Bibliographic record
Abstract
How do states impact whether employers provide work-family benefits like childcare and paid family leave, especially in national contexts of low social policy spending? What out- comes for women’s professional advancement and gender equality more broadly should we expect from these interventions? And what does public opinion tell us about what people think employers ought to be doing privately that the state is not? I explore these questions in this three-paper dissertation. In Chapter 1, I begin by looking at on-site childcare benefits provided privately by em- ployers in the liberal welfare states of Canada and the United Sates, two countries without universal, publicly-available childcare. Using an original panel dataset of high-revenue Cana- dian and US companies, I use a difference-in-differences design to show that state childcare regulation in the United States and provincial subsidies in Canada that include employers, especially in the province of Quebec, increase the supply of on-site childcare. I then deploy a field experiment to show how greater provision of on-site childcare results in greater female demand for the benefit and, in turn, greater professional advancement. In Chapter 2, a paper co-authored with Ana Catalano Weeks, we look at how corporate board gender quotas produce feedback effects on company policies that lead to greater gen- der equality. With a difference-in-differences approach, we use an original panel dataset of corporate reports from Italy, where a board quota was instituted, to Greece, where one was not. We look at changes in company programs and policies beyond the board, especially in the areas of women’s leadership throughout the company, childcare, paid leave, and schedul- ing flexibility. Qualitative analysis helps understand the context in which companies make iii these changes to their internal policies. Finally, in Chapter 3, I look at political preferences for work-family benefits provided by employers rather than the government. I use two case studies – the first with com- parative survey data and the second with an original survey of veterans who have used employer-provided childcare through the US Department of Defense – to understand how organizational and individual experience with employer benefits shapes preference for them. Qualitative interviews with veterans shed light on some of the potential mechanisms behind these pathways. In each chapter, I discuss the political and policy implications of these findings, especially for women, who are the main of childcare and paid family leave as the primary caretakers of children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".