LBWTLine-Based Wavelet Transform
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The dependent variable is a trichotomized indicator of birth-weight and includes: (1) normal BWT (2,500-5,500g), (2) low BWT (<2,500g), and high BWT (>5,500g); a multinomial logistic regression model (Long 1997) is specified for this regression in which normal BWT serves as the reference category and high and low BWT (LBWT) as the effect categories.
Income and LBWT Income has a significant and curvilinear relationship with the probability of LBWT (see model 1, Table 2; and "unadjusted" in Figure 1).
Occupational Grade and LBWT Occupation grade has a marginally significant effect on the probability of being born LBWT (model 3, Table 2) although this effect works largely through improved income status within families (model 4, Table 2).
Income Inequality and LBWT. There are no significant effects of state-level income inequality on the probability of LBWT, neither net of median levels of state income (model 2, Table 2) nor is there a gross effect (not shown).
Given that WIC is a means-tested program requiting both income and nutritional deficiencies for qualification, plotting income only among those for whom it is plausible to have received WIC (observations with incomes below the 50th percentile) may indicate that WIC has the ability to partially stave off the deleterious effects of poverty on LBWT. This interaction indicates the gradient is steepest (i.e., disparities are largest) among the poor and near-poor who did not participate in WIC and virtually nonexistent among those who did participate in WIC.