Chapter 7 Summary of Findings, Conclusions and Recommendations for
7.5 Recommendations for Further Study
With regard to tender price index compilation, this thesis recommends measuring the price movement of the M&E items and broadening the sample base to design and build contracts as two areas well worth pursuing to restore the representative nature of the indices. For design and build, acquiring access to the contract price information such as the contractor’s contract cost plan and the possibility of using such cost information to produce TPI deserve further study. Because of the diversity of M&E items giving comparable performance, it is difficult to stretch the existing current match item index method to measure the price movement of the M&E items.
Therefore, there is a need to depart from the presently adopted method and a hedonic index is an appealing alternative. Although the indices may become less consistent for long period analysis than the existing pure item matching method, this is a trade-off for improving the representativeness.
Looking at the components of the CCIs, the least reliable would seem to be the labour cost components and an in depth study is recommended with specific focus on the change in the composition and skill levels of the construction labour force.
This study suggests the private sector construction new orders series deserve closer examination with a view to explaining, and hopefully removing, , the prima facie inexplicable gap between the output and orders series. To avoid double counting, ONS currently samples only main contractors in the circa 230,000 construction units in IDBR for the construction new orders statistics, whereas for construction output statistics, they survey all contractors and deduct the amounts subcontracted to avoid double counting. Given the difficulty of ascertaining the proportion of specialist contractors obtaining work directly from clients, applying the same method that is
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used for sampling construction output to construction orders deserves further research.
The model presented in chapter 5 is supported by the result of the data driven vector autoregression (VAR) model except that out of the 10 pairs of relationships in the demand and supply model, the VAR model displays 2 pairs (output and unemployment, and tender price and building material price) as different from the demand and supply model. This study recommends including a measure of productivity, which is found important in driving long run relative construction output price, in the model. The model at present assumes linear relationship between growth of the economy and of the construction sector. However the Bon curve (Bon (1992), Pietroforte and Gregori (2006), Ruddock and Lopes (2006) and Strassmann (1970)) proposes a non-linear, bell-shaped, relationship in the long run.
For brevity the model does not allow a more elaborated autoregressive distributed lag structure, so the model is static. Extending and expanding the model to a dynamic system and making use of the demand and supply relationship as the long run driving force in the vector error correction autoregression framework would be a potentially rewarding next step of research.
Last but not least, inflation is a monetary phenomenon, so study of the transmission mechanism by which the central role of monetary policy affects the prices in different sectors of the economy is an overdue agenda item in the field of sectoral research.
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