Labor market accessibility and
unemployment
Therese Norman Maria Börjesson, Christer Anderstig
Purpose
• How do increased labor market accessibility due to transport
investments induce changes in the unemployment rate?
Mechanisms/Hypothesis
• Generosity of welfare system; income taxation increases
unemployment.
• Transport system can correct for this distortion (Ihlanfeldt &
Sjoquist, 1998; Brueckner & Martin, 1997; Brueckner & Zenou,
2003)
• Reservation wages (Åslund, Öst & Zenou; 2010)
• Search costs (Mortensen, 1987; Isacsson, 2003; Duranton &
Puga, 2004; Pilegaard & Fosgerau, 2008; Smith & Zenou, 2003;
Stoll, 1998)
Lower transport costs
-> may reduce commuting cost, hence decrease unemployment
Data
ΔAT
A
1985
1993
ΔAE
1997 ΔE
2002
•
A: Initial job accessibility (1985)
•
•
ΔAT: Changes in job accessibility due to changes in the transport
system (85-97) (changes in generalized travel costs)
ΔE: Employment rate change (93-02)
•
ΔAE: Changes in job accessibility due to employment changes (93-02)
Data
Grouped data of individuals based on (34503 segments):
•
•
•
•
•
Municipality of residence
Gender
Age (6 categories)
Education (4 categories)
Native (Swedish, Nordic, Non-nordic)
Dependent variable:
Model
3
𝐸𝑖,𝑟
=
exp(𝑢)
,
1+exp(𝑢)
𝑢
1
0−2 2
= 𝛼 + β1 𝐸𝑖,𝑟
+ 𝛽2 ln 𝐴0𝑟 + 𝛽3 Δ𝐴0−2
+
𝛽
Δ𝐴
+ 𝛽5 Δ𝐴1−3
4
𝑇,𝑟
𝑇,𝑟
𝐸,𝑟 + β6 𝐴𝑔𝑒𝑖,𝑟 + 𝛽7 𝑀𝑎𝑙𝑒𝑖,𝑟 + 𝛽8−10 𝐸𝑑𝑢𝑖,𝑟
+ 𝛽11−12 𝑂𝑟𝑖𝑔𝑖𝑛𝑖,𝑟 + 𝛽13 Δ𝐿𝑆_𝑠𝑒𝑔𝑖,𝑟 + β15 ∆𝐿𝑆_𝑎𝑔𝑔𝑟 + β15 𝐶𝑖𝑡𝑦𝑟 + 𝑒𝑖,𝑟
Ei,r = Employment rate in group i and municipality r
Ar = Initial accessibility
AT,r = Accessibility change due to transport system
AE,r = Accessibility change due to employment
Accessibility
• 𝐴0𝑟 =
1
𝑠 𝐸𝑠 𝑒𝑥 𝑝
• 𝑐𝑟𝑠 =
𝑜∈𝑟
𝑑∈𝑠
𝑜∈𝑟
0
𝜌𝑐𝑟𝑠
𝑚 𝑇𝑜𝑑𝑚 𝑐𝑜𝑑𝑚
𝑑∈𝑠
𝑚 𝑇𝑜𝑑𝑚
•  is a negative sensitivity parameter
• Accessibility change due to changes in transport
•
Δ𝐴0−2
𝑇,𝑟
=
1
𝑠 𝐸𝑠
1
𝑠 𝐸𝑠
2
𝑒𝑥𝑝 𝜌𝑐𝑟𝑠
0
𝑒𝑥𝑝 𝜌𝑐𝑟𝑠
,
• Accessibility change due to changes in employment
•
Δ𝐴1−3
𝐸,𝑟
=
1
𝑠 𝐸𝑠
3
𝑠 𝐸𝑠
0
𝑒𝑥𝑝 𝜌𝑐𝑟𝑠
0
𝑒𝑥𝑝 𝜌𝑐𝑟𝑠
,
Data
LMR
DEPENDENT VAR.
