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  1. 1. THE EFFECT OF THE STOCK MARKET CRASH ON RETIREMENT DECISIONS JEL classifications: J21, J26, J32 Abstract: (155 words) Since the business cycle peak of March 2001, older Americans are working more than they have in any previous recession. Increasingly, pension income is coming from defined contribution (DC) pensions, sources that are tied to financial markets. Defined contribution pensions can intensify labor shortages during boom periods because higher stock prices induce older workers to retire. Conversely, during recessions declines in DC account values cause retirement postponements which increase labor surpluses. We use cohort averages from the Current Population Survey to estimate that the 40% decline in the S&P 500 since January 2000 caused the labor force participation of older workers aged 55-64 to increase by 2.64% and 5.36% for men and women respectively. Using micro-data from the Health and Retirement Study to perform a difference-in-differences analysis, we find that the probability of retirement for men and women aged 63-64 with DC pensions fell by 24.6 and 20.4 percentage points respectively from 1998 to 2002. 1
  2. 2. I. Introduction 2
  3. 3. Economic downturns highlight the sometimes-underappreciated role Social Security and defined benefit (DB) pension plans play to stabilize the macro-economy and moderate fluctuations in unemployment. Social Security and DB pensions stabilize the economy because older workers eligible for Social Security and DB pensions are more likely to leave the labor force and retire rather than be laid off and look for work. This causes unemployment to be lower than it would otherwise be. This salubrious macroeconomic effect is lost when workers are increasingly covered by defined contribution (DC) type pension accounts, such as 401(k)s. Defined contribution accounts can be automatic destabilizers. When the economy is in an expansion DC values increase, making retirement possible and enticing. Yet, precisely when workers are leaving the labor force because of bull financial markets, employers want more workers. On the other hand, when the economy dips, or a regional industry or company fails, the DC and 401(k) plan values can plummet while companies are laying off workers. These conclusions flow easily from standard macroeconomic theory. The challenge of this paper is to find support for the argument that the effect exists and that the magnitude is not trivial. It is well known that DC-type pensions, including 401(k)s, are supplementing and replacing annuities and defined benefit pension plans. That means a growing number of older workers have assets that are more vulnerable to financial market fluctuations. On average, the percentage of workers with only 401(k) plans has stayed just under 20 percent from 1981 to 1996 (U.S. Department of Labor). However, the rate of increase in such coverage has been tremendous. Over this time period, defined benefit wealth fell 3
  4. 4. 39.5 percent while wealth in marketable accounts, including defined contribution plans, increased by 838 percent.i We find evidence that the recent sharp decline in DC values boosted the labor force participation of older people. For the first set of regressions we use aggregate data including the S&P 500 as an indicator for both the value and perception of an individual’s retirement wealth in a DC pension plan. Second, we perform a difference-in- differences analysis using micro-data from the HRS and find that older workers with DC pensions are much less likely to retire when the stock market value falls significantly. The first part of this study introduces how the value of DC benefits fluctuates with the stock market compared to DB pensions and Social Security. The next section reviews the literature on the extent to which workers drop out of the labor force in response to positive changes in wealth. Third, we perform multiple regression analysis to estimate the effect of changes in the stock market on older workers’ labor supply decisions (holding unemployment and wages constant). Fourth, using a difference-in-differences approach we find that older workers with only DC-type pension plans were dramatically less likely to retire than those with DB plans after the stock market crash of January 2000. We conclude with reflections on further research and the relationship between pensions and macroeconomic stability. II. Labor Force Participation and Wealth Changes Though relatively few workers across the age spectrum have significant wealth in stock (and women much less) – , Gale, Engen, and Uccello (2002) found the 49% ercent of households with retirement accounts had small median values,, nder $20,000,, n 1998 – , oder workers hadveuch higher valued accounts. Purcell (2002) found that 50% ercent of full time older workers (aged 55-64) owned one or more retirement accounts, which have a mean value of $71,040 and a median value of $33,000 and these accounts were 4
  5. 5. linked with stock prices.ii Additionally, recent studies show that precise marginal changes in retirement wealth may not be as important in influencing work and retirement decisions as perceptions of current and future retirement wealth (Gustman and Steinmeier, 2001b) Though people receive benefit statements annually, they receive reports on the stock market daily. Because impressions about the value of financial wealth rely on imperfect knowledge and social norms (Schiller, 2000) it is not unreasonable to conclude workers make their best estimate of retirement affordability based on easily available and widely accepted, though imperfect, signals about the value of their retirement wealth which has increasingly become more dependent on financial markets. Therefore, the S&P 500 is a good proxy for the value and perceived durability of retirement wealth and it is expected to highly influence labor force behavior. After the stock market crash, disappointed Americans related to newspaper journalists how their expectations were affected (Greene, 2002): “I thought I would at least be able to take a break and think about what to do with the second half of my life,” Mr. Pringle, 63, said. “But I didn't have a lot of options when the market went south’…” To many Americans, the sustained slide in the stock market–particularly last week's nose dive–has been something to fret about, a darkening cloud. But to many people at or near retirement age, it has been a colossal jolt.” (Zernike, 2002) Below we test the hypothesis that women and men worked more in the 2001-2002 recession because their pension income is falling due to increased reliance on DC 5
  6. 6. pensions. Other research foresees (Cheng and French, 2000; Sevak, 2001) or supports this claim (Eschtruth and Gemas, 2002). In this paper, we find supporting evidence that older workers are more likely to postpone retirement because of the changes in the structure of retirement wealth away from DB to DC plans (i.e. from an automatic stabilizer form to an automatic destabilizer form) from anecdotal evidence, comparisons of labor force participation trends, insights from other studies and, finally, regression analysis linking retirement behavior to changes in pension wealth. II. A. Older Americans are Working Longer Anecdotal evidence suggests workers did or will postpone their retirement plans because of the unanticipated fall in retirement wealth in the early 2000s. In January, 2002, the Gallup Organization (2002) reported that nearly one in five investors considered postponing retirement by four years on average – the figure is higher for older workers, 26 percent for investors aged fifty and older. Four months later Gallup found that 45 percent of non-retired Americans over age 50 do not expect to have enough money to live comfortably when retired and, on average, predict their retirement at age sixty-three. In 1995, the average estimated age of retirement was sixty (Jones, 2002). The Enron case is a celebrity example where workers with retirement assets concentrated in employer stock are postponing retirement. Enron’s 401(k) has as much as 60 percent of its assets in company stock, which fell from its peak of $90 a share in 2001 to 50 cents a share.iii Many of Enron’s employees saw the value of their 401(k) accounts free fall and said they would delay retirement as a result.iv Eschtruth and Gemus (2002) found that the labor force participation rates for older workers increased since the recession began in March 2001. They also found that labor force participation rates of older workers in this recession are negatively correlated 6
