Targeted Subsidies

in Urban transport The experience thus far

OBA Webinar Series April 24, 2014

Shomik Mehndiratta & Camila Rodriguez

and next steps

with Output-Based Aid

Presentation Outline

• Sector Challenges Need for finding a balance between social inclusive

subsidy schemes and guarantee cost-recovery of public

transport systems

• Experiences with targeted subsidy schemes Bogota, Colombia: Pro-poor targeted subsidy scheme for

SITP (GPOBA financed)

Analytical work in Argentina

• GPOBA Global Study on OBA in Urban

Transport 2

Output Based Aid: core concepts

• Pro-poor targeting: to create incentives for service providers to

provide service access to the poor

• Accountability: performance and financial risk is transferred to

service providers

subsidies are paid on attainment of measurable outputs.

• Innovation: Service providers have an incentive to be innovative

and identify appropriate solutions to achieve outputs & trigger

subsidy payments

• Efficiency: per capita subsidy costs are kept within acceptable

limits – lower cost achieved through competition

• Sustainability: Ideally subsidy is one-off subsidy, cost recovery

is achieved through tariff collection

• Output verification and monitoring: independent verification of

agreed outputs by a third party is needed

3

Targeted subsidies

Examples in urban transport

http://blogs.worldbank.org/transport/will-you-take-me-1000-pesos-making-

sure-public-transport-subsidies-really-target-poor

Cost recovery vs sustainability

• US FTA experience with operating subsidies

Impact on productive efficiency – unsustainable

Neither poor nor service quality benefited

• Buenos Aires fares frozen in 2002

Impact on service quality – unsustainable

Most of the subsidy benefits the middle class

• Bogota, Brazilian cities

Cost recovery

Unaffordable for the poor – particularly the informal sector

• London experience

Competitive concessions to pick operators

Financing from congestion pricing

Improvements in service quality – mode choice

5

What makes for a ‘good’ subsidy scheme?

Efficiency

Productive

- cost per unit of

output ($/pkm)

Administrative

- Cost of

implementaton

Sustainability

Impact on service

-Quality, quantity of

service

-Subsequent system

impacts

Impact on

government

-Overall fiscal impact

-Control over impact

Can not isolate one from others. Well designed

scheme reflects appropriate balance.

Meeting policy goals

- How much of the

target group is getting

served?

- How much of the

subsidy is ‘wasted’

outside target group

Effectiveness

Early experience with targeting

Productive

Efficiency

Effectiveness at

targeting poor

Sustainability

User groups;

elderly, students

No impact Convenient more

than accurate

Does government

pay?

Brazil vale

transport for

employees

Reduces cost

discipline for

operators – users

don’t have stake

in costs

Self-selects poor

employees with

6% threshold BUT

informal workers

left out

Some reselling.

Labor tax on poor

employees

USA TransitChek No impact All employees –

not poor but mode

shift

Government

forgoes tax

revenue

Pereira “free

morning”

No impact Self-selected Low fiscal impact

Chile fuel subsidy No impact Appropriate but

not public

transport

No impact

7

Moving towards targeted subsidies

• Characterizing the travel needs of the

poor

• Who and how much?

Building on existing social programs

“Affordability indices”

• Evaluating alternative schemes

Effectiveness at targeting poor

Operational and financial impacts

• Implementation issues

8

Process

9

Understanding Beneficiary

Travel Patterns

• Use existing databases to identify and target low-income households.

• Survey current transport users as well as beneficiaries currently not using the system.

Indentifying Who to

Subsidize and How Much

• Use the affordability index and other tools to guide political decisions about who to fund and how much.

Comparing Alternative

Subsidy Schemes

• Compare in- and exclusion errors; share of subsidy reaching intended beneficiaries; Fare Affordability Index levels and other measures; and aggregate financial impact.

Considering System

Conditions and Financial

Implications

• Estimate impacts on system conditions, overal ridership, revenues, and operating costs.

Planning for Implementatio

n

• Minimize leaking and abuse by using smartcards, ICT tools, and adminstrative processes.

• Consider user and operator incentives.

