A MODEL OF THE COMMUTING RANGE OF UNEMPLOYED JOB
SEEKERS
Draft version of: McQuaid, R.W. and M. Greig (2001) “A Model of the Commuting
Range of Unemployed Job Seekers”, in: D. Pitfield (ed.), Transport Planning,
Logistics and Spatial Mismatch (London, Pion) pp. 152-166.
Ronald W. McQuaid and Malcolm Greig
Employment Research Institute
Napier University
South Craig
Craighouse Road
Edinburgh
EH10 5LG
Tel. +44 131 455 6033
Fax +44 131 455 6030
r.mcquaid@napier.ac.uk
Published in: McQuaid, R.W. and M. Greig (2001) “A Model of the Commuting
Range of Unemployed Job Seekers”, in: D. Pitfield (ed.), Transport Planning,
Logistics and Spatial Mismatch (London, Pion) pp. 152-166.
2
1. Introduction
The ability or willingness of unemployed people to travel further to a new job will
affect the level of mismatch between job seekers and jobs (Brueckner and Martin
1997; Gabriel and Rosenthal, 1996; Holzer, 1991). Empirical evidence suggests that a
range of socio-economic factors affect the length of time that job seekers are willing
to commute to a new job (McQuaid et al, 2001). However, while there is considerable
research into the travel-to-work times of those already in work there is limited
research on the potential maximum journey to work for unemployed people seeking
work. This paper seeks to identify and examine a range of personal, demographic and
spatial factors that influence the time that unemployed job seekers would be willing to
spend travelling to work if they could obtain employment.
In Section 2 existing evidence on factors affecting commuting time is reviewed.
Section 3 sets out a theoretical model of the attitudes of unemployed job seekers
towards the time they would be prepared to commute to a new job. Section 4 presents
the results of a factor analysis of characteristics of unemployed job seekers and their
attitude towards travel-to-work times. Section 5 presents the conclusions.
2. Empirical Evidence
Considerable existing empirical research has examined factors associated with travelto-work time (e.g. Aronsson and Brannas, 1996) and travel-to-work distance (e.g.
Rouwendal and Rietveld, 1994; Ong and Blumenberg, 1998) in the context of
employed workers. However, research on the potential travel times of those seeking
work is limited. A notable exception is the work by Van den Berg and Gorter (1997)
which quantifies the effect of potential travel-to-work time on the unemployed by
measuring the reservation wage differential required to offset the disutility of
commuting. Van den Berg and Gorter examine the relationship between this disutility
and explanatory variables, which they categorise as person-specific, householdspecific and environment-related. Characteristics of job seekers are often classified as
personal
characteristics
(Rouwendal
and
Rietveld,
1994).
These
personal
3
characteristics can be subdivided into the four categories of: innate social
characteristics; acquired human capital characteristics; financial characteristics which
describe the financial position of individuals; and the characteristics of potential jobs
that the person may apply for. In addition, the characteristics of the local economy in
which the person lives will influence the attitudes of job seekers towards commuting,
and these exogenous factors make up a fifth category of factors affecting attitudes
towards commuting time.
First, the set of social characteristics examined includes gender, where evidence
shows that females are likely to incur shorter travel-to-work times than males (Gordon
et al, 1989). Turner and Niemeier (1997) argue that this is due to the effects of the
household responsibility hypothesis (Johnston-Anumonwo, 1992) in which the greater
household responsibility of females leaves less time for commuting. Evidence of this
gender effect in employed workers is supported by, for example, Brännäs and Laitila
(1992) in studies of the Swedish labour force, and Rouwendal and Rietveld (1994) in
an examination of travel-to-work distance in the Netherlands, and Dex et al (1995) in
the UK. Mensah (1995) argues that the increased effect of the household
responsibility hypothesis among lower income women in the US leads an increased
proportion of part time work and therefore less commuting time due to them working
on fewer days. However, evidence of a gender difference is not supported by either
Van den Berg and Gorter (1997) or by Ong and Blumenberg (1998) who, in a study of
welfare recipients in the US, argue that the limited skills and low wages found in this
sector of society transcend gender boundaries.
Specifically, marital status has been shown to reduce travel-to-work time due to
increased household responsibility, although only for females (Turner and Niemeier,
1997). Contrary to this, Rouwendal and Rietveld (1994) found lack of geographical
mobility resulted in married workers having higher travel-to-work distances.
