.. CENTER for

RETIREMENT
RESEARCH

at BOSTON COLLEGE

ARE OLDER WORKERS GOOD FOR BUSINESS?
Laura D. Quinby, Gal Wettstein, and James Giles

CRR WP 2023-19
November 2023

Center for Retirement Research at Boston College
Haley House
140 Commonwealth Avenue
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Tel: 617-552-1762 Fax: 617-552-0191
https://crr.bc.edu

All of the authors with the Center for Retirement Research at Boston College; Laura D. Quinby
and Gal Wettstein are senior research economists and James Giles is a senior research associate.
The research reported herein was pursuant to a grant from the Alfred P. Sloan Foundation. This
paper is released to inform interested parties of research and to encourage discussion. This
research uses data from the U.S. Census Bureau's Longitudinal Employer Household Dynamics
Program, which was partially supported by the following National Science Foundation Grants
SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854;
and grants from the Alfred P. Sloan Foundation. The U.S. Census Bureau's Disclosure Review
Board and Disclosure Avoidance Officers have reviewed this information product for
unauthorized disclosure of confidential information and have approved the disclosure avoidance
practices applied to this release. This research was performed at a Federal Statistical Research
Data Center under FSRDC Project Number 2387. (CBDRB-FY22-P2387-R9314). Any views
expressed are those of the authors and not necessarily those of the Alfred P. Sloan Foundation,
the U.S. Census Bureau, or Boston College.

© 2023, Laura D. Quinby, Gal Wettstein, and James Giles. All rights reserved. Short sections
of text, not to exceed two paragraphs, may be quoted without explicit permission provided that
full credit, including © notice, is given to the source.
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Center for Retirement Research at Boston College
Haley House
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phone: 617-552-1762 Fax: 617-552-0191
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Affiliated Institutions:

The Brookings Institution
Mathematica - Center for Studying Disability Policy
Syracuse University
Urban Institute
Abstract

The U.S. workforce is aging, which has raised concerns about the implications of older
workers for businesses and the economy. However, little research has been conducted on the
quantitative value of older workers in recent years. This paper attempts to fill that gap by linking
employee and employer data from the U.S. Census Bureau's Longitudinal Employer-Household
Dynamics, Longitudinal Business Database, and Business Register. The analysis finds that in
general, older workers are as productive as younger workers, however they do earn higher
wages. Furthermore, the relationship between the share of older workers, productivity, and

profitability varies substantially by industry.
Introduction

The U.S. workforce is rapidly aging, spurring widespread concern over the implication of
older workers for the economy at large, and for businesses in particular. Despite substantial
interest from labor economists in the past, little research in the last two decades has examined the
quantitative value of older workers to employers in the United States. Widespread
discrimination against older workers in hiring, and employer concerns about the high cost of
older workers, suggest that employers view this demographic trend as a challenge.! Yet, the
evidence from before the turn of the century is decidedly mixed, and recent research on older
workers in the United States tends to look at anecdotal accounts or soft measures, like employee
motivation and engagement, rather than a hard measure of firm performance. Therefore, the
productivity and profitability of employing older workers remains an open question.

One challenge in answering this question is data linking employees to their employers.

In particular, the data necessary for such an analysis needs to include the age structure of the
workforce, the payroll, and the total revenue of different employers. This paper uses restricted
Census data linking employers and workers to compare quantitative measures of worker value -
the actual productivity (revenue per worker) and profitability (revenue divided by wages) - at
firms based on the age distribution of their employees.?

The paper includes two analyses. The first is descriptive, just correlating firm
performance with the share of older workers at the firm. This approach is valuable given the
lack of recent evidence and the mixed nature of the older evidence. However, a concern is that
firms that are expected to decline in the coming years are unlikely to attract young workers,
leading to a negative bias when trying to estimate the causal effect of older workers on firm
outcomes. For this reason, the second analysis supplements the descriptive approach with a two-
stage least squares (TSLS) strategy, providing quasi-experimental evidence for the limited but
important case of the manufacturing industry. In this design, the changing age structure of each

 

! Neumark, Burn, and Button (2019), for example, show experimental evidence of discrimination against older
workers in hiring, while Munnell and Wettstein (2020) show that surveyed employers perceive older workers as
more costly.

