Unemployment Insurance and Job Search in the Great Recession

NBER WORKING PAPER SERIES

UNEMPLOYMENT INSURANCE AND JOB SEARCH IN THE GREAT RECESSION

Jesse Rothstein

Working Paper 17534

http://www.nber.org/papers/w17534

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

October 2011

This paper was prepared for the Brookings Papers on Economic Activity, which provided financial

support. I thank Stephanie Aaronson, David Card, Hank Farber, Lisa Kahn, Anne Polivka, John Quigley,

Gene Smolensky, Rob Valletta, Till von Wachter, the Brookings Papers editors and conference participants,

and seminar participants at Berkeley, NBER, Santa Barbara, and Wharton for many helpful comments

and suggestions. I gratefully acknowledge research support from the Institute for Research on Labor

and Employment and the Center for Equitable Growth, both at UC Berkeley. Ana Rocca provided

excellent research assistance. I served in the Obama administration in 2009-10 and participated in

internal discussions of the Unemployment Insurance extensions studied here, but all opinions expressed

herein are my own.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-

reviewed or been subject to the review by the NBER Board of Directors that accompanies official

NBER publications.

may be quoted without explicit permission provided that full credit, including © notice, is given to

the source.

Unemployment Insurance and Job Search in the Great Recession

Jesse Rothstein

NBER Working Paper No. 17534

October 2011

JEL No. H53,I38,J64,J65

ABSTRACT

Nearly two years after the official end of the "Great Recession," the labor market remains historically

weak. One candidate explanation is supply-side effects driven by dramatic expansions of Unemployment

Insurance (UI) benefit durations, to as many as 99 weeks. This paper investigates the effect of these

UI extensions on job search and reemployment. I use the longitudinal structure of the Current Population

Survey to construct unemployment exit hazards that vary across states, over time, and between individuals

with differing unemployment durations. I then use these hazards to explore a variety of comparisons

intended to distinguish the effects of UI extensions from other determinants of employment outcomes.

The various specifications yield quite similar results. UI extensions had significant but small negative

effects on the probability that the eligible unemployed would exit unemployment, concentrated among

the long-term unemployed. The estimates imply that UI benefit extensions raised the unemployment

rate in early 2011 by only about 0.1-0.5 percentage points, much less than is implied by previous analyses,

with at least half of this effect attributable to reduced labor force exit among the unemployed rather

than to the changes in reemployment rates that are of greater policy concern.

Jesse Rothstein

Goldman School of Public Policy

University of California, Berkeley

2607 Hearst Avenue

Berkeley, CA 94720-7320

and NBER

[email protected]

1 Introduction

While the so-called “Great Recession” oﬃcially ended in June 2009, the labor market remains

stagnant. In September 2011, the unemployment rate remained above nine percent — it

has fallen below that threshold for only 2 of the last 29 months — and nearly 45% of the

unemployed had been out of work for more than six months.

An important part of the policy response to the Great Recession has been a dramatic

expansion of Unemployment Insurance (UI) beneﬁts. Preexisting law provided for up to

26 weeks of beneﬁts, plus up to 20 additional weeks of "Extended Beneﬁts" (EB) in states

experiencing high unemployment rates. But Congress has frequently authorized additional

weeks on an ad hoc basis in past recessions, and starting in June 2008 it enacted a series of

UI extensions that brought statutory beneﬁt durations to as long as 99 weeks.

Unemployment beneﬁts subsidize continued unemployment. Thus, it seems likely that

the unprecedented UI extensions have contributed to some degree to the elevated unemploy-

ment rate. However, the magnitude and interpretation of this eﬀect is not clear. Several

recent analyses have found that extensions of UI beneﬁts contributed around 1.0 percent-

age points to the unemployment rate in 2010 and early 2011 (see, e.g., Mazumder, 2011;

Valetta and Kuang, 2010; Fujita, 2011), while some observers have claimed that the eﬀects

were several times that size (Grubb, 2011; Barro, 2010).

There are two channels by which UI can raise unemployment, with very diﬀerent policy

implications (Solon, 1979). On the one hand, UI extensions can lead recipients to reduce

their search eﬀort and raise their reservation wages, slowing the transition into employment.

On the other hand, UI beneﬁts — which are available only to those engaged in active job

search — also provide an incentive for continued search for those who might otherwise have

exited the labor force. The latter raises measured unemployment but has no eﬀect — or

possibly even a positive eﬀect — on the reemployment of displaced workers. Based in part

on this observation, Howell and Azizoglu (2011) ﬁnd “no support” for the view that UI

extensions have reduced employment. Unfortunately, most studies of the eﬀect of UI on the

duration of unemployment have been unable to distinguish the two channels.

Uncovering the causal eﬀect of UI extensions on labor market outcomes is diﬃcult be-

cause these extensions are badly endogenous by design — UI beneﬁts are extended in severe

recessions precisely because it is seen as unreasonable to demand that workers ﬁnd jobs

quickly when the labor market is weak. Thus, obtaining a credible estimate of the eﬀect of

the recent UI extensions requires a strategy for distinguishing this eﬀect from the confound-

ing inﬂuence of historically weak labor demand.

This paper uses the haphazard roll-out of the EUC and EB programs during the Great

Recession to identify the partial equilibrium eﬀects of the recent UI extensions on the labor

market outcomes of workers who have been displaced from their previous jobs and are

actively seeking new ones. I use the longitudinal structure of the Current Population Survey

to construct hazard rates for unemployment exit, reemployment, and labor force exit that

vary across states, over time, and between individuals displaced at diﬀerent dates.

I explore a variety of strategies for isolating the causal eﬀects of UI extensions. One

strategy exploits the gradual rollout and repeated expiration of EUC beneﬁts through suc-

cessive federal legislation to generate variation in beneﬁt durations across labor markets

facing plausibly similar demand conditions. Second, as in Valetta and Kuang (2010), I use

UI-ineligible job seekers as a control group for eligible unemployed workers in the same

state-month labor markets. A third strategy exploits state decisions to take up or decline

optional EB provisions that alter the availability of EB beneﬁts, using a “control function”

to distinguish the eﬀects of the economic conditions that deﬁne eligibility. Finally, I exploit

diﬀerences in remaining beneﬁt eligibility among UI-eligible workers displaced at diﬀerent

times but searching for work in the same labor markets to identify the eﬀect of approaching

beneﬁt exhaustion.

All of the strategies point to broadly similar conclusions. The availability of extended UI

beneﬁts caused small reductions in the probability that unemployed workers exited unem-

ployment, reducing the monthly hazard in the fourth quarter of 2010 — when the average

unemployed worker anticipated a total beneﬁt duration of 65 weeks — by between one and

three percentage points on a base of 22.4%. Not more than half of the unemployment

exit eﬀect comes from eﬀects on reemployment: My preferred speciﬁcation indicates that

UI extensions reduced the average monthly reemployment hazard of unemployed displaced

workers in 2010:Q4 by 0.5 percentage points (on a base of 13.4%), and reduced the monthly

labor force exit hazard by 1.0 percentage points (on a base of 9.0%).

The labor force exit eﬀect raises the possibility that UI extensions might actually raise

the employment rate of formerly displaced workers in bad economic times, by extending

the time until they abandon their search.

However, estimating this eﬀect requires strong

assumptions, along with ad hoc corrections for shortcomings in the data. Using such as-

sumptions and corrections, I simulate the eﬀect of the 2008-2010 UI extensions on aggregate

unemployment and on the long-term unemployment share. All of the estimates are partial

equilibrium, as I assume that reduced job search from one worker has no eﬀect on the search

behavior or job-ﬁnding rate of any other worker. This almost certainly leads me to overstate

the eﬀect of UI extensions.

Nevertheless, I ﬁnd quite small eﬀects. My preferred speciﬁcation indicates that in the

absence of unemployment insurance extensions, the unemployment rate in December 2010

would have been about 0.2 percentage points lower and the long-term share of the unem-

ployed would have been about 1.6 percentage points lower. Even the speciﬁcation yielding

the largest eﬀects indicates that UI extensions contributed only 0.5 percentage points to the

unemployment rate. Moreover, simulations that include only the labor force participation

eﬀects yield estimates at least half as large as do simulations with both participation and

reemployment eﬀects, suggesting that reduced job search due to UI extensions raised the

unemployment rate by only 0.1 to 0.2 percentage points.

The remainder of the paper is organized as follows. Section 2 reviews recent labor

market trends and discusses the UI extensions that have been an important part of the

policy response. It also presents a simple model of the eﬀects of UI beneﬁt durations and

discusses existing estimates of the eﬀect of the recent extensions. Section 3 discusses the

longitudinally-linked CPS data that I use to study the eﬀects of UI. Section 4 presents my

empirical strategies for isolating the UI eﬀect. Section 5 presents estimates of the eﬀect of

UI beneﬁt durations on the unemployment exit hazard. Section 6 develops a simulation

methodology that I use to extrapolate these estimates to obtain eﬀects on labor market

In addition, UI may reduce hysteresis by increasing labor force attachment and thereby slowing the

deterioration of job skills. If so, UI extensions could make displaced workers more employable when demand

recovers. A related possibility is that UI extensions may deter displaced workers from claiming disability

payments (Duggan and Imberman, 2009; Joint Economic Committee, 2010).

aggregates, and presents results. Section 7 concludes.

2 The Labor Market and Unemployment Insurance in the

Great Recession

2.1 Labor market trends

The recession oﬃcially began in December 2007, but the downturn was slow at ﬁrst: Sea-

sonally adjusted U.S. real GDP fell at an annual rate of only 0.7 percent in the ﬁrst quarter

of 2008. Conditions worsened sharply in late 2008 and GDP contracted at an annual rate

of 6.8 percent in the fourth quarter.

The labor market downturn also began slowly. Figure 1 shows that the unemployment

rate began trending up in 2007, but remained only 5.8% in July 2008. Over the next year,

however, it rose 3.7 percentage points, to 9.5 percent, and has fallen below 9 percent in only

two months since. Employment data show similar trends: Non-farm payroll employment

rose through most of 2007, fell by 738,000 in the ﬁrst half of 2008, and then fell by nearly

6.8 million over the next year. Job losses continued at slower rates in the second half of

2009, followed by modest and inconsistent growth in 2010. As of August 2011, employment

remained 6.9 million below its pre-recession peak.

Figure 1 also shows the long-term unemployment rate, deﬁned as the share of the un-

employed who have been out of work for six months or more. It generally lags the overall

unemployment rate by about six months or perhaps a bit more: It began to increase slowly

in early 2008 and much more quickly in late 2008, reaching a peak around 45% in early 2010

— nearly twenty percentage points higher than the previous record of 25.7%, recorded in

June 1983 — and remaining mostly stable since then.

Figures 2A and 2B illustrate gross labor market ﬂows over the course of the reces-

sion. These are obtained from two sources: The Job Openings and Labor Turnover Survey

(JOLTS), which derives from employer reports, and the gross ﬂows research series computed

by the Bureau of Labor Statistics from matched monthly Current Population Survey (CPS)

household data discussed at length below. Figure 2A shows ﬂows out of work: Quits and

layoﬀs from the JOLTS (“other separations,” including retirements, are not shown), and

gross employment to unemployment (E-U) ﬂows from the CPS. Figure 2B shows ﬂows into

work: Hires from the JOLTS and unemployment to employment (U-E) ﬂows from the CPS.

It also shows unemployment to non-participation (U-N) ﬂows, with both the U-E and U-N

ﬂows expressed as shares of the previous month’s unemployed population.

Together, Figures 2A and 2B shed a good deal of light on the dynamics of the rise

and stagnation of the unemployment rate.

Figure 2A shows that layoﬀs spiked and quits

collapsed in late 2008, indicating an extreme weakening of labor demand; interestingly, the

decline in quits seems to have preceded the increase in layoﬀs by several months. Not

surprisingly, the number of monthly employment-to-unemployment transitions increased by

about one-third over the course of 2008. Layoﬀs returned to (or even below) normal levels

in late 2009, but quits remained just over half of their pre-recession level and E-U ﬂows

remained high, suggesting that weak demand continued to dissuade workers from leaving

their jobs and to impede the usual quick transition of displaced workers into new jobs.

Turning to Figure 2B, we see that the collapse in new hires was more gradual than

the spike in layoﬀs and began much earlier, in late 2007. The rate at which unemployed

workers transitioned into employment also began to decline at this time, then fell much

more sharply in late 2008. Recall that the rapid run-up in long-term unemployment was

in mid-2009, roughly six months later, again suggesting that the usual process by which

displaced workers are recycled into new jobs was substantially disrupted around the time

of the ﬁnancial crisis. U-E ﬂows remain very low through the present day. Finally, the

U-N ﬂow rate fell rather than rose during the recession, despite weak labor demand which

might plausibly have led unemployed workers to become discouraged. This is plausibly a

consequence of Unemployment Insurance beneﬁt extensions, which created incentives for

ongoing search even if the prospect of ﬁnding a job was remote.

2.2 The policy response

Congress responded quickly to the deteriorating labor market, authorizing Emergency Un-

employment Compensation (EUC) beneﬁts in June 2008, but proceeded in ﬁts and starts

See Elsby et al. (2010) for a more detailed examination of these and other aggregate data.

thereafter.

The June 2008 legislation made 13 weeks of EUC beneﬁts available to anyone

who exhausted his or her regular beneﬁts before March 28, 2009. The EUC program was

subsequently expanded in November 2008. That expansion brought basic EUC beneﬁts to

20 weeks, and also added a second “tier” of 13 weeks of beneﬁts in states with unemployment

rates above 6%. A second expansion in November 2009 changed Tier II beneﬁts to 14 weeks

and added Tiers III, 13 weeks of beneﬁts when the unemployment rate was above 6%, and

Tier IV, an additional 6 weeks when the unemployment rate was above 8%. Adding the four

tiers together, individuals in high-unemployment states were eligible for 53 weeks of EUC

beneﬁts. Columns 1-4 of Table 1 show the number of tiers and number of weeks available

over time.