Responsibility for dependant children has been found to reduce travel-to-work,
especially amongst females (Rouwendal and Rietveld, 1994; Turner and Niemeier,
1997). Aronsson and Brännäs found dependant children increased travel-to-work time
for both sexes, arguing that the presence of children necessitates a larger home,
usually involving a move to a suburban location with a corresponding increase in
4
commuting time. Also children related ties, such as schools, may reduce the
propensity to move house when a member of the household changes jobs. It is likely
to often be the case that one working household member has a job with time,
commuting and other characteristics, which allows them to take primary childcare
responsibility during the working part of the week, while the other adult in the
household may commute longer, especially if they change jobs.
There are two schools of thought concerning the effect of the age of a worker upon
travel-to-work. Taylor and Ong (1995) found that travel-to-work time increases with
age and argued this was due to an increase in marketable skills and experience with
age, resulting in an increased geographical job search area. However, Ong and
Blumenberg (1998) found a negative relationship, arguing that this was due to the
higher level of marketable skills in older workers enabling them to compete more
successfully for nearby jobs. The bulk of research finds that travel-to-work times
decreases with age, mainly due to an unwillingness of older workers to travel for long
periods (Brännäs and Laitila, 1992; Rouwendal and Rietveld, 1994; Turner and
Niemeier, 1997).
The issue of race has been found to have a significant effect on travel-to-work by
some (Zax and Kain, 1996; Turner and Niemeier, 1997), although Van den Berg and
Gorter (1997) found it not to be significant. The population and sample in this study
of Eastern Scotland is relatively homogeneous (with under 2% of people classified as
ethnic minorities), so this is not a key issue in the current paper.
Second, the set of human capital factors includes educational qualifications, for which
the research evidence on effects on travel-to-work is mixed. Izrael and McCarthy
(1985) found a negative relationship between education and travel-to-work time and
Van den Berg and Gorter (1987) find no significant relationship. In contrast
Rouwendal and Rietveld (1994) identified a strong positive relationship between
academic training and travel-to-work distance. They argue that this is due in part to
the increasing separation of areas of employment growth and new residences. This is
particularly due to the increased specialisation and segmentation of the labour market
5
resulting in high individual job search radii, and hence higher potential travel-to-work
times for skilled workers.
Ong and Blumenberg (1998) address these ideas from an individual rather than a
macro-perspective, arguing firstly that skill acquisition raises workers into job markets
which are fewer in number and more widely dispersed, hence requiring larger search
areas. Secondly, they argue that a rise in income leads to a rise in demand for larger
homes and hence a move to a more suburban location, further from centres of
employment. As a more precise empirical test of the effects of labour market
segmentation on travel-to-work time a measure of specific professional and vocational
qualifications is included in the analysis below. It is worth noting that Rouwendal and
Rietveld (1994) found the possession of higher vocational qualifications to be
insignificant, and Van den Berg and Gorter (1997) found the level of work experience
to be insignificant.
In addition, to controlling for the type of work experience acquired, a variable to
distinguish manual from non-manual work is included. The demand for skilled, nonmanual labour has increased in recent years whereas unskilled jobs have been
disappearing, especially in inner city locations (Ong and Blumenberg, 1998). While a
fairly crude measure, this variable may allow us to isolate the relative strength of the
previously mentioned ‘labour segmentation’ effect against any demand side and/or
location disadvantage experienced by lower income unskilled workers. To account for
any ‘negative’ human capital incurred during unemployment the variable of
unemployment duration is included. Van den Berg and Gorter (1997) found the length
of unemployment to be insignificant, however, economic literature associates
increased unemployment duration with difficulty in obtaining employment (see for
example: Layard et al, 1991). This increased difficulty may increase the search radius
and hence increase travel-to-work time for any resulting employment. Research has
also highlighted the existence of an opposing ‘discouraged worker effect’ (for
example, Budd et al, 1988) in which unemployed workers’ search activity decreases
with unemployment duration, which may reduce the search radius and hence reduce
travel-to-work time.
6
Third, the financial set of variables are designed to provide a measure of the current
financial position of the unemployed workers. Research has shown that the propensity
to seek work is inversely related to current income (Blomquist and HannsonBrusewitz, 1990). In particular, the relationship between the ratio of state benefits to
average earnings (the replacement ratio) and the level of unemployment has been
shown to be positive (Minford et al, 1990; Layard et al, 1991). It is expected that
higher levels of total household income will reduce incentives to find employment,
thus reducing the job search radius and potential travel-to-work time.
