David Owen and Anne Green, University of Warwick

Transcription

1 Factors associated with commuting behaviour in England and Wales David Owen and Anne Green, University of Warwick Paper presented to British Society for Population Studies Annual Conference, University of Kent, th September Introduction The UK Government s central economic objective is to achieve high and stable levels of growth and employment, in order to enhance economic competitiveness and obviate social exclusion. Work for those who can has been identified by New Labour as the key to addressing poverty and social exclusion (Hills et al., 2002). This means that labour market participation is a central policy issue. Indeed, the Department for Work and Pensions (2005) Five Year Strategy sets out plans to raise the proportion of people of working age in employment to 80%, by increasing participation of the economically inactive as well as the unemployed. The reduction of gaps, between people and between places, is integral to many policy targets. For example, the Department for Work and Pensions has a performance target to increase employment rates of disadvantaged areas and groups (including those with the lowest qualifications) over the economic cycle, and significantly reduce the difference between their employment rates and the overall rate. Likewise, there is also a crossdepartmental target to make sustainable improvements in the economic performance of all English regions and over the long term reduce the persistent gap in growth rates between regions. The adoption of floor targets in social inclusion and neighbourhood renewal policies promotes a minimum standard which underperforming areas and groups are required to achieve, so that headline targets are not met by concentrating resources on the more advantaged at the expense of the disadvantaged. Reducing gaps in performance is recognised as important from equity and efficiency standpoints. There has been considerable debate about how best to achieve full employment and tackle areas of disadvantage and concentrations of worklessness. In the UK in recent years the policy emphasis has been on supply-side solutions aimed at promoting employability. Some commentators (for example, Turok and Webster, 1998; Sunley et al., 2001) have criticised this supply-side emphasis as failing to take into account a lack of suitable jobs in some local areas. Moving jobs to workers in disadvantaged neighbourhoods is intuitively attractive. Observations of the low commuting tolerances of non-employed and low skilled people (Lucas et al., 2001; Social Exclusion Unit, 2003; Lucas, 2004) have been used to argue for the need for local jobs which are proximate to deprived neighbourhoods if residents are to gain work. In theory, such a policy confers social, economic and ecological benefits. However, if these jobs are lost, communities formerly reliant on them are left stranded (Gordon, 2003), and may not have the confidence or experience to search for work further afield in a way that a workers to jobs policy encouraging workers to be mobile might have engendered. The practical limits of a jobs to workers policy are constrained in two main ways. The first is the available stock of footloose jobs. Here it is also salient to note that reference has been made to the existence of unfilled vacancies alongside relatively high levels of unemployment (Hogarth et al., 2003), as highlighted by Chancellor of the Exchequer Gordon Brown at the Urban Summit in 2002: too often there are workers without jobs side by side with jobs without workers. A second constraint on a jobs to workers policy is the way in which local labour markets operate. Job growth in areas with high levels of worklessness 1

2 does not necessarily trickle down to local residents; rather the leakage of jobs to non-local residents, who are not necessarily disadvantaged, is a well known phenomenon (Haughton et al., 1993; McGregor and McConnachie, 1995). Processes of job competition and mobility operate to leave the weakest behind (Gordon, 1999, 2003) because employers will not just seek workers from the local area and they may be reluctant to train local people in preference to recruiting experienced people from outside the local area. Hence, in practice both demand-side and supply-side policies have strengths and limitations, and some combination of both is likely to be required (Green and Shuttleworth, 2004). This paper explores the factors underlying spatial variations in labour market participation and commuting behaviour. A series of regression models are estimated, which identify the separate influence of individual, local labour market and geographical factors. These models explicitly build space into labour market models via a series of explanatory variables which summarise the employment opportunities and workers of different types accessible to a given location. The paper explains how these variables are derived from the population potential model formulation, and illustrates how this approach can be used in other types of local labour market models. The paper draws upon the findings of a research project funded by the Joseph Rowntree Foundation, entitled The geography of poor skills and access to work 2. Variations in commuting behaviour The 2001 Census Special Workplace Statistics were used to calculate distances travelled by comparing geographical centroids of wards, weighting the calculation by the number of persons involved in the commuting flow. These distances are summarised in Figure 1, which presents variations in median journey-to-work distances by occupation. The median is preferred to the mean as a measure of average distance, because the latter is subject to distortion by extremely long work journeys. Figure 1: Average commuting distance by occupation, England and Wales kilometres UQ Median LQ Managers & senior officials Professional Occupations Associate professional & technical occupations Administrative & secretarial occupations Skilled trades occupations Personal service occupations Sales & customer service occupations Process, plant & machine operatives Elementary occupations TOTAL Source: Table W205, 2001 Census 2

