The aim of this project was to describe commuting in rural areas, and compare these to other types of areas in England.
In order to achieve this 1991 Census data was used to analyse commuter flow data. Analysis was undertaken at two levels: a ward level analysis and an individual level analysis. The ward level analysis looked at the origins and destinations of all commuting into and out of all wards in England. The individual level analysis looked at the characteristics of commuters. These analyses aimed to discern patterns of commuting and their potential causes.
This is the first such study of commuting flows using a comprehensive database (census data), and represents the first robust and reliable insight into commuting in England. Use of the Census allowed a range of specific data sources to be used to enhance reliability (Special Workplace Statistics; Small Area Statistics; Sample of Anonymised Records; Special Migration Statistics). Crucially, use of the Census meant that all areas, and a fully representative sample of individuals, could be included in the analysis. This greatly enhances reliability and validity. For example, this is the first study to assess patterns of commuting into and out of all 8,619 wards in England, generating flow matrices of 74,287,161 cells to discern commuting between each of these wards.
Three types of commuting were considered: commuting balance (in- and out-flows between wards); commuting distance; and commuting mode.
In general, outward commuting from wards is significantly related to a number of factors. The more employment opportunities there are nearby and the more migrants and self employed people in a ward, the less outward commuting. However, the higher the population density and percentages of people in social classes I and II, the more outward commuting there will be.
The higher the proportion of people in a ward from social classes I and II, and the higher the percentages of migrants and people with 2 or more cars in the household, the higher will be the distance of the commute. As would be expected, the more employment opportunities there are nearby, the higher the population density, the lower the distance of the commute, as more urban wards retain more residents as employees.
For non-car commuting, the distance was positively associated with the number of households with 2 or more cars, percentage of people in social classes I and II, females in full time employment and the percentage of people who were unemployed.
Individual level modelling shows that long-distance migrants in rural areas are much more likely to commute a long distance than others in rural areas. This group will include those who have moved from urban to rural areas, whilst retaining their place of employment in an urban centre.
The greater the population density within a ward, the greater will be the amount of non-car commuting. Interestingly, the greater the percentages of women in full time employment and the percentages in social classes I and II, the greater the use of non-car modes for commuting.
The reason for the relationship with women in full time employment probably represents uneven access to cars within households, or the fact that the figures include hinterlands of large cities, especially London, where it is both economically necessary, and physically possible for women to have access to the labour market. Indeed, the reason for the positive relationship between outward non-car commuting and social classes I and II is the inclusion of such urban (especially London) hinterlands.
As would be expected, there is a negative association between outward non-car commuting and the percentage of households with 2 or more cars.
Individual level modelling shows that those commuters living in rural areas are significantly more likely to commute by car than commuters living elsewhere - this shows that those living in rural areas are significantly more likely to commute by car controlling for other variables.
Overall, the models we used to help explain commuting patterns performed well. However, they performed least well in 'predominantly urban' and 'predominantly rural' wards. Further work could usefully be conducted in these areas to investigate why this is so.
Rural areas are not, as some would argue, the sources of large levels of daily out commuting. Instead, rural areas have much less commuting activity per se - both in and out. However, when a person commutes from a rural area, they will undertake the trip predominantly by car, and travel a longer distance than average. This holds true throughout England, though proximity to large cities reduces the dominance of the car.
The role of migrants who move to rural areas is of interest. Indeed, when migrants to rural areas are more prevalent, so will the overall distance of commuting undertaken from that area. However, the volume will on average be much lower. This mirrors the experience of rural areas as a whole.
These findings are based on a thorough analysis of a robust data set. The figures provide an accurate insight of behaviour based upon 1991 data. The work provides a good benchmark for the study of commuting, and can form a first step in what would be a useful longitudinal study.