2 For measuring revenue per worker with the LEHD, CBR, and LBD, see Haltiwanger et al. (2017).
manufacturing establishment's commuting zone is used to predict changes in the age structure of
the establishment's workforce.>

The rest of the paper is organized as follows. The next section describes the state of the
literature on the business value of older workers. The third section describes the data used in the
analysis, while the fourth section describes the methods for the descriptive and TSLS analyses.
The fifth section presents the results of the descriptive analysis, while the sixth section lays out
the results of the TSLS approach. The final section concludes that the evidence that older

workers are less valuable to businesses than young workers is weak.

Background

The share of workers over age 55 has increased dramatically over the past few decades,
doubling between 1997 (see Figure 1). Despite this enormous change in the age structure of the
workforce, the question of the impact of workforce aging on productivity and firm performance
remains largely unsettled.

Currently, most research on the productivity of older workers in the United States is both
dated and contradictory. Seminal research on the topic in Haltiwanger, Lane, and Spletzer
(1999) and Hellerstein, Neumark, and Troske (1999) reaches conflicting conclusions; the first
study finds that having a larger share of workers over age 55 at a firm reduces productivity,
while the second study finds (statistically insignificant) evidence that output actually increases
with the share of workers over age 55. Potentially more concerning, these estimates have not
been updated since Haltiwanger, Lane, and Spletzer (2007), using data running up to 1997, fully
two decades ago.

Instead of outcomes measured quantitatively, by output or profit, recent evidence in the
U.S. context tends to rely on qualitative assessments or imperfect proxies of productivity such as
turnover rates. Finkelstein and Block (2015) document the benefits of older workers' skills,
experience, and tenure in small businesses. Aon Hewitt (2015) has similar findings as
Finkelstein and Block, but for large businesses. Meta-analyses of studies using similar "soft"

measures of performance reach diverging conclusions, with Sturman (2003) finding that

 

3 The limitation to manufacturing in this analysis is dictated by the fact that it is an industry with a clear disconnect
between where goods are produced and where they are consumed. This means the age structure of the local
population is largely irrelevant to the market for the goods produced.
advanced worker age is detrimental in general, while Ng and Feldman (2008) conclude that age
is positively associated with most dimensions of work-related performance. Meanwhile,
macroeconomic evidence suggests that, at least at the aggregate level, an older workforce is
detrimental to productivity. Feyrer (2007, 2008) finds that, across countries, older labor forces
are associated with lower productivity at the macro level. Maestas, Mullen, and Powell (2016)
find similar results at the state level across the United States.

Substantially more work exists in the European context. However, here, too, the
conclusions vary widely. Aubert and Crepon (2006) find no evidence that the productivity of
older workers is different from that of prime-age workers; Malmberg, Lindh, and Halvarsson
(2008) and Borsch-Supan and Weiss (2013) find that older workers are more productive; and
Lovasz and Rigo (2013), Vandenberghe (2013), and Ilmakunnas and Maliranta (2016) show that
older workers reduce productivity. Given these variations, it is unsurprising that Mahlberg et al.
(2013) show that the relationship between age and productivity varies by location and sector,
motivating a renewed look at the United States, and underscoring the importance of up-to-date

estimates.

Data Linking Employers to Workers

The data for this analysis are drawn from the U.S. Census Bureau's Longitudinal
Employer-Household Dynamics (LEHD), Longitudinal Business Database (LBD) and Census'
Business Register (CBR). Merging these three databases allows us to track businesses and
establishments over time, while observing their revenues, payroll, and the age composition (and
other demographic characteristics) of their workforces. The data available for this analysis span
the years 1997 to 2014.

