Trade Liberalization and Productivity Growth: Firm-Level Evidence from Cameroon
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it