can be accessed within a given time-frame. When commuters travel by foot or using the minibus network, the share of accessible jobs within an hour doubles from 11% to 21% and from 20% to 42% respectively. This result is important because it demonstrates that careful land use planning and zoning which is coordinated with transport infrastructure can lead to more integrated and efficient labor markets without large costs. Secondly, our results show that accessibility to employment is already a crucial determinant of the spatial layout of Nairobi, with people and employers choosing their locations so as to reduce commuting times and costs. Finally, we find that promoting equity in the access to jobs will result in mixed land use patterns and more even job-housing balance. Small labor markets, matching employment supply and demand locally, will however emerge at the expense of average aggregate accessibility and a fully integrated labor market which is conducive to the manifestation of agglomeration economies.

The paper is structured as follows: Section 2 provides background information about the current urban form in Nairobi County and the mobility of its residents. Section 3 presents the data sources used to investigate accessibility patterns, the distribution of residents and jobs as well as transport costs proxied by transport times for three transport modes: cars, matatus and pedestrians. Section 4 presents the results of the accessibility analysis for Nairobi as it is today. In particular it provides graphic evidence that accessibility is uneven geographically but also depending on whether residents have access to cars. Section 5 then introduces the main innovation of the paper which is to investigate

> 5 Although some authors (Niedzielski 2006; O Kelly and Lee 2005) have paid attention to worker and job characteristics so that the matching pattern yielded by solving the transportation problem is more realistic.

4
accessibility measures under alternate and counterfactual land use patterns. We find that land-use changes can enhance aggregate accessibility in Nairobi and promote a unified labor market even in the absence of transport and building investments. Section 6 concludes. The Appendix discusses methodological and data choices and limitations.

# **2 Context: Urban form and mobility**

Nairobi County is rapidly growing: Its population grew by one million people over the last decade reaching 3.1 million people in 2009<sup>6</sup> and was home to approximately 3.2 million people in 2012<sup>7</sup> . While residential development is spread out, featuring clusters of high density pockets throughout the city, Nairobi’s employment arrangement follows a highly monocentric pattern. Land use areas which are likely to host most formal jobs, such as industrial, commercial and public facilities remain highly concentrated in the urban core, with only limited activity occurring in other emerging centers. New road and highway infrastructure, higher land prices, increased rates of car ownership, and other factors appear to be fueling a discontinuous or “leapfrog” outward growth in peri-urban settlements and along major road corridors.

Small, privately-owned, and artisanally operated buses and vans, known locally as matatus, form the backbone of public mass transportation services in the Nairobi metropolitan region. Matatus and walking dominate the mode share. Eighty-three percent of all trips include walking as the major or secondary mode of travel. Forty-one percent of all trips are walking only, 42 percent involve other modes, mainly matatus (63 percent) and buses (21 percent). Only 17 percent of all trips are made without walking, among which, more than half (54 percent) are completed by passenger cars and 13 percent are made by motorcycle (JICA 2013). Compared to other large cities in Africa, Nairobi has the largest share of walking trips despite rising and relatively high motorization rates compared to other major African cities (JICA 2006; JICA 2014; Kumar and Barrett 2008).

The overall average travel time per trip in Nairobi is 47 minutes, but differs when compared by gender, age, personal income level, mode, and trip purpose. Higher earners generally make longer trips (by time), while average travel times for motorized modes hover between 54 and 60 minutes per trip (excluding motorcycles, which average 38 minutes per trip).

Traffic congestion is a growing problem in Nairobi, resulting from rapidly increasing population and the crowding of motorized traffic onto a limited street network. Indeed, IBM ranked Nairobi fourth highest in its “commute pain” survey in 2011, indicating that the city hosted one of the world’s longest traffic jams, 2.1 hours. According to the World Bank’s Kenya Urbanization Review (World Bank 2016), increasing average commuting speeds from the average recorded 13.5kph to 20 kph would save $54.1 million USD per year and decrease time spent traveling by 30 percent. Increasing average travel speeds to 30 kph would save $93.4 million USD per year and decrease time spent traveling by 54 percent.

> 6 as measured by the Kenyan Census.

> 7 according to landscan data (Bright, Rose, and Urban 2013).

