Unemployment and Subsequent Earnings: Estimating Scarring Among British Men 1984–94

UNEMPLOYMENT AND SUBSEQUENT EARNINGS:ESTIMATING SCARRING AMONG BRITISH MEN

1984 ± 94�

Mary Gregory and Robert Jukes

This paper estimates the impact of unemployment on earnings following re-employment for alarge and representative sample of British men, 1984±94. Unemployment incidence is foundto have only a temporary effect, an average earnings setback of 10% on initial re-engagementlargely eroding over two years. The effect of unemployment duration, by contrast, is perma-nent, a one-year spell adding a further penalty of 10 percentage points. These wage penaltiesare least for young men and the low paid ± those most at risk of unemployment ± and greatestfor prime age and highly paid men.

Unemployment is part of the ongoing process of workforce reallocation, asjobs are created and destroyed, and employers and workers seek suitable jobmatches. The macroeconomic ef®ciency gains from this do not, however,preclude adverse effects on individuals, temporarily or permanently, from thereallocation process or from intervening spells of unemployment. Duringunemployment, work experience cannot be accumulated. Skills previouslyacquired may be eroded, or the acquisition of new skills inhibited. Unemploy-ment may leave a `scar' in the form of lower future earnings.

The impact of an unemployment spell on subsequent earnings for Britishmen is the focus of this paper. Speci®c questions are to be examined: Wherethe transition to a new job has involved a spell of unemployment, do earningstake a different future pro®le from those of otherwise similar workers whohave not been unemployed? Where re-entry into employment is accompaniedby a fall in wages, is this a temporary setback only, with earnings subsequentlyregaining their previous trajectory? Is it the incidence of unemployment or itsduration which is more important? Do the effects vary systematically acrossdifferent groups, particularly as proneness to unemployment differs?

The main vehicle for the analysis is the linked New Earnings Survey Panel(NESPD) and the Joint Unemployment and Vacancies Operating System(JUVOS) dataset which provides combined earnings and unemploymenthistories for 1% of employees in Britain over the period 1984±94. The NESPD

The Economic Journal, 111 (November), F607±F625. # Royal Economic Society 2001. Published by Black-well Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.

[ F607 ]

� This work was supported by the Employment Market Research Unit of the Department forEducation and Employment, who made the NESPD/JUVOS data set available to us and ®nancedRobert Jukes' participation. We are grateful for their support and co-operation. Responsibility for theuse of the data and the views expressed is exclusively the authors'. Part of the work was carried out whileMary Gregory was visiting the Research School of the Social Sciences, ANU, Canberra, whose hospitalityis warmly acknowledged. Helpful comments have been received from Andrew Rees, Bill Wells, AlisonBooth, Mono Chatterji, Bob Gregory, Bruce Chapman, Denise Doiron, Peter Kuhn, Tom Crossley, SteveNickell, Adrian Kalwij, Wiji Arulampalam, Mark Stewart, participants at EALE and AEA conferences,and at seminars at DfEE, Government Economic Service, Universities of Oxford, Essex, Sydney andMelbourne, Murdoch University and ANU. Comments and suggestions from editor Steve Machin andthree referees for this JOURNAL have improved the presentation.

is the pre-eminent source of earnings information for Britain, while JUVOSgives comprehensive coverage of the individual's unemployment claimantrecord, including the number, timing and duration of all unemploymentspells. The linked data contain longitudinal information on the individual'sunemployment experience, some personal and job characteristics, and earn-ings both before and after any spells of unemployment. The analysis focuseson the experience of men, where attachment to the labour force is generallystronger and unemployment measured by claimant status more clear-cut thanin the case of women.

Section 1 brie¯y reviews the predictions from economic theory, and Section2 the evidence from previous studies. The data are described in Section 3.Section 4 reviews the unemployment experience of men in employment. Theestimating equation and econometric issues are discussed in Section 5. Section6 presents the results, and Section 7 concludes.

