Why are the commuting distances of highly educated couples so short? An analysis of the heterogeneity of the location preferences of households

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1 Why are the commuting distances of highly educated couples so short? An analysis of the heterogeneity of the location preferences of households J.W. Weijschede van der Straaten and J. Rouwendal, Netherlands Bureau for Economic Policy Analysis, The Hague Department of Spatial Economics, VU University, Amsterdam Tinbergen Institute, Amsterdam Key words: commutes, regional labour markets, household location, two earner households JEL codes: J12, R21, R23 Abstract Several empirical studies have reported that the commutes of workers in two earner households are similar to those of otherwise comparable workers from single earner households. In this paper we reconsider this evidence and find that, while it is sensitive to the additional of control variables, the impact of the co location problem of two earners is small. Strategic residential location choice might explain this finding and to investigate this issue we develop and estimate a residential sorting model. We find that couples of higher educated individuals are willing to pay considerably more than others for residential location close to large concentrations of jobs. Urban amenities also appear to be an important attractor of such households. Moreover, the sorting model provides a reasonable explanation of spatial differences in housing prices. 1

2 1. Introduction The share of two worker households in the population has increased considerably over the past decades. Two earner households have to solve a co location problem of finding a satisfactory employment housing arrangement for two workers. It is therefore easily conjectured that the rising share of double income households has pushed the average length of commutes upwards and contributes substantially to increasing congestion problems. Formal analysis of search models for instance, joint search theory (see Guler et al., 2009) confirms this idea. However, the empirical evidence for a positive relationship between the number of workers in a household and the length of their commutes is surprisingly weak. Several studies have hit upon the fact that belonging to two worker households does not result in longer commutes, see, for instance, Sultana (2005), Rouwendal and Rietveld (1994) or van Ham and Hooimeijer (2009). Table 1 illustrates this for recent Dutch data by comparing the impact of education with that of the number of workers. Higher educated people often have specialized skills, which implies that the density of suitable jobs is low and that finding a job at a reasonable commute from one s residential location is more difficult. Singles and workers in one earner (multi person) households have longer commutes when they are higher educated. The average commutes of workers in two earner households are also longer, but the impact is smaller and related in part to the higher level of education of workers in two earner households. Costa and Kahn (2000) have argued that the co location problem is especially difficult to solve for couples of higher educated individuals, so called power couples. The figures in the last lines of Table 1 confirm that average commutes are longest for workers belonging to such power couples, but especially for commute times, the difference with the average commute of highly educated workers in one earner households is small. 2

3 Table 1 Commutes and the co location problems : some descriptives Household type Commuting time (in min) Commuting distance (in km) (1) (2) (3) (4) Males Females Males Females All 21.4 (17,701) 18.6 (17,429) Singles All 20.6 (2,432) 20.2 (2,085) Higher educated 23.0 (777) 21.6 (849) Multiperson households One worker 18.6 (4,781) 16.0 (2,463) Higher educated, one worker 23.8 (1,305) 20.2 (498) Two workers 21.8 (10,150) 19.0 (11,610) Power couples, two 24.5 (2,246) 22.0 (2,493) workers Power couples 24.5 (2,712) 21.8 (2,790) Source. Own computations based on the 2006 Labour Force Survey. The number of observations is given in parentheses. The figures for commuting distances are based on a lognormal interpolation of interval data. These are raw data, and it remains to be seen if this prima facie evidence holds if we introduce control variables. To investigate this issue, Table 2 presents regression coefficients that provide information about the effects after controlling for the education of the worker, the weekly number of hours, and some other variables. 1 Single worker households were used as the reference category. For male workers there is no significant effect for belonging to a two worker household or a power couple, or both if we consider commuting time, while if we look at commuting distances, there is a small effect (5%) of belonging to a two worker household. For females the picture is somewhat different: there we find significant positive effects of belonging to a two earner household (but note that the number of female workers belonging to single worker households is a somewhat special group) and of belonging to a power couple. The cross effect is negative, but not significant. When we consider commuting distances, we only find a significant effect of belonging to a two worker household. Summarizing, we find an effect of the co location problem of at most 5% on the commuting 1 Full details of the estimation are available upon request. 3