5
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<!-- Start of picture text --> Rieti: pee aea = By TeX 5 10k<br>tp :<br>Wy RONa nnnSatea MST ari Es =Ve—— S—-Is, ‘ ea *; roti<br>Dy a ee S<br>Te a a Ee, REM eae ]<br>DeeeReaa GDeTOKBoe - eeege aere<br>fy ss oY iS eS % x | “4<br>Le en Mints | in, an o> See<br>Oe, ie aS ele —— ‘4 a i . - -<br>eS ‘ 3 a >) ead ___ Road Network<br>beSospee ot as . Wee — Major roads<br>= Vy | rd "= — Secondary roads<br>et ERS Tertiary roads<br><!-- End of picture text -->
<!-- Start of picture text --> fj 0 5 10 km<br>a? = ——<br>1 =<br>é a a<br>Oe he Se he’s . ae 7<br>* a Se rE ee3 Leah fe weetor :<br>Uwe oo i ae Wea cee<br>= Se i<br>Pig “¥<br>ant =a c<br>-4 Nairobi land uses<br>main opportunity<br>N classes<br>(5) Commercial<br>G3 Educational<br>> GB Industrial<br>HB Public Purpose<br><!-- End of picture text --> <!-- Start of picture text --> % 5 10 km<br>i i<br>eae<br>~— g<br>im “ig<br>ia)" i]En|ie<br>aay 1 | G a eeBSoe zsan a |<br>= | | _ BE = 88 =|<br>‘ | a | a an EB ai |<br>E SESEEREEES SCORE<br>Zz En Hee EEE HSN<br>* oO on Ean || eT a 4<br>f 2B EREEn 8 One 8 .<br>En 8 See fBE<br>5 Ee EEEBe iEEEEEOREEa |8<br>on Son oan SES See<br>En 688 i = Seen<br>et | Li ee<br>aS 6s | i<br>a6 P| a Location and volumeof opportunities<br>= navonal ME ho opportunities [1 175 000 - 225 000<br>ial} 5 ots M0 - 15000 [5] 225 000 - 285 000<br>LJ @TM 15000-45000 |=) 285 000 - 350 000<br>Pe ae HB 45000-80000 a 350 000 - 440 000<br>AS. =I 80 000 - 120 000 ll 440 000 - 550 000<br>[=] 120 000 - 175 000 Bill 550 000 - 700 000<br>Gl > 700000<br><!-- End of picture text -->
<!-- Start of picture text --> . a4 y - oo "Oo 5 10 km<br>A >a= ¥ * : : ’ Ff | irre || |i}<br>_f aan<br>- ~| sie OGRE<br>j . Z ne-) Ef6|BERR|ee| | | i lia |EEEEEE| a<br>BERR EER UL OCU 8<br>a Ci iia En En EERE BB<br>a — | Se |_| CasTT i<br>EELOCO om | | dT i | CE<br>nn eee ERR 6B<br>J Baie TC EEEEEEEEOD Sh OU<br>EERO ERE<br>= SSSGEn 8 mt<br>BEEme a2 a En BEEEESERRE| fy 8ay<br>BEEEEEEEREEEEEEE 68 hh EU Be<br>BEREEE SERRE BESS RRees 6<br>m Tl] {|| et ea | eleslstsley [Te<br> EREEEEE || |ee -o<br>$ a.| Haea Le t HEELEI<br>a oo ; =|<br>ae * = - y<br>ere “2 J Share of accessible opportunities<br>Ez N 2 . by car within an hour<br>Ny=S \ aa HBHBHB °%0-%20%10 -- %20%10%30 GE L)EB %30%50%40 --- %50 %60%40 HEB MMF) %70%90%80 -- %80- %90 %100<br>é (5) %60 - %70<br><!-- End of picture text -->

<!-- Start of picture text --> ef . - : 8 5 10 km<br>{ 2 pe |<br>ia HE | |<br>“ —E BEB<br>oes \ oe i SEED ©<br>4 ; EEEB EERESEEEEER8B<br>EEEEEE 8688<br>BEEREEDDES 8<br>ED EEE<br>EREEOUOCCBH 8H SeeCe<br>En EERE<br>/_ia_iaiee<br>Ee 8S<br>+ - “ ! Share of accessible opportunities<br>&& N ; aaeod . by matatu within., : an hour<br>& MB %0-%10 TD %30-%40 GH %70<br>ey; \ i HBGB %10-%20- %20%30 (7)[] %40-%50%50-%60 MMHE %80%90-- %80 %90<br>y %60- %70 - %100<br><!-- End of picture text -->
<!-- Start of picture text --> Lorenz curve for accessibility - Nairobi<br>1<br>____ matatus<br>0.9 Gini=0.36<br>8 | ___ cars<br>= 0.8 Gini=0.29<br>5 — 45° degree line<br>t 0.7<br>fe)<br>Qa<br>20.6<br>2<br>20.5<br>on<br>3<br>© 0.4<br>Oo<br>©<br>6 0.3<br>2<br>£ 0.2<br>7)<br>0.1 ‘<br>0<br>0 0.2 0.4 0.6 0.8 1<br>Share of population<br><!-- End of picture text -->
<!-- Start of picture text --> Mono-centric |<br><!-- End of picture text -->

<!-- Start of picture text --> Decentralized |<br><!-- End of picture text -->

<!-- Start of picture text --> Urban 3<br><!-- End of picture text --> <!-- Start of picture text --> Mono-centric ci<br>Accessibility<br>metrics<br>m0<br>0.515<br>”<br><!-- End of picture text --> <!-- Start of picture text --> Decentralized ci<br>| | ft | ft tt Accessibility<br>metrics<br>mo<br>0.5<br>15<br>| Tt | | | le:<br>Eee<br><!-- End of picture text -->