1. Pointers from Economic Theory

Three areas of economic analysis give predictions about the impact of unem-ployment on subsequent earnings: human capital, search and imperfectinformation. Their predictions, however, are neither clear-cut nor mutuallyconsistent.

Human capital theory contributes in an obvious way. Through on-the-jobexperience plus any formal training, the worker accumulates ®rm-speci®c skillswhich are rewarded through the wage, as evidenced in the widely documentedassociation between earnings and job tenure.1 To the extent that these skillsare non-transferable, their contribution to the worker's productivity is perma-nently lost when employment with the ®rm is terminated. In addition, it isoften argued that unemployment brings the depreciation of general ortransferable work skills, and even that this depreciation accelerates as theunemployment spell lengthens. These two aspects of the reduction of humancapital resulting from unemployment both point to lower productivity andtherefore a lower wage on re-entry. The loss of ®rm-speci®c skills should re¯ectunemployment incidence, and not be directly in¯uenced by the duration ofthe unemployment spell. Any deterioration of transferable skills, on the otherhand, should be linked to unemployment duration. A further implication ofthe human capital approach is that re-engagement stops, and possibly reverses,the erosion of general human capital while ®rm-speci®c skills start to re-accumulate, with tenure, following re-engagement. The initial earnings setbackshould therefore be followed by the resumption of an upward trend inearnings.

The human capital approach, and particularly its interpretation of thereturn to tenure, has not gone unchallenged. An obvious dif®culty is that all

1 The seminal contributions to the human capital literature are Becker (1975) and the empiricalapplications by Mincer (1974). A particularly extensive application in the context of unemploymentand sequential jobs is Addison and Portugal (1989).

F608 [ N O V E M B E RT H E E C O N O M I C J O U R N A L

# Royal Economic Society 2001

job changes, with or without an intervening spell of unemployment, imply theloss of the ®rm-speci®c element of human capital. Yet many job changes bring,and are clearly motivated by, pay increases. The `matching' view of job tenuredevelops this insight. On this view, a worker changes jobs, incurring anyintervening spell of unemployment required for job search, only to improvehis position. Where a good match is achieved between worker and job, theresulting productivity is re¯ected in the wage. Good (high productivity, highwage) matches are durable, giving rise to the association of higher earningswith tenure. Given proper control for the aspects contributing to the match,much of the return to tenure is an illusion.2 When a good match is disrupted,`match' capital is lost and a subsequent wage setback is to be expected. On theother hand, where the employment relationship is terminated by either sidebecause of the poor quality of the match, future earnings will be enhanced if abetter match is located. To the extent that unemployment allows improved`sorting' of workers among jobs, higher earnings may be expected. Moreover,since unemployment duration re¯ects search for a suitable new job, up to thepoint where the expected return from locating a better match just balancesthe cost of the additional period of unemployment, longer durations shouldpoint to improved subsequent earnings.

The basis for the third approach is the fact that, at the time of engagement,the employer can have only a limited knowledge of a new worker's productiv-ity. He will therefore seek signals which may convey information on this. Theemployee's unemployment history, in terms of both incidence and duration,may be regarded as providing such a signal, and a negative one.3 Thissignalling or asymmetric information approach gives two predictions on theimpact of unemployment on subsequent earnings. The ®rst concerns the timepro®le. The initial wage penalty, attributable to incomplete information at thetime of re-engagement, should be eroded over a relatively short period if thenew worker proves to be of a higher productivity than the employer initiallyinferred from his unemployment history. Second, redundancy due to plantclosure should give a less negative signal than redundancy by selection from aplant which continues in production.

Common to each approach is the implication that unemployment maymatter for the future wage in one or more of three dimensions: incidence,duration and elapsed time relative to the wage observed. They do not,however, yield quantitative predictions about either the magnitude or thetime pro®le of the effects. The objective of our empirical analysis is toincorporate all three aspects, with the aim of evaluating their relative contribu-tions.