4 distances of male workers (les than 1 km) and of at most 20% (slighty more than 3 minutes) on the commuting times and 12% (1.2 km) on the commuting distances of female workers. We conclude that after introducing some control variables, significant effects of the colocation problem on commutes appear to be present, especially for the higher educated. However, the effect on male commutes is modest, while the larger effect on female commutes tends to equalize the commuting behaviour of the two workers and Table 1 shows that the commutes of female workers are still shorter than those of males. Table 2 Commutes and colocation problems: regression coefficients log (travel time in min) (1) Males (2) Females log(distance in km) (3) Males (4) Females Higher education (.027) (.026) (.042) (.037) Two worker household (.014) (.019) (.022) (.028) power couple (.039) (.050) (.058) (.072) power couple * two worker (.040) (.049) (.061) (.072) adj R log ps lik 21, , N 17,701 17,429 16,915 19,977 Source: Own computations based on the Dutch Labour Force Survey 2006 The regression contains controls for age, weekly working hours, number of children below 18, education. Observations with commutes longer than 60 minutes, or reported weekly working hours exceeding 80 have been removed. Standard errors are in parentheses. Statistically significant effects (at the 5 per cent level) are bold. A plausible explanation for these relatively small effects is that households choose their residential and work location so as to deal optimally with the co location problem. Long commutes are in general considered to be a heavy burden, and this provides a strong incentive for households to avoid them. The potential relevance of this behavioural response to the co location problem is underlined by the double income associated with two jobs, which makes it easier to overbid other households for houses on attractive locations. For power couples, this effect is reinforced by the higher income that is generally associated with better education. 4

5 In this paper we examine the relationship between the co location problem of two worker households empirically by a sorting model of the type developed by Bayer, McMillan and Rueben (2004) which is estimated for Dutch data. Costa and Kahn (2000) present strong evidence of strategic location choice at the level of metropolitan areas for highly educated couples, which are often two worker households (see also Compton and Pollak, 2004). Rouwendal and van der Straaten (2004) show that the trend towards increased concentration of highly educated couples in metropolitan areas is also present in the Netherlands. In this paper we look at a smaller spatial level (municipalities) and focus on the role of accessibility to jobs relative to house prices and urban and natural amenities. The prima facie evidence on commutes presented above suggests that two worker households and especially power couples attach more weight to job accessibility than other households types. The paper proceeds as follows. The next section presents the structural location model. The data are introduced in Section 3, where we also discuss some empirical issues. Estimation results are presented in Section 4. Section 5 concludes. 2 A household location choice model 2.1 General discussion The model concerns a given population households that differ in tastes over the residential location alternatives among which they have to choose. The heterogeneity is described by K classes, which are defined on the basis of household characteristics. Each household belongs to one class. These alternatives are rental or owner occupied houses in Dutch municipalities. The N alternatives differ in observed as well as unobserved characteristics and in the price of the available housing. The number of houses available in each alternative is taken as given. 2 Location choice behaviour is modelled by a multinomial logit model. Heterogeneity in tastes is treated by distinguishing K household types, k=1 K. The deterministic part of the utility experienced by a household of type k in choice alternative n (n=1 N) is denoted as v ik. The 2 This can be interpreted as a short run assumption. However, it can be further justified by the tight spatial planning regime in the Netherlands to which our empirical analysis refers. The development of new residential areas is slow and hardly responds to market forces (see Vermeulen and Rouwendal, 2007). 5

6 umber of households of type k is N k, and the number of houses available in alternative n as S n. Demand equals supply if: e K vnk N k 1 vlk e l 1 B S, n 1,..., N. k n (1) We assume that total demand equals total supply ( N K Sn Bk ). A well known restrictive n 1 k 1 property of the logit model is the IIA property, is avoided at the population level by the heterogeneity of the households. Households belonging to different classes have different parameters of the function v. The effect on market demand is similar to the effect of random parameters in the mixed logit model that also results in relaxation of the IIA property. The deterministic part of the utility function is further specified as: M v P x. (2) nk k 0 n km mn n m 1 In this equation P n is the housing price in choice alternative n; x mn is the value of the m th characteristic of location n; and n is a term that reflects unobserved (by the researcher) characteristics of choice alternative n. Equation (1) defines a market equilibrium, and when demand equals supply, the observed prices P n will clearly be functions of the factors affecting supply and demand: P P x, S,. n n In this equation we have expressed the exogenous variables as a matrix x and two vectors, S and ξ, in a self explanatory notation. Equation (3) points to an identification problem that is similar to that in traditional linear supply and demand equations, and was first analysed in the context of discrete choice models by Berry (1994) and Berry et al. (1995). The strategy they suggested to attack this problem is to first estimate a vector of mean utilities for the choice alternatives, δ, and then use 2SLS or a similar technique to estimate the coefficients incorporated in these mean utilities. The discussion that follows makes use of the exposition in Bayer et al. (2004), which adapts the Berry et al. (1995) approach to housing market analysis. We specify the heterogeneity of preferences by relating the β s in (2) to household groups as follows: z z, m 0,..., M. (4) km m km k (3) 6