3 Broadly, commuting distance decreases with declining skill levels: with elementary occupations and personal service occupations (often associated with part-time working) being characterised by the shortest median and upper quartile values. However, those in higher level non-manual occupations both have the longest average work journeys and display a wider range of commuting distances than is evident for lower skill occupations , , Frequency 200 Frequency 300 Frequency med_1 Mean = 7.35 Std. Dev. = N = 8, med_2 Mean = Std. Dev. = N = 8, med_9 a) Managers (SOC 1) b) Professionals (SOC 2) c) Elementary (SOC 9) Figure 2: Distribution of median ward-to-ward commuting distances in England and Wales by SOC major group Mean = Std. Dev. = N = 8, ,727 6, ,235 6, med_all ,945 3,988 7,352 7,008 7,211 7,209 4,076 4,073 7,208 3,712 3,996 3,7757,082 3,834 3,972 7,369 8,372 8,392 8,710 7,862 8, ,385 5,740 4,837 5,974 8,567 2,406 8,473 2,022 8,502 4, ,851 2,886 3, B - North West A - North East G - East F - West Midlands E - East Midlands D - Yorkshire & Humber J - South East H - London W - Wales K - South West Government Office Region Figure 3: Boxplot of median distance by region of ward Greater detail of the geographical pattern of commuting is provided by Figure 2, which depicts the distribution of ward-level median journey-to-work distances (in kilometres) calculated from the Special Workplace Statistics for England and Wales, 2001 for managers, professionals and workers in elementary occupations. Workers from both higher status 3

4 occupations, have higher average commuting distances and have a more extended distribution of distances: workers in many wards have very long median journeys to work. In contrast, workers in elementary occupations have a much more compressed distribution of median distances, with the great majority having very short commuting distances and the tail of longer distances involving relatively few wards. Figure 3 demonstrates that there is little regional variation in ward-level median commuting distances, except for shorter distances and less variation in commuting distance in London (and a number of long median distances in the East of England). The factors underlying commuting behaviour were explored by fitting a regression model to individual journey-to-work distances, using data from the 3% individual Controlled Access Microdata Sample from the 2001 Census (Table 1). Table 1: Relationship of distance to work to social and economic characteristics and occupation B Std. Error Beta T Significa nce (Constant) Hours worked weekly Female no or low qualifications level 4/5 qualification Social rented Private rented Professional Assoc. professional Admin & secretarial Skilled trades Personal service Sales/customer service Process, etc. operatives Elementary The statistical fit of the model was very poor (adjusted R 2 =0.024), but it highlights key patterns of variation in commuting behaviour. The propensity to commute a long distance was lower in each other major occupation group than for managers and administrators, with workers in skilled trades and elementary occupations being least likely to commute long distances. Highly qualified people and those working long hours are more likely to have long journeys to work, while women and those with poor levels of qualification and living in private rented accommodation are more likely to have short commuting journeys. 3. Modelling geographical variations in commuting behaviour The insights gained from analysis of individual variations in commuting were used to build regression models which investigated whether ecological associations (using the average characteristics of an area as a surrogate for the characteristics of people living in those areas) can explain the aggregate behaviour of people living in those areas. The starting point was the work of Coombes and Raybould (2001) and Shuttleworth and Lloyd (2004), who undertook analyses of average travel-to-work distances in England and Wales and in Northern Ireland, based on data from the 1991 Census of Population. Shuttleworth and Lloyd s model implemented the job shortfall measure derived by Coombes and Raybould in the Northern Ireland context. Table 2 describes how the spatial variables 4