The EUC program was originally set to expire on March 28, 2009. However, the program

was reauthorized several times to delay the scheduled expiration. Column 5 of Table 1

shows the scheduled expiration date of EUC beneﬁts over time. For much of the program’s

history, the expiration date was quite close. Indeed, on three occasions, in April, June, and

November of 2010, Congress allowed the program to expire. Each time, Congress eventually

reauthorized it retroactive to the expiration date, but in June this took seven weeks.

The EUC program complemented a preexisting program, Extended Beneﬁts (EB), that

allowed for 13 or 20 weeks of extra beneﬁts in states with elevated unemployment rates. EB

is an optional program, and participating states can choose among several options regarding

the speciﬁc triggers that will activate EB beneﬁts. As costs are traditionally split evenly

between the state and the federal government, many states have opted not to participate or

have chosen relatively stringent triggers. However, the American Recovery and Reinvestment

Act of (February) 2009 provided for full Federal funding of EB beneﬁts. This induced a

number of states to begin participating in the program and to adopt more generous triggers.

Figure 3 shows the number of states in which EB beneﬁts have been available over time,

along with simulated counts of the number of weeks that would have been available had

every state adopted minimal or maximal triggers. At the beginning of 2009, only three

This discussion draws heavily on Fujita (2010). I neglect a number of details of the UI program rules. In

particular, claimants whose previous jobs were short are not eligible for the full 26 weeks of regular beneﬁts

or for the indicated number of weeks of EUC beneﬁts.

The Recovery Act also provided for tax deductibility of a portion of UI beneﬁts, for somewhat expanded

eligibility, and for more generous weekly beneﬁts.

states oﬀered EB beneﬁts, but by July of that year beneﬁts were available in 35 states.

Figure 3 shows that this reﬂected a combination of increased EB participation — which

brought the “actual” series well above the “minimal” series — and deteriorating economic

conditions that would have expanded EB participation even with ﬁxed triggers.

The Figure

also shows that participation plummeted each time the EUC program was allowed to expire:

A number of states wrote their EB implementing legislation to provide for state participation

only as long as the federal government paid 100% of the cost, and this provision expired

and was reauthorized each time along with EUC. Other than these spikes, participation has

been relatively stable over time.

A ﬁnal feature of Figure 3 is that there is a wide disparity between the simulated “min-

imal” and “maximal” series, with relatively few states — and none after mid-2010 — quali-

fying for beneﬁts under the least generous triggers but nearly all states qualifying under the

most generous options. Thus, Alabama and Mississippi, each with total unemployment rates

of 10.4 percent but insured unemployment rates below 4 percent, both qualiﬁed under max-

imal triggers but not minimal triggers in January 2010; because Alabama had adopted the

most generous optional triggers but Mississippi had not, unemployed individuals in Alabama

were eligible for 20 weeks of EB beneﬁts but those in Mississippi were ineligible.

Combining regular beneﬁts (26 weeks), EUC (as many as 53 weeks) and EB (as many

as 20 weeks), statutory beneﬁt durations have reached as long as 99 weeks in many states.

However, this overstates the number of weeks that any individual claimant could expect.

According to EUC program rules, after the program expires participants can draw out the

remaining beneﬁts from any tier already started but cannot transition to the next tier.

Throughout 2010, the expiration date of the program was never more than a few months

away. Thus, no individual exhausting her regular beneﬁts in 2010 could have anticipated

During the period covered by my sample, “minimal” triggers provided EB beneﬁts only when the 13-week

moving average of the insured unemployment rate (IUR) was at least 5% and above 120% of the maximum

of its values one year and two years prior. It is this lookback period that accounts for the decline in the

minimal series in late 2009. “Maximal” triggers also provided beneﬁts in states with 13-week IURs above

6% (regardless of their lagged values) or with three-month moving average total unemployment rates (TUR;

the traditional measure) above 6.5% and 110% of the value either one or two years prior. Each simulated

beneﬁts series allows a state’s status to change no more than once in 13 weeks, following program rules; the

maximal series also assumes that the optional 3-year lookback was adopted when it became available in 2011.

See National Employment Law Project (2011) and Federal-State Extended Unemployment Compensation

Act of 1970 (Undated).

being able to draw beneﬁts from EUC Tiers III or IV absent further Congressional action.

It is not clear how to model workers’ expectations in the weeks prior to a scheduled

EUC expiration. They might reasonably have expected an extension, if only to smooth the

“cliﬀ” in beneﬁts that would otherwise be created. However, each extension has been highly

controversial, facing determined opposition and ﬁlibusters in the Senate. It would have been

quite a leap of faith in mid 2010, in the midst of a Republican resurgence, for an unemployed

worker to assume that the program would be extended beyond its November 30 expiration.

Moreover, even a worker who foresaw an eventual extension might (correctly) have expected

a gap in beneﬁts between the program’s expiration and its eventual reauthorization. For

a UI recipient facing binding credit constraints, beneﬁts paid retroactively are much less

valuable than those paid on time.

Figure 4 provides two ways of looking at the evolution of UI durations. The left panel

shows estimates for the state with the longest beneﬁt durations at any point in time. After

late 2008, this is a state qualifying for 20 weeks of EB beneﬁts and all extant EUC tiers. The

right panel shows the (unweighted) average across states. In each panel, the short dashes

show the maximum number of weeks available by statute over time, while the long dashes

and the solid line show the expectations of a worker just entering unemployment and of a

worker who has just exhausted her regular beneﬁts, respectively, under the assumption that

workers do not anticipate future EUC extensions or trigger events.

The “statutory” series shows a rapid run-up, due primarily to EUC expansions and

secondarily to EB triggers, in 2008 and throughout 2009, followed by repeated collapses in

2010 when the EUC program temporarily expired. However, the other two series show much

more gradual changes from the perspective of individuals early in their allowed beneﬁts.

Newly displaced workers who did not expect further legislative action would have seen the

EUC program as largely irrelevant for most of its existence, as only on three occasions

(roughly, the 3rd quarter of 2008, the 2nd quarter of 2009, and the period since December

2010) was the expiration of the EUC program farther away than the 26 weeks it would take

for a newly displaced worker to exhaust his regular beneﬁts. Workers already exhausting

their regular beneﬁts, by contrast, would have anticipated at least Tier I beneﬁts at all times

except during the temporary sunsets. Even these workers, however, could not look forward

to Tier II, III, or IV beneﬁts for most of the history of the program. It is only in December

2010 and the very beginning of 2011 that any such worker could anticipate eligibility for

Tier IV beneﬁts. A ﬁnal feature to notice is that the average state was quite close to the

maximum from 2009 on, as most states had adopted at least one of the EB options and

most had hit their triggers.

2.3 A model of job search and UI durations

To ﬁx ideas, I develop a simple discrete time model of job search with exogenous wages and

time-limited unemployment insurance. The model yields two main results: First, search

intensity rises as UI beneﬁt expiration approaches, and is higher for UI exhaustees than for

those still receiving beneﬁts. Thus, an extension of UI beneﬁts reduces the reemployment

chances of searching individuals, both those who have exhausted their regular beneﬁts and

those who are still drawing regular beneﬁts and thus not directly aﬀected by the extension.

Second, when UI beneﬁt receipt is conditioned on continuing job search, beneﬁt extensions

can raise the probability of search continuation. Both results imply positive eﬀects of beneﬁt

extensions on measured unemployment. However, because the second channel can increase

search, the net eﬀect on the reemployment of displaced workers is ambiguous.

I assume that individuals cannot borrow or save.

The income — and therefore the

consumption — of an unemployed individual is y

if she does not receive UI beneﬁts and

+ b if she does. Her per-period ﬂow utility is u (c) − s, where c is her consumption and

s is the amount of eﬀort she devotes to search. If she ﬁnds a job, it will be permanent

and will oﬀer an exogenous wage w > y

+ b and ﬂow utility u (w). The probability that

she ﬁnds a job in a period is an increasing function of search eﬀort, p (s), with p

(s) > 0,

(s) < 0, p (0) = 0, p

(0) = ∞, and p (s) < 1 for all s. Although p (s) might naturally be

modeled as a function of changing labor market conditions, to avoid excessive complexity

from dynamic anticipation eﬀects I assume that job seekers treat it as ﬁxed. I assume that

unemployment beneﬁts are available for up to D periods of unemployment. Initially, I model

these as conditional only on continued unemployment; later, I condition also on a minimum

Chetty (2008) ﬁnds that much of the search eﬀect of unemployment insurance is concentrated among

those who are credit constrained, and also that lump-sum severance pay has a similar eﬀect to UI beneﬁt

extensions (see also Card et al., 2007a).

level of search eﬀort.

These assumptions lead to a dynamic decision problem with state variable d correspond-

ing to the number of weeks of beneﬁts remaining. Letting V

(d) represent the value function

of an unemployed individual with d > 0 weeks of beneﬁts remaining, the Bellman equation

(d) = max

u (y

+ b) − s

+ δ [p (s

) V

+ (1 − p (s

)) V

(d − 1)] , (1)

where s

represents the chosen search eﬀort, V

is the value function of an employed worker,

and 1 − δ is the per-week discount rate.

The ﬁrst order condition then implies that the search eﬀort choice satisﬁes

) =

δ (V

− V

(d − 1))

for d ≥ 1. The following results are proved in an appendix.

Proposition 1. The value function V

(d) is increasing in d: V

(d + 1) > V

(d) for all

d ≥ 0.

Proposition 2. Search eﬀort increases as exhaustion approaches, reaching its ﬁnal level in

the penultimate period of beneﬁt receipt: s

d+1

< s

= s

for all d ≥ 2.

Proposition 2 implies that unemployment insurance extensions will reduce job-ﬁnding

rates at all unemployment durations below the new maximum beneﬁt duration D and will

shift the time-until-reemployment distribution rightward. The relative magnitude of the

eﬀect at diﬀerent unemployment durations depends on the shape of the p () function, but

under plausible parameterizations (s

d−1

− s

) declines with d so beneﬁt extensions will have

the largest eﬀects on the search eﬀort of those who would otherwise be at or near exhaustion.

These results neglect the impact of UI job search requirements. To incorporate them,

I assume that an individual is considered a part of the labor force and therefore eligible

to receive UI beneﬁts only if his search eﬀort is at least θ > 0. Those who choose lower

Once beneﬁts are exhausted (d = 0), the problem becomes stationary: V

(0) = max

u (y

) − s

δ [p (s

) V

+ (1 − p (s

)) V

(0)].

For example, this holds under the parameters considered by Chetty (2008, p. 8), which in my notation

correspond to CRRA utility u (c) =

1−γ

with γ = 1.75, y

= 0.25w, b = 0.5w, p (s) = 0.25s

0.9

, δ = 1, and

= 500u (w).

search receive no beneﬁt payments but preserve their beneﬁt entitlements (that is, d is not

decremented). The Bellman equation for an individual with d > 0 weeks remaining is now:

(d) = max











u (b) − s + δ

p (s) V

+ (1 − p (s))

(d − 1)

if s ≥ θ

u (0) − s + δ

p (s) V

+ (1 − p (s))

(d)

if s < θ.

(2)

Unemployment beneﬁts may deter an unemployed individual from exiting the labor

force if search productivity is low (i.e., if p

(θ) <

δ(V

−V

(d−1))

) and if beneﬁt levels are

high relative to θ. It can be shown that:

Proposition 3. Any individual who chooses search eﬀort s ≥ θ with d weeks of beneﬁts

remaining would also choose s ≥ θ with d

weeks remaining, for all d, d

> 0.

Intuitively, an individual who chooses s < θ when her UI entitlement has not yet been

exhausted does not use any of her remaining entitlement so the state variable, and therefore

the optimization problem, is the same the following week. She will thus never re-enter the

labor force. This then implies that the value of the state variable was irrelevant the previous

week, as remaining beneﬁt eligibility has no eﬀect on someone who will never search again.

The only temporally consistent policies are to exit the labor force immediately after a job

loss or to remain in the labor force at least until beneﬁts are exhausted.

UI beneﬁt extensions thus reduce non-participation by delaying the exit of those who

plan to exit when d reaches 0. This implies that the net eﬀect of UI extensions is ambiguous

when job search requirements are enforced: Those who would have searched intensively will

reduce their search eﬀort, while some of those who would have dropped out of the labor

force will increase their eﬀort. The relative strength of these two eﬀects is likely to vary over

the business cycle: When labor demand is strong and search productivity therefore high,

the negative eﬀect is likely to dominate, but when search productivity is low the former may

be more important.

Finally, it is worth mentioning two important factors that are not captured by this model.

First, p (s) may evolve over the business cycle. If p (s) is temporarily low but expected

to recover later, UI extensions might keep individuals searching through the low-demand

period. If search productivity is increasing in past search eﬀort, as is implied by many

discussions of hysteresis, this could lead to higher employment when the economy recovers.

Even without state dependence in p (s), UI extensions may bring discouraged workers back

into the labor force earlier in the business cycle upswing. Second, I do not model search

externalities. Reduced search eﬀort from one person likely increases the productivity of

search for all others — if a UI recipient does not take an available job, this merely makes the

job available to someone else. This is particularly important if the labor market is demand

constrained, but arises anytime labor demand is downward sloping. In the presence of

search externalities, partial-equilibrium estimates of the eﬀect of UI extensions on recipients’

reemployment probabilities will overstate the aggregate eﬀects.

2.4 Prior estimates of the eﬀect of UI extensions in the Great Recession

There have been a number of estimates of the eﬀect of the recent UI extensions on labor

market outcomes. Nearly all involve extrapolations from pre-recession estimates of the eﬀect

of UI durations or from pre-recession unemployment exit rates.

Mazumder (2011) uses estimates of the eﬀect of UI durations from Katz and Meyer

(1990a) and Card and Levine (2000) to conclude that UI extensions contributed 0.8 to

1.2 percentage points to the unemployment rate in February 2011.