2 Leading examples of research developing the `matching' approach are Altonji and Shakotko(1987), Mortensen (1988) and Topel (1991).

3 On the stigma effect of an unemployment record, see Belzil (1995) and Vishwanath (1989).

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2. Evidence from Existing Studies

The most extensive body of analysis of the re-employment earnings experienceof displaced workers relates to the United States, with only a small number ofstudies available for economies with different labour market institutions. Theweight of evidence from the US studies indicates substantial and long-lastingwage setbacks following displacement, but the limited evidence for othereconomies suggests effects which are signi®cantly less strong.

The earnings of American workers before and after displacement have beenextensively examined using the Displaced Worker Survey supplements to theCurrent Population Survey, and the Michigan Panel Study of Income Dy-namics.4 A typical ®nding is that a displaced worker experiences an earningsloss of between 5% and 15% on re-employment, earnings losses being greatestfor long-tenure workers.

However, these `before' and `after' comparisons, focusing only on workerswho have been displaced, are likely to underestimate the extent of wage loss.Given ongoing wage growth for non-displaced workers, even re-engagement atthe previous wage implies a relative loss. Potentially more seriously, severalstudies have drawn attention to the tendency for the relative wages of displacedworkers to decline prior to separation, as deteriorating prospects for the ®rmor emerging ®nancial distress lead to downwards pressure on wages beforeseparations are initiated. Evidence from Jacobson et al. (1993a,b) suggestsdeclining relative wages for long-tenure workers for at least three years prior toplant closures or major redundancies, with the earnings decline being around15%. Similar evidence is presented by Ruhm (1991) and de la Rica (1995),who identify a 10% wage deterioration at least two years prior to separation.

Similar indications that subsequent redundancies have a prior depressiveeffects on wages can be found for Britain. Gregory et al. (1987), using the CBIPay Databank on wage settlements in the ®rst half of the 1980s, report thatwhere `risk of redundancy' was cited by the employer as a `very important' or`important' downward pressure on the pay settlements, the settlement out-come was signi®cantly lower than in settlements where it was cited as unim-portant. Blanch¯ower (1991), using the British Social Attitudes Surveys, ®ndsthat workers who stated that they expected to be made redundant were beingpaid 8% less than otherwise comparable workers.

These ®ndings con®rm that a simple comparison of pre- with post-separa-tion earnings for displaced workers is insuf®cient. To capture the full impactof unemployment on earnings, a longer trajectory of earnings experience isrequired, before as well as after unemployment. Further, the earnings experi-

4 The Displaced Worker Survey, a biannual supplement to the annual Current Population Survey(US Bureau of the Census), is based on retrospective information, usually for the preceding three years.It relates to workers who have been permanently displaced by plant closure, job abolition or slack work,and distinguishes those with at least three years of job tenure from all displaced workers. In the PanelStudy of Income Dynamics displaced workers are de®ned as those who have been permanentlydisplaced, regardless of their length of tenure in the job. The original analysis for the DWS of 1984 isFlaim and Sehgal (1985). See also Hamermesh (1989), Podgursky and Swaim (1988), Swaim andPodgursky (1991), Kletzer (1989), Topel (1990), Farber (1993) and Hall (1995).



ence of workers with unemployment must be set in the context of earningschange for those in continuous employment.

In one of the most comprehensive studies of American experience, Jacobsonet al. (1993a), using data on over 20,000 high-tenure displaced and non-displaced workers in Pennsylvania in the early to mid-1980s, identify fourphases in the impact of unemployment on the earnings of displaced workers.The relative earnings of displaced workers begin to decline substantially threeyears prior to separation, falling by 15% before separation; initial re-engage-ment following displacement brings a further sharp drop, of around one-quarter; the six subsequent quarters bring rapid recovery but, thereafter,further advance is slow. The overall effect is that, ®ve years after separation,earnings losses still amount on average to 25% of pre-displacement earnings.This study emphasises that, within its focus on high-tenure workers, the samebasic picture of earnings losses applies with only minor variations to men andwomen, younger and older workers, and across a broad spectrum of industriesand labour market conditions. Similar estimates are derived by Ruhm (1991)using the Panel Study of Income Dynamics, who ®nds earnings for displacedworkers four years after displacement to be still 10±13% below those ofcomparable non-displaced workers.