5 used in these models are derived. The approach adopted in these studies was to conduct regression analysis using the following variables: 1 Dependent variable: mean commuting distance (in kilometres) per ward Independent variables: relating to people : 2 o deprivation score as a measure of socio economic status and a proxy for income 3 o % of households with no car indicating reliance on public transport 4 o % of individual employed in SOC Major Groups 1-3 as a measure of occupational mix 5 and of ability to seek out a wider range of residential locations and to travel further o % of residents who were Catholic (used in Northern Ireland analysis only, an ethnic group variable is more applicable for England and Wales) relating to place : 6 o job ratio 7 o job shortfall score 8 o an employment accessibility score The first step was to estimate the Shuttleworth and Lloyd model. Not all the independent variables could be replicated: for example, while an official index of deprivation is available for England (the 2004 Index of Multiple Deprivation), no such index had yet been calculated for Wales. Instead, the percentage of the population aged claiming Income Support was used as an indicator of prosperity. This was found to have a very close relationship (R=0.947) with the IMD 2004 in England, and the minority variable was based on ethnic group rather than religion. Table 2: Shuttleworth and Lloyd Place-related variables Job ratio Ratio of the number of jobs located in a ward to the population of working age Job shortfall score The procedure is to: 1. Fill as many jobs as possible with residents of the same ward 2. From this identify wards with job surpluses or deficits. 3. Sort all pairs of wards in terms of distance apart, up to a specified distance threshold. 4. If one ward has a job deficit and the other a surplus, allocate jobs from the latter to the former. an employment This is a potential measure P, i calculated from the number of people accessibility score seeking work (in work and unemployed) E i across all wards in England and Wales, and the distance (d ij ) between them, raised to an exponent β in order to represent the friction of distance. 9 n Ei Pi = dijβ j= 1 1 The precise variables used are not exactly the same in each study for example, a change dimension was included in some of the place variables in England and Wales, but not in the Northern Ireland analysis. 2 Note that these variables are ecological, but are used to capture characteristics of people in the area. 3 Note that an unemployment rate variable could be used, but is highly correlated with deprivation. 4 Included because of its specific transport link, although it is positively correlated with deprivation. 5 And of ability to seek out a wider range of residential locations and to travel further. 6 Note that these variables are specifically related to wards as places. 7 Calculated as the numbers of workers employed in a ward as a ratio of the population of working age (i.e. including the employed and non-employed). 8 Derived by operationalising a set procedure for allocating jobs to the nearest person of working age eligible for employment, up to a pre-defined distance cut-off. 9 The distance exponent is set to 1.5, following Coombes and Raybould (2000) 5

6 Table 3: Replication of Shuttleworth and Lloyd (2004) results Model 1 Model 2 Model 3 Beta T Beta T Beta T Employment accessibility score Job ratio Job shortfall Log% with no car Deprivation % ethnic minorities % SOC Adjusted R Table 3 presents the results of replicating the Shuttleworth and Lloyd (2004) model and following their approach of building up a composite model from individual and spatial models. The main difference is in the dependent variable: the average journey-to-work is measured using the median, which is then log-transformed. The degree of explanation achieved is much poorer for England and Wales than for Northern Ireland. The area characteristics model (2) fits the data much better than the spatial labour market variables (model 1). As in Northern Ireland, the best fit is achieved when both are brought together (in model 3). The influence of the employment accessibility score and job ratio is increased when area characteristics are included. However, the job ratio is a more significant influence than the job shortfall, the opposite of the Shuttleworth and Lloyd (2004) finding. Confirming the results for Northern Ireland, the sign of the beta coefficients on deprivation and % from SOCs 1 to 3 were positive in model 2. In model 3, when both sets of variables are brought together, the importance of the socio-economic variables is preserved, with (the logarithm of) car ownership the most important (negative) influence. Deprivation is the second most important influence, followed by employment accessibility. The relationship with the percent from ethnic minorities is weak, but negative as expected. However, the degree of statistical fit of the model is quite poor. In order to improve the fit, models which introduced improved variable specifications were estimated. The replacement/additional variables were: 1. A dummy variable for London (adding this improved the adjusted R 2 vale to 0.271); 2. An Improved ethnicity specification, to reflect the varying labour market experiences of people from different ethnic groups. Variables representing the percentage of the population (transformed to logarithms) from three ethnic groupings were added : Indian, Chinese, Other-Asian and other ethnic groups (most favoured); Black and Mixed parentage ethnic groups (intermediate position) and Pakistani and Bangladeshi (most deprived). These variables improved the adjusted R 2 vale to 0.290; 3. The percentage of the population from SOC major groups 1 to 3 was replaced by a measure of human capital. In addition, variables which represented a more complex/realistic representation of the spatial labour market were investigated. A series of summary measures of the geographical distribution of workers and jobs were investigated as potential independent variables. However, these were often closely correlated with the employment accessibility score. When added to the regression model, they increased the degree of explanation, but only because the multicollinearity between independent variables distorted the regression fit. After some experimentation, a variable which attempts to summarise job matching within reasonable commuting range was added to the model. This variable is the difference 6

