Do Environmental Regulations Influence Trade Patterns? Testing Old and New Trade Theories
Bibliographic record
Abstract
One approach to examining the impact of environmental regulations on trade patterns is via the standard Heckscher-Ohlin-Samuelson (HOS) framework where comparative advantage is determined by factor endowment differentials. In this approach, net exports are expressed as a function of factor endowments, including environmental regulations. The aim of this paper is to examine the impact of environmental regulations on trade patterns within the traditional comparative advantage based model and within the 'new' trade-theoretic framework. In the former we will test whether the stringency of a country's environmental regulations influences its net exports of pollution-intensive output. In the 'new' trade model we are asking a slightly different question. Since this approach is concerned with bilateral trade and the share of intra- and inter-industry trade within total trade, we are testing whether environmental regulations, like other factor endowments, influence the composition of trade, i.e. the extent to which countries trade within the same, or different, industries. With regard to the HOS framework, we extend Tobey's (1990) analysis in a number of ways: (i) we use a larger and more up to date dataset that allows us to assess whether the impact of regulations on trade patterns has changed since the mid-1970s; (ii) we test two alternative measures of environmental regulations; (iii) where possible, we include industry dummies to control for unobserved industry characteristics that may affect the relationship between regulations and net exports; (iv) we control for the potential endogeneity of environmental regulations. Turning to the 'new' trade model, we are unaware of any previous study that tests the effect of environmental regulations within a framework of this type. More specifically, we include environmental regulation differentials alongside other factor endowment differentials as a possible explanation of the share of inter-industry trade within total trade, with determinants of the share of intra-industry trade also included. We, again, control for possible endogeneity thereby providing the first cross-country trade analysis to incorporate the possible endogeneity of environmental regulations. The remainder of the paper is organised as follows: Section 2 provides the econometric analysis based on a model of comparative advantage, Section 3 estimates the 'new' trade model and Section 4 provides an interpretation of the results. Section 5 summarises and concludes.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".