<abstract xmlns="http://www.w3.org/1999/xhtml">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; and2) 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.</abstract>

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; and2) 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.

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

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Using Administrative Data to Evaluate Sampling Bias in a Business Panel Survey"}

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; and2) the...

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

Online veröffentlicht: 26. März 2019

Seitenbereich: 67 - 92

Eingereicht: 01. Juli 2016

Akzeptiert: 01. Apr. 2018

DOI: https://doi.org/10.2478/jos-2019-0004

Schlüsselwörter
Business surveys, panel samples, panel attrition, administrative data, auxiliary information

© 2019 Leandro D’Aurizio et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 26. März 2019

Seitenbereich: 67 - 92

Eingereicht: 01. Juli 2016

Akzeptiert: 01. Apr. 2018

DOI: https://doi.org/10.2478/jos-2019-0004

SchlüsselwörterBusiness surveys, panel samples, panel attrition, administrative data, auxiliary information

© 2019 Leandro D’Aurizio et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Business surveys, panel samples, panel attrition, administrative data, auxiliary information