  7. 7. with changes in the value of the S&P 500. This suggests that changes in the structure of pensions, which have made them more sensitive to financial markets, are major determinants of the recent increase in labor force participation. However, we find that when male and female rates are viewed separately the relationship is driven mainly by women. Graphs 1 and 2 show the increase in women’s labor force participation is steeper than men’s and it appears that older women’s (age 55-64) labor force participation rates are more sensitive to the value of financial assets than are the labor force participation rates of older men. Simple two-way correlations reveal that the proportion of the change in older women's labor force participation attributable to the change in the S&P 500 is 0.843 (measured by the square of the Pearson product moment correlation coefficient) and the correlation is 0.827 for older men. The increase in the labor force participation rate of older workers since the stock market fell is due in large part to increases in older women’s work effort. As we will show later in the paper, this conclusion is also supported by our regression estimates, which reveal a larger and negative marginal effect of the S&P 500 variable on labor force participation rates for older women than for older men. The micro-analysis shows that both men and women with DC pensions were less likely to retire than those with DB pensions.v Insert Graphs 1 and 2 about here. II. B. Wealth Shocks and Retirement Timing 7
  8. 8. Studies of retirement timing and unanticipated changes in pension wealth can help us assess the magnitude of an expected increase in labor force participation due to a fall in retirement wealth, although existing studies focus on unanticipated good news (see, especially, Gustman and Steinmeier, 2002). We expect the labor force participation response to be greater when income falls as people work to maintain target income rather than target leisure. Gustman and Steinmeier’s (2002) micro-simulations based on observations from the Health and Retirement Study of individuals near retirement age suggested that the stock market boom of the 1990s increased retirement rates by three percentage points. They infer that a cessation of the boom will have the opposite effect of nearly the same magnitude. Friedberg and Webb (2000) argue that the change in pension plans, from DB to DC, affects retirement patterns because DC plans do not have early retirement incentives and eliminate the “DB-induced spikes” in retirement ages at certain ages of eligibility. Using data from the Health and Retirement Study (HRS) (1992-1994) and Survey of Consumer Finances (SCF), they show that financial incentives in DB plans lead people to retire almost 2 years earlier. The increasing number of DC plans since 1983 has raised the median retirement age by 2-4 months and they predict a further increase in retirement age by 2-5 months as more people rely on DC plans. Friedberg and Webb also estimate that a 100 percent increase in financial wealth (using the mean of $37,182) will increase the probability of retirement by about a half percentage point. Munnell, Cahill, and Jivan (2003) also found that DC plans increase the expected retirement age. Cheng and French (2000) argue that the stock market run-up caused labor supply to fall. The stock market run-up caused a $5.8 trillion shock to national wealth – 15 percent of individuals age 55+ had a wealth shock of over $50,000. Using data from the PSID, HRS, and CRSP, they estimate that the labor force participation for people over 8
  9. 9. age 55 would have been 1.16 percent higher without the stock market boom. They also found that the labor supply response was greater at older ages. Sevak (2001) shows that positive wealth shocks increase the probability of retirement. Unexpected increases in wealth (i.e. large capital gains from the stock market boom of the 1990s) lead to earlier retirement. Data from the HRS (1992-1998) shows that a $50,000 wealth shock leads to a 1.9 percent increase in retirement probability of people age 55-60 and that the elasticity of retirement flows (1996-1998) with respect to wealth is between .39 and .50 for men. We also found a labor force participation elasticity that is negatively related to non-labor income, although we do not expect that it will be of the same magnitude. It is reasonable to believe that the labor force participation decision is more reactive to declines in wealth rather than increases in wealth because people aim to maintain a basic level of consumption. As Cheng and French (2000) show, the effect of stock market gains on an individual’s labor supply is dampened by their overwhelming response to increase consumption. In the case of stock market losses, however, individuals will be resistant to reduce their consumption below a certain target level; their labor supply decisions will be more sensitive to reductions in wealth in order to maintain this level of consumption. Coronado and Perozek (2001) show that labor force participation declines in response to positive wealth shocks. Data from the HRS (1992-1998) indicates that one- third of respondents retired earlier than expected. Those who retired earlier had much larger gains in the value of their stock portfolios ― the average increase in the value of stocks was $93,000 for early retirees, compared to an average gain of $58,000 for the entire sample. The greater the share of equities in the portfolio, the earlier the retirement date. Unforeseen increases in wealth from Social Security provide another test of the responsiveness of labor force participation to positive wealth shocks. Burtless (1986) and 9
  10. 10. Anderson, Burkhauser and Quinn (1986) found that unanticipated boosts in Social Security benefits from 1969 to 1973 induced more retirements. Further evidence about labor force responses from changes in wealth are gleaned from studies on the effect of unearned income (i.e. lottery winnings) on labor earnings. Imbens, Rubin, and Sacerdote (2001) find that the propensity to earn becomes larger and negative as unearned income increases. Data from a survey of lottery players in Massachusetts in the mid-1980s reveals a marginal propensity to consume leisure of 11 percent (larger effects for people age 55-65). They conclude it takes time for winners to adjust their labor supply. Additionally, the authors note that there is no evidence of negative effects of lottery payments on the labor supply of low earners, yet there is a large and significant negative effect of unearned income on retirement savings. II. C. Current Trends in Employer-Provided Pensions It is important to note that the structure of pensions has helped reduce the effects of industrial retrenchment on unemployment. In the recently shrinking sectors of the economy manufacturing, mining, etc., firms have used a combination of voluntary retirements induced by early retirement buyouts from DB plansvi and layoffs to reduce payroll. Voluntary retirements are a better outcome than layoffs. Layoffs do not change labor force participation at best, and, at worst, seems to induce older women to seek work (presumably because their spouses lose their jobs). Services and retail are the fastest growing industries in the last two decades. Employment in retail jumped 23 percent from 1989 to 2001, but the percent of pay going to pensions is the smallest (1.4 percent in 2001) compared to the all-industry average of 3.5 percent. In addition, service employment jumped 30 percent and their share of pay going to pensions is only 2.5 percent in 2001 (Bureau of Labor Statistics, Employment Cost Index, various years). These trends imply that when downturns occur in these industries, layoffs will be the 10