Evidence on the effect of labour (earned) income upon travel-to-work behaviour
amongst employed workers is mixed. Izrael and McCarthy (1985) and Brännäs and
Laitila (1992) found that travel-to-work time increased with earnings, while Aronsson
and Brännäs (1996) suggest the opposite. They argue that this occurs due to the fact
that leisure is a normal good, i.e. as incomes rise more leisure is demanded, so travel
time falls to facilitate this. However, to date, research on the effects of non-labour
income for the unemployed is lacking.
A high level of personal savings may act as a similar disincentive to job search as a
high level of non-labour income, with a corresponding negative effect on travel-towork time. However, a high level of savings is an indicator of past wealth as well as
current financial security and may therefore be associated with unemployed workers
who were previously in higher earning professions. Such individuals may have higher
potential travel-to-work times enforced by labour market segmentation, as discussed
previously.
The ownership of private transport is a significant factor in improving the
employment status of welfare recipients (Ong and Blumenberg, 1998). Access to
private transport would enable unemployed workers to search for employment outside
public transport corridors and may make travel more comfortable, thus widening their
job search radius and increasing potential travel-to-work time. In addition, the
ownership of private transport may be a proxy for past wealth, equivalent to savings
as discussed above, and may similarly increase travel time. Travel by private transport
is also usually accepted as being faster, more convenient and often more reliable than
7
public transport (Turner and Niemeier, 1997). However, while likely to increase the
distance travelled to work, access to private transport may reduce the time spent
travelling. Unemployed job seekers may recognise this and it may be reflected in their
stated potential travel-to-work times.
The fourth set of factors examined is that of potential job characteristics, which
measure the importance unemployed workers attach to specific job features. In
particular, the minimum wage the worker is willing to accept, the reservation wage,
has been tested in previous research. Blau (1991) found it to be insignificant in the job
acceptance decision process. Van den Berg and Gorter (1997) used differences in the
reservation wage to measure the utility of travel-to-work time. They found that there
are threshold values of reservation wage for which they can state with confidence that
an unemployed worker would or would not be prepared to travel for more than one
hour. The reservation wage was found to be higher for a high travel-to-work time.
It can be argued this particular relationship is of a bi-causal nature in that the wage
offered by the employer and that demanded by the job seeker will be influenced by the
travel-to-work time. A high travel-to-work time dictates, ceteris paribus, that a higher
reservation wage will be required by the unemployed worker, and the higher the
reservation wage offered, the longer the worker will be prepared to travel. In addition,
it can be argued that the higher the reservation wage required by the worker,
independent of travel time (for instance because the individual is well qualified and/or
experienced in a profession), the further that individual will expect to have to travel to
meet his or her wage requirements.
Further potential job characteristics that should be examined are the unemployed
workers’ attitudes to shift work, the importance of promotion prospects and the
willingness to accept part-time or temporary jobs (Adams et al, 2000). A greater
desire to avoid shift work may force job seekers to widen their search radius in order
to obtain employment that satisfies their work pattern requirements. However, it may
also be the case that such a desire to avoid shift work may stem from substantial levels
of family responsibility which, consistent with the household responsibility hypothesis
as outlined by Turner and Niemeier (1997), would reduce potential travel-to-work
8
time. The importance of promotion prospects may be expected to increase potential
travel time, again because strict criteria for employment should entail a wider search
radius. Unemployed workers seeking jobs with promotion prospects may also be
younger and seeking work in more skilled occupations, both of which would be
expected to increase potential travel-to-work time. Part-time jobs should be associated
with shorter travel-to-work times as they involve a higher ratio of work to travel time.
Similarly as a new job involves an investment in finding and possibly investing in
new means of transport, temporary jobs are likely to be associated with shorter
commutes.
Fifth, the location characteristics need to be considered. This issue of opportunities for
employment and the characteristics of the local economy forms a further category of
factors influencing a job seeker’s travel-to-work attitudes, termed below as location
issues. Poor accessibility to a potential work location may discourage people from
searching or applying for jobs there. Factors such as access to (including car
ownership) and cost of transport will be important, as will the availability of different
transport modes and potential to integrate work journeys with other trips (for example
shopping or taking children to school).
The attitudes and practices of local employers and the characteristics of the
neighbourhood economy affect opportunities for local job seekers (Adams et al, 2000;
Preston and McLafferty, 1999). The accessibility to job opportunities will also be
affected by the density of jobs and other factors, such as the number of other job
seekers nearer to the jobs (McQuaid et al, 1996). In addition, the flow of information
is influenced by distance with different communication channels between employer
and job seeker being used at different times in the search process and for different
types of jobs (Russo et al, 1996). Hence those living further from job opportunities
may be at a disadvantage so they may be willing to travel further to work to open
greater numbers of potential job opportunities, or conversely they may become
discouraged and stop seeking work. For details of the accessibility measure used, see
McQuaid et al (2001).
