1. Public transport fare elasticity in HRT 2014
BEST / Sampo Kantele

2. What is fare/price elasticity
It refers to the responsiveness of demand for tickets to (small) changes in their prices.
"If I lower the price of a product, how much more will sell?"
"If I raise the price of one good, how will that affect sales of this other good?"
"If the market price of a product goes down, how much will that affect the amount that firms will be willing to supply to the market?"
Most basic form is direct elasticity where change in price on given ticket type (product ) affects demand of same ticket type.
When elasticity coefficient is -0.3 (”industry standard”), upcharge of 10% in price will result 3% deduction in demand
In reality, upcharge of given ticket type will affect demand of other ticket types (cross elasticity)
There is also cross elasticity between price change and proportion of different modes of transport

3. Price elasticity cont. Can be/is used in financial planning
Short vs. long run elasticity
Short run can be interpreted as next (budget) year and long run when individual/given change is not affecting any more (~7 years)
In long run one has means to react by moving, change employer, buy car …
Long run elasticities are 2-3 times larger than short run
Factors that influence elasticity
User type (financial means increase ways to adopt)
Type of trip (work vs. recreational)
Region (larger region diminishes elasticity: supply, congestion, parking)
Baseline of price change
Direction of change (price cut is not symmetrical to upcharge)
Timing of change (economy …)
Mode of public transportation (elasticity is lower in rail-based modes ) -

4. Description, methodology and limitations of the study
The aim has been to find out how the small changes in public transport ticket prices affect the ticket demand
The changes in adult ticket prices and its effect to ticket sales in HRT area was studied between
Previous equivalent study was made in 1999
Statistical research method used was a time series model
Dependent variable was yearly amount of tickets sold per capita
Model has been estimated by using ticket sale statistics and price information
Other background information
changes in public transport supply (route km)
changes in the factors that affect to the demand (such as consumer price index, incomes, population growth, employment and unemployment rate)

5. The development of ticket sales amounts in different ticket categories between 2005-2013
During the period considered the population in HRT area has grown approximately 10 %
In 2009 price-relation change was made in internal ticket prices
Sales of the single tickets have decreased significantly
Sales of value tickets have increased
In 2012 the internal ticket prices have been standardized in the same level in all municipalities
Ticket prices were decreasing especially in the municipality of Vantaa
The Travel Card system founded in 2002 (between studies)
Single ticket
Value ticket
Seasonal ticket
Regional
-17,4%
37,2 %
12,3 %
Helsinki internal
-31,2 %
35,2 %
22,8 %
Espoo internal
-38,6 %
41,6 %
43,2 %
Vantaa
internal
-13,9 %
46,4 %
68,7 %
Values above present the change in number of tickets sold

6. Elasticity coefficient and the comparison to the literature review
Short run elasticity means the elasticity value within one (budget) year after the price change
Long run elasticity means the balanced elasticity value several years after the price change (approximately 5 years)
Short run elasticity of the seasonal ticket is quite similar to generally used fare elasticity value -0,3.
The report contains also a literature review of the studies in Great Britain, Australia and in the Nordic countries
In the literature review the short run elasticity is between -0,2 and -0,5
Long run elasticity is significantly bigger
Ticket type
Short run elasticity
Long run elasticity
Seasonal ticket
-0,36
-0,78
Value ticket
-0,32
-0,60
Single ticket
-0,50
-1,40

7. Time series model and elasticity coefficients
Dt = demand of given ticket type per capita at time point t
Pt = price of given ticket at time point t
Tit =independent variable at time point t, i= 1…n
 = estimated coefficient of lag term of demand
0 = estimated coefficient of price variable
1 n are estimated coefficients of dependent variables
 = estimated constant
t = error term of the model at time point t
Price elasticity coefficients
E0 = 0
Short term elasticity of demand
E1 =
*0
Change in demand in year t+1, when price change happened in year t
E2 =
2*0
Change in demand in year t+2, when price change happened in year t 
En =
n*0
Change in demand in year t+n, when price change happened in year t
E =
Long run elasticity of demand

8. To conclude
Elasticity of route kilometers (~service level) is at same level but in opposite direction (-0.36 vs. 0.36)
Additional year or two would have made it easier to estimate parameters
New Zone Model (2016 or later) will “reset the data”
Report can be found: -> Joukkoliikenteenhintajoustotutkimus
Kiitos !!