The LEHD links state administrative unemployment insurance records, IRS employer tax
information, and Census data on establishments to provide an accurate and comprehensive

account of employment and earnings at detailed geographical and industry levels. Not all states
have elected to participate in this program. For the years included in this analysis, 29 states
participate in the LEHD,* covering 95 percent of employment in the United States.'

The LBD provides information on firm payroll and number of employees. The CBR is a
comprehensive census of U.S. businesses and establishments. It contains data on the location
and industry of all domestic U.S. business establishments. Linked with the LEHD and LBD, the
resulting dataset allows matching of workers to establishments, and calculating the share of each
establishment's workers falling in different age categories, young (under 30), prime-age (30-54),
and older (55 and over). The linked data further document the industry of each establishment
and its precise geographic location. The latter is aggregated up to the commuting-zone level,
which is the relevant geography for studying labor markets.

Table 1 shows descriptive statistics for the sample of firms. In this sample, the average
revenue per worker is $292, while the average revenue per dollar of payroll is $6.2. The share of
workers age 55 and over is 15 percent, while prime-age workers make up 56 percent of the

workforce.

Descriptive and Quasi-Experimental Estimation of the Value of Older Workers

The analysis in this paper has two parts. The main analysis estimates the association
between the share of a firm's workers age 55 and over and that firm's productivity and
profitability, and how these associations vary by industry. The secondary analysis focuses on a
limited but important industry, manufacturing, to see whether a more causal analysis in this

context sheds a different light on the question.

Descriptive Estimates of the Productivity and Profitability of Older Workers
This analysis aims to estimate the productivity and profitability of workers age 55 and
over. Productivity is defined as the total revenues of a firm divided by its employees.

Profitability is defined as how much revenue is generated per dollar of payroll.

 

" The states are: Alabama, Arizona, California, Colorado, Connecticut, Delaware, Indiana, Iowa, Kansas, Maine,
Maryland, Nebraska, Nevada, New Jersey, New Mexico, New York, North Dakota, Ohio, Oklahoma, Pennsylvania,
South Dakota, Tennessee, Texas, Utah, Virginia, Washington, Wisconsin, and Wyoming.

5> The LEHD is a quarterly dataset. However, because the other linked datasets are at an annual resolution, for these
quarterly series we take the fourth-quarter values.
This relationship likely varies substantially across different industries, so firms are
divided into their two-digit NAICS industries. The exception is that agriculture, mining, utilities,
and construction are considered one industry, ensuring that these relatively small industries are
broad enough that privacy is maintained for the relevant establishments. Also, public
administration is omitted since it is unclear that revenue is a good indicator of productivity in the
context of public services.

Since having a retirement plan at the firm could allow marginal older workers to retire,
leaving only more productive workers, the analysis controls for plan availability. The analysis
also controls for firm size and age, and other demographics of the workforce (shares of women,
members of different races/ethnicity, and shares with different educational attainments) to ensure
the comparison is between firms with otherwise similar workforces. Finally, the estimated
regressions control for geography and secular trends in productivity: fixed effects for each firm's
commuting zone are included, as are state-by-year fixed effects allowing for nonparametric time
trends that differ arbitrarily across states.®

These criteria yield the following equation, estimated by OLS:

Yer = a + B1S55¢; + B3SPAs, + B4S55¢: * SPAf; + BsXpr + &5 ¢))

Here Y ; is the productivity or profitability of firm fin year z. S55¢; is the share of
workers in firm fin year ¢ that were ages 55 and over, while SPA¢ , is the share of prime-age
workers in the firm-year. X, is a vector of other controls described above." Standard errors are
clustered by commuting zone, and the regressions are weighted by the number of employees at
the firm.

