Division of Strategic Human Resources Policy 

Center for Workforce Information and Systems Requirements 

Workforce Information and Planning Group 

An Analysis of Federal Employee 

Retirement Data
 Predicting Future Retirements 

and Examining Factors Relevant to 

Retiring from the Federal Service 

March 2008 
An Analysis of Federal Employee Retirement Data 

Introduction 

This paper discusses Federal retirement statistics in order to gain a better understanding of the 

future makeup of the Federal workforce. A significant number of employees are eligible or will 

become eligible to retire in the near future, making a deeper analysis of the retirement of Federal 

civilians more timely and meaningful. The findings will hopefully provide valuable insight into 

workforce planning as the workforce ages and the needs of the Federal Government continue to 

evolve. 

Included among the findings is the median number of years an employee stays with the 

Government after first becoming eligible to retire is four years. Nearly 25 percent remain for nine 

or more years. 

How Retirement Eligibility is Calculated 

Retirement eligibility is determined based on factors including type of retirement system, age, 

length of service, and minimum retirement age, as described below. 

CSRS (Civil Service Retirement System) 

employees are eligible to retire if they are: 

a) At least 55 years of age and have at least 30 years of service; or 

b) At least 60 years of age and have at least 20 years of service; or 

c) At least 65 years of age and have at least 5 years of service. 

FERS (Federal Employees’ Retirement System) 

employees are eligible to retire if they are: 

a) Of minimum retirement age (MRA – see Table 1) and have at least 30 years of service; or 

b) At least 60 years of age and have at least 20 years of service; or 

c) At least 62 years of age and have at least 5 years of service; or 

d) Of minimum retirement age (MRA) and have at least 10 years of service (with a reduced 

annuity, as explained on page 2). 

The DC Police and Firemen’s Retirement Fund 

employees are eligible to retire at any age if they have a minimum of 20 years of service. 

FSPS (Foreign Service Pension System) employees are eligible to retire if they are: 

a) At least 50 years of age and have at least 20 years of service; or 

b) At least 62 years of age and have at least 5 years of service; or 

c) Of minimum retirement age (MRA) and have at least 10 years of service (with a reduced 

annuity, as explained on page 2). 

The Foreign Service Retirement and Disability System 

employees are eligible to retire if they are: 

a) At least 50 years of age and have at least 20 years of service; or 

b) At least 60 years of age and have at least 5 years of service. 

2 
Minimum retirement age (MRA) is calculated based on year of birth, as shown in Table 1. 

Table 1 – Minimum Retirement Age (MRA) for FERS and FSPS Employees 

Birth Year MRA 

Before 1948 55.00 

1948 55.17 

1949 55.33 

1950 55.50 

1951 55.67 

1952 55.83 

1953 – 1964 56.00 

1965 56.17 

1966 56.33 

1967 56.50 

1968 56.67 

1969 56.83 

After 1969 57.00 

MRA/10 Rule for FERS and FSPS 

As shown above in part (d) of the FERS rules and (c) of the FSPS rules for retirement eligibility, 

employees who have reached their minimum retirement age (see Table 1) and have at least 10 

years of service are eligible to retire. However, employees who wish to retire under this rule 

receive an annuity reduction of 5 percent a year for each year under 62 years of age. Very few 

employees eligible to retire only under this rule actually exercise this option. In FY 2006, this 

figure was a mere 3.4 percent; therefore, including employees eligible to retire under the MRA/10 

rule seriously overestimates calculations of retirement statistics. In all calculations of retirement 

eligibility in this paper, the MRA/10 rule for FERS and FSPS is excluded unless otherwise noted. 

Current and Future Retirement Eligibility Statistics 

As of March 2007, approximately 18 percent of the non-seasonal full-time permanent workforce 

was eligible to retire. Table 2 shows the number and percent of the non-seasonal full-time 

permanent employees on-board as of October 2006 by fiscal year through fiscal year 2016. By the 

year 2016, an ostensibly large 60.8 percent of the non-seasonal full-time permanent workforce as 

of October 2006 will be eligible to retire. However, the projected percentage of retirements is only 

37.3 percent of the full-time permanent workforce as of October 2006, as explained in the 

following section, Retirement Predictions. 

