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Spatially varying predictors of teenage birth rates among counties in the United States

Author

Listed:
  • Carla Shoff

    (Pennsylvania State University)

  • Tse-Chuan Yang

    (State University of New York at Albany)

Abstract

Background: Limited information is available about teenage pregnancy and childbearing in rural areas, even though approximately 20 percent of the nation’s youth live in rural areas. Identifying whether there are differences in the teenage birth rate (TBR) across metropolitan and nonmetropolitan areas is important because these differences may reflect modifiable ecological-level influences such as education, employment, laws, healthcare infrastructure, and policies that could potentially reduce the TBR. Objective: The goals of this study are to investigate whether there are spatially varying relationships between the TBR and the independent variables, and if so, whether these associations differ between metropolitan and nonmetropolitan counties. Methods: We explore the heterogeneity within metropolitan/nonmetropolitan county groups separately using geographically weighted regression (GWR), and investigate the difference between metropolitan/nonmetropolitan counties using spatial regime models with spatial errors. These analyses were applied to county-level data from the National Center for Health Statistics and the US Census Bureau. Results: GWR results suggested that non-stationarity exists in the associations between TBR and determinants within metropolitan/nonmetropolitan groups. The spatial regime analysis indicated that the effect of socioeconomic disadvantage on TBR significantly varied by the metropolitan status of counties. Conclusions: While the spatially varying relationships between the TBR and independent variables were found within each metropolitan status of counties, only the magnitude of the impact of the socioeconomic disadvantage index is significantly stronger among metropolitan counties than nonmetropolitan counties. Our findings suggested that place-specific policies for the disadvantaged groups in a county could be implemented to reduce TBR in the US.

Suggested Citation

  • Carla Shoff & Tse-Chuan Yang, 2012. "Spatially varying predictors of teenage birth rates among counties in the United States," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(14), pages 377-418.
    • Handle: RePEc:dem:demres:v:27:y:2012:i:14
    DOI: 10.4054/DemRes.2012.27.14
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    References listed on IDEAS

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    1. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, December.
    2. Changshan Wu, 2012. "Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications edited by Manfred M. Fischer and Arthur Getis," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 386-388, May.
    3. Santelli, J.S. & Lindberg, L.D. & Finer, L.B. & Singh, S., 2007. "Explaining recent declines in adolescent pregnancy in the United States: The contribution of abstinence and improved contraceptive use," American Journal of Public Health, American Public Health Association, vol. 97(1), pages 150-156.
    4. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
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    Cited by:

    1. Hoehun Ha & Wei Tu, 2018. "An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States," IJERPH, MDPI, vol. 15(4), pages 1-16, April.
    2. Ibolya Török, 2018. "Qualitative Assessment of Social Vulnerability to Flood Hazards in Romania," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    3. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312.

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    More about this item

    Keywords

    geographically weighted regression; teenage birth rates; nonmetropolitan; spatial nonstationarity; local modeling;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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