IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v27y2012i14.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol27/14/27-14.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2012.27.14?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, April.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Ibolya Török, 2018. "Qualitative Assessment of Social Vulnerability to Flood Hazards in Romania," Sustainability, MDPI, vol. 10(10), pages 1-20, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Olaru, Doina & Mulley, Corinne & Smith, Brett & Ma, Liang, 2017. "Policy-led selection of the most appropriate empirical model to estimate hedonic prices in the residential market," Journal of Transport Geography, Elsevier, vol. 62(C), pages 213-228.
    3. Stamatis Kalogirou, 2012. "Testing local versions of correlation coefficients," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(1), pages 45-61, March.
    4. Mendieta, Rodrigo & Ontaneda, Diego & Pontarollo, Nicola, 2019. "Canton growth in Ecuador and the role of spatial heterogeneity," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    5. Stamatis Kalogirou, 2011. "Testing local versions of correlation coefficients," ERSA conference papers ersa10p529, European Regional Science Association.
    6. Wang, Chih-Hao & Chen, Na, 2017. "A geographically weighted regression approach to investigating the spatially varied built-environment effects on community opportunity," Journal of Transport Geography, Elsevier, vol. 62(C), pages 136-147.
    7. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
    8. Chih-Hao Wang & Na Chen, 2021. "A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths," Transportation, Springer, vol. 48(4), pages 1967-1986, August.
    9. Christos Agiakloglou & Cleon Tsimbos & Apostolos Tsimpanos, 2019. "Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes," Empirical Economics, Springer, vol. 57(5), pages 1613-1631, November.
    10. López-Carr, David & Davis, Jason & Jankowska, Marta M. & Grant, Laura & López-Carr, Anna Carla & Clark, Matthew, 2012. "Space versus place in complex human–natural systems: Spatial and multi-level models of tropical land use and cover change (LUCC) in Guatemala," Ecological Modelling, Elsevier, vol. 229(C), pages 64-75.
    11. Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
    12. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi & Dominguez, Alberto, 2012. "Modelling transport and real-estate values interactions in urban systems," Journal of Transport Geography, Elsevier, vol. 24(C), pages 370-382.
    13. Daisuke Murakami & Morito Tsutsumi, 2015. "Area-to-point parameter estimation with geographically weighted regression," Journal of Geographical Systems, Springer, vol. 17(3), pages 207-225, July.
    14. Chocholatá Michaela & Furková Andrea, 2017. "Regional Disparities in Education Attainment Level in the European Union: A Spatial Approach," TalTech Journal of European Studies, Sciendo, vol. 7(2), pages 107-131, October.
    15. Rey, Sergio, 2015. "Bells in Space: The Spatial Dynamics of US Interpersonal and Interregional Income Inequality," MPRA Paper 69482, University Library of Munich, Germany.
    16. Padovano, Fabio & Petrarca, Ilaria, 2014. "Are the responsibility and yardstick competition hypotheses mutually consistent?," European Journal of Political Economy, Elsevier, vol. 34(C), pages 459-477.
    17. Atems, Bebonchu, 2013. "The spatial dynamics of growth and inequality: Evidence using U.S. county-level data," Economics Letters, Elsevier, vol. 118(1), pages 19-22.
    18. Diana Gutiérrez Posada & Fernando Rubiera Morollón & Ana Viñuela, 2018. "Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(3), pages 332-349, July.
    19. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    20. Manfred M. Fischer & Nico Pintar & Benedikt Sargant, 2016. "Austrian Outbound Foreign Direct Investment in Europe:A spatial econometric study," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 10(1), pages 1-22, JUNE.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dem:demres:v:27:y:2012:i:14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.