9
Research has shown that place of residence can have an impact on travel-to-work
(DeSalvo, 1985; Ong and Blumenberg, 1998). The inflexibility of certain types of
housing tenure limits workers’ ability to move, and this can increase travel-to-work
time. Housing tenure is therefore also included as a social factor. So, in areas with
high shares of public housing as in the study area (below) the housing characteristics
(e.g. owner occupier or renter, public or private) may be important. The variable of
private house tenant is included in the current study as this is the group likely to face
the highest housing costs and therefore most likely to be caught in a ‘benefits trap’.
These various issues are now combined in a basic model.
3. Theoretical Model
The length of time that individuals would be willing to spend travelling to work is
determined by their utility function. Rational individuals will maximise utility subject
to a budget constraint. If we define a workers utility as:
 =  (C, L; Z )
(1)
Where C is consumption, L is leisure and Z a set of exogenous factors. These
functional determinants of utility are themselves determined by
C = c(Y)
(2)
LE = T - H t - H w
(3)
LU = T – S
(4)
Where LE is leisure for those in employment, LU is leisure for those unemployment, Y
is household income, T is total time available, H T is hours spent travelling to work,
HW is hours spent working and S is time spent searching for employment. For
simplicity T includes essential household functions, such as house cleaning, although
there may be a trade-off between L and T. Leisure time will be total time minus time
10
spent working or travelling for employed workers, and total time minus time spent on
job search for unemployed workers. There is a trade-off involved between leisure and
work where the hours worked will increase income and consumption but will reduce
leisure time, individuals preferences on this will be dependant upon their own utility
functions. The trade-off will also be affected by the intrinsic benefits of different types
of leisure activities.
Similarly with travel-to-work time, individuals willing to spend less time travelling to
work may derive greater utility from a unit of leisure, for example because of specific
personal or economic circumstances. Hence, it would be expected that unemployed
workers who would be prepared to spend less than a specified time, say up to 30
minutes, travelling to work would exhibit a different set of characteristics from those
who would be willing to travel for longer. No continuous travel time data were
available. Respondents were asked to state their potential travel times within given
ranges corresponding to the values below. As we were dealing with expected travel
time, it was felt that any more precise measure given would be subject to a high
margin of error. Also many respondents would most likely round to the nearest 15
minutes anyway, so responses may otherwise be inconsistent. We therefore define
unemployed job seekers in terms of four potential travel-to-work groups:
K = 1 willing to travel up to 30 minutes to work
K = 2 willing to travel 31-45 minutes to work
K = 3 willing to travel 46-60 minutes to work
K = 4 willing to travel over 60 minutes to work.
The model simplifies into equation:
Ti  X i   i
(5)
Where Ti represents the maximum travel-to-work time that an individual i was
prepared to travel. X i is the matrix of factors isolated in the factor analysis which
may influence the probability that individual i will travel for a longer period each day
11
with a parameter vector  and i is a normally distributed random variable which
allows for measurement errors and unmeasured effects. The exogenous explanatory
variables were selected to represent the broad groups of job seeker attributes as
discussed earlier as: social, acquired human capital, financial, potential job
characteristics and location characteristics. The complete list of variables is given in
Appendix 1.
A factor analysis of job seeker attributes was then performed and a regression
conducted on these factors, using travel-to-work time as the dependent variable.
Factors were extracted using Principal Component Analysis using the Varimax
rotation method with Kaiser normalisation, which enhances interpretation of both the
factors and the variables within these. The scores (values with respect to each
observation) for these factors were then analysed using a censored grouped data
regression, taking as a dependant variable travel to work time, measured on a fourpoint scale as defined by the boundaries above.
The data are derived from a survey of 306 unemployed job seekers (72% male) carried
out in 13 job centres in the Bathgate and Edinburgh travel-to-work areas (TTWAs).
All interviewees were seeking full-time work. Edinburgh is the capital city of
Scotland and a financial centre with a TTWA population of around half of a million
and Bathgate is a contiguous industrial and mining area with higher unemployment
and rural hinterland and a relatively high level of commuting to Edinburgh.
Unemployed job seekers were asked about the way they looked for work, what jobs
they were looking for, their personal characteristics, and what was the maximum time
that they were prepared to spend travelling (one-way) to work each day.
