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Temporary Agency Work and Firm Competitiveness: Evidence from German Manufacturing Firms

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Trade Credit and Temporary Employment

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

Abstract

This chapter addresses the relationship between the utilization of temporary agency workers by firms and their competitiveness measured by unit labor costs, using a rich, newly built, data set of German manufacturing enterprises. The analysis is conducted by applying different panel data models while taking the inherent selection problem into account. Making use of dynamic panel data models allows us to control for firm specific fixed effects as well as for potential endogeneity of explanatory variables. The results indicate an inverse U-shaped relationship between the extent that temporary agency workers are used and the competitiveness of firms.

This chapter was originally co-authored with Alexander Schiersch and has been reproduced here with permission from John Wiley and Sons.

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Notes

  1. 1.

    Although this paper focuses on temporary agency work, which is defined as a triangular relationship between worker, leasing company and client (Burgess and Connell 2005), we do not explicitly distinguish between fixed-term contracts and temporary agency work in this literature review, because the discussed effects are rather similar for both forms of employment. This paper does also not discuss the institutional framework and its development in Germany. For more information, see Schmidt and Wuellerich (2011), Antoni and Jahn (2009), Mitlacher (2008) and Pfeifer (2005).

  2. 2.

    The questionnaire can be downloaded for each year. See http://fdz.iab.de/en/FDZ_Establishment_Data/IAB_Establishment_Panel/IAB_Establishment_Panel_Working_Tools.aspx. Moreover, Städele and Müller (2006) provide a detailed description for each variable up to 2005.

  3. 3.

    Employment duration for more than 50 % of all temporary agency workers is less than 3 months (Schmidt and Wuellerich 2011).

  4. 4.

    Some descriptive statistics indicate that this observation problem exists and that there is a difference between date data and annual data. According to Hirsch and Mueller (2012), about 24.4 % of all plants in their sample (1,556 out of 6,375) have used temporary agency at least once in the observation period (2003–2009). In our dataset, we find that roughly 63 % of all firms in the sample used temporary agency work at least once. Now, these shares are not easy to compare: Firstly, the observation unit in the IAB establishment panel is an establishment, while it is a company in our data set. Secondly, the observation periods differ, as Hirsch and Mueller (2012) analyze the 2003–2009 period, while this study analyzes the 1999–2006 period. Though, using an earlier period should rather have the effect that the share in our data set is lower, given the raising number of temporary agency workers since 2000. Eventually, the explanation for the difference might simply be the fact that the IAB questionnaire specifically asked for the number of temporary agency workers at the 30th of June.

  5. 5.

    The data are confidential and can only be used by remote execution. However, they are not exclusive. For more information see Zuehlke et al. (2004) and http://www.forschungsdatenzentrum.de/en/index.asp.

  6. 6.

    The subsamples are compiled in 1999 and 2003.

  7. 7.

    For more information about the Cost Structure Census surveys in Germany, see Fritsch et al. (2004).

  8. 8.

    Starting in 2010 the cut-off limits were increased to 50 employees in order to reduce bureaucratic burdens for smaller firms. However, this does not affect our analysis since we use data from before 2010.

  9. 9.

    ULC as indicator for competitiveness is, for instance, provided and used by the OECD or the Bureau of Labor Statistics. See: http://stats.oecd.org/mei/default.asp?rev=3, http://www.bls.gov/news.release/prod4.t03.htm

  10. 10.

    For international comparisons, the purchasing power parity and exchange rates must also be considered. See for an introduction see van Ark et al. (2005).

  11. 11.

    We are aware of the critics and limitations of ULC. First, changes in the second input category, capital, are not explicitly taken into account. Second, the way ULC is defined it can also be interpreted as a measure of the share of labor income on output (Felipe 2007). We address these issues by considering the capital intensity of production as an explanatory variable in the estimation.

  12. 12.

    Given this data set, we construct ULC by using gross value added deflated by the producer price index at a two digit industry classification and the sum of all labor costs. The latter include wages, social security expenditures, provisions for firm pensions etc. Moreover, we also include the costs of temporary agency workers in the denominator.

  13. 13.

    The capital stock is not given in the data. It is approximated by a program recently published by Wagner (2010a).

  14. 14.

    We do not make use of all available variables in the selection equation because variables in the selection model and in the regression models should not be identical in order to avoid multicollinearity between the inverse Mills ratio and the other exogenous variables (Briggs 2004; Puhani 2000).

  15. 15.

    The table with the results for the VIFs is available on request from the authors. To check the robustness of our results regarding a changing exclusion restriction, we estimate three alternative exclusion restrictions. First a dummy that takes the value of one if at least one owner is working in the firm; second we use a combination of legal status dummies and the dummy variable taking value of one if at least one owner is working in the firm and third we estimate the model without any exclusion restriction only identifying it via nonlinearity. Our results of the second stage regressions are not affected by changes in the exclusion restriction: the respective results are available on request from the authors.

  16. 16.

    The fixed effect model was also estimated without dummy variables. We do not report the results, since the level of the coefficients changes minimally, while signs and significances do not change.

  17. 17.

    Following Roodman (2009b), we reduce the number of instruments by collapsing them because too many instruments could lead to a bias in estimates. Without collapsing, the number of instruments increases by 2.5 times from 65 to 163. This heavily affects the Hansen test of exogeneity of instruments. However, the estimated coefficients are minimally affected. Although the tables are omitted from this paper, the results are available upon request from the authors.

  18. 18.

    The literature also discusses whether the relationship between firm size and firm performance is nonlinear. Schiersch (2013), for example, finds a U-shaped relationship between technical efficiency and firm size in the case of the German mechanical engineering industry. We therefore test if this relationship can also be found between ULC and firm size, and whether it has any influence on the relationship between the use of temporary agency workers and ULC. The coefficients of Share and Share2 are not affected if we include a squared firm size term. Although not presented here, results are available upon request from the authors.

  19. 19.

    Hansen tests for different subsets of instruments show that the rejection of the null hypothesis stems from a correlation between the error terms and the industry dummies. A correlation between the error terms and the industry dummies indicate, that the error terms vary systematically between the different industries.

  20. 20.

    The classification of 11 groups is based on NACE rev. 1.1, as used, for example, by OECD STAN. To reduce the number of reported estimations within the respective table and to make sure that each industry group includes enough observations, we make use of this less detailed industry classification, compared to the one reported in Table 4.2.

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    Sebastian Nielen

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Nielen, S. (2016). Temporary Agency Work and Firm Competitiveness: Evidence from German Manufacturing Firms. In: Trade Credit and Temporary Employment. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-319-29850-4_4

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