3 
Table 2 – Retirement Eligibility Counts and Percentages by Fiscal Year 

Fiscal Year 

Retirement Eligibility Counts for 

Full-Time Permanent Employees 

on-board as of October 1, 2006 

Percent of Full-Time Permanent 

Employees on-board as of 

October 1, 2006 that will be 

Eligible to Retire 

End of FY 2006 Count 1,572,855 -

through 2007 360,373 22.90% 

through 2008 428,167 27.20% 

through 2009 494,619 31.40% 

through 2010 566,801 36.00% 

through 2011 637,645 40.50% 

through 2012 707,750 45.00% 

through 2013 775,035 49.30% 

through 2014 836,516 53.20% 

through 2015 896,335 57.00% 

through 2016 956,613 60.80% 

Retirement Predictions 

The Office of Personnel Management’s Workforce Information and Planning group has attempted 

to predict future retirement probabilities from FY 2007 through 2016 using past retirement 

probability data from the Central Personnel Data File (CPDF). The methodology is 

as follows: 

The full-time permanent Executive Branch workforce as of September 30, 2004, was crosscategorized by sex (2 categories), white-collar occupational category (Professional, Administrative, 

Technical, Clerical, and Other – PATCO – 6 categories), retirement system (3 categories), and 

number of years since or until retirement eligibility (13 categories), resulting in 468 distinct 

subcategories. 

For each subcategory, the proportion of employees retiring during the FY 2005 one-year period 

from October 1, 2004, through September 30, 2005, was computed. For employees not retiring or 

otherwise separating during that year, their retirement eligibility was recalculated as of September 

30, 2005. In each subcategory, the proportion of employees retiring in FY 2006 (from October 1, 

2005, through September 30, 2006) was computed. This resulted in two one-year retirement 

probabilities for each of the 468 subcategories. The estimated one-year probability of retirement in 

each subcategory was derived by computing the average of the FY 2005 and the FY 2006 

retirement probabilities. 

The full-time permanent Executive Branch workforce as of September 30, 2006, was crosscategorized, as in (1), by sex, PATCO, retirement system, and number of years since or until 

retirement eligibility. 

Retirement eligibility for each employee was also calculated as of September 30 for years 2007 

through 2016. We assumed the future one-year retirement probabilities within a given subcategory 

4 
are identical to the past one-year probabilities. Using this assumption and taking into account an 

employee’s eligibility at the beginning of each year, we calculated the probability of retirement in 

each year for each employee. Each computation assumes an employee has not previously retired or 

otherwise separated. 

Step 5 results in 10 retirement probabilities for each employee corresponding to each of the next 10 

years (FY 2007 – 2016). 

Retirement projections for each year are calculated by summing that year’s retirement probabilities 

for each employee. 

The FY 2007 – 2016 retirement projections are shown in Table 3. For each FY, there is a column 

for the following: predicted count of retirements; predicted percent of end of FY 2006 count of 

retirements; predicted cumulative count of retirements, a predicted cumulative percent of end of 

FY 2006 count of retirements; the retirement eligibility counts from Table 2; and a predicted 

cumulative count of retirements as a percent of eligibility counts. 

If we compare the cumulative counts and cumulative percentages to the counts and percentages in 

Table 2, we see not nearly as many employees will retire as are eligible. Of the 956,613 employees 

who are eligible to retire through FY 2016, it is predicted 586,339 employees (61.3 percent) will 

retire during that period. 

Evaluation of past retirement projections confirms this method of projecting retirements is 

reasonably accurate. Table 4 contains FY 2002 – 2006 retirement projections calculated in 2001. A 

comparison of predicted and actual retirement projections results in differences of no more than 0.7 

percent. 

Another source of data with which to compare the projections is the 2006 Federal Human Capital 

Survey (FHCS), administered at the end of FY 2006. The 2006 FHCS is a survey of full-time 

permanent Federal civilian employees that measures employees' perceptions of whether, and to 

what extent, conditions characterizing successful organizations are present in their agencies. A 

weighted count of 58,717 responded they intended to retire within one year, a difference of only 

2,024 fewer than the number projected for FY 2007. 

