The General Equilibrium Wage Impact of Trade-Induced Shifts in Industrial Compositions of Employment in Brazilian Cities, 1991-2000
Bibliographic record
Abstract
Conventionally, it is presumed that restructuring of industrial composition of employment only modestly affects the average wage. This is because in a partial equilibrium setting such a restructuring affects the calculation of the average wage only through changes in employment shares of industries used as weights on constant industry wages. On the contrary, this paper brings substantial evidence indicating that aside from such partial equilibrium shift-share effects, a change in industrial composition sizably impacts all industry wages through general equilibrium (G.E.) feed-backs from the average wage – as a reservation wage in all industries in a search and bargaining framework – onto all industry wages. In particular, this paper uses Brazilian census data for years 1991 and 2000 to study the G.E. wage impacts of exogenous shifts in industrial compositions in cities of Brazil induced by substantial trade liberalization in this country during the 1990s. A restructuring of industrial composition in a city favoring high-wage industries that modestly raises the average wage in this city by only 1% through shift-share accounting, is estimated here to increase all industry wages in the city in average by at least twice as much – between 2 to 4 percent – in the long-run through the G.E impacts, resulting in an overall increase of 3 to 5 percent in the average wage. Concerns about endogeneity is address by using an IV strategy that exploits distance of a city from major international commercial ports as an indicator of how the change in trade policy impacted its industrial composition. The result is also robust to correcting for sample selection bias generated by regional migrations and to the presence of alternative explanatory mechanisms. The finding here highlights the importance of considering G.E. interactions in policy evaluations. It also indicates that major changes in national industrial or trade policies in developing countries such as Brazil, with already non-uniform distribution of economic development across regions, create geographical winners and losers depending on how the impacts are distributed across different localities sub-nationally. If the distribution of impacts is such that the losers-to-be regions are those already suffering, then balancing measures are necessary to avoid spatially uneven sub-national economic development.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".