BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics 

OFFICE OF EMPLOYMENT RESEARCH

AND PROGRAM DEVELOPMENT

Do Older Workers Respond to Changes in Social

Security Benefits? A Look at the Time Series Evidence

Jay Stewart

Working Paper 271

November 1995

 

Some of the research for this paper was conducted while I was an economist with Unicon Research Corporation. I thank National

Institute on Aging for funding (grant number 5 R01 AG0613303), and Tim Bremer, Harley Frazis, Maury Gittleman, Fran Horvath, Mark

Loewenstein, Robert Moffit, Jim Spletzer, and Mike Ward for helpful comments. Any errors or omissions are my responsibility. All views

and opinions expressed in this paper are my own and do not necessarily reflect views of the Bureau of Labor Statistics, the National

Institute on Aging, or Unicon Research Corporation.
Do Older Workers Respond to Changes in Social Security

Benefits?

A Look at the Time Series Evidence

Jay Stewart*

Office of Employment and Unemployment Statistics

Bureau of Labor Statistics

Room 4965

2 Massachusetts Avenue, NE

Washington, D.C. 20212-0001

ph. (202) 606-6345

* I conducted some of the research for this paper while I was an economist with Unicon

Research Corporation. I thank National Institute on Aging for funding (grant number 5 R01

AG06133-03). I also thank Tim Bremer, Harley Frazis, Maury Gittleman, Fran Horvath, Mark

Loewenstein, Robert Moffitt, Jim Spletzer, and Mike Ward for helpful comments. All views

and opinions expressed herein are those of the author and do not necessarily reflect views of the

Bureau of Labor Statistics, the National Institute on Aging, or Unicon Research Corporation.

The usual disclaimer applies.
Do Older Workers Respond to Changes in Social Security

Benefits?

A Look at the Time Series Evidence

ABSTRACT

This paper uses time series data to examine the effect of Social Security on the participation

rates of older men. The evidence indicates that changes in the level of Social Security benefits

have a large affect on the participation rates of 62-64 and 65-69 year-old men, and that this

effect occurs with a lag of 1-3 years. Increases in Social Security benefits accounted for 37% of

the decrease in participation rates of 65-69 year-old men between 1954 and 1990. For 62-64

year-old men, Social Security accounts for 40% of the decline since 1963.
-1-

I. Introduction

Over the past 30 years, the labor force participation rates of older men have decreased

sharply. At the same time, Social Security benefits have increased dramatically. Naturally, one

suspects a link between these two trends. A number of studies examine this link, and attribute

the high retirement rates at ages 62 and 65 to kinks in the budget constraint caused by the Social

Security benefit formulas.1 Despite this effect on retirement age, these studies also find that

changes in the level of Social Security benefits (and hence, Social Security wealth) have little

effect on participation rates.

Burtless and Moffitt [1984,1985] estimate a structural model of retirement using the

Retirement History Survey (RHS). They find that Social Security has a statistically significant

effect on retirement age. But this effect is rather small; a 20% reduction in benefits would

increase retirement age by only 2-3 months. Studies by Burtless [1986] and Hausman and Wise

[1985] come to similar conclusions: that the benefit increases of the early 1970's had a very

small effect on retirement age. Both studies use the RHS data, but use different techniques.

Hausman and Wise estimate a proportional hazard model, while Burtless estimates a structural

model

Fields and Mitchell [1984a,1984b] estimate the combined effect of Social Security and

private pensions on retirement age. Their results imply that the proposed changes in Social

Security benefits will have a rather small effect on retirement ages. For example, if the normal

retirement age is increased to 67, benefits would fall by 13%. But average retirement age

would increase by only 1.1 months.

 1 See Ippolito [1990]. He argues that increases in Social Security benefits in the early 1970's caused the age 62

spike to increase and the age 65 spike to decrease. Diamond and Hausman [1984] find that the availability of

Social Security benefits accounts for half of all retirements of men aged 62-64.
-2-

Another set of papers by Fields and Mitchell [1984b,1984c,1987] examines the effect of

Social Security reforms on retirement age and retirement incomes of older workers using the

RHS. They find that increasing actuarial reductions for early retirement would increase

retirement age by 3 months, while increasing the normal retirement age to 68 would increase

retirement age by 1.6 months. Other changes, such as delaying cost of living adjustments

(COLAs) by 6 months and increasing the delayed retirement credit, would have negligible

effects on retirement age. Hence, Fields and Mitchell conclude that changes in the Social

Security benefit formulas will have only a small effect on retirement ages.