  11. 11. dominant method of downsizing, which will increase labor force participation and exacerbate unemployment for older workers. The overall trend in employer expenditures for pensions (a good proxy for quality and coverage) was a decline of 22 percent between 1978 and 1998 (Medoff and Calabrese, 2001, p. 134). Twenty years ago 43 percent of women workers were covered by a pension plan; in 1996 just 39 percent were. The fall is worse for men – they are more likely to be in industries that have DB plans than women. More than half of male workers had pensions in 1979, now only 48 percent do. This decline is affecting all workers at all income levels (Ellwood, 2000). The fall in the value of non-labor income, regardless of source, will induce older workers to work longer (ceteris paribus). Furthermore, in early December 2002 the Treasury Department lifted the moratorium on defined benefit plans being converted to another form of a defined benefit plan, cash balance plans. The conversions suggest, and the consensus among experts is that these changes will lower the benefits for older, long-service employees. Following the analysis above, this policy will induce more work among the elderly and further loosen the labor market. III. Cohort Regression Strategy and Analysis In the regression analysis we emphasize the effect on labor force participation of being exposed to a retirement plan whose value depends on the financial market. We expect, ceteris paribus, that labor force participation would fall in recessions because many studies confirm that an increase in the unemployment rate causes more retirements, both voluntary and involuntarily (Burtless, 1986, p. 132; Burtless and Moffitt, 1985). 11
  12. 12. However, older women increase their labor force participation in contractions on average, since the trough of 1954, by 3.0 percent, whereas older men withdraw from the workforce. Men age 65 and older withdrew from the labor force in contractions on average by 2.3 percent; while women age 65 and older hardly changed their labor force participation (see Table 1). These trends suggest that DC plans affect the retirement behavior of men and women differently. Insert Table 1 here. It is also possible that any causal relationship between an implied loss of pension income and more work effort is an illusion. Women and men may have faced different job opportunities inducing them to change their labor force participation in different ways. The first part of the analysis tests this possibility. The second set of analysis uses a difference-in-differences approach and finds that having only a DC plan, rather than a DB plan, decreased the probability of retirement for individuals aged 61-64 in 2002. The differences in retirement behavior between these two groups changed significantly from 1998 to 2002. III. A. Labor Market Conditions for Older Men and Women Older women’s (age 55-64) labor force participation rate increased by 8.1 percent since the bubble burst in the stock market, from 52.0 percent in January 2000 to 56.2 12
  13. 13. percent in October 2002, while older men’s labor force participation rate rose by 3.7 percent. During the same time period, unemployment increased more for older men than women. The unemployment rate rose, on average per month, by 2.03 percent for men and 1.31 percent for women. Additionally, weekly earnings rose, on average, 0.23 percent and 0.63 percent for older men and women respectively (see Table 2). This data suggests that labor market opportunities were relatively better for older women than men. Insert Table 2 here. Since the recession started in March 2001, older women’s labor force participation rates continue to increase more than men’s corresponding to faster growth in wages. However, the level of wages for older women is still significantly lower than for men. The likelihood of finding employment has fallen for older women in the recession as evidenced by their rising average unemployment rate, which is now larger than the growth rate of unemployment for older men. The marginal labor market advantages of older women relative to men have deteriorated in the current recessionary period (see Table 3). Insert Table 3 about here. If non-labor income falls, we expect (ceteris paribus) that labor force participation would rise. If wages increase and the substitution effect dominates the income effect, we expect labor force participation to increase as the rise in wages causes individuals to work more because leisure is now more expensive.vii 13
  14. 14. III. B. Data 14
  15. 15. In our regression analysis, we use two sets of data to test the hypothesis that older workers with DC pension wealth will postpone retirement in a recession. The first set of regressions uses average monthly data divided by sex and age group. The variables include labor force participation rates, unemployment rates, average weekly earnings, and the level of the S&P 500. The level of the S&P 500 is used to proxy primarily for changes in the expectations of retirement income security, and secondarily for the value of DC or 401(k)-type pension plans. The unemployment rate and average weekly earnings proxy for changes in labor demand. The sample means are presented in Appendix Table 1. We run regressions for each sex and age group for three time periods: from January 1994 to October 2002, from the beginning of the bear stock market (January 2000) to October 2002, and from the peak of the business cycle (March 2001) to October 2002. In the analysis in Section 4, we use data from the HRS for 1998 and 2002 and RAND’s contribution to the HRS (version C). The original HRS interviewed individuals born in 1931-1941 who were 61-71 years of age in 2002. The RAND data provide an extensive number of variables that have been aggregated from various questions in the HRS. Variables used in the regressions include: age, sex, race, educational attainment level, marital status, good or bad health, self-reported retirement status, type of pension plan (DB only or DC only)viii, industry code, health insurance (public, private and/or employer-provided), availability of retiree health insuranceix, single year age dummy variables, and an indicator for observations from 2002 (post-treatment period). Table 4 details the characteristics of the sample in 1998 and 2002. We restrict our sample to respondents aged 61-64 in both waves to create mutually exclusive age cohorts which are near the age of usual retirement. We further restrict our sample to respondents who have only a DB plan or only a DC plan. Including older workers who have both 15