24.0%

20.1%

4.5%6.4%

39.4%

5.6%11.7%

36.1%

1.2%5.8%

38.2%

6.9%

Automobile

On foot

Subway

Railway

Bus

Other

Poor Non-Poor

. Modal Split by Income Quintile in the Metropolitan Area of Buenos Aires

(Trips in Percent)

Notes: “Other” comprises trips by taxis, motorcycles, bicycles, private/school buses and charters. Poor are households in income quintile 1. Non-poor are households in other income quintiles. A trip on foot implies that the person walks more than ten (10) blocks. Fuentes: CIPPEC based on ENMODO (2010).

Around two fifths of the trips by the poor (income quintile 1) are made by bus. Another 36% of the poor-trips are on foot. Only around 6% and 1% of the poor-trips are made by railway and subway, respectively.

Characterizing travel patterns of the poor

11

85% of the trips of the poor (income quintile 1) are made by bus and only around 13% and 3% by train and subway, respectively. Use of buses and railways decreases with income, whereas the use of the subway increases with income quintile.

Public Transport Modal Split (Legs) by Income Quintile in the Metropolitan Area of BA

84.5% 81.7% 80.5% 77.5%72.9%

12.8%12.8% 13.2%

13.0%12.1%

2.7% 5.5% 6.3% 9.6%15.0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Bus Railway SubwayPoor

Characterizing travel patterns of the poor

• Trip frequency increases with income quintile. • Around a third of the poor (income quintile 1) make only one or

two round trips per week. 12

Trips per Week by Income Quintile (%)

23.0% 22.5% 21.3% 19.9% 18.2%

5.3% 4.9% 4.8% 5.8% 5.9%

6.4% 6.0% 8.5% 8.1% 7.3%

2.6% 2.5% 3.2% 3.6% 4.6%

52.3% 52.7% 50.7% 52.2% 55.3%

10.3% 11.5% 11.5% 10.4% 8.8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

6 a week

5 a week

4 a week

3 a week

2 a week

1 a week

Poor

Characterizing travel patterns of the poor

13 13

Fuentes: CIPPEC sobre la base de ENMODO (2010).

Social Program Beneficiaries in Buenos Aires

16,2%

3,6% 5,1%13,9%

1,9%

0,4% 2,1%

1,8%

6,1%

4,7%5,0%

5,8%

5,6%

2,8%6,6%

5,5%

1,4%

0,5%0,9%

1,3%

68,8%

88,0%80,4%

71,7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Bus Subte Tren Total

Estudiantes Beneficiarios de Plan Social Jubilados

Empleados domésticos Discapacitados Otros

Who? Build on other social programs

• Bogota: SISBEN

• Buenos Aires: All social program beneficiaries

(a) Average household (b) Quintile 1 8.4%

7.5%

6.1%

4.8%

3.8%

3.3%3.6%

2.8%

2.2%2.4%

3.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

43.1%

34.6%

27.3%

21.7%

16.6%

13.9%15.0%

11.6%

8.6% 8.6%

11.2%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

14

For the typical household in BA, expenditure in public transport as a share of total income

decreased from 8.4% in 2003 to 2.4% in 2012; and,

For the average household in income quintile 1, expenditure in public transport as a

share of total income fell from 43.1% in 2003 to 11.2% in 2013.

