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Household-Scale Data and Analytical Approaches

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International Handbook of Population and Environment

Part of the book series: International Handbooks of Population ((IHOP,volume 10))

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Abstract

Much of today’s population-environment research centers on the interaction between households and individuals and their natural environment. The microdata used for such analyses are diverse with respect to their structure, substantive content, and geographic and temporal scope, raising both costs and benefits to users. The goal of this chapter is to put such issues in context by reviewing the major sources of household-level data and corresponding analytic methods used to examine the relationship between environmental change and demographic outcomes among individuals and households. In addition to reviewing examples of census, survey, surveillance, and administrative data, this chapter discusses key issues about the measurement of environmental exposures, adjustment for confounding variables, identification of causal pathways, and substantive interpretation of findings. It concludes by discussing opportunities for innovation, including in the manner that existing data are disseminated and in new data collection efforts. The recent, rapid expansion and improvement of data and methods provides population-environment researchers with many novel opportunities, but fundamental questions about conceptualization, measurement, and modeling remain salient.

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Notes

  1. 1.

    This chapter focuses on studies of demographic outcomes at the household or individual levels. It is also possible to aggregate household-scale data up to study ecological relationships (e.g., Abel et al., 2019; Mastrorillo et al., 2016). However, with one exception, such approaches are excluded from this discussion for brevity.

  2. 2.

    In many cases, a representative sample (e.g., 1%, 5%) of the census data is released to the public for analysis.

  3. 3.

    County identifiers are suppressed for some counties in the public-use samples to maintain confidentiality.

  4. 4.

    While many population-environment researchers would prefer such data to be collected on a higher-frequency basis (Fussell et al., 2014b; Headey & Barrett 2015), such longer inter-censal intervals are likely preferred by implementing governments, citizens, and other stakeholders. Indeed, given resource constraints and the track record of censuses globally, a consistently implemented decadal census would represent a considerable improvement over the status quo.

  5. 5.

    Even among the surveys that lack these geocodes, geographic identifiers and corresponding GIS data for first-order sub-national units are widely available.

  6. 6.

    To reduce disclosure risk, the location of urban clusters (i.e., communities) are displaced up to two kilometers, and the location of rural clusters is displaced by up to five kilometers, with an additional (randomly selected) 1% of rural clusters displaced by up to 10 km. This displacement does have non-trivial consequences for estimating the effects of some contextual variables, particularly those measuring distance to a given point (Elkies et al., 2015). See Grace et al. (2019) for further discussion.

  7. 7.

    Evidence of systematic misreporting of birth month (Larsen et al., 2019) suggests caution is needed when constructing such datasets at a monthly resolution.

  8. 8.

    The most common data on migration in these surveys includes (a) years in current residence; and (b) type of place of previous residence (e.g., rural vs. urban). However, even these two basic variables have not been collected in every round of the DHS. Notably, a number of recent DHS surveys have included information on place of previous residence (e.g., region of previous residence in the 2016 Ethiopian DHS).

  9. 9.

    These systems are sometimes also referred to as Health and Demographic Surveillance Sites (HDSSs).

  10. 10.

    A number of other studies have used U.S. Internal Revenue Service tax return data to study the effects of environmental change on migration. These important studies—including Curtis et al. (2015), Fussell et al. (2014a), and Hauer (2017)—aggregate these data (e.g., to the county level) and conduct ecological analyses, and are therefore not featured in the text above.

  11. 11.

    It is common to use the spatial mean, but conceptually it is possible to develop other measures of exposure such as population-weighted means.

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Authors and Affiliations

  1. Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, PA, USA

    Brian C. Thiede

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  1. Brian C. Thiede
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Correspondence to Brian C. Thiede .

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  1. Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA

    Lori M. Hunter

  2. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Clark Gray

  3. Campus Condorcet, National Institute for Demographic Studies, Aubervilliers Cedex, France

    Jacques Véron

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Thiede, B.C. (2022). Household-Scale Data and Analytical Approaches. In: Hunter, L.M., Gray, C., Véron, J. (eds) International Handbook of Population and Environment. International Handbooks of Population, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-76433-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-76433-3_5

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