5 
Table 3 – FY 2007 – 2016 Retirement Projections 

Fiscal Year Predicted Count of Retirements Predicted Retirements as a Percent of End of FY 2006 Count Predicted Cumulative Count of Retirements 

Predicted 

Retirements as 

a Cumulative 

Percent of End of 

FY 2006 Count 

Retireme

Counts f

Permanent Em

Oct

End of FY 2006 Count 1,572,855 1,572,855 1,572,855 1,572,855 

through 2007 60,741 3.9% 60,741 3.9% through 2008 61,702 3.9% 122,442 7.8% through 2009 62,019 3.9% 184,461 11.7% 

through 2010 61,748 3.9% 246,209 15.7% 

through 2011 60,950 3.9% 307,159 19.5% 

through 2012 59,547 3.8% 366,707 23.3% 

through 2013 57,839 3.7% 424,546 27.0% 

through 2014 55,919 3.6% 480,464 30.5% 

through 2015 53,946 3.4% 534,411 34.0% 

through 2016 51,928 3.3% 586,339 37.3% 
Table 4 – Evaluation of Past Retirement Projections 

FY 

Number of 

Predicted 

Retirements 

Predicted 

Retirement 

Percentage 

Number of 

Actual 

Retirements 

Actual 

Retirement 

Percentage 

Difference of 

Predicted and 

Actual Number 

of Retirements 

Difference of 

Predicted and 

Actual 

Retirement 

Percentages 

2002 51,011 3.4% 41,705 2.8% 9,306 0.7% 

2003 54,218 3.6% 50,240 3.4% 3,978 0.4% 

2004 56,650 3.8% 53,649 3.6% 3,001 0.4% 

2005 58,129 3.9% 59,609 4.0% -1,480 0.2% 

2006 59,269 4.0% 57,649 3.9% 1,620 0.4% 

A Study of Occurrence and Timing of Retirement 

An important question, thus far unanswered, is: How long after an employee becomes eligible to 

retire does he or she actually retire? To answer, we can look at a sample of employees which first 

become eligible to retire between the years 1997-1999 and track their personnel actions to 

determine if and when they retired, and construct a life table - a tool of longitudinal data analysis -

to summarize the data. 

The methodology for this analysis is as follows: 

A 5 percent simple random sample of employees was selected from a population which first 

became eligible to retire sometime between 1997 and 1999. 

Each of these employees’ retirement and other separation personnel actions were collected from 

the CPDF Dynamics files through FY 2006, and the most recent data available. 

For all employees who retired, the time of retirement was calculated by determining the number of 

days after first becoming eligible that the retirement action occurred, converting the number to a 

year by dividing by 365.25. 

Employees who did not retire within the allotted time period, or employees who otherwise 

separated from the Government, were “censored”, or flagged as having an unknown retirement 

time. 

The data was summarized in a life table (see Table 5). Two important functions in the life table are 

the hazard function and the survivor function. The hazard function is the conditional probability an 

employee will retire in a specific year, given the did not retire or otherwise separate in a previous 

year. The survivor function is the probability an employee will remain employed in the 

Government past a specific year. 

7 
Table 5 is an informative summarization of time of retirement. Within one year of first becoming 

eligible, 15.3 percent of employees retired Approximately 24 percent of employees remained in the 

Government nine years after first becoming eligible. The median number of years an employee 

remained after first becoming eligible is about four years, as indicated by the survivor function. 

Figures 5a and 5b are graphical representations of the hazard and survival probability functions, 

respectively. This data indicates many employees wait several years to retire after first becoming 

eligible. 

Table 5 – Life Table Describing the Number of Years Federal Employees 

Remain in Government after First Becoming Eligible to Retire 

Hazard Function Survivor Function 

Time 

(proportion of employees

at the beginning of

the year that left 

during the year) 

(proportion of

all employees 

still employed at the 

end of the year) 

Year 0 . 100.0% 

Year 1 15.3% 84.7% 

Year 2 14.3% 72.6% 

Year 3 15.3% 61.4% 

Year 4 16.6% 51.3% 

Year 5 15.9% 43.1% 

Year 6 19.1% 34.9% 

Year 7 16.5% 29.1% 

Year 8 15.7% 24.5% 

Year 9 2.8% 23.9% 

8 
Figure 5a 

Hazard Probability 

0.25 

0.2 

Survival Probability Hazard Probability 0.15 

0.1 

0.05 

0
 

1 2 3 4 5 6 7 8 9
 

Years Remaining in Government after First Becoming Eligible to Retire 

Figure 5b 

Survival Probability 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

1 2 3 4 5 6 7 8 9 

Years Remaining in Government after First Becoming Eligible to Retire 

The life table gives insight as to when employees retire. But why do some retire within the first 

year of becoming eligible, while some wait five or even nine years? We can attempt to answer the 

question of why with a discrete-time hazard statistical model of the data. 

9 
A discrete-time hazard model estimates an individual’s conditional probability of retirement in 

year t, given he or she did not retire in a previous year, and he or she has particular values for 

predictors in the model. While there is a potentially infinite number of factors related to an 

employee’s decision to retire, our analysis is restricted to those variables available in CPDF. The 9 

CPDF variables selected for potential inclusion in the discrete-time hazard model are described in 

Table 6. For simplification, all variable values were recorded at the time the employee first became 

eligible to retire. 

