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Working from Home

What is the Effect on Employees’ Effort?

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Schmalenbach Business Review Aims and scope

Abstract

This paper investigates how working from home affects employees’ work effort. Employees who have the possibility to work from home have a high autonomy in scheduling and organizing their work and are therefore assumed to have a higher intrinsic motivation. Thus, we expect working from home to positively influence work effort of employees. We introduce a theoretical model that analyzes whether intrinsic motivation influences the impact of working from home on employees’ work effort. To account for the self-selection into working locations, we use an instrumental variable (IV) estimation strategy. Our empirical results indicate that working from home has a positive influence on employees’ work effort. In addition, we show that working from home indeed increases intrinsic motivation and thus employees’ work effort. Moreover, we find that the frequency of working from home also matters. The more frequently employees work from home, the higher the work effort they provide is.

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Notes

  1. For further information on the SOEP, see Wagner et al. (2007).

  2. Monthly earnings up to a threshold of 400 euros (450 euros since 2012) characterize German “mini-jobs”. Employees who have a “mini-job” are socially subsidized. For instance, they do not need to pay the employee contribution to the pension insurance system.

  3. Table 10 in the appendix shows how many observations were excluded in each step.

  4. In this case, all communication with colleagues and clients is through information and communication technologies (Bailey and Kurland 2002).

  5. We distinguish between the sixteen German federal states.

  6. The dataset provides information on employees’ working location in the 1997, 1999, 2002, and 2009 waves. We decided to use the working location in 2002 as an instrument because this information is closest to that for 2009.

  7. In the dataset, we only have information on employer-provided computers or laptops in the 2006 and 2008 waves. Because the motivational impact of fringe benefits received in 2008 might influence employees’ work effort one year later, we use information on employer-provided computers from 2006.

  8. A reason for over-reporting working hours is that individuals might want to give socially desirable responses. Another possible explanation might be that individuals have problems recalling the exact timing of last week’s working hours (Robinson et al. 2011), or employees might over-report their actual workload because they aim to receive a promotion.

  9. For instance, employees who report 40 actual working hours per week overestimate their real working hours by 2 h on average (Robinson et al. 2011). Therefore, in this example, we correct employees’ work effort by subtracting 2 h from their reported actual working hours.

  10. Table 11 in the appendix displays the estimation results of the control variables. The empirical results discussed here refer to specifications (5), (6), and (7) of the model displayed in Table 2.

  11. For the robustness checks, we exclude the interaction term (\(\textit{WFH}\times \textit{IM}\)) from Eq. 2.

  12. Bartel (1995) argues that investments in human capital increase employees’ productivity. At the same time, the investment in human capital leads to a wage growth. Therefore, wages are an appropriate proxy for measuring employees’ productivity.

  13. Another explanation for the empirical results than the crowding-out of motivation might be that distinct incentive schemes induce different degrees of dishonesty (e. g., Erat and Gneezy 2012). Thus, the observed different impacts on reported work effort in specifications (1) and (2) might be more strongly related to the payment regime than to the working location or employees’ motivation.

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Acknowledgements

The authors would like to thank Alfred Wagenhofer (Editor-in-Chief), Peter Jost (Special Section Guest Editor), Simone Balestra, Christian Rupietta, Kurt Schmidheiny, and two anonymous referees for their very helpful comments and suggestions and are also grateful to the German Institute for Economic Research (DIW) for data provision. Moreover, the authors thank Stephanie Wyss for excellent research assistance.

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Authors and Affiliations

  1. Faculty of Business and Economics, Human Resources and Organization, University of Basel, Peter Merian-Weg 6, 4002, Basel, Switzerland

    Kira Rupietta & Michael Beckmann

  2. Faculty of Politics, Law, and Economics, Organizational Economics, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany

    Kira Rupietta

Authors
  1. Kira Rupietta
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  2. Michael Beckmann
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Correspondence to Kira Rupietta.

Appendix

Appendix

Table 10 Exclusion of Observations. (Source: SOEP (wave 2009), own calculations)
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Table 11 Working from Home (WFH) and Work Effort (OLS Estimation), Including Regression Coefficients for Control Variables. (Source: SOEP (wave 2009), own calculations)
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Rupietta, K., Beckmann, M. Working from Home. Schmalenbach Bus Rev 70, 25–55 (2018). https://doi.org/10.1007/s41464-017-0043-x

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