But UI durations in

the current recession are longer and labor market conditions are diﬀerent in a variety of

ways than in the periods used for the earlier studies. The eﬀect of UI durations in the

earlier estimates largely reﬂects a spike in the unemployment exit hazard in the weeks

immediately prior to beneﬁt exhaustion. Katz and Meyer (1990b) ﬁnd that much of this

spike is attributable to laid oﬀ workers recalled to their previous job; these recalls are thought

to have become much less common in recent years. Card et al. (2007a,b) suggest that much of

the remaining spike is attributable to labor force exit rather than reemployment, highlighting

the importance of distinguishing these two channels.

Aaronson et al. (2010), Fujita (2010), and Elsby et al. (2010) use similar strategies and obtain similar

results.

Another potential explanation for large spikes in at least some of the earlier studies is heaping in reported

unemployment durations. Katz (1986) and Sider (1985) suggest that in retrospective reports much of the

observed heaping — especially prominent at 26 weeks (or 6 months), the duration of regular UI beneﬁts —

reﬂects recall error or other factors (Card and Levine, 2000) rather than UI eﬀects.

Fujita (2011) extrapolates from reemployment and labor force exit hazards observed in

2004-2007 to infer counterfactual hazards in 2009-2010 had UI beneﬁts not been extended.

To absorb confounding eﬀects from changes in labor demand, he controls linearly for the job

vacancy rate. He ﬁnds larger eﬀects of UI extensions on unemployment than does Mazumder

(2011), primarily attributable to reduced reemployment rather than reduced labor force exit.

However, these conclusions are based on the extrapolated eﬀects of a reduction in the job

vacancy rate that is roughly twice as large as the range observed in the earlier period.

Daly et al. (2011), drawing on Valetta and Kuang (2010), contrast changes in the unem-

ployment durations of job-losers, many of whom are eligible for UI beneﬁts, and job-leavers,

who are not, over the course of the recession. They conclude that UI extensions raised

the unemployment rate by 0.8 percentage points in 2009 and early 2010. This comparison

identiﬁes the UI eﬀect in the presence of arbitrary changes in demand conditions, so long as

the two groups are otherwise similar. However, the collapse in the quit rate seen in Figure

2A suggests that UI extensions may not be the only source of changes in the relative out-

comes of job losers and job leavers. If the remaining job leavers come largely from sectors

where job openings are plentiful while job losers come from those hit hard by the recession

(e.g., construction), the comparison between them will overstate any negative eﬀect of UI

extensions.

A larger estimate comes from Barro (2010), who assumes that the long-term unemploy-

ment rate in 2009 would have been the same as in 1983 if not for the UI extension. Barro

concludes that extensions raised the unemployment rate by 2.7 percentage points. Grubb’s

(2011) literature review comes to quite similar conclusion, while Howell and Azizoglu (2011)

conclude that any eﬀect is much smaller and primarily attributable to reduced labor force

exit induced by the UI job search requirement.

A ﬁnal relevant paper is by Farber and Valletta (2011). That paper was written simul-

taneously with and independently of this one, but pursues a similar strategy of using recent

data and competing risks models to identify the eﬀect of UI on reemployment and labor

force exit hazards. Unsurprisingly, Farber and Valletta obtain very similar results to those

presented below. Relative to Farber and Valletta, I (a) explore several alternative speci-

ﬁcations that isolate diﬀerent components of the variation in UI beneﬁts; (b) explore the

sensitivity of the results to unavoidable ad hoc assumptions made about expected beneﬁt

availability; and (c) address an important discrepancy in the CPS data, discussed below,

that leads to drastic understatement of the long-term unemployment rate and that has the

potential to substantially obscure eﬀects of UI extensions on unemployment durations.

3 Data

I use the Current Population Survey (CPS) rotating panel to measure the labor market

outcomes of a large sample of unemployed workers in the very recent past. Three-quarters

of each month’s CPS sample is targeted for another interview the following month, and it is

possible to match over 70% of monthly respondents (94% of the attempted reinterviews) to

employment statuses in the following month. (The most important source of mismatches is

individuals who move, who are not followed.) This permits me to measure one-month-later

employment outcomes for roughly 4,000 unemployed workers each month during the Great

Recession, and thereby to construct monthly reemployment and labor force exit hazards

that vary by state, date of unemployment, and unemployment duration.

The CPS data have advantages and disadvantages relative to other data that have been

used to study UI eﬀects. Advantages include larger and more current samples, the ability to

track outcomes for individuals who have exhausted their UI beneﬁts or who are not eligible,

and the ability to distinguish reemployment from labor force exit.

These are oﬀset by important limitations. First, the monthly CPS does not contain

measures of UI eligibility or receipt. Only displaced workers — those who were laid oﬀ from

their previous jobs rather than having quit or being new entrants to the labor force — are

eligible for UI beneﬁts. Past research has found that less than half of the eligible unem-

ployed actually receive UI beneﬁts (Anderson and Meyer, 1997). This appears to have risen

somewhat in the current recession; I estimate that over half of displaced workers unemployed

more than three months in early 2010 received UI beneﬁts.

Although the participation

rate is far less than 100%, I simulate remaining beneﬁt durations for all displaced workers,

Observations in February, March, and April can be matched to data from the Annual Demographic

Survey, which includes questions about UI income in the previous calendar year. In early 2010, 56% of

job-leavers whose unemployment spells appear to have started before December 1, 2009 reported non-zero

UI income, up from 39% in early 2005.

assuming that each is eligible for full beneﬁts. As I estimate relatively sparse speciﬁcations

without extensive individual controls, the estimates can be seen as the “reduced form” av-

erage eﬀect of available durations on the labor market outcomes of all displaced workers,

pooling recipients and non-recipients. To implement the simulation, I match the CPS data

to detailed information about the availability of EUC and EB beneﬁts at a state-week level

and compute eligibility for beneﬁts in each week between the time of displacement and the

initial CPS interview (including those paid retroactively due to delayed reauthorizations).

I assume that one week of eligibility has been used for each week of covered unemployment

(including retroactive coverage due to delayed reauthorizations).

In modeling expectations for beneﬁts subsequent to the CPS interview, I assume in my

main speciﬁcations that the individual anticipates no further legislative action or “triggering”

of beneﬁts on or oﬀ after that date, as in Figure 4. Insofar as unemployed individuals are

able to forecast future legislation, I may understate the duration of expected beneﬁts and

overstate the amount of variation across unemployment entry cohorts within the same state.

It is unclear in which direction we would expect this nonclassical measurement error to bias

my results; I explore speciﬁcations aimed at reducing this bias below.

A second limitation of the CPS data is that employment status and unemployment

durations are self-reported, and respondents may not fully understand the oﬃcial deﬁnitions.

Oﬃcially, someone who is out of work, is available to start work, and has actively looked for

work at least once in the last four weeks should be classiﬁed as “unemployed,” with a duration

of unemployment reaching back to the last time he/she was not in this state. Someone who

has not actively searched or is unavailable to start a job is out of the labor force. But the line

between unemployment and non-participation can be blurry, particularly when there are few

suitable job openings to which to apply or when job search is intermittent. The data suggest

that reported unemployment durations often stretch across periods of non-participation

or short-term employment back to the perceived “true” beginning of the unemployment

spell. Reinterviews with CPS respondents in the 1980s indicate important misclassiﬁcation

of labor force status, particularly for unemployed individuals who are often misclassiﬁed

as out of the labor force. This leads to substantial overstatement of unemployment exit

probabilities (Poterba and Summers, 1984, 1995; Abowd and Zellner, 1985).

Relatedly,

examination of the unemployment duration distributions indicates substantial heaping at

monthly, semi-annual, and annual frequencies, suggesting that many respondents round their

unemployment durations.

To minimize the misclassiﬁcation problem, my primary estimates count someone who

is observed to exit unemployment in one month but return the following month — that is,

someone whose three-month trajectory is U-N-U or U-E-U — as a non-exit.

This means

that I can only measure unemployment exits for observations with at least two subsequent

interviews. I have also estimated alternative speciﬁcations that count all measured exits or

that exclude many of the “heaped” observations, with similar results.

I discuss these issues

at greater length in Section 6.

Finally, the CPS does not attempt to track respondents who change residences between

interviews. Mobility and nonresponse lead to the attrition of roughly 8% of the sample — and

10% of the unemployed respondents — each month. If UI eligibility aﬀects the propensity

to move (Frey, 2009; Kaplan and Schulhofer-Wohl, 2011), this could bias my estimates in

unknown ways. However, when I estimate my main speciﬁcations using mobility as the

dependent variable, I ﬁnd no sign that it is (conditionally) correlated with my UI duration

measures.

Table 2 presents summary statistics for my full CPS sample, which pools data for inter-

views between May 2004 and January 2011, matched to subsequent interviews in each of the

next two months. (Rotation groups that would not have been targeted for two follow-up in-

terviews are excluded.) Figure 5 presents average monthly exit probabilities for unemployed

workers who report having been displaced from their previous jobs (as distinct from new

entrants to the labor force, reentrants, and voluntary job leavers) over the sample period.

The overall exit hazard fell from about 40% in mid 2007 to about 25% throughout 2009

CPS procedures were altered in 1994, in part to reduce classiﬁcation error. There are no public-use

reinterview samples from the post-1994 period. However, my analysis of data supplied by Census Bureau

staﬀ suggests that the misclassiﬁcation of unemployment remains an important issue even after the redesign.

Fujita (2011) also recodes some U-N-U trajectories as U-U-U. I am grateful to Hank Farber for helpful

conversations about this issue.

I am unable to address a related potential problem: although the CPS data collection is independent

of that used to enforce job search requirements, these requirements may lead some true non-participants to

misreport themselves as active searchers. This may lead my estimates of the eﬀect of UI on reported labor

force participation to overstate the eﬀect on actual job search.

and 2010.

The Figure also reports exit hazards for those unemployed 0-13 weeks and 26

weeks or more. The hazard is higher for the short-term than for the long-term unemployed.

However, both series fell similarly to the overall average in 2007 and 2008, indicating that

only a small portion of the overall exit hazard decline can be attributable to composition

eﬀects arising from the increased share of long-term unemployed with low exit rates.

4 Empirical Strategy

The matched CPS data allow me to measure whether an unemployed individual exits un-

employment over the next month, but do not allow me to follow those who do not exit to

the end of their spells. I thus focus on modeling the exit hazard directly. I assume the

monthly hazard follows a logistic function. To distinguish between the diﬀerent forms of

unemployment exit, I turn to a multinomial logit model that takes reemployment, labor

force exit, and continued unemployment as possible outcomes.

Let n

ist

be the number of weeks that unemployed person i in state s in month t has been

unemployed (censored at 99); let D

ist

be the total number of weeks of beneﬁts available to

her, including the n

ist

weeks already used as well as weeks she expects to be able to draw

in the future; and let Z

be a measure of economic conditions. Using a sample of displaced

workers, I estimate speciﬁcations of the form:



ist

1 − λ

ist



= D

ist

θ + P

ist

; γ) + P

; δ) + α

+ η

. (3)

ist

is the probability that the individual exits unemployment by month t + 1;

and

are ﬁxed eﬀects for states and months; and P

and P

are ﬂexible polynomials. This logit

speciﬁcation can be seen as a maximum likelihood estimator of a censored survival model

with stock-based sampling and a logistic exit hazard, with each individual observed for

only two periods.

However, as I discuss below, modeling survival functions in the CPS

This is a lower exit rate than is apparent in the BLS gross ﬂows data, which also derive from matched

CPS samples but do not incorporate my adjustment for U-N-U trajectories.

In principle, individuals can be followed for three periods in the CPS data. (Although the CPS is a

4-period rotating sample, I cannot measure exit between period 3 and period 4 because I require a follow-up

observation to measure temporary exits.) Accounting for this would give rise to a somewhat more complex

likelihood function. I treat an individual observed for three periods as two distinct observations, one on exit

data is challenging due to inconsistencies between stock-based and ﬂow-based measures of

survival. In Section 6, I develop a simulation approach to recovering survival curves from

the estimated exit hazards that are consistent with the observed duration proﬁle. For now,

I focus on modeling the hazards themselves.

After some experimentation, I settled on the following parameterization of P

ist

; γ) = n

ist

+ n

ist

+ n

−1

ist

+ 1 (n

ist

≤ 1) γ

. (4)

This appears ﬂexible enough to capture most of the duration pattern. I have also estimated

versions of (3) using fully nonparametric speciﬁcations of P

ist

; γ), with little eﬀect on

the results.

As discussed above, the main challenge in identifying the eﬀect of D

ist

is that it covaries

importantly with labor demand conditions. Absent a source of true random assignment of

ist

, I explore several alternative strategies, aimed at isolating diﬀerent components of the

variation in D

ist

that are plausibly exogenous to unobserved determinants of unemployment

exit.

My ﬁrst strategy attempts to absorb labor demand conditions through the P

function.

In my preferred speciﬁcation, P

is a cubic polynomial in the state unemployment rate. I

also explore richer speciﬁcations that control as well for cubics in the insured unemployment

rate — an alternative measure of unemployment based only on UI-eligible workers — and

the number of new UI claims in the CPS week (expressed as a share of the employed, eligible

population). The remaining variation in D

ist

comes primarily from the haphazard roll-out

of EUC, which creates variation over time in the relationship between Z

and the number

of weeks of available UI beneﬁts. Additional variation derives from the repeated expiration

and renewal of the EUC program and from state decisions about whether to participate

in the optional EB program. Note that labor demand is likely negatively correlated with

the availability of beneﬁts, so speciﬁcations of P

that do not adequately capture demand

conditions will likely lead me to overstate the negative eﬀect of UI beneﬁts on job-ﬁnding.

