The dynamics of car ownership in E.U countries

1. The dynamics of car ownership in E.U countries A comparison based on the European Community Household panel with a dynamic probit model Joyce Dargay, Laurent Hivert and Diègo Legros

2. Introduction • Many disaggregated models of household car ownership have been proposed during the past decade. • These developments have been driven by the recognition that aggregated time-series models which have previously dominated automobile demand forecasting were deficient in some aspects. • Specifically they do not capture the causal relationship underlying household behavior thus limiting their accuracy, versatility and policy sensitivity (see Manski et al, 1978 for a review of such models).

3. In contrast with these models, disaggregated models which are formulated at the household level possess the necessary structure to depict the causal mechanisms that drive household behavior. • However, it has been recently acknowledged that disaggregated models based on cross sectional data are subject to their own sets of limitations. They may be flawed because elasticities which are estimated using such data may not be the same as longitudinal elasticities. These latter are related to changes over time for each behavioral unit, thus they may not offer accurate forecasts.

4. The presence of unobserved factors which are correlated with the observed variables will lead to biased estimates, which will in turn produce false elasticities and forecasts. This motivates the use of dynamic models which are based on longitudinal observation of behavioral units. • There are many reasons, as well as behavorial as statistical, to favor such dynamics models (Heckman, 1981; Davies and Pickles, 1985; Goodwin et al, 1987; Goodwin et al., 1989; Kitamura, 1989). • For example, Goodwin and Mogridge (1981) note the « resistance to change » as one of the dynamic aspects of car ownership behavior that previous cross-sectional models have failed to account for.

5. Factors that motivate the use of dynamic models include: • Asymmetry in the magnitude of response (i.e. elasticity may be different as regards the direction of change, say, income increase and decrease). • Asymmetry in the speed of response (i.e. the time lag between the time when a change takes place in the travel environment and the time a response takes place may be different depending on the direction of change). • Influence on behavior of past experiences on our future expectations (brand loyalty) • Effects of temporal changes and trends (increasing the licence ownership).

6. Plan • Presentation of the model structure • Data and regressors • Estimation method • Results • Concluding remarks

7. Dynamic Probit model • The model that we estimate is the following :

8. The error term of this equation can be written : Where is an individual effect that is a time-invariant random component. • is a time and individual specific error term uncorrelated with the regressors and uncorrelated accross individuals but which may be serially correlated.

9. Data and explanatory variables • European Community Household Panel (ECHP) from Eurostat. • For descriptive statistics, see Dargay and Hivert, COST action, Berlin, december, 2005. • These dynamic probit models have been estimated for countries present during the period from 1994-2001: Spain, Portugal, Denmark, Netherland, Belgium, France, Ireland, Italy and Greece. • Some issues in the UK case: the maximisation algorithm did not converged.

11. The choice of the included explanatory variables was limited by the data for the largest number of countries and periods. These variables are the following : • Own a car in (t-1) • Net income • Three or more adults in the household • Single • Children aged less 16 years old • One or more person aged 65 or more • The household reference person is a female • The household reference person is unemployed

12. Estimation method • For each moment of time and for each household, we observe the car ownership : • The probability of observing the sequence for a particular household is : • Whereis the normal density function with mean zero and variance one and with and are defined in this way:

14. We have used the maximum simulated likelihood method in order to estimate this model. • This estimation procedure by simulation replaces the functions which are computationally intractable using numerical or analytical methods, by random approximations (simulators). • The maximum simulated likelihood is a conceptually simple extension of the maximum likelihood estimation: instead of gathering the log-likelihood through analytical or numerical methods, the log-likelihood is simulated and then maximized to obtain maximum simulated estimators of the model parameters.

15. The implementation of simulated maximum likelihood estimation requires a simulator for the probabilities that enter the log-likelihood function. There are many alternatives available for the simulation of multivariate normal rectangle probabilities (Hajivassiliou et al., 1996; Vijverberg, 1997). • In this study, we use Smooth Recursive Conditional simulator (Hyslop, 1999) = identical to the GHK (Geweke-Hajivassiliou-Keane) simulator in the case of a Normal distribution.

16. Results • The most interesting result is the following : once we account for unobserved heterogeneity, the past car ownership strongly influences the current car ownership. • This strong persistence in car ownership is not a persistence in unobserved differences what Heckman (1981) names as a spurious dependance.

17. Results concerning other regressors • As expected, income has a significant positive influence on car availability in all countries. The coefficient ranges from 0.18 in Greece to 75.77 in Ireland. The income shows the tendancy to have a greater influence in the lower-income countries suggesting that the income elasticity declines with increasing income, but the evidence is not clear cut. • The impact of the number of adults in the household changes between countries. We note a positive and significant impact of this variable in Belgium (0.22), Portugal (0.10), Italy (0.13) and Denmark (0.43).

18. To be single has a significant negative effect in all countries. • The effects of having children on car availability are far less clear cut. The coefficient is not statistically different from zero at the 5% level in 4 of the 9 countries. This variable seem to have no effect in Belgium, Netherland, France and Ireland. In the remaining countries, Spain, Portugal, Italy, Denmark and Greece, it is positive. It is unclear why the effect of having children should differ among countries.

19. In all countries, households composed of only of one or 2 over 65 persons are significantly less likely to have cars. • Being a woman as head of household reduces the car availability in all countries. The fact that households with female head are less likely to have car then those with male head (controlling for income differences and children) is puzzling and requires further investigation. • When the household person reference is unemployed has no effect on the car ownership except in Belgium and in Ireland.

20. Conluding remarks • Investigate the puzzling results. • To solve the non-convergence of the maximisation algorithm for UK. • To explain some national specificities. • To split the income variable into 2components :the permanent income and the transitory income. • To make the income endogenous.