1. Evaluating the Impacts of the Dar es Salaam BRT System
Team
Ronald Lwakatare, John Shauri, Ami Srivastava (DART)
Gharad Bryan, Clare Balboni, Adam Cole (London School of Economics)
Melanie Morten, Chris Becker (Stanford University)
Yonas Mchomvu, Bilal Siddiqi, Cecilia Escalante (World Bank)
Rachel Steinacher, Jessica Mahoney, Nicolas Lusingu (Innovations for Poverty Action)

2. Dar es Salaam Bus Rapid Transit System
Phase 1 of (6 phase) Dar es Salaam BRT
Opened May 2016
21km of bus ways, 5 terminals, 2 depots, 27 bus stations
Connects high-income CBD with middle/low-income residential areas
First operational BRT in East Africa

3. We would like to know: does the Dar BRT…
Reduce travel times?
Create jobs and boost income?
Encourage firm productivity, trade and growth?
Improve property values?
Make roads safer?
Increase the health, wealth and happiness of Dar residents – especially the poorest?

4. Evaluation approach aims to…
…estimate the impacts of BRT Phase 1
Understand how the BRT has affected the lives and welfare of residents
As well as the impacts on urban neighborhoods, development and growth
… and understand the mechanisms through which impacts happen
e.g. through greater mobility? Relocation of people/firms?
…while laying the groundwork to evaluate future phases
Phases 2, 3 planned in the near future, 4-6 later on
Don’t want to reinvent the wheel for future evaluations!
…and providing information relevant to for other contexts!
Should inform BRTs planned in other African cities, elsewhere in the world

5. Combination of methods used to achieve this
Spatial “triple-differences” approach
Compare places (i) near and far from Phase 1 with places (ii) near and far from planned Phase 2, (iii) before and after Phase 1.
Mechanism experiments (randomized controlled trials)
Temporary fare subsidies to encourage (some) commuters to use BRT – compare to control group to understand impacts of using the BRT
“Spatially conditional” cash transfers to encourage (some) residents to stay where they are, or relocate – to understand BRT’s impacts on relocation and urban development
Structural modeling to estimate general equilibrium impacts
Mechanism experiments will produce key elasticities for model
Allow findings to be applied to other contexts

6. What we have been up to so far
Jan 2016: Baseline surveys
Travel time survey
Household survey
May 2016: BRT Phase 1 Opens
Mar 2017: Tracking baseline sample
Household tracking survey
Mobile survey enrolment
May 2017 (onwards): Mobile survey
SMS- and call-based
Semi-monthly to panel of respondents

7. Spatial triple differences approach
We are trying to understand if the BRT caused changes in outcomes of interest
Want to make sure that changes were not caused by other things
If BRT went to neighborhoods that were richer, we would see more jobs, higher income – but not caused by BRT!
If rich people moved away from BRT areas, we would see less income – but not caused by BRT either!
Need a comparison (“control”) group
Find locations and individuals that look just like those affected by BRT
Compare “treatment” and “control” groups – just like a medical experiment!

8. Spatial triple differences approach
Compare places (i) near and far from Phase 1 with places (ii) near and far from planned Phase 2, (iii) before and after Phase 1.
Difference 1: Compare A* to B* = ∆
Difference 2: Compare ∆ to difference between A’ and B’
Difference 3: Compare these differences before and after Phase 1

9. Baseline data collection (Jan 2016)
Dense geographical sampling strategy ensures coverage inside and outside catchment areas of Phase 1
Surveyed 1749 households from sampled neighborhoods
Data can be used as baseline for impact evaluation of future phases

10. Examples of baseline data (Jan 2016)

11. Baseline sample tracking and mobile surveys
Very early results from first tracking attempt
Baseline sample: 1749 households, 2 respondents/HH
Through phone/tracking, so far reached and enrolled 1315 individual respondents in monthly mobile surveys
Of these, 902 one of the baseline survey respondents
Asked 3 simple questions
Going forward, hope to get (almost) full sample…
Full follow-up survey of baseline respondents Sep 2017
We expect results to change

12. Who is riding the BRT?
Of 902 tracked baseline respondents, 32% rode the BRT last week
Socioeconomic status:
Basic Needs Poverty Line: 24% below vs 34% above
Unemployed: 32%
Informal sector workers: 31%
Age % vs %
Female 30% vs Male: 33%
Private vehicle users 44%, vs Daladala 35%, Walk: 31%

13. Changes in hours worked
Scale
Red/orange: hours worked went up
Blue/green: hours worked went down
No conclusive relationship (yet) between BRT Phase 1 and hours worked
But suggests focusing on time-use
If people save time from commuting, what do they use the time for?

14. What we plan to do next! Follow-up survey of baseline sample
Estimate short-term (18 month) impacts
Experiments to understand mechanisms of change – to develop this week
Experiment 1: Fare subsidies
Objectives: (i) to measure direct effects of riding the BRT and (ii) to understand access issues
Experiment 2: “Spatially Conditional” Cash Transfers
Objective: to understand relocation and urban development due to BRT

15. Experiment 2: “Spatially Conditional” Cash Transfers

16. What we plan to do – for the next 3 years!
July-Aug 2017 – Qualitative study of urban housing market
To inform the feasibility and design of housing RCT
Sep 2017 – Follow-up survey 1
Reinterview all baseline respondents
Oct-Dec 2017 – Analysis
First set of spatial triple-diff results
Presented and disseminated
Jan-Feb 2018 – Housing RCT launch
Sample enrolment and baseline survey
Disbursement of first set of cash transfers
Oct 2018 – Follow-up survey 2
Will provide 2-year impacts
Nov-Dec 2018 – Fare RCT launch
Sample enrolment and baseline survey
Disbursement of 6-month smartcards
Jan 2019 – Housing RCT endline
Feb–Jun 2019 – Analysis
Second set of spatial triple-diff results
Results from housing RCT
Presented and disseminated
July 2019 – Fare RCT endline
Aug 2019-Jun 2020 – Analysis
Results from fare RCT
Results from structural modeling exercise
Final results presented and disseminated