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Does new entry drive out incumbents? The varying roles of establishment size across sectors


Using establishment-level data for Japan, we examine the effects of new entry on the probability that incumbents will exit from the market. In particular, we estimate how the effects vary depending on the size of both entrants and incumbents and whether the effects of new entry differ across sectors. We find that while new entry increases the probability that incumbents will exit, the effect depends on the size of both entrants and incumbents. We also find that the effect differs significantly across sectors: It is largest in nontradable services, but fairly limited in the case of manufacturing and tradable services. Furthermore, in the case of the tradable services sector, very large-scale entries are less likely to drive out incumbents than medium- or small-scale entries. On the other hand, new entry is most likely to affect incumbents in the nontradable services sector, probably because it is difficult for incumbents in this sector to expand their customer base outside the region. Although small establishments are the most likely to be driven out by new entries in all sectors, very large incumbents are not always the most competitive and, in the case of the tradable services sector, medium-sized establishments are the least likely to be affected by new entry.

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Fig. 1


  1. 1.

    The microdata underlying the census are available to researchers for academic purposes only because of considerations of confidentiality. The microdata were obtained and compiled for the purpose of our research project at the Research Institute of Economy, Trade and Industry (RIETI).

  2. 2.

    Although in some industries the number of public establishments is relatively large, their share in the total number of establishments in 2006 was only 2.5 %. Moreover, public establishments are concentrated in certain industries, such as forestry, utilities, education, and public administration, and their share in most other industries is very small.

  3. 3.

    Although comprehensive data are available from the early 1990s, we were unable to obtain reliable converters for establishment identification codes for the 1990s. Therefore, we were unable to link the establishment-level data over time from the early 1990s and decided to restrict our analysis to the 2000s.

  4. 4.

    In fact, a number of studies on Europe, such as Koster et al. (2012), Bosma et al. (2011), and Fritsch and Schroeter (2011) employ data at a detailed regional level akin to the commercial areas used here rather than larger administrative units.

  5. 5.

    The reason for excluding the mail industry (JIP industry code 79) is that Japan’s postal services were privatized in 2006. This means that in 2001, most establishments in the mail industry were in the database for public establishments, while in 2006 they were in the database for private establishments. As we were unable to obtain consistent data for such privatized postal service establishments, we excluded this industry from our analysis.

  6. 6.

    As already mentioned, we link establishments in the 2001 Census and the 2006 Census using converters of establishment ID codes (establishment ID codes for individual establishments are not permanent). The figures in Table 2 are largely consistent with figures reported in the official statistics published by the Ministry of Internal Affairs and Communications, suggesting that we were able to link the two Censuses successfully. However, we should note that we treat relocated establishments as new establishments, which are also true for the official publication. In the Census, establishment codes consist of the location (prefecture and city/town/village) information and the establishment-level code. If an establishment moved outside a particular municipality (city/town/village), the establishment is given a new code in a new location. Therefore, in our analysis such establishments are classified as exits in the previous location and as new entries in the new location, because, unfortunately, we cannot identify whether establishments truly entered or exited or simply relocated.

  7. 7.

    The figures exclude establishments set up in or after 1996 that exited before the 2001 Census.

  8. 8.

    It should be noted that while Table 3 shows figures for relatively broad industry categories, the number of small and very large new establishments in each industry was calculated for each JIP industry (see Table 1), and the figures were then aggregated for Table 3.

  9. 9.

    The very high entry and exit rates for the telecommunications industry are explained by the entry and exit of many small shops owned by major telecommunications companies. The number of such shops has increased since the mid-1990s along with the rapid spread of the use of mobile phones. Such shops sell and provide various services related to mobile phones.

  10. 10.

    Size distributions for entrants and incumbents as of 2001 are shown in Appendix Table 1. Although the size difference between the two groups is not large, the difference in means is statistically significant and entrants tend to be larger than incumbents.

  11. 11.