A second strategy uses job seekers who are not eligible for UI, either because they are

from period 1 to period 2 and another on exit from period 2 to period 3 (if she survives in unemployment

in period 2), allowing for dependence of the error term across the observations.

new entrants to the labor market or because they left their former jobs voluntarily, to control

non-parametrically for state labor market conditions (Valetta and Kuang, 2010; Farber and

Valletta, 2011). Using a sample that pools all of the unemployed, I estimate:



ist

1 − λ

ist



= D

ist

ω + e

ist

θ + P

ist

, e

ist

; γ) + e

ist

; δ) + α

, (5)

where

is a full set of state-month indicators and e

ist

is an indicator for whether individual

i is a job loser (and therefore presumptively UI-eligible). P

ist

, e

ist

; ) represents the

full interaction of the unemployment duration controls (4) with the eligibility indicator,

while e

ist

; δ) indicates that the relative labor market outcomes of job losers and

other unemployed are allowed to vary parametrically with observed labor market conditions.

The D

ist

measure of the number of weeks available is calculated for everyone, eligible and

ineligible alike, and is entered both as a main eﬀect that will absorb any correlation between

cohort employability and beneﬁts and interacted with the eligibility indicator e

ist

. The

causal eﬀect of UI duration is θ, and identiﬁed from covariance between UI extensions and

changes in the relative unemployment exit rates of job losers and other unemployed who

entered unemployment at the same time, over and above that which can be explained via

the Z

controls.

This speciﬁcation has the advantage that it does not rely on parametric controls to

measure the absolute eﬀect of economic conditions on job-ﬁnding rates. However, recall that

Figure 2A indicated that the quit rate has been low throughout the recession. If the ineligible

unemployed during the period when beneﬁts were extended are disproportionately composed

of people who have relatively good employment prospects, the evolving prospects of the

population of ineligibles may not be a good guide to those of eligibles, leading speciﬁcation

(5) to overstate the causal eﬀect of UI beneﬁts. I attempt to minimize this by adding controls

for individual covariates — age, education, gender, marital status, and former occupation

and industry — to (5).

My third strategy returns to the eligible-only sample but narrows in on the variation

in UI durations coming from state decisions about which EB triggers to adopt, using a

control function to absorb all other variation in D

ist

. I augment (3) with a direct control

for the number of EUC weeks available. This leaves variation only in EB beneﬁts (and,

incidentally, eliminates my reliance on assumptions about job-seekers’ expectations of future

EUC reauthorization, as the EB program is not set to expire). I also add controls for the

availability of EB beneﬁts in the s–t cell under maximal and minimal state participation

in EB (as graphed in Figure 3), along with indicators for the status of each of the four

EB triggers.

With these controls, the only variation in D

ist

should come from diﬀerences

among states in similar economic circumstances in take-up of the optional EB triggers.

My ﬁnal strategy turns to an entirely diﬀerent source of variation, focusing on the in-

teraction between the number of available weeks in the state and the number of weeks that

the individual has used to date. Equations (3) and (5) model the eﬀect of UI extensions as

a constant shift in the log odds of unemployment exit, reemployment, or labor force exit;

in some speciﬁcations I allow separate eﬀects on those unemployed more or less than 26

weeks. But this is a crude way of capturing the eﬀects, which the model in Section 2.3

suggests are likely to be strongest for those facing imminent exhaustion that for those for

whom an extension only adds to the end of what is already a long stream of anticipated

future beneﬁts. To focus better on this, I turn to a speciﬁcation that parameterizes the UI

eﬀect in terms of the time to exhaustion:



ist

1 − λ

ist



= f (d

ist

; θ) +

v=0

1 (n

ist

= v) γ

+ α

. (6)

Here, d

ist

= max {0, D

ist

− n

ist

} represents the number of weeks of beneﬁts remaining,

with f (·; θ) a ﬂexible function; I impose only the normalization that f(0; θ) = 0, implying

that UI extensions have no eﬀect on job searchers who have already exhausted even their

extended beneﬁts. The second term in (6) is a full set of indicators for unemployment

duration, and the third is a full set of state-by-month indicators. There are two sources of

variation that allow separate identiﬁcation of the eﬀects of d and n, within state-by-month

cells, without parametric restrictions. The ﬁrst is the nonlinearity of the mapping from D

ist

and n

ist

to d

ist

: across-(s, t) variation in beneﬁt availability has one-for-one eﬀects on d

ist

Three of the triggers are described in note 5. The fourth is is activated when the 3-month moving

average TUR exceeds 8% and is above 110% of the minimum of its one-year and two-year lagged values.

States adopting optional trigger 3 are required to also adopt 4, which when activated provides an additional

7 weeks of EB beneﬁts on top of the normal 13.

for those who have not yet exhausted beneﬁts but not for those who have. Second, the

EUC expiration rules mean that the addition of new EUC tiers extends d for those who will

transition onto the new tiers before the EUC program expires but not for those with lower

ist

who expect the program to have expired before they reach the new tiers.

5 Estimates

Panel A of Table 3 presents logit estimates of equation (3), with standard errors clustered

at the state level. The table shows the unemployment duration coeﬃcient and its standard

error. Below these, it also shows the estimated eﬀect of the UI extensions on the average

exit hazard in the fourth quarter of 2010, computed as the diﬀerence between the average

ﬁtted exit probability and the average ﬁtted probability implied by the model with beneﬁt

durations set to 26 weeks for the entire sample.

Column 1 is estimated using only

displaced workers who are presumed to be eligible for UI beneﬁts, and includes state and

month ﬁxed eﬀects and the n

ist

controls indicated by (4), but no controls for economic

conditions in the state. It indicates a signiﬁcant negative eﬀect of UI beneﬁt durations on

the probability of unemployment exit, with a net eﬀect of the UI extensions on the 2010:Q4

exit rate of -2.3 percentage points (on a base of 22.4%). Columns 2–5 add additional controls:

First a control for the state unemployment rate, then a cubic in that rate, then cubics in

three other measures of slackness — the number of UI claimants and the number of new UI

claims, each expressed as a share of insured employment, and the state employment growth

rate — and then ﬁnally a vector of individual-level covariates, including education, age,

marital status, and indicators for previous industry. The estimated UI eﬀects move around

a bit as the covariate vector is expanded, but within a fairly narrow range: The implied

eﬀects of UI expansions on the exit hazard in 2010:Q4 range from -1.7 to -2.3 percentage

points.

Columns 6 and 7 turn to my second strategy, adding to the sample over 60,000 unem-

ployed individuals who left their jobs voluntarily or are new entrants to the labor force and

Strictly, I use observations from the September–November surveys. December observations are excluded

because the EUC program had expired and not yet been renewed at the time of the December survey; see

Section 2.2.

are therefore not eligible for UI beneﬁts. As indicated by equation (5), this allows me to add

state-by-month ﬁxed eﬀects.

I also include an indicator for (simulated) UI eligibility and

its interaction with the duration and unemployment rate controls, as well as a “simulated

UI duration” control that is common to both the job-losers and the job-leavers and designed

to capture any unobserved cohort eﬀects that are common to both groups but correlated

with my UI measure. Column 7 also adds the full vector of individual covariates, as a

guard against the possibility that there are important diﬀerences in employability between

the job-losers and the job-leavers comparison group. With or without these covariates, the

estimates indicate notably smaller eﬀects than in columns 1-5.

There is no particular reason to think that beneﬁt extensions have the same eﬀects on

those near exhaustion as on those just beginning their spells. As a ﬁrst step toward loosening

this assumption, in Panel B I allow D

ist

to have distinct eﬀects on those unemployed more

and less than 26 weeks. The negative eﬀect of D on unemployment exit is found to be

entirely concentrated among those unemployed 26 weeks or more, with estimated eﬀects on

the shorter-term unemployed that are close to zero, never statistically signiﬁcant, and in

many cases positive. The coeﬃcients for the long-term unemployed are somewhat larger

than in Panel A, though the diﬀerences are small. The implied eﬀects of UI extensions on

exit hazards are smaller than those in Panel A in columns 1–5, but larger in columns 6 and

7, narrowing the gap between the two sets of speciﬁcations.

Table 4 presents several speciﬁcations aimed at gauging the sensitivity of the estimates

to the measurement of expected future beneﬁts. Column 1 repeats the baseline speciﬁcation

from Table 3, Panel B, Column 3. Column 2 replaces the anticipated UI duration measure

with an alternative calculated under the assumption that all recipients expect the EUC

program to be extended seamlessly and indeﬁnitely.

This leads to larger estimated UI

eﬀects, more than doubling the eﬀect on the monthly exit rate.

Measurement error in the two beneﬁt duration proxies is likely concentrated in the

months shortly preceding expiration of the EUC program, when the two expectations mod-

els yield quite diﬀerent durations; the simulated beneﬁt durations should match recipient

For computational reasons, I estimate the speciﬁcation by conditional logit, then back out consistent

but ineﬃcient estimates of the α

ﬁxed eﬀects for use in predicted exit probabilities.

This is the measure used by Farber and Valletta (2011).

expectations much more closely in subsamples where the two expectations models are in

closer agreement. Column 3 presents a speciﬁcation that builds on this intuition. I measure

the absolute diﬀerence between the Ds calculated under the two expectations models, and

interact this diﬀerence with the simulated beneﬁt duration (using my “myopic” expectations

model). I interpret the D main eﬀect in this speciﬁcation — the eﬀect of durations when

the two expectation models are in agreement — as indicating the causal eﬀect of D, and

I interpret the interaction as a measure of the bias due to mismeasurement of D when

EUC expiration approaches. Point estimates for the main eﬀects are intermediate between

those in Columns 1 and 2; the interaction coeﬃcients are negative for both the short- and

long-term unemployed, but are imprecisely estimated.

Column 4 takes a diﬀerent approach to the diﬃculty of forecasting EUC extensions:

I simply control directly for the (simulated) number of EUC weeks available. With this

control the only remaining variation in D comes from EB beneﬁts, which are not directly

dependent upon EUC reauthorization. The estimated UI eﬀects are somewhat larger than

in my baseline speciﬁcation but in the same general range.

Finally, Column 5 turns to my third strategy for identifying the UI eﬀect, using a control

function to isolate variation in EB beneﬁts coming from state decisions about which version

of the EB triggers to use.

I add to the Column 4 speciﬁcation controls for the status of each

of the four EB triggers and for simulated EB beneﬁts under the most and least generous

versions of the triggers. This inﬂates the coeﬃcients, which indicate that UI extensions

reduced the monthly exit rate by 3.1 percentage points.

Next, I explore the distinction between reemployment and labor force exit. Table 5

reports multinomial logit estimates of several of the speciﬁcations from Tables 3 and 4, using

three outcomes: Continued unemployment (the base case), exit to employment, and exit to

non-participation in the labor force. For the long-term unemployed, the results indicate

that beneﬁt durations have negative, signiﬁcant eﬀects of roughly similar magnitude on

Identiﬁcation in this speciﬁcation comes from variation in state take-up of a program that was for much

of the period under study entirely funded by the federal government. Insofar states that turned down this

free money — an important consideration seems to be the presence of a governor who was vocally opposed to

federal economic stimulus in 2009 — experienced sharper downturns in labor market conditions (conditional

on my controls), this strategy may lead me to overstate the eﬀect of UI. Of course, an association in the

opposite direction would lead me to understate this eﬀect.

the logit indexes for both types of unemployment exit. For the short-term unemployed,

estimates indicate positive eﬀects on reemployment and negative eﬀects on labor force exit,

both insigniﬁcant in most speciﬁcations. The bottom rows show the eﬀects of UI extensions

on average exit hazards in 2010:Q4. Beneﬁt extensions appear to lead to larger reductions

in the probability of labor force exit than in the probability of reemployment, reﬂecting in

part the positive point estimates for reemployment of the short-term unemployed. Given

the imprecision in those estimates, however, eﬀects of comparable magnitude on the two

margins are clearly within the conﬁdence intervals.

The multinomial logit model requires the “independence of irrelevant alternatives” (IIA)

assumption, which corresponds to independent risks of reemployment and labor force exit.

This may be incorrect here, particularly if (as in the model in Section 2.3) search eﬀort is

continuous and labor force participation simply corresponds to an arbitrary eﬀort threshold.

However, note that the labor force exit and reemployment eﬀects indicated in the last rows

of Table 5 sum to a net eﬀect on unemployment exit that is, in each column, quite similar

to the eﬀect implied by the corresponding binomial logit model. This is at least suggestive

that violations of IIA are not dramatically biasing the results.

Two additional considerations support the same general conclusion. The most likely

source of IIA violations is unobserved heterogeneity: Individuals with low job-ﬁnding prob-

abilities may be most likely to exit the labor force (and vice versa). Recall from Table 3,

however, that controlling for unobservables has little eﬀect on the estimated UI eﬀects. The

same is true in the multinomial speciﬁcations (compare Column 3 of Table 5, which includes

the individual covariates, with Column 2, which does not). This is at least suggestive that

neglected individual heterogeneity is not driving the results. Second, insofar as heterogeneity

is producing IIA violations, it likely leads me to overstate the negative eﬀect of UI extensions

on reemployment: If extensions dissuade low-job-ﬁnding-probability individuals from labor

force exit, this will reduce average job-ﬁnding rates among the unemployed through a pure

composition eﬀect, on top of any eﬀect operating through UI’s disincentive for intensive

search. My estimates of the reemployment eﬀect will thus be biased downward. As even

the estimated eﬀects in Table 5 are quite small, it seems safe to conclude that UI extensions

have not had large eﬀects on the job-ﬁnding probabilities of the unemployed.

Table 6 presents a number of alternative speciﬁcations of the multinomial logit regression,

focusing on the implied eﬀects of UI extensions on the 2010:Q4 exit hazards. The ﬁrst

row repeats the results from Table 5, column 2. Row 2 allows the UI eﬀect to diﬀer for

those with initial durations under 26 weeks, exactly 26 weeks, and over 26 weeks, as there

is substantial heaping at 26 in the raw data (due, presumably, to rounding of durations

reported in months). Although point estimates (not shown) show that eﬀects are largest for

those with exactly 26 weeks, this group is not large enough to change the overall average

exit hazards.

Row 3 oﬀers another approach to investigating the impact of duration heaping: I exclude

from my sample anyone who reported a duration of exactly 26, 52, or 78 weeks when ﬁrst

asked about his unemployment spell (in his ﬁrst month in the CPS sample), as well as anyone

who reports an inconsistent duration from one month to the next.

