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Racial harassment, job satisfaction, and intentions to remain in the military

Journal of Population Economics volume 22, pages 713–738 (2009)Cite this article

Abstract

Our results indicate that two thirds of active-duty military personnel report experiencing offensive racial behaviors in the previous 12 months, whereas approximately one in ten reports threatening racial incidents or career-related discrimination. Racial harassment significantly increases job dissatisfaction irrespective of the form of harassment considered. Furthermore, threatening racial incidents and career-related discrimination heighten intentions to leave the military. Finally, our results point to the importance of accounting for unobserved individual- and job-specific heterogeneity when assessing the consequences of racial harassment. In single-equation models, the estimated effects of racial harassment on both job dissatisfaction and intentions to leave the military are understated.

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Notes

  1. See Milliken and Martins (1996) for a review of the organizational psychology literature on the effects of workplace diversity. Lazear (1999) examines the incentives for diversity in team building, whereas Alesina and La Ferrara (2005) consider the relationship between ethnic diversity and economic performance generally. Finally, Hamilton et al. (2004) present empirical evidence on the impact of team diversity on productivity.

  2. In 1999, minorities made up fully 36.4% of all active-duty personnel (Dansby et al. 2001, p. 221).

  3. Similar arguments can be made regarding sexual harassment and the integration of women into the US military (Antecol and Cobb-Clark 2006). In particular, sexual harassment has been linked to a reduction in unit cohesion and combat readiness (Rosen and Martin 1997).

  4. In the analysis, we will also consider harassment of Asians and Hispanics. Although harassment of these groups is more likely based on ethnicity rather than race, we will continue to refer to this as “racial” harassment for simplicity.

  5. Empirical estimates of labor market discrimination are generally derived from residual differences in aggregate outcomes once observable productivity-related characteristics have been taken into account. Omitted variables, unobserved heterogeneity, and measurement error can all confound residual-based estimates of labor market discrimination, however, leading to an increased interest in the use of direct survey data to measure discrimination (e.g., Kuhn 1987; Hampton and Heywood 1993; Laband and Lentz 1998; Johnson and Neumark 1997; Antecol and Kuhn 2000; Shields and Wheatly Price 2002a, b; Antecol and Cobb-Clark 2006, 2007).

  6. See Clark (1996), Clark and Oswald (1996), Heywood and Wei (2006), and Shields and Ward (2001) for reviews of the economics literature on job satisfaction.

  7. Exceptions are Shields and Wheatly Price (2002b) who examine the effect of racial and ethnic harassment on both job dissatisfaction and the intention to leave nursing. Additionally, Laband and Lentz (1998) and Antecol and Cobb-Clark (2006) study the effect of sexual harassment on the job satisfaction and intended job change of female lawyers and female military personnel, respectively.

  8. Installations are a particularly meaningful measure of organizations in our case because they reflect geographically separate groups of individuals who live and work together and whose day-to-day experiences are ultimately under the command of a single individual. In particular, DoD directives make equal opportunity a commander’s responsibility (Dansby and Landis 2001).

  9. Although the initial non-proportional stratified random sample consisted of 76,754 active-duty personnel, 3,258 of them were found to be ineligible for the target population because they had left the military service (Elig et al. 1997; Wheeless et al. 1997).

  10. Approximately 40% (70%) of overseas personnel (members of the Coast Guard serving in the USA) have missing installation codes, whereas roughly 13, 6, 4, and 4% of members of the Army, Navy, Marine Corps, and Air Force serving in the USA, respectively, have missing installation codes.

  11. Similar results are found if we consider only those installations with at least 50 active-duty members and are available upon request.

  12. A unique feature of the AF-EOS data is that it contains basic demographic information for both respondents and non-respondents. We find that, while whites and Asians were disproportionately likely to respond to the survey, blacks are underrepresented among respondents. Moreover, respondents are less likely to be in the Marines and more likely to be in the Air Force. These differences—while significant—are generally minor, suggesting that the characteristics of the two groups are much the same. Similar results are found when comparing our analysis sample to both non-respondents and respondents who were excluded from our analysis (i.e., members of the coast guard, personnel serving at small military installations (i.e., less than ten active-duty personnel), overseas personnel, installations with missing zip codes, installations with missing identifiers, and characteristics with missing information). Unfortunately, however, we can only speculate about the ways in which the harassment experiences of these individuals might differ from the individuals in the sample.

  13. Scarville et. al. (1997) used a principal component analysis with orthogonal rotation to assign each of the 31 types of encounters into six broad categories. As four of their categories (assignment/career, evaluation, punishment, and training/test scores) all pertain to racial discrimination with respect to aspects of one’s military career, we have combined these four categories into one broad category that we label “career-related.” The remaining categories are identical to those considered by Scarville et. al. (1997). See Table A.1 in Appendix for a detailed list of the question wording and specific behaviors that make up each type of racial harassment.

  14. Given the disjuncture between standard measures of discrimination and perceptions of discrimination (Antecol and Kuhn 2000), we suspect that—even if harassment could be objectively measured—it is perceptions of harassment that are important in understanding individual behavior.

  15. Responses to the first question include: very dissatisfied, dissatisfied, neither, satisfied, and very satisfied. Responses to the second question include: very unlikely, unlikely, neither, likely, and very likely.

