Mergers in the European telecommunications industry

Introduction

The consolidation of mobile operators in various countries has raised questions about the impact of reduced competition on consumer prices. Mergers in the telecommunications industry can potentially lead to efficiency gains, yet there is concern that fewer operators might also result in increased market power, allowing remaining operators to raise prices (Genakos et al., 2017). This study, conducted by Louise Aimene, Francois Jeanjean, and Julienne Liang and affiliated with Orange, France, uses a Difference-in-Differences (DiD) approach to evaluate how such mergers affect unit prices for data and voice services across different countries. Research on telecom consolidation suggests that consolidation can have mixed impacts on pricing, depending on the competitive dynamics of each market, which underscores the relevance of this study (Aimene et al., 2019).

Company and Author Background

The study’s authors are linked to Orange, a major telecommunications operator in France, which has a vested interest in understanding the effects of market consolidation. With increased merger activity in the telecom sector, operators like Orange seek empirical evidence on how such changes impact pricing dynamics. The company’s involvement in this research underscores the industry's focus on balancing competitive market structures with operational efficiency. By exploring these effects, Orange aims to inform policy decisions and shape public opinion on consolidation's impact on consumers (Aimene et al., 2019).

Research Problem

The primary research question addressed in this study is: "How does mobile operator consolidation influence unit prices for data and voice services?" In the context of reduced competition, there is concern that fewer players could lead to higher prices due to increased market power(Aimene et al., 2019). However, efficiency gains from consolidation could potentially reduce costs for operators, leading to lower prices for consumers. The authors use DiD to analyze the price effects by comparing countries with and without operator mergers, thereby isolating the causal impact of consolidation on pricing (BEREC., 2018) This approach allows for a nuanced understanding of how competitive dynamics shift post-merger.

Data and Methodology

The study utilizes country-level data on revenue from data services, cellular traffic, and voice services to evaluate the effect of mobile operator consolidation on unit prices. By employing the Difference-in-Differences (DiD) method, the authors are able to compare changes in unit prices between countries where mergers occurred (treatment group) and those where they did not (control group). This method accounts for potential confounding variables that could independently affect prices, thereby isolating the causal effect of mergers (Aimene et al., 2019). The DiD methodology provides a robust framework for assessing the impact of industry consolidation, making it suitable for studies requiring precise causal inference in competitive market settings.

Table A-1: Difference-in-Differences regressions for each merging country, showing the impact of mergers on data unit prices (lnpdata). This table highlights the variables and their respective coefficients in the analysis, indicating the methodology's setup across different countries (Source: Aimene et al., 2019).

This figure illustrates the timeline of mergers in different countries, which can help understand when the 4-to-3 mergers occurred. It provides context for the treatment period in the DiD analysis, showing when the “intervention” (merger) occurred in each country (Source: Aimene et al., 2019).

Findings and Analysis

The study's findings reveal a complex impact of telecom mergers on pricing. For data services, unit prices tend to decrease after consolidation, which the authors attribute to economies of scale achieved by the merged entities. This suggests that efficiency gains from consolidation may pass on benefits to consumers through lower prices. However, for voice services, the analysis finds an increase in unit prices, likely due to reduced competitive pressures within the market. This outcome indicates a trade-off where the efficiency benefits of consolidation in data services are counterbalanced by the negative price effects on voice services stemming from diminished competition (Aimene et al., 2019). These results emphasize the importance of carefully assessing the competitive landscape in the telecommunications industry following consolidation to balance efficiency gains with consumer welfare.

Table 2: Summary of Difference-in-Differences regression results for four merging countries. The table illustrates the impact of mergers on unit prices for data and voice services, showing significant decreases in data prices and increases in voice prices, consistent with observed efficiency gains and market power effects in the telecommunications sector (Source: Aimene et al., 2019).

This figure highlights the trend in data prices over time for merging and non-merging countries. It visualizes the decrease in data unit prices post-merger, supporting the analysis that consolidation can lead to lower data prices due to economies of scale (Source: Aimene et al., 2019).

Conclusion and Limitations

This study provides useful recommendations to policymakers and regulators in the telecommunications sector. While mergers can drive efficiencies, leading to lower data prices, the observed increase in voice prices suggests a need for regulatory measures to ensure these consolidations do not lead to anti-competitive behavior. Policymakers are encouraged to balance the benefits of operational efficiencies with the risks associated with reduced competition. The authors acknowledge certain limitations, including the possible influence of unobserved country-specific factors that might affect pricing independently of mergers. Additionally, the reliance on country-level data may overlook regional variations within countries, limiting the generalizability of the results (Aimene et al., 2019). Future studies could address these limitations by incorporating more granular data and considering additional factors that influence telecom markets.