This leads to larger

eﬀects of UI extensions on labor force exit, but does not change the substantive story. Row

4 excludes individuals who have been unemployed for less than 8 weeks at the ﬁrst survey.

This reduces the precision of the estimates, and a test of the hypothesis that the eﬀects of

UI durations on labor force exit of the short- and long-term unemployed are both zero now

is only marginally signiﬁcant (p=0.06). However, the basic pattern is again similar to that

seen earlier.

Row 5 explores the sensitivity of the result to the deﬁnition of unemployment “exit.”

Where my main speciﬁcations count only exits that don’t backslide into unemployment

the following month, in order to exclude those most likely to be spurious consequences of

measurement error in employment status, this speciﬁcation counts all exits. This allows me

to expand the sample by over 50%, as I only require one follow-up interview to measure

exit. It raises the baseline hazards substantially, particularly for labor force exit, but has

little impact on the estimated eﬀect of UI extensions.

The remaining rows of Table 6 show estimates on diﬀerent subsamples. Rows 6 and 7

show that the eﬀect of UI extensions is concentrated among prime-age workers; for workers

over 55, extensions appear to raise the unemployment exit probability (though only the

That is, an unemployment duration of 9 weeks in interview 2 would be considered inconsistent unless

the individual reported in interview 1 being unemployed for between 3 and 6 weeks.

eﬀect on reemployment is statistically signiﬁcant). Rows 8 and 9 show eﬀects by gender;

there is no clear pattern here. Rows 10 and 11 show that the labor force exit eﬀect is con-

centrated among non-college workers, though reemployment eﬀects are similar for more- and

less-educated workers. Finally, rows 12 and 13 show that labor force exit eﬀects are con-

centrated among workers in the construction and manufacturing sectors, where employment

was especially hard hit in the recession, while reemployment eﬀects derive from workers

displaced from other sectors.

Next, I turn to my fourth strategy, as described in equation (6), allowing the eﬀects of

UI durations to operate through the time to exhaustion. As in the baseline speciﬁcations

earlier, I control for state and month indicators and a cubic in the state unemployment rate.

I also include an extremely ﬂexible parameterization of the unemployment duration

. As

discussed in Section 4, the time-until-exhaustion eﬀects are identiﬁed due to variation across

state-month cells in the number of weeks available D

— with one-for-one eﬀects on d

ist

only for those whose durations do not exceed the higher D value — and to variation in D

ist

across unemployment cohorts within cells due to the projected expiration of EUC beneﬁts

at ﬁxed calendar dates, which means that earlier unemployment cohorts expect to be able

to start more EUC tiers than do later cohorts.

I begin with a multinomial logit speciﬁcation that allows for unrestricted d

ist

eﬀects.

The d coeﬃcients from this speciﬁcation are illustrated as the solid lines in Figure 6.

The

reemployment coeﬃcients, in the left panel, show a clear pattern of negative coeﬃcients that

are perhaps falling as d

ist

falls toward about 10, then rise toward zero as d

ist

falls further.

This is consistent with the general pattern one would expect from reasonably parameterized

search models (see Section 2.3), with depressed search eﬀort from those with many weeks

left and increasing eﬀort as beneﬁt exhaustion approaches that reaches a maximum value

at the time of exhaustion, with constant search eﬀort thereafter.

The labor force exit

The duration density gets thin at above one year, and most respondents seem to round their durations

to the nearest month. I thus include weekly duration indicators for durations up to 26 weeks and monthly

indicators thereafter, plus separate linear weekly duration controls within each of 8 bins (26-30 weeks, 31-40,

41-50, 51-60, 61-70, 71-80, 81-90, and 91-99).

The maximum value of d

ist

in my sample is 83, but the frequency of individual values above 35 is often

quite low, so I show coeﬃcients only for the lower portion of the distribution.

The increase in the exit rate as d approaches zero is consistent with the presence of a “spike” in the

exit rate at or near the exhaustion of beneﬁts (i.e., at d = 0 or d = 1; see, e.g., Katz and Meyer, 1990a).

The CPS data are not well suited to the identiﬁcation of sharp spikes, however, as the monthly frequency

coeﬃcients, in the right panel, show a roughly similar pattern: Negative and fairly stable for

large d

ist

values, rising as d

ist

falls from 10 toward 0. This time, however, the coeﬃcients are

generally positive for the lowest d

ist

values, indicating that those very near exhaustion are

more likely to exit the labor force than are those who have already exhausted their beneﬁts.

This, too, is consistent with the search model presented earlier, which indicated that beneﬁt

exhaustion would trigger labor force exits among at least a subset of UI claimants.

Based on the pattern of coeﬃcients in Figure 6, I next turn to a semi-parametric spec-

iﬁcation that allows for three duration terms: A linear term in d

ist

; a second linear term

in max {0, d

ist

− 10} that allows for a change in the slope when d

ist

exceeds 10; and an

intercept that applies to all individuals with remaining beneﬁts (i.e. with d

ist

> 0). Es-

timates from a logit speciﬁcation are shown in the ﬁrst row of Table 7. As in Figure 6,

exit rates are lower for those with many weeks of remaining beneﬁts than for those whose

beneﬁts have been exhausted, roughly constant across d greater than 10 — the main d term

and the additional term for d > 10 cancel out — and sharply increasing as d falls from 10

toward 0. There is no signiﬁcant diﬀerence in exit rates between those in their last weeks

of beneﬁts and those who have already exhausted, holding constant the length of the spell.

The rightmost column of Table 7 shows that the implied eﬀect of UI beneﬁts on the UI exit

rate is somewhat smaller than those implied by the earlier estimates.

The second row of Table 7 shows a speciﬁcation that includes a full set of state-by-month

indicators. This yields very similar results to those in the less restrictive speciﬁcation. In row

3, I return to the control variables from row 1, but use a multinomial logit that distinguishes

alternative types of exit from unemployment. (Coeﬃcients from this speciﬁcation are plotted

as dashed lines in Figure 6.) As before, we see substantial eﬀects of UI beneﬁts on both

margins, but the impact on unemployment exit hazards is smaller than in the earlier analyses.

smooths out week-to-week changes.

In the model, exits occur either immediately upon job loss or upon exhaustion. Thus, the model does

not perfectly ﬁt the data, which show positive rates of labor force exit even for non-exhaustees. The gradual

rise in labor force exit rates as the date of exhaustion approaches is also inconsistent with the model, but

may be explained by an imperfect correspondence between my simulated exhaustion date and the true one.

6 Simulations of the Eﬀect of Unemployment Insurance Ex-

tensions

The results in Tables 3 – 7 indicate that the UI beneﬁt extensions enacted in 2008-2010

reduced both the probability that a UI recipient found a job and the probability that he ex-

ited the labor force, with somewhat larger estimated impacts on the latter than the former.

Moreover, the results are quite stable across a variety of speciﬁcations that exploit diﬀerent

components of the variation in UI beneﬁts. However, the magnitudes are diﬃcult to inter-

pret. This section presents simulations of the net eﬀect of the extensions on labor market

aggregates, obtained by comparing actual unemployment exit hazards with counterfactual

hazards that would have been observed in the absence of UI beneﬁt extensions.

6.1 Stocks and ﬂows in the CPS

Extrapolation of the estimated hazards to the aggregate level requires confronting an impor-

tant limitation of the longitudinally linked CPS data: The exit hazards seen in the data are

inconsistent with the cross-sectional duration proﬁle. Figure 7 illustrates this by plotting

survival curves computed in two diﬀerent ways. The solid line uses the CPS as repeated

cross sections, without attempting to link observations between months. The estimated

survival rate to duration n of the cohort entering unemployment in month m is simply the

ratio of the number of unemployed observations in month m + n with duration n to the

number of unemployed observations in m with duration 0.

To smooth the estimated rate,

I pool both numerator and denominator across all entrance months in calendar year 2008.

The dotted and dashed lines are Kaplan-Meier survival curves based on unemployment

exit hazards estimated from the linked CPS sample described in Section 3. The survival

rate to duration n is computed as

n−1

t=0

p (m + t, t), where p (x, t) represents the share of

unemployed individuals in month x at duration t who remain unemployed in month x + 1.

The dotted line uses two-month panels to estimate p, counting as survivors only those who

In practice, the unemployment duration measure is in weeks, where the CPS sample is monthly. For

Figure 7, I compute the duration in months as ﬂoor(

/4.3), where n is the duration in weeks and 4.3 is the

average number of weeks in a month. Note also that this construction does not constrain the survival curve

to be downward sloping, and indeed the data show upward slopes at 6, 12, and 18 months, presumably a

reﬂection of rounding in reported durations.

report being unemployed in the second month (that is, only U-U transitions). The dashed

line uses my preferred survival measure, using a three-month panel to measure persistence

of exits and only counting exits between month 1 and month 2 where the person does not

return to unemployment in month 3 (that is, U-E-E, U-N-N, U-N-E and U-E-N transitions

count as exits between months 1 and 2 but U-E-U and U-N-U cycles are treated as survival

into month 2). As with the cross-sectional curve, both of the Kaplan-Meier curves are

computed by pooling all unemployment entry cohorts from calendar year 2008.

Both of the Kaplan-Meier survival curves are substantially below the curve computed

from repeated cross-section data. The most important contributor to this discrepancy is the

phenomenon highlighted in Section 3: It is not uncommon for an unemployed individual in

month t to report being out of the labor force or employed in t+1 and then unemployed again

(often with a long unemployment duration) in t + 2. While some of these transitions are

real, a large share appear to be artifacts of measurement error in the t + 1 labor force status

(Abowd and Zellner, 1985; Poterba and Summers, 1986, 1984). The alternative Kaplan-

Meier survival curve based on the three-month panel substantially reduces the discrepancy

with the repeated cross section data.

Extensive exploration of the CPS data points to two other factors contributing to the

remaining discrepancy. The ﬁrst is so-called “rotation group bias”: The measured unemploy-

ment rate is higher in the ﬁrst month of the CPS panel than in later months, even though

each rotation group should be a random sample from the population (see, e.g., Bailar, 1975;

Solon, 1986; Shockey, 1988). Second, individuals starting a new unemployment spell often

report long durations. This phenomenon is particularly common when the employment spell

that precedes the entry into unemployment is short, suggesting that respondents may be

conﬂating what appear to be distinct spells into a longer super-spell. However, this does not

seem to be a complete explanation. In 2006 and 2007, for example, there are nearly 2,400

respondents observed to be employed for three consecutive months and then unemployed

in the fourth month; 10% of these report unemployment durations in the fourth month of

longer than 6 weeks.

6.2 Reconstructing survival curves consistent with the observed stocks

A full econometric model of measurement error in CPS labor force status and unemployment

durations is beyond the scope of this paper. Instead, I use ad hoc procedures similar in spirit

to the “raking” algorithm that the Bureau of Labor Statistics uses in constructing the gross

ﬂows data (Frazis et al., 2005) to force consistency between the Kaplan-Meier survival curve

and the cross-sectional duration proﬁle. I take the view that the cross-sectional proﬁle is

correct, and that diﬀerences between this proﬁle and my (adjusted) Kaplan-Meier survival

curve are due to “late entries” into unemployment.

I use two diﬀerent adjustments; I

argue below that one approach is likely to lead me to somewhat overstate the eﬀect of UI

extensions while the other is likely to understate it.

Let u (m, n, s) be the count of individuals observed in month m in state s with duration

n (in months) obtained from cross-sectional data; let p (m, n, s) represent the probability

that an individual in month m in state s with duration n persists in unemployment by

month m + 1; and let p

(m, n, s) be the counterfactual persistence probability that would

be observed in the absence of unemployment insurance extensions. Both p and p

are

obtained from ﬁtted values from the exit regressions presented in Section 5.

The unemployed at duration n are the survivors from among the unemployed at n − 1

one month prior. This creates a link between the u () and p () functions:

u (m, n, s) = u (m − 1, n − 1, s) p (m − 1, n − 1, s) + e (m, n, s) . (7)

In population data without measurement error, the residual e (m, n, s) would be identically

zero. The actual residual in (7) has two components. The ﬁrst is mean-zero sampling error,

which may cause the number of unemployed in newly entering rotation groups to diﬀer from

the number rotating out. The second is the “late entry” phenomenon discussed above, which

leads to E [e (m, n, s)] > 0 for most n.

We wish to compare u (m, n, s) to the counterfactual unemployment u

(m, n, s) that

would be observed had the persistence probabilities been p

rather than p. To do this,

The UI system tabulates the number of individuals who exhaust their (regular program) beneﬁts each

month, providing an independent measure of survival. The implied exhaustion rates are much more nearly

consistent with the cross-sectional survival curve than with the Kaplan-Meier curve.

I assume that entry into unemployment at duration 0 is not aﬀected by UI extensions:

u (m, 0, s) = u

(m, 0, s) for all m and s. My two approaches diﬀer in their assumptions

about the counterfactual values of e (m, n, s).

My ﬁrst approach begins with an expression for u (m, n, s) obtained by recursively

substituting into the right side of (7):

u (m, n, s) = u (m − n, 0, s)

n−1

t=0

p (m − n + t, t, s) + E (m, n, s) , (8)

where E (m, n, s) ≡

r=1

[e (m − n + r, r, s)

t=r

p (m − n + t, t, s)]. (Hereafter, I sup-

press the month and state subscripts, understanding that increments to duration require

corresponding increments to the month of observation in order to maintain a focus on the

same entry cohort.) In this approach, I assume that the cumulative count of surviving late

entries E (n) is unaﬀected by UI extensions. I estimate

E (n) ≡ u (n) − u (0)

n−1

t=0

p (t).

This is simply the vertical distance between the solid and long-dashed lines in Figure 7,

evaluated at duration n. I use (8) to construct a counterfactual unemployment count

ˆu

(n) ≡ u (0)

n−1

t=0

(t) +

E (n) . (9)

My second approach assumes instead that the per-period late entries e (n) are unaﬀected

by UI extensions but that the subsequent persistence of these late entrants is aﬀected.