  16. 16. types of pension plans would muddy our results because these plans are likely to differ widely in value and in their effect on the retirement decision. Limiting our sample in this way reduces our observations from 1,415 to 914 in 1998, and from 1,772 to 1,033 in 2002. We use the HRS respondent-level weights to make our sample nationally representative. Insert Table 4 about here. III. C. Cohort Regression Analysis and Results We regressed monthly changes in the labor force participation rate on three independent variables: the average monthly level of the S&P 500, the average monthly unemployment rate and the average weekly earnings for each month for the relevant age and sex group for three different time periods. The longer time period between 1994 and 2002 is used as a benchmark for the bear market and recessionary time periods. All independent variables are lagged one month in the regressions. The model for the regression equation is: LS = a + B1S&P500(t-1) + B2 U(t-1) + B3w(t-1)+ ε. The unemployment rate supplements the wage rate as a measure of the marginal benefit from working considering the chance of getting work and the S&P 500 proxies for changes in the value of financial assets. In the second time period, since the stock market slide began, the coefficient on the S&P 500 level is negative and significant at the 1 percent level in the regressions for 16
  17. 17. older workers (both men and women), aged 55-64. We also get a significant and negative coefficient for the S&P 500 level in the third time period. From the beginning of the recession in March 2001 to October 2002x, older women’s labor force participation was 52.8 percent and rose to 56.2 percent by October 2002, a 6.4 percent increase. Table 5 contains our regression results for all three time periods. Insert Table 5 about here. We interpret the results to mean that older men and women have been induced to work since the recession began because of real declines in the value of their DC pensions and expectations that their retirement is more insecure because of the fall in the S&P 500. The inducement is larger for women – the elasticity is -0.101 percent for women and -0.064 percent for men.xi The result from the first set of regressions implies that, evaluated at the mean, a 10 percent or 109.01 point drop in the S&P 500 causes older men’s labor force participation rate to increase from 68.63 percent to 69.07 percent. Similarly, older women’s labor force participation rate would increase from 53.90 percent to 54.44 percent. In the second set of analysis we will see that the effect of having only a DC plan is even larger for men and women aged 63-64 from 1998 to 2002. The S&P 500 had fallen by 630.83 points or 42.47 percent since its peak during this time period. Our elasticities indicate that this drop would cause the labor force participation rates of older workers to increase from 67.7 percent to 69.77 percent for men and from 51.8 percent to 54.84 percent for women. The actual values of labor force participation rates for older men and women in October 2002 are 70.1 percent and 56.2 percent respectively which are slightly higher than predicted. Again, women’s labor force participation decisions are more sensitive to levels of the S&P 500, which proxies for the value of their non-labor income and wealth. 17
  18. 18. The results for women who are retirement age (age 65) reveal similar significant effects, but not so for men in the first period. The S&P 500 significantly affects the labor force participation rate for women aged 65 and older when evaluated from January 2000 to October 2002. The elasticity is such that a 10 percent drop in the S&P 500 would cause retirement-age women’s labor force participation rate to increase from its mean of 9.61 percent to 9.73 percent. The S&P 500 variable is not significant for retirement-age men in this time period. The third time period, between 1994 and 2002 is a benchmark. The coefficient on the lagged S&P 500 variable is significant and positive for the 1994-2002 time period, yet the elasticities of the S&P 500 variable during this time period are much smaller than the elasticities in the later two time periods. The results suggest that individuals did not increase their labor force participation in response to a fall in the stock market until the last few years when defined contribution plans made their ascendancy in the American retirement scene. In 1999, a national magazine dubbed the phenomenon “401(k) Nation” (Smith, 1999). Since the presence of autocorrelation is possible among these variables we used the Granger causality (Greene, 2000, pp. 742-743) test to find evidence that causality is such that changes in the S&P 500 (lagged) affects labor force participation rates.xii The Granger causality test passes for men and women age 55-64 in the 2000-2002 time period. The test indicates that the S&P 500 level lagged one month is driving the labor force participation rate and not the reverse. Similarly, the Granger causality test also passes for women age 65 and older in the two most recent time periods.xiii In the other regressions, including those for men 65 and older, the test either does not pass or implies causality in both directions. The unemployment rate does not significantly affect older men’s or women’s labor force participation rates since the decline in the stock market (January 2000) or 18
  19. 19. since the recession began. Unemployment seems to have no greater effect on labor force participation for older or retirement-age men and women in the longer time period (January 1994-October 2002). The next section offers another way to examine the sensitivity of labor force participation to changes in the financial market exposure of pension wealth. IV. A. Difference-in-Differences Analysis Using individual-level data from the HRS, we can assess whether or not DC plans caused older workers’ retirement rates to fall after the stock market decline. We use a difference-in-differences approach to compare the change in retirement rates for older workers with only DB or DC pension plans. This approach avoids problems with measurement of error in the HRS wealth data as suggested by Sevak (2001) since it does not utilize actual measures of wealth. Respondents with both DB and DC pension plans are restricted from the sample in order to isolate the effect of market-based retirement wealth (i.e. DC-only plans) on retirement timing. We focus on workers aged 61-64 in two different time periods, 1998 and 2002. The treatment in our model is the dramatic fall in the stock market which began in January 2000. Thus, 1998 is our pre-treatment period and 2002 is our post-treatment period. The mean retirement rates for two age groups (61-64 and 63-64 year olds) by sex and pension type in 1998 and 2002 is found in Table 5. Retirement rates fell dramatically from 1998-2002 for those with DC pensions. Retirement rates declined by over 11 percentage points from 1998-2002 for workers aged 61-64 with DC pensions. The difference-in-differences model allows us to control for unobserved characteristics that are time invariant. For example, we know the structure of DB plans causes spikes in retirement rates at certain ages of eligibility, whereas DC plans do not 19