Krueger and Pischke [1991] use evidence from the "notch" cohorts2 to estimate the effect

of Social Security on labor supply. They construct synthetic cohorts from aggregated Current

Population Survey (CPS) data and find that, despite the decrease in benefits, participation rates

continued to fall.

These results imply that, while Social Security is largely responsible for the spike in the

retirement hazard at ages 62 and 65, changes in Social Security wealth have a small impact on

the retirement decision. This implies that the decrease in benefits brought about by the 1983

reform will not have the desired effect of substantially increasing participation rates.3

The studies mentioned above use microdata - most use the RHS - to estimate the effects

of Social Security on retirement behavior. Microdata has the advantage of permitting the

researcher to control for individual characteristics. But, as Moffitt [1987] points out, these

studies do not properly measure the effect of changes in the system because the same law covers

all individuals. Differences in Social Security wealth reflect differences in lifetime earnings

across individuals, rather than any change in the general level of benefits.

 2 The notch cohorts, those born between 1917 and 1921, received lower benefits than previous cohorts as a result

of the 1977 legislation.

3 However, Gustman and Steinmeier [1985,1986] find the reverse. In the long run, the 1983 reforms will increase

the percentage of older workers (65+) who are full time workers.
-3-

To remedy this, Moffitt uses time series data to estimate the effect of Social Security on

labor supply. He bases his estimation procedure on a life-cycle model of labor supply, where

workers maximize a Stone-Geary utility function subject to a lifetime wealth constraint. From

this model he derives a labor supply function, which he then estimates using aggregate data from

the CPS and the Social Security Administration. Regressing average annual hours on Social

Security wealth, he finds that unanticipated increases in Social Security wealth had a small effect

on labor supply in the 1965-75 period; they can explain at most 20% of the decline in labor

supply.

I take another look at the time series data and come to the opposite conclusion: Social

Security has had a large impact on participation (and hence, retirement) rates. Between 1954

and 1990, Social Security accounted for 37% of the decrease in participation rates of 65-69 yearold men. And for 62-64 year-olds, Social Security accounts for 40% of the decrease in

participation rates between 1965 and 1990.

The different results are due to the different time period used in estimation, and different

definitions of the Social Security variable. Moffitt estimates his model over the 1955-1981

period. And as he notes, trends in Social Security wealth and income are highly correlated,

which makes it difficult to distinguish between the effects of these two variables. My analysis

extends the period of estimation another 10 years to 1991. This extension is important because

the decline in Social Security benefits between 1981 and 1984 allows me to better distinguish

between the effects of changes in income and changes in Social Security benefits.

In defining the Social Security variable, Moffitt makes the distinction between

anticipated and unanticipated changes in the level of benefits, whereas I do not. When I make

the distinction using my data, I find that (dollar for dollar) unanticipated changes in the level of

benefits have a much larger effect on participation than do anticipated changes. But the

magnitude of the unanticipated changes is so small that the total effect is negligible. Anticipated

changes in benefits are an order of magnitude larger, which results in their having a larger

impact on participation rates.
-4-

II. Social Security and Labor Force Participation

Theory predicts that exogenous increases in Social Security benefits, which increase

wealth, lead workers to reduce labor supply by retiring earlier. To illustrate, suppose that

individuals live for two periods; they are young in the first period, and old in the second period.

Hence, individuals derive utility from leisure in period 1, leisure in period 2, and consumption of

goods and services.4 Further suppose that Social Security benefits exogenously increase at the

beginning of period 2. If workers fully anticipate the increase at the beginning of the first

period, the income effect causes them to increase consumption and reduce labor supply in both

periods. However, individuals can draw Social Security benefits only if they stop working (or

decrease labor supply sufficiently), which causes individuals to increase labor supply in period 1

and retire earlier in period 2. This implies that labor supply unambiguously decreases in period

2, but could increase or decrease in period 1.5 On the other hand, if workers do not anticipate

the increase in benefits they can adjust their labor supply only in period 2, which leads to a

decrease in retirement age. In either case, increases in Social Security benefits cause individuals

to retire earlier.

Time Series Evidence

Figures 1 and 2 show the relationship between the non-participation rate (1 minus Labor

Force Participation Rate [LFPR]) of older men and the generosity of Social Security benefits. I

measure generosity (PIA/Earnings) as the ratio of the average Primary Insurance Amount (PIA)6

 4 I ignore the timing of consumption.

5 If the increase in benefits also increases the marginal gain to working, then workers have an incentive to

increase labor supply in all periods. This effect will be weaker when workers are old since they must quit working

to collect benefits.