7 In this equation z k is a dummy indicating membership of group k, and z is the average value of this indicator over the population. 3 Eq. (4) thus states that each group specific β km is the sum of an average part β m that is common to all households and a group specific. If km is positive, households belonging to group k derive more utility from part z z km k location characteristic m than most other households. For instance, the discussion in the introductory section suggests that power couples attach more weight than other households to employment accessibility, and for such households we therefore expect a positive km if locational characteristic m is accessibility to employment. Substitution of (4) into (2) gives: 0 0 m v z z P z z x nk k k k n m mk k k mn n P x z z P z z x z z P z z x 0 n m m mn n 0k k k n m mk k k mn n 0k ik k n m mk ik k mn The last line of (5) separates the mean utility from the group specific part by placing the relevant terms in brackets, while the third line denotes the mean utility of location n as δ n. Estimation of the model proceeds in two stages. For the first stage we substitute the third line of (5) into the logit model and estimate the km s in the conventional way while treating the δ n s as alternative specific constants. The δ n s are computed by the contraction mapping suggested by Berry et al. (1995). See Bayer et al. (2004) for further discussion. The second stage is a 2SLS regression on the δ n s, using the M location characteristics as explanatory variables and treating the price as endogenous. (5) 2.2 Incorporating rental housing The description in the previous subsection made clear that the equilibrating function of prices plays a large role in the model. In the Netherlands almost 40% of the housing stock is rent controlled social housing. In this part of the housing market prices do not equilibrate demand and supply. Allocation of households over dwellings is taken care of by a rationing system based that uses waiting lists and gives priority to those who are viewed to be especially in need of a house. Since we have no detailed information about effective waiting 3 K That is: z z B k 1 k k 7

8 times for social rental housing per group of households and per municipality, we are unable to model this allocation process. One possible response would be to ignore the rental sector completely, but since rental housing is an important substitute for owner occupied housing and its availability is different depending on the location chosen by a household, we judged this to be unsatisfactory. We have therefore included the rental sector as an alternative to the owner occupied sector for each municipality, but we allow the coefficients of the utility function (the km estimated in the first stage as well as the m s estimated in the second stage) to be different for the rental and owner occupied parts of the market. The reason for proceeding in this way is that the rationing process on the rental part of the market probably disturbs the revelation of preferences. The coefficients we estimate for the rental sector should therefore be interpreted as the result of interaction between preferences and a non market allocation system that probably cannot be used to investigate the willingness to pay for the attributes of the rental housing alternatives. Since rents do not differ over space after controls for quality have been taken into account 4 it makes no sense to introduce a price variable for the rental part of the market. To take into account the effect of the rationing system we allowed the unobserved heterogeneity variable n to be different for the rental and owner occupied houses at each location. We expect this term for the rental sector to take up special features of that tenure type. For instance, a huge excess demand for rental housing in the urbanized part of the country makes it less attractive to choose this alternative, and this results in a relatively low value of. 5 Moreover, in the first stage (logit) estimation we introduced interactions between indicators for household groups and a dummy for rental housing to allow for the probable presence of selection effects in the functioning of the rationing system for rental housing. We also allowed the parameters to be different for rental and owner occupied housing. In this way we attempt to do full justice to the presence of rental housing as an alternative for owner 4 The reason is that maximum rents are determined by a quality points, which are related to housing attributes, but not to the location of the house. We verified this by running the appropriate hedonic regression. 5 An alternative way to deal with this is to concentrate on the owner-occupied market by assuming that households take their decision about tenure choice at an early stage, i.e. before they choose a residential location. This implies the assumption that the large differences in the ratio of the house price and rent over the country has no effect on the tenure choice of households locating in a particular municipality. 8

9 occupied housing, while care is taken that the alternative allocation system on the rental part of the market does not affect the measurement of the preference parameters for those who choose the owner occupied part. Since the rationing system for rental housing may affect many of the parameters of the utility function that we estimate for rental housing alternatives, our second stage estimates, and the willingness to pay measures based on them, will only use the average estimated utilities of the owner occupied alternatives. 3. Data 3.1 The Housing Needs Survey The data we use are obtained from the Housing Needs Survey (in Dutch: Woon Onderzoek usually abbreviated as WoON) of The survey contains a wealth of information about household characteristics and the housing situation of a large sample of Dutch households. We exclude households with a negative disposable income or with an income higher than 400,000 per year. The total number of observations is 55,823. If we divide households by education level, Table 3 shows that the majority of households consist of couples, of which both members are lower educated. The share of power couples, couples of which both members are higher educated equals 10 per cent. If we compare the share of households with at least one working person 6, the share of twoworker households is equal to 40 per cent. Single earner households and single workers both form 30 per cent of the household population per cent of the households have one or more children younger than 18 years. The average disposable household income equals 29,64 per year. The average age of the head of the household is 50 years. 6 A working person is defined as one who works 12 hours or more. 7 Most power couples (72 per cent) are double-income workers. However, if we only look at double-income workers, one out of four is a power couples. 9

