Oli Hawrylyshyn’s Contribution to the Economic Valuation of Unpaid Domestic Work
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Oli Hawrylyshyn (1943-2020) played an important role in recognizing unpaid domestic work as part of the national production. He suggested ways to include it in national accounting. While Gary Becker (1965) studied how people divide their time between paid and unpaid activities, Hawrylyshyn (1977) focused on measuring the economic value of domestic work. Hawrylyshyn was a Canadian economist who studied at MIT under Evsey Domar and first specialized in migration and economics. In the 1970s, he shifted to researching unpaid household work, responding to growing criticism of GDP measures, especially from feminist movements that argued for recognizing unpaid labor. As part of the International Association for Research in Income and Wealth, he developed methods to estimate the value of domestic work and showed why it should be included in national statistics. Hawrylyshyn disagreed with Becker’s strict use of opportunity cost to measure domestic work, arguing that it overestimated the economic value of unpaid work. He introduced a key distinction between direct utility (personal satisfaction from doing a task) and indirect utility (the economic value of the service provided). He showed that some household tasks cannot be outsourced without reducing well-being. One of his most important contributions is the so-called third-person criterion: a task is considered an economic activity if someone else could be paid to do it without reducing its value to the household. This helped determine which domestic tasks should be counted in national income. He compared studies on domestic work from different countries and found that the results varied based on the method used for estimation. He identified three main approaches: opportunity cost (which gave the highest estimates), housekeeper wages (which gave moderate values), and market pricing of services (which gave the lowest values). He also noted that factors like family size, women’s employment status, and the age of the youngest child affected these estimates. Hawrylyshyn criticized GDP for not including unpaid work, especially by women. He argued for better data collection, such as time-use surveys and wage comparisons, to better value domestic labor. His approach, which combined theory and data, continues to influence efforts to include unpaid household work in national accounts. By combining theory with practical methods, Hawrylyshyn made unpaid domestic work more visible in national accounting and economic policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".