Heckle and Chide:

Empowering matatu passengers to enforce

better driving behavior in Kenya

James HabyarimanaGeorgetown University

and

William JackGeorgetown University

Motivation• “Accidents happen!” he says, with a cheerful

shrug. "Maniacs? Maybe we are a little bit - but you've got to drive fast to get the money!”– A matatu driver in Kenya

• “Taxi drivers put money first and passengers' and pedestrians' lives second”– Patrick Ayumu, Ghana

Objectives of the project

• Evaluate a randomized intervention aimed at reducing matatu accidents by

– empowering passengers to….

– .…enforce better driving behavior

• Using evocative messages placed inside the matatu

WHY road safety?

• Major cause of injury and death– Rising share in global deaths

• Economically costly – 2% of national income in Kenya– Vulnerable population: working age (15-44)

accounts for 75% of RT fatalities (Odero (2003))

Source: Mathers and Loncar (2006)

WHY matatus ?

• They account for a large share of inter-city passenger transport– Vulnerable population in road traffic injuries

• They are involved in 20% of recorded crashes– But larger share of injuries/fatalities

• They are well suited to our intervention

WHY so many crashes?

• Road conditions

• Vehicle conditions

• Behavior of other road users

• Behavior of matatu drivers Focus ofstudy

WHO can affect driver behaviour?

Matatudrivers

Owners /Operators

Government(incl. police)

Passengers

Focus ofstudy

HOW do we empower passengers?

• Tell them to speak up!

• “Heckle and Chide”

• Insert stickers with messages inside matatus

WHICH stickers: heckle and chide imperatives?

WHICH stickers: The soft touch?

WHICH stickers: Shock therapy?

Sticker Placement Plan

Side door

Front ofmatatu

Driver’sseatAjaliFoot

Leg

Sit

Vibaya

HOW do we evaluate impact of the intervention?

• RCT– compare randomly selected matatus with

stickers to a control group of matatus without stickers

• Outcome measures– Crash rates

• Associated injuries/fatalities

– Survey results of passenger and driver behavior

Motivating the intervention

• Are accident rates efficient?– Collective action problems inside matatus

• If not, what is the role of regulation?– Enforcement problems in public regulation

• Stickers could either:– increase perceived benefit of action – if people

underestimate the effects of accidents; or– reduce the cost of taking action

• stickers legitimize heckling• Focal point for passenger action

Matatu-land, Nairobi

Recruitment

A challenging research environment

Outcome variable

Timeline

August 2007

March 2008

May 2008

Recruitment

Pilot recruitment • Weekly Raffles

• Accident Data Collection• Follow up surveys• Trip observations

January 2000

Data• Sample of 2,276 matatus from 21 SACCOs*

– 6 SACCOs account for about 50% of the sample• account for 166-312 vehicles

• 40% of sample had been assigned during pilot phase– Random assignment from SACCO lists to

treatment status: p=0.625

• 60% new matatus– assignment based on last digit of plate number– Odd Treatment– Even Control* Savings and Credit Cooperatives

03

06

09

01

20

# M

ata

tus

Control Treatment

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

Compliance to assignment rule: New Matatus

Consent and Compliance

• Informed consent obtained from drivers

• Consent from owners very difficult

• Better compliance in pilot sampleShares of matatus that at least one sticker inserted

Assignment Entire Sample Old Sample New Sample

Control (no stickers) 0.16 0.06 0.22Treatment (stickers) 0.84 0.86 0.82Total 0.52 0.52 0.52

Partial compliancePercent receiving each treatment

Number of Stickers Control Treatment

0 84.4 16.1

1 0.3 3.6

2 0.2 3.1

3 0.5 8.0

4 0.1 0.7

5 14.5 68.5

Sample BalanceCovariates Control Treatment Difference

Significant

Owns cell phone0.89

(0.01)0.91

(0.01)No

Odometer reading356,506(7,236)

[327,365]

361,386(6,350)

[343,602]No

Capacity (passengers)14.52(0.05)

14.52(0.05)

No

Uses tout0.45

(0.02)0.48

(0.01)No

Number of weekly trips20.19(0.36)[14]

19.60(0.30)[14]

No

Average daily distance

420.48(6.14)[400]

414.10(5.33)[400]

No

Has speed governor1.00

(0.00)1.00

(0.00)No

Share owned by large Cooperative

0.49(0.02)

0.51(0.01)

No

Involved in accident in last 12 months

0.004(0.002)