7 between the number of jobs located within 20 km of the ward and the number of people working within 20km of the ward, expressed as a percentage of the number of jobs within this 20km radius. It can be summarised as a job excess measure. This variable increased the fit of the model to an adjusted R 2 of 0.298, and was highly statistically significant. 3.1 Spatially smoothed variables Coombes and Raybould (2001) present a series of regression models which predict commuting distance using similar measures to those used by Shuttleworth and Lloyd (2004). However, they also introduce what they refer to as smoothed variables. These are calculated using the population potential formula (e.g. the employment accessibility measure detailed in Table 2). Their variables include the job ratio and unemployment change. These variables are calculated by calculating potential values for the numerator and denominator used in calculating these ratio variables and then dividing the potentialised numerator by the potentialised denominator. Thus, to calculate the smoothed unemployment rate, it is necessary to calculate a potential for unemployment and a potential for the economically active population. The smoothed unemployment rate (URS ) i is therefore the former divided by the latter, multiplied by 100; n Uj B j= 1 dij URSi = n EAj B dij j= 1 Two of the variables used by Coombes and Raybould (2001) - the smoothed unemployment rate and the smoothed job ratio were added to the regression model. The correlation between these two variables is fairly weak (R=0.185). However, the smoothed job ratio has a close negative correlation (R=-0.691) with the job shortfall measure, and is even more closely positively correlated (R=0.814) with the employment accessibility score, which was therefore dropped from the model. These variables are mapped in Figures A1 and A2. The job ratio variable (Figure A3) highlights the availability of jobs in the centres of population (particularly the major cities) and the lack of jobs in peripheral areas. The smoothed unemployment rate (Figure A2) represents the average labour market conditions within commuting range and has a strong north/south contrast, with higher unemployment in London and Birmingham, but very low unemployment rates to the west of London. The combined effect of adding the spatial labour marked variables was to increase the degree of fit marginally (adjusted R 2 of 0.352), with the best fit for managers and poorest fit for elementary occupations. The most important influences are the percentage of households with no car and the smoothed job ratio, both of which act to reduce the median distance commuted. The median commuting distance is higher in London, while areas of higher smoothed unemployment rates have longer commuting distances, having taken other influences into account. The median commuting distance is lower the higher the percentage of the population from Pakistani and Bangladeshi or Indian and Other ethnic groups, but higher where the percentage from Black and mixed parentage ethnic groups is greater. There is a weak positive association between commuting distance and the human capital index, but no association with the percentage on income support, once other influences are taken into account. The job shortfall variable is no longer significantly associated with median distance. 7