10 Table 3 Descriptive statistics of the household characteristics mean Share of households, by educational level Single, low educated (%) Single, higher educated power singles (%) Couple, both partners low educated (%) Couple, one spouse higher educated (%) Couple, both partners higher educated power couples (%) 9.66 Share of households, by labour market participation Single worker (%) One earner couple (%) Two earner couple (%) Other household characteristics Households with children Income 29,636 Age of head of household 50,11 The number of households is The descriptives of the household characteristics are weighted using the household weight, provided by the Housing Need Survey (2006). Table 4 gives some prima facie evidence of the importance of strategic location choice of two worker and other households in the Netherlands. It shows estimation results of a simple logit model for choice of a municipality size. Estimation results confirm that two worker couples and singles are more likely to live in one of the larger urban areas than single earner multi person households, which is taken as the reference category. Also the chance that highly educated couples and singles live in large urban areas is large. Table 4 Distribution of households over municipalities that differ in size 20,000 50,000 inhabitants 50, ,000 inhabitants 100, ,000 inhabitants 150, ,000 inhabitants At least 250,000 inhabitants Constant (0.067) (0.072) (0.078) (0.091) (0.087) Single (0.058) (0.062) (0.066) (0.076) (0.071) Highly educated single (0.095) (0.096) (0.098) (0.109) (0.099) Two earner household (0.040) (0.043) (0.049) (0.058) (0.056) Power couple (0.057) (0.061) (0.065) (0.076) (0.071) Household with children (0.038) (0.041) (0.045) (0.052) (0.050) Hh income (x1000) (0.000) (0.001) (0.001) (0.002) (0.001) Age of head of household (0.001) (0.002) (0.002) (0.002) (0.002) The table reports estimates of a multinomial logit regression of households with at least one working person. Municipalities with the lowest number of inhabitants (0 20,000 inhabitants) is chosen as the reference category. Standard errors are given in parentheses. Statistically significant effects (at the 5 per cent level) are in bold. Number of observations is equal to 37,343 and includes both tenants and owner occupied households. Source: Housing Needs Survey of

11 3.2 Characteristics of the residential location In the next section we report estimation results for the model introduced above. This model has been applied to the location choice of households in the Netherlands, treating the municipalities as our basic spatial entities. In 2007 their number was 443. For each municipality we distinguish between rental and owner occupied housing, which make the total number of choice alternatives equal to 886. However due to the fact that in the dataset two municipalities does not have observations of rental houses, the total number of choices are Using different data sets, we include four types of location characteristics: (1) accessibility; (2) regional wage; (3) amenities; and (4) the cost of living. Table 5 gives an overview of the average values of the characteristics in the Netherlands, the Randstad, the Intermediate Zone and the Periphery. Table 5 Average values of the variables of the residential location The Netherlands Randstad Intermediate Zone Periphery Accessibility of the location Distance to large labour market (km) Distance to motorway slip road (km) Distance to railway station (km) Regional wage difference Regional wage (differences from mean) Amenities of the location Urban attractivity index (x100) Percentage of nature (%) Percentage of agricultural land (%) Amenities close by Distance largest four cities (km) Percentages of nature (%) Price of living Price of a standard owner occupied house The number of municipalities in the Netherlands is 443; 167 in the Randstad; 155 in the Intermediate Zone; and 121 municipalities in the Periphery (in the year 2007). In the remainder of this section we provide a description of the four types of variables just mentioned. A complete list of variables, their sources, and definitions are summarized in Table A.1 in Appendix A, the Data Appendix. (1) Accessibility of the residential location: The accessibility variables include the accessibility to large labour market and the accessibility to transportation facilities. 8 These two municipalities are Ten Boer and Rozendaal. 11

12 The relation of the location of residence and the size of the labour market is taken into account by the (Euclidean) distance to the nearest employment centre with at least 100,000 jobs of each municipality. If the distance to the jobs is low, it means that the residential location is located close to a large labour market. Figure 1 shows that in the Randstad and around cities the distance to the labour market is low. With respect to the accessibility to transport facilities, we use the (Euclidean) distance to the nearest motorway slip road and the (Euclidean) distance to the nearest inter city railway station. Because the intercity railway stations are usually located in the city centre of the main city, this variable also reflects the distance to urban amenities. The Randstad and the municipalities that include a large city have short distances to these amenities. Figure 1 Distance to jobs (km) 12

13 (2) Regional wage difference: The existence of an urban wage premium in the United States (US) is well established (see, for instance, Glaeser and Maré, 2001). Groot et al. (2009) show that, as in the US, in the Netherlands the wage of a standard worker is highest in the most urbanized regions and the lowest in the peripheral areas. In their analysis, the Mincer regression is used, in which the natural logarithm of the hourly wage is explained by the employee s characteristics such as age, gender, being an immigrant or not, part time worker, education level, occupation, and the NUTS 3 work location. Figure 2 Urban attractivity index (3) Amenities of the residential location: The variables that indicate the amenity of the residential location include the urban attractivity index and the percentage of nature coverage and agricultural coverage. The urban attractivity index describes the availability of cultural, catering, and retail facilities. Its value is based on three categories: (1) the number 13

