Patterns of Corporate Diversification in Canada: An Empirical Analysis
Bibliographic record
Abstract
Using a comprehensive micro-database of Canadian firms in conjunction with industry-level data on commodity flows, we develop a profile of corporate diversification within the Canadian economy. Our analysis has two major objectives. First, we decompose corporate diversification into horizontal and vertical components based on the degree to which industries are linked by inter-industry trade flows. Horizontal and vertical decompositions serve as useful proxies for the strategic factors that underlie diversification strategies. We find that over 80% of corporate diversification is horizontal in nature, occurring across industries that do not exhibit strong buyer/seller relationships. In the main, this suggests that many firms pursue diversification strategies in order to spread risk and to take advantage of special assets, more so than as a means of improving vertical efficiencies. Seventy-one percent of corporate diversification is also broad-spectrum, representing an expansion of corporate activities across (as opposed to within) 2-digit industry groups. Our second objective is to ascertain whether diversification patterns are closely associated with certain industry characteristics. Here we consider industry-level factors that are generally posited to affect the level of diversification (e.g., growth, concentration, knowledge-intensity) along with other variables designed to evaluate whether diversified ownership structures are associated with inter-industry commodity flows. Our regression analysis draws on three empirical measures of diversification: first, the amount of total entropy (i.e., diversification) within an industry; second, the average entropy per firm; and last, the percentage of firms within an industry that diversify. Within our sample of 132 commercial industries, total diversification is positively associated with the intensity of inter-industry trade flows. Hence, the more diversified an industry's buyer/seller linkages with other sectors, the greater the level of corporate diversification that one would expect to find. This provides some evidence that inter-industry trade intensity plays a significant role in explaining overall levels of corporate diversification, where this level is, in turn, determined by (i) the number of diversified firms, and (ii) the average level of diversification within these firms. This said, inter-industry trade flows are not related to the average level of diversification (the second of these effects), nor do they help explain the percentage of firms within an industry that become diversified. On these issues, we look to other factors for explanation. Industry concentration and average firm size are both positively associated with the amount of diversification per firm. This is consistent with the "constrained optimization" view of diversification - large firms in concentrated markets look to diversification strategies as a means of achieving growth, as the potential for fu
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".