Nonmarket Strategies of New Entrants and Incumbents: Evidence from Ridesharing and Taxi Firms
Bibliographic record
Abstract
Technological innovations by digital platform-based firms have led to public debates about the legitimacy of new entrants’ business strategies in a range of industries, prompting governments to revisit industry regulations – and triggering competition between new entrants and incumbents in the nonmarket environment in order to shape the future “rules of the game”. In this paper, we examine the lobbying strategies employed by market incumbents and new entrants to shape emerging industry regulations. Our central thesis is that the asymmetry in political resources and capabilities of incumbents and new entrants will result in them pursuing divergent lobbying strategies. Focusing on the key legislator attributes of committee assignment, seniority, and ideology, we hypothesize that incumbents will leverage their superior political experience and relationships to lobby members of industry-relevant committees and more senior legislators. On the other hand, new entrants circumvent the resulting political barriers to entry by focusing on legislators outside of the committee, those who have more recently been elected, and those who are relatively more pro-competition. We also argue that the response of firms to their rivals’ lobbying will be contingent on the nature of their political support, and as such, incumbents and new entrants will respond differently to the lobbying actions of their rivals. Utilizing a novel dataset of lobbying contacts made by incumbent taxi firms and new entrant Uber with legislators in Toronto from 2014 - 2016 we find support for our hypotheses. Our findings emphasize how heterogeneous political resources drive nonmarket actions of firms when they compete with each other to influence policy outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".