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Imputing rent in consumption measures, with an application to consumption poverty in Canada, 1997–2009

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  • Sam Norris
  • Krishna Pendakur

Abstract

We consider two econometric problems in the measurement of poverty, both relating to rent imputation. First, we account for quality differences correlated with selection into owner‐occupied versus rental tenure. This correction increases estimated household consumption by 5% over uncorrected estimates and decreases estimated poverty rates quite dramatically. Second, we propose that measurement error induced by the imputation be corrected by imputing a consumption distribution, rather than a consumption level, for each household. This correction increases estimated poverty rates slightly. We use our methods to measure consumption poverty in Canada, and find that the imputation strategy used influences the patterns observed. For example, measured poverty among the elderly barely declines when one uses our methods, in contrast to the almost 6 percentage point reduction we find using traditional methods. In our assessment of the over‐time evolution of consumption poverty, we find that substantial progress has been made on overall poverty and on child poverty, but that poverty among the elderly hardly changed. Les auteurs considèrent deux problèmes économétriques dans la mesure de la pauvreté, tous deux reliés à l'imputation du loyer. D'abord, on prend en compte les différences de qualité liées au choix entre logement occupé par son propriétaire et logement locatif. La correction accroît le niveau de consommation des ménages de 5% par rapport aux estimations non‐corrigées, et diminue les taux de pauvreté estimés de manière dramatique. Ensuite, on suggère que l'erreur de mesure introduite par l'imputation soit corrigée en dérivant une distribution de consommation plutôt qu'un niveau de consommation pour chaque ménage. Cette correction augmente la pauvreté estimée un peu. Les auteurs utilisent leurs méthodes pour mesurer la pauvreté de consommation au Canada, et découvrent que la stratégie d'imputation utilisée influence les patterns observés. Par exemple, la pauvreté mesurée chez les aînés diminue à peine quand on utilise leurs méthodes, contrairement à la diminution de presque 6% que proposent les méthodes traditionnelles. Dans leur évaluation de l’évolution de la pauvreté de consommation dans le temps, ils découvrent qu'on a fait des progrès substantiels pour ce qui est de la pauvreté en général et de la pauvreté des enfants, mais que la pauvreté chez les aînés n'a à peu près pas changé.

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  • Sam Norris & Krishna Pendakur, 2013. "Imputing rent in consumption measures, with an application to consumption poverty in Canada, 1997–2009," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 46(4), pages 1537-1570, November.
  • Handle: RePEc:wly:canjec:v:46:y:2013:i:4:p:1537-1570
    DOI: 10.1111/caje.12054
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    References listed on IDEAS

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    1. Blundell, Richard & Robin, Jean Marc, 1999. "Estimation in Large and Disaggregated Demand Systems: An Estimator for Conditionally Linear Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 209-232, May-June.
    2. Matthew Brzozowski & Thomas F. Crossley, 2011. "Viewpoint: Measuring the well‐being of the poor with income or consumption: a Canadian perspective," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 88-106, February.
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    7. Kevin Milligan, 2008. "The Evolution of Elderly Poverty in Canada," Canadian Public Policy, University of Toronto Press, vol. 34(s1), pages 79-94, November.
    8. Krishna Pendakur, 2001. "Consumption Poverty in Canada, 1969 to 1998," Canadian Public Policy, University of Toronto Press, vol. 27(2), pages 125-149, June.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    Cited by:

    1. Rahul Deb & Yuichi Kitamura & John K H Quah & Jörg Stoye, 2023. "Revealed Price Preference: Theory and Empirical Analysis," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(2), pages 707-743.
    2. Chen, Feifei & Qiu, Huanguang & Zhang, Jun, 2022. "Energy consumption and income of the poor in rural China: Inference for poverty measures," Energy Policy, Elsevier, vol. 163(C).
    3. Serena Yu, 2016. "Retiree Welfare and the 2009 Pension Increase: Impacts from an Australian Experiment," The Economic Record, The Economic Society of Australia, vol. 92(296), pages 67-80, March.
    4. Sam Norris & Krishna Pendakur, 2015. "Consumption inequality in Canada, 1997 to 2009," Canadian Journal of Economics, Canadian Economics Association, vol. 48(2), pages 773-792, May.
    5. Carlos Felipe Balcázar & Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2017. "Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 881-898, December.
    6. Krishna Pendakur, 2018. "Welfare analysis when people are different," Canadian Journal of Economics, Canadian Economics Association, vol. 51(2), pages 321-360, May.
    7. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    8. Yanfeng Chen & Qingjie Xia & Xiaolin Wang, 2021. "Consumption and Income Poverty in Rural China: 1995–2018," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(4), pages 63-88, July.
    9. Li, Lianyou & Song, Ze & Ma, Chao, 2015. "Engel curves and price elasticity in urban Chinese Households," Economic Modelling, Elsevier, vol. 44(C), pages 236-242.

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    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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