  20. 20. appear to cause such spikes in retirement rates (Friedberg and Webb, 2000). Because we do not expect this effect of DB pension structure to change over time, it is controlled for in our model. We are emphasizing not the difference in retirement rates between individuals with DB and DC pensions but, rather, how that difference changes when there has been a negative shock to the stock market. Our probit model is: Retit* = α + β*DCit + δ*Y2002t + γ*DC_2002it + εit Retit = 1(Retit* >= 0) where α is a constant, DCit is a dummy variable indicating DC pension coverage, Y2002t is a dummy for the year 2002, DC_2002it is an indicator for whether a respondent has a DC plan in 2002, and εit is the error term.xiv The coefficient of interest is γ, which represents the difference-in-differences due to the treatment. A negative and significant coefficient on γ implies that retirement rates of older individuals with DC plans declined due to the negative shock to the stock market. Table 6 clarifies the significance of γ in our model. The difference-in-difference estimator, γDD, is defined as γDD = [E(Retit|DCit=1, Y2002t=1) – E(Retit|DCit=1, Y2002t=0)] – [E(Retit|DCit=0, Y2002t=1) – E(Retit|DCit=0, Y2002t=0)]. Insert Table 6 about here. A comparison of the samples in 1998 and 2002 does show that they are quite similar. The average age is 62.43 in 1998 and 62.39 in 2002. Pension coverage rates by type are also very consistent across the two waves. The proportion of women in the sample increases slightly from 43.91 percent in 1998 to 45.14 percent in 2002. Health insurance coverage also increases for this sample by 5.53 percentage points. The aged 61-64 group in 2002 has slightly lower educational attainment. The percentage of workers in the business and repair services industry rises from 7.52 percent to 10.51 percent in 2002, but most other changes in industry density are quite small. Table 7 20
  21. 21. indicates that the 1998 and 2002 cohorts are very similar overall and suggests that they are good comparison groups. Insert Table 7 about here. IV. B. Results of Difference-in-Differences Analysis We estimated the regression above for our sample of 61-64 year olds with either a DB or DC pension plan (not both) and found the coefficient of γ to be negative and significant at close to the 10 percent level. The regression was re-estimated separately for men and women and we found that the coefficient of γ was negative for both, but only significant for men in this age group. The results for the 63-64 age group improve in significance. The difference-in-differences estimator is negative and significant for both men and women aged 63-64 at the 5 percent level, which suggests that the decline in the stock market was more likely to cause workers aged 63-64 with DC plans to postpone retirement. To check the robustness of our results, we included a set of explanatory variables and re-estimated the regressions to see if the significance of γ changed. The explanatory variables included dummy variables for the following: single year of age, race (white, black, other), educational attainment level (less than high school, high school, some college, college, and post-college), industry (13 different groupings), martial status, health (good or bad), health insurance, and retiree health insurance. The difference-in-differences estimator remained negative and significant at the 10 percent level for men aged 61-64 and at the 5 percent level for both sexes aged 63-64 (see Table 8). Insert Table 8 about here. The marginal effect of having a DC plan in 2002 for men aged 61-64 is a reduction of 10.7 percentage points in the probability of retirement. For men and women 21
  22. 22. aged 63-64 this reduction was significantly greater at 24.6 and 20.4 percentage points respectively (see Table 9). The mean values of the variables used in these regressions are found in Appendix Table 2. Insert Table 9 about here. Note that Sevak (2001) estimated a similar model to determine if the run-up in the stock market caused retirement rates to increase for individuals with DC plans between 1992 and 1998. Sevak, however, uses a linear probability model (LPM) to test the significance of her difference-in-differences estimator. Concerns about the shortcomings of the LPM (see Greene, 2000, p. 813) led us to use a probit regression instead. However, we also performed the LPM estimation to compare our results. This regression is: Retit = α + β*DCit + δ*Y2002t + γ*DC_2002it + εit Our results with the LPM are comparable to those with the probit model, however the significance improves. The difference-in-differences estimator is significant for men aged 61-64 and for both sexes aged 63-64 at the 5 percent level. V. Conclusions, Further Research, and Policy Implications Defined contribution plans could be destabilizing because they are positively correlated with stock market growth. Concentration of employer stock in 401(k) plans heightens this destabilization problem. We find older workers are sensitive to the financial markets. Unfortunately, the sensitivities are such that older workers, especially women, are entering the labor force as the stock market declines and the unemployment rate increases. When compared to similar individuals with DB pensions, men and women aged 63-64 with only DC pensions have experienced a significant reduction of 22
  23. 23. between 20 to 24 percentage points in their probability of retirement in response to a decline in the stock market. The destabilizing effect we have described should become even stronger in the future if current coverage rates hold or continue in this direction. Data from the Survey of Income and Program Participation (SIPP) indicates that younger workers, age 25-39, are more likely to have DC plans and less likely to have DB plans than older workers. In 1996, 21.9 percent of younger workers, aged 25-39, were covered by only 401(k)-type plans compared to 15 percent for workers over age 55. Further research can go in many directions: some industries could be more affected by the destabilizing effect of DC plans because, in these industries, aggregate demand and financial markets are highly correlated. If the auto industry used DC plans and not DB plans, auto-producing regions could become more unstable. Since African- Americans and Hispanics have far fewer individual level retirement accounts and rely on social security, the destabilizing effects may not affect them. Since the United States–the only OECD nation that bans forced retirement–expects the elderly to work for pay, erosions in the private pension system may force workers to spend their longer lives in the workforce, which reverses decades of improvements in workers’ retirement opportunities. 23
  24. 24. Graph 1 LFP of Older Men and the S&P 500 since January 2000 Labor Force Participation of Older Men and the S&P 500 since January 2000 1550 74 73 1350 72 1150 71 S&P 500 Index 70 950 S&P 500 69 LFP Rate 750 Men 55-64 68 550 67 66 350 65 150 64 00 01 02 0 1 2 0 1 2 -0 -0 -0 -0 -0 -0 n- n- n- ep ep ep ay ay ay Ja Ja Ja M M M S S S 24
  25. 25. Graph 2 LFP of Older Women and the S&P 500 since January 2000 Labor Force Participation of Older Women and the S&P 500 since January 2000 1550 60 59 1350 58 1150 57 S&P 500 Index 56 LFP Rate 950 S&P 500 55 Women 55-64 750 54 550 53 52 350 51 150 50 00 02 01 1 0 2 1 0 2 -0 -0 -0 -0 -0 -0 n- n- n- ep ay ay ay ep ep Ja Ja Ja M M M S S S 25
  26. 26. 26
  27. 27. 27
  28. 28. Table 1 Men and Women Act Differently in Labor Market Contractions: LFP Changes from Peak to Trough* by Sex and Age November 1948 – March 2001 28
  29. 29. Peak Trough 25-34 25-54 55-64 65+ Men Women Men Women Men Women Men Women Nov-48 Oct-49 0.40% -2.60% 0.40% 1.40% -1.90% 12.10% -1.00% 9.90% Jul-53 May-54 0.10% 4.80% 0.00% 2.60% 0.70% 2.00% -1.00% -18.90% Aug-57 Apr-58 0.20% -0.60% 0.10% 1.00% 1.50% 1.70% -3.80% 2.90% Apr-60 Feb-61 -0.30% 0.80% -0.30% 0.90% 1.20% 4.30% 2.40% 7.50% Dec-69 Nov-70 0.00% 2.50% -0.10% 1.20% -1.30% 1.60% -3.30% 0.00% Nov-73 Mar-75 -0.50% 4.30% -0.90% 1.30% -1.90% 0.20% -0.40% -6.90% Jan-80 Jul-80 -0.20% -0.50% -0.10% 0.20% -0.40% -0.50% -3.60% -2.40% Jul-81 Nov-82 -0.20% 2.70% 0.10% 2.50% -0.40% 1.70% -3.90% -2.50% Jul-90 Mar-91 -0.20% -0.40% 0.10% -0.30% -0.60% -0.40% -4.30% 1.20% 1-Mar 2-Nov -1.30% -1.60% -0.80% -1.40% 2.80% 7.00% -3.90% 5.30% Average change in Labor Force Participation 0.90% -0.20% 0.90% 0.00% 3.00% -2.30% -0.40% Notes: These are the relevant quarterly rates. Bolded numbers indicate the change in labor force participation was greater than in the previous recession. 29