    That said, anecdotal evidence suggests that new manufacturing establishments tend to be more capital intensive and hire fewer employees than incumbent establishments. As we measure establishment size using the number of employees because of data constraints, it is therefore possible that we may have inadvertently classified capital-intensive establishments as relatively small establishments, which may be the reason for the relatively small share of very large new establishments in these manufacturing industries.

  12. 12.

    We are grateful to an anonymous referee for pointing out this issue. In fact, previous studies such as Honjo (2000) show that young firms are less likely to survive than old ones.

  13. 13.

    Unfortunately, information on the output of establishments (such as shipments, production, or value added) is not available in the Censuses; therefore, we cannot measure productivity.

  14. 14.

    As mentioned above, because of data constraints it was not possible to perfectly match headquarters and branches. Although it was partly possible to match headquarters and branches in the 2006 Census, the information required for such matching was not available in the 2001 Census. This creates the following problem: If an establishment of a multi-establishment firm exits, the decision may be made based on the conditions or performance of other establishments as well, so that in our estimation we should control for the behavior of other establishments of the same firm. However, because it was not possible to match headquarters and branches, we were unable to do so; therefore, there is a risk that our estimation results are biased. Therefore, in order to check the robustness of our results, we ran an estimation restricting the sample to single establishments only. The results, which are available from the authors upon request, are very similar to our baseline results, indicating that any bias from our inability to match headquarters and branches seems to be limited.

  15. 15.

    The population variables are constructed using data from the Population Census, which is conducted in years ending in 0 or 5. Therefore, the variables are constructed using the 1995 and 2000 Population Censuses.

  16. 16.

    Our construction of the variable for industry growth follows the example of Sakakibara and Porter (2001). Industry-level real gross output data are taken from the JIP Database 2011.

  17. 17.

    The observations for the mail industry (JIP industry code 79) have already been excluded since the beginning of this study because of the postal service privatization in 2006 as already mentioned in footnote 5. We also ran the estimations including observations for the utilities and public service sectors, and the results were very similar. Although the magnitude of only the estimated coefficients and marginal effects for the Hirschman–Herfindahl Index variable (INDHHI) changed significantly, the results were qualitatively same.

  18. 18.

    The full results of the estimation are shown in Appendix Tables 3 to 5. In this paper, we focus on discussions of the effects of new entry and do not explain the estimated coefficients on various control variables to conserve space.

  19. 19.

    The estimation results are available from the authors upon request.

  20. 20.

    The full results of the estimation and the average marginal effect of each explanatory variable are shown in Appendix Tables 9 to 11 and in Appendix Fig. 1.

  21. 21.

    The estimation results are available from the authors upon request.

  22. 22.

    The estimation results are available from the authors upon request.

  23. 23.

    This result may also imply that incumbents/entrants quickly notice new entry/exit and respond immediately. Although the speed of impact of entry is an interesting issue that deserves further scrutiny, it is not a focus of our current study and we would like to leave this for our future study.

  24. 24.

    The estimation results are available from the authors upon request.


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This research is conducted as part of the “East Asian Firm-Level Productivity Project” at the Research Institute of Economy, Trade and Industry (RIETI). The authors would like to thank Hiroyuki Odagiri, Yuji Honjo, Shoko Haneda, participants of workshops at Chuo University and RIETI, two anonymous referees and the editor Alexander Kritikos for their detailed and helpful comments. The authors are also grateful to Yuji Matsuoka for helping us to compile the dataset. This research was supported by grants from the Japan Society for the Promotion of Science (KAKENHI 23243050 and 26780161).

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Correspondence to Keiko Ito.

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Ito, K., Kato, M. Does new entry drive out incumbents? The varying roles of establishment size across sectors. Small Bus Econ 46, 57–78 (2016).

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  • New entry
  • Entrant
  • Incumbent
  • Survival
  • Exit
  • Japan

JEL Classifications

  • L10
  • M10
  • L26