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Do fewer people mean fewer cars? Population
decline and car ownership in Germany
by Nolan Ritter (RWI) and Colin Vance (RWI / JUB)
June 19, 2013
Research Questions
◮ What are the determinants of car ownership in privatehouseholds?
◮ What is the future expected level of car ownership in Germanyconsidering socio-economic and socio-demographic factors?
Outline of Analysis
◮ We employ a multinomial logit model to estimate the impactof socio-economic and socio-demographic factors on carownership.
◮ In addition, we project private car ownership levels fordifferent scenarios until 2030.
◮ We contrast our findings with those from an ordered probitbut find that the fit of the multinomial logit better predictsobserved car ownership levels.
◮ Moreover, tests indicate that the parameters for the carownership levels can not be collapsed into a binary variable.
Motivation
◮ Mobility is indispensable for a functioning economy.
◮ Labor and capital can only be combined when both are in thesame place.
◮ Households use a battery of travel modes including publictransport, bicycles, and cars to achieve mobility.
◮ One of the most important travel modes is the individualmotor car.
Motivation
Figure : Performance of travel modes in Germany (BMVBS, 2010)0
200
400
600
800
1000
billi
ons
of p
erso
n ki
lom
eter
s
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
individual motor car traffic airtrain public transport
Motivation
Figure : Population and cars in Germany (BGL, 2007; Destatis, 2012a)
5060
7080
popu
latio
n in
mill
ions
010
2030
4050
mill
ions
of v
ehic
les
1950 1960 1970 1980 1990 2000 2010
cars population
Motivation
Figure : Length of highways and federal roads (BMVBS, 2010)0
2040
60ro
ad n
etw
ork
in 1
,000
km
1950 1960 1970 1980 1990 2000 2010
highway federal roadstotal
Motivation
Figure : CO2 emissions in Germany (BMVBS, 2010)
0.12
0.13
0.14
0.15
0.16
0.17
Sha
re o
f em
issi
ons
from
roa
d tr
affic
200
400
600
800
1000
1200
CO
2 em
issi
ons
in m
illio
n m
etric
tonn
es
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
total emissions emissions from road trafficshare of emissions from road traffic
Motivation
Figure : Road traffic accidents (Destatis, 2012b, 2009; BMVBS, 2010)0
500
1000
1500
2000
2500
road
acc
iden
ts in
1,0
00
1950 1960 1970 1980 1990 2000 2010
total number of accidents with physical injurieswith property damage
Data
Table : Descriptive Statistics Part 1 (N=10,743)
Variable Description Mean Std. Dev. Minimum Maximum
cars number of privatelyowned cars
1.09 0.73 0.00 3.00
household size number of householdmembers
2.12 1.07 1.00 5.00
share 20 to 39 share of members who are20 to 39 years old
0.20 0.33 0.00 1.00
share 40 to 64 share of members who are40 to 64 years old
0.43 0.41 0.00 1.00
share 65+ share of members who are65 and older
0.25 0.41 0.00 1.00
ln(income) logged monthly householdincome in Euros
2186.22 865.95 250.00 4750.00
commute distance commute in km. summedover all household mem-bers
12.66 24.14 0.00 437.00
ln(fuel price) logged fuel price, lagged3-year moving average
1.04 0.11 0.76 1.29
urban 1 if household lives in ur-ban area
0.35 0.48 0.00 1.00
Std. Dev. stands for standard deviation.
Data
Table : Descriptive Statistics Part 2 (N=10,743)
Variable Description Mean Std. Dev. Minimum Maximum
minutes walking minutes to nearestpublic transit stop
5.68 4.84 0.00 85.00
rail 1 if nearest public transitstop is a rail station
0.22 0.42 0.00 1.00
company cars number of company carsin household
0.07 0.28 0.00 3.00
open space share of agricultural andforest area in polygon
0.73 0.24 0.00 0.99
firm density number of companies persquare kilometer in poly-gon
108.71 211.57 0.85 2392.47
insurance vehicle insurance class 6.21 2.92 1.00 12.00
Std. Dev. stands for standard deviation.