4. Results
The factor analysis produced ten main factors that are summarised in Table 1. These
factors were chosen according to the Kaiser criterion, with eigenvalues of more than
one and they can also be reasonably interpreted (Table 2). Factor 1 profiles job seekers
who are willing to travel longer to work (see below) as having a high level of formal
12
human capital: with positive years educated, YEARSED(+), and level of education
reached, HEDQUAL(+), educated to degree level, DEGREE(+) but not educated
solely to vocational level, SCOTVEC(-). Factor 1 could be interpreted as ‘high human
capital’ job seekers as they represent acquired human capital characteristics. Factor 2
represents a job seeker with high income, TOTINC(+), older, AGE(+), with access to
private transport, PRTRANS(+), MARRIED(+), with dependants, NDEPS(+) and
professionally qualified, PROFDUM(+). Factor 2 could be interpreted as a ‘better-off
and settled life-stage’ variable and represents both social and financial characteristics.
TABLE 1 ABOUT HERE
TABLE 2 ABOUT HERE
Factor 3 is a ‘high density location’ variable with high accessibility to job centres in
the area, ACC(+), high local job vacancy levels, VACLEVEL(+), employment in the
local (postcode) area, PSDEMP(+), and the level of local (postcode) unemployment,
PSDU(+). This Factor clearly represents the location characteristics. Factor 4 could be
interpreted as a ‘part-time, temporary workers’ variable with willingness to accept
part-time, FUTPT(+), and temporary, FUTTEMP(+), jobs, representing job
characteristics. Factor 5 is a ‘non-post-school qualified’ variable with people with
their highest qualification being advanced level or equivalent High School
qualifications, ALEVEL(+), but negatively related with those having only vocational
post-school qualifications, SCOTVEC(-). This Factor again represents acquired
human capital characteristics. Factor 6 is a ‘female flexible worker’ variable with
unwillingness to do shift work, DUMSHIFT(+), female, but negative in terms of
needing to work full-time, FUTFT(-), representing job characteristics.
Factor 7 is a ‘requiring suitable permanent jobs’ variable in that job seekers perceive
their own work quality positively, QUALITY(+), and require a full-time permanent
job, FUTPERM(+). This Factor can be interpreted as representing both a combination
of acquired human capital and innate characteristics (qualities) and job characteristics.
It loads positively against travel time and so is consistent with Factor 4 which loads
negatively (see Table 3 below). Examining the components of Factors 4 and 7, it
should be noted that FUTPERM is not an inverse of FUTTEMP – these variables test
13
whether a worker is prepared to take a permanent or temporary job respectively and
are therefore not mutually exclusive. The presence of these variables in each factor
does not therefore lead to collinearity between these factors. An interpretation is that
F7 represents job expectations of workers, whereas F4 is a measure of the
characteristics of a worker and their geographical location.
Factor 8 has wishing to find jobs that offer promotion prospects, DUMPROM(+), use
the job seekers skills, DUMSKILL(+), and offer a high expected wage, EXPW(+).
Hence, it may be termed the ‘good jobs’ variable and represents job characteristics.
Factor 9 is a ‘low skilled and long-term unemployed’ variable with job seekers who
are manual workers, MANDUM(+), and have been out of work for a long time,
LENGTHU(+). These represent low human capital characteristics. Finally, factor 10
includes high accessibility to job opportunities, ACC(+), and job seekers in private
rented housing, PRENT(+), which in the survey area indicates residence in highdensity properties close to centres of economic activity and can be termed ‘city centre
dwellers’. These represent location characteristics.
A grouped data logistic regression was then carried out upon the factor scores for
factors 1-10, using the of length of time job seekers are willing to travel-to-work as
the dependent variable.
TABLE 3 ABOUT HERE
The results (Table 3) show that Factors 6 to 10 ‘female flexible workers’, ‘requiring
suitable permanent jobs’, ‘good jobs’, ‘low skilled and long-term unemployed’ and
‘accessible private housing’ respectively are significant in the regression at the 10% or
less levels. Factor 1, ‘high human capital’, narrowly misses significance at the 10%
level. Factor 6 is the most highly significant in the equation. The interpretation of the
significance of this is that job seekers who most closely fit this profile, i.e. are
unwilling to do shift work, female and not requiring a full-time job, are, as expected,
likely to travel less. Likewise, job seekers who fit the ‘low skilled and long-term
unemployed’ profile of Factor 9 are also less likely to travel for long periods, which is
consistent with our expectations.
