This dissertation comprises two chapters that are related to the research topics in Korean Job Training Programs using administrative data. The first chapter examines the effectiveness of the programs on the probability of re-employment and on wages. This observational study tackles multiple biases by adopting: (1) a propensity score matching approach for selection into treatment, and (2) a principal stratification framework for selection into employment when looking at wages. This chapter finds positive effects on employability (0.026), but negative effects on wages (-8.4%), both of which are statistically significant. We conduct sensitivity analysis to violations of our main identifying assumption that confirms our results are robust to unobserved confounding. The second chapter analyzes the heterogeneous treatment effects of the programs on employability using a recent causal forest estimator, which is a machine learning technique. This chapter finds that almost a third of trainees (31.0%) is likely to experience negative effects, in spite of a positive and significant average treatment effect on the treated (0.029). We illustrate distinctive characteristics of the two groups that are affected positively and negatively. Based on these characteristics, we suggest some alternative assignment rules and find that several of the suggested assignment rules outperform the current one.
","abstract_html":"<p>This dissertation comprises two chapters that are related to the research topics in Korean Job Training Programs using administrative data. The first chapter examines the effectiveness of the programs on the probability of re-employment and on wages. This observational study tackles multiple biases by adopting: (1) a propensity score matching approach for selection into treatment, and (2) a principal stratification framework for selection into employment when looking at wages. This chapter finds positive effects on employability (0.026), but negative effects on wages (-8.4%), both of which are statistically significant. We conduct sensitivity analysis to violations of our main identifying assumption that confirms our results are robust to unobserved confounding. The second chapter analyzes the heterogeneous treatment effects of the programs on employability using a recent causal forest estimator, which is a machine learning technique. This chapter finds that almost a third of trainees (31.0%) is likely to experience negative effects, in spite of a positive and significant average treatment effect on the treated (0.029). We illustrate distinctive characteristics of the two groups that are affected positively and negatively. Based on these characteristics, we suggest some alternative assignment rules and find that several of the suggested assignment rules outperform the current one.</p>","abstract_has_math":false,"creators":["Hwang, Seokchae"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Alfonso Flores-Lagunes","Leonard M. Lopoo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-06-28T07:00:00Z","date_published":"2019-06-28T07:00:00Z","updated_at":"2026-07-24T04:55:50Z","subjects":["causal inference","heterogeneous treatment effect","job training program","korea","random forest","selection bias","Social and Behavioral Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/1091","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alfonso Flores-Lagunes","Leonard M. Lopoo"]},{"key":"dc:creator","label":"Author","values":["Hwang, Seokchae"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["causal inference","heterogeneous treatment effect","job training program","korea","random forest","selection bias","Social and Behavioral Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/1091"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation comprises two chapters that are related to the research topics in Korean Job Training Programs using administrative data. The first chapter examines the effectiveness of the programs on the probability of re-employment and on wages. This observational study tackles multiple biases by adopting: (1) a propensity score matching approach for selection into treatment, and (2) a principal stratification framework for selection into employment when looking at wages. This chapter finds positive effects on employability (0.026), but negative effects on wages (-8.4%), both of which are statistically significant. We conduct sensitivity analysis to violations of our main identifying assumption that confirms our results are robust to unobserved confounding. The second chapter analyzes the heterogeneous treatment effects of the programs on employability using a recent causal forest estimator, which is a machine learning technique. This chapter finds that almost a third of trainees (31.0%) is likely to experience negative effects, in spite of a positive and significant average treatment effect on the treated (0.029). We illustrate distinctive characteristics of the two groups that are affected positively and negatively. Based on these characteristics, we suggest some alternative assignment rules and find that several of the suggested assignment rules outperform the current one.
"]},{"key":"dc:title","label":"Title","values":["Empirical Essays on Causal Effects of Job Training Programs: Evidence From Korea"]}]}],"canonical_facts":{"dc:contributor":["Alfonso Flores-Lagunes","Leonard M. Lopoo"],"dc:creator":["Hwang, Seokchae"],"dc:description.abstract":["This dissertation comprises two chapters that are related to the research topics in Korean Job Training Programs using administrative data. The first chapter examines the effectiveness of the programs on the probability of re-employment and on wages. This observational study tackles multiple biases by adopting: (1) a propensity score matching approach for selection into treatment, and (2) a principal stratification framework for selection into employment when looking at wages. This chapter finds positive effects on employability (0.026), but negative effects on wages (-8.4%), both of which are statistically significant. We conduct sensitivity analysis to violations of our main identifying assumption that confirms our results are robust to unobserved confounding. The second chapter analyzes the heterogeneous treatment effects of the programs on employability using a recent causal forest estimator, which is a machine learning technique. This chapter finds that almost a third of trainees (31.0%) is likely to experience negative effects, in spite of a positive and significant average treatment effect on the treated (0.029). We illustrate distinctive characteristics of the two groups that are affected positively and negatively. Based on these characteristics, we suggest some alternative assignment rules and find that several of the suggested assignment rules outperform the current one.
"],"dc:identifier":["https://surface.syr.edu/etd/1091"],"dc:subject":["causal inference","heterogeneous treatment effect","job training program","korea","random forest","selection bias","Social and Behavioral Sciences"],"dc:title":["Empirical Essays on Causal Effects of Job Training Programs: Evidence From Korea"],"thesis:degree_discipline":["Economics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:55:50Z"}