Table 6 – Variables Selected for Potential Inclusion in Discrete-Time Hazard Model 

Variable Description Values 

Sex employee's gender Female=1 

Male=0 

Supervisor employee's supervisory status Supervisor=1 

Non-supervisor=0 

Agency_Type employee's agency type Defense*=1 

Non-Defense=0 

Location employee's work location DC area=1 

All other locations=0 

Age employee's age 0-100 

Length_of_Service employee's length of service in Government 0-100 

Salary employee's salary $4,000-$200,000 

PATCO employee's occupational category Professional or Administrative=1 

All other categories=0 

Retirement_Plan employee's retirement plan CSRS=1 

FERS=0 

* Defense agencies are Department of the Army, Department of the Air Force, Department of the Navy, and all other defense activities 

The response variable in this model, whether or not a person retired in a specific year, is binomially 

distributed. Therefore, logistic regression, a type of regression in which the response variable is 

binomial, is used to fit the model. A logistic regression model expresses the log odds (commonly 

referred to as logits) of a binomial dependent variable as a linear combination of predictors. 

The form of a logistic regression discrete-time hazard model for the retirement data is 

 logit h(tj) = [α1Y1 + α2Y2 + ... + α9Y9] + [β1X1 + β2X2 + ... + βpXp], 

where h(tj) is the hazard function (the conditional probability an employee will retire in a specific 

year, given they did not retire or otherwise separate in a previous year); α1, α2, ..., α9 are the 

intercept parameters that correspond to the value of the logit hazard (log odds of retirement) in that 

year; Y1, Y2, ..., Y9 are indicator variables of the value of year (i.e., if the time period is year 3 after 

first becoming eligible to retire, then Y3 = 1 and all other Yi = 0); β1, β2 , ..., β9 are slope 

10 
parameters that indicate how a one unit difference in the corresponding predictor variable affects 

likelihood of retirement; and X1, X2 , ..., Xp are the predictors. 

There are 9 variables we could potentially include as predictors in this model, as well as many 

interactions among these variables. The objective is to select predictors that result in a simple and 

practical, yet informative, model. Model building is a complex task requiring much 

experimentation, and we must use statistical tests for verification. 

The final model chosen for this data includes the following predictors: Retirement plan, Patco, 

Location, Sex, and PATCO*Sex (interaction term of PATCO and Sex). The fit of this model was 

assessed using several statistical tests. A likelihood ratio test was used to determine if additional 

terms in a model contributed significantly more information about likelihood of retirement than a 

smaller number of terms in a nested model. A Hosmer-Lemeshow goodness-of-fit test was used to 

assess the fit of the model. The p-value of this test for the final model was .0585, indicating we 

cannot reject the hypothesis that the model fits the data well at the .05 level of significance. 

The parameter estimates, their standards errors, and tests of significance are in Table 7 below. All 

parameters are significant at the .05 level, as proven by the last column. 

First we will interpret the parameter estimates for year variables, Y1-Y9. We can convert these 

estimates into hazards of the baseline group (employees that have a value of 0 for every non-year 

predictor – Retirement_plan = 0, Patco = 0, Location = 0, and Sex = 0) by taking the antilogit: h(t ) 

= 1 / (1 + e−α ), as done in Table 8, for easier interpretation. The baseline group’s estimated hazard, 

or conditional probability of retirement at time t, given the employee did not already retire or 

separate, is approximately 16 percent in the first year. It dips slightly in the second year, increases 

and peaks at year six, drops slightly at years seven and eight, and sharply drops in year nine. 

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Table 7 – Parameter Estimates, Standard Errors, and Significance Tests 