6 The primary insurance amount is the benefit an individual would receive at age 65. I use average PIA rather

than actual benefit amounts because PIA is not affected by early retirement decisions or number of dependents
-5-

of new awards to average taxable earnings,7 both from the Social Security Bulletin Annual

Statistical Supplement.8 It is natural to measure generosity this way because workers weigh

their Social Security benefits against what they could earn by working.9 The bottom series in

Figure 1 shows the non-participation rate of 65-69 year-old men, while the bottom series in

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(any adjustments for early retirement or dependents are expressed as percentages of PIA) and hence, measures the

general level of benefits.

7 This measure of generosity was first used in Ward [1984].

8 The Social Security Administration estimates earnings for individuals who exceed the maximum taxable

earnings.

9 Note that PIA/Earnings does not measure replacement rates because the average earnings figures include all

covered workers. As noted in Moffitt [1987], earnings data are not available by age group.
-6-

Figure 2 shows the non-participation rate of 62-64 year-old men. Both series are computed from

the Current Population Survey (CPS).10

These figures suggest a strong relationship between the generosity of Social Security

benefits and the non-participation rate of older workers. Beginning in 1963, the nonparticipation rate follows changes in generosity with a 2-4 year lag. Prior to 1963, the lag was

closer to 8 years. A similar pattern exists for 62-64 year-old men (see Figure 2). However, a

notable difference between the two series is that the non-participation rate of 62-64 year-olds

increases between 1963 and 1970, while the non-participation rate for 65-69 year-olds is

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PIA/Earnings

Non-participation rate

 10 I thank John Stinson of BLS for providing me with the data for 62-64 year-old men.
-7-

relatively constant. The increase in the non-participation of 62-64 year-olds coincides with the

availability of Social Security benefits, which began in 1962.

To better estimate the effect of Social Security on the participation rates of older men, I

used the above data to run two sets of regressions. In both sets, the dependent variable is the

labor force participation rate. The independent variables include current and lagged values of

PIA/Earnings, and a time trend. Since I estimated the equations in first differences, the time

trend reduces to a constant term.11

Tables 1 and 2 contain regression results for 65-69 and 62-64 year-old men. The

regressions indicate that increases in Social Security benefits cause a decrease in the

participation rates, but with a lag. The lag is 2-3 years for 65-69 year-olds and 1-3 years for 62-

64 year-olds .

The regression in column (1) of Table 1 includes the current value of PIA/Earnings as

well as values lagged 1-4 years. Here, PIA/Earnings(t-3) has the largest effect, and

PIA/Earnings(t-2) has the next largest effect, although only the former is statistically significant.

Moving from column (1) to column (5), one can see the effect of shortening the lag structure.

Clearly, dropping PIA/Earnings(t-3) has the largest effect on R2, which indicates that it explains

the bulk of the variation in participation rates. The next largest drop in R2 occurs when I drop

PIA/Earnings(t-2) from the regression. The regression that "performs the best" includes

PIA/Earnings(t-2) and PIA/Earnings(t-3), and is reported in column (6).12

For 62-64 year-olds, the effects are distributed over a three year period, and the lag is

shorter. Although none of the coefficients in column (1) of Table 2 are statistically significant,

 11 I tried alternative specifications that used moving averages of GNP (a 5 year or a 10 year average) and the

current unemployment rate instead of the time trend. Since there was very little difference in the results, I report

the simpler specification.

12 I could have included PIA/Earnings(t-4) in the regression in column (6). There is no difference in the

explanatory power of the two equations, but excluding PIA/Earnings(t-4) makes it comparable to column (6) in

Table 2.
-8-

Table 1

65-69 Year-Old Men

Dependent variable: Labor Force Participation Rate.

