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Exports of firms and diversity: an empirical assessment for Germany

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Abstract

The international trade literature highlights the importance of firm productivity and economies of scale on the firm’s international export success. In the context of agglomeration economies, firms enjoy productivity gains when they are located close to related firms and they gain from knowledge spillovers and other positive externalities. They may also benefit from a potentially large supply of diverse workers that possess distinct knowledge and problem-solving skills. In such environments, firms may be more prone to export. In this paper, we employ a comprehensive German data set that combines survey and administrative data. We ask whether German firms (i.e., establishments) export more as a result of localization and urbanization externalities, and labor market pooling associated with workforce diversity, while controlling for a variety of establishment characteristics. Using a fractional response model, we provide evidence that manufacturers and smaller establishments benefit more from externalities and especially from knowledge spillovers. There is less evidence supporting the benefit of workforce diversity; however, that factor may be associated with between-establishment variation.

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Notes

  1. First, ordinary least squares (OLS) fails to estimate export shares within the desired range. One can overcome this shortcoming with a simple log-transformation and applying OLS. There is a cost to such a transformation, however, in that the zero-boundary values cannot be transformed. The same disadvantage occurs when using the beta-distribution (Ramalho et al. 2011; Wagner 2001). A censored Tobit model with censoring at zero and one may seem appropriate for our study, but there is no censoring following Tobit approach—i.e., the export proportion is between zero and one per the definition and not by censoring.

  2. OLS and Tobit estimation techniques are used to ensure robustness. Because of the nonlinear approaches used by Tobit and FRM, marginal effects are computed as average marginal effects.

  3. Firms may self-select into specific regions, which would require a firm location-choice model with the given data set. Specifically, we consider over 400 regions, which forms the choice set of the location decision for each firm. Further, when adding industry- and region-fixed effects to the other control variables, such a location choice model is unlikely to converge. Irrespective, the required dataset is not available.

  4. We classified regions by their 1992 industry concentration and identified the 10 closest regions using an Euclidian distance matrix. Then, the instruments are constructed from the distance-weighted arithmetic mean of the industry-specific regional values.

  5. To construct the shift-share instrument, we use “historical” regional data on immigrant shares by continent/country of origin (see Card 2005). Because this data is only available with a relatively large time lag for West Germany, we do not consider East Germany in this test.

  6. Jacobebbinghaus (2008) suggests a test procedure to secure validity of the linkage, which was developed for the IAB LIAB, which is comparable only in parts to our IAB-EH and further assumptions have to be met. Limiting the data set to “valid” cases leads to similar results, but a large reduction in sample size yields efficiency losses.

  7. Eickelpasch and Vogel (2011) find that the share of part-time work is inversely related to exports.

  8. The productivity measure relates to the annual revenue divided by total annual work volume and therefore it is measured in Euros per fulltime equivalent employee per day.

  9. Evidence on firm age is mixed, providing little guidance as to our expected coefficient sign. Stiebale (2011) and Roberts and Tybout (1997) find a positive firm age-export link, potentially due to stronger transportation networks and more experience in foreign markets as Love and Mansury (2009) argue. Contrary, Contractor et al. (2007) contend that older firms are often inflexible and less able to adapt to market changes. Sjöholm (2003) finds a negative relationship. Finally, Barrios et al. (2003) find a nonlinear, inverse U-shaped age-export intensity relationship. Having dichotomized firm age we are able to identify a potentially nonlinear structure.

  10. There might be second-generation migrants born in Germany who did not receive German citizenship and therefore are registered as foreigners when they become employed. However, it is unclear as to the significance of this issue. If this group is relatively large and completed their German schooling, then their skill-base is probably more “German” than “foreign.” Such a possibility would lead to a downward bias in the diversity-related estimates.

  11. The productivity measure may be affected by measurement errors because of its construction from two data sources. We alternatively estimate the models using employment levels as reported by the IAB-EP: the results remain robust against the change although the parameter becomes slightly different. We also use the Jacobebbinghaus’s test procedure to assess if additional data outliers affect our results, but again the results remain mainly unchanged. We stick to our measure because the survey information on employment suffers under missing values and implies a significant reduction of the sample size by almost one-half.

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Authors and Affiliations

  1. Institute for Employment Research, Nuremberg, Germany

    Stephan Brunow & Luise Pestel

  2. University of Applied Labour Studies, Wismarsche Str. 405, 19055, Schwerin, Germany

    Stephan Brunow

  3. Ohio State University, Columbus, USA

    Mark Partridge

  4. Jinan University, Guangzhou, China

    Mark Partridge

  5. GSSI, L’Aquila, Italy

    Mark Partridge

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  1. Stephan Brunow
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  2. Luise Pestel
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Correspondence to Stephan Brunow.

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Brunow, S., Pestel, L. & Partridge, M. Exports of firms and diversity: an empirical assessment for Germany. Empirica 46, 151–175 (2019). https://doi.org/10.1007/s10663-018-9425-7

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