This article investigates the impact of military service on the socioeconomic status of African American and white, non-Hispanic women veterans of the post-1973 U.S. allvolunteer force. Earnings and family income are used as measures of socioeconomic status. Data from the 1990 Public Use Microdata Sample L were used in this analysis. This data set is a .45 percent sample drawn from the 1990 Census. Using semilogarithmic regression, we found that, overall, African American women veterans did not differ significantly from their non-serving counterparts, controlling for several factors associ ated with socioeconomic status. White, non-Hispanic women veterans, however, suffered an earnings and family income penalty relative to similar non-serving women. The data suggest that this veteran disadvantage may be due to the interaction of military service. child-bearing patterns, and educational attainment.
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12. The benefits associated with the G.I. Bill vary significantly depending upon which version of the G.I. Bill was in place during the veteran's period of service. The first G.1. Bill was implemented in 1944 and provided, at no cost to the veteran, a relatively generous allowance of $500 per year for tuition (enough to cover tuition at the nation 's most prestigious and expensive universities, such as Harvard) and a living allowance of $50 to $65 per month. However, the value of G.I. Bill benefits has generally declined with each successive version of this program and, beginning in 1976, veterans must have contributed a portion of their active-duty pay to the program in order to receive benefits. Just within the period of the AVF, it is possible for veterans to have fallen under three different educational benefits programs: the post-Korean/Vietnam-era G.l. Bill, the Veterans Educational Assistance Program (VEAP), and the Montgomery G.I. Bill. Thus, some differences in the educational attainment (and subsequent status attainment) of veterans may be due to their participation in different programs with different levels of benefits. Currently, G.l. Bill benefits would not even cover half of a veteran 's tuition at the nation's most prestigious schools (based on authors' calculations given $985 monthly G.l. Bill benefits and Harvard's $24,630 tuition for the 2002-2003 school year). Theda Skocpol, “The G.l. Bill and U.S. Social Policy, Past and Future,”Social Philosophy & Polic+14, 2 (Summer 1997): 95-115; “The Evolving GI Bill,” Educational Record 75, 4 (Fall 1994): 50, 51, 53.
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72. The natural logarithm of a number is the base-e logarithm of that number, where e is defined as the limit of (I + l/x)X as x goes to infinity (approximately 2.71828). In other words, the natural logarithm answers the question, “To what power must e be raised to equal a given number?” (e.g., In (e') = 2). Practically speaking, transforming the dependent variables using the natural logarithm function makes the distribution of those variables more normal (i.e., less skewed) and provides a better fit between the data and our regression models. Once the variables are transformed. however, the interpretation of differences in earnings and family income between groups (e.g., veterans and non-veterans), as well as the changes in the dependent variables associated with changes in the independent variables in regression analysis, is also transformed. Using Equations I and 2, such differences can be converted to percentage differences between two groups or, in the case of regression, the percentage change in the dependent variable associated with a change in an independent variable.
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74. All of the regression models used in this analysis are semilogarithmic models. In this case, such models relate a logarithmically transformed dependent variable (see note 72, above) to a set of non-transformed independent variables using ordinary least squares (OLS) regression. The simplest form of this model could be represented by the equation: ln(Y) = a + bX, where ln(Y) is the transformed dependent variable, X represents an independent variable, and b represents the change in the ln(Y) associated with a change in X. The coefficient b can be converted to the percentage change in Y associated with a change in X using Equation 2.
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76.
76. The coefficients of the interaction terms in such a model can be interpreted as the difference in the coefficients between the reference group (in this case, non-serving individuals) and veterans. Their test of significance is the test that the difference between the veteran and non-serving coefficients is statistically different from 0.
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