Distribution of Observations
Figure : Distribution of Surveyed Households
Legend
indicates polygon with at least one sampled household
Distribution of Observations
Figure : Distribution of Open Space
Legend
0% - 17%
18% - 37%
38% - 50%
51% - 62%
63% - 72%
73% - 80%
81% - 87%
87% - 92%
92% - 95%
96% - 100%
Model Specification
Uim = Vim + ǫim (1)
with Vim = αm + xim · β
P(Vim + ǫim > Vik + ǫik) = P(ǫik − ǫim < Vim −Vik), ∀k 6= m (2)
Assuming the error terms to be identically and independentlydistributed as a log Weibull distribution, the multinomial logitmodel results, with choice probabilities equal to (Long and Freese,2006, p. 228):
P(yi = m) =exp(xi · βm)J∑
j=1
exp(xi · βj)
, (3)
where yi is a discrete variable denoting the number of cars owned.
Regression Results
Table : Multinomial Logit Regression Results (Part 1)
1 vs. 0 Cars (j=1) 2 vs. 0 Cars (j=2) 3+ vs. 0 Cars (j=3) Joint Test
Variable Param. Std. Err. Param. Std. Err. Param. Std. Err. P-Values
household size: 2 0.778∗∗ 0.108 2.332∗∗ 0.166 1.181∗∗ 0.352 0.000household size: 3 1.458∗∗ 0.221 4.168∗∗ 0.271 4.791∗∗ 0.399 0.000household size: 4 1.867∗∗ 0.367 4.600∗∗ 0.427 6.681∗∗ 0.538 0.000household size: 5 1.951∗∗ 0.550 5.214∗∗ 0.604 7.665∗∗ 0.713 0.000share 20 to 39 1.230∗∗ 0.450 4.198∗∗ 0.569 8.698∗∗ 0.767 0.000share 40 to 64 1.427∗∗ 0.433 4.107∗∗ 0.547 9.047∗∗ 0.738 0.000share 65+ 0.782 0.427 2.118∗∗ 0.546 6.631∗∗ 0.782 0.000ln(income) 1.990∗∗ 0.122 4.034∗∗ 0.196 4.956∗∗ 0.450 0.000commute distance 0.005 0.003 0.011∗∗ 0.004 0.013∗∗ 0.004 0.002ln(fuel price) −0.814 2.272 −1.075 3.122 −3.966 5.477 0.910urban −0.423∗∗ 0.144 −0.639∗∗ 0.203 −1.102∗∗ 0.420 0.005minutes 0.044∗∗ 0.012 0.075∗∗ 0.014 0.087∗∗ 0.018 0.000rail −0.275∗∗ 0.101 −0.925∗∗ 0.144 −0.920∗∗ 0.245 0.000
log-likelihood: −8, 200.19
Wald χ2(81): 1, 752.15∗∗
number of observations: 10, 743
Param. stands for parameter, Std. Err. stands for robust standard error. ** (*) indicates significance at the
1% (5%) level.
Regression Results
Table : Multinomial Logit Regression Results (Part 2)
1 vs. 0 Cars (j=1) 2 vs. 0 Cars (j=2) 3+ vs. 0 Cars (j=3) Joint Test
Variable Param. Std. Err. Param. Std. Err. Param. Std. Err. P-Values
company cars −1.897∗∗ 0.135 −3.947∗∗ 0.214 −4.625∗∗ 0.395 0.000firm density 0.000∗ 0.000 −0.001 0.001 −0.002 0.001 0.123open space 1.019∗∗ 0.309 1.862∗∗ 0.448 2.179 0.950 0.000insurance −0.014 0.018 −0.003 0.024 0.012 0.040 0.000dummy for 2000 −0.132 0.133 0.170 0.183 0.378 0.340 0.727dummy for 2001 −0.150 0.310 0.439 0.424 0.959 0.746 0.093dummy for 2002 −0.615 0.433 0.012 0.595 0.225 1.048 0.209dummy for 2003 −0.514 0.471 0.152 0.649 0.782 1.145 0.337dummy for 2004 −0.350 0.482 0.432 0.668 0.808 1.175 0.392dummy for 2005 −0.138 0.518 0.615 0.717 1.366 1.278 0.458dummy for 2006 −0.011 0.657 0.560 0.907 1.340 1.604 0.773dummy for 2007 −0.204 0.743 0.483 1.021 1.633 1.753 0.664dummy for 2008 −0.083 0.850 0.326 1.169 1.272 2.017 0.902dummy for 2009 −0.181 0.756 0.388 1.038 0.941 1.792 0.861intercept −15.287∗∗ 1.111 −37.199∗∗ 1.731 −51.873∗∗ 3.960 0.000
log-likelihood: −8, 200.19
Wald χ2(81): 1, 752.15∗∗
number of observations: 10, 743
Param. stands for parameter, Std. Err. stands for robust standard error. ** (*) indicates significance at the
1% (5%) level.