<!-- Start of picture text --> Urban village city<br>Ft | Et Ly Accessibility<br>metrics<br>mo<br>0.5<br>15<br>Pit et<br>anne<br><!-- End of picture text -->
|**Ranking of these stylized cities in terms of accessibility**|
|---|
|**Mono-centric city**<br>**Decentralized city**<br>**Urban village city**|
|**1?, 2?, 3?**<br>1?,2?,3?<br>1?,2?,3?|

**Table 2 : Which stylized city performs better in generating accessibility?**

# **5.2 How does Nairobi perform?**

To move away from the stylized cities presented above but still with the idea of evaluating whether alternative spatial layouts could enhance accessibility we conduct a series of tests. Keeping constant the transportation network and the overall building stock<sup>10</sup> , we randomly re-shuffle all opportunities and people throughout the urban area and re-compute for each outcome indicators of how the city performs in connecting households with opportunities. Proceeding in this manner, 10,000 different distributions are produced.

> 10 This assumption is tantamount to not allowing for building stock densification. As a result, the urban footprint of Nairobi County is not allowed to shrink. Hence, the exercise conducted here does not investigate the accessibility outcome associated with smaller urban areas and overall densification. It is solely based on the reallocation of jobs, households and building infrastructure for a given amount of invested capital in building and firm structure and in the transportation network.

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<!-- Start of picture text --> s / OS<br>+ at xr o 5 10 km<br>ID 231 t |<br>~~ Pop density: x231 is<br>Opportunities: y231 a<br>= one ss os<br>—. ia GEER | tt 4<br>i p20 S0eee 6 oi<br>Sc S088 82 Seeeee i=]<br>a ge” EEEES BE 8 ES a<br>2 S5SS08 SERRE5RS 8 i<br>)| ee ie | | | a) ee<br>PTTi oT<br>i i i ma IDPop640 density: Xeao opulation density - Landscan 2012<br>_ Opportunities: yeso —_persons / km?)<br>- — wates00- Mend () 1101514885 -- 1488520634<br>' MB 0-1036 = 20634 - 27340<br>sci res heres ~ GHB 1036 - 2988 ty 27340 - 33256<br>’ GB 2988 - 5096 tay 33256 - 41426<br>GiB 5096 - 7532 gam 41426 - 48973<br>49 © 7532 - 11015 gay 45073 - 53793<br><!-- End of picture text -->

<!-- Start of picture text --> % 5 10 ken<br>ehntest 5 iv<br>ID 231 Ma<br>~ 'g Pop density: x231<br>. Opportunities: y231 an<br>x ay | | ba a Bee<br>es s 6 TToe | |<br>— ae 8 | Ef: San Ba |<br>| | | o.\ a @SH BEESEEne Hee8<br>‘<br>a He ae om<br>a B tia BE BESHE | E08BE58Baan a<br>Gea See ae | |<br>os oom | oo. oe<br>a || a 8 ID 640<br>2 6aeoe ‘<br>a : | 7 »»harete Opportunities:Pop density:a ae xeao.3 yeao olumeenh[=] 225 000ofaes. opportunities- asi285 000<br>LI : : ‘ bl ar aii (5) 285 000 - 350 000<br>te ay te<br>~ 45000-80000 Mal 350 000 - 440 000<br>; GB 80 000 - 120000 BM 440 000 - 550 000<br>© 120 000 - 175 000 BMi 550> 700000000- 700 000<br><!-- End of picture text -->
<!-- Start of picture text --> Distribution of accessibility indexes<br>Distribution of<br>90 accessibility indexes<br>80 e= allcurr p e ntrmutationsNairobi |<br>2 70<br>2<br>£ 60<br>—]<br>£<br>® 50<br>Qa<br>2 40 |<br>Cc<br>a<br>8 30<br>20<br>10<br>(@) _<br>40 45 50 55 60 65 70 75 80<br>Share of accessibile opportunities within 60 min (%)<br><!-- End of picture text -->
# **5.3.1 Searching for accessibility improving spatial layouts: Methodology**

Another way of exploring the potential permutations is required. This second exercise aims to assess the most efficient spatial coordination of land uses, keeping, as previously, the transportation network, population, number of jobs and building stock constant. The methodology relies on a hill-climbing optimization procedure which only switches grid cells 2-by-2 starting from the current spatial layout of Nairobi (instead of permuting all grid cells at once as in the previous exercise). When this initial permutation increases overall accessibility, the new spatial layout becomes the baseline and a new iteration is performed. If this permutation is not successful, the permutation is discarded and another one is tested. This process is repeated a number of times to try to converge to an optimum. A hill climbing procedure is conducted for each mode and for the combination of them and also for each time threshold. Therefore, an aggregate accessibility spatial layout is found for example for the special case where only matatus and walking are available as a transport mode and where the time threshold for calculating available opportunities is 45 minutes. This spatial layout will differ from the aggregate accessibility improving land use pattern found through hill climbing when the only available transport option is cars and the threshold is 45 minutes again. It will also differ from the spatial layout where the only transport option is matatus and walking but where the time threshold is 60 minutes. In total, twelve aggregate accessibility improving spatial layouts are computed: for all modes (walking, walking and matatus, cars, combination of any mode) and for all time thresholds (30, 45 and 60 minutes).

