Commuting preferences: habit formation or sorting?

Transcription

1 Commuting preferences: habit formation or sorting? Jack Zagha Hop Technical University of Denmark, Bygningstorvet Building 116 Vest, 2800 Kgs. Lyngby, Denmark Ismir Mulalic Technical University of Denmark, Bygningstorvet Building 116 Vest, 2800 Kgs. Lyngby, Denmark Jos Van Ommeren VU University, FEWEB, De Boelelaan, 1081 HV Amsterdam, the Netherlands Keywords: commuting, commuting preferences, habit formation, residential sorting. Abstract The theory of habit formation states that people s perceptions and judgments can be affected by their earlier experiences. Related to commuting preferences, it assumes that the commuting environment that someone observes will have an effect on his/her commuting choices. At analyzing people that have moved to another residence, using Danish register based data from 2001 to 2010; we find that, for Danish movers, there are no signs of habit formation. Furthermore, the individual preferences for commuting, represented by the previous movers commuting distance in the area of origin, have a positive and significant, effect in the commuting choices in the new area. In other words, people that preferred to commute longer in their past residence area will also choose longer commutes in the new one regardless of their previous commuting environment. 1. Introduction The theory of habit formation assumes that the environment to which someone is exposed has an effect on his individual preferences. This concept is based on a term coined by psychologists known as contrast effects, which states that people s perceptions and judgments can be affected by their earlier experiences. In the field of economics, the work examining such preferences is very scarce (Rozen, 2010). This paper finds that although past consumption has an impact in consumption choices, its influence fades over time. 1

2 The relationship between the theory of habit formation and location choices, and more specifically commuting behaviour, has been studied by Simonsohn (2006). At analysing the commuting behaviour of people that moved between two different cities, he shows that the average commute length in the city of origin has, at least temporarily, a positive effect on the movers chosen commute in the city of destination. This result can be interpreted as habit formation. That is, the movers developed a higher tolerance for longer commutes by living in a city where longer commutes are more likely to happen. An alternative explanation to Simonsohn s results is that movers originally residing in cities with large commuting distances experience a smaller reduction in utility of a longer commuting distance compared to non-movers. That is, some (unobserved) heterogeneity in preferences could be leading to the observed higher commutes in the movers' cities of destination (compared to the non-movers), so his finding can be fully explained by (residential) sorting. In other words, movers present different commuting choices than not movers, differentiating themselves from the rest of the population. When analysing Danish data, this heterogeneity can be observed: movers show different individual preferences for commuting, in average, they commute 20% longer distances compared to the general population. They are also younger and have less children in average compared to those of the general population. The main objective of this paper is to identify the effect of habit formation on commuting preferences and more specifically, the choice of commuting distances by controlling for this heterogeneity. In order to intend identifying the existence of habit formation, we use Danish registerbased micro data from 2001 to We identify individuals that moved residence with a minimum distance move of 50 kilometres. This selection guarantees that the movers included in the study face sufficient variation in their new commuting environment to contrast their habits in their new city. Using the individual movers commuting length in the area of destination as a dependent variable, we estimate the effect of our two variables of interest on the movers commuting choices. The first, representing habit formation is the average commuting length in the area of origin. The second is the individual commuting distance in the city of origin that is the ex-ante 2

3 commuting preferences. We will see that the effect of the average commuting distance in the area of origin is small and not significant, that is, there are no signs of habit formation once that we control for individual characteristics. Furthermore, the individual preferences for commuting, represented by the previous commuting distances in the area of origin, have a positive effect in the commuting choices in the new area. In other words, people that preferred to commute longer in their area of origin will also choose longer commutes in the new area regardless of their previous commuting environment. The remainder of this paper is organized as follows. The relevant literature is briefly discussed in Section 2. Our empirical approach is explained in Section 3. The data is described in Section 4. We will discuss the main results in Section 5, and Section 6 concludes. 2. Background literature Recent literature on urban economics states that household location choices are affected by the accessibility to employment opportunities, but also by the accessibility to urban amenities (Brueckner et al., 1999; Glaeser et al., 2001). The value that each household attaches to these factors depends on the level of education, income, age, marital status, etc. Households sort among their cities according to their wealth, social characteristics and commuting preferences. This heterogeneity of characteristics and preferences influences the diversity that can be observed between the neighbourhoods of a city. In order to develop and evaluate public policies with consistent estimations of the welfare implications, it is utmost important to clearly understand the sources of preference heterogeneity (Kuminoff et al., 2012)). As mentioned above, an important factor in the sorting process is the commuting preferences. An attempt to understand the utility (or disutility) of commuting lengths was made by Simonsohn (2006). His argument is strongly based on a term coined in the field of psychology called the theory of habit formation. This theory assumes that the environment to which someone is exposed has an effect on his individual preferences. Until recently, in the field of economics, the work examining the habit formation theory is very scarce, and although it has contributed on the knowledge of economic behavior there is no clear consensus on how to formulate it (Rozen, 2010). Related to commuting preferences, Simonsohn (2006) analyses 3

