Efficiency and productivity analysis of deregulated telecommunications industries: a comparative study of the cases of Canada and Nigeria
Bibliographic record
Abstract
Following telecommunications industry deregulation in United Kingdom and the introduction of competition in the United States of America's long distance telecommunications services in the 1980s, telecommunications industries in other developed and developing countries have been deregulated. Contributing to the deregulation are the influences of globalization, technological advancement, fiscal policy restraint, lending institutions' requirements, regulatory costs curtailment and the desire for improved performance. However, the benefits of deregulation remain uncertain. The motivation for this research is to investigate the efficiency and productivity performance of telecommunications industries in deregulated environments. Comparatively analyzing the experiences of Canada and Nigeria, this research addresses two broad questions. First, how did deregulatory policies influence competitiveness in the industries in the two countres? This was addressed by: (i) investigating the forces that drove deregulation, (ii) exploring the similarities and differences in the deregulatory milieu in the two countries, and (iii) evaluating competitiveness in the industry. Second, how did the industries perform in the deregulated environments? The outcomes shed lights on the efficiency, productivity and the influence of environmental factors on efficiency performance. It also imbues the applicability of structure-conduct-performance model in the understanding of deregulatory outcomes. The approach adopted entailed empirical analysis of the two countries in the context of 17 other telecommunications industries from High Income Countries and Middle Income Countries over a 13-year period (2001–13). The study used non-parametric Data Envelopment Analysis (DEA) and the Malmquist Productivity Index to assess the efficiency and productivity changes and a random effect (RE) panel Tobit model was used to evaluate the effect of environmental factors on efficiency performance. Furthermore, responses from industry participants were obtained to complement the DEA findings. The DEA results suggest that operating in deregulated environment improves efficiency and productivity performance; a finding validated by the views of the industry participants involved in the study. The two countries, though inefficient, showed improved technical efficiency. The productivity analysis revealed both countries experienced productivity growth but it has slowed. Also, the Mann-Whitney test showed that the two countries have comparable productivity change. The Canadian telecommunications industry experienced technological progress and efficiency improvement, but the productivity change was mainly due to efficiency improvement attained through managerial effectiveness. On the other hand, the Nigerian telecommunications industry experienced technological retardation but efficiency progression. Its productivity change was due to efficiency improvements attained through enhanced operational scale. The investigation of the influence of environmental factors on efficiency reveals that the number of years in deregulation has an insignificant negative influence on technical and scale efficiency. However, as a quadratic term, the effect is positive but remained insignificant. Revenue per subscription positively influences technical and scale efficiencies and is statistically significant. This indicates that higher prices may result in better technical efficiency and operational scale. Industry concentration level was found to have a positive but not statistically significant effect on technical and scale efficiencies and a negative but also statistically insignificant effect on pure technical efficiency. This signifies that telecommunications industry concentration is not consequential to performance. Capital expenditure to revenue ratio has no significant influence on technical efficiency but a statistically significant negative influence on scale efficiency. This signifies that scale efficiency could be attained by optimizing capital expenditure through full capacity utilization and by avoiding infrastructure duplication. Labour productivity influences technical efficiency but has an unimportant negative effect on scale efficiency. This implies that technical efficiency could be enhanced through labour productivity improvements. Also, change in real gross domestic product per capita has a negative and insignificant effect on technical and scale efficiencies. However, as a quadratic term, it has significant positive influence on scale efficiency, suggesting that countries with higher economic growth and wealth would display better scale efficiency performance. Inflation has significant positive influence on technical and scale efficiency performance. The level of development has insignificant relationship with technical and scale efficiency scores, implying that it is not an essential determinant of performance. The interaction of labour productivity and capital intensity undermines technical efficiency, signifying that efficiency improvement through labour productivity and increased use of capital is not sufficient to neutralize efficiency loss from increased capital intensity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".