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Calibrating Time-Use Estimates for the British Household Panel Survey

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  • Cristina Borra

    ()

  • Almudena Sevilla
  • Jonathan Gershuny

Abstract

This paper proposes an innovative statistical matching method to combine the advantages of large national surveys and time diary data. We use data from two UK datasets that share stylised time-use information, crucial for the matching process. In particular, time-diary information of an individual from the Home On-line Study, our donor data set, is imputed to a similar individual from the British Household Panel Survey, our recipient dataset. Propensity score methods are used in conjunction with Mahalanobis matching to increase matching quality. Copyright Springer Science+Business Media Dordrecht 2013

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File URL: http://hdl.handle.net/10.1007/s11205-012-0198-2
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Bibliographic Info

Article provided by Springer in its journal Social Indicators Research.

Volume (Year): 114 (2013)
Issue (Month): 3 (December)
Pages: 1211-1224

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Handle: RePEc:spr:soinre:v:114:y:2013:i:3:p:1211-1224

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Web page: http://www.springer.com/economics/journal/11135

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Related research

Keywords: Statistical matching; Propensity score; Mahalanobis distance; Childcare time;

References

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  1. Sutherland, Holly & Taylor, Rebecca & Gomulka, Joanna, 2002. "Combining Household Income and Expenditure Data in Policy Simulations," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(4), pages 517-36, December.
  2. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
  3. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
  4. Jutta M. Joesch & C. Katharina Spiess, 2006. "European mothers’ time spent looking after children - differences and similarities across nine countries," electronic International Journal of Time Use Research, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 3(1), pages 1-27, August.
  5. Man Kan & Jonathan Gershuny, 2009. "Calibrating Stylised Time Estimates Using UK Diary Data," Social Indicators Research, Springer, vol. 93(1), pages 239-243, August.
  6. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
  7. Connelly, Rachel & Kimmel, Jean, 2007. "Spousal Influences on Parents' Non-Market Time Choices," IZA Discussion Papers 2894, Institute for the Study of Labor (IZA).
  8. Gershuny, Jonathan, 2000. "Changing Times: Work and Leisure in Postindustrial Society," OUP Catalogue, Oxford University Press, number 9780198287872, September.
  9. Andreas Peichl & Thilo Schaefer, 2009. "FiFoSiM - an integrated tax benefit microsimulation and CGE model for Germany," International Journal of Microsimulation, Interational Microsimulation Association, vol. 2(1), pages 1-15.
  10. Zhao, Zhong, 2005. "Sensitivity of Propensity Score Methods to the Specifications," IZA Discussion Papers 1873, Institute for the Study of Labor (IZA).
  11. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
  12. Ridder, Geert & Moffitt, Robert, 2007. "The Econometrics of Data Combination," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 75 Elsevier.
  13. Jens Bonke, 2005. "Paid Work and Unpaid Work: Diary Information Versus Questionnaire Information," Social Indicators Research, Springer, vol. 70(3), pages 349-368, 02.
  14. Hans Bloemen & Silvia Pasqua & Elena Stancanelli, 2010. "An empirical analysis of the time allocation of Italian couples: are they responsive?," Review of Economics of the Household, Springer, vol. 8(3), pages 345-369, September.
  15. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
  16. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
  17. Jonathan Gershuny, 2004. "Costs and Benefits of Time Sampling Methodologies," Social Indicators Research, Springer, vol. 67(1), pages 247-252, June.
  18. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
  19. Man Kan, 2008. "Measuring Housework Participation: The Gap between “Stylised” Questionnaire Estimates and Diary-based Estimates," Social Indicators Research, Springer, vol. 86(3), pages 381-400, May.
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