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Shopping Hours and Entry - an Empirical Analysis of Aldi’s Opening Hours


Aldi, the biggest discounter in Germany, started to systematically extend shopping hours of its stores in 2016. We interpret the decision to extend opening hours of a specific Aldi store as entry into a new market. By using a novel data set containing the opening hours of nearly all German grocery retailers, we find the following interesting correlations: The probability that a given Aldi outlet extends its shopping hours past 8 p.m. (i) increases if nearby Aldi outlets already extended shopping hours and (ii) decreases if nearby stores run by Aldi’s close competitors did not expand shopping hours past 8 p.m.. These results seem surprising in conjunction with cannibalization and residual demand, but can be explained by consumer and firm learning or market expansion.

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Fig. 1


  1. Aldi has 4,195 stores and Lidl, the second largest discounter, 3,187 outlets (See Section 5).

  2. Exceptions are some markets located in shopping centers. Systematic extensions of opening hours started in 2015, see

  3. See, or Aldi’s online store locator ( for detailed information.

  4. Aldi is and always was a family-led firm and information on its policies and plans are extremely scarce. After the death of the two founders in 2010 and 2014, respectively, changes in the firm’s conduct were expected by experts (See;;

  5. See BVE (2016);

  6. Exceptions are Bavaria and the Saarland where stores are allowed to be open from 6 a.m. until 8 p.m. only.

  7. Note that it is possible that the cost function is not continuous because of, e.g., legal restrictions on the maximum number of working hours per employee.

  8. Other models analyzing spatial and temporal competition are Hosseinipour and Sandoh (2013) and Sandoh et al. (2015).

  9. A complete review of this literature is beyond the scope of this article. An early review of the empirical literature on entry can be found in Geroski (1995). We will outline the relevant links to that strand of literature in Section 3.

  10. This framework ignores potential effects of cannibalization within the Aldi group, i.e., between different Aldi outlets. Anticipating the empirical findings, cannibalization seems to be no issue in our empirical analyses because an Aldi outlet’s probability to extend shopping hours increases in the number of nearby Aldi outlets where shopping hours have already been expanded.

  11. Note that contrary to Toivanen and Waterson (2005), we do not consider a duopoly. In the baseline model, we consider the outlets of all four major German food retailers as explained in the introduction.

  12. Typically, more employees are present during daytime. However, also at nighttime at least some employees have to be present if the outlet is open.

  13. See BVE (2016);

  14. See Lebensmittelzeitung, 25, 19.06.2015,

  15. See Lebensmittelzeitung, 40, 02.10.2015, and ALDI Press Release, 11.05.2016,

  16. See law on shop opening hours of each federal state (Ladenöffnungsgesetz) as well as the federal law on business hours (Gesetz über den Ladenschluss).

  17. See,,, and (last accessed on 30 June, 2017)

  18. For instance, the German Federal Cartel Office uses a range of 20-min driving time in order to define a local market in their analyses of the German retail grocery industry (Bundeskartellamt 2015).

  19. (last accessed on 30 June 2017)

  20. This data is provided by Axciom Deutschland GmbH, Neu-Isenburg, 2016.

  21. See Handelsblatt online, (last accessed on November 7, 2016)

  22. We conjecture that this observation can be explained by the close distance to Aldi’s headquarters located in Essen and Mühlheim, where they potentially started to extend shopping hours of its stores.

  23. Most Edeka outlets are owned by independent retailers. See also Edeka concept accessed on December 8, 2016).

  24. A store is weighted by inverse distances (in seconds of driving time).

  25. Aldi and Lidl have not only have similar business policies, product ranges and prices, but Lidl is also the biggest discounter after Aldi, when focusing the turn over (see Lebensmittelzeitung online, 15.03.2018,, last accessed on June 13, 2019).

  26. Indeed, the test indicates that there are is multicollinearity in the estimations. However, the results are qualitatively the same when skipping (subsets of) the variables that are potentially susceptible to multicollinearity, as stated in the Appendix.


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Correspondence to Samuel de Haas.

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We would like to thank the anonymous referees as well as Niklas Dürr, Justus Haucap, Paul Heidhues, Michael Hellwig, Georg Götz and Peter Winker for discussing earlier versions of this article.



As a robustness check, we run a Logit regression as stated in Eq. 2 with a different definition of a ’local market’: Instead of a maximum of 20 minutes driving time, we assume a maximum of 15 minutes driving time. Corresponding results are shown in Table 3.

Table 3 Logit estimation results

The results are qualitatively the same as the ones discussed in Section 5.3.

As mentioned in Section 5.3 it is surprising that the coefficients of the variables PurchasingPower and Unemployment have the same signs. This may in part be due to multicollinearity effects. To test this, we run a Variation Inflation Factor (VIF) test. Results are stated in Table 4.

Table 4 VIF of regression in Table 2

Indeed, the VIFs of the variables 2Person-Household, 1Person-Household, PurchasingPower, Households< 40 and Unemployment are problematic because they are above 10 (rule of thumb, see Wooldridge (2015)). Therefore, we run the Logit regression as stated in Eq. 2 again, but only with a subset of the problematic variables. The results are stated in Table 5.

Table 5 Logit estimation results

The results are qualitatively the same as the ones discussed in Section 5.3. A corresponding VIF test shows that there are no more problems with multicollinearity, as stated in Table 6.

Table 6 VIF of regression in Table 5

When skipping other subsets of variables, instead of the one skipped in the underlying regression of Table 5, the results do not change (qualitatively). Corresponding regression results are available from the authors on request.

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de Haas, S., Herold, D. & Schäfer, J.T. Shopping Hours and Entry - an Empirical Analysis of Aldi’s Opening Hours. J Ind Compet Trade 20, 139–156 (2020).

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  • Shopping hours
  • Retailing
  • Coordination
  • Market entry

JEL Classification

  • L22
  • L41
  • L81