(1) (2) (3) (4) (5) (6) (7)

PIA/Earnings(t) -.0800 -.0716 -.0792 -.1194 -.1193

(.1335) (.1327) (.1425) (.1453) (.1411)

PIA/Earnings(t-1) .0986 .0941 .0432 -.0003 .0932

(.1345) (.1340) (.1422) (.1447) (.1343)

PIA/Earnings(t-2) -.1434 -.1639 -.2396 -.1607 -.1736

(.1300) (.1276) (.1330) (.1255) (.1277)

PIA/Earnings(t-3) -.2703 -.2987* -.2863 -.2999

(.1256) (.1212) (.1202) (.1227)

PIA/Earnings(t-4) -.1099

(.1225)

Observations 37 37 37 37 37 38 38

F Statistic 2.28 2.67 1.33 0.35 0.72 4.87 3.36

Significance Level .0707 .0500 .2820 .7089 .4034 .0136 .0299

R2 .2691 .2501 .1077 .0200 .0200 .2178 .2288

Adjusted R2 .1512 .1563 .0266 -.0376 -.0080 .1731 .1607

Standard Errors are in parentheses.

Bold* with and asterisk indicates significance at the 1% level using a 1-tailed test.

Bold indicates significance at the 5% level using a 1-tailed test.

Italics indicates significance at the 10% level using a 1-tailed test.

All regressions were estimated in first differences and include a constant term. The data used for these

regressions come from the CPS (participation rates) and the Social Security Bulletin Statistical

Supplement (average PIA and average earnings) and cover the period from 1954-1992. Since the

Social Security data are available only to 1991, omission of PIA/Earnings(t) in columns (6) and (7)

yield an additional observation.
-9-

Table 2

62-64 Year-Old Men

Dependent variable: Labor Force Participation Rate.

(1) (2) (3) (4) (5) (6) (7)

PIA/Earnings(t) -.0939 -.0820 -.0963 -.1719 -.2379

(.1672) (.1591) (.1649) (.1580) (.1671)

PIA/Earnings(t-1) -.2286 -.2328 -.3190 -.3480 -.2338

(.1640) (.1598) (.1565) (.1579) (.1655)

PIA/Earnings(t-2) -.1688 -.1904 -.2199 -.2449 -.2158

(.1757) (.1572) (.1620) (.1563) (.1546)

PIA/Earnings(t-3) -.2498 -.2564 -.3337 -.2561

(.1613) (.1564) (.1562) (.1626)

PIA/Earnings(t-4) -.0501

(.1648)

Observations 26 26 26 26 26 27 27

F Statistic 2.45 3.18 3.10 3.60 2.03 4.35 4.68

Significance Level .0690 .0344 .0474 .0435 .1674 .0245 .0266

R2 .3799 .3771 .2974 .2386 .0779 .2659 .3245

Adjusted R2 .2249 .2584 .2016 .1724 .0395 .2047 .2363

Standard Errors are in parentheses.

Bold* with and asterisk indicates significance at the 1% level using a 1-tailed test.

Bold indicates significance at the 5% level using a 1-tailed test.

Italics indicates significance at the 10% level using a 1-tailed test.

All regressions were estimated in first differences and include a constant term. The data used for these

regressions come from the CPS (participation rates) and the Social Security Bulletin Statistical

Supplement (average PIA and average earnings), and cover the period from 1966-1992 (I omitted the

first three years because the start-up period is not representative of the rest of the sample). Since the

Social Security data are available only to 1991, omission of PIA/Earnings(t) in columns (6) and (7)

yield an additional observation.
-10-

one can see that the coefficients on PIA/Earnings are the largest for values lagged 1-3 periods.

Omitting PIA/Earnings(t-4) has only a slight effect on R2, but R2 drops significantly when

PIA/Earnings(t-3), (t-2), and (t-1) are dropped (columns (3), (4), and (5)). The equation in

column (7), which includes PIA/Earnings (t-1) through (t-3), seems to perform the best.

The results in Tables 1 and 2 are similar in that they indicate that increases in Social

Security benefits reduce the participation rates of both groups with a lag of 2-3 years for 65-69

year olds and 1-3 years for 62-64 year olds.

To interpret the lag in the effect of Social Security on participation rates, one must

remember that the dependent variables represent age groups. Since there is no strong

contemporaneous effect, decreases in the participation rate have two possible interpretations.

The decrease in participation could be due to increased retirement at ages 62 and 65. Or it could

reflect increased retirement at earlier ages. Consider the former interpretation first.