MEAN
Small
MIN
Large
MAX
MIN
Small
MAX
Large
0.95
0.95
0
1
0
1
𝑨𝟎𝒓
8.57
10.47
7.00
9.28
9.28
12.78
𝚫𝑨𝟎−𝟐
𝑻,𝒓
1.30
1.09
0.85
5.57
0.46
3.75
𝚫𝑨𝟏−𝟑
𝑬,𝒓
1.01
1.08
0.84
1.23
0.86
1.17
∆𝑷𝒐𝒑𝒓
-0.06
0.03
-0.19
0.30
-0.13
0.30
0.88
0.89
0
1
0
1
𝑬𝟑𝒊,𝒓
MUNICIPAL EXPLANATORY VAR.
SEGMENTAL EXPLANATORY VAR.
𝑬𝟏𝒊,𝒓
Employment
Job accessibility
All
municipalities
Small LMR
Large LMR
(1)
(3)
(4)
0.165***
0.020
0.302***
(0.007)
(0.021)
(0.010)
∆AT
0.358***
0.248***
0.344***
(0.038)
(0.050)
(0.073)
∆AT2
-0.062***
-0.048***
-0.061***
(0.008)
(0.010)
(0.018)
0.707***
1.235***
0.437**
(0.128)
(0.209)
(0.182)
Indep. Var.
lnA0
Improvements of the
transport system
increases the
employment rate with an
elasticity of 0.01.
∆AE
D_City
∆LS_agg
-0.507***
(0.017)
(0.020)
-0.739***
3.020***
-1.423***
(0.119)
(0.251)
(0.141)
ΔLS_seg
-1.621***
0.111
-2.854***
(0.152)
(0.296)
(0.214)
E93
3.012***
2.736***
3.026***
(0.051)
(0.083)
(0.066)
-0.018*
-0.045**
-0.012
(0.010)
(0.020)
(0.012)
-0.115***
-0.173***
-0.100***
(0.004)
(0.008)
(0.005)
0.207***
0.129***
0.221***
(0.013)
(0.026)
(0.015)
0.468***
0.596***
0.455***
(0.017)
(0.043)
(0.020)
0.744***
0.923***
0.722***
(0.019)
(0.055)
(0.021)
-0.809***
-0.635***
-0.829***
(0.014)
(0.039)
(0.017)
-0.265***
-0.436***
-0.250***
(0.024)
(0.040)
(0.030)
0.165***
0.020
0.302***
(0.007)
(0.021)
(0.010)
29,869
13,478
16,391
0.228
0.243
0.225
D_Male
Age
Diminishing returns
-0.409***
D_Education2
D_Education3
D_Education4
D_Non-nordic
D_Nordic
Constant
Observations
AIC
Segmented on education level
Education level
Elasticity of E
w.r.t. AT
Primary
0.013
Secondary
0.010
Higher level: ≤ 3 years
0.010
Higher level: ≥ 4 years
0.005
Education level proxy for wage prospects
Reservation wages more important for low income groups .
Methodological issues
• Reflecting the transport system by using detailed data:
-The accessibility measures is taken directly from a transport model, taking
into account commuting behavior and perceived generalized costs of all travel
modes in use. This also makes it compatible with measures used in standard
cost-benefit analyses.
-Reducing endogeneity:
-The change in job accessibility from a previous period is estimated rather
than the level.
-The accessibility measures are divided into two variables in order to pinpoint
the accessibility change induced by (i) changes in the transport system rather
than (ii) changes in the number of jobs.
-Controlling for changes in the socio-economic composition of the population
by use of disaggregated data.
Case study “Åtgärdsplanen 2010-2020”
Conclusions
• Improvements in job accessibility due to changes
in the transport system have a positive impact on
employment level, elasticity 0.01
• The impact increases with lower education level
(income level)
• Consistent with theories on reservation wages