4 the commuting behaviour of people that moved between different cities in the U.S. He argues that the disutility derived from previous observed commutes (measured by the average commuting length in a city) will have an effect on current commuting choices. In other words, a longer observed average commute distance in a city will induce a higher tolerance (or acceptance) of its citizens for longer commutes. We believe that an alternative explanation to the results above is that the heterogeneity in preferences could be the cause of the longer commutes in the movers' cities of destination. The main objective of this paper is to identify the effect of habit formation on commuting preferences and more specifically, the choice of commuting distances by controlling for this heterogeneity to shed more light on the sorting process around the cities. 3. Empirical specification In our analysis, we follow the model presented by Simonsohn (2006). We assume a reducedform specification of commuting distance for a worker i in city (of destination) c at year t: logd i,c,t = β 0 + β 1 D k c,t 1 + β 2 D c,t + β 3 D i,c,t 1 + β 4 D i,c,t 2 + β 5 X i,t + Y t + u i,t,c (1) where D k c,t 1 denotes average commuting distance in origin, β 2 D c,t denotes the average commuting distance in the destination, and D i,c,t 1 and D i,c,t 2 denotes the previous individual commuting choices or commuting consumption in the previous two years respectively before the move. Furthermore, X i,t denotes exogenous time-varying controls for worker characteristics, Y t controls for the year of the move, and u i,t,c is the overall error term. The coefficients β are unknown, and our main interest are β 1, β 2, β 3 and β 4. The first coefficient of interest, β 1, estimates the effect of habit formation on commuting choices. We expect that, if there is indeed a habit formation effect, the coefficient must be positive and significant. In other words, if the mover observed longer commutes in his/her area of origin, (s)he will develop a higher tolerance, and will opt, for longer commutes. β 2 must have a positive effect as well, and we expect that this coefficient has a large impact on the movers commuting distance as it describes the current commuting environment (in an area with longer commutes, the 4

5 mover will commute longer and vice versa). Finally, β 3 and β 4 identify the individual preferences for commuting, i.e. the previous commuting distances in the past two years respectively, so we expect that they have a positive effect on the current commuting choice. A main concern of our model is in relationship with the data selection. Our use of a sample of households who move residence in the period of observation may suffer from selection bias (unobserved residential sorting). Below, we will explicitly address this issue by estimating a Heckman selection model (Heckman, 1978). In this model, we used the presence of children between the ages of 7 to 17 as an instrument for the people that moved residence. We expect for this instrument to have a significant impact on relocation decision (because of moving social costs, i.e. school, friends, etc.). On the other side, it is expected that the instrument will not have an effect on commuting distance after we control for number/presence of children. Estimating the model (1) raises other concerns. First, the model might present endogeneity issues due to time-invariant unobserved worker characteristics. For example, the productivity level may affect wage and commuting distance. In line with standard arguments, we can address this by using an individual fix effect estimator. Second, it can be argued that income and commuting distance are mutually affected; therefore another endogeneity issue might arise. This can be solved with the use of instrumental variables. We will explicitly explore these issues by estimating instrumental variable and fixed effects models. 4. Data description Our analysis is focused on Danish register-based micro data from 2001 to We have selected full time workers between the ages of 20 to 65. The objective of this selection is to analyse the people that make choices on their commuting behaviour. Moreover, we have restricted the data based on commuting distance in order to avoid outliers or people that do not commute at all. That is, our sample consists on people that commute more than 0 and less than 50 kilometres every day to their workplaces. Due to the large amount of observations (around 1.5 million people fulfil the characteristics mentioned above) we have selected a 30% random sample from our data set for the estimations. We ran the estimations several times 5