Observations vs. Predictions
Table : Millions of predicted and observed privately owned cars.
2000 2001 2002 2003 2004 2005 2006 2007
predicted total cars 42.1 42.2 38.9 41.2 42.3 44.7 43.5 43.0observed total cars − 39.1 39.6 39.9 40.3 40.6 41.2 41.6difference in % − 7.9% −0.2% 3.2% 4.9% 9.9% 5.6% 3.4%
As of 2008, the KBA changed its counting procedure to only include privately owned cars that are registered
over the entire year.
Future Population
Figure : Population by Age Cohort (Destatis, 2006)
0
10
20
30
40
50
60
70
80
90
Tot
al P
opul
atio
n in
Mill
ions
1990 2000 2010 2020 2030year
Age Cohort 65+ Age Cohort 40 to 64
Age Cohort 20 to 39 Age Cohort 0 to 19
Future Household Structure
Figure : Households by Size (Destatis, 2006)
0
10
20
30
40
50
Tot
al H
ouse
hold
s in
Mill
ions
1990 2000 2010 2020 2030year
5−Person Households 4−Person Households
3−Person Households 2−Person Households
1−Person Households
Extrapolations
Figure : Simulation Results: Changes in Explanatory Variables
30
35
40
45
50
55
Mill
ions
of C
ars
2007 2009
2000 2010 2020 2030Year
Predicted Car Count Official Car Numbers
Baseline Scenario Constant Income
Increased Income
Extrapolations
Figure : Simulation Results: Changes in Explanatory Variables
30
35
40
45
50
55
Mill
ions
of C
ars
2007 2009
2000 2010 2020 2030Year
Predicted Car Count Official Car Numbers
Baseline Scenario Increase in Urbanization
Fewer and Bigger Households Higher Density
Summary
◮ Data:◮ The estimates are based on data from the German Mobility
Panel (MOP, 2011).
◮ Method:◮ We employ multinomial logit regression.
◮ Main Results:◮ Household income is a major determinant of car ownership.◮ Under constant income, the number of privately owner cars
may drop.◮ Changes in the number of households and in the composition
of households have limited impact on the level of privatelyowned cars.
BGL (2007, April). Fahrzeugbestand Lkw und Pkw imBundesgebiet 1950-2007. Frankfurt am Main: BundesverbandGuterkraftverkehr Logistik und Entsorgung e.V.
BMVBS (2010). Verkehr in Zahlen 2009 / 2010. Berlin: FederalMinistry of Transport, Building and Urban Development.
Destatis (2006). Germany’s population by 2050: Results of the11th coordinated population projection. Wiesbaden: GermanFederal Statistical Office.
Destatis (2009). Verkehrsunfalle - Zweiradunfalle imStrassenverkehr 2008 (Artikelnummer 5462408087004).Wiesbaden: German Federal Statistical Office.
Destatis (2012a). Bevolkerung nach dem Gebietsstand.Wiesbaden: German Federal Statistical Office.
Destatis (2012b). Strassenverkehrsunfalle, Verungluckte.Wiesbaden: German Federal Statistical Office.
Long, S. J. and J. Freese (2006). Regression models for categoricaldependent variables using Stata (2nd ed.). Stata Press.
MOP (2011). German Mobility Panel. Karlsruhe Institute ofTechnology.