Abstract
Despite a slight increase in the labor force participation rate of women age 18–55 in the U.S. between 1990 and 2010, the labor force participation rate of military wives in this age cohort fell from 63 to 57 %. The goal of this paper is twofold: to document and analyze the labor force participation of military wives between 1990 and 2010, using the U.S. Census and American Community Survey data, and to compare the relationship between migration and labor force participation for military and non-military wives. We find that the primary suspects to explain the widening gap are the repeated migration for military wives, and the deepening of the recession.
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Notes
An examination of the many effects of military service on families is beyond the scope of this paper. Interested readers are directed to Routon (Forthcoming) and Savych (2008) for comprehensive literature reviews on the topic.
For example, in the 2010 ACS data there are 22,381 military wives and 3364 military husbands.
Lleras-Muney (2010) provides support for the assumption that the location of military families is exogenous. Although approximately one-third of military personnel report that they are assigned to their preferred location, it has been shown that this number is biased upwards as listed preferences are commonly chosen to be in-line with the known preferences of the military. Officers may have more influence on their location decision. As a robustness check, we drop officers from the regressions (see Table 4) but find that their exclusion has no impact on the outcome.
Defined specifically as those whose husbands list their occupation as Military Specific Occupations in the Census and ACS Occupation variables for each year.
Although it would be interesting to capture wives whose spouses are overseas, if the spouse is not present in the household, we do not have any information on the spouse, including whether or not he is in the military and so cannot identify these households.
Hispanic is an ethnic category. Those identifying as Hispanic may be of any race; hence both the dichotomous Hispanic variable and race categories need to be included in the anaylsis.
All summary statistics presented are weighted using the HHWT variable.
We are analysing data that spans the 1990s and 2000 decades. Clearly, the situation facing military personnel post-2011 differs from that of the previous decade, and it is likely that this would affect the behavior of military spouses. Unfortunately with our data, we are unable to determine the number or length of deployments, and so cannot test the impact of deployments specifically. We discuss this in more detail in the conclusion.
The wage gaps presented were calculated only including wage earners.
General patterns in demographics between military wives and non-military wives have not changed dramatically over the decades considered.
In our analysis, labor force participation is a dichotomous variable which classifies those employed and unemployed but actively seeking work as participants in the labour force, and those who do not fit this criterion as being not in the labour force.
We estimated expected wage using a standard Heckman selection approach. The estimated wage is calculated for all women, whether working or not, and included in the LFP regression. The first stage results are not included in the paper for space considerations but are available on request. To estimate predicted wages, we first calculated hourly wages using income, usual hours worked, and weeks worked for each individual. We did not analyse wages of military wives and non-military wives in depth as the wage information in these datasets are imprecise. Regression results are similar with and without the inclusion of expected wage.
Education groups are collapsed to five: less than high school, high school or GED, more than HS, Bachelor’s degree, more than Bachelor’s degree. There are nine region variables, categorized by the region definitions of the U.S. Census Bureau. Race is categorized as White, Black and Other with Hispanic being a separate indicator variable.
Our data included the Duncan Socioeconomic Status index. The regressions that we present in this paper do not control for socioeconomic status (SES), as we control directly for the variables from which the index is constructed and other variables that correlate highly with SES. In our preliminary work, we ran the regressions with SES included as an independent variable, but the inclusion of this variable did not change the results. These results are available upon request.
Information on previous migrations, the duration of marriage, the length of time in the military, and whether or not the husband joined the military before or after the beginning of their marriage is not included in the datasets.
All results are presented as odds ratios, with standard errors in parentheses. Full results are available upon request.
These coefficients should be compared to each other as the base category is Not Available. We kept these observations with missing values on this variable, as deleting these observations would reduce the sample by 9 %.
Those living on military bases would potentially have even better support systems in place. Unfortunately, we are unable to identify those living on military bases.
Defining the two equations as \({\rm{LF}}{{\rm{P}}^{{\rm{NMW}}}} = {\beta _{{\rm{NMW}}}}{X_{{\rm{NMW}}}}\)and \({\rm{LF}}{{\rm{P}}^{{\rm{NMW}}}} = {\beta _{{\rm{NMW}}}}{X_{{\rm{NMW}}}}\)for military wives and non-military wives, respectively, the gap can be described as the decomposition \({\rm LF}{{\rm P}^{\rm NMW}} - {\rm LF}{{\rm P}^{\rm MW}} = {\beta _{\rm MW}}({X_{\rm NMW}} - {X_{\rm MW}}) + ({\beta _{\rm NMW}} - {\beta _{\rm MW}}){X_{\rm MW}} + ({\beta _{\rm NMW}} - {\beta _{\rm MW}})({X_{\rm NMW}} - {X_{\rm MW}})\) where the first term estimates the contribution of the endowments, the second the effect of coefficients and the third term is the joint effect.
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Acknowledgment
We would like to thank Wayne Simpson, the participants at the Gender Wage Gap Workshop, University of Guelph and at the Canadian Economic Association meetings for helpful comments.
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Appendices
Appendix 1: Military Wife Heckman Selection Model
We assume that military wife status is selected. As a test, we run a Heckman selection model in which the military wife variable is estimated in the first stage. The second stage LFP on the military wife sample includes the inverse mills ratio.
The selection equation for the Heckman selection model controls for region, metropolitan status, age group, race, education, whether or not the individual is Hispanic, language, propUSAF5, and M2024URATE. The last two variables capture part of the marriage market situation facing the respondent when she was aged 20. PropUSAF5 captures the proportion of men in her state who were in the US military that year (using a 5 year moving average), and M2024URATE is the state level unemployment rate of men aged 20 to 24 during the year she turned 20.
The labor force participation equation controlled for labor force status, migration status, region, whether the individual has children, whether the individuals has children under the age of 5, metropolitan status, age group, race, education, whether or not the individual is Hispanic, language and year (if necessary).
For all second stage regressions, the coefficient on athro (Fisher’s Z transformation of the correlation), its standard error, and its associated z-score and p-value, as well as the coefficient for rho and its associated standard error for each of the data sets are shown in the table below.
Appendix 2
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Whitby, B., Compton, J. The labor supply of military wives in the US. Rev Econ Household 16, 513–539 (2018). https://doi.org/10.1007/s11150-016-9352-y
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DOI: https://doi.org/10.1007/s11150-016-9352-y