6 with different random samples and the results appear to be insensible to this selection. After doing this, we count with 1.8 million observations in total, meaning 181,342 full time workers per year. As mentioned above, the objective of this study is to analyse the commuting behaviour of people that moved residence in their new commuting environment in order to contrast their commuting habits in their new city. We have identified the movers based on changes on their residence from one year to another. It is important to notice that a person can move to another residence in the same area and thus, face the same commuting environment as before. This leaves us with the option of identifying movers between zones 1 and the 98 municipalities. Both options allow us to study the commuting choices of movers confronting more diverse environments. The disadvantage of these specifications is a border effect, that is, people that leave in the border of a zone or municipality can be more influenced by the commuting environment of their neighbour entities. One way to cope with this issue is to calculate the distance between one geographical zone/municipality with another, and restrict the study to people that moved at least a certain distance from their zone/municipality of origin. 2 The geographical delimitation of municipalities is too aggregate to apply this solution being that Denmark only counts with 98 municipalities in its territory and the observations will decrease drastically. Therefore the study will be conducted on people that moved between different zones. The total territory of Denmark is divided in around 900 zones and they present sufficient variation between them in commuting environments. Table 1 shows the number of movers from residences, zones and municipalities. In total, we count with 120,000 people that changed residence during 2001 to 2010, representing 6.6% of the sample. It is important to mention that the absolute numbers cannot be interpreted as the real amount because these are based on a 30% random sample of all full time workers in 1 We use more than 900 zones designed for the purpose of detailed traffic modelling. 2 Because of privacy regulations in the Danish register-based data, we cannot identify the distance between house addresses. 6

7 Denmark. However, the percentage of movers (6.6%) can give us a clear view of the housing market frigidness in this country. More on our interest, there are 77,399 people that moved between different zones during the period of study. It can be observed that the yearly amount has been decreasing, from 5.9% of the sample to 2.9%, more evidence that the housing market in Denmark is very static. Table 1. Number of movers. Year Moved residence % of sample Moved between zones % of sample Moved between municipalities % of sample , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Total 120, , , Total observations: 1,813,420. Total observations per year: 181,342. The register-based data includes information on daily commuting distances, as well as household income, gender, age, and other socio-economic variables of interest. Table 2 presents the summary statistics of these variables, making the distinction between movers, not movers and the total sample. The differences in characteristics between movers and not movers are evident. It can be observed that movers are 5 years younger in average than the people that didn t move. Moreover, they have fewer children and more of them are single households (34% of movers are single compared to 18% of not movers). Another difference arises in the household income, in which the movers earn significantly less in average than not movers. This might be related to the differences in average age between both groups. 7

8 Table 2. Summary statistics. Not movers Movers of zone ( ) All observations Mean S.D. Mean S.D. Mean S.D. Commuting (Km) Household income (DKK) 421, , , , , ,602 Male Age Number of children Single households City size Total observations 1,736,021 77,399 1,813,420 Notes: City size variable distinguishes between the city of Copenhagen, areas with population of over 100,000, areas with population 50,000-99,999, 20,000-49,999, 10,000-19,999, 5,000-9,999, 1,000-4,999, , , , and inhabitants. Finally, the heterogeneity between the two groups is also present in commuting choices. In their city of origin, movers commuted in average 1.5 kilometres more than the people that stayed in the same residence in the following year. In other words, it can be argued that movers present a bigger ex-ante preference for longer commutes. This difference can be better observed in Figure 1 where we show the distribution of commuting distances for movers and not movers. We can see that movers tend to choose longer commutes as the histogram for this group is more steered towards longer distances in comparison with not movers. Figure 1. Difference in commuting distances between movers and non-movers. Not-movers Movers Density Commuting distance (Km) 8