# **5.3.2 Accessibility gains and land use profiles**

Performing 100,000 iterations using this hill climbing procedure it is shown that overall accessibility can increase substantially in Nairobi. Results reported in Table 3 indicate that alternative land use patterns can increase overall accessibility by 15% for cars (from 77% to 92.5% of accessible opportunities) and can even double the share of formal economic opportunities which can be reached within an hour using matatus (from 20% to 42%) or traveling by foot (from 11% to 21%). This is the central result of the paper.

These conclusions indicate that even if Nairobi’s spatial layout performance is tolerable in connecting people to opportunities as demonstrated in the first exercise, there is still considerable room to increase accessibility through better land use coordination. Achieving such outcomes, however, will require very strong governance structures, enforcement policies and planning capabilities.

15
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<!-- Start of picture text --> 7A =o 5 10 km<br>Nee =<br>*<br>: e .<br>3<br>- Bi<br>a a5a onC<br>. SeSRSSS00888stfaaees =S<br>, SERRE RRee<br>5 Bo Seene5<br>O SS500 S58055”<br>OS SRBSRRE Oo<br>Pe22ootSen<br>5 SSee0c8Sn8o0m8<br>SEGS0 000000885<br>BH EE EH<br>an Cate<br>.<br>=<br>; -<br>— J Share of accessible opportunities<br>E N _— . by matatu within an hour<br>MB 0-410 GB %30- %40 HE %70- 80<br>~ GB %10-%420 KD %e0-%50 ME %80 - %90<br>f GB % 20 - %430 BBI %60-%50- %70%660 ME %90 - %100<br><!-- End of picture text --> <!-- Start of picture text --> te. i 0 5 10km<br>i I a |<br>‘ * Oe<br>* Co<br>~ - - Coo is<br>c ar<br>ae aaee 6B<br>- aoCo<br>va Hi<br>a8 bySe5e886 oc |<br>3 i a aE 5a<br>‘ aon Pry<br>La on oan<br>faET SHEE SSSE0R8<br>acl tet PCSSSSS00800880i ee ©<br>oe Hp 5<br>ao 2 sence<br>Co Poo<br>a a © 1<br>LitCCC a oanoo<br>in|Coo)| oie a<br>i oe o<br>Z LOO Ul -<br>F =! - ; Share of accessible opportunities<br>aN _— . by car within an hour<br>> 7 ME %0-%10 GD %30-%40HE %70-%80<br>“ GB %10-%20 7) %e40- 50 ME %480-%690<br>= a Gy %20- %30 BW(7) %50-%60%60-%70 ME %90-%100<br><!-- End of picture text -->
<!-- Start of picture text --> = 0 5 10 km<br>‘ ———<br>rin aan @<br>nae | t.<br>~ —— ty oo ang| tT a<br>omtm =onSBier8TyooSESEEEEEEsLfLe 6SalltanOeom BB|| at aa wo| Leaon<br>an Gn Beene S58 Seon<br>a ae Sano Ge Se [|<br>a on BEEEESS Seon<br>ae G HHS<br>Ge Eee i) a |<br>a on BEEOS BBE 8<br>On 558 SES BRS0Snen8e8<br>on 88 | a seen<br>a an 5 Ll<br>a 6 = 6<br>Ls | a | a<br>iat alee WS|| (ag Oy Location and volume of opportunities<br>- HB no opportunities (_] 175 000 - 225 000<br>ES M0 - 15000 (=) 225 000 - 285 000<br>Mt oe : 15[14580000000000-80 - - 120 45.000000000 (J=)El 350285 000440 000000 --- 350.000440550 000000<br>gn tt © 120 000 - 175 000 fllBB >550 700000000- 700 000<br><!-- End of picture text -->

