The Effects of Tariff Reduction on Total Factor Productivity in the U.S. Manufacturing Sector: A Firm-Level Analysis
Bibliographic record
Abstract
(ProQuest: ... denotes formulae omitted.)INTRODUCTIONThis study investigates the impact of trade protection on total factor productivity (TFP) in the U.S. manufacturing sector. Firm-level accounting data obtained from Compustat and nominal tariff rates at the six-digit NAICS level are used to test hypotheses regarding the relationship between trade protection and TFP. Research conducted using recent micro-level data allows investigations regarding variations in productivity among firms that reside within the same industry. Research shows that, at the micro-level, firm characteristics and export orientation create a pathway for international trade to influence aggregate productivity. The role of free trade in facilitating productivity and increasing living standards make trade's impact on productivity growth an interesting topic for industrial economists. This study controls for firm characteristics, based on industrial organization theory and research, as well as the endogeneity of trade policy, to estimate the effect of nominal tariff rates on firm-level total factor productivity. The study contributes to the literature by applying existing theory and analysis to accounting data from the U.S. manufacturing sector in order to investigate the effects of tariff reduction on both firm-level and aggregate productivity. Because most previous studies focus on trade liberalization in developing nations, this study contributes to the literature by examining the relationship between trade protection and total factor productivity for the United States.Melitz (2003) examines the differential impact of trade on the productivity of firms operating within the same industry. Melitz' model states that, prior to market entry, productivity level is unknown to the firm. It is only after entry occurs that the firm's productivity function is realized. Melitz (2003) argues that open trade leads more productive firms within an industry to enter into the export market, while the less productive firms continue to operate domestically and the least productive firms exit the market. Melitz (2003) believes that resources are reallocated from less productive firms within an industry to the more productive firms. Melitz (2003) goes on to state that the inter-firm reallocation that takes place within an industry increases average industry productivity and aggregate productivity, ultimately providing welfare gains.Micro-level empirical research: Research using recently available micro-level data allows investigations regarding variations in productivity among firms that reside within the same industry. The micro-level research suggests that firm characteristics and export orientation create a pathway for international trade to influence aggregate productivity. Tybout's (2003) survey of trade liberalization studies reveals that the intra-industry reallocations of resources trump interindustry reallocations that are stressed by comparative advantage. The increases in employment associated with intra-industry reallocations are greater than the increases implied by comparative advantage. Similar results apply for developed nations. This analysis addresses concerns that increases in productivity may not result solely from changes in trade policy, because the liberalization could have been part of a broader economic reform in the developing nation. Trefler (2004) and Bernard, Jensen, and Schott (2006) find similar results in their investigations using data from Canada and the United States respectively. Bernard, Jensen, and Schott (2006) find that the probability of plant deaths rises as trade costs decline with the greatest impact on plant deaths for lowest productivity plants.Bernard et al. (2007) note several stylized facts emerge regarding U.S. firms and exporting. First, exporting is relatively rare at the firm level, with roughly 4% of all firms engaged in exporting and only 15% engaged in the industries that produce tradable goodsmanufacturing, mining, agriculture. …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".