Buenos Aires: Affordability of Public Transport, 2003-2013

How much? Affordability index

• Actual use vs Basket of trips

• Complementarity of transport and land markets

15

Promedio Quintil 1

1 Sao Paulo 727.7 72.8 78.1 10.7% 107.3%

2 Rio de Janeiro 1,193.8 119.4 75.2 6.3% 63.0%

3 Brasilia 1,082.1 108.2 64.1 5.9% 59.2%

4 Cape Town 1,204.3 120.4 45.5 3.8% 37.8%

5 Buenos Aires (2004) 552.1 102.7 33.8 6.1% 32.9%

6 Mumbai 715.4 293.3 67.3 9.4% 23.0%

7 Kuala Lumpur 1,529.3 336.4 73.0 4.8% 21.7%

8 Mexico City 818.3 126.8 23.6 2.9% 18.6%

9 Chennai 309.8 127.0 23.6 7.6% 18.6%

10 Manila 813.1 219.5 37.8 4.6% 17.2%

11 Krakow 1,298.3 473.9 78.4 6.0% 16.5%

12 Amsterdam 2,347.5 856.8 136.0 5.8% 15.9%

13 Moscow 1,346.2 329.8 50.8 3.8% 15.4%

14 Guangzhou 763.8 229.1 33.1 4.3% 14.4%

15 Buenos Aires (2010) 930.2 218.6 24.9 2.7% 11.4%

16 Warsaw 2,168.7 791.6 85.5 3.9% 10.8%

17 New York 4,311.6 1,164.1 120.0 2.8% 10.3%

18 Los Angeles 3,540.3 955.9 96.0 2.7% 10.0%

19 Chicago 4,025.0 1,086.8 108.0 2.7% 9.9%

20 Singapore 3,233.1 808.3 78.2 2.4% 9.7%

21 Beijing 1,198.3 359.5 33.1 2.8% 9.2%

22 Seoul 1,398.7 559.5 51.3 3.7% 9.2%

23 Buenos Aires (2012) 1,101.0 277.0 18.8 1.7% 6.8%

24 Shanghai 1,734.5 520.4 33.1 1.9% 6.4%

25 Cairo 593.1 255.0 15.7 2.6% 6.1%

26 Budapest 1,842.2 921.1 53.6 2.9% 5.8%

27 London 4,421.4 1,348.5 69.8 1.6% 5.2%

28 Prague 2,729.8 1,419.5 52.8 1.9% 3.7%

29 Bangkok 1,698.8 526.6 19.3 1.1% 3.7%

Índice de accesibilidad Ingreso mensual

per cápita

U$PPP

Ingreso mensual

per cápita Quintil 1

U$PPP

Gasto mensual en

transporte

público

Ciudad

Affordability index allows benchmarking

16

Buenos Aires

proposed Bogota adopted

Errors of exclusion (% intended beneficiaries left out) 13.6% 68%

Errores of inclusion (% beneficiaries that are unintened) 19.8% 0%

Share of total subsidy obtained by the poor 35.1% 100%

PT Affordability index for the poor 9.5% 7.1%

PT Affordability index for the population as a whole 6.4% NA

Subsidy as share of total operator revenue 1.6% 5.6%

Evaluating alternative schemes

Effectiveness

Notes: [1] For Buenos Aires, the proposed fare structure reflects an increase in the general fare with a reduced fare for card-holders

of identified ‘categories’ (elderly, students, household workers, unemployed). The poor /intended beneficiaries are defined as the

poorest quintile of households; [2] In Bogota, the proposed fare structure reflects the structure actually implemented by Bogota (see

Box 2). The intended beneficiaries are households with a SISBEN score below 100. For budget reasons, however, the subsidy has

been extended only to a section of this population with SISBEN scores below 40,excluding those who have a car, are younger than

16 years old and have another type of public transit subsidy (disabled or old age). SISBEN is recognized as only an approximate

measure to identify potential beneficiaries of social programs in Bogota and is being continuously and incrementally improved.

Indicator was only calculated for trips taken in zonal buses of the SITP, does not include trips in trunk corridors. With proposed

subsidy, indicator decreased from 8.2% to 7.1%.

Bogota: Impact of 30% Fare Discount

on SITP’s Revenues & Costs

6%

5%

4%

3%

2%

1%

0 Net

Impact 2015+

4.84%

Cost Km

0.76%

Cost Fleet

0.05%

New Revenue Increase

0.82% ~110k trips

Revenue

Decrease

4.85% ~650k trips

% SITP Revenues

Operational & financial impacts

• How many of the

existing trips are

made by the

beneficiaries?

• How many new

trips will the

scheme crowd in?

Evaluating alternative schemes

Targeted Subsidy Implementation

Registration Delivery Usage

Biometrics

Means-tested

• Online

• By phone

• Customer Service

Desk

• Courier Service

• Verification of users identity:

• Drivers revision

• Supervision staff

• Distinctive set of lights

• Cards including photo ID

• Time restriction of 75 minutes in

the same station.

• Subsidy locked in non-used cards.

• Cards differently colored.

• Algorithms analysis to detect

suspicious patterns of use

• Biometric identification and authentication systems, seem to offer a promising tool to

support governments deploy targeted subsidies efficiently.

