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European Mobile Phone Industry: Demand Estimation Using Discrete Random Coefficients Models

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Modeling, Dynamics, Optimization and Bioeconomics IV (ICABR 2017, DGS 2018)

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Abstract

In this paper, we combine the literature from marketing and industrial economics about the mobile industry to build up the basics for estimating heterogeneous demand models. We also had a look at the general statistics for better understanding the market and proposing logit and Random Coefficient Logit (RCL) models to estimate the demand equations. Based on the observation on the market and the aggregate format of the market intelligence data, we follow the methodology of [1, 2] since we consider the RCL an attractive approach for estimating discrete purchases demand from aggregate data. The significant contribution of our approach is to provide a practical method for estimating price elasticities for demand systems involving many similar datasets using market intelligence data. The applications to other related electronics industry would be quite simple, considering the similarities in characteristics, perishable nature of selling prices, heterogeneous demand, and demographic differences in tastes.

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Notes

  1. 1.

    See also La lecture par l’Autorité de régulation des télécommunications de l’article L.1425-1 (2005). An MVNO is a wireless communications services provider that does not own the wireless network infrastructure over which the MVNO provides services to its customers.

  2. 2.

    It might be better to feel the difficulty in the reality of our dataset. Even without considering characteristics and demographics, we will already have to estimate at least \(N^{2}\cdot M \cdot 36\) months (here N is the number of products and M is the number of markets). For our final clean dataset, this means 4,148,928 parameters.

  3. 3.

    Some sites are www.idealo.de, www.gsmarena.com, www.kimovil.com, or www.bardtech.

  4. 4.

    Further discussion would be out of the scope of our paper. However, actually many other interesting facts are found based on the analysis of the raw data. Summary statistics are shown in the Appendix, Figs. 18, 19, 20 and 21. We can observe, for example, that the number of models found in basic tier increase with time, while the actual sales portion of the same tier decrease. Then, there are high competition even though the demand is decreasing. This is due to low entry barriers for manufactures and saturation of European mobile market, making consumers prefer phones with better features. The number of models in Premium tier also increase with time, and consequently current sales portion of the same tier skyrocket. This explain why high-tier and premium markets are the focus of well-known firms (the cash-cow).

  5. 5.

    We use R 3.4.0 for the analysis, using status-of-the-art BLPestimatoR-package for the analysis. This package provides the estimation algorithm described in [2].

  6. 6.

    In many of the estimations with more than 5 variables, we do not get convergence of the GMM method or the results are meaningful at the optimum. We opt for choosing only 6 variables (price plus 5 product characteristics) entering linearly the utility function, out of the 21 possible explanatory variables (1 price + 20 product characteristics).

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Acknowledgements

We thank Alberto A.Álvarez-López for his insightful comments and encouragement, but also for some useful comments and suggestions, which incentivized us to widen our research from various perspectives.

Author information

Authors and Affiliations

  1. Department of Economic Theory and Mathematical Economics, UNED, Paseo Senda del Rey, 11, Madrid, 28040, Spain

    Kyung B. Kim & José M. Labeaga

Authors
  1. Kyung B. Kim
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  2. José M. Labeaga
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Corresponding author

Correspondence to Kyung B. Kim .

Editor information

Editors and Affiliations

  1. Department of Mathematics and LIAAD—INESC TEC, Faculty of Science, University of Porto, Porto, Portugal

    Alberto Pinto

  2. Department of Agricultural and Resource Economics, University of California, Berkeley, CA, USA

    David Zilberman

Appendix

Appendix

See Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 and 31.

Fig. 11
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Some selected models and characteristics (1)

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Fig. 12
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Some selected models and characteristics (2)

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Fig. 13
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Some selected models and characteristics (3)

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Fig. 14
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Results in models without demographics

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Fig. 15
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Results in models with demographics

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Fig. 16
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Own and cross-product elasticities (average for 10 countries)

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Fig. 17
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Brand own-price elasticities—statistics for 10 countries

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Fig. 18
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Number of products per brand and pricing group

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Fig. 19
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Development of Shares of Tiers

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Fig. 20
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Average sales price of handsets in Europe (ITC)

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Fig. 21
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Number of brands per pricing group in Europe (G2K)

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Fig. 22
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Cross product-price elasticities of some products, France

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Fig. 23
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Cross product-price elasticities of some products, Germany

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Fig. 24
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Cross product-price elasticities of some products, Spain

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Fig. 25
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Cross product-price elasticities of some products, Italy

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Fig. 26
figure 26

Cross product-price elasticities of some products, Austria

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Fig. 27
figure 27

Cross product-price elasticities of some products, Belgium

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Fig. 28
figure 28

Cross product-price elasticities of some products, Croatia

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Fig. 29
figure 29

Cross product-price elasticities of some products, Czech Republic

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Fig. 30
figure 30

Cross product-price elasticities of some products, Poland

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Fig. 31
figure 31

Cross product-price elasticities of some products, Portugal

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Kim, K.B., Labeaga, J.M. (2021). European Mobile Phone Industry: Demand Estimation Using Discrete Random Coefficients Models. In: Pinto, A., Zilberman, D. (eds) Modeling, Dynamics, Optimization and Bioeconomics IV. ICABR DGS 2017 2018. Springer Proceedings in Mathematics & Statistics, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-78163-7_12

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