Federally-mandated child support award guidelines: Intrafamily allocation of income via child support awards

Mary Joan Combs, Fordham University

Abstract

States' child support award guidelines adopted under the 1984 Child Support Enforcement Amendment and the 1988 Family Support Act were empirically tested. A synthetic data set, created by matching married women from the 1990 Current Population Survey to child support-eligible women from the 1990 March/April Match File on Child Support and Alimony, was used to impute fathers' incomes needed for calculating theoretical child support awards under the Wisconsin-Fixed Percent, Wisconsin-Varying Percent, and Income Shares guideline models. Differences or gaps between actual and theoretical awards and reported receipts were examined across parents' characteristics using multiple regression analysis. Mother's schooling, number of children, African-American race, age of the child support award, fathers' household earnings, fathers' days of contact, and guideline model used displayed strong and significant effects on the gaps examined, while AFDC recipiency status, never-married status, mother's work status, and Hispanic origin had little or no effect. Guideline model played an important role in determining size of the awards and size of the gaps, with Income Shares cases offering both smallest awards and smallest gaps. A Chow test revealed structural differences in regression equation coefficients estimated for appropriate subsets of data for different guideline models. All matching routines and statistical analysis were performed using the Convex C3840 of the National Center for Supercomputing Applications at the University of Illinois under grant SES930004N. ^

Subject Area

Law|Economics|Individual & family studies

Recommended Citation

Combs, Mary Joan, "Federally-mandated child support award guidelines: Intrafamily allocation of income via child support awards" (1995). ETD Collection for Fordham University. AAI9530022.
https://fordham.bepress.com/dissertations/AAI9530022

Share

COinS