  16. Antecol and Cobb-Clark (2006) find that endogeneity leads single-equation estimates of the effect of sexual harassment on job satisfaction and intended quits to be overstated. At the same time, Shields and Wheatly Price (2002b) conclude that, although significant correlations exist between the error terms in their racial harassment, job satisfaction, and intended job change equations, their results based on single-equation models are generally robust to endogeneity concerns.

  17. To investigate this identifying assumption, we reformulated the estimation model, allowing harassment to have both direct and indirect effects on intended job change. The direct effect of harassment on intended quits was insignificant in our specifications, and the overall results were substantially the same. (These results are not presented here, but are available upon request).

  18. All estimation is preformed in Stata 8 using a trivariate probit estimation routine developed by Cappellari and Jenkins (2003). This routine is based on the GHK smooth recursive simulator that has been found to be quite accurate and is often used in computing functions involving multivariate normal integrals (see Greene 2000, pp. 196–197). The square root of the number of observations is used to determine the number of draws used by the trivariate probit estimation routine.

  19. This estimation framework also implicitly assumes that military personnel who are neither satisfied/dissatisfied (neither likely to remain/quit) are the same as military personnel who are satisfied (likely to remain). To investigate this, we re-estimate equation (2), replacing “dissatisfied” with “satisfied” [equaling one for individuals reporting that they are (very) satisfied with their job as a whole] and “quit” with “stay” [equaling one for individuals reporting that they are (very) likely to remain in military employment]. We also re-estimate equation (2), replacing dissatisfied with satisfied but leaving intentions to leave military employment. In both cases, the results did not substantially differ from those presented in the paper. Additional results are available upon request.

  20. The exceptions are the model of offensive racial encounters and career-related discrimination estimated for whites.

  21. Specifically, in addition to their basic pay, military personnel receive additional payments that depend, in part, on the number of dependents they have. Housing allowances and the value of medical benefits also explicitly vary with the number of dependents (Kilburn et al. 2001). Many components of military pay and benefits are nontaxable.

  22. Unlike the previous case that relies only on the univariate cumulative standard normal distribution, this result also necessitates the use of the trivariate cumulative normal distribution. We calculated standard errors by using a Cholesky decomposition of \(\Sigma \) (including the estimated correlations) to obtain \(p\prime \). Using \(\kappa = \widehat{\varphi } + p\prime \eta _{{ij}} \) where \(\widehat{\varphi } = (\widehat{\beta },\widehat{\gamma },\widehat{\delta },\widehat{\rho }_{{12}} ,\widehat{\rho }_{{13}} ,\widehat{\rho }_{{23}} )\), we randomly sampled \(\eta _{{ij}} \)(N = 1,000) from a standard normal distribution and recalculated the marginal effect using alternative values of \(\kappa \) in equation (4). Standard errors are based on the distribution of these results. These calculations were performed using Gauss 7.0.

  23. We also estimate the model separately by race. The same patterns are found across racial groups, although the link between job dissatisfaction and intended job change is generally strongest for black and Asian personnel and weakest (and often insignificant) for white and Hispanic personnel. Although we can only speculate, these differences may stem from racial and ethnic differences in non-military labor market opportunities. Results are available upon request.

  24. The conditional probability of harassment on intended job change in the single equation framework, using the chain rule, simply reduces to the marginal effect of racial harassment in the dissatisfaction equation times the marginal effect of job dissatisfaction in the intended job change equation. The standard errors are calculated using the “delta” method.

  25. Similarly, Shields and Wheatly Price (2002b) conclude that racial harassment is a considerable problem for the National Health System in the UK.

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Acknowledgment

We thank the Defense Manpower Data Center for providing us access to variables related to location from the confidential files of the 1996 AF-EOS. Many helpful comments were provided by seminar participants at RAND and three anonymous referees. None of the views expressed in this paper represent the official views of the US Department of Defense, and all errors remain our own.

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Authors and Affiliations

  1. Department of Economics, Claremont McKenna College, 500 E. 9th Street, Claremont, CA, 91711, USA

    Heather Antecol

  2. Social Policy Evaluation, Analysis and Research Centre, Economics Program, Research School of Social Sciences, Australian National University, Canberra, ACT 0200, Australia

    Deborah Cobb-Clark

  3. Institute for the Study of Labor (IZA), Bonn, Germany

    Heather Antecol & Deborah Cobb-Clark

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  1. Heather Antecol
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  2. Deborah Cobb-Clark
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Correspondence to Heather Antecol.

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Responsible editor: Christian Dustmann

Appendix

Appendix

Table A.1 Racial harassment, job dissatisfaction, and intention to leave questions
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Table A.2 Independent variable definitions
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Table A.3 Sample means
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Table A.4 Validity of exclusion restrictions (2SLS linear probability models)
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Table A.5 Correlation coefficients
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Antecol, H., Cobb-Clark, D. Racial harassment, job satisfaction, and intentions to remain in the military. J Popul Econ 22, 713–738 (2009). https://doi.org/10.1007/s00148-007-0176-1

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Keywords

  • Job satisfaction
  • Racial harassment
  • Quits

JEL Classification

  • J16
  • J28