14 of social and cultural facilities, namely, theatres, museums and cinemas; (2) the number of retail facilities; and (3) the number of catering facilities, namely hotels and restaurants. (For a fuller description, see Appendix A.) As shown in Figure 2, large cities, like Amsterdam and Rotterdam, have a high urban attractivity. The percentages of nature coverage and agriculture coverage give an indication about the outdoor recreation facilities of the location. Nature includes natural areas, such as dunes, heath, and forests. The largest percentages of nature coverage are located in the East, Middle and South of the Netherlands, and along the coast. The lowest coverage of nature occurs in the centre of the Randstad, also called The Green Heart. Although agricultural land is most of the time not accessible, it is regarded as open space. In the periphery of the Netherlands and in the Green Hart the coverage of agricultural land is high. Because households can easily enjoy amenities of the neighbouring municipalities close by, think for example of municipalities that are closely located to large cities, the characteristics of the surrounding municipalities can also influences the location choice of households. Therefore we also include the average percentage of nature coverage and agricultural coverage of the surrounding municipalities. Furthermore we include the distance towards the largest four cities in the Netherlands (Amsterdam, The Hague, Rotterdam and Utrecht). (4) Cost of living: The Netherlands is a small country and spatial variation in the cost of living is dominated by house prices. We will thus concentrate on that aspect. Because the rental sector is highly regulated in the Netherlands and regulated rents show no variation over space, we focus on the price of owner occupied housing. We estimated a hedonic regression with housing characteristics and municipality dummies and use the results to compute an spatial index for the price of a standard house. For a fuller description, see Appendix A, the Data Appendix. The average price of a standard owner occupied house is 184, Estimation results The first step in estimating the model proposed in Section 2 is the estimation of a logit model with alternative specific constraints (compare equation (1)). The sample we use is person based, and weighting is therefore needed in order to get a representative sample of the household population in each municipality. The weights, provided by the Housing Needs 14

15 Survey, also correct for differences in response rates. 9 The independent variables of the logit estimation include the 886 alternative specific constants and 60 interaction variables of the characteristics of the location 10 with the household characteristics. 11 The results show, for instance, that the interaction of power couples with the distance to a large labour market is significantly negative. This result complies with the hypothesis that locations close to a large labour market are attractive for the higher educated. The coefficients of the interaction parameters of the conditional logit estimation are presented in Appendix A. These results are used, together with the estimation results of the second step, to estimate the heterogeneity in the willingness to pay (WTP) of households in their preferences for location characteristics. The second step consists of a weighted two stage least squares estimation, with the dependent variable being the estimated coefficients of the alternative specific constants of the first step. To give large municipalities more influence, the numbers of households of each municipality in 2006 are used as weights. Due to the absence of regional price differences in standard rental house, we only include the regional price of the (standard) owner occupied houses as the indication of the cost of living. We use average characteristics of neighbouring communities as instruments, following a procedure suggested by Klaiber and Phaneuf (2010). 12 One concern with these instruments is that characteristics of neighbouring municipalities may affect the attractiveness of the municipality. We have therefore constructed an alternative instrument that gives the equilibrium prices that would obtain in the absence of the unobserved characteristics, following a suggestion by Bayer et al. (2007). The results of the are shown in Table 6. 9 As a result, the share of weighted observations of households living in a particular municipality in our sample is identical to the share of households in the Dutch population living in that municipality. However, our selection of households with at least one worker and an annual income below 400,000 may have slightly disturbed this identity. It can also not be guaranteed that it holds for both the owners and for the tenants. 10 The characteristics of the residential location include: the distance to large labour market, a highway slip road, and the distance to a railway station, regional wage differences, the urban attractivity index, percentage of nature coverage, distance to the largest four cities, average percentage of nature surrounding municipalities and the price of a standard house. 11 The household characteristics include: double-income households, power couples, single worker, power single worker, households with children, and age of the head of the household. 12 The municipalities of Ameland and Texel are (officially) islands in the Wadden Sea and therefore do not have adjacent neighbouring municipalities. Their instrumental variables are set equal to zero. The instrumental variables include the distance to a large labour market, distance to the nearest highway slip road, distance to the nearest railway station, percentage of nature coverage and the urban attractivity index. Because the regional wage differences are estimated at the COROP-level, this variable is not used as instrumental variable. 15

16 Table 6 Estimation results for mean utilities OLS 2SLS, Klaiber and Phaneuf inst. 2SLS, Bayer inst. Coefficient Standard error Coefficient Standard error Coefficient Standard error Constant , , LN (price of house) Distance to large labour market (km) Distance to motorway slip road (km) Distance to railway station (km) Regional wage differences Urban attractivity index (x100) Percentage of nature (%) Percentage of agricultural land (%) Distance largest 4 cities (km) Surrounding nature (%) The table reports the second stage estimation results of a 2SLS. Standard errors are in parentheses. Statistically significant effects (at the 5 per cent level) are in bold. The number of observations is equal to