9 As mentioned above, we have computed the distance between zones in order to identify the movers that faced a different commuting environment once they moved. For this to happen, the movers will have to switch jobs on their new zone of residence. Table 3 presents the average commuting distance and for several groups of movers depending on how further away they moved. Figure 2 presents graphically this relationship between commuting choices and the distance of the move. It seems that the people that moved less than 50 kilometres away from their previous residence maintained their same work place. This is explained by the similarity in commuting distances for people that moved less than 20 kilometres and the very high commuting distance for the people that moved between 20 and 50 kilometres (close to 20 km in average). This means they moved further away from their workplaces and now they commute longer distances in order to keep the same job. The distribution of people that moved 50 to 100 kilometres present a more complex story. Even though they present a very high average commuting distance of almost 23 kilometres the standard deviation is around 16. This can be explained by the U-shape of the distribution in Figure 2. It can be observed that part of the movers kept the same job and had to commute even longer. However, the smaller commutes in the distribution shows that part of them have changed it and found a new workplace that is closer to their new residence. This means that, in average, with a move of at least 50 kilometres away, some people will start to face a totally different commuting environment. The movers from 100 and more kilometres away from their previous residence confirm this argument as they only commute 12 kilometres in average. In the next section this distinction will become clear. Table 3. Commuting distances per year (km). Mean commuting distance Standard deviation Number of observations Not-movers ,736,021 Movers up to 20 km ,939 Movers from 20 to 50 km ,033 Movers from 50 to 100 km ,201 Movers from 100 to 150 km Movers from 150 km and more ,378 9

10 Figure 2. Distribution of commuting distances. Movers 0_20 km Movers 20_50 km Movers >150 km Density Movers 100_150 km Movers 50_100 km Non-movers Commuting distance (km) 4.1 Distance decay and average commuting distance In order to identify the habit formation in commuting choices we have to compute an average commuting distance that truly represents the commuting environment which people are exposed to. We have argued that utilizing geographical specifications can be misleading as people that live in the borders can be more influenced by their neighbor entities than by its own. Therefore, we have computed the average commuting distances delimitated by the area of influence based on a distant decay function. This average commuting distance is a weighted average of the commuting environment in which the closest neighbors have a bigger influence (or weight) than the ones that are far away and it decreases up to a distance of 50 kilometers. In other words, the habit formation can be developed by watching their neighbor s behavior. We believe that this measure is more accurate than the average commuting in the whole city as it is far less aggregated and closer to reality as the commuting environments can vary significantly from one neighborhood to another in the same city being the commuting choices more influenced by the people living nearby. Therefore, the weighted average commuting distance in the year of the move will be our explanatory variable for habit formation. 10

11 The average commuting distance has been compiled using the observed commuting distance (D) for each zone applying the following equation: D n = D m e δd n,m m where D is the commuting distance, n is the zone index, d is the distance from zone n to zone m, and δ is a parameter. We set δ to 0.05 in order that D approaches zero at the maximum observed commuting distance for the workers in our sample. Figure 3 presents the distribution of the average commuting distance variable. It can be observed that it presents sufficient variation for an explanatory variable. Figure 3. Distribution of average commuting distances..5 0 Density Average commuting distance (Km) 5. Empirical results 5.1 Standard analysis In this section, we present the results from our main estimations. In order to identify the effect of habit formation on commuting choices, we proceeded as follows. As mentioned before, we have selected the people that moved from one address to another and identified the distance from the zones of both addresses. This, in order to measure how far (and how different) the commuting behaviour in the mover s destination will be. Table 4 presents the results for the 11

12 people that have moved at least 50 kilometres away from their zone of origin. The dependent variable in all specifications is commuting distance measured in kilometres. In all specifications we have controlled by individual characteristics of the movers which are the number of children and a dummy variable that states if the head of the household is single. We included other control variables as the size of the city of destination and a year dummy to control for temporal effects. Apart from the control variables, Specification [1] includes the household income as an explanatory variable. The sign is positive and significant as expected. This means that people with higher income prefer to live further away from the city centre (main concentration of jobs) and attach more utility to living space that is cheaper in the suburbs. In average, an increase of 1% in the yearly income will lead to an increase of roughly 2% in the commuting distance. Also with Danish data, Gutierrez-i-Puigarnau et al. (2014) finds that the mean commuting distance increase by income quartile. 3 Specification [2] includes the average commuting distance in the zone of destination (measured in log). This variable identifies the commuting behaviour of the mover s zone of destination as it is a weighted average of the new neighbours commuting distances. Expectedly, this variable has an important effect on the individual commuting distance. The positive sign simply states that if someone moved to an area of more frequent long commutes, he/she will opt for longer commutes as well. Specification [3] includes the habit formation variable that is measured by the weighted average commuting distance in the zone of origin. That is, the old neighbours commuting behaviour. The theory of habit formation states that the past experiences will have an effect on the future choices. However, it can be observed that this variable is not significant in all specifications. This means that, related to commuting choices, the movers do not develop a higher (or lower) tolerance to commuting due to their past commuting environment but rather, as soon as they move to their destination, they adapt to the new one. Specifications [4] and [5] include the individual preferences for commuting distances, measured by the individual 3 However, they that the causal effect of households income on worker commuting distance is negative (an increase of 1% in the household s disposable income will lead to a decrease of approximately 1% in the commuting distance). 12

