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Using Engel Curves To Estimate Bias In The Canadian Cpi As A Cost Of Living Index

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  • Beatty, Timothy K.M.
  • Larsen, Erling Roed

Abstract

. Semiparametric Engel curves are used to infer bias in the Canadian CPI as a Cost of Living Index. The budget share of food has long been used as an indicator of welfare. We compare households with the same levels of CPI deflated total expenditure over the period 1978–2000. Differences in the expenditure share of food are attributed to the CPI failing to capture changes in costs of living. We employ a novel econometric approach using a single index penalized linear spline model. Over the period, we find that the CPI overstated changes in the cost of living between 1.33 and 1.86% for the four household types considered. JEL classification: D1, C1 L’utilisation des courbes d’Engel pour évaluer la distorsion de l’IPC en tant qu’indice du coût de la vie. On utilise les courbes semi‐paramétriques d’Engel pour identifier le biais de l’IPC comme indice du coût de la vie au Canada. La part du budget qui va à la nourriture a depuis longtemps été utilisée comme un indicateur de bien‐être. On compare des ménages qui ont les mêmes niveaux de dépenses totales (dégonflées par l’IPC) pour la période de 1978 à 2000. Les différences dans la portion des dépenses qui va à la nourriture sont attribuées au fait que l’IPC ne saisit pas les changements dans les coûts de la vie. On emploie une approche économétrique inédite utilisant une fonction spline linéaire avec pénalités pour produire un seul indice. Sur l’étendue de la période, on découvre que l’IPC surévalue les changements dans le coût de la vie de 1.33 à 1.86 pour cent pour les quatre types de ménages étudiés.
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Suggested Citation

  • Beatty, Timothy K.M. & Larsen, Erling Roed, 2004. "Using Engel Curves To Estimate Bias In The Canadian Cpi As A Cost Of Living Index," Working Papers 15836, University of British Columbia, Food and Resource Economics.
    • Handle: RePEc:ags:ubcwps:15836
    DOI: 10.22004/ag.econ.15836
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    References listed on IDEAS

    as
    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
    2. Alan G. White, 2000. "Outlet types and the Canadian Consumer Price Index," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 33(2), pages 488-505, May.
    3. Yu Y. & Ruppert D., 2002. "Penalized Spline Estimation for Partially Linear Single-Index Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1042-1054, December.
    4. Allan Crawford, 1998. "Measurement biases in the Canadian CPI: An update," Bank of Canada Review, Bank of Canada, vol. 1998(Spring), pages 39-56.
    5. Alan G. White, 2000. "Outlet types and the Canadian Consumer Price Index," Canadian Journal of Economics, Canadian Economics Association, vol. 33(2), pages 488-505, May.
    6. Bruce W Hamilton, 1998. "The True Cost of Living: 1974 - 1991," Economics Working Paper Archive 395, The Johns Hopkins University,Department of Economics.
    7. Yatchew, Adonis & Sun, Yiguo & Deri, Catherine, 2003. "Efficient Estimation of Semiparametric Equivalence Scales with Evidence from South Africa," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 247-257, April.
    8. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521012263, September.
    9. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    10. Dora L. Costa, 2001. "Estimating Real Income in the United States from 1888 to 1994: Correcting CPI Bias Using Engel Curves," Journal of Political Economy, University of Chicago Press, vol. 109(6), pages 1288-1310, December.
    11. Jerry Hausman, 2003. "Sources of Bias and Solutions to Bias in the Consumer Price Index," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 23-44, Winter.
    12. Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
    13. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    14. Bruce W. Hamilton, 2001. "Using Engel's Law to Estimate CPI Bias," American Economic Review, American Economic Association, vol. 91(3), pages 619-630, June.
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    More about this item

    Keywords

    Consumer/Household Economics;

    JEL classification:

    • D1 - Microeconomics - - Household Behavior
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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