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<!-- Start of picture text --> of a; . 0 5 10 km<br>None Ss ia<br>nan 4 |<br>< amt t Wy ga T-<br>ta camexBA.D nt ag)leeepo<br>gepu Xt Sr \<br>_ Saf 5<br>( is Population density: matatus<br>iets ne Kpeteone;) Km)<br>j| : é (00-00matatu lines [_)©) 11015 14885-- 14885 20634<br>| * wong MTMo-1021 ) 20634-27340<br>al sw GlGl 29881021-- 5096 2088) GE)27340 33256-- 4142633256<br>\ A «vata _ 2(Dl 50967532-11015-7532 HlMl 4142648973-- 4897353793<br><!-- End of picture text -->
much less segregated with more of a mixed land use outcome. The difference between these two outcomes can tentatively be explained by the fact that matatus only run on major roads (matatus in essence provide localized services) whereas cars can use secondary or even tertiary roads and thus can reach locations (close and more distant) easier and without relying on walking for a portion of the trip. These spatial outcomes and their differences, which we do not claim to fully explain, appear consistent with the observations and the literature concerned with urban form and travel behaviors which indicates that mixed land use patterns are better suited to a combination of collective transport and walking (Cervero 2002; Cervero and Kockelman 1997). Both accessibility improving spatial layouts tend to have highest population and formal economic opportunity densities along major roads and in close vicinity to the CBD of Nairobi. It is worth pointing out that the tendency towards central concentrations of households and economic opportunities is achieved in both cases without accounting for agglomeration economies which would tend to encourage further this phenomenon. It should equally be noted that this exercise does not account for potential increase in congestion levels as a result of modified commuter flows (see Appendix for a more detailed discussion of the implications of such a simplification). Congestion forces would conversely tend to discourage higher concentration which is linked to higher land or real estate prices and longer travel times because of increased traffic congestion.

**5.3.3 Is there an efficiency vs equity tradeoff? How about losers?**

The results presented above show that on average residents of Nairobi could achieve higher accessibility levels to employment opportunities with alternate spatial layouts. However, this does not address whether some people would lose from land use reorganization or whether the new land use configuration reduces inequality in the access to jobs across the urban area. In other words is there an efficiency vs equity tradeoff associated with aggregate accessibility improving spatial layouts?

Figure 12 shows the spatial inequalities in access to formal economic opportunities for residents in Nairoby County in the aggregate accessibility improved spatial outcomes for matatus and cars. For cars, the Lorenz curve is very similar to the one displayed in the current Nairobi (Figure 5) and the associated GINI coefficient remains virtually identical. For matatu users conversely, the spatial layout which maximizes their aggregate accessibility is associated with a rise in spatial inequality. The GINI coefficient increases from 0.36 to 0.40 and the Lorenz curve is more convex. This rise in inequality in access to employment opportunities using matatus is due to the fact that the land use reorganization will tend to benefit the most centrally located people more than the rest of the metropolitan population.

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<!-- Start of picture text --> Lorenz curve for accessibility - Improved spatial layout<br>:<br>0.9 Gini=0.40<br>2 | __ cars<br>= 0.8 Gini=0.29<br>5 —— 45° degree line<br>5 0.7<br>20.6<br>oo<br>20.5<br>i<br>8 0.4<br>8<br>‘6 0.3<br>2<br>£ 0.2<br>7)<br>0.1<br>0 ,<br>0 0.2 0.4 0.6 0.8 1<br>Share of population<br><!-- End of picture text -->

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opportunities within an hour’s travel than in the baseline while the loser only has access to 5% fewer economic opportunities.

There are several conclusions which can be drawn from looking at how alternative aggregate accessibility improving spatial layouts will affect Nairobians and how gains and losses are distributed. First, many more will gain than lose from the spatial reconfiguration: 80% at least. Second, there is however a non-negligible share of losers ranging from 2% to 20% with average shares for pedestrians and matatu riders between 16% and 20%. Third, the average gains in accessibility for the winners are much larger than the average losses for the losers and this is particularly true for pedestrians and matatu riders. Finally, the average accessibility losses for the losers are typically limited for pedestrians and for matatu riders: from 1% to 5% depending on the travel time threshold. So, although the concern that a spatial reconfiguration will create losers should be acknowledged and addressed to the extent possible, we argue that it should not impede the overall goal of providing many more with increased accessibilities.

# **5.4 Localized accessibility: Inclusive vs integrated labor markets?**

In this section the aim is to investigate accessibility improving spatial configurations where the objective is no longer to promote market wide accessibility but more localized accessibility, i.e. to increase the share of people that have access to a significant share of formal economic opportunities. We carry out this exercise for two reasons. First, following up on the previous section and the discussion of winners and losers from land use reorganization, to investigate counterfactual scenarios where the focus is on labor market inclusion (even if partial) rather than on overall efficiency. We wish to understand if the spatial outcomes of these two exercises differ drastically or not. Second, because while their benefits are well documented, the exact criteria of what constitutes large and integrated labor markets is not<sup>13</sup> .

## **5.4.1 Without agglomeration economies**

Instead of investigating the average access to jobs in Nairobi County we are interested in assessing the share of residents that have access to a “reasonable” amount of formal economic opportunities within a given time-frame and how land use patterns can affect this share. As a first step, we define the reasonable amount of economic opportunities to be 10%<sup>14</sup> of the total in Nairobi County. We then, as previously, use a hill climbing procedure to simulate accessibility improving spatial layouts (again keeping the housing stock and transportation network constant). We perform this analysis for all transportation modes, but will focus mainly in this section on the localized accessibility of pedestrians and matatu riders as they are the most likely to benefit from improved local accessibility as the distance they can travel in a given time-frame is much shorter than for car drivers.