13 commuting distance one year and two years before moving to the commuting environment respectively. The positive and significant coefficients of both variables imply that movers do bring their individual preferences for commuting to their new faced commuting environment. For example, the coefficient for the own commuting distance one year before moving (t-1) in specification [5] infers that a 1% increase in the mover s preferred commuting distance before he/she moves will also increase the current commuting distance by 1%. This suggests that movers bring their own individual preferences on commuting but not their past environment. In other words, the commuting choices are made by heterogeneous people with different preferences and these are not affected by past experiences once that the estimations are controlled by these individual commuting preferences. The control variables (number of children, marital status and city size) are significant in all specifications. It is interesting that the coefficient of city size of destination is positive; meaning that if a mover is moving to a smaller city, she will commute longer. This confirms that movers indeed present different commuting preferences. In specification [6] we show a Heckman selection model in which we used the presence of children between the ages of 7 to 17 as an instrument for the people that moved 50 kilometres or more away from the previous residence. This variable appears to be a good instrument as it is significantly different from zero with a t-statistic of and a negative coefficient (as expected) of It can be observed that the results are very stable, proving that our estimations do not suffer from a sample selection bias. Table 4. OLS with movers 50 km away from their previous residence. Variable [1] Base model Household income (in log) Average commuting in destination (in log) Avg. Commute in origin (in log) [2] Adds Avg. Commute in destination 0.213** 0.220** (0.059) (0.058) ** (0.539) [3] Adds Avg. Commute in origin 0.214** (0.058) 4.628** (0.541) (0.590) [4] Adds own commute t ** (0.058) 4.505** (0.533) (0.583) [5] Adds own commute t ** (0.062) 3.992** (0.587) (0.635) [6] Heckman selection model 0.215** (0.063) 3.822** (0.813) (0.684) 13

14 Own commute t-1 (in log) Own commute t-2 (in log) ** (0.013) ** (0.023) 0.087** (0.024) 0.103** (0.024) 0.086** (0.024) Number of children ** ** ** ** ** ** (0.019) (0.019) (0.019) (0.019) (0.020) (0.027) Single ** ** ** ** ** ** (0.058) (0.058) (0.058) (0.057) (0.062) (0.078) City size of destination 0.070** (0.004) 0.078** (0.004) 0.078** (0.004) 0.074** (0.004) 0.076** (0.005) 0.079** (0.011) Year dummy Included Included Included Included Included Included Mills ratio (0.345) Number of observations 4,941 4,941 4,940 4,940 4,085 4,085 Notes: Dependent variable: Commuting distance (in log). ** and * indicate that estimates are significantly different from zero at the 0.05 and the 0.10 level, respectively. Standard errors are presented in parentheses. 5.2 Sensitivity analysis In this section we provide several robustness checks for our results. In order to provide more insights about the distance of the move and habit formation, the first robustness check deals different relocation distances. The second robustness check adds an individual fix effect estimator in order to solve endogeneity issues due to time-invariant unobserved worker characteristics. Next, we deal with the endogeneity between income and commuting distance with the use of instrumental variables and finally, we add car ownership into our model The relocation distance In order to examine the robustness of the results, we have estimated specification [5] of the model for different distances which people have moved. 4 Table 5 presents the results of our main specification for movers of residence regardless of the distance they have moved, as well as of people that moved more than 20, 50, 100 and 150 kilometres away from their previous zone of residence. These results are arranged in columns (a) to (e) respectively. The results appear to be stable regarding the distance specification. It can be observed that the habit 4 We also estimated several specifications with different control variables and the results were very robust. 14

