Using Administrative Data to Evaluate Sampling Bias in a Business Panel Survey

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We examine two sources of bias for the Bank of Italy’s panel business survey of Industrial and Services Firms:

1) the bias caused by panel attrition; and

2) the bias created by delays in the distributional data on the reference population, needed for computing the survey weights.

As for the first source of bias, the estimates strongly dependent on big firms’ values are less affected by panel attrition than those representing firms’ average behavior, independent of their sizes. Positive economic results make it easier to enroll new firms in the survey, in order to replace firms dropping out because of bad economic performances. However, the economic results of new entrances become more aligned to those of the population, once they enter the sample.

A very different result emerges for the second source of bias, since, when the population size is highly variable, the information delays produce a bias for the estimates influenced by the contribution of great firms, but the effect is negligible for the estimates not dependent on firm size.

Afonso, L.M. 2015. “Correcting for Attrition in Panel Data Using Inverse Probability Weighting: An application To the EU15 Bank System.” Doctoral dissertation (Lisbon School of Economics and Management, Working Paper). Available at: (accessed January 2019).

Ardilly, P. and P. Lavallée. 2007. “Weighting in Rotating Samples: The SILC survey in France.” Survey Methodology 33(2): 131–137.

Bank of Italy. 2005. Supplements to the Statistical Bulletin, Sample Surveys, Survey of Industrial and Service firms, Year 2003, Volume XV, 20 October 2005. Available at: (accessed January 2019).

Bank of Italy. 2014. Supplements to the Statistical Bulletin, Sample Surveys, Survey of Industrial and Service firms, Year 2013, New Series, Year XXIV, 24 July 2014. Available at: (accessed January 2019).

Bank of Italy. 2015. Supplements to the Statistical Bulletin, Sample Surveys Survey of Industrial and Service firms, Year 2014, New Series, Year XXV, 1st July 2015. Available at: (accessed January 2019).

Bank of Italy. 2017. Annual Report for 2016. Available at: (accessed January 2019).

Black, C., D.C. Broadstock, A. Collins, and L. Hunt. 2007. “A Practical Guide to Developments in Data Imputation Methods.” Traffic Engineering and Control 48(8): 358–363. Available at: (accessed january 2019).

Cameron, A.C. and P.K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge university press.

Cochran, W.G. 1977. Sampling Techniques. New York: Wiley.

Deng, Y., D.S. Hillygus, J.P. Reiter, Y. Si, and S. Zhen. 2013. “Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples.” Statistical Science 28(2): 238–256. Doi:

Deville, J.-C., C.-E. Särndal, and O. Sautory. 1993. “Generalized Raking Procedures in Survey Sampling.” Journal of the American Statistical Association 88(423): 1013–1020. Doi:

Doms, M. and T. Dunne. 1998. “Capital Adjustment Patterns in Manufacturing Plants.” Review of Economic Dynamics 1(2): 409–429. Available at: (accessed January 2019).

Fabbris, L. 1989. L’indagine campionaria. Nuova Italia Scientifica.

Faiella, I. 2010. “The use of survey weights in regression analysis.” Bank of Italy’s Working Paper n. 739. Doi:

Hollander, M. and D.A. Wolfe. 1973. Nonparametric Statistical Methods. John New York: Wiley.

Istat. 2010. Available at: (accessed January 2019).

Istat. 2014. Archivio Asia. Avaiable at: (accessed January 2019).

Knapp, M., A. Gart, and M. Chaudhry. 2006. “The Impact of Mean Reversion of Bank Profitability on Post-merger Performance in the Banking Industry.” Journal of Banking and Finance 30(12): 3503–3517.

Lavallée, P. and S. Labelle-Blanchet. 2013. “Indirect Sampling Applied to Skewed Populations.” Survey Methodology 39(1): 183–215.

Little, R. and D. Rubin. 2002. Statistical Analysis with Missing Data. New York: Wiley.

Martin, E., D. Abreu, and F. Winters. 2001. “Money and Motive: Effects of Incentives on Panel Attrition in the Survey of Income and Program Participation.” Journal of Official Statistics 17(2): 267–284. Available at: (accessed January 2019).

Rogers, W. 1994. “Regression Standard Errors in Clustered Samples.” Stata Technical Bulletin 3(13). Available at: (accessed January 2019).

Särndal, C.-E. and S. Lundström. 2005. Estimation in Surveys with Nonresponse. New York: Wiley.

Solon, G., S.J. Haider, and J. Wooldridge. 2015. “What Are We Weighting For?” Journal of Human Resources (2): 301–316. Doi:

Trivellato, U. 1999. “Issues in the Design and Analysis of Panel Studies: a Cursory Review.” Quality & Quantity (33): 339–352. Doi:

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