8 Table 4: Model incorporating improved ecological and spatial variables All SOC 1 SOC 2 SOC 9 Beta T Beta T Beta T Beta T Job ratio Job shortfall % households with no car % on income support, London pakistani/bangladesh i log indian and other log mixed and black % job excess within 20km Smoothed unemployment rate Smoothed job ratio Human capital index Adjusted R * specific to each SOC major group 2.2 Urban morphology Figure 4: Median distance travelled by ODPM urban/rural classification med_all ,955 6,449 6,450 6,711 6,586 3,972 3,973 6,944 4,019 2,603 2,597 2,530 4,062 2,860 6,690 2,514 6,603 2,235 6,945 7,385 5,740 4,888 7,384 4,930 8,029 6,896 3,322 8,251 1,403 7,514 3,450 5,566 2,741 6,727 6,729 3,618 5,972 7,211 7,209 7,369 8,392 3,657 med_ ,947 6,711 2,514 3,972 4,019 3,973 6,943 6,455 6,944 6,586 2,530 4,062 2,860 6,690 7,011 7,065 2,525 6,955 6,450 2,528 7,026 5,995 5,740 4,901 5,882 6,896 2,857 5,647 4,874 5,915 5,337 5,881 4,873 5,334 2,235 7,012 7,602 5,646 5,0325, ,116 6,366 4,935 5,551 6,003 3,585 3,645 3,492 6,727 5,974 5,119 6,729 5,043 4,928 3,478 5,123 5,054 4,897 3,618 5,972 6,988 5,137 3,299 5,587 5,343 3, Urban > 10k - Town and Village, Hamlet Urban > 10k - Town and Village, Hamlet Urban > 10k - Town and Village, Hamlet Urban > 10k - Sparse Fringe - Sparse and Isolated Less Sparse Fringe - Less and Isolated Sparse Fringe - Sparse and Isolated Less Sparse Dwellings - Sparse Dwellings - Less Dwellings - Sparse Sparse Sparse Combined code Combined code a) All workers b) SOC1 (managers) Town and Village, Hamlet Fringe - Less and Isolated Sparse Dwellings - Less Sparse ,699 7, med_ ,539 6,453 4,763 6,727 7,027 7,003 7,015 4,573 7,006 7,013 7,014 5,132 6,599 7,023 8,567 5,744 5,915 4,223 5,123 6,363 7,612 7,012 8,029 6,719 6,590 6,496 7,556 4,764 8,013 3,400 2,235 1,694 6,350 8,695 2,004 6,814 7,780 5,337 3,586 6,736 5,486 3,610 5,616 med_ ,711 6,727 6,729 2,886 3,828 3,076 3,117 6,502 2,633 6,619 7,089 8,598 55,018 2,239 6,427 6,555 6,586 2,860 2,235 3,200 2,741 6, ,825 4,540 6,450 2,603 2,530 3,597 3,718 7,048 3, ,449 6,955 Urban > 10k - Town and Village, Hamlet Urban > 10k - Town and Village, Hamlet Urban > 10k - Town and Village, Hamlet Urban > 10k - Sparse Fringe - Sparse and Isolated Less Sparse Fringe - Less and Isolated Sparse Fringe - Sparse and Isolated Less Sparse Dwellings - Sparse Dwellings - Less Dwellings - Sparse Sparse Sparse Combined code Combined code c) SOC 2 (professionals) d) SOC 9 (elementary) Town and Fringe - Less Sparse Village, Hamlet and Isolated Dwellings - Less Sparse 8

9 The Office of the Deputy Prime Minister in association with the Office for National Statistics and the Department for Rural Affairs (DEFRA) has recently released a new urban/rural classification of wards in England and Wales. This distinguishes broad urban size, location in an urban core or fringe and location in areas of sparse population. Figure 4 presents contrasts in median commuting distance in the six categories of this classification for all workers and workers from SOCs 1, 2 and 9. There are some variations in distance travelled by level of the urban-rural classification; distances tend to be shorter in sparsely populated areas and longer in urban fringes than in urban centres for SOCs 1 and 2. Accordingly, five urban/rural morphology dummy variables were added to the model, representing all levels of the classification except in an urban area greater than 10 thousand people in a non-sparsely populated area, as most wards in London would fall into this category (Table 5; details in Table A1). Table 5: Results of adding morphological variables All SOC 1 SOC 2 SOC 9 Beta T Beta T Beta T Beta T Job ratio Job shortfall % households no car % on income support London pakistani/bangladeshi log indian and other log mixed and black % job excess <20km Smoothed unemp. rate Smoothed job ratio Human cap index Urban>10k sparse Town&fringe sparse Village etc. sparse Town&fringe less sp Village etc. less sparse Adjusted R * specific to each SOC major group The model achieves a higher R 2 value for all four regression models, with particular improvements for all flows and SOC major group 1. The influence of the car ownership variable is substantially reduced, but it is still the most significant influence. The urban/rural variables are highly significant, revealing lower commuting distances in more sparsely populated and rural wards and longer commuting distances in the urban fringe in more densely populated regions. The dummy variable for London is still positive and highly significant. The smoothed unemployment rate has a positive sign and the smoothed job ratio a negative sign. The coefficients for Pakistanis/Bangladeshis, and Indians and other Asians are negative, that for Black and Mixed is positive. The sign on the human capital index is positive. For SOC major group 1, lack of car ownership and the smoothed job ratio were the most significant (negative) influences, with strong negative signs in sparsely populated areas. The only significant ethnicity variable is the positive sign on mixed parentage and Black people. For SOC major group 2, the smoothed unemployment rate is not significant. The urban/rural dummy variables are all negative in sparsely populated regions and positive in more densely 9