  30. 30. 30
  31. 31. *Data is from the IRS Form 5500 Reports and the CPS (1996). Table 2 31
  32. 32. Women and Men Face Different Incentives to Retire Since the Bubble Burst in the S&P 500 (Average Monthly Change in LFP, Wages, and Unemployment by Sex and Age since the Crash in Financial Markets, January 2000 – October 2002) Sex and Age Average Monthly Change Average Monthly Average Monthly Group in Labor Force Change in Earnings Change in Participation January January 2000 – Unemployment 2000 – October 2002 October 2002 January 2000 – October 2002 Women 55-64 0.24% 0.63% 1.31% Men 55-64 0.11% 0.23% 2.03% Women 65 + 0.36% 0.92% 6.19% Men 65 + 0.13% 0.95% 2.98% Table 3 32
  33. 33. Women and Men Face Different Incentives to Retire Since the Recession Started in March 2001 (Average Monthly Change in LFP, Wages, and Unemployment by Age and Sex, March 2001 – October 2002) Sex and Age Group Average Monthly Average Monthly Average Monthly Change in Labor Change in Earnings Change in Force Participation March 2001 – Unemployment March 2001 – October 2002 March 2001 – October 2002 October 2002 Women 55-64 0.34% 0.74% 2.45% Men 55-64 0.18% 0.48% 1.94% Women 65 + 0.39% 0.67% 9.97% Men 65 + -0.07% 0.78% 4.36% 33
  34. 34. 34
  35. 35. Table 4 Mean Retirement Rates by Age Group, Sex, and Pension Type Before and After the Stock Market Decline 1998 2002 Retirement Rate Retirement Rate Pension Type Men Women Total Men Women Total Age 61-64 46.2 DB only 49.35 42.58 4 56.91 43.05 50.71 32.9 DC only 35.32 29.2 1 20.83 22.64 21.67 Difference (DB- 13.3 DC) 14.03 13.38 3 36.08 20.41 29.04 Difference-in- Differences 22.05 7.03 15.71 (1998-2002) (0.086) (0.648) (0.106) Age 63-64 DB only 60.62 53.36 57.32 65.71 59.22 62.95 DC only 44.93 50.4 46.72 31.26 28.18 29.93 Difference (DB- DC) 15.69 2.96 10.6 34.45 31.04 33.02 35
  36. 36. Difference-in- 22.42 Differences 18.76 28.08 (0.008 (1998-2002) (0.06) (0.042) ) Notes: P values are in parenthesis. Individuals are coded as retired if they responded in the HRS that their labor force status is “retired.” Respondent level weights from the HRS are used to create a nationally representative sample. Table 5 Multiple Regression Analysis Monthly Labor Force Participation Rate By Sex and Age and Time Period Since 1994 January 1994 – January 2000 March 2001 October 2002 -October 2002 -October 2002 (n=105) (n=34) (n=20) Women 55-64 BENCHMARK BEAR MARKET CONTRACTION Constant 34.74 (1.523)*** 57.843 (2.351)*** 56.415 (3.498)*** Weekly earnings 1 0.029 (0.003)*** 0.002 (0.003) 0.002 (0.004) Unemployment rate 0.179 (0.233) 0.451 (0.263)* 0.66 (0.423) S&P500 0.002 (.0005)*** -0.006 (0.0008)*** -0.005 (0.002)** Adjusted R-squared 0.69 0.851 0.623 Men 55-64 Constant 60.32 (1.997)*** 74.511 (2.974)*** 81.08 (4.908)*** Weekly earnings 0.005 (0.003)* -0.003 (0.003) -0.011 (0.005)* 36
  37. 37. Unemployment rate 0.264 (0.199) 0.25 (0.191) 0.473 (0.209) S&P500 0.002 (0.0005)*** -0.004 (0.0009)*** -0.004 (0.001)*** Adjusted R-squared 0.403 0.81 0.738 Women 65+ Constant 7.246 (0.434)*** 11.62 (0.471)*** 11.753 (0.748)*** Weekly earnings 0.005 (0.001)*** -0.001 (0.001) -0.0006 (0.001) Unemployment rate -0.013 (0.064) 0.014 (0.049) 0.027 (0.072) S&P500 0.0004 (0.0001)*** -0.001 (0.0002)*** -0.002 (0.0005)*** Adjusted R-squared 0.217 0.606 0.421 Men 65+ Constant 14.405 (0.535)*** 16.509 (0.821)*** 16.355 (1.213)*** Weekly earnings 0.003 (0.001)*** 0.002 (0.001)* 0.002 (0.0015) Unemployment rate 0.136 (0.077)* 0.17 (0.076)** 0.159 (0.092) S&P500 0.0006 (0.0002)*** -0.0004 (0.0003) -0.0002 (0.0006) Adjusted R-squared 0.262 0.261 0.14 1.) The means and definition of variables for the table in Appendix Table 1. Note: *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level. Table 6 Expected Value of the Retirement Rate for Pre and Post-Treatment Periods Expected Value of the Retirement Rate Pension Type 2002 1998 Difference DC α+β+δ+ γ α+β Δ+γ DB α+δ Α Δ Difference in Differences γ 37
  38. 38. Table 7 Characteristics of Age 61-64 Sample with DC-Only or DB-Only Pensions in 1998 and 2002 (in percentages except for age) Variables 1998 2002 DB only 68.95 68.75 DC only 30.8 30.47 Male 56.09 54.86 Female 43.91 45.14 White 87.61 87.08 Black 9.09 9.42 Other 3.18 3.31 Age 62.43 62.39 Married 75.14 73.89 Good Health 82.75 83.19 Bad Health 17.17 16.75 Retired 42.05 41.53 Health Insurance 78.54 84.07 Retiree Health Insurance 51.75 51.93 High School Degree or Less 50.93 47.78 Agriculture, Forestry, Fishing 1.92 1.86 38
  39. 39. Mining & Construction 6.54 8.08 Manufacturing – Durable 9.7 10.8 Manufacturing - Nondurable 16.54 15.36 Transportation 11.13 11.39 Wholesale 6.28 6.26 Retail 11.66 12.06 Finance, Insurance, and Real Estate 8.25 9.52 Services - Business and Repair 7.52 10.51 Services – Personal 2.81 2.74 Entertainment and Recreation 2 3.17 Professional and Related Services 37.84 37.95 Public Administration 9.09 9.99 n=914 n=1,033 Note: Respondent-level weights from the HRS are used Table 8 Regression Results for Probit Model (Difference-in-Differences), Age 61-64 Men 61-64 Women 61-64 Explanatory Coefficient Robust Coefficient Robust Variables (dF/dx) Standard Error (dF/dx) Standard Error DC Pension Only 0.258*** 0.227 0.202*** 0.05 Year2002 0.02 0.259 0.048 0.054 DC*Year2002 -0.107* 0.321 -0.032 0.054 Age62 0.088 0.23 0.012 0.044 Age63 0.152*** 0.214 0.118** 0.066 Age64 0.284*** 0.224 0.19*** 0.064 Black -0.004 0.227 0.085* 0.051 Other -0.122** 0.363 0.401** 0.217 Less Than HS 0.026 0.199 -0.087** 0.03 Some College -0.044 0.221 0.019 0.041 College Degree -0.069 0.263 -0.014 0.059 Post-College -0.078 0.288 -0.017 0.053 Agriculture -0.126** 0.442 n/a n/a Construction -0.1** 0.257 -0.009 0.084 Manufacturing- -0.068 0.215 -0.025 0.052 39
  40. 40. Durable Manufacturing- Nondurable -0.074* 0.22 -0.094*** 0.025 Transportation -0.039 0.203 -0.113*** 0.022 Wholesale -0.094* 0.279 -0.108*** 0.02 Retail 0.022 0.214 -0.116*** 0.024 Finance -0.011 0.297 -0.094*** 0.024 Services-Personal -0.13** 0.503 -0.066 0.04 Entertainment -0.082 0.416 n/a n/a Professional -0.049 0.25 -0.141*** 0.051 Public -0.049 0.305 -0.041 0.045 Married 0.055 0.222 0.031 0.033 Poor Health 0.147*** 0.183 0.007 0.039 Health Insurance -0.207*** 0.207 -0.058 0.05 Retiree Health Insurance -0.003 0.19 -0.091** 0.037 Notes: Coefficients reported are marginal effects; baseline = Age 61, White, High School, Services-Business; *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level. Table 9 Regression Results for Probit Model (Difference-in-Differences), Age 63-64 Men 63-64 Women 63-64 Explanatory Coefficient Robust Coefficient Robust Variables (dF/dx) Standard Error (dF/dx) Standard Error DC Pension Only 0.424*** 0.072 0.449*** 0.105 Year2002 0.157 0.126 0.070 0.101 DC*Year2002 -0.246** 0.098 -0.204** 0.074 Age64 0.162** 0.071 0.012 0.068 Black 0.020 0.096 0.067 0.088 Other -0.221** 0.050 n/a n/a Less Than HS 0.177* 0.110 -0.131 0.064 Some College -0.130 0.070 -0.039 0.080 College Degree -0.219*** 0.059 -0.026 0.110 Post-College -0.129 0.102 -0.037 0.102 Agriculture -0.225*** 0.040 n/a n/a Construction -0.191** 0.061 0.092 0.234 Manufacturing- -0.098 0.080 -0.067 0.115 40
