Essays on labor markets and social policy
Bibliographic record
Abstract
In chapter 1, I examine how the child's educational outcomes are impacted by the mother's participation in an employment guarantee program versus the mother's participation in the regular labor force in India. Using the survey data from India Human Development Survey I and II, I estimate this effect by analyzing the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) 2005. I use household-specific and parent-specific characteristics as controls. Regression analysis and propensity score matching techniques are used to determine the causal relationship. The results show that the participation of mothers in MGNREGA work leads to a decrease in the test scores of the children. While MGNREGA is an important source of employment for households in the rural areas, especially women, this paper shows that increased participation can lead to negative spillover effects for the children. This suggests that employment programs must be designed alongside childcare support and educational interventions to mitigate adverse consequences. Chapter 2, co-authored with Md Wahid Ferdous Ibon examines the wage differentials between Indian immigrants in the United States and Canada, focusing on how differences in immigration policies and labor market structures impact earnings. Using data from the 2021 Canadian Census, the 2021 American Community Survey (ACS), and India’s Periodic Labor Force Survey (PLFS) 2020-21, we compare the wages of Indian immigrants in both host countries to those of Indian residents. We employ the DiNardo, Fortin, and Lemieux (DFL) decomposition to separate the effects of skill endowments and returns to skills. Our results indicate that Indian immigrants in the United States earn significantly higher wages than their counterparts in Canada, with a greater share of this wage advantage attributable to higher returns to skills rather than differences in educational attainment or experience. The U.S. system, which relies on employer sponsorship, appears to facilitate better skill-job matching, whereas Canada has a points-based system that despite selecting highly educated immigrants, results in lower wage returns due to factors such as credential recognition barriers and wage compression. Chapter 3 investigates the impact of Medicaid expansion under the Affordable Care Act (ACA) on child maltreatment rates in the United States. While existing literature extensively examines Medicaid expansion’s effects on healthcare access and financial security, its influence on child welfare remains underexplored. Using state-level panel data from the National Child Abuse and Neglect Data System (NCANDS) (2010–2019), this study employs a Difference-in-Differences (DiD) approach and the Callaway & Sant’Anna (2021) estimator to account for staggered Medicaid adoption across states. My findings indicate that Medicaid expansion does not significantly reduce overall child maltreatment rates, though it is associated with a 16% decline in reported physical abuse cases. Additionally, I observe an increase in reported maltreatment cases for infants under one year old, likely driven by increased healthcare visits and subsequent detection. These results suggest that while Medicaid expansion may alleviate financial stress and improve parental well-being, its protective effects on child maltreatment are limited.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".