LEP Economic Profiles - How do local assets affect productivity?
Local Productivity Analysis
Note that some Local Authorities are in more than one LEP area. These authorities can be identified by the darker colouration.
The lighter bars show the full range of possible values, the darker bars show the values that apply to the selected LEP.
Standard economic studies only provide a partial picture
The standard approach is to gather series of economic indicators and to describe each individually, showing how an area performs relative to a given benchmark, whether it be a regional/UK average or a set of comparator areas. This analysis is then used to infer area characteristics that will contribute or hinder development prospects.
That approach does not help policy-makers to understand:
- how the mix of local attributes can help to determine the future prospects of an area;
- which of the particular local attributes under review is of most significance in determining future potential;
- how the mix of attributes varies from other benchmark areas or more economically successful parts of the country.
An integrated analysis offers more insight into performance
The key to understanding economic performance and potential is to comprehend the way in which the assets of an area come together to improve or limit performance prospects.
We have looked at eight asset groups:
- The proportion of micro (<5) firms, Business density, and Business vibrancy
- Labour Market
- Activity rate and Job density
- Full-time employment rate and Net Commuting
- Location/ Accessibility
- Rail journey time to London, Accessibility to 8 largest business centres, and Accessibility to large airports
- Industrial Base
- GVA-weighted sector employment distribution, Knowledge-based industries concentration, and Agglomeration elasticity
- Managerial and professional jobs, NVQ level 4+ qualifications rate, and the rate of no qualification
- Council Tax A&B band properties and Private sector stock
- Property mix
- Factory workspace and Office workspace
In order to understand the variety, pattern, and interaction of a wider range of indicators, we employ two statistical techniques.
- Factor analysis identifies common patterns in sets of different variables. This means that we can ‘summarise’ all of the indicators within a few newly constructed variables.
- These summary variables are then used in a regression analysis to determine how they explain variation in GVA per hour performance across areas, and to estimate the extent to which the local asset base assists/hinders local performance.