14
Those ‘requiring suitable permanent jobs’ in terms of the job seekers having a high
opinion of their own skills and work attitudes, and requiring a full-time job (Factors 7)
are more likely to travel longer. Similarly, as is expected in theory, those requiring
‘good jobs’ (Factor 8) in terms of promotion prospects, expected wages and skills
needed are more likely to commute longer. Finally, Factor 10, ‘city centre dwellers’,
are likely to be young professional people seeking jobs at the high end of the labour
market and who are less likely to have family commitments. Although these people
live in accessible areas, they may be willing to endure a higher travel time to secure
appropriate employment and they recognise the long travel times in city traffic.
The other factors are not significant in the regression analysis, but the signs are all as
expected. This suggests that the most strongly correlated variables (Factors 1-5) were
less influential on travel time. Factor 1, ‘high human capital’, are more willing to
travel longer to work as their expected future income (pecuniary and non-pecuniary)
should be higher, especially in the long-term. This is consistent with other evidence
cited above. Factor 2, ‘better-off and settled life-stage’ similarly represents job seekers
willing to travel longer as expected with their higher incomes and access to private
transport (although the coefficient here is very small). However, this factor suggests
increasing age is linked to willingness to travel, although it is expected to increase
with age before declining again in later working life.
The next three factors are each associated with a lower willingness to travel longer to
work. Factor 3, ‘high density location’, suggests that job seekers profiled as living in
areas with high accessibility to centres with high employment and vacancies (and
unemployment due to the relatively large population) are willing to travel less, due to
the relative abundance of local opportunities. Factor 4, ‘part-time, temporary
workers’, suggests that those willing to accept part-time or temporary jobs are less
willing to travel. This is expected given the balance between travel and work time in a
part-time job and the effort and expense of arranging travel for a temporary job. Factor
5 is a ‘non-post-school qualified’ variable with those not going to Further or Higher
Education or who only have vocational qualifications, willing to travel less, again
15
suggesting that greater human capital investment is associated with longer travel-towork.
5 Conclusions
The factor analysis has produced results that present a picture which is consistent with
a number of expectations identified in the empirical literature in terms of the specific
influences upon the job seeker potential travel-to-work times. However, the results of
this research expand on this by offering an insight as to how these may be combined
and displayed in job seekers within the sample.
Ten Factors were identified: ‘high human capital’, ‘better-off and settled life-stage’,
‘high density location’, ‘part-time, temporary workers’, ‘non-post-school qualified’,
‘female flexible workers’, ‘requiring suitable permanent jobs’, ‘good jobs’, ‘low
skilled and long-term unemployed’ and ‘accessible private housing’ respectively.
All but two of the10 Factors identified fell into only one of the main groups of
characteristics set out in the paper: social, acquired human capital, financial, potential
job characteristics and location characteristics. Factors 1, 5 and 9 were composed of
human capital characteristics, Factors 4, 6 and 8 of job characteristics, Factors 3 and
10 of location characteristics. In two cases there were factors from two sets of
characteristics. In the case of Factor 2, ‘better-off and settled life-stage’ job seekers
this represented both social and financial characteristics (age, total income, married,
private transport, dependants and professional qualifications). Factor 7 was composed
of both a combination of innate social and human capital characteristics (own
perceived work quality) and job characteristics (requiring a full-time permanent job).
When a grouped data logistic regression was then carried out upon the factor scores,
Factors 6 to 10 were found to be significant. Of these significant factors, ‘female
flexible workers’ and the ‘low skilled and long-term unemployed’ were associated
with willingness to travel for shorter times. Factors ‘requiring suitable permanent
jobs’, ‘good jobs’, and ‘accessible private housing’ showed a willingness to commute
16
for longer times. The other factors had the expected signs (with higher human capital
being associated with willingness to commute longer and lower human capital, parttime and temporary workers or those living in high job opportunity areas being less
willing to commute). The significant factors represented high human capital, job and
location characteristics. Interestingly, the social factors were not found to be
significant. This suggests that greater research is needed to consider the effects of
human capital, job and location characteristics on the attitudes towards travel-to-work
of unemployed job seekers.
Acknowledgements
This work arose from a larger project involving John Adams and Andrew
Charlesworth-May, without whose help it could not have been completed. The authors
would also like to thank an anonymous referee for useful comments.