Following (7), I estimate ˆe (d) = u (n) − u (n − 1) p (n − 1), then deﬁne the counterfactual

count iteratively as:

ˆu

(n) = u

(n − 1) p

(n − 1) + ˆe (n) . (10)

This can be rewritten to yield an intuitive expression for ˆu

(n) in terms of actual counts

u (n) and two adjustments:

ˆu

(n) ≡ u (n) + u (n − 1) [p

(n − 1) − p (n − 1)] (11)



ˆu

(n − 1) − u (n − 1)



(n − 1)

The ﬁrst adjustment — the second term on the right side of (11) — reﬂects diﬀerences

between the actual and counterfactual scenarios in unemployment persistence at duration

n − 1, while the second adjustment — the third term in (11) — captures diﬀerences in exit

at durations t < n − 1, multiplied by the probability of surviving from n − 1 to n.

Neither assumption about the late entries is particularly plausible. First, there is no

reason to expect that the job search behavior of “late entrants” to unemployment will be

unaﬀected by UI extensions, particularly if these late entrants are in part an artifact of

measurement error in the pre-unemployment labor force status. If the late entrants are in

fact aﬀected, E

(n) < E (n) and ˆu

(n) > u

(n). This implies that the UI eﬀect inferred

from the comparison of u (n) with ˆu

(n) will understate the magnitude of the eﬀect of UI

extensions.

On the other hand, insofar as the late entries reﬂect people cycling from unemployment

to non-participation and back, UI extensions that reduce the ﬂow from unemployment into

non-participation would also likely reduce the number of subsequent late entries. This would

imply e

(n) > e (n) and ˆu

(n) < u

(n), so a UI eﬀect inferred from the comparison of u (n)

with ˆu

(n) will likely overstate the magnitude of the eﬀect of UI extensions on employment.

Thus, there is reason to think that the two counterfactuals should bracket the true eﬀect of

UI extensions (assuming, of course, that the eﬀects of UI extensions on exit hazards obtained

from the speciﬁcations in Section 5 are accurate).

6.3 Results

Figure 8 presents the two counterfactual simulations of the number of unemployed, using the

model from Table 5, Column 2 to construct p and p

and aggregating across all durations

at each point in time. The solid line shows the actual, non-seasonally-adjusted counts from

the monthly CPS. The two counterfactual simulations ˆu

and ˆu

are plotted as short and

long dashes, respectively. Counterfactual approach 1 indicates essentially no eﬀect of the UI

extensions, making the short-dashed line hard to distinguish from the solid “actual” series.

State-by-month level estimates of E (n) and e (n) are extremely noisy. However, national-level monthly

estimates can be obtained by aggregating across states. The time-series relationship between

E (n) and

UI beneﬁt durations is robustly negative, consistent with the view that method 1 understates the eﬀect of

UI extensions. The estimated relationship between ˆe (n) and beneﬁt durations is weaker and generally not

statistically signiﬁcant.

Counterfactual approach 2 oﬀers only a slightly diﬀerent conclusion, suggesting that the UI

extensions increased unemployment in 2010 and early 2011 by about 2.6%.

Table 8 presents more results from the simulations, using each of my four main strategies

to generate predicted exit hazards and then simulating aggregate unemployment and the

long-term unemployment share in January 2011.

The ﬁrst speciﬁcation is the one graphed

in Figure 8, using a cubic in the state unemployment rate to absorb endogeneity in the

availability of extended UI beneﬁts. The second speciﬁcation uses the comparison of job-

losers to job-leavers reported in Table 3, Column 6 to generate the exit hazards. Third,

I use the control function speciﬁcation from Table 5, Column 5 — identiﬁed from state

decisions about whether and how to participate in the EB program. Finally, I use the

time-to-exhaustion model from Table 7, Row 3.

The estimates indicate that UI extensions raised the number of unemployed in January

2011 by between 5,000 and 759,000, the unemployment rate by 0.1 to 0.5 percentage points,

and the long-term unemployment share by between 0.3 and 2.8 percentage points. In each

case the largest estimates come from counterfactual method 2 and the control function

speciﬁcation; leaving these out, the upper end of the ranges are 370,000 unemployed, 0.2

percentage points on the unemployment rate, and 1.6 percentage points of long-term unem-

ployment. These are much smaller eﬀects than are indicated by the extrapolations discussed

in Section 2.4.

The lower panel of Table 8 presents an alternative and more speculative set of counter-

factual simulations. An important question regarding the eﬀects in Panel B of Table 8 is

whether the eﬀect of UI extensions on unemployment reﬂects reduced job search behavior

or simply reduced labor force exit. As a ﬁrst eﬀort to assess this, I re-run the simulations,

turning oﬀ the eﬀects of UI on the propensity to become reemployed and retaining only

the eﬀects on the labor force exit propensity. Speciﬁcally, let X

ist

be the observed values

of the explanatory variables and let β

and β

be the full vectors of covariates from the

employment and non-participation equations, respectively, of the multinomial logit model.

I count anyone unemployed 6 months or more as long-term unemployed. This means that I generally

include people who report being unemployed for exactly 26 weeks on the survey date, where the BLS long-

term unemployment deﬁnition uses durations of 27 weeks or more. This accounts for the discrepancy between

the baseline long-term unemployment rate in Table 8 and the published rate of 42.2%.

The one-period survival probability is then p

ist

= [1 + exp (X

ist

) + exp (X

ist

)]

−1

and

the counterfactual survival probability used for the simulations in Panel B of Table 8 is

ist

= [1 + exp (X

ist

) + exp (X

ist

)]

−1

, where X

ist

represents the explanatory variables

in the counterfactual scenario where beneﬁts are ﬁxed at 26 weeks. In Panel C, I use instead

ist

= [1 + exp (X

ist

) + exp (X

ist

)]

−1

. Comparisons of simulations based on p

ist

and

ist

reveal how much of the overall eﬀect revealed by the p

ist

-p

ist

comparison is due to labor

force exit. The results in Panel C indicate that just turning oﬀ the eﬀect of UI extensions

on labor force exit reduces unemployment by more than half as much as did turning oﬀ

both UI eﬀects in Panel B.

In other words, the majority of the eﬀect of UI extensions

on overall unemployment and on long-term unemployment operates through the labor force

exit channel, by keeping people in the labor force who would otherwise have exited, rather

than through reduced reemployment rates.

These last results must be interpreted with some caution, as they rest importantly on the

assumption of independent risks. With this assumption, an individual who is dissuaded from

exiting the labor force in one month has approximately a 13% chance of becoming reemployed

the next month, the same as would an individual who never considered abandoning his job

search. This is probably not realistic; one might expect that the unemployed with the worst

employment prospects are the most likely to exit the labor force. Thus, the results in Table

8, Panel C might overstate the share of the UI eﬀects that is attributable to labor force exit

decisions. Even so, it is clear from Panel B alone that any negative reemployment eﬀect

must be small.

7 Discussion

The design of unemployment insurance policy trades oﬀ generosity to workers who have

experienced negative shocks against the disincentive to return quickly to work created by

the availability of generous non-work beneﬁts. In bad economic times, displacement from a

job is a much larger shock, as it can take much longer to ﬁnd new work. Moreover, insofar

as weak labor markets reﬂect a shortage of labor demand, the negative consequences of

I do not report estimates for Strategy 2 in Panel C, as the multinomial logit version of this speciﬁcation

is computationally intractable.

reduced search eﬀort among the unemployed may be relatively small.

It thus stands to

reason that one might want to extend unemployment insurance beneﬁt durations during

bad times (Landais et al., 2010; Kroft and Notowidigdo, 2011; Schmieder et al., 2011). Such

extensions can have macroeconomic beneﬁts as well, as the unemployed likely have a high

marginal propensity to consume and UI payments thus have relatively large multipliers

(Congressional Budget Oﬃce, 2010).

However, the advisability of long UI extensions depends importantly on the view that

the reduced job search induced by these extensions will not overly slow the labor market

matching process. Many commentators have argued that the 99 weeks of beneﬁts available

through the EUC and EB programs in 2010 and 2011 have gone too far, some pointing to

the apparent outward shift of the Beveridge Curve in 2010 (Elsby et al., 2010) as evidence

that UI extensions have reduced labor supply suﬃciently to noticeably slow the recovery of

the labor market.

It is ultimately an empirical question whether UI extensions lead to large reductions

in job ﬁnding. But the eﬀect of extensions on job ﬁnding rates is hard to identify, because

extensions are usually implemented in response to poor labor market conditions. Fortunately

for the researcher (if not for UI recipients themselves), the haphazard way that the EUC

program was gradually expanded and then repeatedly renewed generates a great deal of

variation in beneﬁt availability that is plausibly exogenous to the demand conditions that

otherwise confound eﬀorts to estimate the beneﬁt duration eﬀect.

Using a variety of comparisons that isolate diﬀerent components of the variation in beneﬁt

availability, I ﬁnd that extended beneﬁts do reduce the rate at which unemployed workers

reenter employment. But the reductions are small, in most speciﬁcations smaller than eﬀects

of extended beneﬁts on labor force exit and always much smaller than what one would have

expected based on older estimates in the literature. The two eﬀects both lead to increases

in measured unemployment, but combined they have raised the unemployment rate by only

about 0.2 percentage points, implying that the vast majority of the 2007–2009 increase in the

unemployment rate was due to demand shocks rather than to UI-induced supply reductions.

See, e.g., Kroft and Notowidigdo (2011). Schmieder et al. (2011) ﬁnd evidence in Germany, however,

that the reemployment eﬀect of UI durations is relatively constant across the business cycle.

Moreover, less than half of the small UI eﬀect comes from reduced reemployment rather

than from reduced non-participation (i.e., from increased labor supply).

Any negative eﬀects of the recent unemployment insurance extensions on job search are

clearly quite small, too small to outweigh the consumption-smoothing and equity-promoting

beneﬁts of UI (Gruber, 1997). The latter are likely to be particularly large when the marginal

recipients have been out of work for over a year in conditions where job-ﬁnding prospects are

bleak. Moreover, the estimates herein should be seen as reﬂecting the partial equilibrium

eﬀects of UI, as they do not account for search externalities — when jobs are scarce, a job

claimed by one searcher reduces the probability that other searchers will ﬁnd employment.

Incorporating these spillovers would make extensions more attractive, as reduced job search

among a subset of the unemployed would not translate one-for-one into reduced employment

but would rather simply shift jobs from the UI recipients to other job seekers (Landais et

al., 2010). The evidence here thus supports the view that optimal UI program design would

tie beneﬁt durations to labor market conditions, to give displaced workers realistic chances

of ﬁnding new employment before their beneﬁts expire.

References

Aaronson, Daniel, Bhashkar Mazumder, and Shani Schechter, “What is behind

the rise in long-term unemployment?,” Economic Perspectives (Federal Reserve Bank of

Chicago), 2nd Quarter 2010, 34 (2).

Abowd, John M. and Arnold Zellner, “Estimating gross labor-force ﬂows,” Journal of

Business & Economic Statistics, 1985, 3 (3), 254–283.

Anderson, Patricia M. and Bruce D. Meyer, “Unemployment Insurance takeup rates

and the after-tax value of beneﬁts,” Quarterly Journal of Economics, 1997, 112 (3), 913–

937.

Bailar, Barbara A., “The eﬀects of rotation group bias on estimates from panel surveys,”

Journal of the American Statistical Association, 1975, 70 (349), 23–30.

Barro, Robert, “The folly of subsidizing unemployment,” Wall Street Journal, August 30

2010, August 30.

In principle, estimates identiﬁed from across-state-month comparisons should capture these externalities.

However, because my samples for these estimates exclude large fractions of job seekers, only a portion of the

externality is captured.

Card, David and Philip B. Levine, “Extended beneﬁts and the duration of UI spells:

Evidence from the New Jersey extended beneﬁt program,” Journal of Public Economics,

2000, 78 (1-2), 107–138.

, Raj Chetty, and Andrea Weber, “Cash-on-hand and competing models of intertem-

poral behavior: New evidence from the labor market,” Quarterly Journal of Economics,

2007, 122 (4), 1511–1560.

, , and , “The spike at beneﬁt exhaustion: Leaving the unemployment system or

starting a new job?,” The American Economic Review, 2007, 97 (2), 113–118.

Chetty, Raj, “Moral hazard vs. liquidity and optimal unemployment insurance,” Journal

of Political Economy, 2008, 116 (2), 173–234.

Congressional Budget Oﬃce, “Policies for increasing economic growth and employment

in 2010 and 2011,” Publication 4077 January 2010.

Daly, Mary, Bart Hobijn, and Rob Valetta, “The Recent Evolution of the Natural

Rate of Unemployment,” Working paper 2011-05, Federal Reserve Bank of San Francisco

January 2011.

Duggan, Mark and Scott Imberman, “Why are the DI rolls skyrocketing? The contri-

bution of population characteristics, program changes, and economic conditions,” in David

Cutler and David Wise, eds., Health at Older Ages, Chicago, IL: University of Chicago

Press, 2009.

Elsby, Michael W, Bart Hobijn, and Aysegul Sahin, “The labor market in the Great

Recession,” Brookings Papers on Economic Activity, 2010, Spring.

Farber, Henry S. and Robert Valletta, “Extended unemployment insurance and un-

employment duration in the Great Recession: The U.S. experience,” manuscript June 24

2011.

Federal-State Extended Unemployment Compensation Act of 1970, http:

//workforcesecurity.doleta.gov/unemploy/EB_law_for_web.pdf. Accessed June 28,

2011. Undated.

Frazis, Harley J., Edwin L. Robison, Thomas D. Evans, and Martha A. Duﬀ, “Es-

timating gross ﬂows consistent with stocks in the CPS,” Monthly Labor Review, September

2005, 128 (9), 3–9.

