Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Censu...
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Contextual research on time and place requires a consistent measurement instrument for neighborhood conditions in order to make unbiased inferences about neighborhood change. We develop such a time-invariant measure of neighborhood socio-economic status (NSES) using exploratory and confirmatory factor analyses fit to census data at the tract level from the 1990 and 2000 U.S. Censuses and the 2008–2012 American Community Survey. A single factor model fit the data well at all three time periods, and factor loadings—but not indicator intercepts—could be constrained to equality over time without decrement to fit. After addressing remaining longitudinal measurement bias, we found that NSES increased from 1990 to 2000, and then—consistent with the timing of the “Great Recession”—declined in 2008–2012 to a level approaching that of 1990. Our approach for evaluating and adjusting for time-invariance is not only instructive for studies of NSES but also more generally for longitudinal studies in which the variable of interest is a latent construct.
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Although some single factor models also include measures of census tract composition by race/ethnicity and nativity or even English language ability,19 , 21 other studies have shown that models including these variables are best fit with a multifactorial structure, with a separate but correlated factor for the presence of racial/ethnic segregation and/or immigrant enclaves.8 , 10 , 21 , 27
In factor analysis, the measured variables are considered to be outcome variables with their scores predicted by the latent construct; hence the intercept is the expected value of the measured variable when the latent construct takes the value of zero.
The LTDB provides transformation coefficients and a tract correspondence matrix for harmonizing 2000 geographic boundaries to 2010 geographic boundaries. The methodology is similar to an earlier harmonization method developed for harmonizing 1990 boundaries to 2000 boundaries.45 We were able to use these data to produce transformation coefficients for (reverse) harmonizing from 2010 to 2000.
The rescaled level of education = 0 × (a) + 1 × (b) + 2 × (c).
The reference period for questions about income and sources of income in 1990 and 2000 Censuses is the last calendar year, while estimates from the ACS 2008–2012 refer to income in the last 12 months and come from respondents surveyed between 2008 and 2012.
The reference period for all surveys was employment in the last week; however, as noted above, ACS estimates come from respondents who may have been interviewed at any of the year-round survey dates between 2008 and 2012.
Although the SOC classifies occupations employ four levels of hierarchical detail, an indicator for professional and managerial occupations collapses over ten of the major occupation groups at the highest level of this detail, providing a less nuanced indicator of occupations than even the highest level of the SOC. By so doing, this indicator for professional and managerial occupations is understood to respectively capture either two of the ten “big class” categories of the Featherman-Hauser classification system, or one of the five “big class” categories in the Erikson-Goldthorpe classification system, after excluding self-employed occupations.72
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RAND Corporation, Santa Monica, CA, USA
Jeremy N. Miles, Margaret M. Weden, Diana Lavery, José J. Escarce & Regina A. Shih
University of California, Los Angeles, CA, USA
José J. Escarce
University of Chicago, Chicago, IL, USA
Kathleen A. Cagney
Correspondence to Margaret M. Weden.
Jeremy N. Miles and Margaret M. Weden contributed equally to this work.
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Miles, J.N., Weden, M.M., Lavery, D. et al. Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Census Tracts. J Urban Health 93, 213–232 (2016). https://doi.org/10.1007/s11524-015-9959-y
Published17 December 2015
Issue DateFebruary 2016
- Neighborhood socio-economic status
- Neighborhood disadvantage
- Neighborhood change
- Confirmatory factor analysis
- Measurement bias
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