17
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19
Table 1 Factor Analysis Results – Rotated Component Matrix
Rotated Component Matrixa
BATHGATE
ACC2
VACLEVEL
TOTINC
YEARSED
AGE
MANDUM
PRTRANS
FEMALE
MARRIED
NDEPS
PRENT
QUALITY
FUTFT
FUTPT
FUTPERM
FUTTEMP
DUMSHIFT
DUMPROM
DUMSKILL
LENGTHU
PSDEMP
PSDU
EXPW
PROFDUM
HEDQUAL
DEGREE
ALEVEL
SCOTVEC
1
1.438E-02
9.236E-02
.118
-6.95E-02
.935
-.102
-.339
8.123E-02
8.209E-02
-8.01E-02
.129
5.870E-02
-8.23E-02
3.733E-02
9.417E-02
1.085E-02
.131
-4.36E-02
.167
.101
-.123
4.127E-02
-1.83E-02
.110
3.940E-02
.846
.945
5.681E-02
-.439
2
6.508E-03
-4.30E-02
-7.52E-03
.677
1.855E-02
.664
-4.36E-02
.521
-.131
.838
.552
-4.61E-02
5.376E-02
-9.94E-02
.199
9.870E-02
6.589E-02
4.361E-02
-.340
7.356E-02
1.554E-02
4.825E-03
-1.39E-02
.318
.440
-.167
2.860E-03
-1.74E-02
-.276
3
-.105
.645
.605
1.710E-04
6.445E-02
2.938E-03
.121
-4.87E-02
-3.56E-02
9.462E-03
-7.07E-02
4.554E-02
2.318E-03
-1.40E-02
.117
6.308E-02
4.729E-02
9.026E-02
-7.27E-02
7.816E-02
-1.04E-02
.925
.818
-.299
6.538E-02
7.015E-02
4.244E-02
1.260E-02
7.359E-02
Extraction Method: Principal Component Analys is.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 14 iterations.
4
.742
-7.41E-02
-.439
.173
7.446E-02
-6.50E-02
-.141
.152
.401
2.852E-02
6.509E-02
-7.12E-02
-1.56E-02
-4.21E-02
.659
-9.94E-03
.722
-.156
-7.51E-02
-.257
-3.43E-02
4.315E-02
.134
.260
-.151
.125
1.381E-02
.112
6.101E-02
Component
5
6
-5.11E-02
-.162
-4.74E-02 4.425E-02
.150
.243
.123
.258
.195 -6.71E-02
.163 2.112E-02
-.152
-.140
1.062E-02
-.112
5.556E-02
.554
1.792E-02 1.665E-02
-.125 -7.66E-04
1.450E-02 -8.60E-03
.241
.107
2.986E-02
-.693
4.232E-02
.288
-.274
-.146
.149 -1.33E-02
-7.87E-02
.587
5.771E-02 1.128E-02
.228
.213
2.967E-02 7.091E-02
4.897E-02 2.723E-04
-.120 -2.35E-02
-.160
-.296
-.223
-.188
.169 2.737E-03
-.184 1.045E-02
.872 -5.20E-02
-.589 7.632E-02
7
.233
-.104
6.298E-02
6.356E-02
-5.80E-02
-.142
-2.56E-02
.176
.203
3.501E-02
.181
5.399E-02
.566
.183
-1.41E-04
.695
-.291
7.386E-02
.318
3.169E-02
3.210E-02
3.033E-02
2.931E-02
-6.16E-02
-.326
5.602E-02
-5.17E-02
4.956E-02
.120
8
-.111
8.295E-03
-.200
.124
7.026E-02
.154
-7.10E-02
.195
-6.38E-03
-8.71E-02
-.241
4.892E-03
9.563E-02
6.640E-02
-8.64E-02
-4.44E-02
-1.11E-02
.129
.481
.644
7.269E-02
5.653E-03
2.892E-02
.557
.349
7.316E-02
3.571E-02
6.293E-02
-5.71E-02
9
-2.89E-02
-8.29E-02
-2.57E-02
-2.94E-02
-8.55E-02
.306
.462
-.283
-3.74E-02
-.169
.202
6.092E-02
-2.75E-02
-5.89E-02
-3.28E-02
3.755E-02
-.107
-2.01E-02
-.188
.108
.790
3.965E-03
.114
.106
-3.19E-02
-.242
-4.08E-02
-.118
-.248
10
-.205
.511
4.981E-02
.215
2.587E-02
-6.41E-02
-.102
-.183
8.566E-02
-4.24E-02
-1.70E-02
.857
1.122E-02
-.135
3.841E-02
-3.73E-03
1.549E-02
-.215
.165
-3.33E-02
8.675E-02
.110
-.181
-2.15E-02
-.125
4.308E-02
3.005E-02
1.607E-02
1.725E-02
20
Table 2 Eigenvalues of Extracted Factors
Total Variance Explained
Component
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Initial Eigenvalues
% of
Cumulativ
Total
Variance
e%
3.517
12.127
12.127
3.184
10.981
23.108
2.621
9.038
32.146
2.121
7.313
39.459
1.702
5.869
45.328
1.397
4.819
50.147
1.279
4.409
54.556
1.141
3.936
58.492
1.066
3.674
62.166
1.041
3.590
65.756
.965
3.328
69.085
.893
3.079
72.164
.871
3.002
75.166
.844
2.911
78.076
.810
2.792
80.868
.724
2.495
83.363
.697
2.403
85.766
.634
2.186
87.952
.580
2.001
89.953
.558
1.922
91.875
.473
1.632
93.507
.453
1.561
95.068
.377
1.300
96.368
.323
1.113
97.481
.289
.998
98.479
.213
.734
99.213
.160
.552
99.765
5.089E-02
.175
99.940
1.731E-02 5.968E-02
100.000
Extraction Method: Principal Component Analys is.
