Examining the relationship between commuting patterns, employment growth and unemployment in the NSW Greater Metropolitan Region.
Bill, Anthea ; Mitchell, William ; Watts, Martin 等
Introduction
Analysis of regional labour markets in Australia reveals persistent
disparities in rates of labour utilisation (Mitchell and Bill, 2005b).
In particular, unemployment dispersion has not fallen despite the
decline in the national unemployment rate since 1993. There is
increasing evidence that regional labour market outcomes are not
determined exclusively by the national business cycle, even if account
is taken of industrial structure, so that reliance on indiscriminate
Keynesian macroeconomic policy will not redress persistent inequality in
labour utilisation rates (Mitchell and Juniper, 2006). In addition,
regions differ in their composition of unemployment between short and
long term, but notwithstanding the spatial persistence of unemployment,
the evidence does not support the commonly held view that long term
unemployment is irreversible (Mitchell and Bill, 2005a).
Moreover, when employment growth is spatially uneven as it has been
over the 1990s, regionally localised growth (and stagnation) may promote
strong migratory and commuting responses, as relatively advantaged
workers seek out employment opportunities. Gordon (2003: 56) argues that
few barriers to labour market adjustment exist at the small area level.
While interactions between labour markets are strongest between
proximate or neighbouring regions (Mitchell and Bill, 2004 and 2005a,
2005b for empirical application to Australia), adjustments to
disequilibria travel across sub-markets relatively quickly. Such
adjustments occur through commuting and migration; and the majority, of
migration is through small moves between neighbouring regions (Gordon,
2003: 59). Migration and commuting are likely to play a greater role in
times of buoyant economic activity than recession (Gordon, 2003).
Further, unevenness in the distribution of employment opportunities is
likely to be the key motivating factor, rather than differentials in the
rewards and risks of the destination region (Gordon, 2003).
From a policy view point, commuting and migration are liable to
directly impact on the effectiveness of local employment growth in
reducing local unemployment (Renkow, 2003). In-commuting may frustrate
the attempts of local policymakers to deliver opportunities to resident
unemployed or to stimulate local business via increased resident
purchasing power. On the other hand, local job creation strategies may
not be strictly necessary to revitalise flagging local economies, if
resident workers are able to secure employment in neighbouring regions.
This reliance on residential mobility to remedy regional downturns may
heavily disadvantage low-skilled workers who are less likely to commute or migrate (Mitchell and Bill, 2005b).
In this paper we employ the labour market accounts (LMA) framework
for the period 1996-2001 to analyse these labour market responses in the
NSW GMR, one of the most buoyant economic regions in Australia over the
1990s. This framework decomposes the movements in working age population
(WAP) and labour force (LF) for a particular area to determine who fills
the jobs arising from changing employment levels. We provide estimates
for the following components: (a) labour force changes due to
demographic processes, which are broken down into natural increase and
net in-migration; (b) labour force changes due to changes in the labour
force participation rate; (c) changes in unemployment, which are broken
down into changes arising from demographic processes and changes arising
from changes in the percentage of the labour force that are unemployed;
and (d) changes in net in-commuting.
Regression models are estimated to assess the relative strength of
the relationships between these adjustment responses and percentage
employment change. Separate models are estimated for men and women to
test whether their respective adjustment processes are different. We
also augment the regressions to determine whether the initial
occupational structure of each area impacts on the adjustment process.
The results show that migration and commuting responses are
dominant with employment growth between 1996 and 2001 eliciting
substantial changes in commuting behaviour. There are clear differences
between men and women, with men showing relatively greater in-commuting
responsiveness to employment growth. The important implication is that
as a result of this mobility, unemployment changes in local areas have
been muted.
In Section 2, we review the LMA literature, followed in Section 3
by a presentation of the LMA framework and the data to be used. Section
4 utilises the decomposed labour market responses in regression models
to estimate the relation between employment change and the components of
labour market adjustment. Concluding comments and policy implications
are presented in the final section.
The labour markets accounts literature
A number of UK studies of cities have analysed the 'sectoral
and spatial shifts for different sections of the labour force'
(Bailey and Turok, 2000: 631) arising from the processes of
de-industrialisation and de-urbanisation within the LMA framework. An
equivalent approach to regional labour market analysis which has been
extended to analyse localised fiscal impacts of growth was developed
separately in the US by researchers within the Community Policy.