  41. 41. Durable Manufacturing- Nondurable -0.260*** 0.050 -0.080 0.101 Transportation -0.103 0.081 -0.077 0.103 Wholesale -0.198*** 0.054 -0.150* 0.051 Retail 0.018 0.094 -0.194*** 0.048 Finance -0.148 0.085 -0.026 0.123 Services-Personal n/a n/a -0.101 0.086 Entertainment 0.023 0.200 n/a n/a Professional -0.098 0.091 -0.072 0.124 Public -0.104 0.107 0.002 0.161 Married 0.075 0.086 0.115 0.068 Poor Health 0.105 0.105 0.047 0.095 Health Insurance -0.211** 0.107 -0.061 0.101 Retiree Health Insurance -0.142* 0.080 -0.241*** 0.077 Notes: Coefficients reported are marginal effects; baseline = Age 63, White, High School, Services-Business; *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level. Appendix: Appendix Table 1 Means and Variable Definitions For Regressions in Table 5 January 1994 – January 2000 March 2001 – October 2002 -October 2002 October 2002 Women 55-64 (n=105) (n=34) (n=20) LFP rate 51.10 53.04 53.90 Weekly earnings 493.76 532.99 548.21 Unemployment rate 3.00 2.86 3.09 S&P500 954.10 1222.49 1090.11 Men 55-64 LFP rate 67.34 68.08 68.63 Weekly earnings 810.75 846.98 856.28 Unemployment rate 3.30 3.29 3.83 S&P500 954.10 1222.49 1090.11 41
  42. 42. Women 65+ LFP rate 9.05 9.61 9.76 Weekly earnings 286.64 309.81 314.65 Unemployment rate 3.47 3.21 3.52 S&P500 954.10 1222.49 1090.11 Men 65+ LFP rate 17.09 17.69 17.78 Weekly earnings 480.26 514.95 528.83 Unemployment rate 3.39 3.26 3.27 S&P 500 954.10 1222.49 1090.11 Definition of variables: 1) Monthly labor force participation rate seasonally adjusted collected from the Current Population Survey (CPS), Department of Labor, home.htm, 2) Average weekly earnings from the CPS’s Basic Monthly Survey, calculated (in 2000 $) for each sex and age group of individuals with weekly earnings greater than or equal to 1,, 3) Monthly unemployment rate seasonally adjusted collected from the CPS, Department of Labor,, 4) Average monthly level of the S&P 500, Appendix Table 2 Means for Difference-in-Difference Regressions Explanatory Variables Men 61-64 Women 61-64 Men 63-64 Women 63-64 DC Pension Only 0.528 0.433 0.616 0.435 Year2002 0.432 0.431 0.443 0.476 DC*Year2002 0.251 0.206 0.283 0.235 Age62 0.275 0.290 n/a n/a Age63 0.243 0.187 n/a n/a Age64 0.207 0.213 0.461 0.529 Black 0.106 0.197 0.114 0.182 Other 0.048 0.019 0.046 n/a Less Than HS 0.189 0.162 0.196 0.165 Some College 0.175 0.218 0.192 0.206 College Degree 0.135 0.087 0.151 0.094 Post-College 0.179 0.136 0.142 0.135 Agriculture 0.032 n/a 0.041 n/a Construction 0.124 0.016 0.137 0.024 42
  43. 43. Manufacturing-Durable 0.122 0.084 0.128 0.076 Manufacturing- Nondurable 0.195 0.077 0.187 0.071 Transportation 0.145 0.061 0.137 0.071 Wholesale 0.118 0.033 0.128 0.041 Retail 0.127 0.150 0.110 0.124 Finance 0.076 0.087 0.073 0.100 Services-Personal 0.022 0.052 n/a 0.035 Entertainment 0.022 n/a 0.023 n/a Professional 0.255 0.583 0.279 0.576 Public 0.092 0.087 0.091 0.071 Married 0.888 0.578 0.895 0.571 Poor Health 0.173 0.159 0.169 0.165 Health Insurance 0.819 0.808 0.776 0.824 Retiree Health Insurance 0.600 0.562 0.548 0.582 Note: Respondent level weights from the HRS are used to create a nationally representative sample 43
  44. 44. Bibliography Ameriks, John and Stephen B. Zeldes, “How Do Household Portfolio Shares Vary With Age?” Working Paper, TIAA-CREF Institute, 2001. Anderson, Kathryn H., Richard V. Burkhauser, and Joseph F. Quinn, “Do Retirement Dreams Come True? The Effect of Unanticipated Events on Retirement Plans.” Industrial and Labor Relations Review, Vol. 39, No. 4, pp. 518-26, July 1986. Associated Press, “Lucent to Offer Early Retirement.” Toronto Star Newspapers, p. E05, June 6, 2001. Bureau of Labor Statistics, Employer Costs for Employee Compensation. http:/stats.bls.goc/news.release/ecec.nws.htm, various years. Burtless, Gary, “Social Security, Unanticipated Benefit Increases, and the Timing of Retirement.” The Review of Economic Studies, Vol. 53, No. 5, pp. 781-805, 1986. Burtless, Gary, and Robert Moffitt, “The Joint Choice of Retirement Date and Post-Retirement Hours of Work.” Journal of Labor Economics, Vol. 3, No. 2, pp. 209-236, April 1985. Cerulli Report,, 2001. 44
  45. 45. Cheng, Ing-Haw, and Eric French., “The Effect of the Run-Up in the Stock Market on Labor Supply.” Economic Perspectives – The Federal Reserve Bank of Chicago, Vol. 24, No. 4, pp. 48-65, 2000. Committee on Education and the Workforce, U.S. House of Representatives, “The Enron Collapse and Its Implication for Worker Retirement Security.” Statement of Thomas O. Padgett, pp. 104-106, dbname=107_house_hearings&docid=f:81198.pdf, February 7, 2002. Coronado, Julia Lynn, and Maria Perozek, “Wealth Effects and the Consumption of Leisure: Retirement Decisions During the Stock Market Boom of the 1990s.” Unpublished Manuscript, Federal Reserve Board, 2001. Ellwood, David, “Winners and Losers in America.” In David Ellwood and Karen Lynn-Dyson, eds., A Working Nation: Workers, Work, and Government in the New Economy, New York: Russell Sage Foundation, 2000. Eschtruth, Andrew D., and Jonathon Gemus, “Are Older Workers Responding to the Bear Market?” Just the Facts on Retirement Issues, Center for Retirement Research, September., 2002. Francis, Theo, and Ellen Schultz, “Enron Pensions Had More Room at the Top.” The Wall Street Journal, January 23, 2002. Friedberg, Leora and Anthony Webb, “The Impact of 401(k) Plans on Retirement.” University of California, San Diego, Discussion Paper No. 30, 2000. 45
  46. 46. Gale, William, Eric Engen and Cori Uccello, “The Adequacy of Household Saving.” Center for Retirement Research, Boston College, 2000. Greene, Kelly, “More Older Investors May Delay Retirement as Portfolios Shrink.” The Wall Street Journal, February 14, 2002. Greene, William H., Econometric Analysis, Fourth Edition. New Jersey: Prentice-Hall, Inc., 2000. Gustman, Alan, and Thomas L. Steinmeier, “Retirement and the Stock Market Bubble.” Working Paper No. w9440, National Bureau of Economic Research, December 2002. Gustman, Alan, and Thomas L. Steinmeier, “Imperfect Knowledge, Retirement, and Saving.” Working Paper No. w8406, National Bureau of Economic Research, August 2001. Imbens, Guido, Donald Rubin, and Bruce Sacerdote, “Estimating the Effect of Unearned Income on Labor Supply, Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Winners.” American Economic Review, Vol. 91, No. 4, pp. 778-795, 2001. Jones, Jeffrey M., “Americans Counting on 401(k)s, Not Social Security: Retirement Savings Tops List of Americans’ Financial Worries.” The Wall Street Journal, April 25, 2002. Lazear, Edward P., “Labor Economics and the Psychology of Organizations.” Journal of Economic Perspectives, Vol. 5, No.2, pp. 89-110, 1991. 46
  47. 47. Medoff, James, and Michael Calabrese, “The Impact of Labor Market Trends on Health and Benefit Coverage and Inequality.” The Pension & Welfare Benefit Agency. U.S. Department of Labor, Feb. 28, 2001. Munnell, Alicia H., Kevin E. Cahill, and Natalia A. Jivan, “How Has the Shift to 401(k)s Affected the Retirement Age.” Center for Retirement Research,, September 2003. Purcell, Patrick, “Retirement Savings and Household Wealth in 200: Analysis of Census Bureau Data.” Congressional Research Service, Library of Congress, order code RL30922, December 2, 2002. Schiller, Robert, Irrational Exuberance. Princeton University Press, 2000. Sevak, Purvi, “Wealth Shocks and Retirement Timing: Evidence from the Nineties.” Unpublished Manuscript, University of Michigan, 2001. Smith, Alex Kates, “Roads to Riches.” U.S. News and World Report, pp.67, June 28, 1999. Survey of Income and Program Participation, Wave 7,, 1996. U. S. Department of Labor, Abstract of 1993 Form 5500 Annual Reports (table F5) and 47
  48. 48. U. S. Department of Labor, U.S. Bureau of Labor Statistics, Current Population Survey, Series ID LFS1603301, LFS21003301, LFS1604901, LFS1604901Q, LFS21004901, LFS1606501, LFS1606501Q, LFS21006501, LFS1603302, LFS21003302, LFS1604902, LFS1604902Q, LFS21004902, LFS1606502, LFS1606502Q, LFS21006502, VanDerhei, Jack L., “Company Stock in 401(k) Plans: Results of a Survey of ISCEBS Members.” EBRI Special Report, 2002. Wolff, Edward, Retirement Insecurity, Economic Policy Institute, Washington D.C., 2002. Zernike, Kate, “Stocks' Slide Is Playing Havoc With Older Americans' Dreams.” New York Times, p. 1, July 14, 2002. Endnotes 48