17 Using the results of the first and second step we are able to calculate the average marginal willingness to pay (MWTP) and the heterogeneity in the MWTP for characteristics of the residential location. We used the 2SLS results based on the Klaiber Phaneuf (2010) procedure, but results for the Bayer et al. (2007) are similar. The MWTPs are reported in Table 7. Table 7: Marginal willingness to pay (MWTP) for characteristics of the residential location (1) mean MWTP Accessibility Distance to large labour market 2169 (km) (256) Distance to motorway (km) 320 (254) Distance to railway station (km) 7 (206) Regional wage difference Regional wage difference (1% increase) 18 (614) Amenities Urban attractivity index (x100) (2964) Percentage of nature (%) 696 (107) Percentage of agricultural land 56 (%) (55) (2) Twoearner couple (3) Power couple (4) Single worker (5) Power single worker (6) Household with children (7) Average age + 10 years Surroundings Distance to nearest of 4 large cities (km) (34) Percentage nature (surr.) (%) 413 (135) The first column shows the MWTP of the characteristic of the residential location of the average household (the mean MWTP). Columns (2) to (7) show the deviation with respect to the mean MWTP of the corresponding household characteristic. The standard errors of the mean MWTP have been computed on the basis of the 2SLS standard errors using the delta method, and are given in parentheses in the first column. The first column of this table shows the MWTP with standard errors given in parentheses. On average, households are willing to pay 2,169 to live 1 km closer to a large labour market. The WTP of a household in order to have a higher urban attractivity index is equal to 10,176. (However one must take into account that a marginal change of the urban 17

18 attractivity index has a large influence). 13 With respect to nature, households are willing to pay 696 in order to have 1 per cent more nature in their residential location. Households prefer to live close to one of the four largest cities, and are willing to pay 624 extra for a house that is 1 km closer. Households prefer to live close to one of the four largest cities in the Netherlands. On average, households are indifferent to being located closer to a motorway slip road, to an intercity railway station, regional wage rate and percentage of agricultural land in their residential location. These results show that households find it important to have good accessibility to large labour market, urban amenities and nature, and that they are willing to pay a higher price for that. Columns (2) to (7) show the deviation of the MWTP of a household characteristic with respect to the first column, the mean MWTP. 14 Column (3) shows that power couples are willing to pay 600 extra (that is, in addition to the mean value of 2,169) in order to be located close to a large labour market. Living close to a railway station and urban attractivity are also important characteristics for power couples, and they are willing to pay 118 extra in order to be located close to a railway station, and 2,404 extra to have more urban facilities. Surprisingly, power couples seem to prefer to live in regions with a low wage. All wtp s are in terms of higher or lower bids for a standard house. Power single workers show similar preferences for residential characteristics as power couples. Power single workers are willing to pay 600 extra to live close to a large labour market and 2296 to be located in a municipality with a higher urban attractivity. Our results show that households with children have different location preferences. Their preference for being close to jobs, railway station and urban amenities are less strong. An interesting research topic for the near future would be to further analyse the influence of the household s lifecycle stage on location preferences. 13 The index is the summation of three ratios (each accounting of 1/3 of the total value). The ratio of each category is equal to the number of facilities (for example, retail facilities) divided by the total number of facilities in the Netherlands. Hence, a marginal change of the value of the index implies a large influence of the facilities in the municipality. 14 Please note that due because the estimation method involves two separate regressions, we are not able to calculate the standard error of the marginal prices of the heterogeneity of the WTP of the characteristics. 18

19 These results can be applied to compare the WTP of households for living in municipalities that differ in characteristics. We compare the municipality of Amsterdam with the municipality of Almere. Almere, which was built in the 1970s, was designated as a growth centre to accommodate the population growth of Amsterdam. In Table 8 the distance to a large labour market, distance to railway station, regional wage difference and the urban amenity index is shown for Amsterdam in column (1), and for Almere in column (2). The variables distance to motorway slip road and nature are left out because the coefficients of these variables were not significant, see Table 7. Table 8 Application: WTP of living in Amsterdam and Almere (1) Amsterdam (2) Almere (3) Difference [=(1) (2)] (4) MWTP average household (5) WTP average household [=(3)*(4)] (6) MWTP power couple (7) WTP Power couple [=(3)*(6)] Distance to large labour market (km) Urban amenity index (x100) Percentage nature (%) Distance to largest four cities (km) Average percentage of nature in the neighbouring communities (%) Total WTP (euro) Column (3) shows the differences between column (1) and column (2), and it can be seen that the distance to a large labour market is 12 km less in Amsterdam than in Almere. Also the number of urban facilities is much higher in Amsterdam than in Almere. Column (4) shows the WTP of the average household for a marginal change of the characteristics; the marginal willingness to pay (MWTP). If we assume that the changes with respect to the characteristics are marginal, we are able to calculate the WTP, which is shown in column (5). The total willingness to pay (TWPT) of the average households to be located in Amsterdam instead of Almere, is shown in the last line of column (5). An average household is willing to pay 85,519 extra to be located in Amsterdam instead of Almere. This result complies with 19