Analysis Network (see Scott and Johnson, 2000; Renkow, 2003). The major
differences between the two approaches relate to the analytical methods
used and applications targetted.
Bailey and Turok (2000) examined the impact of job loss on the
labour market adjustment process across major cities in Britain from
1981 to 1991. They found high rates of adjustment occurred through
migration and changes in commuting patterns, but some of these changes
arose from workers relocating out of the cities, but continuing to work
in them. For some of the resident workforce, however, the adjustment
took the form of higher levels of economic inactivity, which combined
with out-migration led to unemployment falling despite lower employment.
The authors identified major gender and occupational differences in
responses to employment changes. Women were more likely to drop out of
the labour force in response to employment loss, and women in less
skilled occupations had a much higher rate of inactivity than their more
skilled counterparts. Also cities with high shares of manual workers
experienced less out-migration and greater increases in inactivity when
employment fell. The authors attribute these results to a number of
factors. First more qualified individuals have higher incomes and are
able to commute greater distances. Also, women tend to be more
constrained than men due to their higher level of domestic
responsibilities, and greater incidence of part-time work. Second, less
qualified workers are alleged to experience greater barriers to
migration than professional and managerial employees, which can be
attributed to income levels, moving costs and barriers to migration
arising from the social housing system. Bailey and Turok (2000: 648)
suggest that there are likely to be few direct benefits for residents
from creating professional and managerial employment because: (a) there
are few unemployed residents in these occupations; and (b) the potential
applicants for these jobs have wide commuting fields and hence
significant choice about housing location. Conversely; job creation for
less qualified workers brings direct local benefits. Over half of the
jobs are obtained by residents who were previously unemployed or
inactive; while more than a quarter go to in-migrants or those who would
have out-migrated. Few jobs are lost to commuters.
Renkow (2003) employs the LMA framework to explore the labour
market adjustment process across both urban and rural counties in the
U.S. over the period 1980-90. The motivation for his study is both who
secures new jobs created in a particular county, but also the public
finance implications, since 44 per cent of local public expenditures in
rural North Carolina are funded by residential property taxes. Changes
in commuting patterns and the size of the labour force account for most
of the labour market adjustment associated with employment change,
rather than the unemployment rate, which is consistent with Owen et al.
(1984). Significant differences in the pattern of labour market
adjustment are found between rural and metropolitan counties. The
significant take up of new jobs via in-commuting suggests that leakages
associated with employment shocks may be substantial (Renkow, 2003:
510).
The labour market accounts (LMA) model
The LMA framework decomposes movements in working age population
(WAP) and labour force (LF) for a particular area to determine who fills
the jobs arising from employment growth. The approach is useful for
analysing the extent to which a community enjoys higher incomes as a
result of employment growth Barkley et al. (2002) and provides a basis
for measuring the shortfall of jobs in a local area (Bailey and Turok,
2000).
Figure 1 presents a stylised version of the LMA framework to show
the seven sources of take-up of new local jobs. Following Barkley et al.
(2002), local residents who are currently not in the labour force, may
choose to become economically active (A) by increasing their labour
force participation. Local unemployed residents may gain local
employment (B). Local residents who are in employment (locally or not)
may take additional jobs (C), or they may quit and take new local jobs
(D, E). Residents from outside the local area may also in-commute (F) or
'in-migrate' into the local area (G) and secure employment
there.
[FIGURE 1 OMITTED]
The system of labour market accounts used in this paper draws on
the contemporary approach of Bailey and Turok (2000), conceptualised in
Figure L Bailey and Turok (2000) note that employment change over time
in an area gives rise to three interrelated changes, namely labour force
variations, which incorporates the level of net in-migration, changes in
the number of these residents who are unemployed and changes in net
commuting flows. In the process of estimating this relationship the
labour force change is given to be a function of change in the working
age population due to demographic factors (say new labour market
entrants) and net out-migration. Changes in unemployment are similarly
broken down into a component associated with changes in labour force
participation and a function of the unemployment rate. The final
component of the accounts arises from the change in the net in-commuting
associated with the local area, expressed as the difference between the
change in local employment and the change in the level of employment of
residents, some of which is local. Detailed working is included in Bill
et al.(2005).
