Ronald R.Rindfuss and Minja KimChoe (Eds.) Low and Lower Fertility: Variations across Developed CountriesSpringer, 2015. 196 p. $129.00; $122.55 (pbk).Ronald R.Rindfuss and Minja KimChoe (Eds.) Low Fertility, Institutions, and their Policies: Variations across Industrialized CountriesSpringer, 2016. 303 p. $99.99.
Bibliographic record
Abstract
Over recent decades fertility throughout the developed world has declined to below the replacement level, with fertility in some countries falling to one birth per woman. This trend has become a concern for policymakers primarily because low fertility is resulting in rapid aging of populations. Aging in turn threatens the viability of social security and health care systems, which for the most part rely on pay-as-you-go financing, i.e., the contributions of the working-age population. These two volumes contain the revised papers from two workshops conducted in December 2013 and August 2014 and organized by the Korean Institute for Health and Social Affairs and the East-West Center. The first volume includes chapters on Australia, China, Hong Kong, Japan, South Korea, Netherlands, Singapore, and the United States. The second covers Austria, Canada, Czech Republic, France, Hungary, Italy, Norway, Spain, Taiwan, and the United Kingdom. While this set of countries is representative of the developed world, a few large countries—in particular Germany and Russia—are excluded. Each chapter is written by a country expert who presents recent fertility trends and discusses the social, economic, institutional, historical, and cultural factors affecting fertility as well as policies. In addition to the country chapters, each volume contains an introductory chapter by the editors that summarizes the key themes that run through the volume, including labor market factors, child care availability, parental leave, gender equity, education system, availability of housing, and government subsidies. A final chapter provides a cross-country comparison of individual perceptions about obstacles to having children, based on survey data, and government support for families. The central puzzle the authors address is the lack of a simple explanation for the diversity of fertility levels (ranging from 2.1 to 1). The main message is that a wide range of factors affect fertility positively or negatively and that this multiplicity of factors differs from country to country, yielding a unique net outcome. Any given level of fertility can be reached through different pathways. Additional complexity results from variation in causes over time. Government policy can affect fertility levels through a wide range of subsidies and regulations, although effects of specific interventions tend to be small. The role of government as measured by the percent of GDP devoted to family benefits ranges from 3.5 percent in Nordic countries to less than 1.5 percent in Southern Europe, Japan, South Korea, and Taiwan. It is not surprising that the level of public investments in families correlates positively with the level of fertility. The two volumes are an invaluable resource for students, researchers, and policymakers, providing comprehensive and highly informative expositions of the causes of low fertility in high-income countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.033 |
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".