Employment status transitions and shifts in daily activity-travel behavior with special focus on shopping duration

This study is positioned in the overlap of activity-based analysis of travel behavior and life trajectory studies. According to the activity-based analysis framework, travel should be viewed as a result of demand, induced by the fact that individuals have to be involved in activities and consequent travel in their pursuit of realizing particular needs and desires. In order to satisfy these needs and achieve associated aspirations (targets), they need to conduct particular activities. The realization of particular needs may require (formal) commitments. For example, in order to generate income, individuals need to become involved in work activities. Work arrangements tend to be institutionalized, implying that individuals sign formal work contracts, inducing commitments and (semi-)formal start and end times of work activities for particular days of the week and weeks of the year. Institutionalized commitments tend to have a certain fixed duration. Although the choice to become involved in work activities is in principle a discretionary choice, institutionalized work arrangements and commitments subsequently constrain the timing and flexibility of involvement in non-work activities.

Because the supply of facilities allowing individuals to conduct activities is distributed across space, the decision to participate in activities logically implies (induced) travel. In more general terms, individuals face the problem how to organize their activities in time and space, subject to varying degrees of flexibility in deciding on the timing, duration, and location to conduct these activities and various types of locational, temporal, spatio-temporal and institutional constraints such as to satisfy their needs and concomitant aspirations. We assume that individuals learn about the opportunities and constraints in their environment by personal experience (i.e. their activity-travel behavior) and several external information sources. They build up particular beliefs about the state of the environment, act upon these and experience the positive and negative consequences of their decisions. Through learning, updating of beliefs, and exploration, they develop certain routines, scripts and habits that produce perhaps not optimal, but certainty satisfactory behavior that meets their constraints and is congruent with their aspirations, conditional on the constraints they face. Such behavior may be context-dependent. Over time, individuals develop a repertoire of context-dependent routine-like, habitual activity-travel patterns. This reasoning articulates the complex, context-dependent, interdependencies between activity participation, timing and duration, location choice and transportation mode choice decisions, subject to individual, household, institutional and spatio-temporal constraints, that generate observed activity-travel patterns. Developments in activity-based modelling (see Rasouli and Timmermans 2014 for a recent review) can be viewed as alternative ways of (partly) modelling these interdependencies.

Although this framework implies that under quasi-stationary conditions, individuals will develop habitual patterns over time, leading to a system in equilibrium, in reality constraints may change, individual may change their needs and aspirations levels and in addition the state of the system may change either exogenously or endogenously due to the accumulated decisions of individuals. In turn, such change may trigger individuals to reconsider and perhaps adjust their habitual repertoire of activity-travel behavior. Of particular relevance for the present study are changes induced by lifecycle events, such as marriage, birth of a child and the focus of the present study change in employment status. A limited set of previous studies has documented the influence of lifecycle events on changes in activity-travel patterns. Examples include Lanzendorf (2003, 2010), Prillwitz et al. (2006, 2007), Verhoeven et al. (2006), Levinson (2009), and Oakil et al. (2011).

Take the transition from being employed to the state of unemployment as an example. The shift from employment to unemployment means less income. Consequently, the individual needs to decide how to compensate for this loss of income. Adaptation to this new situation may involve participating less in activities, spending less on (particular) activities, traveling less frequently, shifting to less costly transportation modes, visiting less distant locations or travel more efficiently by increasing trip chaining, using savings or some combination of these strategies. At the same time, however, the transition to unemployment also implies that the work time is released, triggering the individual to decide how to use this extra time and to consider rescheduling the remaining activities, as prior constraints may no longer hold. Thus, the shift to unemployment enforces or intensifies resource constraints but simultaneously reduces the influence of spatio-temporal constraints and time pressure due to work commitments.

Vice versa, the transition from unemployment to employment induces increasing demands on the activity agenda in the sense that the individual is expected to show up for work, most likely at a particular location and at particular times, depending on the flexibility of the work arrangement. It will also increase the time for the commuter trip. However, a new job will also come with additional income, which can be spend on additional resources, such as a (second) car, and on increased expenditures for activities and travel. The reduction in available time for non-work activities should be compensated in some way. Individuals may consider participating (less) in non-work activities, spending less time on the activities in which they participate, shift activities to other days of the week, choose faster transportation modes, increase the amount of trip chaining or some combination of these. In principle, more complex adaptation strategies some of which are beyond the individual might be considered, such as multi-tasking, (de-) fragmentation of time, task re-allocation within the household, family, social network or hiring help. As these strategies are not considered in the present study, we will not elaborate here on their potential relevance and impact.

Data were taken from the Puget Sound Transportation Pane. It is the first general-purpose travel panel survey in an urban area in the United States. The panel is composed of approximately 1700 households in the four county central Puget Sound region (King, Kitsap, Pierce and Snohomish counties). It records all trips during two consecutive days of all household members. The data cover 10 waves from 1989 to 2002.

