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 $)
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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 $)