The primary data for this analysis are Statistical Local Area (SLA)
data drawn from ABS Census 2001. A simple comparison of the working-age
profile over the five years yields the natural increase in the
working-age profile from individuals getting older minus any deaths in
that age group plus the level of net in-migration. The natural change in
the working-age profile is obtained by age adjusting the 1996
working-age profile. SLA level death rates were devised using ABS
(2001a) and ABS (2001b). An estimate of deaths across the age
distribution for men and women in each SLA over the 5 year period is
obtained (see Bill et al. (2005) for details). While the NSW Greater
Metropolitan Region (GMR) study area officially comprises 70 SLAs, only
55 SLAs were included in this study--the Sydney metropolitan area,
Newcastle, Blacktown, Sutherland Shire but omitting SLAs in the upper
and northern Hunter. This smaller dataset was imposed by the restricted
availability of 1996 Census data. Data availability also does not permit
complete disaggregation of labour market accounts by occupation. A
complete analysis would require unemployment by occupation and gender
for each spatial area.
Modelling labour market responses to employment growth
Overview of labour market responses
Summary statistics of the labour market responses to employment
change between 1996 and 2001 (Table 1), show that Greater Metropolitan
Sydney gained on average 8.6 per cent of their male labour force and 8.8
of their female labour forces over this period via demographic changes
with net in-migration dominating (4.6 per cent for males and 5.3 per
cent for females). In this growth period, changes in male labour force
participation reduced the available labour force on average across the
areas whereas female labour force participation increased. On average,
employment growth only had a muted impact on the unemployment of
residents. Interestingly changes in net in-commuting represented the
dominant labour market response to the extra employment generated over
the period. This is true for both males (5.6 per cent on average) and
females (4.5 per cent on average) for the study areas shown.
Figures 2 and 3 illustrate the individual LMA components of the
employment change between 1996 and 2001 for men and women, respectively.
Clearly, Sydney dominates the other SLAs for both men and women. The
muted response of unemployment revealed in Table I also translates into
a lack of variation in the unemployment responses across the SLAs.
Employment growth between 1996 and 2001 did not significantly reduce
residents' unemployment, partly because it was not of a sufficient
strength, given movements in workers from other areas, through migration
and commuting.
[FIGURES 2-3 OMITTED]
That the Sydney SLA emerges as an outlier in terms of demographic
changes, in-migration and commuting in both Figures is hardly
surprising. As a result of a considerable inner-city economic revival
the workforce of Sydney expanded by 16 per cent over the 5 years. Most
of this growth was in high-skill managerial and professional occupations
in line with industrial shifts favouring the 'new economy'
(Raskall, 2002: 284). Sydney's residential population almost
doubled from 1991 to 1996 and again from 1996 to 2001. In August 2001 a
well-educated workforce more than 16 times larger than the residential
population of the city commuted inwards (Raskall, 2002: 285).
Regression analysis of labour market responses
Bailey and Turok (2000: 639) use regression models "to examine
the relative strength of the relationships between employment change and
each of the labour market adjustment variables." The first male and
female equations involved regressing each of the labour market
components outlined in Section 3 expressed as a percentage of the 1996
labour force on total local employment change between 1996 and 2001
expressed as a percentage of the 1996 labour force. The constant term
captures labour market adjustments not attributable to employment
change. We also employed systems estimation imposing the cross-equation
restriction that the sum of the slope coefficients for the % change in
employment should sum to one. The restricted results (not reported)
generally accord with the unrestricted results.
We also seek to determine whether the initial occupational
structure of an area impacts on adjustment. It is expected that areas
with higher proportions of manual workers (labourers and tradespersons)
would experience lower rates of adjustment, so that the adjustment
processes of men and women would be different. While this arises partly
due to occupational differences, women are more likely instrumentally
attached to the labour force.
The male and female labour market adjustment responses to
employment change are shown in Table 2 and Table 3. As noted above, the
sample period was one of consolidated growth, following the 1991
recession. There was considerable adjustment to employment change in the
form of net in-migration and net in-commuting with the latter
dominating. For every 1000 male jobs created in an area, net
in-commuting by men rose by 846 and 274 economically-active men migrated
into the same area. The goodness of fit measures (adjusted [R.sup.2])
indicate that the relationships are strong (0.97 and 0.86, respectively
for in-commuting and in-migration). So both out-migration and
out-commuting occur in areas where employment losses arise.
