Modelling UK household expenditure: economic versus noneconomic drivers

Abstract

This article attempts to quantify the contributions of economic and noneconomic factors that drive UK consumer expenditure for 12 COICOP categories of goods and services using the structural time series model (STSM) over the period 1964Q1 to 2006Q1. This approach allows for the relative quantification of the impact of noneconomic factors on UK household expenditure demand (via a stochastic trend and stochastic seasonal) in addition to the economic factors (income and price). The results suggest that the contribution of the noneconomic factors is generally higher for ‘housing, water, electricity, gas and other fuels’, ‘health’, ‘communication’ and ‘education’; hence, they have an important role to play in these sectors. The message for policymakers is therefore that, in addition to economic incentives such as taxes which might be needed if they wish to restrain future expenditure, other policies that attempt to influence lifestyles might also need to be considered.

Acknowledgements

This work is part of the interdisciplinary research group RESOLVE ¹⁴ funded by the ESRC (Award Reference: RES-152-25-1004) and their support is gratefully acknowledged. We thank members of RESOLVE for many discussions about the work, in particular David Broadstock, Angela Druckman and Tim Jackson. Of course, the authors are responsible for any remaining errors.

Notes

¹‘Classification of Individual Consumption by Purpose’, for more information see http://unstats.un.org/unsd/sna1993/glossform.asp?getitem=54.

²Almost ideal demand system.

³Differential consumer demand systems known as Central Bureau of Statistics (CBS).

⁴Linear expenditure system.

⁵Perhaps adequate demand system.

⁶This includes nonnormality, heteroscedasticity, autocorrelation and predictive failure tests. In addition, LR tests are performed for restrictions of a deterministic time trend and deterministic seasonal dummies. For further details, see Hunt and Ninomiya (2003).

⁷This work is part of ongoing research attempting to quantify the impact of ExNEF on consumer expenditure and demand; see, for example Chitnis and Hunt (2009a, b) and Broadstock and Hunt (2010).

⁸Previously known as underlying energy demand trend (UEDT); for example Hunt and Ninomiya (2003).

⁹ www.statistics.gov.uk

¹⁰The exceptions being ‘recreation and culture’ that suffers from autocorrelation despite some experimentation with different specifications and/or dummy variables.

¹¹For ‘housing, water, electricity, gas and other fuels’ and ‘health’ both the income and price coefficients are insignificant. ‘Education’ expenditure has a negative income coefficient (giving negative income elasticities in both the short and long run).

¹²By restricting the variance of the level and/or the slope to be zero.

¹³Charts showing the estimated underlying expenditure trend and seasonality for each sector can be found in Chitnis and Hunt (2009a). Note, all charts use the preferred models re-estimated over the whole period, up to and including 2006Q1.

¹⁴ www.surrey.ac.uk/resolve/