  49. 49. i Edward Wolff (2002) helps assess the meaning of defined contribution plans for the retirement readiness of older workers using the Survey of Consumer Finances. Drawing a distinction between marketable wealth and Social Security and defined benefit wealth, he shows that as marketable wealth has increased – from individual pension accounts primarily – overall adequacy in future retirement income has declined. Workers are less likely to be covered by defined benefit plans in 1998 compared to 1983; coverage rates were 21 percent and 35 percent respectively (Medoff and Calabrese, 2001). ii In addition to holding more stocks as a form of retirement wealth, if offered, workers hold “too” much employer stock, and tend to not rebalance their asset allocation which may have exacerbated their severe, first time aggregate losses starting in 2000 -- 401(k) plan assets declined by $72 billion in 2000 (Cerulli, 2002). Though financial advisors (Ameriks and Zeldes, 2001) do not recommend individuals hold more than 10 percent of an individual’s portfolio invested in employer stock, the share of 401(k) assets in company stock is 19 percent (VanDerhei, 2002). Also contributing to the destabilizing effect of DC plans is the human psychological tendency towards inertia. A failure to change 401(k) asset allocations over one’s lifecycle causes them to be more risky and inappropriate for the needs of the individual. Ameriks and Zeldes (2001) explained their puzzling finding that people hold more stock as they age because they do not change their initial allocation and stocks increase in value faster than fixed-income assets. iii Enron also used an ESOP offset arrangement to permanently cut the value of pensions earned between January 1987 and January 1995 (Francis and Schultz, 2002). iv For example, Thomas O. Padgett, a long time Enron employee, testified before the Committee on Education and the Workforce that he planned to retire this year but now estimates having to work
  50. 50. another ten years (Committee on Education and the Workforce Hearings from February 7, 2002). v An initial reaction may be that DC plans have nothing to do with the increase in labor force participation rates for older women workers because the rate of labor force participation for all ages of women has increased steadily in the Post-World War II period. We have the onus to show that the recent increase is more exaggerated than this long term trend. vi Employers who are aiming to layoff workers may be wishing they had a way to use the 401(k) plan to manage their labor supply. Pension plans are traditionally used as human resource tools (Lazear 1991). When product demand falls, early retirement programs help shrink payroll. Lucent used their DB to manage one of the biggest corporate layoffs in history. It used the DB plan to offer early-retirement packages to over 15,000 U.S. managers (Associated Press, Toronto Star, 2001). Generous severance payments have similar effects, but struggling companies often don’t have the cash to offer and rely on pre-funded pensions to induce the voluntary attrition. Early retirement plans help shrink payroll and are funded by pensions. v vii The determinants of labor demand include product demand, productivity, and prices of other resources and are shown in a familiar labor demand function: LD = [v/(αP1/αA1/αw(α-1)/αβ(1-α)/α] α/(α+β-1) , , where A represents technology, K and L are capital and labor inputs respectively, P is the price of the product, and v and w are the costs of capital and labor respectively. A, α, and β are constants such that A > 0, αє(0,1), βє(0,1), α + β < 1.
  51. 51. Labor demand is a negative function of the wage rate (and cost of capital) and a positive function of technology and the price of the good, which represents demand for the product and coincides with increases in the values of financial assets (which we proxy here as the S&P 500). For our purposes we hold all other factors constant and obtain LD = [v/(αP1/] α/(α+β-1) LD′ P > 0. The determinants of labor supply include non-labor income (NLY) and preferences for work versus leisure (a more elaborate model would model the choice between time and market intensive goods but this serves our purposes). An individual’s preference function is defined as U(L,C), where L is leisure and C is consumption (income, Y, is equal to consumption multiplied by the price). The budget constraint is NLY + w(T-L) = Y, where T is the total time available. Hours of work is defined as H = T – L. To obtain labor supply, we maximize the preference function for work versus income, U(H,Y), with respect to the budget constraint, NLY + wH = Y. Ls = F(NLY, w) If NLY = F(P) then LS′ P < 0 viii Pension variables were constructed using data from the current wave and also data carried forward from previous waves allowing for at least partial vesting in DB plans after 5 years and rollovers in DC plans after changing employers.
  52. 52. ix This variable is constructed from questions that ask “can you continue your health insurance coverage up to age 65 if you left your current employer?” This variable may be capturing the presence of retiree health insurance or COBRA coverage, which can also be continued after leaving the employer but at full cost to the individual. x The BLS has seasonally adjusted data only until December 2002 and there are no plans to make the seasonally adjusted data available in the future. We plan to update the analysis by using unseasonally adjusted data. xi The elasticity is -0.072 percent or -0.00072 for men and -0.138 percent or -0.00138 for women in the 55-64 age group in the time period from January 2000 to October 2002. x xii “Causality in the sense defined by Granger (1969) and Sims (1972) is inferred when lagged values of a variable, say xt, have explanatory power in a regression of a variable yt on lagged values of yt and xt” (Greene, 2000, pp. 742-743). If the Granger causality test passes, then previous changes in xt do help explain movements in yt even in the presence of the lagged value of yt. xiii x The Granger causality test passes at the 5 percent level for women age 65 and over in the time period from January 2000 to October 2002. The test passes at the 10 percent level for this same group in the time period from March 2001 to October 2002. xiv Probit: Prob(Y=1) = Ф(β′x )
  53. 53. Where Ф is the distribution function of the standard normal. The standard normal distribution used in probit models is similar to the logistic distribution used in logit models, although the logistic distribution has fatter tails. To obtain the marginal effects, calculate the partial derivative ∂F(β′x)/∂x = f(β′x)β, which, for the probit, is φ (β′x) β where φ is the density function of the standard normal. For the binary independent variables, the marginal effects are given by Prob[Y=1|xbar*,d=1] – Prob[Y=1|xbar*,d=0], where xbar* is the mean of all the other variables in the model. Alternatively, as Greene points out, “simply taking the derivative with respect to the binary variable as if it were continuous provides an approximation that is often surprisingly accurate.” (Greene, 2000, p. 817). The probit model is estimated using the maximum likelihood method. The likelihood equation is L = Π[Ф(β′xi)]yi[1- Ф(β′xi)]1-yi, where the product is taken from i=1 to n. Taking the natural log of the equation gives lnL = Σ{yi ln Ф(β′xi) + (1-yi) ln [1- Ф(β′xi)]}, where the sum is taken from i=1 to n. To perform maximization, the first derivative of the log- likelihood function is taken with respect to β and set equal to zero, ∂lnL/∂β = Σ (yi - Ф (β′xi))/ [Ф (β′xi)(1- Ф (β′xi))] φ (β′xi) xi = 0 where the sum is taken from i=1 to n. Solving the first derivative for βhat yields the estimated parameters.