The focus of the analysis is the following thought experiment: how would productivity
and profits change if the share of older workers were increased by one percentage point at the
expense of reducing the share of workers under age 30 (the omitted group)? This tradeoff
between the oldest and youngest workers makes sense since, unlike prime age workers, the more

extreme-age workers are more likely to be part time, less likely to be strongly attached to the

 

® The location of a firm is based on the commuting zone that has its modal number of employees.

7 Specifically, the firm-level controls: size and age of the firm and an indicator for whether it offers a retirement
plan; the demographics of its workforce: share female, Black, Asian, Pacific Islander, mixed-race, Hispanic, those
with some college and those with a college or postgraduate degree; and the commuting zone and state-by-year fixed
effects.
employer, and generally have more elastic labor supply.® In conducting this thought experiment,

it is important to account for possible complementarity between older and younger workers.'
The estimated relationship between older workers and firm outcomes thus depends on the

existing share of prime-age workers who might be complemented or substituted by these older

workers. The object of interest is, therefore, B; + B,SPAs, , where SPAg ; is evaluated at the

industry mean of prime-age workers for the firm's industry.

Quasi-Experimental Evidence of the Productivity of Older Workers in Manufacturing

While the regression above will show the correlation between the share of older workers
and the productivity and profitability of the firm, controlling for observable characteristics and
retirement plan coverage, it will not yield a causal estimate of the effect of older workers on
productivity. For example, if firms with low productivity tend not to hire new workers, the
estimate will show that older workers reduce productivity when, in fact, it is low productivity
that leads to an older workforce. To provide a different perspective on the question of how older
workers affect firm productivity, the paper turns next to a TSLS approach, using the age of the
labor market the firm occupies as an instrument.

The TSLS is based on the intuition that firms are simultaneously facing two markets: a
market for products, in which firms are the supplier, and a market for inputs, in which firms are
the consumer. To the extent that these markets are not simultaneously determined, changes in
the supply of labor a firm faces may be distinguishable from changes in the demand for products
the firm sees in the product market. Furthermore, some industries, particularly manufacturing,
do not seem to have a clear link between their geographical location and their product market. In
contrast, labor is relatively immobile, and firms must draw their workers from the local labor
market of their establishments.

Building on these assumptions, the TSLS approach will restrict attention to single-
establishment manufacturing firms and consider their commuting zones. The first stage predicts
the age structure of establishments based on the age structure of their commuting zone;
meanwhile the second stage estimates the correlation of predicted age structure with

establishment productivity and profitability. In this way, the variation in firm age structure is

 

8 Fiorito and Zanella (2012).
% Backes-Gellner and Veen (2013).
driven by the variance in availability of workers of different ages, rather than changes in the
demand for the firm's product. The regressions estimated in this stage will mirror those above,
except that they will not control for the share of prime-age workers and S55¢; will be replaced
by S§5\f,t, the predicted share of workers over age 55 based on the following first-stage
regression:

8557, =y + 6,CZ55¢; + 8, X5 + 65 2
Where CZ55¢, is the share of the firm's commuting zone's population over age 55. In these
regressions, standard errors will be clustered at the commuting-zone level, and results are
weighted by employment. X¢, here includes all of the controls in equation (1), except for the
relative shares of young and prime-age workers.

While TSLS can estimate causal effects, it relies on strong assumptions. In this case, the
assumption is that the age structure of an establishment's commuting zone has no effect on
productivity except through its effect on the establishment's available workforce. While the
focus on manufacturing attempts to ensure that the local age structure does not affect the product
demand faced by the firm, potential sources of bias still exist. For example, firms may select

where to locate their establishments due to the age structure of the local population.

Descriptive Estimates of the Value of Older Workers

The main purpose of this analysis is to provide industry-specific estimates of the
association between the age structure of the firm and firm performance. Table 2 shows the share
of prime-age workers by industry. Meanwhile, Figure 2 displays the estimates of §; + fi4TAf,t
for the different industries for revenue per worker (a measure of productivity), while Figure 3
does the same for revenue per wages (a measure of profitability).!!