10 populated regions, especially on the urban fringe. The only significant ethnicity variable is the negative sign on Indian and other. The sign on job excess is significant and negative. For SOC major group 9 (elementary occupations), the smoothed unemployment rate is positive and highly significant. The sign on mixed parentage and black people is positive and significant. The smoothed job ratio is the most important variable, followed by lack of car ownership. The sign on the human capital index is significant and negative. These models have demonstrated that adding spatial variables which represent the labour market and the urban/rural structure improve the explanatory power of regression models which relate commuting distance to the ecological characteristics of areas. However, the degree of variance in the data accounted for by the models presented here is not as high as in Northern Ireland. The commuting pattern in England and Wales is much more complex than that of Northern Ireland, with the dominant commuting focus (London, comparable to Belfast) supplemented by major cities such as Birmingham and Manchester and much more diverse commuting flows in the diffuse urbanisation pattern of south-east England. 4. Geographical variations in employment rates 1, ,400 1,000 1, , Frequency 600 Frequency 400 Frequency Working age employment rate Mean = Std. Dev. = N = 8, Age low qual employment rate Mean = Std. Dev. = N = 8,800 All of working age Aged with no or low qualifications Figure 5: Employment rates by ward, England and Wales Age high qual employment rate Aged with high qualifications Mean = Std. Dev. = N = 8,800 Regression models were also developed to account for geographical variations in employment rates across wards in England and Wales. Figure 5 presents the distribution of employment rates across the 8800 standard Census wards in England and Wales, with the normal distribution curve superimposed. Clearly, the distribution of values is negatively skewed, with a clustering around the modal value, which is well above the mean employment rate in each case. Table 6 presents variations in employment rates by region, socio-economic cluster type and urban morphology. Working age employment rates are highest in the South East and East of England and lowest in London, Wales and the North East. There is less regional variation in employment rates for the highly qualified than for those with no or low qualifications. Employment rates are highest in suburban areas and the commuter belt and lowest in the inner cities and older industrial areas. Employment rates were highest in the less sparse regions of the country, again highest in the urban fringe, suburbs and commuter villages. Employment rates are mapped in Figures A5 and A6 for year olds with high and no or low qualifications. 10

11 Table 6: Geographical variations in mean economic activity and employment rates by ward Employment rates for people aged 25 to 49 Working age employme nt rate with no or low qualificatio ns with high qualificatio ns J - South East G East K South West E - East Midlands F - West Midlands D - Yorkshire & Humber B North West H London W Wales A North East All regions Prospering Suburbs Suburbs Accessible Countryside Commuter Suburbs Out of Town Manufacturing Countryside Senior Communities Prospering Metropolitan Out of Town Housing Industrial Areas Built-up Areas Transitional Economies Student Communities Northern Ireland Countryside Inner City Multicultural Built-up Manufacturing Multicultural Areas All groups Urban > 10k Sparse Town and Fringe Sparse Village, Hamlet and Isolated Dwellings - Sparse Urban > 10k - Less Sparse Town and Fringe - Less Sparse Village, Hamlet and Isolated Dwellings - Less Sparse All urban/rural categories Regression models for ward-level employment rates were estimated for all people of working age and for people aged with no or low educational qualifications and highlevel educational qualifications. These models were informed by regressions on individual employment probabilities, estimated using CAMS data. The individual-level models highlighted the importance of material deprivation, gender and household structure in determining the probability of being in employment. 11