15 formation variable (the weighted average commuting distance in the area of origin) is significant in columns (a) and (b). This can be explained by the fact these are short distance movers, and their area of origin most likely is overlapped with their area of destination. Remember that the average commuting distances were computed to a distance decay of 50 kilometres. This is why, as we increase the distance, and both areas now represent two different commuting environments, the coefficients for habit formation in columns (c), (d) and (e) are not significant. This means, no signs of habit formation. Another interesting result is that the coefficients for individual preferences (at least for one year before the move) are significant in all distance specifications. Finally, all the control variables are similar for all distance specifications. Table 5. OLS with movers from residence for different distances (only specification 5). Variable Household income (in log) Average commuting in destination (in log) Avg. Commute in origin (in log) (a) Regardless of distance 0.250** (0.013) 1.047** (0.314) 2.733** (0.313) Own commute t-1 (in log) 0.202** (0.005) Own commute t-2 (in log) 0.109** (0.005) Number of children ** (0.004) Single ** (0.012) City size 0.072** (0.001) (b) more than 20 km 0.305** (0.029) 3.073** (0.384) 1.648** (0.390) 0.144** (0.011) 0.089** (0.011) ** (0.009) ** (0.028) 0.063** (0.002) (c) more than 50 km 0.212** (0.062) 3.992** (0.587) (0.635) 0.101** (0.023) 0.087** (0.024) ** (0.020) ** (0.062) 0.076** (0.005) (d) more than 100 km 0.131* (0.075) 2.598** (0.685) (0.760) 0.058* (0.032) 0.033* (0.024) (0.026) ** ( ** (0.006) (e) More than 150 km (0.084) 2.748** (0.749) (0.870) 0.079** (0.038) (0.038) (0.031) * (0.087) 0.072** (0.007) Year dummy Included Included Included Included Included Number of observations 59,536 16,404 4,085 2,393 1,755 Notes: Dependent variable: Commuting distance (in log). ** and * indicate that estimates are significantly different from zero at the 0.05 and the 0.10 level, respectively. Standard errors are presented in parentheses. 15

16 5.2.2 Individual fixed effects As mentioned in the empirical specification, the model might present endogeneity issues due to time-invariant unobserved worker characteristics. For example, the productivity level may affect wage and the commuting distance. In line with standard arguments, we can address this by using an individual fix effect estimator. Table 6 presents the results for all the specifications using individual fixed effects. It can be observed, that even by controlling for the unobserved individual characteristics, there are still no signs of habit formation. Moreover, the rest of the variables are very stable with this specification. Table 6. Individual fixed effects with movers 50 km away from their previous residence. Variable (1)Base model (2) Adds Avg. Commute in destination Household income (in log) 0.227** 0.220** Average commuting in destination (in log) Avg. Commute in origin (in log) (0.059) (0.058) ** (0.539) (3) Adds Avg. Commute in origin 0.221** (0.058) 4.608** (0.541) (0.591) (4) Adds own commute t ** (0.058) 4.488** (0.539) (0.585) Own commute t-1 (in log) ** (0.013) Own commute t-2 (in log) (5) Adds own commute t ** (0.062) 3.972** (0.586) (0.636) 0.099** (0.023) 0.087** (0.024) ** (0.020) ** Number of children ** ** ** ** (0.019) (0.019) (0.019) (0.019) Single ** ** ** ** (0.058) (0.058) (0.058) (0.057) (0.062) City size 0.069** 0.078** 0.078** 0.074** 0.076** (0.004) (0.004) (0.004) (0.004) (0.005) Year dummy Included Included Included Included Included Individual fixed effect Included Included Included Included Included Number of observations 4,941 4,941 4,940 4,940 4,085 Notes: Dependent variable: Commuting distance (in log). ** and * indicate that estimates are significantly different from zero at the 0.05 and the 0.10 level, respectively. Standard errors are presented in parentheses Income and Commuting distance endogeneity It can be argued that income and commuting distance are mutually affected; therefore another endogeneity issue might arise. A common method of dealing with this endogeneity is the use of 16