Figure 2 does not show a clear pattern of a negative relationship with an older workforce.
Excluding finance, which is a clear outlier (for both productivity and profitability of older

workers), other industries are roughly evenly distributed around 0, with those displaying a

 

10 A further threat to identification is that older commuting zones may be served by local governments with a
smaller tax base, or with higher public health expenses, leaving fewer resources for production-enhancing public
goods such as infrastructure. The state-year and commuting zone fixed effects partially control for such differences
but cannot completely eliminate them.

11 For full regression results, see Appendix Table 1.
negative relationship with age (such as "other [non-public] services") generally more than
counterbalanced by industries displaying a positive relationship (such as retail).!?

In particular, manufacturing, retail, and management all have estimates of the association
of increased older worker share and productivity exceeding $100 per worker (all significant at
the 5-percent level). In other words, given the share of prime-age workers in these industries and
the complementarities of older and prime-age workers in the industries, increasing the share of
older workers at the expense of younger workers is associated with an increase of more than
$100 per worker in firm revenues. Conversely, only other services and finance display losses of
revenue-per-worker of around $100 or more from increasing the share of older workers (none of
the other negative estimates are statistically significant at the 5-percent level, either).!3

The picture on profitability is more lopsided, with the estimates generally indicating that
a larger share of older workers is associated with lower profits. The magnitude of the
relationship is substantial; for many industries, a 1-percentage-point increase in the share of older
workers is associated with a $1 or more decline in revenue to payroll, of which information,
finance, real estate, arts, and other services are significant at the 5-percent level (see Figure 3),
while the mean ratio of revenue to payroll is $6.2 (see Table 1). The only industry displaying a
significantly positive relationship between the share of older workers and profits is
Accommodations (and the magnitude of the relationship is modest, at 72 cents per dollar of
wages for every 1-percentage-point increase in the share of older workers).

Furthermore, considering a more comprehensive measure of compensation beyond
payroll is likely to aggravate this relationship: older workers are likely to cost more in health
insurance premiums and in deferred compensation such as defined benefit pension costs or
employer matches to defined contribution plans. This pattern is consistent with a large body of
empirical evidence that wages continue to increase with tenure even after productivity growth
has flattened out. Many theories seek to explain this pattern, but the consensus is that older
workers get paid more than their younger counterparts for reasons beyond simple productivity.!4

All in all, older workers appear as productive as younger workers, but they cost more.

However, a concern with all these estimates is that a declining firm may have a large share of

 

12 Other non-public services is a broad category including disparate services such as automotive repair, personal
care, pet care, and religious organizations.

13 Real estate shows a loss of close to $100, but it is not significant at the 5-percent level.

14 For a review of this literature see de Hek and van Vuuren (2011).
older workers because young workers do not wish to enter it. If that is the case, we would find a
negative association between the share of older workers and firm performance, but this
association would not reflect the causal impact of an increasingly older workforce. The next

section shows the results of the TSLS estimation aimed at addressing this concern.

TSLS Estimates of the Value of Older Workers in Manufacturing

Firms must draw their workers from the labor markets surrounding their establishments.
While this geographical link may have weakened with pandemic work-form-home flexibility, in
the relevant years workers would generally work in person. Furthermore, in the manufacturing
industry working from home is particularly challenging. Thus, the age composition of local
labor markets in large part determines the age of workers available to local manufacturing
establishments. This intuition underlies the identification strategy adopted here.

Table 3 shows the first stage of the TSLS. The share of the commuting zone population
age 55 and over is indeed predictive of the share of older workers in the commuting zone's
manufacturing establishments. Thus, the analysis proceeds to use the share of the population age
55 and over as the basis for estimating the firm's older-worker share.

Table 4 shows the TSLS results alongside equivalent OLS estimates. Columns 1 and 2
display the results for revenue per worker, while columns 3 and 4 show the parallel results for
revenue per dollar of payroll. The OLS results here differ from the results presented in the
previous section for manufacturing for two reasons: these regressions no longer control for the
share of prime-age workers, and they encompass only single-establishment firms. Nevertheless,
the share of workers age 55 and over is still not related to firm productivity or profitability under
these conditions when estimated with OLS (columns 1 and 3).