It is well known that the kinks in the budget line at ages 62 and 65 give workers an

incentive to retire at one of those ages. Given the strong incentive to retire at age 62, workers

who have not retired by age 63 or 64 have exhibited a strong attachment to the labor force, and

are likely to wait until 65 or later to retire. For workers in the labor force beyond age 65, the

bond is even stronger. Assume, for now, that increases in Social Security benefits affect

whether workers retire at ages 62 or 65, so that any observed decrease in labor force

participation rates is due to an increase in the number of people who retire at these ages, rather

than any change in the retirement rate of 63-64 year-olds or 66-69 year-olds.13

Consider the effect of an increase in Social Security benefits in year t. By assumption,

this increase does not immediately affect the retirement decision of 63-64 year-old workers

because these workers cannot retroactively retire at 62. However, the coefficient on

PIA/Earnings(t) indicates that there may be a small (though not statistically significant)

 13 This assumption is a reasonable approximation to observed changes in the retirement hazard over time.
-11-

contemporaneous effect on 62 year-olds. In year t+1, the 62-64 year-old group now includes

people who were 61 in year t and excludes people who were 64 in year t. The higher retirement

rates at age 62 will reduce the participation rates of the 62-64 year-old group. The coefficient on

PIA/Earnings(t-1) in column (1) suggests that this is the case. A similar argument applies to

years t+2 and t+3 regarding workers who were 60 and 59 in year t. As each cohort reaches 62, a

larger proportion of them retires.

The above argument holds for 65-69 year-olds as well. First, 65-69 year olds who are

still in the labor force in year t have already displayed a strong attachment to the labor force so

that one is not surprised by the small coefficient on PIA/Earnings(t). However, one expects a

higher percentage of people who are 62-64 in year t to retire when they reach 65. This is

consistent with the large negative coefficients on PIA/Earnings(t-2) and PIA/Earnings(t-3).

Note that the results in Table 1 are also consistent with the second interpretation that

retirement rates are higher at younger ages. The large coefficients on PIA/Earnings(t-2) and

PIA/Earnings(t-3) could be due to higher retirement rates at ages 62-64 in years t and t+1.

However, if this were the case, I would have found a large contemporaneous effect in the

regression on 62-64 year-olds. Thus, it appears that changes in the level of Social Security

benefits affect retirement rates primarily at ages 62 and 65.

To summarize, an increase in Social Security benefits in year t has a negligible

contemporaneous effect on the retirement rates of men aged 62-64 and 65-69. But the increase

in benefits causes a larger proportion of 59-61 year-olds to retire when they reach 62. Similarly,

a larger proportion of 62-63 year-olds will retire when they reach 65.

These results suggest a simple way of capturing the effect of Social Security; use a single

variable that is a moving average of PIA/Earnings. The simplified regressions in Table 3 tell the

same story as Tables 1 and 2. For 65-69 year-olds, equations with 3 year moving averages

lagged 2 or 3 years explain the most variation. Note that the contemporaneous 3 year moving

average explains the least variation, confirming the findings in Table 1 that Social Security

affects retirement behavior with a lag of 2-3 years. Note also that the 10 year moving average
-12-

Table 3

Moving Average Equations

Dependent variable: Labor Force Participation Rate

Independent variable: Moving average of PIA/Earnings

PIA/Earnings

Standard

Error R2 F Statistic

Significance

Level Obs.

65-69 Year-Old Men

 5 Year -.5689* .2063 .1786 7.61 .0092 37

 10 Year -.7933* .2980 .1684 7.09 .0117 37

 3 Year -.2939 .1996 .0583 2.17 .1499 37

 3 Year (t-1) -.4268 .1811 .1337 5.56 .0240 38

 3 Year (t-2) -.5191* .1591 .2283 10.65 .0024 38

 3 Year (t-3) -.5017* .1572 .2205 10.18 .0029 38

62-64 Year-Old Men

 5 Year -.8109* .2264 .3482 12.82 .0015 26

 10 Year -.7312 .3381 .1661 4.78 .0388 26

 3 Year -.6233* .2091 .2701 8.88 .0065 26

 3 Year (t-1) -.7080* .2047 .3237 11.96 .0020 27

 3 Year (t-2) -.6012* .2131 .2415 7.96 .0092 27

 3 Year (t-3) -.5701* .2086 .2300 7.47 .0114 27

Bold* with and asterisk indicates significance at the 1% level using a 1-tailed test.

Bold indicates significance at the 5% level using a 1-tailed test.

Italics indicates significance at the 10% level using a 1-tailed test.

