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10-03-2017

Comparing survey data with an official administrative population: assessing sample-selectivity in the IAB Establishment Panel

Published in: Quality & Quantity | Issue 2/2018

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Abstract

The IAB Establishment Panel is a large annual establishment survey and serves as a data source for many empirical analyzes, e.g., on labor demand, wages and industrial relations but also for influential policy evaluations. We analyze the selectivity of the IAB Establishment Panel compared with the administrative population of all establishments in Germany to test whether the IAB Establishment Panel is biased towards “good employers”. By design the survey over-samples large, thus high paying and stable establishments. After flexibly controlling for elements of the sampling design, we do not detect any meaningful difference between establishments participating in the survey and the full population regarding key economic performance indicators. If anything, we observe a slight overrepresentation of stable and slow growing establishments. For applied research, the results highlight that “typical” econometric specifications may not be sufficient to control for selectivity.

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Bollen et al. (2016) argue similarly and highlight that most research in social sciences follows a ‘tradition’ of generally not using weights (even when using complex survey data) when modeling relationships in regression models. While it is clear that “not weighting” must not be confused with “not correcting for selectivity”, it seems that a lot of applied econometric work does not reflect selectivity issues of survey designs in a sufficient way.

Detailed information about the IAB Establishment Panel can be retrieved from Ellguth et al. (2014) or Fischer et al. (2009).

Approximately 70% of the interviews are conducted in face-to-face mode (see Ellguth et al. 2014).

In practice, we cannot rule out that administrative and survey information refer sometimes to different entities of the establishment (or even the company). Therefore we conducted additional robustness tests for a subsample of establishments which are less likely to differ in their definition of the establishment. This check did not change our conclusions.

We refer to the survey for 2010. There were changes in the classification of industries in other years. Modifications became necessary due to revisions of the underlying official classifications. We adjust our categories for some analysis accordingly.

A detailed description of the BHP is presented in Gruhl et al. (2012). Our version differs from the publicly available data set in that it is the 100% sample.

We checked whether this restriction is selective with regard to the probability of participation in the IAB Establishment Panel and found no correlation (see Table 4).

But IAB Establishment Panel aims nevertheless at being representative for a given year's population of establishments as most of its questions refer to a reference date which is 30 June of the respective year.

As empirically assessed by Janik and Kohaut (2012) there is selectivity in panel continuation, which can be explained by operative variables. E.g. when interviewers change, the likelihood of panel continuation shrinks by about 12% points. But the question in this regard is whether or not this is also related with the “quality” of the establishment or any other economic relevant concepts.

The results are qualitatively similar for other years of the data.

We do not present the constant in our tables as it is not very informative. Instead, we present baseline probabilities in Table 1.

We show implications of such strategies in Sect. 6.1.

In the administrative data about 15% of the individual wages are top-coded. Using the establishment-level median wage should be robust towards this data limitation. By contrast, average wages are severely biased.

The spike at a daily median wage slightly above €10 represents establishments at which the median person is in a marginal employment contract (Mini-Job). In 2010, these are jobs with a monthly salary below € 400.

Remember that we have a substantial amount of missing wage information, see Table 1. This is mainly due to the fact that the median wage in the BHP is computed for full time employees only.

It is known that closures may reflect also other events like take-overs, retirement decision and not necessarily economic failure (Müller and Stegmaier 2015), but for practical reasons and due to data constraints we stick to closures.

These definitions are based on employment flows and require a sufficient number of employees. Hence, the smallest size category is omitted and the number of observations decreases (see Table 1) when we analyze closures according to this concept.

The results are qualitatively similar for all three definitions of closure. For simplicity we present the results only for the first definition.

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Title: Comparing survey data with an official administrative population: assessing sample-selectivity in the IAB Establishment Panel
Publication date: 10-03-2017
Published in: Quality & Quantity / Issue 2/2018
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-017-0495-6

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