This lack of systematic relationship between the share of older workers and firm
outcomes holds in the TSLS estimation as well. Column 2 indicates that the share of older
workers is not statistically significantly related to productivity, while Column 4 shows the same
null result for profitability. Thus, in this causal analysis, we find no evidence that older workers

are harmful to firm performance.
Conclusions

As the working population ages, a prevalent worry is that these demographic trends will
undermine prospects for economic prosperity. From the narrower perspective of employers,
workforce aging raises the possibility that productivity and profitability will be set back. The
evidence of age discrimination in hiring suggests that some employers are being influenced by
these fears in practical management decisions. And advocates for longer working lives must
contend with the fact that only quite limited evidence exists on whether such fears have any basis
in fact.

The current analysis lays out evidence based on the most recent available data, for the
largest possible sample, covering the vast majority of US employment. The findings give reason
to hope that, in general, workforce aging is not a major cause for concern regarding firm
productivity and profitability. While estimates vary by industry, as a whole we find little
evidence that older workers are systematically associated with lower productivity as compared to
very young workers. Furthermore, a secondary analysis aimed at addressing issues of reverse
causality similarly finds no evidence that older workers causally reduce productivity or
profitability in the limited but important manufacturing sector.

Together with results from previous work in this series of papers, the results here are
cautiously optimistic. The previous papers found that - when surveyed - employers perceive
older workers to be costlier but more productive than younger workers. Other findings from the
previous papers suggest that older workers face a reasonably broad selection of job openings,
albeit with a relative dearth of fringe benefits, so that robust demand for older workers is not
likely to be reversed by the shifting landscape of occupations in the U.S. economy over the next
decade. The analysis here finds that, for their part, employers should not be hesitant about
employing older workers - we find little evidence that these workers are less valuable to their

employers than younger workers.

10
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11
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12
Tables and Figures

Figure 1. Share of Workers Ages 55 and Over, by Year

30%

 

20% /-/\

10%

 

 

0%

 

1997 2002 2007 2012 2017 2022

Source: Authors' calculations from the Current Population Survey (1997-2023).

13
Figure 2. Estimated Effect of Increasing the Share of Workers Ages 55 and Over on Revenue per

Worker, by Industry

Agriculture, mining, utilities, construction
Manufacturing
Wholesale
Retail
Transportation
Information
Finance

Real estate
Professional
Management
Administrative
Educational
Health care

Arts
Accommodation
Other services

 

-630.900

 

-11.680

-99.140

 

 

 

  

58.000

104.100

317.700

 

 

-800 -600 -400 -200

Note: Solid bars are statistically significant at least at the 5-percent level.
Source: Authors' calculations based on the Longitudinal Employer-Household Dynamics (LEHD) (1997-2014).

0

200 400

Figure 3. Estimated Effect of Increasing the Share of Workers Ages 55 and Over on Revenue Per

Wages, by Industry

Agriculture, mining, utilities, construction
Manufacturing
Wholesale
Retail
Transportation
Information
Finance

Real estate
Professional
Management
Administrative
Educational
Health care

Arts
Accommodation
Other services

 

 

 

 

 

 

 

-15

Note: Solid bars are statistically significant at least at the S-percent level.
Source: Authors' calculations based on the LEHD (1997-2014).

