Building New Plants or Entering by Acquisition? Estimation of an Entry Model for the U.S. Cement Industry
Bibliographic record
Abstract
In many industries, firms usually have two choices when expanding into new markets: They can either build a new plant (greenfield entry) or they can acquire an existing incumbent. In the U.S. cement industry, the comparative advantage (e.g., TFP or size) of entrants versus incumbents and regulatory entry barriers are important factors that determine the means of expansion. Using a rich database of the U.S. Census of Manufactures (1963-2002), an entry game is proposed to model this decision and estimate the supply and demand primitives to determine the importance of these factors. Two policies that affect the entry behavior and industry equilibrium are considered: An asymmetric environmental policy that creates barriers to greenfield entry and a policy that creates barriers to entry by acquisition. In the counterfactual analysis it is found that a less favorable environment for acquisitions during the Reagan-Bush administration would decrease the acquired plants by 90% and increase greenfield entry by 21%. Also, the Clean Air Act Amendments of 1990 increased the number of acquisitions by 3.5%. Furthermore, my simulations suggest that regulations that create barriers to greenfield entry are less favorable in terms of welfare than regulations that create barriers to entry by acquisition. Finally, it is shown how the parameter estimates change with the traditional approach in the entry literature where entry by acquisition is not considered, and when using a simple OLS estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".