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Accounting for household heterogeneity in general equilibrium economic growth models

Author

Listed:
  • Melnikov, N.B.
  • O'Neill, B.C.
  • Dalton, M.G.

Abstract

We describe and evaluate a new method of aggregating heterogeneous households that allows for the representation of changing demographic composition in a multi-sector economic growth model. The method is based on a utility and labor supply calibration that takes into account time variations in demographic characteristics of the population. We test the method using the Population-Environment-Technology (PET) model by comparing energy and emissions projections employing the aggregate representation of households to projections representing different household types explicitly. Results show that the difference between the two approaches in terms of total demand for energy and consumption goods is negligible for a wide range of model parameters. Our approach allows the effects of population aging, urbanization, and other forms of compositional change on energy demand and CO2 emissions to be estimated and compared in a computationally manageable manner using a representative household under assumptions and functional forms that are standard in economic growth models.

Suggested Citation

  • Melnikov, N.B. & O'Neill, B.C. & Dalton, M.G., 2012. "Accounting for household heterogeneity in general equilibrium economic growth models," Energy Economics, Elsevier, vol. 34(5), pages 1475-1483.
    • Handle: RePEc:eee:eneeco:v:34:y:2012:i:5:p:1475-1483
    DOI: 10.1016/j.eneco.2012.06.010
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    References listed on IDEAS

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    1. De Cian, Enrica & Dasgupta, Shouro & Hof, Andries F. & van Sluisveld, Mariësse A.E. & Köhler, Jonathan & Pfluger, Benjamin & van Vuuren, Detlef P., 2020. "Actors, decision-making, and institutions in quantitative system modelling," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    2. N. B. Melnikov & A. P. Gruzdev & M. G. Dalton & M. Weitzel & B. C. O’Neill, 2021. "Parallel Extended Path Method for Solving Perfect Foresight Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 517-534, August.
    3. van Ruijven, Bas J. & O’Neill, Brian C. & Chateau, Jean, 2015. "Methods for including income distribution in global CGE models for long-term climate change research," Energy Economics, Elsevier, vol. 51(C), pages 530-543.
    4. Volker Krey, 2014. "Global energy-climate scenarios and models: a review," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(4), pages 363-383, July.
    5. Wei, Taoyuan & Zhu, Qin & Glomsrød, Solveig, 2018. "How Will Demographic Characteristics of the Labor Force Matter for the Global Economy and Carbon Dioxide Emissions?," Ecological Economics, Elsevier, vol. 147(C), pages 197-207.
    6. Kulmer, Veronika & Seebauer, Sebastian, 2019. "How robust are estimates of the rebound effect of energy efficiency improvements? A sensitivity analysis of consumer heterogeneity and elasticities," Energy Policy, Elsevier, vol. 132(C), pages 1-14.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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