The current analysis is based on observations of two-day activity-travel patterns of 7135 individuals, after filtering out students, Individuals participate in multiple waves. Table 1 gives an overview of sample characteristics. It shows that the sample has an approximately equal share of men and women. The percentage of the age categories monotonically decreases with increasing age. The distribution of occupation varies, although the share “unknown” is high. The number of responses and reporting diary for weekends is very few.

The dataset was disaggregated by employment status in the different waves. By comparing employment status between consecutive waves, transitions in employment status could be derived. Logically, individuals can keep their employment status (employed or unemployed) or may shift their status (from employed to unemployed, or vice versa). Table 2 gives an overview of transitions. Note that this procedure does not capture any transitions that have occurred in between two successive waves, but these probably do not significantly affect activity-travel patterns. On the other hand, although the employment status may not change, people may change the location of their job, which in turn may dramatically influence their activity travel patterns. Therefore, such respondents were deleted from the sample that was used for analysis and model estimation. It creates more pure contrasts between the employment status groups.

Table 2 gives an overview of transitions in employment status. The total number of transitions in the sample is 20,676. Of these, 62.2 % stays employed between two consecutive waves. Another 29.6 per cent keeps their unemployed status. Interesting for our analysis is the remaining 8.2 %. The transitions from employed to unemployed equal 4.8 %, whereas the reverse transition amounts to 3.4 %.

In this paper, we report the results of descriptive analyses and the estimates of a random parameters panel effects regression model of change in time allocation to shopping between two successive waves. A full descriptive analysis, guided by the adopted activity-based framework which articulates the interdependencies between different facets of activity-travel patterns, would include participation rates, time expenditures on different types of activities, classified into mandatory and discretionary activities, travel time by activity type, travel distance by activity type, total activity time and travel distance for respectively work and non-work activities and choice of transportation mode for different activity types. Due to space limitations, we restrict reporting the results of these analyses to changes in total travel time and in travel time by activity, and to changes in total activity duration and activity duration by activity.

The model generalizes these descriptive analyses by regressing the various transitions in employment status on changes in activity duration to several activity types, after controlling for a set of socio-demographic variables and day of the week. The paper reports the results for shopping.

A first analysis is the change in average travel time for different activities. Table 3 shows that average travel time drops for almost all trip purposes/activities after respondents have become unemployed. The exception is for school and college, which may suggest that former workers have decided to start taking lessons to increase their job opportunities and/or escort their children more often to school. Comparing these results for the results pertaining to the employed–employed category (quo status) suggests that respondents who became unemployed are conducting some activities closer to home compared to the employed respondents.

The pattern for the reverse transition (from unemployed status to employment status) is more interesting. As expected, average travel time for work increases. The reduced time budget due to work commitments has to be compensated in part by spending less time on travel and the execution of other activities. Table 3 demonstrates that such reduction is not the same for all trip purposes. School is not affected. It seems that travel times related to the conduct of personal activities and appointments even slightly increase which may be due to work, while shopping and free time are considerably more affected. These findings are in line with the findings mentioned in the literature review. It seems that being employed triggers participating in some personal and social activities, leading to more appointments, further away from home. In turn, the extra time for these activities furthers reduces the time budget for other activities such as shopping.

Table 4 lists the changes in activity duration associated with a transition in employment status. Respondents in the panel who became unemployed reported a strong reduction in time expenditure to work, although the average time reported is still 40 min per day. Time expenditure to several other activities also slightly dropped. In contrast, these respondents increased their time expenditure to school and college. In case of a transition from unemployed to employed, time spent on work substantially increased. Coinciding with this shift in time allocation, time spent on school and college decreased, as did the amount of free time and time spent on visiting. Interestingly, time allocated to personal activities increased, which suggests that being employed increases time spent on personal commitments.

Our prime interest is to examine the effect of transitions in employment status on time allocation to different activities. Panel data are the best kind of data for such analysis. However, every data set, including panel data, has its specific peculiarities, the effect of which should be minimized.

The model specification was therefore based on the following considerations. First, because the interest is to examine to what extent the four different classes that stem from the sequence of employment status in subsequent waves, a comparison against the mean is most revealing. Consequently, all explanatory variables in the model were effect coded to ensure that the estimated parameters have a direct meaning in this context. Second, an accurate assessment of the effects of employment status transitions necessitates controlling for the effects of other potentially influencing variables. Regardless of employment status transition, the literature on activity-based analyses has shown that the demand for different activities is a function of socio-demographic variables. Moreover, time expenditure on different activities tends to vary between days of the week. Finally, general trends in society may affect the dynamics of time allocation.

