Essays on the Economics of Immigration
Bibliographic record
Abstract
My dissertation consists of three papers studying the impact of social networks and employment mobility on Canadian immigrants, and the effects of economic conditions and immigration policy changes on international Ph.D. students in Canada. In the first paper, I use the Longitudinal Survey of Immigrants to Canada (LSIC) to examine the effects of social networks on labour market outcomes of newly arrived Canadian immigrants. I find that the presence of initial networks at landing significantly increases the probability of getting a network job and reduces the probability of getting a formal job after landing. Across immigration categories, network effects vary, with the largest effect among the Refugees, followed by the Family Class, and then the Economic Class immigrants. In each class, low-educated immigrants rely more on networks to find a job than high-educated ones. By separating close ties into kinship and friendship, I find that family has stronger effects on employment outcomes. Moreover, the development of the network is important over time. Economic immigrants gain from more diverse networks, while the Refugees improve their employment outcomes by frequently contacting their networks. Finally, social networks play a limited role in determining the quality of immigrants' first jobs. The second paper examines employment mobility and its effects on long-run earnings inequality among Canadian male immigrants using the Longitudinal Immigration Database (IMDB) and linked tax data. Incorporating employment risk and earnings mobility, I find long-run earnings inequality among male immigrants is up to 34 percent lower than the current inequality (a 90/10 ratio of 4.92 versus 7.51). Further, I find that around 70 to 80 percent of the total long-run inequality reduction happens within the first 5 years with the remainder occurring by 15 years. Among immigration categories, the Refugees experience the highest level of both earnings mobility and employment risk, while employment mobility mainly happens at the bottom of the earnings distribution for the Family Class and Economic Class. These findings indicate high current earnings inequality among the immigrant population is not persistent in the long run. This is good news. One concerning factor is that the employment risk is concentrated at the bottom of the earnings distribution, especially for the Refugees. In the third paper, I study the effect of changing economic conditions and immigration policies on international Ph.D. students in Canada. After arriving in a host country, they are prone to economic conditions like domestic students and are also likely to be affected by immigration policies. Using the IMDB, I find that, unlike domestic students, international doctoral students experience a shorter study duration under adverse economic conditions. At the same time, a higher unemployment rate negatively affects international Ph.D. students as it associates with a lower probability of both getting permanent resident (PR) status during the study and remaining in Canada in the following year after finishing their studies. Immigration policies are also found to significantly correlate with the students' outcomes. When PR policies are less restrictive, international students have shorter study durations and are more likely to get PR while studying and stay in Canada after studying. Although there is no evidence that relaxed work permit policies affect the study duration of international Ph.D. students, they are shown to negatively correlate with their probability of getting PR during the study period and to substantially improve the retention likelihood.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".