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Record W3124477828 · doi:10.1257/jep.26.2.141

Why is the Teen Birth Rate in the United States So High and Why Does It Matter?

2012· preprint· en· W3124477828 on OpenAlexaboutno aff
Melissa S. Kearney, Phillip B. Levine

Bibliographic record

VenueThe Journal of Economic Perspectives · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBirth rateDemographic economicsFace (sociological concept)EconomicsDemographyFertilitySociologyPopulation

Abstract

fetched live from OpenAlex

Teens in the United States are far more likely to give birth than in any other industrialized country in the world. U.S. teens are two and a half times as likely to give birth as compared to teens in Canada, around four times as likely as teens in Germany or Norway, and almost 10 times as likely as teens in Switzerland. Among more developed countries, Russia has the next highest teen birth rate after the United States, but an American teenage girl is still around 25 percent more likely to give birth than her counterpart in Russia. Moreover, these statistics incorporate the almost 40 percent fall in the teen birth rate that the United States has experienced over the past two decades. Differences across U.S. states are quite dramatic as well. A teenage girl in Mississippi is four times more likely to give birth than a teenage girl in New Hampshire--and 15 times more likely to give birth as a teen compared to a teenage girl in Switzerland. This paper has two overarching goals: understanding why the teen birth rate is so high in the United States and understanding why it matters. Thus, we begin by examining multiple sources of data to put current rates of teen childbearing into the perspective of cross-country comparisons and recent historical context. We examine teen birth rates alongside pregnancy, abortion, and "shotgun" marriage rates as well as the antecedent behaviors of sexual activity and contraceptive use. We seek insights as to why the rate of teen childbearing is so unusually high in the United States as a whole, and in some U.S. states in particular. We argue that explanations that economists have tended to study are unable to account for any sizable share of the variation in teen childbearing rates across place. We describe some recent empirical work demonstrating that variation in income inequality across U.S. states and developed countries can explain a sizable share of the geographic variation in teen childbearing. To the extent that income inequality is associated with a lack of economic opportunity and heightened social marginalization for those at the bottom of the distribution, this empirical finding is potentially consistent with the ideas that other social scientists have been promoting for decades but which have been largely untested with large data sets and standard econometric methods. Our reading of the totality of evidence leads us to conclude that being on a low economic trajectory in life leads many teenage girls to have children while they are young and unmarried and that poor outcomes seen later in life (relative to teens who do not have children) are simply the continuation of the original low economic trajectory. That is, teen childbearing is explained by the low economic trajectory but is not an additional cause of later difficulties in life. Surprisingly, teen birth itself does not appear to have much direct economic consequence. Moreover, no silver bullet such as expanding access to contraception or abstinence education will solve this particular social problem. Our view is that teen childbearing is so high in the United States because of underlying social and economic problems. It reflects a decision among a set of girls to "drop-out" of the economic mainstream; they choose non-marital motherhood at a young age instead of investing in their own economic progress because they feel they have little chance of advancement. This thesis suggests that to address teen childbearing in America will require addressing some difficult social problems: in particular, the perceived and actual lack of economic opportunity among those at the bottom of the economic ladder.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.280
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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