> 13 Angel and Blei (2015a) for example claim that what matters for productivity is not metropolitan wide potential access to jobs but to ensure that residents can access their actual job within a reasonable travel time.

> 14 Other thresholds were tested (5% and 20%). The results for the 5% threshold look very similar to those of the 10% threshold. For the 20% threshold however, the urban form is closer to that of the overall accessibility increasing spatial layout.

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<!-- Start of picture text -->
- = 7o 5 10km<br>——<br>EN Share of economic opportunities<br>2 7 accessible to pedestrians<br>ey ( within an hour<br>MB < 10% MB > 10%<br><!-- End of picture text -->

<!-- Start of picture text --> “ = 7o 5 10km<br>——<br> EN Share of economic opportunities<br>= 7 accessible to matatu riders<br>yy within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text --> <!-- Start of picture text --> = 7o 5 10km<br>j ——<br>EN Share of economic opportunities<br>ia 7 accessible to pedestrians<br>\ within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text --> <!-- Start of picture text --> = 7o 5 10km<br>j ——<br> EN Share of economic opportunities<br>La 7 accessible to matatu riders<br>/ \ within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text -->
**Figure 14: Distribution of formal economic opportunities (left) and population (right) in the initial and local accessibility improving spatial layouts for pedestrians or matatu transportation**

It can be seen from Figure 14 that when the objective is to increase the share of residents that have access to a “reasonable” amount of economic opportunities, the resulting spatial patterns (in panel b/ and c/ of Figure 14) display much higher levels of dispersion mostly in economic opportunities but also to a lesser extent in population compared to the highly monocentric and concentrated initial spatial layout. This can be seen from the scattering of green colored cells representing high levels of economic opportunities. Simulations therefore confirm that increasing mixed land uses can improve local accessibility levels to some economic opportunities. This is not surprising as it locally improves the jobhousing balance.

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The question that follows is whether there is a fundamental tradeoff between focusing on inclusive labor markets and the integrated labor market which increases average accessibility to employment investigated in section 5.3. Table 5 provides some relevant results.

|**Share**|**of Nairo**|**bi’s opportu**<br>**mode used**|**nities acces**<br>**in the curr**|**sible wit**<br>**ent and a**<br>|**hin a give**<br>**lternative**<br>|**n time-frame**<br>**spatial layou**<br>|**depend**<br>**ts of Na**|**ing on the**<br>**irobi**<br>|**transport**<br>|
|---|---|---|---|---|---|---|---|---|---|
|||Cars||M|atatus + w|alking||Walking|only|
||**Initial**|**Local**<br>**access**|**Aggregate**<br>**access**|**Initial**|**Local**<br>**access**|**Aggregate**<br>**access**|**Initial**|**Local**<br>**access**|**Aggregate**<br>**access**|
|**<30 mins**|31%|23%|55%|4%|4%|7%|3%|3%|5%|
|**<45 mins**|58%|46%|83%|10%|12%|21%|7%|9%|13%|
|**<60 mins**|77%|71%|96%|20%|17%|42%|11%|11%|21%|

**Table 5: Comparison of average share of accessible formal economic opportunities in the initial spatial layout, in the local accessibility improving spatial layout and in the aggregate accessibility improving spatial layout**

Table 5 shows the average share of accessible jobs in Nairobi County in the initial spatial layout and the two alternatives which have been discussed in this paper: the aggregate accessibility improving and the local accessibility improving spatial layouts. There are two important messages that come out from these numbers. First, the local accessibility improving spatial pattern performs less well on average than the aggregate accessibility improving spatial pattern in connecting people and opportunities for all modes and all time thresholds and for a given transportation network. For the 60 minute time threshold the figures read 21% of accessible opportunities vs 11% for pedestrians, 42% vs 17% for matatus riders and 96% vs 71% for car passengers. Second, with two exceptions (for pedestrians and matatu riders for the 45 minute threshold) the local accessibility improving scenario does not perform better and sometimes worse (for car drivers/passengers for all time thresholds and for matatu riders for the 60 minute threshold) than the initial baseline in providing residents with overall access to opportunities. Nairobi planners and policy makers should therefore be aware of the trade-off that exists between locally promoting, through land use planning decisions, a balance between jobs and housing and a unified and thick countywide labor market. Whereas the first option can provide some measure of access to economic opportunities, it might fail to deliver a strong integrated labor market and hence hinder an efficient pooling of workers and employers which is one channel through which agglomeration economies are achieved.

