Formal vs. Informal Institutional Distances and the Competitive Advantage of Foreign Subsidiaries in Latin America
Bibliographic record
Abstract
By focusing on the tacit and explicit characteristics of informal and formal institutional distances, this study investigates the competitive advantage of foreign subsidiary firms from developed countries and emerging markets operating in Latin America. Following recent research on distances in international management, this study measured the size and direction of distances and computed formal institutional distances based on the world governance indicators from the World Bank, whereas informal institutional distances are calculated using the four original dimensions of Hofstede. Considering that culture is tacit, whereas formal institutions are explicit, it is argued that these differences affect the ability to convert experience dealing with cultural and formal institutional conditions in the home country into firm specific advantages (FSAs) in a foreign host country. These assumptions are tested quantitatively using data from the Orbis database, a sample that includes over 4200 firm-year observations covering 10 of the largest economies in Latin America. In a departure from previous studies investigating the implications of FID direction, it is shown that the effects in specific directions are different for foreign subsidiaries from developed countries and from emerging markets. The results reveal that emerging market firms are at an advantage when operating in less developed host countries, whereas foreign subsidiaries from developed countries can adjust more positively when operating in host countries with strong formal institutions. On the other hand, the effects of the different CD dimensions depend on the direction towards host countries with specific cultural profiles. These findings indicate that foreign subsidiaries from emerging markets have a clear advantage in dealing with institutional voids in Latin America (i.e., FID towards less developed host countries), whereas the effects of CD are the same for all firms. This suggests that the cultural profile of the host country is what really matters.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".