17 IVs estimation. This approach is not easy to apply as it is difficult to find suitable instruments for the household income, as emphasized in Gutierrez-i-Puigarnau et al. (2014). The instrument we propose in this study is the years of working experience. It is expected as the working experience affects the income positively but has no effect on the chosen commuting distance. Table 7 shows the results for the IV estimation using the years of experience per worker as an instrument for the household income. Again, the results appear to be very stable and the results do not present signs of habit formation. Table 7. IV estimation with movers 50 km away from their previous residence. Variable (1)Base model (2) Adds Avg. Commute in destination Household income (in log) 0.876** 0.907** --Instrumented-- Average commuting in destination (in log) Avg. Commute in origin (in log) (0.355) (0.353) ** (0.548) (3) Adds Avg. Commute in origin 0.927** (0.353) 4.538** (0.551) (0.599) (4) Adds own commute t * (0.353) 4.452** (0.538) (0.588) (5) Adds own commute t ** (0.452) 3.903** (0.598) (0.646) Own commute t-1 (in log) ** (0.014) Own commute t-2 (in log) ** (0.024) 0.084** (0.024) ** (0.035) Number of children ** ** ** ** (0.030) (0.030) (0.030) (0.030) Single (0.238) (0.236) (0.237) (0.236) (0.299) City size 0.072** 0.081** 0.081** 0.076** 0.079** (0.005) (0.005) (0.005) (0.005) (0.005) Year dummy Included Included Included Included Included Number of observations 4,941 4,941 4,940 4,940 4,085 Notes: Dependent variable: Commuting distance (in log). ** and * indicate that estimates are significantly different from zero at the 0.05 and the 0.10 level, respectively. Standard errors are presented in parentheses Car ownership Finally, we have included car ownership in our preferred specification in order to verify the robustness of the results; Table 8 shows the results. Column 1 and column 2 present the results of specification 5 without and with car ownership respectively. It can be observed 17

18 that car ownership has a positive impact on commuting distance; however, the other coefficients remain very stable and habit formation is still not present. Table 8. OLS with control for car ownership (only specification 5). Variable [5] Without car ownership Household income (in log) 0.212** (0.062) 3.992** Average commuting in destination (in log) (0.587) Avg. Commute in origin (in log) (0.635) Own commute t-1 (in log) 0.101** (0.023) Own commute t-2 (in log) 0.087** (0.024) Car ownership [5] With car ownership 0.236** (0.069) 4.131** (0.630) (0.708) 0.079** (0.026) 0.108** (0.027) 0.192** (0.042) ** (0.022) Number of children ** (0.020) Single ** (0.062) (0.069) City size 0.076** 0.076** (0.005) (0.005) Year dummy Included Included Number of observations 4,085 3,351 5 Notes: Dependent variable: Commuting distance (in log). ** and * indicate that estimates are significantly different from zero at the 0.05 and the 0.10 level, respectively. Standard errors are presented in parentheses. 6. Conclusions With the objective of verifying the existence of habit formation in commuting choices, we identified individuals that moved residence with a minimum distance of 50 kilometres. These movers were forced to face a new commuting environment and make new commuting choices in their new residence area. Using the individual movers commuting distance in the area of destination as a dependent variable, we estimated the effect of habit formation (measured as the average commuting distance in the area of origin) and the individual preferences for commuting (measured as the movers previous commuting distances). The results of this paper 5 The lower number of observations for this specification is due to lack of information about car ownership. The earliest available year with data is

19 proved that the previous commuting environment does not have an effect on future commuting choices. In other words, there is no habit formation in the choice for commuting distance. The nonexistence of habit formation proves that there is a clear distinction between individual and developed preferences, being the individual preferences the main drivers of commuting choices. Therefore, it is important to increase the knowledge of these preferences as these are significant shapers of the residential composition in our cities. 19

20 References Brueckner, J. K., J. F. Thisse and Y. Zenou. (1999). Why is Central Paris rich and downtown Detroit poor? An amenity-based theory. European Economic Review, 43, pp Glaeser, E. L., J. Kolko and A. Saiz. (2001). Consumer city. Journal of Economic Geography, 1, pp Gutiérrez-i-Puigarnau, E., Mulalic I. and N. van Ommeren J. (2014) Do rich households live farther away from their workplaces? Journal of Economic Geography. October 29, 2014 doi: /jeg/lbu046 Kuminoff, N.V., V.K. Smith and C. Timmins. (2012). The new economics of equilibrium sorting and policy evaluation using housing markets. Journal of Economic Literature, 51(4), pp Rozen, K. (2010). Foundations of intrinsic habit formation. Econometrica, Journal of Econometric Society, 78(4), pp Simonsohn, U. (2006). New Yorkers commute more everywhere: contrast effects in the field. The Review of Economics and Statistics, 88(1), pp