Frey, William H., “The great American migration slowdown: Regional and metropolitan

dimensions,” Technical Report, The Brookings Institution December 2009.

Fujita, Shigeru, “Economic eﬀects of the unemployment insurance beneﬁt,” Business Re-

view (Federal Reserve Bank of Philadelphia), Fourth Quarter 2010.

, “Eﬀects of extended unemployment insurance beneﬁts: Evidence from the monthly CPS,”

Working paper 10-35/R, Federal Reserve Bank of Philadelphia January 2011.

Grubb, David, “Assessing the impact of recent unemployment insurance extensions in the

United States,” Working paper, OECD May 25 2011.

Gruber, Jonathan, “The consumption smoothing beneﬁts of unemployment insurance,”

American Economic Review, March 1997, 87 (1), 192–205.

Howell, David R. and Bert M. Azizoglu, “Unemployment beneﬁts and work incentives:

The U.S. labor market in the Great Recession,” Working Paper 257, Political Economy

Research Institute, University of Massachusetts Amherst May 2011.

Joint Economic Committee, “Extending unemployment insurance beneﬁts: The cost of

inaction for disabled workers,” Report May 2010.

Kaplan, Greg and Sam Schulhofer-Wohl, “Interstate migration has fallen less than

you think: Consequences of hot deck imputation in the Current Population Survey,” Staﬀ

Report 458, Federal Reserve Bank of Minneapolis June 2011.

Katz, Lawrence, “Layoﬀs, recall, and the duration of unemployment,” Working paper

1825, National Bureau of Economic Research January 1986.

Katz, Lawrence F. and Bruce D. Meyer, “The impact of the potential duration of

unemployment beneﬁts on the duration of unemployment,” Journal of Public Economics,

1990, 41 (1), 45–72.

and , “Unemployment Insurance, recall expectations, and unemployment outcomes,”

The Quarterly Journal of Economics, November 1990, 105 (4), 973–1002.

Kroft, Kory and Matthew J. Notowidigdo, “Should unemployment insurance vary with

the unemployment rate? Theory and evidence,” working paper 17173, National Bureau

of Economic Research June 2011.

Landais, Camille, Pascal Michaillat, and Emmanuel Saez, “Optimal unemployment

insurance over the business cycle,” working paper 16526, National Bureau of Economic

Research November 2010.

Mazumder, Bhashkar, “How did unemployment insurance extensions aﬀect the unem-

ployment rate in 2008-10?,” Chicago Fed Letter, April 2011, 285.

National Employment Law Project, “Q&A: The basics of the Extended Beneﬁts pro-

gram,” Fact Sheet February 3 2011.

Poterba, James M. and Lawrence H. Summers, “Response variation in the CPS:

Caveats for the unemployment analyst,” Monthly Labor Review, 1984, 107 (3), 37–43.

and , “Reporting errors and labor market dynamics,” Econometrica, November 1986,

54 (6), 1319–1338.

and , “Unemployment beneﬁts and labor market transitions: A multinomial logit

model with errors in classiﬁcation,” The Review of Economics and Statistics, May 1995,

77 (2), 207–216.

Schmieder, Johannes F., Till von Wachter, and Stefan Bender, “The eﬀects of

extended unemployment insurance over the business cycle: Evidence from regression dis-

continuity estimates over twenty years,” Working paper March 2011.

Shockey, James W., “Adjusting for response error in panel surveys,” Sociological Methods

& Research, 1988, 17 (1), 65.

Sider, Hal, “Unemployment duration and incidence: 1968-82,” The American Economic

Review, June 1985, 75 (3), 461–472.

Solon, Gary, “Labor Supply Eﬀects of Extended Unemployment Beneﬁts,” The Journal of

Human Resources, Spring 1979, 14 (2), 247–255.

, “Eﬀets of rotation group bias on estimation of unemployment,” Journal of Business and

Economic Statistics, January 1986, 4, 105–109.

Valetta, Rob and Katherine Kuang, “Extended unemployment and UI beneﬁts,”

FRBSF Economic Letter, April 2010, 2010 (12).

Appendix: Proofs of propositions

All proofs are by induction.

Proof of Proposition 1. An individual’s decision problem in state d > 0, holding search eﬀort

for all lower d ﬁxed, is to choose s to maximize

(s, d) = u (y

+ b) − s + δ [p (s) V

+ (1 − p (s) V

(d − 1))] .

The optimal s is labeled s

, and by deﬁnition satisﬁes V

, d) = V

(d).

Note that the maximization problem is identical when d = 1 as when d = 0. (Compare

equation (1), evaluated at d = 1, with the problem in note 7 — they diﬀer only by an

additive term u (y

+ b) − u (y

) > 0 that is invariant to search eﬀort.) Thus, s

= s

and

(1) − V

(0) > 0. Second, assume V

(x) > V

(x − 1) for some x > 0. Then

(x + 1) − V

(x) = V

x+1

, x + 1) − V

, x)

≥ V

, x + 1) − V

, x)

= δ (V

(x) − V

(x − 1)) (1 − p (s

)) > 0. (12)

Thus, V

(d + 1) > V

(d) for all d.

Proof of Proposition 2. See above for s

= s

. For d ≥ 1, s

satisﬁes the ﬁrst order condition

) =

δ(V

−V

(d−1))

. Proposition 1 thus implies that p

d+1

) < p

), so p

(s) < 0

implies s

d+1

> s

Proof of Proposition 3. Let ˜s

= arg max

(s, d), where

(s, d) =

(

u (y

+ b) − s + δ [p (s) V

+ (1 − p (s) V

(d − 1))] if s ≥ θ

u (y

) − s + δ [p (s) V

+ (1 − p (s) V

(d))] if s < θ,

and let η

= 1 (˜s

≥ θ). I show that η

d+1

6= η

for any d > 0 yields a contradiction. Without

loss of generality, suppose that η

= η

d−1

= · · · = η

; this merely means that we have chosen

the smallest d such that η

d+1

6= η

Begin by considering the case where η

= 1, so ˜s

≥ θ for all x ≤ d. Then an argument

identical to that above implies that the search requirement is never binding: ˜s

= ˜s

and

for all x > 0,

(x + 1) −

V (x) > 0 and ˜s

x+1

> ˜s

. In particular, ˜s

d+1

> ˜s

, so η

d+1

= 1.

Next, suppose that η

= 0 but η

d+1

= 1. The former implies that

(x) = max

s<θ

u (0) − s + δ

p (s) V



1 − p (s)

(x)

i

= max

s<θ

u (0) − s + δp (s) V

1 − δ (1 − p (s))

(13)

for all 0 ≤ x ≤ d. Note that the right-hand side of (13) does not vary with x, so the left

side does not either. In particular,

(d) =

(d − 1). Moreover, because labor force exit

with s = ˜s

< θ is a feasible option with d + 1 weeks of beneﬁts available, it must be the

case that

(d + 1) >

(d). Next, note that

(d) <

(d + 1)

(˜s

d+1

, d + 1)

= u (b) − ˜s

d+1

+ δ

p (˜s

d+1

) V



1 − p (˜s

d+1

)

(d)

i

(˜s

d+1

, d) + δ (1 − p (˜s

d+1

))



(d) −

(d − 1)



(d) + δ (1 − p (˜s

d+1

))



(d) −

(d − 1)



, (14)

where the ﬁnal inequality follows from a revealed preference argument for beneﬁt duration

d. This implies that

(d) >

(d − 1), a contradiction.

There are thus only three possible values for the η

sequence: η

= 1 for all d ≥ 0; η

= 0

for all d ≥ 0; or η

(

0 if d = 0

1 if d > 0

. Unemployment to non-participation transitions thus

occur only when beneﬁts are exhausted; beneﬁt extensions will delay these transitions for

those who would otherwise have exhausted their beneﬁts.

"#!

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Unemployment rate (L axis)

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2004 2005 2006 2007 2008 2009 2010 2011

Year

Hires (JOLTS), L. axis U-E flow rate (CPS), R. axis

U-N flow rate (CPS), R. axis

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2008 2009 2010

Date

Actual

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Year

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2008 2009 2010 2011

Year

Statute

Expectation if at 26 weeks

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0102030

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Reemployment

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Counterfactual simulation 2

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Date

I II III IV

(1) (2) (3) (4) (5)

Jun. 30, 2008 13 Mar. 28, 2009

Nov. 21, 2008 20 13 C Mar. 28, 2009

Feb. 17, 2009 20 13 C Dec. 26, 2009

Nov. 6, 2009 20 14 13 C 6 H Dec. 26, 2009

Dec. 19, 2009 20 14 13 C 6 H Feb. 28, 2010

Feb. 28, 2010 0 0 0 0 n/a

Mar. 2, 2010 20 14 13 C 6 H Apr. 5, 2010

Apr. 5, 2010 0 0 0 0 n/a

Apr. 15, 2010 20 14 13 C 6 H Jun. 2, 2010

Jun. 2, 2010 0 0 0 0 n/a

Jul. 22, 2010 20 14 13 C 6 H Nov. 30, 2010

Nov. 30, 2010 0 0 0 0 n/a

Dec. 17, 2010 20 14 13 C 6 H Jan. 3, 2012

Scheduled EUC

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Weeks available under EUC Tier

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ABC!(4/0,'(!D8&:!/%.&:E8.E(4/0,'!@F!#F!"F!%5!GH!!+,,!(&4&8(&8I(!-('!ABC!D'8J:&(H!!C-6(4/0,'!

8.!I%,-/.(!KE=!'LI,-2'(!%6('574&8%.(!D8&:!/8((8.J!%5!4,,%I4&'2!,46%5!9%5I'!(&4&-(!8.!&:'!

64('!(-57'1!%5!8.!'8&:'5!%9!&:'!&D%!9%,,%D8.J!8.&'578'D(F!%5!D8&:!4,,%I4&'2!-.'/0,%1/'.&!

2-54&8%.!8.!&:'!64('!(-57'1H!

Job

losers

Job leavers /

entrants /

reentrants

Job

losers

Job leavers /

entrants /

reentrants

(1) (2) (3) (4)

N 95,485 77,913 77,813 61,105

Share matched to one follow-up interview 91% 91% 100% 100%

Share matched to two follow-up interviews 85% 83% 100% 100%

Unemployment duration (spells in progress)

Average (weeks) 22.7 21.8 23.1 22.2

Share 0-13 weeks 54% 59% 54% 59%

Share 14-26 weeks 17% 15% 17% 15%

Share 27-98 weeks 23% 20% 24% 20%

Share 99+ weeks 5% 6% 5% 6%

Share exiting unemployment by next month

Counting all exits (1+ follow-ups)

Total 39% 52% 38% 51%

To employment 23% 20% 23% 20%

Out of labor force 15% 32% 15% 31%

Not counting U-N-U or U-E-U transitions (2+ follow-ups)

Total 30% 42% 29% 41%

To employment 20% 18% 20% 18%

Out of labor force 10% 24% 10% 24%

Anticipated weeks of unemployment benefits

Total 43.9 -- 44.2 --

Remaining 24.1 -- 24.0 --

Total (anticipating EUC reauthorization) 56.7 -- 57.0 --

State unemployment rate 7.7% 6.9% 7.7% 6.9%

All unemployed

Subsample with 2+

follow-up interviews

"#!

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>6A6<7!/0!&>'!B322!(64C2'!%B!D%E52%('<(!/(!FG9HIJ!/0!&>'!FK.K)LH!(3E(64C2'!/&!/(!FF9HI9!!

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/0!&>'!E'=/00/0=5%B54%0&>!(3<;'?Q,!/&(!(R36<',!/&(!/0;'<(',!607!60!/07/16&%<!B%<!E'/0=!0'N2?!

30'4C2%?'7!PS!.!N''OQ9!!TC'1/B/16&/%0(!/0!U60'2!V!62(%!/01237'!60!/07/16&%<!B%<!E'/0=!

30'4C2%?'7!F8!N''O(!%<!4%<'9!!W07/;/7362!1%;6</6&'(!/0!1%2340(!"!607!+!6<'!='07'<J!

46</&62!(&6&3(J!6!B'462'546<</'7!/0&'<61&/%0J!607!6=',!'7316&/%0,!607!C<'530'4C2%?4'0&!

/073(&<?!7344/'(!P8,!H,!607!."!16&'=%</'(,!<'(C'1&/;'2?Q9!!T''!&'@&!B%<!7'(1</C&/%0!%B!&>'!

1%;6</6&'(!/0!1%2340(!8!607!+9!TC'1/B/16&/%0(!/0!1%2340(!.5"!3('!&>'!XUT!(64C2'!N'/=>&,!

N>/2'!1%2340(!8!607!+,!N>/1>!6<'!'(&/46&'7!E?!1%07/&/%062!2%=/&,!3('!&>'!6;'<6='!XUT!

N'/=>&!/0!&>'!(&6&'54%0&>!1'229!!:22!(&6076<7!'<<%<(!6<'!123(&'<'7!6&!&>'!(&6&'!2';'29!

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

Panel A: Constant effect of UI across all durations

-0.33 -0.27 -0.31 -0.34 -0.37 -0.15 -0.19

(0.10) (0.10) (0.10) (0.10) (0.10) (0.10) (0.10)

-2.1 p.p. -1.7 p.p. -1.9 p.p. -2.1 p.p. -2.3 p.p. -0.9 p.p. -1.2 p.p.

Controls

State FEs Y Y Y Y Y

Month FEs Y Y Y Y Y

State-by-month FEs Y Y

Unemp duration controls Y Y Y Y Y Y Y

State unemployment rate linear cubic cubic cubic

State insured unemp rate cubic cubic

State new UI claims rate cubic cubic

State employment growth rate cubic cubic

Individual covariates Y Y

Job loser indicator Y Y

Unemployment duration X job loser Y Y

Unemployment rate X job loser Y Y

# of weeks of benefits if elig. Y Y

Panel B: Allowing effect to vary by individual unemployment duration

Weeks of benefits (/100) X

0.08 0.20 0.13 0.10 0.10 -0.11 -0.13

unemployed < 26 weeks

(0.15) (0.15) (0.15) (0.14) (0.14) (0.19) (0.19)

Weeks of benefits (/100) X

-0.37 -0.30 -0.34 -0.36 -0.40 -0.19 -0.23

unemployed 26+ weeks

(0.09) (0.10) (0.09) (0.09) (0.09) (0.10) (0.11)

-1.5 p.p. -1.0 p.p. -1.3 p.p. -1.4 p.p. -1.6 p.p. -1.0 p.p. -1.3 p.p.