Overall the labour force responses due to demographic processes are
smaller for women (Table 3). Further, while the net in-commuting
response is lower for females (745 jobs per 1,000 extra jobs, compared
to 846 for males), the statistically significant net in-migration
coefficient indicates that for every 1,000 jobs generated net female
in-migration on average is 306 (compared to 274 for males). Consistent
with Bailey and Turok (2000) we find that females have exhibited a more
dramatic participation response compared to males, a 2.3 per cent (on
average) labour force increase which is not surprising given a period of
consolidated employment growth. This is in contrast to the decline in
the male labour force.
Strikingly, employment growth only had a small impact on
unemployment for both males and females (1000 extra jobs reducing
unemployment by 4 via reductions in the unemployment rate for males and
3 for females (although statistically this result is barely significant
for males) but increasing it by 15 as a result of demographic processes
for males and by 20 for females (including the hidden unemployed). For
males with every 1000 jobs created, 61 workers dropped out of the labour
force via participation rate changes. Given this surprising result, we
explored the role of different age cohorts, but found no significant
variations by age.
The picture of labour adjustment emerging within the Greater
Metropolitan Sydney is that both men and women rely heavily on commuting
responses across regions to gain income-earning opportunities, with
migration being the second most significant response. This suggests that
labour mobility between neighbouring regions is a strong adjustment
factor in the Australian economy during the late 1990s.
Bailey and Turok (2000: 642) suggest that "part of the
explanation for these changes must lie in the changes for different
occupational groups." With a rising proportion of jobs in the
professional and other skilled occupations, it would be expected that a
larger proportion of the employment opportunities would be taken by
in-commuters. This reflects the fact that the more advantaged population
cohorts have greater choice of housing and transport and as a result
tend to commute longer distances than the more disadvantaged segments of
the population. Equally, the declining participation rate for the
resident populations is consistent with a smaller proportion of lower
skilled job opportunities.
The regression models for males and females were extended by adding
control variables to the right hand side. A metropolitan dummy which
took the value of 1 for the metro region and 0 otherwise was added. We
also consider two occupational groups at opposite ends of the wage
distribution, manual workers and professionals. The percentage of manual
male workers in total male and female employment and the percentage of
professional male workers in total male and female employment for each
area were added to the basic regressions. If significant differences
were found, the results would contribute to an explanation of persistent
regional unemployment differentials based on the regional occupational
structure.
In all cases the fit of the regressions is improved with the
introduction of occupational shares (see Table 4 and Table 5), in some
cases, substantially. For males the labour market responses to
employment growth are similar to those in Table 2. For every 1,000 jobs
created, net in-migration rises by 150 and net in-commuting rises by
966, other things equal. The impact of adding occupational controls is
similar for females--reducing the net in-migration response and
increasing the net in-commuting response.
The adjusted results suggest that males have larger net-migration
and net in-commuting responses compared to females after controlling for
occupational structure.
For males, metropolitan regions have lower net in-migration and
lower unemployment due to demographic processes, other things equal than
non-metropolitan areas. Females in metropolitan regions have lower
in-migration (similar to males), lower labour force participation rates
(in stark contrast to males), higher net in-commuting (in accord with
males but with a stronger relative impact) and overall lower
unemployment compared to non-metropolitan areas. All other labour market
responses are insensitive to this geographic distinction.
For males in SLAs where the manual employment share is higher, the
role of natural increase is higher, but, other things equal, the
responses of net in-migration, labour force participation and
unemployment from changes in the unemployment rate, are lower. The
results for SLAs with higher female manual employment share are very
similar.
Conclusion
This paper is the first to apply the LMA framework to Australian
data and yields two policy-relevant results. First, commuting, followed
by migration, was the main labour market adjustment mechanism for both
men and women in the late 1990s. Thus considerable leakages exist in
local employment creation, with the effects of local employment shocks
rippling out across the Greater Sydney Metropolitan region. Such
leakages, in upturns and downturns, need to be considered by
policy-makers when estimating the returns to local residents of local
employment generation. Men rely more heavily on commuting across local
areas than women to gain income-earning opportunities in response to
employment growth. Second, employment growth had only a small impact on
the change in unemployment for both males and females. While this may
partly be due to increased job-competition from in-migrants and
in-commuters it remains that the overall employment growth has not been
sufficient to generate enough jobs to satisfy the desires of the
workers.
