The Department for Transport appraises projects in a sustainable development framework, setting out components of social welfare benefits and costsof a scheme, relative to not implementing it. Whilst most welfare gains from transport schemes are increases in gross domestic product (GDP), some are not. Such effects include the changes in agglomeration following an investment, benefits arising from changes in competition, and economic benefits from increased employment and productivity. It is this latter effect which is of interest in this study, commissioned by the Department for Transport. Specifically this effect, the increase in labour productivity in a particular sector, or GP3, can be measured as the sum over all areas of the product of the change in employment in each sector/region, the level of productivity in that sector/region and the national average industry GDP in that sector. The aim of this project is to identify regional productivity differentials which can be used for this calculation. Since transport investments often have their greatest effect within a relatively small geographic area, the productivity measurements need to be highly disaggregate. In particular the Local Authority District (LAD) level for Great Britain (405 districts in England, Scotland and Wales) is considered. Productivity studies are concerned with modelling output per unit of input, where output can be measured based on a single input such as labour, or in the context of multiple inputs using total-factor productivity or multifactor productivity techniques. Given the data available and time constraints, labour productivity is used as the productivity measure. Under the assumption thatthe labour market is in equilibrium, there is equality between the hourlywage and marginal product of labour. Using data from the Annual Survey ofHours of Employees (ASHE) for 2004-2006, a reduced form wage equation is estimated using regression analysis of hourly wage on vectors of individual characteristics (including age, gender and occupation), employer characteristics (industry and firm size) and a vector of location dummies at the LAD level. Each regions productivity can then be estimated relative to thenational average. Whilst controlling for regional industry composition increases the range of differentials, it is found that the standard errors of the regional coefficients are considerably smaller in the model without interaction terms. This reduces the number of regions where productivity is statistically different from the national average With both models, London areas LADs dominate the most productive areas, with the City of London,Tower Hamlets and Westminster being the first 3 LADs in terms of productivity, and the same 8 LADs appear in the top 10. Regarding the LADs with the lowest productivity, both specifications agree that Isles of Scilly and Penwith are the bottom two, and that the Isle of Wight, North Norfolk, Kerrier, Torridge, Arun and Hastings are among the bottom 10. There is a larger geographic spread of the least productive regions. The results have a lower spread over regions than raw earnings differentials, i.e. by controlling for personal and firm level characteristics, the regional differentials in productivity are lower so use of the difference between regional wages and the national average as a measure of a regional labour productivity will provide a significant over- or underestimate of regional productivitydifferentials. This suggests caution should be used when employing income-based estimates of labour productivity defined in terms of gross value added in appraisal. For the covering abstract see ITRD E145999
Samenvatting