20 the difference in price of a standard house in Amsterdam and Almere, which is equal to 128, Column (6) of Table 8 shows the MWTP of power couples with respect to the residential characteristics. The last column (7) shows the WTP of power couples to be located in Amsterdam instead of Almere. The last line shows that power couples are willing to pay to be located in Amsterdam instead of Almere. With respect to the heterogeneity of preferences between households, we can conclude that power couples have a strong preference for being located closer to a large labour market and urban amenities. Both preferences imply a higher housing services demand, which means that power couples are willing to pay extra for their house. Therefore, we can conclude that, in order to solve their co location problem and because of their preferences for urban amenities, power couples do indeed use their purchasing power to outbid others, so they can locate close to large urban areas. This result complies with the hypothesis that power couples strategically choose their residential location. It should be noted that these results are in line with the results found by van Ham et al. (2000), who find that suburban locations in between major employment centres are clearly superior for households with highly skilled workers. However, our results show that, besides accessibility towards the labour market, also the presence of urban amenities is regarded as important in the location choice of the higher educated. 5 Conclusions, implications and further research suggestions This paper has focussed on the location choices of power couples and analyses whether power couples, differ with respect to their preferences for various characteristics of their residential location in order to solve their co location problem. Own analyses and those of others do show that power couples are more likely to be located in areas with good labour market facilities. However, the choice of a residential location is a result of a trade off between the many aspects involved. Therefore, in the residential sorting model, we include, besides labour market characteristics, several other characteristics of the residential location such as transport facilities, recreational facilities, urban facilities and the cost of living. 15 The price of a standard owner-occupied house in Amsterdam is 285,846. The price of a standard owneroccupied house in Almere is 156,

21 The results show that an average household would like to live close to a large labour market; and have more amenities like urban facilities and nature. Households are indifferent with respect to transport facilities and regional wage difference. In comparison with the average households, double income couples do not deviate with respect to their preference for the various characteristics. However, power couples are willing to pay more than average to be located close to large labour markets and a railway station, and to have good urban facilities in their residential location. These results are in line with our hypotheses that power couples use their purchasing power to locate at their preferred location in order to solve their the co location problem. The results show that the location choice is not simply more connected with just the working place. Although accessibility to the workplace is still important, the amenities that the location offers are also regarded as important, especially for power couples and (power) single workers. This explains why they are more likely to live in large urban areas. A possible research topic for the near future would be to correct for differences in the diversity of the labour market. Although large urban areas offer more potential job matches, and hence the probability of drawing a good initial match, or a subsequent match, is higher, a dense labour market by itself is not necessarily more attractive to power couples as a solution for their co location problem. In a dense labour market, there are not only more jobs, but also more workers, and, as a result, there will be more competition for jobs. In an empirical analysis of the overqualification of the trailing spouse and its relation to the size of the location, McGoldrick and Robst (1996) found no significant relationship between population size and the likelihood of the trailing spouse (women) being overeducated. The authors suggest that it is not the market s size which is important but its job composition, although more research is needed to answer this question. Our analyses have focussed on the regional scale, but it would also be interesting to apply future analyses on the more detailed scale of neighbourhoods. Especially in large cities there can be much heterogeneity at a lower spatial level, and, hence, the neighbourhood level might be more relevant than the municipality. However, estimating the model at the neighbourhood level would imply a huge increase in the number of choice alternatives, and, moreover, some data is not available at that level. Another interesting application of the 21

22 model would be to analyse the differences between the lifetime of households and the implications with respect to their location preferences. 22

23 References Bayer, P., F. Ferreira and R. McMillan (2007) A unified framework for measuring preferences for schools and neighbourhoods. Journal of Political Economy, 115,.. Bayer, P., F. R. McMillan and K. Rueben (2004) Residential segregation in general equilibrium, Economic Growth Center Working paper, 885. Berry, S.T., 1994, Estimating discrete choice models of product differentiation, The RAND Journal of Economics, 25, Berry, S., J. Levinsohn and A. Pakes, 1995, Automobile prices in market equilibrium, Econometrica, 63, Compton J. and R.A. Pollak, 2004, Why are power couples increasingly concentrated in large metropolitan areas, Journal of Labor Economics, 25, p Corvers, F., M. Hensen and D. Bongaerts, 2009, Delimitation and Coherence of Functional and Administrative Regions, Regional Studies, 43, Costa, D.L. and M.E. Kahn, 2000, Power couples: changes in the locational choice of the college educated, , Quarterly Journal of Economics, 115, Groot, S.P.T., H.L.F. de Groot and M.J. Smit, 2009, Regional wage differences in the Netherlands: Micro evidence on agglomeration externalities, presented at the workshop Cities agglomeration externalities and spatial planning, The Hague, 13 May Guler, B., F. Guvenen and G.L. Violante, 2009, Joint search theory: New opportunities and new frictions, NBER working paper, no van Ham, M., P. Hooimeijer and C. Mulder, 2000, Urban form and job access: Disparate realities in the Randstad, Tijdschrift voor Economische en Sociale Geografie, 92,