0.015(0.004)

Yes

Selected Covariates COMPLIANT NON-COMPLIANTControl Treatment Control Treatment

Owns a cell phone 0.87(0.01)

0.92(0.01)

0.97(0.01)

0.84(0.03)

Odometer Reading 352773.86(7884.45)

[321587.00]

361055.90(6909.05)

[339677.50]

377001.21(18188.49)[401230]

363190.79(16168.82)[361696.5]

Passenger Capacity 14.54(0.05)

14.49(0.05)

14.43(0.13)

14.67(0.09)

Proportion use tout 0.45(0.02)

0.50(0.02)

0.40(0.04)

0.39(0.04)

Age, years 2.29(0.10)

2.78(0.10)

3.00(0.27)

2.67(0.23)

Number of weekly trips 20.22(0.40)[14]

19.72(0.32)[14]

20.00(0.87)[14]

18.95(0.74)[14]

Average daily distance, km

421.07(6.41)[400]

415.35(5.86)[400]

417.32(18.62)[400]

407.50(12.78)[400]

Proportion large Saccos 0.46(0.02)

0.51(0.02)

0.65(0.04)

0.51(0.04)

Proportion had accident in last 12 mths

0.005(0.002)

0.015(0.004)

0.00 0.011(0.008)

What drives selection into actual treatment?

Outcome data: accidents• Main outcome of interest is accidents

– Accident occurrence

– Severity - # injured, killed per accident

• Collected data from two sources

– Insurance companies• All vehicles are required to have minimal coverage• In theory all accidents should be observable – submission

of claims endogenous

– Our own data collection efforts

Other outcome data• Survey data from drivers and passengers to

assess behavior of both

– Safety of drivers

– Heckling and chiding by passengers

• Direct observation of driver behavior

– Send anonymous passengers on matatu trips?

Empirical Specification

• Difference-in-differences strategy to estimate – Parallel trends assumption

• Main concern is that treatment status is potentially endogenous

• Estimate intent-to-treat parameter

• Use assignment to treatment as instrument– IV estimates

Actual Treat status

Before(2007)

After(2008)

Difference

Control .045(.007)

.041(.006)

-.004(.009)

Treatment .057(.006)

.025(.005)

-.032(.007)

Difference .012(.009)

-.016(.007)*

-0.028(.012)*

Average treatment effect

Standard errors in parentheses, + significant at 10%; * significant at 5%; ** significant at 1%

Assignment Before After Difference

Control .045(.007)

.040(.006)

-.005(.008)

Treatment .057(.007)

.026(.005)

-.031(.008)

Difference .012(.01)

-.014(.008)+

-.026(.011)*

Intent-to-Treat Estimator

Standard errors in parentheses, + significant at 10%; * significant at 5%; ** significant at 1%

Actual Treatment Intent-to-Treat Instrumental Variables

(1) (2) (3) (4) (5) (6)

Post -0.005 -0.004 -0.005 -0.004 0.001 0.002(0.588) (0.662) (0.580) (0.654) (0.928) (0.840)

Treatment status 0.011 0.015 0.012 0.013 0.018 0.020(0.227) (0.116) (0.189) (0.155) (0.189) (0.161)

Post * Treatment -0.027 -0.028 -0.026 -0.027 -0.038 -0.040(0.026)* (0.021)* (0.032)* (0.025)* (0.032)* (0.025)*

Constant 0.045 0.034 0.045 0.034 0.042 0.031(0.000)** (0.001)** (0.000)** (0.001)** (0.000)** (0.007)**

Management Controls

X X X

Observations 4322 4318 4322 4318 4322 4318R-squared 0.003 0.013 0.003 0.013 0.003 0.013

Average Treatment Effects: LPM

P-values in parentheses, + significant at 10%; * significant at 5%; ** significant at 1%

05

10

15

20

Eve

nts

per

10

00

mata

tus

-4 -3 -2 -1 0 1 2 3Quarter since recruitment

Treatment Control

Quartely Events

Results so far

Claims rate

(% p.a.)

% Change over

baseline

Accidents avoided

Deaths/ injuries avoided

Baseline 5.1

Sticker effect

Average treatment effect -2.7 -53% ? ?

ITT /Reduced form -2.6 -51% ? ?

IV -3.8 -75% ? ?

Next Steps

• Examine data on possible mechanisms

• Collect more detailed claims data from insurance companies– Includes data on injuries – Types of events being affected by intervention

• Direct observation