Effect on retirement
wealth
\label{effect-on-retirement-wealth}
Given the short and long-term effects on earnings that we found in the
previous subsection, we also investigate how the unemployment rate
prevailing at the time of exit from the military influences accumulated
wealth and financial preparedness for retirement. Figure 3 show the mean
and median wealth of male veterans in the HRS, between the ages of 60
and 62, broken out by wealth component. In our analysis, we focus on the
largest components of retirement wealth: social security prospective
wealth (from the datasets contributed by Kapinos et al.), employer
pension wealth (from the datasets contributed by Gustman, Steinmeier and
Tabatabai), primary residency net worth, and net financial wealth (which
includes, savings and financial investments in stocks, bonds, etc.). In
addition, we also look at Individual Retirement Accounts (IRAs). These
last three components of wealth (and the other categories in Figure 3)
are obtained from the RAND HRS file.
Figure : Average and median wealth of male veterans, 60-62 years old
(2016 $)
[CHART]
We estimate the linear regression models depicted in equation (4), where
\(y_{\text{it}}\) is the wealth variable of interest (in 2016 $). As
was the case above, we also estimate a 2SLS model. The model in equation
(4) is similar to the one in equation (3) but given that we have far
fewer observations, we estimate only linear effects of the time since
re-entering the civilian life, instead of several discrete time-varying
effects as in equation (3). We estimate a version that is centered with
the “time passed variable” around 35 years, which is closed to the
mean in the sample (Table 1). In this way, the we can interpret the
coefficient \(\beta_{1}\) in equation (4) as the average effect of one
additional percentage point in the unemployment rate at the time of
reentering the civilian sector on wealth 35 years later (which
corresponds in our sample to an age of, on average, 57 years).
\begin{equation}
{}_{\text{it}}=\alpha+\beta_{1}UR_{i}+\beta_{2}\left(\text{Time}_{\text{it}}-35\right)+\beta_{3}\left[\left(\text{Tim}e_{\text{it}}-35\right)\times UR_{i}\right]+\beta_{4}X_{i}+{\gamma_{t}+\epsilon}_{\text{it}}\nonumber \\
\end{equation}
The estimation results are shown in Table 5 (we’ll need a table that is
formatted landscape; should inclue ‘Wealth Measures’ results from excel
sheet). The model that includes the unemployment rate indicates that
veterans who entered the labor market during times of higher
unemployment have lower prospective social security wealth and lower
household prospective social security welath (although their spouses’
social security welath is not related to unemployment rate at entry).
Several of the other measures are estimated to be slightly lower, but
the differences generally are not statistically significant. The results
of the model centered at 35 years since leaving active service produces
a mixture of positive and negative estimates, but none are statistically
significant. This suggests that service members who enter the civilian
workforce during poor economic conditions eventually ‘catch up’ with
their peers. In the next subsection, we investigate long-term effect of
the unemployment rate at entry on variables related to labor force
participation and retirement.
Table : Effect of unemployment rate at time of exit from active service
on several wealth measures (2016 $)