Essays on Labor, Immigrants, and Inputs
Bibliographic record
Abstract
In my dissertation, I concentrate on the broad topic of trade and its effects on domestic markets, employing both empirical analysis and theoretical frameworks. This study delves into two primary facets of trade's influence: labor and production. While the impact of trade with China on labor markets on manufacturing employment is well studied, I contribute to existing literature by investigating its effects on STEM (Science, Technology, Engineering, and Math) occupations. Despite considerable attention to STEM education, there is little research on these specific occupations. I extend this analysis in my second chapter by examining how immigrants are affected differently from U.S.-native workers within these sectors. Additionally, I analyze the repercussions of tariff adjustments on input goods and their impact on final goods production within a country. By highlighting these indirect effects, my research advances our understanding of trade dynamics, by examining often overlooked segments of the economy. My first chapter studies the effect of Chinese import penetration on U.S. labor. Recently, several papers have established that imports from China have been detrimental to manufacturing employment. I extend the analysis to non-manufacturing occupations, paying special attention to STEM and non-STEM designations. Following the literature, I exploit regional variation of local labor markets and measure the varying levels of import exposure. Results from this paper show positive labor outcomes from imports from China: while we do see that manufacturing employment drops, we find significant positive effects on the non-manufacturing sectors. Chinese imports increased STEM wages, which are mostly non-manufacturing. These effects are long-lasting, still present 13 years after China's accession to the WTO. Non-STEM, non-manufacturing wages also increase. This suggests that Chinese imports produced a reallocation of resources away from manufacturing sectors and into non-manufacturing ones. In my second chapter my co-author, Dr. Bikramjit Saha, and I investigate how increased trade with China impacted immigrant and native-born workers in the US. Our analysis covers both STEM and non-STEM occupations across manufacturing and nonmanufacturing industries by building on the work done in my first chapter. We examine responses among both immigrant and US-born workers to import penetration at the local labor market level. We uncover the trade-induced gains and losses for US-born STEM workers resulting from changes in the movement of STEM immigrant workers across sectors and regions impacted by imports from China. Furthermore, the shift of non-STEM native-born workers from manufacturing to nonmanufacturing industries could potentially boost the wages of foreign-born workers. Understanding these dynamics has implications for how to shape immigration policy in response to trade agreements and broader sectoral shifts in labor markets. My last chapter starts to examine how changes in the tariff rate on Canadian input goods impact Canadian final goods. To conduct this analysis, I utilize data from the Canada–United States Free Trade Agreement. In order to address this research question, I adopt an empirical approach and I construct a measure of an input tariff rate. This measure quantifies the changes in the tariff rate of the inputs used by the final good. The dataset used in this study consists of data on Canadian industries and the changes tariff rate from 1991 to 2001. The findings indicate that a reduction in the tariff rate of inputs for Canadian industries has a negative impact on the sales growth rate of final good producers, regardless of the firm's size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".