# **5.4.2 With agglomeration economies**

Here the idea to be investigated is associated with the intuition that planning options which would favor access to a reasonably sized labor market instead of to all opportunities could result in more employment options for many residents but also decrease the possibility of achieving industrial or commercial clusters of a sufficient size to reap the benefits of increasing returns and knowledge spillovers. To represent these agglomeration economies we will associate a bonus to grid cells neighbored by cells with high formal employment opportunity counts. This is done in a simplified manner by calculating the sum of formal economic opportunities within a 3km radius of each pixel and by associating a share of this sum (typically 5% in this exercise) to the pixel. The total amount of jobs in Nairobi County will therefore vary depending on the distribution of employment opportunities in the alternative scenarios. The more clustered the employment areas (the pixels), the higher the absolute amount of economic opportunity in Nairobi. In terms of accessibility, this exercise will tend to favor short travel times to large employment zones. While there are many other ways of representing the 23
agglomeration economies associated with intra-metropolitan area firm clustering<sup>16</sup> and the choice of the parameters can also be discussed (Gaigné, Riou, and Thisse 2013; Lucas and Rossi-Hansberg 2002), this exercise nonetheless captures the idea that employment density and firm clustering are productivity enhancing.

**Figure 15: Distribution of formal economic opportunities (left) and population (right) in the initial and local accessibility improving spatial layouts for pedestrians or matatu transportation building in agglomeration economies**

It can be seen from Figure 15 that the absolute amount of formal economic opportunities changes with the introduction of agglomeration economies with higher maximums and many less cells with no opportunities. However, the conclusions that we reach as to the distribution of households and 16 For example, we could have introduced a distance decay function to weigh the benefits of employment opportunities differently according to the distance to any given pixel.

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economic opportunities in the local accessibility improving scenarios only marginally change compared to the maps presented in section 5.4.1 for local accessibility without agglomeration economies. We can however observe slightly less dispersed employment opportunities and more polycentric patterns as the agglomeration economies favor clustering of economic activity.

Angel and Blei (2015a; 2015b) rightfully claim that self-organization processes ensure that as metropolitan areas grow, they also become more productive in a large part because commuting costs do not increase at the same pace as population nor land consumption. We concur with this analysis and have ourselves provided indications (see section 5.2) that the spatial layout of Nairobi County is the result of a complex self-organization process where accessibility to economic opportunities and commuting costs play a central role in the location of residents and economic opportunities. Angel and Blei demonstrate for U.S. cities that there are three processes which can explain why commuting costs only marginally increase with population: “increases in population density, locational adjustments of residences and workplaces to be within tolerable commute range of each other and increases in commuting speeds brought about by shifts to faster roads and transit systems”.

There are a few differences which we however believe deserve a specific discussion. A main difference between our study and Angel and Blei’s is that we focus on potential accessibility whereas theirs looks at actual pairs of residences and jobs. We believe that focusing on potential accessibility makes sense in the Nairobi context for at least three reasons: 1/ when land markets are imperfect (high transaction costs involved in land buying/selling, unclear land titles...), as is often the case in developing country cities, moving costs for firms or residents are high which indicates that intra-urban locational adjustments to remain within reasonable travel times of jobs may not be possible or not smooth nor costless. Therefore, if and when one workplace moves, there is value in having access to a wider array of employment opportunities within a given time frame to increase the employer/worker selection process. 2/ In cities where unemployment levels are high, the spatial mismatch literature (Gobillon, Selod, and Zenou 2007) shows that spatial isolation from large job pools can diminish employment outcomes of residents in part because geographic segregation and low accessibility increases job search costs. 3/ Part time jobs – whether formal or informal – are widespread in developing country cities and increasingly so in the developed world. Having access to more than one employment opportunity is therefore central.

Angel and Blei also advocate that more transport connections are needed and that there should be a focus on increasing travel speeds. We agree in part with this statement, and Nairobi, suffers crippling congestion costs which need to be addressed. But many developing country cities also face huge challenges in terms of housing, sanitation and access to basic services which are worsened by enormous population influxes with only limited financial capacity to cope. And transportation investments – although necessary – are hugely costly. There is therefore value in investigating options which could increase employment accessibility at lower costs. We believe smart planning which would be attentive to residential access to economic opportunities could provide at least part of the answer.

# **6 Conclusions**

This study has demonstrated that land-use change can substantially contribute to the creation of a unified and deep labor market in Nairobi even in the absence of investments in transportation networks and in the building stock. Our central result is that the share of jobs that can be accessed 25
within an hour doubles from 11% to 21% and from 21% to 42% for pedestrian and matatu users respectively with alternative spatial layouts. Achieving such an outcome is central to reaping benefits from agglomeration economies by allowing matching and sorting between potential employers and workers. The long travel times associated with very high shares of walking and reliance on matatus currently displayed in Nairobi are indeed indications of a fragmented labor market where simple geographic proximity is outweighed by commuting costs. This lack of accessibility has negative consequences for productivity, as argued throughout this paper, but also for livability as they are indications that in order to remain within reasonable travel times of economic opportunities, households may be willing to compromise on housing conditions and access to basic services.