12 The geographical employment rate model drew upon this analysis and the results of the commuting distance models in developing a regression model which combined the influence of ecological characteristics of the area with regional location, the spatial labour market and urban morphology structure. Deprivation was represented by conducting a principal components analysis on the following variables; % households in social rented accommodation, % aged 25+ long-term unemployed, % aged on income support, 2001 and % of working age permanently sick. The first component extracted accounted for 78.3 per cent of the variance in these variables, and was added as the composite deprivation index. In addition, two spatial variables which proved successful in modelling geographical variations in commuting distances were added in order to further explore the significant geographical variations revealed in the results described above. These were the employment accessibility measure (representing the likelihood that proximity to job opportunities will increase the percentage in work) and the smoothed unemployment rate, representing average labour market conditions within the vicinity of a ward (expected to be negatively associated with the employment rate). Table 7: Regression models for employment rates Working age 25-49, low or no qualifications 25-49, high qualifications Beta T Beta T Beta T aged as % of working age aged 55+ as % of working age Deprivation indicator % of working age who are carers human capital index * % mixed parentage % Pakistani/Bangladeshi % Indian and other % Black North East North West Yorkshire & the Humber East Midlands West Midlands Eastern South East South West Wales Town>10k sparse Urban fringe, sparse Village, sparse Urban fringe, not sparse Village, not sparse Employment accessibility index Smoothed unemployment rate Adjusted R * year olds for working age, year olds for others For all people of working age, the percentage of year olds is the most important influence on the employment rate, representing the influence of students on depressing the 12

13 employment rate. The next most important influence reducing the employment rate was the deprivation indicator. The higher the percentage of carers and older people, the lower the employment rate, and employment rates were much reduced where the percentage of Pakistani and Bangladeshi people was highest. The regional variables were much less influential, but employment rates were higher in the West Midlands and North East and lower in the South East, South West and Wales having controlled for other influences. Employment accessibility emerges as a positive and statistically significant influence on the employment rate. The smoothed unemployment rate is one of the most significant negative influences on the employment rate. Thus, the expectation that holding deprivation and other factors constant, proximity to job opportunities increases the probability of employment while being located in an economically depressed local labour market decreases the likelihood of being in work is confirmed by these results. For people aged with no or low qualifications, the broad pattern is similar, with deprivation the most important factor. The human capital index is strongly negatively associated with the employment rate. Spatial variables are important, with strongly positive signs on the northern and West Midlands regional dummies. Employment accessibility is not a significant influence, but the smoothed unemployment rate is a very significant negative influence. For people aged with higher level qualifications, deprivation is the major factor depressing the employment rate. The human capital index is negatively associated with the probability of employment. The employment accessibility index is positively associated with the probability of being in employment, and the negative influence of the smoothed unemployment rate is stronger than for people with no or low level educational qualifications. There are strong negative regional dummies for the south-eastern regions and Wales. 5. Conclusion This paper has demonstrated how the addition of spatial labour market variables and indicators of spatial structure can improve the degree of fit of geographical models relating commuting distances and employment rates to the socio-economic characteristics of local areas. The models are informed by analyses of individual-level commuting and employment characteristics. The spatial variables build upon the work of Shuttleworth and Lloyd (2004) and Coombes and Raybould (2001). The models reveal that both people- and place-based factors influence participation in work. Age and gender are key determinants of labour market participation, but location in a deprived neighbourhood is associated with a lower probability of being in work, and prevailing conditions in the local labour market also influence participation. Participation in employment is substantially lower for those with poor skills than for those with higher level skills Moreover, other disadvantaging attributes such as poor health or being of Pakistani/Bangladeshi origin impact with greater force on those with poor skills than on the more highly qualified. They are constrained geographically in how far they travel to work. Irrespective of occupation, most people travel only short distances to work especially if they are working part-time, but those in elementary and other low skill occupations are characterised by shorter average commuting distances than highly skilled workers. 13

14 The analysis reveals that geography matters most for those with poor skills. There are long established and marked spatial variations in employment rates, and these spatial variations are particularly pronounced for those with poor skills even when other individual and other contextual factors have been taken into account. Employment rates are lower than expected, taking account of the age and gender structure of the population, in many large urban centres. In several northern cities the shortfall in employment participation is especially concentrated amongst males and older workers, whereas in some London boroughs especially those with a substantial ethnic minority population it is more evenly spread across age and gender sub-groups. By contrast, in many rural areas and throughout much of the prosperous south of England, employment rates are higher than expected, and especially so amongst those sub-groups (such as older males and the poorly skilled) who appear especially vulnerable in the more traditionally depressed areas. In these high employment rate areas, the share of the non-employed with recent experience of employment is higher than the national average. London emerges as distinctive in having lower employment rates than its overall level of prosperity would suggest. The policy challenge here is enabling more local people to compete successfully for available jobs. 14

