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.