Several other findings emerge from our analysis. First, accessibility is already a central determinant of the locational decisions of households and employers. This is demonstrated by Nairobi’s current spatial layout largely outperforming 10,000 random counterfactuals in terms of aggregate accessibility to employment opportunities. This finding supports urban economic theories, beginning with Alonso (1964), which aim to explain the spatial distribution of people and land rents in urban areas as a tradeoff between the proximity to jobs and larger associated housing costs. Carefully designed plans coordinated with current and future transportation networks can facilitate the search for accessible locations, thereby allowing agglomeration economies to emerge (Rossi-Hansberg 2004). Second, the promotion of spatial layouts that would provide some minimum level of employment accessibility to as many households as possible would lead to small scale local labor markets at the expense of an integrated labor market. We interpret this as a tradeoff between equity and efficiency or in other terms between inclusive and efficient labor markets. However, our simulations also show that winners from spatial reorganizations aimed at increasing employment accessibility at large are far more numerous than losers. Winners’ gains in terms of accessible jobs are also larger than losers’ losses.

This study explicitly links employment accessibility to intra-city distribution of people and activity. As such, it is, to the best of our knowledge, an innovative attempt to tackle accessibility through a land use and planning lens instead of through a more commonly used transportation approach. This methodology suffers from some limitations that we hope future work can address. While we detail all these limitations extensively in the Appendix, we want to discuss the main drawback briefly here. The methodology we employed is a-economic in the sense that it disregards the economic forces which guide the structure of an urban area. In particular, it does not account for centripetal congestion forces which can take the form of traffic jams, higher land and housing costs and which would tend to discourage the concentration of people and activities and the associated increase in accessibility. An avenue for future work would be to incorporate our accessibility approach which focuses on land uses to an economically founded model that explicitly describes household and employers’ locational decisions.

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# **Appendix: Data and methodological issues**

This study relies on a relatively small number of assumptions and simplifications to work around the absence of data in some areas. It also constitutes only a first step towards a more comprehensive tool to integrate land use changes as a policy relevant option to achieve thicker and more efficient labor markets. As such some methodological choices and data related issues deserve to be discussed.

## **Data choices:**

- <u>Imperfect data about number and location of jobs: the amount and location of formal economic</u> opportunities are inferred from a detailed land use map, associating them with the quantity of land used for industrial, commercial and public utility purposes. This is suboptimal as it does not provide exact information about job numbers and location. The correlation between land uses and economic opportunities is loose: it is indeed uneasy to associate number of square meters used for industrial purposes with a number of industrial jobs. The same applies for commercial activities etc... But even accepting the bold idea that job numbers are proportional to number of square meters, the land use map used in this study does not provide information about building heights, only land plot size. Therefore, one storey buildings are considered in the same way as six storey buildings. The strong assumptions that we make are the unfortunate consequence of the lack of data. We will however pursue efforts aimed at identifying or producing more accurate sources for counting and locating formal jobs in the urban area of Nairobi County. As a follow up to this study the team will apply the same methodology to assess accessibilities and their potential enhancements through land use occupation changes in Kampala, Uganda. In Kampala, detailed registries on employment quantities and locations exist and will increase the accuracy of our conclusions.

- <u>Other public or collective transportation options besides matatus:</u> In parallel to the informal minibus system – matatus – discussed in this paper, there exists both a formal bus system and railway commuter service in Nairobi. Buses operate on roughly 67 routes and carry a smaller percentage of the population than matatus (350,000-400,000 passengers per day). With competing companies, bus service is not provided in an efficient manner, maintenance suffers, and profitability margins are low. Contributing to the inefficiencies of the public transport system is the fact that many of these buses travel along the same routes as the matatus, competing for limited space in the already congested CBD (Consulting Engineering Services (India) Private Limited 2010). Also complicating matters is that routes have been optimized to maximize efficiency for the operator and not coordinated to an efficient level in maximizing passenger comfort or convenience. Nairobi was founded as a railway town, and its rail infrastructure remains well connected to the city center. Rift Valley Railways (RVR) offers twice daily service on five commuter rail routes in the Nairobi metropolitan region, averaging 2,300 to 6,900 passengers per route. While commuter rail is an economical mode of public transport, at present it serves only a tiny fraction of the daily commuting needs of the Nairobi general public. The most commonly cited reasons by Nairobians for not utilizing the system are concerns with safety and comfort, along with protracted travel times and difficult access to stations (Consulting Engineering Services (India) Private Limited 2010). Both these transport modes carry only a fraction of daily commuters in Nairobi so their initial omission is not too problematic; in the future however the team will look to integrate these networks into the analysis.

- <u>Monetary costs of the various transportation options: The analysis conducted in this study only</u> considers transport times in determining accessibilities. A more complete analysis would reason in

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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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