It may be, in the case of the NSW GMR that the occupational and
industrial pattern of job growth, has benefited those employed in new
economy rather than those in old economy industries, and has resulted in
skill or spatial mismatch amongst local unemployed. Unfortunately only
an occupational or industrial breakdown of these adjustment processes
can shed light on this phenomenon, the subject of future work. It is
often claimed that supply-side policies (such as retraining) ought to be
directed towards the local unemployed when employment growth in their
region is largely absorbed by in-commuters (Sunley et al., 2006: 61).
However, when there are insufficient employment opportunities
macro-wide, and workers are highly mobile as they are within the NSW
GMR, the most eligible job-seekers will out-compete the local unemployed
for local jobs. Without a demand-led strategy. (more job creation
overall), supply-side policies to address localised unemployment will
have minimal impact on the probability of employment. Gordon (1999)
concludes that the solution may lie in employment creation strategies
which target a less leaky area, such as a metropolitan wide area, and
perhaps demand-side solutions might best be designed on a larger,
region-wide scale (cited in Sunley et al., 2006).
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Anthea Bill, William Mitchell and Martin Watts (1)
(Endnotes)
(1) The authors are Research Officer, Centre of Full Employment and
Equity (Bill), Director of Centre of Full Employment and Equity and
Professor of Economies (Mitchell), and Deputy Director, Centre of Full
Employment and Equity and Associate Professor in Economics (Watts), all
at the University of Newcastle, Australia.
Table 1: Summary of labour market responses
to employment change, 1996-2001
Std.
Mean Dev. Maximum Minimum
Males
LF changes due to
demography 8.6 14.8 105.0 -3.6
Natural increase in LF 4.0 2.9 11.8 -1.6
Net in-migration to LF 4.6 15.1 105.2 -6.1
LF changes due to DLFPR -2.1 4.0 4.6 -24.9
Change in UN due to
demography 0.5 1.0 6.0 -0.6
Change in UN due to DUR -1.6 1.1 0.4 -4.4
Change in net in-commuting 5.6 44.0 319.2 -30.1
Std.
Females Mean Dev. Maximum Minimum
LF changes due to
demography 8.8 16.5 118.8 -3.4
Natural increase in LF 3.4 3.0 10.5 -3.1
Net in-migration to LF 5.3 16.6 117.9 -5.4
LF changes due to DLFPR 1.6 3.6 8.3 -17.3
Change in UN due to
demography 0.8 1.2 8.0 -0.4
Change in UN due to DUR -1.4 1.2 0.2 -4.6
Change in net in-commuting 4.5 38.8 283.1 -19.6
Note: components are expressed as a percentage of 1996 labour force,
for males and females, respectively. LF refers to the Labour Force,
--LFPR is the change in the labour force participation rate,
UN is unemployment and--UR is the change in the unemployment rate.
Table 2: Labour market adjustment responses
to employment change for males
Labour market Constant Coefficient
adjustment (%) for %
component change
employment
Change in residents Labour
Force
Due to demographic processes 5.208 0.265
natural increase 4.067 -0.009
net in-migration 1.141 0.274
Due to change in LFPR rate -1.343 -0.061
Increase in net in-commuting 5.174 0.846
Change in unemployment
Due to demographic processes 0.294 0.015
Due to change in
unemployment rate 1.536 -0.004
Labour market t-statistic for Adjusted
adjustment % change [R.sup.2]
component employment
Change in residents Labour
Force
Due to demographic processes 16.29 0.83
natural increase -1.18 0.01
net in-migration 18.05 0.86
Due to change in LFPR rate -9.17 0.61
Increase in net in-commuting 4,126 0.97
Change in unemployment
Due to demographic processes 10.07 0.65
Due to change in
unemployment rate -1.41 0.02
Table 3 Labour market adjustment responses
to employment change for females
Coefficient
Labour market for %
adjustment Constant change
component (%) employment
Change in residents Labour
Force
Due to demographic processes 4.915 0.301
natural increase 3.488 -0.005
net in-migration 1.427 0.306
Due to change in LFPR rate 2.286 0.050
Increase in net in,-commuting -5.066 0.745
Change in unemployment
Due to demographic processes 0.510 0.020
Due to change in
unemployment rate -1.397 -3
t-statistic
Labour market for % Adjusted
adjustment change [R.sup.2]
component employment
Change in residents Labour
Force
Due to demographic processes 18.65 0.87
natural increase -0.61 -0.01
net in-migration 20.90 0.89
Due to change in LFPR rate -7.52 0.51
Increase in net in-commuting 39.13 0.97
Change in unemployment
Due to demographic processes 11.92 0.72
Due to change in
unemployment rate -0.96 0
Note: LFPR is labour force participation rate.