The data used for these regressions come from the CPS (participation rates) and the Social

Security Bulletin Statistical Supplement (average PIA and average earnings) and cover the

period from 1954-1992 for 65-69 year-olds and 1966-1992 for 62-64 year-olds. Each row

represents one regression. All regressions were estimated in first differences and include a

constant term. Since the Social Security data are available only to 1991, equations with

lagged moving averages have an additional observation.
-13-

does not explain very much variation. This indicates that changes in the level of Social Security

benefits affect retirement behavior primarily in the short term. Longer term changes may be

captured by the constant (time trend) term, or may result in changes in labor supply at younger

ages.

For 62-64 year-olds the 5 year moving average and the 3 year moving average lagged 1

year explain the most variation, while the 10 year moving average explains the least. This

implies that, for 62-64 year-olds, changes in the level of Social Security benefits affect

retirement with a shorter lag.

To estimate the magnitude of Social Security's effect on participation rates relative to the

time trend, I used estimates from the moving average equations. For 65-69 year-olds, I used the

3 year moving average lagged 2 years. And for 62-64 year-olds, I used the 3 year moving

average lagged 1 year. I estimate that increases in Social Security benefits account for 37% of

the total decline in participation rates of 65-69 year-old men between 1954 and 1992. The

general downward trend in participation rates accounted for the rest. For 62-64 year-old men,

the figure is slightly larger. Social Security accounted for 40% of the total decrease in the

participation rate between 1963 and 1990.14

Comparisons to Moffitt [1987]

These estimates are so much larger than the 20% reduction in labor supply found in

Moffitt [1987] that it is worth noting some of the differences in approach. The primary

 14 I also broke down the effect of Social Security into (1) the effect of the availability of Social Security on the

trend in the participation rate, and (2) the effect of increasing benefit levels on participation. I found that Social

Security accounted for 43 percent of the decline in the participation rate of 62-64 year old men. Of this, 19% was

due to acceleration of the downward trend in participation rates after 1963 caused by the availability of benefits to

62-64 year olds in 1962. The direct effect of increased benefits accounted for the remaining 24%. To estimate

the effect on the time trend, I needed participation rates from the pre-1963 period. Since these data are not

available for 62-64 year-olds, I used the changes in the rates for the 60-64 year-old group. Although the

participation rates of the two groups differ, the first differences are quite close.
-14-

differences are: the specification of the Social Security variable, the age groups included in the

sample, and the time period covered.

Moffitt defines his Social Security variable as unanticipated changes in Social Security

wealth net of taxes. He computes actual and expected Social Security wealth, and uses the

difference to measure unanticipated changes in Social Security benefits. This variable enters

into the equations as either a 5 year or a 10 year moving average.

My specification uses current and lagged values of average PIA divided by average

earnings. Expressing the Social Security variable as a ratio of average PIA to average earnings

does not affect the results. I estimated several equations entering PIA and Earnings separately,

and got very similar results. Table 3 indicates that the 5 and 10 year moving averages do not

perform as well as the 3 year moving averages, but the parameter estimates are reasonably close.

I chose not account for taxes because the link between benefits and taxes is weak; current taxes

do not finance future benefits, but rather the payments of those currently receiving benefits.

Moffitt's use of unanticipated benefits does seem to matter. I estimated moving average

equations that included separate variables for anticipated and unanticipated changes in Social

Security benefits, and found that unanticipated changes in Social Security benefits had very little

effect on participation rates.15 Table 4 contains the results of these regressions. For both 65-69

 15 The equation used to estimate anticipated changes in SS benefits is a variation on the model proposed by

Turner [1984] and Doescher and Turner [1988] to explain the relationship between group size and the level of

benefits that a group will receive. I assume that workers make 5 year predictions so that current values of the

predicting variables are used to predict real PIA 5 years in the future. The variables used in the prediction

equations are: the ratio of 65-69 year olds to the number of people aged 25-59 in the labor force, the number of

people aged 25-59 in the population, the number of 65-69 year olds in the population, real PIA, real GDP

("current" and lagged 1 year), the unemployment rate ("current" and lagged 1 year). The independent variables

are the estimated anticipated and unanticipated changes in SS benefits divided by real earnings (I do not break

down earnings into anticipated and unanticipated components).

I also experimented with concurrent predictions and 10 year predictions. The 5 and 10 year predictions

seemed to perform a little better than the concurrent predictions. I chose to use the 5 year predictions because the

10 year predictions used up 5 more degrees of freedom.
-15-

Table 4

Moving Average Equations

Dependent variable: Labor Force Participation Rate

Independent variable: Moving average of Anticipated and Unanticipated PIA/Earnings

65-69 Year-Old Men

PIA/Earnings

Unanticipated

Anticipated

Standard

Error R2 F Statistic

Significance

Level Obs.

