Abstract
Recent research has highlighted a profound reversal in the well-documented relationship between increasing development and falling fertility at a global scale. This reversal shows that the total fertility rates of the most developed nations in the world are actually increasing. The trend means different things. In some cases, it is simply a data product of the slowing average age at first birth for women in highly developed areas. For others, it is a cultural shift towards larger families in response to a surplus of economic resources. Using multiple data sources, this research examines this shift in the United States by examining levels of relative sub-national human development at the county level and associated relationships to county total fertility rates. Results highlight a non-linear relationship that suggests sub-national human development is related to sub-national variations in fertility behaviors similar to recent research on a global scale. The policy implications of such a reversal in fertility are very important for government officials and public policy makers as they move forward in addressing future issues associated with population aging, labor force turnover, and in the providing of services to youth.
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Notes
See Morgan and Rackin (2010) for a review of the fertility change in the last half century. The article does a good job of identifying the distinction between the economic and cultural determinants as well as highlighting the importance and role of each.
The Total Fertility Rate is used here given its association with human development and the HDI at the global level as well as its wide usage in many studies of fertility. It is worth noting that much work has gone into understanding the potential inherent biases associated with the use of multiple indicators of fertility. For a good review of this argument see the first few paragraphs of Bongaarts and Freeney (1998).
Ultimately, this represents individuals without the basic literacy skills needs to properly function in society due to problems associated with comprehension of text and not necessarily associated with the inability to read or write.
The constructs measuring urban and rural character follow previous work by Porter and Howell (2009). All variables were initially examined in bivariate form in order to test for potential issues with non-orthogonal exogenous variables. Once, the correlations were deemed to be appropriate for multivariate analysis, colinearity diagnostics were examined in an exploratory set of regression models to ensure that high levels of colinearity would not affect the reliability of the coefficient estimates. Ultimately, indicators of education and income correlated very highly with the SHDI and reported very high levels of variance inflation (via the VIF indicator). These were removed from any further modeling and the final models include only the variables associated with urbanization as indicators of socioeconomic status independent of the inherent properties associated with the SHDI.
The urban and rural constructs are not in direct opposition to one another as might be first expected. Instead, they are measures of urban and rural character that exists within a county. Thus, it may be possible for both to exist at different levels and in different regions of the same county. The correlation between the two is −0.160, which suggests that the two are negatively associated with one another. However, it also suggests that the two are not opposite ends of a county-level classification system but instead two independent indicators of the level at which this urban and rural character exist within the county.
Subsequent tests of reliability provided the following alpha coefficients: urban = 0.78, rural = 0.89. Regression based scores were saved from the data reduction procedure and provide a standardized measure of the urban and rural character in the county.
An initial step in identifying spatial dependence across any type of spatially referenced data is to setup the neighborhood weight matrix in order to identify which counties should be considered neighbors. The selection of the neighborhood definition in this case was set to identify any contiguous county that shared a geographic border with another county to be considered its neighbor.
The most common case is that of positive autocorrelation in which the local unit’s (i) value on a variable of interest is significantly, and positively, correlated with the average neighborhood (j) value. Less frequently, negative autocorrelation refers to an instance when a local unit’s (i) value pertaining to a specific variable is significantly in opposition to the neighborhood’s (j) average value. For more information see Anselin (1995).
The Lagrange Multiplier test is a formal test for the type of dependence associated with the spatial clustering of the TFR. Simultaneous autoregression indicates a correlation of error terms and thus a violation of the assumption of non-correlated error terms. The conditional autoregressive model indicates a more substantive linkage between the TFR and the neighborhood indicating an unmeasured spatially-centered determinant of the TFR. Both are discussed at greater length in the following paragraphs.
The 19 % is calculated as the difference in the non-spatially weighted R 2 obtained from the exploratory analysis in Fig. 4 and the R 2 obtained in the spatially-weighted and isolated Model 1. The change from 0.12 (Fig. 4) to 0.31 is associated with the only difference in the estimation being the inclusion of a spatial-weight. To some degree this will vary given the improvement of the estimation procedure through the ability to account for spatial dependence, however, it should still be taken into account when discussing the total amount of variation in the TFR accounted for by the model-specific set of predictors.
It is important to note that this upward slope is based on a sub-group of the counties that have a SHDI score above 0.75 (13 total counties). While it is a population of all counties with a SHDI score above 0.75, it is still a very small group of cases to make any kind of probability based inferences about. Also of import here, the distortion of origin must be taken into account when estimating any new linear relationship with a subset of counties from a particular point in the SHDI. Here the origin is much lower than the full model because fertility has already reached a “bottom”. However, the evidentiary identification of an upward linear slope lends itself to support previous research which has highlighted such a curvilinear relationship at larger scales.
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Porter, J.R. Human Development and the Fertility Reversal: A Spatially Centered Sub-national Examination in the US. Spat Demogr 5, 43–72 (2017). https://doi.org/10.1007/s40980-016-0019-3
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DOI: https://doi.org/10.1007/s40980-016-0019-3