Calibrating Time-Use Estimates for the British Household Panel Survey
- 353 Downloads
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.
KeywordsStatistical matching Propensity score Mahalanobis distance Childcare time
This paper has benefited from comments provided by Man Yee Kan, David Berrigan, and Oriel Sullivan. Any remaining errors are our own. This paper was partly prepared while Dr. Borra was an Academic Visitor in the Centre for Time Use Research at the University of Oxford (summer 2009, autumn 2010). The authors would like to express their thanks for the financial support provided by the Spanish Ministry of Education and Science (Project ECO2008-01297 and Movility Grant “José Castillejo” Convocatoria 2010), by the Andalusian Government (Convocatoria IAC 2009 2), and by the Economic and Social Research Council (Grant Number RES-060-25-0037).
- Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricists companion. Princeton: Princeton University Press.Google Scholar
- Becker, S. O., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. Stata Journal, 2(4), 358–377.Google Scholar
- Bittman, M. (2004). Parenting and employment what time-use surveys show. In Michael. Bittman & Nancy. Folbre (Eds.), Family time: The social organization of care (pp. 152–170). London, New York: Routledge.Google Scholar
- Gershuny, J. (2000). Changing times: Work and leisure in postindustrial society. Oxford: Oxford University Press.Google Scholar
- Gershuny, J. (2004). Costs and benefits of time sampling methodologies. Social Indicators Research, 67, 247–252.Google Scholar
- Gershuny, J. (2012). Too many zeros: A method for estimating long-term time-use from short diaries. Annales d’Ėconomie et de Statistique, 105(106), 247–270.Google Scholar
- Gu, X., & Rosenbaum, P. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2, 405–420.Google Scholar
- Judson, D. H., & Poppoff, C. L. (2004). Selected general methods. In J. S. Siegel & D. Swanson (Eds.), The methods and materials of demography (pp. 667–732). San Diego, CA: Elsevier.Google Scholar
- Kum, H. & Masterson, T. (2008). Statistical matching using propensity scores: Theory and application to the levy institute measure of economic wellbeing. Economics working paper archive wp_535, Levy Economics Institute. http://www.levyinstitute.org/pubs/wp_535.pdf. Accessed December 15, 2010.
- Peichl, A., & Schaefer, T. (2009). FiFoSiM: An integrated tax benefit microsimulation and CGE model for Germany. International Journal of Microsimulation, 2(1), 1–15.Google Scholar
- Räessler, S. (2002). Statistical matching: A frequentist theory, practical applications and alternative Bayesian approaches. New York: Springer.Google Scholar
- Ridder, G., & Moffitt, R. (2007). The econometrics of data combination. In J. J. Heckman & E. E. Leamer (Eds.), Handbook of econometrics (Vol. 6, pp. 5469–5547). Amsterdam: Elsevier.Google Scholar
- Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.Google Scholar
- Royston, P. (2004). Multiple imputation of missing values. Stata Journal, 4, 227–241.Google Scholar
- Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics, 4, 87–94.Google Scholar
- Schulz, F. & Grunow, D. (2012). Comparing diary and survey estimates on time use. European Sociological Review, 28(5), 622–632.Google Scholar
- Smith, J. (2000). A critical survey of empirical methods for evaluating active labor market policies. Swiss Journal of Economics and Statistics, 136(3), 247–268.Google Scholar