Analysis of Maximum Likelihood Estimates 

Parameter DF Estimate Standard 

Error 

Wald ChiSquare Pr > ChiSq 

Y1 1 -1.6226 0.0737 484.1383 <.0001 

Y2 1 -1.7155 0.0778 485.7564 <.0001 

Y3 1 -1.6297 0.0802 412.7041 <.0001 

Y4 1 -1.5422 0.0829 346.2032 <.0001 

Y5 1 -1.5798 0.0876 324.9982 <.0001 

Y6 1 -1.3331 0.0884 227.6434 <.0001 

Y7 1 -1.5291 0.0978 244.3847 <.0001 

Y8 1 -1.5605 0.127 150.8692 <.0001 

Y9 1 -3.4292 0.4185 67.1488 <.0001 

Retirement_plan 1 0.1485 0.0568 6.8398 0.0089 

PATCO 1 -0.2539 0.0558 20.7287 <.0001 

Location 1 -0.188 0.0591 10.1183 0.0015 

Sex 1 -0.2182 0.0714 9.3333 0.0023 

PATCO*Sex 1 0.369 0.0939 15.4511 <.0001 

Table 8 – Taking Antilogits of Paramater Estimates to get 

Hazard Estimates for the Baseline Group 

Year Parameter 

Estimate Hazard 

Y1 -1.6226 0.16485 

Y2 -1.7155 0.15245 

Y3 -1.6297 0.16387 

Y4 -1.5422 0.17622 

Y5 -1.5798 0.17082 

Y6 -1.3331 0.20865 

Y7 -1.5291 0.17813 

Y8 -1.5605 0.17357 

Y9 -3.4292 0.0314 

12 
Now we can interpret the parameter estimates of the main predictors. Here, we convert the 

parameter estimates in Table 7 to odds ratios in Table 9, the ratio the odds of retirement for the two 

values of the variable, by taking the antilog: odds ratio = eβ . 

Table 9 – Taking Antilogs of Parameter Estimates to get Odds Ratios 

Predictor Parameter 

Estimate Odds Ratio 

Retirement_plan 0.1485 1.1600928 

PATCO -0.2539 0.7757694 

Location -0.188 0.8286147 

Sex -0.2182 0.8039646 

PATCO*Sex 0.369 1.4462876 

The odds ratios interpretations are as follows: 

Retirement_plan: the estimated odds of retirement in every year are 16 percent higher for 

employees in CSRS as opposed to FERS, controlling for all other variables. 

Location: the estimated odds of retirement in every year are about 17 percent less for employees 

working in the D.C. area in comparison to non-D.C. area employees, controlling for all other 

variables. 

Since PATCO and Sex are involved in an interaction term, their interpretations are bit more 

complicated. Given an employee is Professional or Administrative, retirement is 16 percent more 

likely in any year if the employee is female, controlling for all other variables. Given the employee 

is not Professional or Administrative, females are about 20 percent less likely than males to retire 

in any year, controlling for all other variables. Given an employee is female, she is about 12 

percent more likely to retire in any year if Professional or Administrative, as opposed to all other 

occupational categories, controlling for all other variables. Given an employee is male, he is 

approximately 22 percent less likely to retire in any year if Professional or Administrative, as 

opposed to all other occupation types, controlling for all other variables. 

The odds ratios indicate no variables in the model are strongly associated with time of retirement, 

but they nonetheless provide some information. There are many other factors that may have 

stronger association with time of retirement, such as amount of money saved for retirement, the 

current state of the economy, number of children in college, whether or not a spouse is still 

employed, etc. However, these types of variables are not readily available, so we must make use of 

demographic variables available in CPDF. 

The life table and the discrete-time hazard model have provided important insight as to when 

employees retire and what demographic factors may be associated with time of retirement. This 

study could be repeated in the future to determine if timing of retirement stays relatively consistent 

in later years. 

13 
Conclusion 

Retirement projection statistics, calculated using past retirement and other demographic 

information, imply the percentage of employees that will actually retire is smaller than the 

predicted number of employees deemed eligible to retire. The study of occurrence and time of 

retirement indicates the median number of years an employee stays with the Government after first 

becoming eligible is four years, and nearly 25 percent remain for nine years or more. Several 

demographic variables provide some information as to time of retirement, but there are likely other 

factors unavailable to us that have a much larger impact. 

Suggested Further Research 

This study focuses on static data available to the U. S. Office of Personnel Management. There are 

likely any number of external factors that affect a person’s decision to retire, data about which are 

unavailable to OPM or even immeasurable. Such factors include familial situations, illness, 

caretaker status, children in college, the cost of tuition for their children, and others. 

A survey of Federal retirees and employees eligible to retire could provide more information on 

these and other factors that influence timing of retirement. Some suggested topics include 

preparation for retirement, savings and investments, dependents, and job satisfaction. 

Consideration might also be given to correlation or examination of larger external factors such as 

the economy as measured by the stock market indices and inflation rates, both of which may 

heavily influence financial status and decisions. 

If you have questions concerning this analysis please e-mail Fedstats@opm.gov 

and reference this report in the subject line. 

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