15 References Coombes, M. and Raybould, S. (2000) Policy-relevant Surfaced Data on population Distribution and Characteristics, Transactions in GIS, 4(4), Coombes, M. and Raybould, S. (2001) Commuting in England and Wales, in D. Pitfield (ed.) Transport Planning, Logistics and Spatial Mismatch. European Research in Regional Science 11. pp Department for Work and Pensions (2005) Department for Work and Pensions Five Year Strategy: Opportunity and security throughou t life. Norwich: TSO. Gordon I. (1999) Move on up the car. Dealing with structural employment in London, Local Economy 14, Gordon I. (2003) Unemployment and spatial labour markets: strong adjustment and persistent concentration in Martin R. and Morrison P.S. (eds.) Geographies of Labou r Market Inequality. London: Routledge, Green A.E. and Shuttleworth I. (2004) Widening Mental Maps, Breaking Down Spatial Barriers: a review of policy initiatives. Report for the Department for Employment and Learning Northern Ireland. Haughton G., Johnson S., Murphy L. and Thomas K (1993) Local Geographies Of Unemployment: Long Term Unemploymen t In Areas Of Local Deprivation. Aldershot: Avebury. Hills J., Le Grand J. and Piachaud D. (2002) Understanding Social Exclusion. Oxford: Oxford University Press. Hogarth T., Shury J., Vivian D., Wilson R. and Winterbotham M. (2004) National Employers Skills Survey 2003: Main Report. Coventry: Learning and Skills Council. Lucas K. (2004) Running on Empty: transport, social exclusion and environmental justice. Bristol: Policy Press. Lucas K., Grosvenor T. and Simpson R. (2001) Transport, the environment and social exclusion. York: York Publishing Services. McGregor A. and McConnachie M (1995) Social exclusion, urban regeneration and economic reintegration, Urban Studies 32, Shuttleworth, I. and Lloyd, C. (2004) Analysing Average Travel-to-Work Distances in Northern Ireland Using the 1991 Census of population: The Effects of Locality, Social Composition, and Religion, School of Geography, The Queen s University of Belfast. ocial Exclusion Unit (2003) Transport and Social Exclusion. London: Social Exclusion Unit, Cabinet Office. Sunley P., Martin R.L. and Nativel C. (2001) Mapping the New Deal: local disparities in the performance of welfare-to-work, Transactions of the Institute of British Geographers 26, Turok I. and Webster D. (1998) The New Deal: jeopardised by the geography of unemployment?, Local Economy 12,

16 Table A1: Dependent Variable: Log 10 Median journey-to-work distance B Std. Error Beta T Significan ce All flows Zeroorder Partial Part Toleranc e (Constant) Job ratio Job shortfall % households with no car % on income support, London log pakistani/bangladeshi log indian and other log mixed and black % job excess within 20km Smoothed unemployment rate Smoothed job ratio Human capital index Urban>10k sparse Town&fringe sparse Village etc. sparse Town&fringe less sparse Village etc. less sparse VIF 16

17 Figure A1: Employment potential surface Employment potential 196,000 32,000 21,000 13,000 4,000 Figure A2: Smoothed unemployment rate Unemployment rate potential

18 Figure A3:Job ratio potential surface Figure A4: Ward-level employment rate for residents aged years with no qualifications, 2001 England and Wales Job ratio potential No qualifications % in w ork

19 Figure A5: Employment rate for residents aged years with high level qualifications, 2001 local authorities in England and Wales Highly qualified % in w ork 90 to (54) 80 to (322) 70 to (13) Figure A6: Employment rate for residents aged years with no qualifications, 2001 local authorities in England and Wales No qualifications % in w ork 80 to (4) 70 to (142) 60 to (150) 50 to (74) 40 to (13) 30 to (6) 19