In lieu of an abstract, here is a brief excerpt of the content:

in the U.S. military, for example, lead to a force disproportionately composed of members with family obligations. This may or may not be consistent with the pressures of military life. The use of alternative compensation schemes to create a familyfriendly atmosphere in the military arose, in part, from the competitive pressures stemming from the implementation of the AVF. In many countries, military service is viewed as an obligation of citizenship. In the United States, however, significant difficulties with the use of government authority to compel service via the draft during the Vietnam era compelled a different policy. In a sense, current policies substitute a policy of “mutually beneficial exchange” (in the sense that joining the military is a labor market choice) for the use of government authority. While this may make participation in the military more “democratic” and less “authoritarian,” such policies can discourage the recruitment of individuals concerned with public rather than private gain. As Waldo’s discussion of how best to recruit a “governing class” demonstrates, this is an enduring problem with no easy solution. It is interesting to note that military recruiting and retention problems , during the economic expansion of the 1990s, were least severe for the U.S. Marine Corps. In contrast to the advertising schemes used by other branches of the services (which focused on military service providing a useful set of technical skills highly valued in the private sector ), the Marine Corps recruiting strategy focused on other values including personal challenge and growth, honor, tradition, discipline, hard work, and service of country. Perhaps governmental leaders are not well advised to compete for personnel using corporate labor market practices. In considering the most appropriate way to recruit a governing or administrative class devoted to public service, those leaders instead might stress the prestige of public employment, the opportunities for personal growth, and the moral or ethical rewards. APPENDIX: RESEARCH METHODOLOGY To establish the findings reported earlier in the chapter, data on marriage and dependents status in the military obtained from the Defense Manpower Data Center (DMDC) were compared with data from the March supplement to the CPS from 1995 to 1999.31 Differences in marital and dependents status based on military service were evaluated using regression analysis. Because marriage and dependents status are binary variables, the logistic model was used. In the first model, the dependent variable was marital status (1 if married, 0 otherwise), and 146 Cadigan the independent variables were the constant, age in years, sex (1 if male, 0 otherwise), income in dollars per year, high school diploma (HSD; 1 if had a high school diploma, 0 otherwise), and civilian status (Civstat; 1 if not in the military, 0 otherwise). The regression output is as follows: Model 1: Logistic Regression of Marital Status ln P/(1 ⫺ P) ⫽ ⫺6.3 ⫹ .27 Age ⫺ .54 Sex ⫹ (3.51E ⫺ 6) Income ⫺ .27 HSD ⫺ .92 Civstat P-value: (.0001) (.0001) (.0001) (.0001) (.0001) (.0001) P-value for Score statistic with 5 DF ⫽ .0001 Percentage of concordant predicted probabilities and observed responses ⫽ 77.8 The coefficient on civilian status is negative and significant well below the 1 percent level. The results suggest that even when controlling for a variety of factors, military service is associated with a higher probability of marriage. Each of the other variables contained in the regression is significant and has a sign consistent with expectations. Also, the Score statistic (which tests for the joint significance of the explanatory variables) and the high percentage of concordant predicted and observed responses (which assess the extent to which higher predicted probabilities are associated with more observances of marriage) suggest that the model fits the data well. Estimation of two other models , the Probit model and the linear probability model, both generate similar results. Model 2 presents the results for dependents status, maintaining the independent variables developed previously. Model 2: Logistic Regression of Dependents’ Status ln [P/(1 ⫺ P)] ⫽ ⫺4.68 ⫹ .20 Age ⫺ .27 Sex ⫺ (2.1E ⫺ 7) Income ⫺ .76 HSD ⫺ 1.02 Civstat P-value: (.0001) (.0001) (.0001) (.0001) (.0001) (.0001) P-value for Score statistic with 5 DF ⫽ .0001 Percentage of concordant predicted probabilities and observed responses ⫽ 76.8 The coefficient on civilian status is negative and significant at well below the 1 percent level. This suggests that even after controlling for a variety of factors, being in the military is associated with a higher probability of having dependents. Each of the other variables contained in the regression is significant and has a sign consistent...

Share