 5 Year -1.171*

 -.5979*

.3090

.2275

.4696 11.07 .0004 28

 10 Year .0037

 -.4243

.8424

.4565

.0448 .47 .6325 23

 3 Year .3516

 -.5441*

.2437

.2186

.1986 3.35 .0503 30

 3 Year (t-1) -.6610*

 -.5442*

.2346

.2104

.2927 5.59 .0093 30

 3 Year (t-2) -.7560*

 -.5161*

.2241

.2026

.3503 7.01 .0037 29

 3 Year (t-3) -.5608

 -.3618

.2325

.2102

.2174 3.47 .0467 28
-16-

Table 4 (continued)

62-64 Year-Old Men

PIA/Earnings

Unanticipated

Anticipated

Standard

Error R2 F Statistic

Significance

Level Obs.

 5 Year -1.022

 -.7231*

.4276

.2742

.3578 6.41 .0061 26

 10 Year -1.299

 -.3918

.9440

.5115

.1434 1.67 .2128 23

 3 Year -.6204

 -.6251*

.2872

.2421

.2702 4.26 .0267 26

 3 Year (t-1) -.6643

 -.7373*

.2705

.2384

.3255 5.79 .0089 27

 3 Year (t-2) -.4190

 -.7371*

.2735

.2483

.2754 4.56 .0210 27

 3 Year (t-3) -.4909

 -.6353

.2704

.2531

.2370 3.73 .0389 27

Bold* with and asterisk indicates significance at the 1% level using a 1-tailed test.

Bold indicates significance at the 5% level using a 1-tailed test.

Italics indicates significance at the 10% level using a 1-tailed test.

The data used for these regressions come from the CPS (participation rates) and the Social

Security Bulletin Statistical Supplement (average PIA and average earnings) and cover the

period from 1954-1992 for 65-69 year-olds and 1966-1992 for 62-64 year-olds. Anticipated

changes in Social Security benefits were computed from a regression using values of the

predicting variables lagged 5 years (see text for a description of the prediction equations).

Each row represents one regression. All regressions were estimated in first differences and

include a constant term. Since the Social Security data are available only to 1991, equations

with lagged moving averages have an additional observation.
-17-

year olds and 62-64 year olds, the 5 year moving average equations explain more of the variation

in participation rates than the other equations. As before, the 10 year moving average explains

the least variation. The coefficients on anticipated and unanticipated changes in SS benefits

indicate that, dollar for dollar, unanticipated changes have a much larger effect than do

anticipated changes. However, the prediction equation does such a good job of predicting

benefits that the average unanticipated change in benefits is an order of magnitude smaller than

the average anticipated change. As a result the total effect of unanticipated changes in Social

Security benefits is quite small.16 On the other hand, anticipated changes in SS benefits explain

a sizable portion of the decrease in participation rates. The equations reported in Table 4

attribute even more of the decline in participation rates to SS than the equations using actual

levels in Table 3.

For 65-69 year olds, unanticipated changes in benefits were negative on average, which

means that they tended to increase the participation rate. After netting out the effect of

unanticipated changes in benefits, Social Security accounted for 47 percent of the decline in the

participation rate of 65-69 year old men. Social Security accounted for a smaller portion, 39

percent, of the decline in participation rates of 62-64 year old men. In this equation,

unanticipated changes in benefits are positive on average17 so that they tend to reduce

participation rates. Unanticipated changes in Social Security benefits accounted for 3.4 percent

of the decline in the participation rate of 62-64 year old men, while anticipated changes in

benefits account for the remaining 35.6 percent.

These results are rather surprising in that one would not expect anticipated changes in

benefits to have a very large effect on participation rates. But one must consider the time

 16 In most equations, the unanticipated changes were negative so that the effect of unanticipated changes in

benefits was to increase participation rates.

17 The average change in unanticipated benefits are different signs in the two equations because the sample

periods are slightly different, and the changes are so small in absolute value.
-18-

horizon over which expectations are formed. That is, when do workers anticipate changes in

benefits? To illustrate, consider the reduction in benefits brought about by the 1977 legislation.

