Plugging the Baby Gap? The Struggle to Reverse Demographic Decline in Russia
Bibliographic record
Abstract
Recently, Russia has been struggling to reverse plunging birthrates by adopting a number of radical policies designed to encourage women to have more babies. The breakup of the Soviet Union and the ensuing economic and political instability prompted a decade-long decline in fertility rates, which dropped from 1.72 children per woman in 1991 to 1.2 children per woman in 2000. As a result, Russia lost nearly 6 million inhabitants. Relative stability and high oil prices in the decade that followed saw fertility rates settle at around 1.6 children per woman in 2012 and 1.71 children per woman in 2013, which is still below the needed replacement rate of 2.1 children per woman. This article focuses on the maternal capital subsidy for the birth of two or more children that took effect in 2007 and will run until 2016. It deals with two questions. The first question is, why has maternal capital fraud been so prevalent? The second question is, does maternal capital make a difference when it comes to increasing Russia’s birthrate? In exploring these questions, the article considers the future of maternal capital subsidy, specifically focusing on the social, economic, and political outcomes of the current Ukraine crisis and Russia’s annexation of the Crimean peninsula. The article concludes that the overly restrictive design of the maternal capital program provides a fertile ground for fraud and that this subsidy fails to address the many complex causes underlying Russia’s declining fertility rates, thus limiting its effectiveness. Mothers and their families want the maternal capital money here and now because they do not believe that the money will be available in the future (in part, such belief is justified by the turbulent history of the 1990s and several bank collapses). The other side of the coin is that the state does not trust its citizens to use maternal capital money in a responsible fashion and has thus prescribed very limited usages for these funds. This lack of trust on both sides creates fertile ground in which fraud and corruption flourish.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".