• Aracaju (Brazil). The city compiled information on biometric characteristics

(fingerprints) of users that could benefit from discounted fares (elderly, students,…), and

then installed biometric validation in buses/stations. The total costs of adapting the

system were US$3,100 per bus/station. After two years, 91% of users succeed

fingerprint validation on their first try, and of the 3% validation failures recorded, 2.6% are

fraud attempts and 0.4% is due to recognition problems.

Roll-Out in Bogota

19 Source: Webpage explaining the transit subsidy for SISBEN 1 and 2 beneficiaries.

http://www.sitp.gov.co/publicaciones/beneficios_de_transporte_para_personas_sisbenizadas_pub

• Feb 2014 roll-out

• Eligibility:

• Beneficiaries, defined as

“SISBEN 1 and 2 users” (score

<40 points) can receive a public

transit subsidy equal to a 40%

discounted fare capped at 21

trips per month.

• Registered in the SISBEN

database.

• Hold a public transit smartcard

with sufficient credit on the card.

• To obtain the card, beneficiaries can

register online or visit a city service

center. Once their ID is validated

against the SISBEN database, the

beneficiary receives the smartcard in

person at a city center in about three

business days.

Experience so far in Bogota

20

• Still very early on but as of March: o Estimated total beneficiary population ≈750,000 (SISBEN 1 and 2

users, except those who have a car, are younger than 16 years old,

and have other subsidies such as old age, disabled)

o 32 city centers have been enabled with personnel and computers to

register potential beneficiaries

o 9,102 appointments had been scheduled to register for the subsidy,

2,079 cards actually distributed, and 10,649 subsidized trips

effectively used (March 23, 2014 Transmilenio)

• Next steps: work with Bogota to design and launch an

impact evaluation of the subsidy policy.

• Blog: http://blogs.worldbank.org/transport/will-you-take-me-1000-pesos-

making-sure-public-transport-subsidies-really-target-poor

Output Based Aid: core concepts

• Pro-poor targeting: to create incentives for service providers to

provide service access to the poor

• Accountability: performance and financial risk is transferred to

service providers

subsidies are paid on attainment of measurable outputs.

• Innovation: Service providers have an incentive to be innovative

and identify appropriate solutions to achieve outputs & trigger

subsidy payments

• Efficiency: per capita subsidy costs are kept within acceptable

limits – lower cost achieved through competition

• Sustainability: Ideally subsidy is one-off subsidy, cost recovery

is achieved through tariff collection

• Output verification and monitoring: independent verification of

agreed outputs by a third party is needed

21

Next Steps: Global Study

Objectives:

• Define OBA in urban transport

• Identify needed conditions to implement OBA

in urban transport

different level of development of urban transport

systems

• Identify range of OBA schemes that could be

implemented

e.g. ongoing direct-user subsidy transitional

subsidies for introducing new schedules or routes

22

Next Steps: Global Study

Objectives:

• Identify cities in GPOBA priority countries to

develop OBA schemes (that could benefit from

GPOBA support)

• Proposed TA activities to develop OBA

schemes

Priority countries mostly in Sub-Saharan Africa and

South Asia: India, Nepal, Bangladesh, Thailand, Pakistan,

Afghanistan, Tajikistan, Kyrgyzstan, Syria, Occupied

Palestinian Territories, Yemen, Somalia, Ethiopia, Kenya,

Sudan, South Sudan, Uganda, Rwanda, Tanzania, Democratic

Republic of Congo, Zambia, Malawi, Zimbabwe, Mozambique,

South Africa, Nigeria, Ghana, Liberia, Sierra Leone.

23

Next Steps: Mexico

• Diagnostics of existing subsidy

schemes

• Empirical data collection on Metro

• Targeted subsidy as part of a

package of measures

24

Next Steps: OBA in Urban

Transport - priority countries

• OBA as a spur for broader reform

for formalization of public transport

• Other options

OBA in informal systems?

Targeted concessions – feeder

services?

Concession contracts with OBA

features – demand risk for the poor

Interventions in land-use markets

25

Conclusions

• OBA has potential to create efficient

mechanisms that focus subsidy to those

who need it most

• Targeted subsidy for the poor is one step

‘social infrastructure’ provides a base for

mechanism design

‘smartcard’ technologies can help monitoring

• More pilots and innovation still needed

Informal systems

Addressing OBA concerns of sustainability &

risk

26