These changes started to take effect in 1979, and were in full effect in 1983. It would be

difficult to argue that workers foresaw these changes during the 1960's. Given rapid increase in

benefits in the early 1970's coupled with demographic trends, workers may have anticipated

these changes as early as the mid 1970's. By 1977, the handwriting was on the wall. The benefit

decreases were already law so that the coming benefit decreases were fully anticipated. At the

other end of the spectrum, some of the benefit decreases resulting from the 1983 legislation will

not take effect for 20-30 years. Presumably these changes will be fully anticipated.

The bottom line is that the "anticipated" changes in benefits used in the equation were

probably not anticipated when workers were making important labor supply decisions earlier in

life. As a result, they have had a large effect on the retirement pattern.

Moffitt estimates his model on 4 age groups (25-34, 35-44, 45-64, and 65+) over the

period from 1955 through 1981. He combines these four age groups into one large dataset rather

than estimating separate equations for each group. Although his theoretical model predicts that

increases in Social Security wealth will reduce labor supply at all ages, it does not account for

the possibility that workers might increase labor supply when young so they can retire earlier.

This substitution effect dampens the effect of any increase in Social Security benefits at younger

ages, which reduces the total effect of Social Security on labor supply. To determine the effect

of a broader sample, I reestimated the 5 year and 10 year moving average equations on the same

age groups used by Moffitt, and found that aggregating all age groups into a single equation

weakens the results. When I estimated the equations by age group, I found that Social Security

had no effect on the participation rate of younger workers (25-34 year-olds and 35-44 year-olds),

and a strong effect on older workers (45-64 year-olds and 65-69 year-olds). These results are

consistent with the discussion above: it is difficult for workers to anticipate changes in the level

of benefits that occur in the distant future. Overall, Moffitt's aggregation over the four age

groups accounts for a relatively small part of the difference in results.
-19-

Moffitt's shorter estimation period explains much of the difference in results. During the

1955-1981 period covered by Moffitt's data, Social Security benefits were rising and

participation rates were falling. In contrast, during the 1980's, Social Security benefits declined.

As a result, regressions estimated on the 1955-1980 data attribute relatively more of the decline

in participation rates to the time trend than do regressions estimated over the 1955-1991 period.

I found considerable differences in the estimated effect of Social Security when the equations

were estimated over the shorter time series. Regressions on the 1955-1981 data explain less

variation in participation rates and estimated coefficients are smaller and less significant. The

difference is more pronounced for the 10 year moving average equations because computation of

the 10-year moving average requires omission of 5 years during which benefits were rising

rapidly.

Why Does Social Security Affect Retirement With a Lag?

The time series evidence implies that changes in the level of Social Security benefits

affect retirement with a lag, which is consistent with work done by Bernheim [1987]. He finds

that individuals are fairly accurate in estimating their retirement dates, but that forecasts become

less accurate18 as they predict retirement dates that are further in the future. Interestingly,

accuracy drops off sharply after 2 years. That is, predictions 3 years into the future are

considerably less accurate than predictions 2 years into the future. This suggests that workers

make firm plans only 2 years into the future, and implies that changes in Social Security benefits

will primarily affect people who are 3 or more years away from retirement.

Liquidity constraints may explain why workers do not change their plans immediately.

To illustrate, Kahn [1988] argues that liquidity constraints are responsible for the spike in the

retirement hazard at age 62. Many individuals who retire at age 62 would prefer to retire earlier,

 18 For a given current year/predicted year of retirement cell accuracy is the percent who predict their retirement

date to within one year.
-20-

but do not have sufficient liquid assets to retire without Social Security. To carry the argument

further, suppose that workers set a target retirement date and choose a desired level of liquid

assets based on their current wealth and expected Social Security benefits. An unexpected

increase in Social Security benefits reduces the desired level of liquid assets, and moves up the

target retirement date. But unless they have already reached their new target, they will not retire

immediately.

IV. Conclusions

The time series evidence presented here implies that Social Security has had an enormous

impact on the participation rates of 62-69 year old men. For 62-64 year-old men, Social

Security affects retirement rates with a 1-3 year lag. For the 65-69 year-old group, the lag is

slightly longer, 2-3 years. Increases in Social Security benefits account for 37% of the decline in

participation rates of 65-69 year-old men between 1954 and 1990. For 62-64 year-olds, Social

Security accounted for 40% of the decline between 1963 and 1990.

These results imply that increasing the normal retirement age will slow the decrease in

participation rates by reducing benefits. But raising the age of first eligibility from 62 would

have an even larger effect because liquidity constraints would force many individuals to delay

retirement if they cannot draw Social Security benefits.
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