Rotation Sums of Squared Loadings
% of
Cumulativ
Total
Variance
e%
2.977
10.265
10.265
2.792
9.628
19.893
2.494
8.601
28.494
2.200
7.586
36.080
1.630
5.621
41.701
1.622
5.594
47.296
1.371
4.729
52.024
1.343
4.632
56.657
1.329
4.582
61.239
1.310
4.518
65.756
21
Table 3 Estimated Grouped Data Regression Equation Coefficients for Time
Willing to Travel-to-work
Factor
Characteristics
Coefficient estimate
F1 ‘high human capital’
Human capital
1.8401
F2 ‘better-off and settled life-stage’
Social and financial
0.0299
F3 ‘high density location’
Location
-1.5746
F4 ‘part-time, temporary workers’
Job
-0.6018
F5 ‘non-post-school qualified’
Human capital
-0.2427
F6 ‘female flexible workers’
Job
-4.5691***
F7 ‘requiring suitable permanent jobs’
Human capital/social, job
2.2834*
F8 ‘good jobs’
Job
2.7644**
long-term Human capital
-2.9543**
F9
‘low
skilled
and
unemployed’
F10 ‘accessible private housing’
Constant
*** significant at 1% level
** significant at 5% level
*significant at 10% level
Location
2.3606*
51.394***
22
APPENDIX 1 DEFINITION OF VARIABLES
TTWT =
job seeker’s maximum stated daily travel-to-work time (minutes)
All have positive co-efficients except those marked (-).
Factor 1 variables
YEARSED = years of education
HEDQUAL = level of education reached
DEGREE =
educated to degree level
SCOTVEC = vocational qualifications only (-)
Factor 2 variables
TOTINC =
monthly non-earned income
AGE =
age of job seeker in years
PRTRANS = 1 if job seeker has access to private transport, 0 otherwise
SINGLE =
1 if the job seeker is single, 0 otherwise
NDEPS =
1 if the job seeker has dependent children, 0 otherwise
PROFQUAL = level of professional/vocational qualifications from 0 (none) to 3
(advanced)
Factor 3 variables
ACC =
accessibility index measuring travel time from job seeker’s residence to
major centres of employment
VACLEVEL = level of vacancies in local Job Centre
PSDEMP =
number employed in postal district
PSDU =
number unemployed in postal district
Factor 4 variables
BATHGATE= 1 if job seeker is resident in Bathgate TTWA, 0 if Edinburgh TTWA
FEMALE =
1 if the job seeker is female, 0 if male
FUTPT =
prepared to take part-time job
FUTTEMP = prepared to take temporary job
VACLEVEL= level of vacancies in local Job Centre
23
Factor 5 variables
ALEVEL =
highest qualification is school Advanced level or equivalent
SCOTVEC = has vocational qualifications (-)
Factor 6 variables
FUTFT =
needing to work full-time (-)
FEMALE =
1 if the job seeker is female, 0 if male
DUMSHIFT = willing to undertake shift work
Factor 7 variables
QUALITY =
self-perceived quality index of transferable skills
FUTPERM = prepared to take a permanent job
Factor 8 variables
DUMPROM = it is important that a new job has promotion prospects
DUMSKILL = it is important that a new job utilises the job seeker’s skills
EXPW =
expected wage
Factor 9 variables
MANDUM = 1 if job seeker is seeking manual occupation, 0 otherwise
LENGTHU = number of weeks that job seeker has been unemployed
Factor 10 variables
ACC =
accessibility index measuring travel time from job seeker’s residence to
major centres of employment
PRENT =
private rented accommodation
Other variables in the factor analysis
TOTINC =
total household income
MARRIED = married