To include these considerations in the model specification, we focused the analysis on differences in time expenditure between different waves for particular activities. Because trends in society may impact all individuals, we included the four possible transition states as the main independent variable in the model. Effect coding was used to represent these transitions. It means that changes in time expenditure can be compared between the four different transitions against the mean. Because observations of activity-travel behavior may involve different days of the week, we controlled for day of the week in the model. Respondents completed an activity-travel diary for two consecutive days. Thus, days of the weeks could only be controlled for the block of 2 days. Differences in time expenditure may reflect differences in days of reporting between successive waves. However, because the vast majority of the panel reported their activity-travel behavior for the same block of two consecutive days, days of the weeks was included once in the model specification. Considering the possibility that being unemployed may have a lagged effect, the years of unemployment was introduced as an interaction with the transition Unemployed  → Employed (years).

Similarly, a set of socio-economic variables was included to pick up any change in time expenditure triggered by transitions in these variables between consecutive waves. Because in addition to these observed characteristics, unobserved heterogeneity may affect the observations, a panel effect was estimated. Finally, to allow heterogeneity in behavioral response, random parameters were estimated.

Technically, for each activity a, a random parameters, panel effect regression model was estimated. It may be expressed as follows:where \((\Delta y_{ia}^{k \to k + 1})\) is the difference between the time that is spent on activity a by individual i from wave k to wave k + 1, δ is a vector of indicator variables representing transitions in employment status from wave k to wave k + 1 of individual i, γ is a vector of marginal effects of transitions in employment status on changes in time spent on the considered activity, β is a vector of marginal effects of a vector of social demographic variables S _i of individual i, α is a vector of marginal effects of day of the week W _a for doing activity a, φ is a vector of marginal effects of interactions between socio-demographic profiles and transition in employment status. ϑ a vector of marginal effects of interactions between day of the week when activity a is conducted and transition in employment status, \(({L^{k \to k + 1}})\) is equal to the length of unemployment if the transition is from unemployed to employed and zero otherwise, τ is the corresponding parameter, and \(\otimes\) is the Kronecker product.

The parameter vectors are assumed independently and identically normal distributed:

Maximum likelihood estimation was used to estimate the model. Results for the shopping activity are listed in Table 5. Shopping was selected for this formal analysis because the results of the descriptive analysis indicated it represents an interesting activity category. Except for grocery shopping, shopping is a discretionary activity, implying that individuals and households have more flexibility deciding how much time to spend on shopping.

Table 5 shows that the estimated constant is −3.62, which indicates that on average time expenditure to shopping during the 2 days of observation between two consecutive waves of the panel has decreased with around 4 min per day. Here, it should be noted that the panel data do not show any shift in participation in shopping activities beyond the 2 days of observation. For example, we cannot rule out that some of the respondents due to a change in employment status shifted their shopping from weekdays to weekends.

Both employment transition categories significantly deviate from the sample average. The positive sign of the estimated effect for a shift from employed to unemployed suggests that, keeping every else constant, respondents belonging to this category on average have increased the time they spend on shopping with almost 8 min between two consecutive waves of the panel survey.

Because many previous studies have provided strong evidence of gender differences in shopping participation and duration, gender was included as another explanatory variable in the model. To include the possibility of gender differences related to the employment status transitions, both main effects and interaction effects between gender and transition categories were estimated. Neither the main effects nor the interaction effects are statistically significant at conventional levels. It provides further evidence of the contention that changes in time allocation to shopping between two consecutive ways of the panel are primarily driven by shifts in employment status transitions. The effect of gender, both generally and for the specific transitions categories, remains relatively constant. Similarly, Table 5 shows that the main effects of occupation categories are not significant at the five per cent probability level.

The literature on activity-travel analysis has found evidence for the existence of day of the week preferences for conducting particular activities. Day of the week was therefore included in the model. Because each respondent provided activity-travel diary data for two consecutive days, while very few observations were recorded for weekend, only four categories were distinguished. As before, both main and interaction effects were estimated to not rule out the possibility that different employment status transition groups have different day of the week preferences

Results indicate that the estimated interaction between the transition from unemployed to employed is significantly negative for Wednesday, while the transition from employed to unemployed is significantly positive for Wednesday. This suggests that time spent on shopping has changed significantly, particularly on the (working) days when the job status changed. It further emphasizes the interpretation that the transition to work status forces these individuals to reorganize their shopping activities across the days of the week.

The number of years in the unemployment state was included in the model to capture any possible effect of the period of unemployment on the difference in shopping activity time between the employment and unemployment state. As the results indicate, with increasing duration of unemployment, the difference in time spent on shopping increases when people get employed.

In general, the insignificant coefficients for many attributes, except employment status change, gender and interaction of some other attributes with employment status changes suggests that socio-demographic characteristics and day of the week per se do not have a strong influence on changes in shopping duration from one wave to the other, but that rather changes in employment status are the main impetus for changing shopping behavior.

As indicated, the random parameters panel effects regression model also estimates a distribution of effects, assuming a normal distribution. Table 5, however, illustrates that none of the estimated scale parameters is significant at the 5 per cent confidence level. It indicates that the sample is quite homogeneous in terms of changes in time allocated to shopping after controlling for the selected socio-demographic variables and days of the week.