Demographically Induced Changes in the Structure of Final Demand and Infrastructure Use

Abstract

As consumption patterns differ between households depending on the age and number of household members, demographic change alters the structure of overall consumption expenditure. This chapter presents an extended input–output model, which was used to study the impact of demographically induced changes in the structure of consumption expenditure on infrastructure use. The analysis is performed for the cases of Germany, Hamburg, and Mecklenburg-Western Pomerania. Selected sustainability indicators (energy use and emissions of CO₂ and NO_X) are quantified to study the implications for sustainable development. The results show that although the shift in consumption patterns tends to reduce energy use and emissions, it does not achieve the required decoupling of GDP growth and emissions.

Appendix

In regional economics, it is often necessary to construct an input–output table for the regional economy by means of a nonsurvey method, because the limited availability of time and money makes the construction of a survey-based table impossible. Traditional nonsurvey methods, however, generate biased results because they underestimate the importance of interregional trade. This problem can be solved by using the more advanced techniques developed by Flegg, Webber, & Elliott (1995) and Flegg and Webber (2000). However, these techniques require the modeller to select a certain value for a key parameter. Although recent work (Bonfiglio, 2009) has provided a range of “good” parameter values, the choice of this value remains to a large extent arbitrary. Therefore, an alternative approach was selected.

The CHARM approach (Kronenberg, 2009a) follows the recommendation by Richardson (1985) “to develop improved nonsurvey adjustments that correct for the effects of cross-hauling”. It is based on the observation that cross-hauling is more common when products are highly heterogeneous. For practical purposes, it is assumed that for each product i the amount of cross-hauling q _i depends on the product’s degree of heterogeneity h _i , regional production x _i , regional intermediate use z _i and regional final use d _i in the following fashion:

$$q_i = h_i (x_i + z_i + d_i )$$

((4.4))

At the national level, the quantities q _i , x _i , z _i , and d _i can be calculated from the national input–output table. This means that h _i , a theoretical construct indicating the degree of product heterogeneity, can be calculated from the national data. At the regional level, the quantities x _i , z _i , and d _i can be estimated using the traditional nonsurvey methods (or, with some luck, they could be obtained from official statistics). Assuming that the value of h _i is the same in all regions, Eq. (4.1) can then be used to calculate the regional q _i . Thus, the CHARM method provides an estimate of regional cross-hauling. Incorporating this into the regional input–output table leads to more realistic estimates of regional trade and output multipliers than the traditional nonsurvey methods.