Trade Liberalization and Productivity Growth: Firm-Level Evidence from Cameroon
Bibliographic record
Abstract
Using a panel of firm-level data, this paper assesses the effects of Cameroon's trade liberalization in the late 1980s and early 1990s on firm productivity growth in the manufacturing sector. A two-step approach is employed. First, a single production function for the whole manufacturing sector is run on the pooled sample of pre-and post-reform periods as well as separately on the pre-and immediately post-reform periods using the Levinsohn and Petrin methodology, and firm productivity indexes are derived. Second, the correlation between trade liberalization and firm productivity growth rates is examined in a regression framework. We focus on the interaction between trade liberalization shocks and firm, industry and environment characteristics. We find a systematic shift in the firm productivity distributions from the pre- to post-liberalization periods in the direction of higher productivity. The manufacturing sector total factor productivity drops in the pre-reform and improves considerably in the post-reform periods. The estimations using pooled pre-and post-liberalization as well as sub-periods firm productivity growth rates show that reductions in effective protection and, even more, increases in export shares are the principal mechanisms of firm productivity improvements. Interestingly, firm, industry and business environment characteristics such as capital intensity, size, age, age squared, competition across industries, and political instability appear to have no influence on the effect of trade liberalization on firm productivity growth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".