Trade Liberalization and Productivity Growth: Firm-Level Analysis from Kenya
Bibliographic record
Abstract
We analyze the impact of trade liberalization on firm productivity growth in Kenya’s manufacturing sector, using a panel spanning 8 years; 1992-1999. Our analysis reveals that liberalizing trade generates high productivity improvements in the manufacturing sector. We find that a one-unit reduction in import duties as a percentage of total imports significantly increases firm-level productivity in the manufacturing sector by 5.7%. When we examine this effect on the firm’s share of exported output, we find that lowering of import duties significantly increases the share of output exported by 0.7%. Further, we sought to assess how the effect of import duties varied across the different industries in our sample. Examining the effect of import duties on industrial performance, we find a negative and statistically significant relationship in some of the industries. Our results show heterogeneous effect of reduction of import duties on industrial performance. Not all industries benefited from the lowering of import duties, especially the food and bakery, and garment industry, where productivity did not increase. These findings have important policy implications for improving the manufacturing sector. Consequently, formulating policies that effectively relax restrictive barriers to trade in the economy could speed up firm-level productivity in the manufacturing sector.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".