Sample is job-losers

Effect of UI extensions on

avg. exit hazard in 2010:Q4

Effect of UI extensions on

average exit hazard in

2010:Q4

# of weeks of UI benefits

(/100)

Sample is all

unemployed

"#!

!"#$%&'(&&)%*+,-,.,-/&01&-2%&3%+4$-+&-0&3%5,6,%*-&%76%5-"-,0*+&809%$&

$%&'()!!*+',-.-,/&-%0!-0!1%2340!5!-(!&6/&!-0!1%2340!7!%.!8/92'!7:!;/0'2!<=!!*/4+2'!-(!->'0&-,/2!

-0!(39('?3'0&!,%2340(@!(''!&'A&!.%B!>'(,B-+&-%0!%.!/>>-&-%0/2!,%C/B-/&'(=!

(1) (2) (3) (4) (5)

Panel A: Constant effect of UI across all durations

Weeks of benefits (/100) X

0.13 -0.08 0.07 0.02 -0.12

unemployed < 26 weeks

(0.15) (0.17) (0.20) (0.26) (0.22)

Weeks of benefits (/100) X

-0.34 -0.44 -0.43 -0.48 -0.62

unemployed 26+ weeks

(0.09) (0.17) (0.19) (0.34) (0.27)

Weeks of benefits (/100) X -0.20

UE<26 weeks X

(0.62)

abs(expectations range)

Weeks of benefits (/100) X -0.62

UE<26 weeks X

(0.39)

abs(expectations range)

-1.3 p.p. -3.0 p.p. -1.8 p.p. -2.1 p.p. -3.1 p.p.

Controls

State FEs Y Y Y Y Y

Month FEs Y Y Y Y Y

Unemp duration controls Y Y Y Y Y

State unemployment rate cubic cubic cubic cubic cubic

Forecast EUC reauthorization? N Y N N N

EUC weeks available Y Y

EB trigger status Y

EB availability under alternative rules Y

Effect of UI extensions on

average exit hazard in 2010:Q4

""!

!"#$%&'(&&)*$+,-./,"$&$.0,+&/.1%$2&3.4&4%%/5$.6/%-+&"-1&$"#.4&3.47%&%8,+&9%42*2&

7.-+,-*%1&*-%/5$.6/%-+&

#$%&'(!!)*+,-&'!*./!',&01210*%1$.'!*3&!*'!1.!%4&!1./10*%&/!0$-5+.'!$2!6*7-&'!8!*./!9:!5'1.;!*!

+5-%1.$+1*-!-$;1%!2$3!*!%3104$%$+$5'!$5%0$+&!<5.&+,-$=+&.%:!&+,-$=+&.%:!$3!.$%!1.!-*7$3!

2$30&>!1.!,-*0&!$2!%4&!-$;1%'!2$3!/104$%$+$5'!$5%0$+&'!<5.&+,-$=+&.%!?&3'5'!.$.@

5.&+,-$=+&.%>!5'&/!1.!%4$'&!%*7-&'A!!B?&3*;&!+$.%4-=!4*C*3/'!1.!%4&!25--!'*+,-&!*3&!

DEAEF!2$3!3&&+,-$=+&.%!*./!EAGF!2$3!-*7$3!2$30&!&H1%I!1.!%4&!JKDK(L9!'57'*+,-&!%4&=!*3&!

D8A9F!*./!EAKF:!3&',&0%1?&-=A!

T3, C1 T3, C3 T3, C5 T4, C3 T4, C5

(1) (2) (3) (4) (5)

Reemployment

Weeks of benefits (*100) X

0.19 0.24 0.18 0.48 0.01

unemployed < 26 weeks

(0.19) (0.19) (0.19) (0.24) (0.33)

Weeks of benefits (*100) X

-0.44 -0.42 -0.47 -0.29 -0.64

unemployed 26+ weeks

(0.13) (0.14) (0.14) (0.21) (0.37)

Labor force exit

Weeks of benefits (*100) X

-0.19 -0.12 -0.11 -0.41 -0.32

unemployed < 26 weeks

(0.21) (0.21) (0.21) (0.45) (0.26)

Weeks of benefits (*100) X

-0.38 -0.34 -0.42 -0.55 -0.58

unemployed 26+ weeks

(0.13) (0.13) (0.15) (0.37) (0.34)

Effect of extensions on average hazards in 2010:Q4

Reemployment -0.6 p.p. -0.5 p.p. -0.7 p.p. 0.2 p.p. -1.2 p.p.

Labor force exit -1.2 p.p. -1.0 p.p. -1.2 p.p. -2.0 p.p. -1.8 p.p.

Specification & sample

"#!

!"#$%&'(&&)$*%+,"*-.%&/0%1-2-1"*-3,/&4&/5#/"60$%/&

$%&'()!*+(',-.'!(/'0-1-0+&-%.!-.!2%3!4!-(!&5+&!16%7!8%,97.!:!%1!;+<,'!"=!$>??@A4BC!2%3!:!

+DD(!+.!-.D-0+&%6!1%6!D96+&-%.(!%1!'E+0&,F!:#!3''G(!+.D!-&(!-.&'6+0&-%.!3-&5!.97<'6!%1!

3''G(!%1!HI!<'.'1-&(!+J+-,+<,'C!2%3!B!D6%/(!%<('6J+&-%.(!35%('!9.'7/,%F7'.&!D96+&-%.!+&!

&5'!<'K-..-.K!%1!&5'!(/',,!%6!&5'!<+('!8LM!-.&'6J-'3!3+(!:#@!":@!%6!?A!3''G(@!+(!3',,!+(!

&5%('!-.!7%.&5N-.N(+7/,'!:!&5+&!+6'!-.0%.(-(&'.&!3-&5!&5'!D96+&-%.!-.!7%.&5!4=!$>#4@A"OC!!

2%3!O!D6%/(!+,,!%<('6J+&-%.(!3-&5!D96+&-%.(!9.D'6!A!3''G(=!$>OP@A":C!!2%3!"!0%9.&(!+,,!HN

$!+.D!HNQ!&6+.(-&-%.(!+(!'E-&(!16%7!9.'7/,%F7'.&@!'J'.!&5%('!R&5'!HN$NH!+.D!HNQNH!

&6+.(-&-%.(S!&5+&!6'&96.!&%!9.'7/,%F7'.&!&5'!1%,,%3-.K!7%.&5C!2%3(!#N4B!9('!&5'!<+(',-.'!

(/'0-1-0+&-%.!%.!D-11'6'.&!(9<(+7/,'(@!3-&5!(+7/,'!(-T'(!+(!-.D-0+&'D!-.!&5'!&+<,'C!!I&+,-0-T'D!

'(&-7+&'(!-.!0%,97.(!:!+.D!O!-.D-0+&'!(/'0-1-0+&-%.(!35'6'!&5'!HI!'11'0&(!3'6'!U%-.&,F!

-.(-K.-1-0+.&!+&!&5'!"V!,'J',C!

Avg.

hazard in

2010:Q4

Effect of UI

extensions

Avg.

hazard in

2010:Q4

Effect of UI

extensions

(1) (2) (3) (4)

Alternative specifications & samples

(1) Baseline 13.4% -0.5 p.p. 9.0% -1.0 p.p.

(2) Separate effect on 26 wks 13.4% -0.5 p.p. 9.0% -1.0 p.p.

(3)

Drop round number & inconsistent

durations

12.8% -0.5 p.p. 7.9% -1.5 p.p.

(4)

Drop very short durations 14.2% +0.1 p.p. 9.6% -1.1 p.p.

(5) Count all UE exits 16.5% -0.6 p.p. 13.7% -1.3 p.p.

Subsamples

Age

(6) Age 25-54 (N=53,104) 14.4% -1.0 p.p. 7.5% -1.8 p.p.

(7) Age 55+ (N=13,990) 11.6% +1.4 p.p. 9.7% +0.5 p.p.

Gender

(8)

Men (N=47,782) 13.7% -0.2 p.p. 7.3% -1.2 p.p.

(9)

Women (N=30,031) 13.0% -1.0 p.p. 11.7% -0.8 p.p.

Education

(10)

HS or less (N=43,628) 13.3% -0.4 p.p. 10.0% -1.8 p.p.

(11)

Some college or more (N=34,185) 13.7% -0.5 p.p. 7.8% -0.1 p.p.

Industry

(12)

Const./manuf. (N=25,584) 14.2% +0.4 p.p. 7.4% -2.1 p.p.

(13)

All other industries (N=52,229) 13.1% -0.9 p.p. 9.7% -0.4 p.p.

Reemployment

Labor force exit

"#!

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$%&'()!!*+,-!./01'2'3!2%4!2'52'('.&(!+!('5+2+&'!(5',676,+&6%.8!!9::!6.,:/3'!6.36,+&%2(!7%2!

&-'!3/2+&6%.!%7!&-'!/.'05:%;0'.&!(5'::<!6.!4''=(!>/5!&%!?@A<!5:/(!+!:6.'+2!(5:6.'!46&-!=6.=(!

+&!BC<!DC<!"C<!@C<!#C<!EC<!+.3!FC!4''=(8!!G%4(!H!+.3!B!6.,:/3'!(&+&'!+.3!0%.&-!6.36,+&%2(!

5:/(!+!,/16,!6.!&-'!(&+&'!/.'05:%;0'.&!2+&'I!2%4!?!2'5:+,'(!&-'('!46&-!(&+&'J1;J0%.&-!

6.36,+&%2(8!!K+:,/:+&6%.!%7!4''=(!/.&6:!LM!'N-+/(&6%.!6(!1+('3!%.!&-'!'N5',&+&6%.!0%3':!

3'(,261'3!6.!&-'!&'N&<!+55:6'3!&%!&-'!3+&'! %7! &-'!1+(':6.'!(/2O';8!

Any weeks

left

# of

weeks left

max(0,

# of weeks -

10)

Effect of UI

extensions in

2010:Q4

(/10) (/10) (p.p.)

(1) (2) (3) (4)

Logit for unemployment exit

(1) 0.12 -0.36 0.39 -0.7 p.p.

(0.08) (0.10) (0.11)

(2) 0.10 -0.33 0.37 -0.5 p.p.

(0.08) (0.11) (0.12)

(3) Multinomial logit with state, month, UR controls

Reemployment -0.03 -0.29 0.35 -0.0 p.p.

(0.11) (0.13) (0.14)

Labor force exit 0.20 -0.36 0.35 -0.6 p.p.

(0.10) (0.12) (0.13)

Logit for unemployment exit

with state, month, UR controls

Logit for unemployment exit

with state-by-month controls

"#!

!"#$%&'(&&)**%+,&-*&./&%0,%123-12&-1&$"#-4&5"46%,&"774%7",%2&31&8"19"4:&;<==&

$%&'()!!*+,-./&',!'00'.&(!/1'!&2'!,-00'1'+.'!3'&4''+!&2'!/.&5/6!6'7'6!8%1!1/&'9!/+,!/!

(-:56/&',!6'7'6!8%1!1/&'9!%3&/-+',!4-&2!3'+'0-&!,51/&-%+(!2'6,!0-;',!/&!<=!4''>(!&21%5?2%5&!

&2'!<@@AB<@CC!D'1-%,E!5(-+?!.%'00-.-'+&(!01%:!&2'!-+,-./&',!(D'.-0-./&-%+(F!!GH'&2%,!CI!/+,!

GH'&2%,!<I!1'0'1!&%!/6&'1+/&-7'!&1'/&:'+&!-+!&2'!.%5+&'10/.&5/6!%0!1'(-,5/6(!%3&/-+',!01%:!

(-:56/&-+?!&2'!/.&5/6!,/&/J!(''!&';&!0%1!,'&/-6(F!!K-:56/&-%+(!-+!L/+'6!M!/((5:'!&2/&!-+!&2'!

.%5+&'10/.&5/6!(.'+/1-%!&2'!:56&-+%:-/6!6%?-&!-+,';!0%1!&2'!6/3%1!0%1.'!';-&!%5&.%:'!4%56,!

.2/+?'!35&!&2'!-+,';!0%1!&2'!1'':D6%N:'+&!%5&.%:'!4%56,!3'!5+/00'.&',F!

Method 1

Method 2 Method 1 Method 2 Method 1 Method 2

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

Panel A: Baseline

Actual in January 2011

Panel B: Full effect of UI extension

Strategy 1 (Table 5, Col. 2) +87 +370 +0.1 p.p. +0.2 p.p. +0.5 p.p. +1.6 p.p.

Strategy 2 (Table 3, Col. 6) +131 +297 +0.1 p.p. +0.2 p.p. +0.3 p.p. +0.9 p.p.

Strategy 3 (Table 5, Col. 5) +283 +759 +0.2 p.p. +0.5 p.p. +0.9 p.p. +2.8 p.p.

Strategy 4 (Table 7, Row 3) +5 +226 +0.0 p.p. +0.1 p.p. +0.6 p.p. +1.5 p.p.

Panel C: Effect operating through labor force participation

Strategy 1 (Table 5, Col. 2) +98 +264 +0.1 p.p. +0.2 p.p. +0.3 p.p. +0.9 p.p.

Strategy 3 (Table 5, Col. 5) +183 +476 +0.1 p.p. +0.3 p.p. +0.5 p.p. +1.6 p.p.

Strategy 4 (Table 7, Row 3) +92 +208 +0.1 p.p. +0.1 p.p. +0.3 p.p. +0.8 p.p.

Level (1,000s)

Rate

Long-term unemp.

Unemployment

14,937

9.0%

45.5%