Table 4: Male labour market adjustment responses
to employment change with occupational structure
and metro dummy
Labour market Constant Coeff % t-stat
adjustment (%) change change
component emp emp
Change in residents
Labour Force
Due to demographic
processes 3.97 0.173 6.90
natural increase 1.51 0.023 1.78
net in-migration 2.45 0.160 8.06
Due to change in LFPR rate 0.31 -0.085 -9.15
Increase in net in commuting -0.21 0.966 32.06
Change in unemployment
Due to demographic processes 0.27 0.005 2.18
Due to change in unemployment -0.20 0.002 0.61
rate
Labour market Coeff t-stat Coeff
adjustment manual % manual % profs %
component total total total
emp emp emp
Change in residents
Labour Force
Due to demographic
processes -0.039 -0.14 0.362
natural increase 0.388 2.73 -0.111
net in-migration -0.427 -2.06 0.473
Due to change in LFPR rate -0.443 -4.27 0.076
Increase in net in commuting -0.167 -0.50 -0.480
Change in unemployment
Due to demographic processes -0.02 -0.90 0.041
Due to change in unemployment -0.179 -4.00 -0.033
rate
Labour market t-stat Coeff on
adjustment profs metro
component % total dummy
emp
Change in residents
Labour Force
Due to demographic
processes 4.59 -3.570
natural increase -2.76 1.057
net in-migration 6.07 -4.627
Due to change in LFPR rate 2.58 1.150
Increase in net in commuting -5.05 2.719
Change in unemployment
Due to demographic processes 6.30 0.041
Due to change in unemployment -2.60 -0.033
rate
Labour market t-stat Adj
adjustment metro [R.sub.2]
component dummy
Change in residents
Labour Force
Due to demographic
processes -1.74 0.88
natural increase 1.01 0.16
net in-migration -3.03 0.93
Due to change in LFPR rate 1.50 0.77
Increase in net in commuting 1.10 0.98
Change in unemployment
Due to demographic processes -2.28 0.80
Due to change in unemployment 1.79 0.42
rate
Note: Adj R2 is the adjusted R2. Coeff refers to the estimated
coefficient; emp is employment, profs is professionals.
Table 5: Female labour market adjustment responses
to employment change with occupational structure
and metro dummy
Labour market Constant Coeff % t-stat
adjustment (%) change change
component emp emp
Change in residents
Labour Force
Due to demographic
processes 0.53 0.119 5.42
natural increase 0.68 0.000 0.01
net in-migration -0.14 0.119 7.03
Due to change in LFPR rate 2.7 -0.109 -9.05
Increase in net in commuting -6.20 0.909 25.54
Change in unemployment
Due to demographic processes 0.36 0.004 1.84
Due to change in unemployment -0.28 0.000 0.07
rate
Labour market Coeff t-stat Coeff
adjustment manual % manual % profs %
component total total total
emp emp emp
Change in residents
Labour Force
Due to demographic
processes -0.413 -2.25 0.580
natural increase 0.472 3.54 0.003
net in-migration -0.885 -6.27 0.583
Due to change in LFPR rate -0.304 -3.02 0.184
Increase in net in commuting 0.972 3.27 -0.506
Change in unemployment
Due to demographic processes -0.019 -1.08 0.052
Due to change in unemployment -0.205 4.90 0.017
rate
Labour market t-stat Coeff on
adjustment profs metro
component % total dummy
emp
Change in residents
Labour Force
Due to demographic
processes 9.45 -3.764
natural increase -0.07 -0.21
net in-migration 12.33 -3.554
Due to change in LFPR rate 5.46 -2.061
Increase in net in commuting -5.08 4.579
Change in unemployment
Due to demographic processes 8.65 -0.775
Due to change in unemployment -1.22 -0.620
rate
Labour market t-stat Adj
adjustment metro [R.sub.2]
component dummy
Change in residents
Labour Force
Due to demographic
processes -2.82 0.95
natural increase -0.22 0.21
net in-migration -3.45 0.97
Due to change in LFPR rate -2.81 0.68
Increase in net in commuting 2.11 0.98
Change in unemployment
Due to demographic processes -5.97 0.91
Due to change in unemployment 2.03 0.49
rate
Notes: see Table