14

0.83
-11.92
0.34
0.01
0.72
-10 -5 0 5
Table 1. Summary Statistics at the Firm Level

 

 

Mean Standard deviation
Revenue / wages 6.20 46.71
Revenue / workers 292.10 755.00
Revenue 29,120,000 68,430,000
Wages 4,238,000 7,070,000
Workers 123,600 266,800
Firm age 27.92 7.11
Has plan 78.16% 41.32%
Age>=55 15.31 18.25
Prime age 56.20 24.42
Female 42.34 28.26
Black 10.25 14.09
Asian 3.39 8.00
Pacific Islander 0.20 1.26
Two+ races 1.42 348
Hispanic 9.75 14.33
Some college 31.12 17.28
Bachelor's+ 31.61 25.68
Age >= 55 in commuting zone 20.31 3.65

 

Sources: Authors' estimates from the LEHD, Longitudinal Business Database (LBD), and Census' Business
Register (CBR) (1997-2014).

15
Table 2. Share of Prime-age Workers by Industry

 

 

 

Industry Share prime age
Agriculture, mining, utilities, construction 60%
Manufacturing 63
Wholesale 62
Retail 46
Transportation 60
Information 60
Finance 61
Real Estate 56
Professional 60
Management 56
Administrative 56
Educational 56
Health Care 58
Arts 47
Accommodation 38
Other services 54
Overall 56

 

Source: Authors' calculations from the Current Population Survey (1997-2014).

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Table 3. TSLS First Stage

 

Share age >= 55

 

 

Share age >= 55 in commuting zone 0.846**

(0.414)

Has plan -0.017%**
(0.00385)

Firm size 3.51E-07**

(1.59¢-07)

Firm age 0.00119%**
(0.000188)

Female -0.0471***
(0.0117)

Black -0.0829%**
(0.0236)

Asian -0.082]1 ***
(0.0236)

Pacific Islander -0.185%**
(0.0515)
Two+ races 0.0446
(0.132)

Hispanic -0.115%**
(0.0152)
Some college -0.00175
(0.0174)

Bachelor's+ 0.0806%**
(0.0239)
Constant -0.00481
(0.0940)
Observations 2,050,000

 

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05.
Source: Authors' estimates from the LEHD, LBD, and CBR (1997-2014).

17
Table 4. TSLS and OLS Equivalents

 

 

 

 

(1) 2) 3) 4)
OLS revenue IV revenue OLS revenue IV revenue
per worker per worker per wages per wages
Has plan 8.948 25.22 0.0864 0.263
(7.325) (18.18) (0.0972) (0.204)
Age>=155 -15.66 939.7 -0.0381 10.31
(24.46) (869.6) (0.261) (9.429)
Firm size 0.000268 -0.0000636 -0.000000308 -0.0000039
(0.000203) (0.000427) (2.47¢-06) (4.36¢-06)
Firm age 6.005%** 4.868*** 0.0571*** 0.0447***
(0.363) (1.129) (0.00618) (0.0137)
Female -56.52%* -11.63 -0.332 0.154
(23.67) (49.95) (0.365) (0.619)
Black 65.85%* 145.3* 1.354%* 2.214%*
(36.18) (87.12) (0.534) (1.016)
Asian 28.46 107.2 0.504 1.358
(29.35) (75.99) (0.523) (0.953)
Pacific Islander -44.25 134.5 -1.196 0.74
(186.6) (241.6) (2.510) (2.950)
Two+t races 152.6 110 1.344 0.883
(109.2) (172.7) (1.371) (2.085)
Hispanic -33.8 76.51 -0.122 1.073
(25.36) (102.0) (0.349) (1.135)
Some college 81.1%%* 82.37%* 0.237 0.251
(28.46) (32.31) (0.415) (0.436)
Bachelor's+ 144 2% 66.64 0.748** -0.0916
(23.42) (68.48) (0.301) (0.812)
Constant 825.5%** 695%** 16.31%** 14.90%**
(191.4) (245.9) (3.422) (4.128)
Observations 2,050,000 2,050,000 2,050,000 2,050,000

 

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Source: Authors' estimates from the LEHD, LBD, and CBR (1997-2014).

18
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All working papers are available on the Center for Retirement Research website
(https://crr.be.edu) and can be requested by e-mail (crr@bc.edu) or phone (617-552-1762).

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