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Imputing Rent in Consumption Measures, with an Application to Consumption Poverty in Canada 1997-2009

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Measures of household consumption-­which may be used to investigate average consumption growth, mor consumption poverty and inequality-­must account for the rental flow from owned accommodation. We consider two econometric problems relating to the imputation of rental flows for owned accommodation that have been thus far ignored in the literature on consumption poverty and inequality. First, using a Heckman-type correction, we account for quality differences correlated with selection into owner-occupied versus rental tenure. Using this correction increases the estimate of average household consumption by about 4 per cent in comparison with uncorrected estimates. Second, we propose a new way to measure poverty (or inequality) that accounts for the measurement error induced by the rent imputation. In particular, we argue that the researcher should impute a consumption distribution, rather than a single consumption level, for each household. We apply our methods to the measurement of consumption poverty in Canada. We estimate the rate of consumption poverty for all people, and for children and the elderly, over the period 1997-2009, allowing for interprovincial and intertemporal variation in commodity prices. Using a better rent imputation strategy matters a lot: the over-time pattern in poverty rates is quite different with the selection- and measurement-­error corrected approach. We find that poverty declined dramatically over the study period, and that, although substantial progress was been made on overall poverty and child poverty, poverty among the elderly did not decrease very much.

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  • Krishna Pendakur & Sam Norris, 2012. "Imputing Rent in Consumption Measures, with an Application to Consumption Poverty in Canada 1997-2009," Discussion Papers dp12-12, Department of Economics, Simon Fraser University.
  • Handle: RePEc:sfu:sfudps:dp12-12
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    Cited by:

    1. 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.
    2. Krishna Pendakur, 2018. "Welfare analysis when people are different," Canadian Journal of Economics, Canadian Economics Association, vol. 51(2), pages 321-360, May.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.

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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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