24 van Ham, M. and P. Hooimeijer, 2009, Regional differences in spatial flexibility: Long commutes and job related migration intentions in the Netherlands, Applied Spatial Analysis, 2, Klaiber, H.A. and D.J. Phaneuf (2010) Valuing open space in a sorting model of the Twin Cities. Journal of EnvironmentalEconomics and Management, 60, Madden, J.F, 1981, Why women work closer to home, Urban Studies, 18, McGoldrick, K. and J.Robst, 1996, Gender differences in overeducation: A Test of the theory of differential overqualification, The American Economic Review, 86, van Oort, F., R. Ponds, J. van Vliet, H. van Amsterdam, S. Declerck, J. Knoben, P. Pellenbarg, and J. Weltevreden, 2007, Verhuizingen van bedrijven en groei van werkgelegenheid, NAi uitgevers, Ruimtelijk Planbureau, Den Haag. Rouwendal, J. and P. Rietveld, 1994, Changes in commuting distances of Dutch households, Urban Studies, 31, Rouwendal, J. en J.W. van der Straaten, 2004, Dual Earners, Urban Labor Markets and Housing Demand, in: R. Capello and P. Nijkamp (eds), Urban Dynamics and growth, Advances in Urban Economics, Elsevier, Singell L.D. and J.H. Lillydahl, 1986, An empirical analysis of the commute to work patterns of males and females in two earner households, Urban Studies, 2, Sultana, S. (2005) Effects of married couple dual earner households on metropolitan commuting: Evidence from the Atlanta metropolitan area. Urban Geography, 26, Vermeulen, W. and J. Rouwendal (2007), Housing Supply in the Netherlands, CPB Discussion Paper 87, Den Haag. 24

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26 Appendix A: Data appendix Table A.1 Variable definition and sources Variable Definition Source Household characteristics Double income couple Couple both member of which work 12 hours or more. HNS (2006) Single earner couple Couple one member of which works 12 hours or more. HNS (2006) Single worker Single who works 12 hours or more. HNS (2006) Power couple Couple of which both members are higher educated. HNS (2006) Power single worker A higher educated single. Household with children Household with child( ren) under the age of 18. HNS (2006) Income (x1000) Average disposable income of the household. HNS (2006) Age of head of household Age of the head of the household. HNS (2006) Accessibility of the location Distance to large labour market The weighted average of the (Euclidean) distance to 100,000 jobs of the municipality. 16 Ruimtescanner (2000) Distance to motorway ramp (km) Distance to railway station (km) Amenities of the location Urban attractivity index (x100) The weighted average of the (Euclidean) distance to the nearest highway slip ABF (2005) road of a municpality. 17 The weighted average of the (Euclidean) distance to (intercity) railway station ABF (2000) of a municipality. 17 The urban attractivity index describes the availability of cultural, catering, and retail facilities. The urban attractivity index includes three categories: (1) the number of social and cultural facilities, such as theatres, museums and cinemas; (2) the number of retail facilities; and (3) the number of catering facilities, such as hotels and restaurants. For each category the national share of the number of facilities are calculated and weighted by one third. The value of the urban attractivity index of a location always lies between 0 and 1. Because the average value of the index is very low (namely, 0.002), we multiplied the index by 100. ABF/CBS (2007) Percentage of nature Percentage of nature coverage in the residential municipality ABF (2003) Distance to largest four cities (km) Percentage of nature surrounding municipalities Cost of living and regional wage difference Price of standard owneroccupied house The (Euclidean) distance towards the closest cities of Amsterdam, Rotterdam, The Hague or Utrecht The average percentage of nature in the surrounding municipalities The regional price of a standard owner occupied house. The standard house is a terraced house, with a volume of 361 m 3, a floor area of 121 m 2, built in the period and sold in the months April to July. The estimation results of the hedonic price function are available on request. ABF/CBS (2007) ABF(2003) NVM (2006) 16 The distance variables to jobs, the nearest highway slip road and the nearest intercity railway station describes the Euclidean distance of the municipality to the characteristic concerned. The value is a weighted average of the relevant value for the 4 digit postal code areas of the municipality. Each value of the 4 digit postal code is weighted according to the number of inhabitants in the corresponding postcode area, in order to give highly populated postal code areas more weight. 26