The small number of comparable studies available for European countriestend to indicate a rather different pattern. For Belgium, where unemploymentdurations are among the longest in the OECD, a preliminary study by VanAudenrode and Leonard (1995) ®nds that re-employed workers experience atmost only limited wage losses. Similarly Ackum (1991) ®nds that unemploy-ment does not signi®cantly reduce re-employment wages for young people inSweden (although, at that time, Sweden's experience of unemployment wasmuch more limited than in Britain). For Austria, on the other hand, Pichel-mann and Riedel (1993) ®nd that a 10% increase in the duration of unemploy-ment is associated with a 0.8% reduction in re-employment earnings, with theloss persisting over the medium run.

This suggests that, in the United States, while displaced workers bene®t fromrelatively rapid re-employment, the process of re-engagement brings not somuch a `sorting' of workers into more suitable new jobs, but a general slippagedown the earnings ladder, with earnings losses which are substantial and long-lasting. On the other hand, the limited evidence for European countries is thatlosses on re-engagement are lower, possibly negligible and, where they exist,are more quickly overcome. Against this, however, the longer duration of theaverage unemployment spell causes much higher immediate forgone earnings.

3. The NESPD-JUVOS Dataset

The NESPD-JUVOS dataset links the New Earnings Survey Panel Dataset(NESPD), which contains longitudinal data on individual earnings, with theJoint Unemployment and Vacancies Operating System (JUVOS) data onindividual unemployment experience. The New Earnings Survey (NES) is thepremier source of earnings information in Great Britain. The survey is sampled



on individuals whose National Insurance number ends in a speci®ed pair ofdigits, generating a 1% random sample of all adults. Since the same pair ofterminating digits is used for each year's sample, a panel is automaticallygenerated within the surveys. The NESPD contains the NES information inpanel form from 1975. The questionnaire is directed to the employer, whocompletes it from payroll records for a speci®ed week in April of each year.The legal obligation on the employer to return the questionnaire and its basisin payroll records ensure a high response rate and a high degree of accuracy inthe earnings information. Moreover, should an individual not be included inthe NES in any year, due to unemployment, temporary withdrawal from thelabour force, or a failure of sample location, the sampling frame ensures thathe should be located for the survey in any future year when he is in employ-ment. Consequently, absence from the sample frame or failures of samplelocation do not lead to cumulative attrition.

The process of sample location, through screening of employers' payrollreturns to local tax of®ces, in principle generates a random sample of allemployees in employment, irrespective of sector, ®rm size, and type of job. Inpractice, however, failures of sample location occur. Where the employee'searnings and hours put him below income tax and National Insurance thresh-olds, there is no obligation on the employer to make a return to the tax of®ce.While some employers supply computerised payroll records covering all em-ployees, the majority return only those records where tax is involved. This leadsto signi®cant under-sampling of the low paid and particularly of part-timeworkers. While women make up the majority in these categories, low-paid menand men in `small' jobs are also under-represented in the NESPD data. Afurther limitation arises from `void' returns from employers. Although theresponse rate for the NES is generally around 95%, as measured by theproportion of questionnaire forms returned, where the employee has left hisprevious employer between the date of location of the sample and thereference pay week, the employer can ®le only a void return. This will be thecase even if the employee has made a direct job-to-job transition, as he will nothave been entered in his new employer's payroll return to the tax of®ce. Thisunder-sampling of recent job changers gives rise to further under-representa-tion of the low-paid and of employees in sectors where job changes arefrequent. It should also be noted that the earnings information in the NESrelates to a speci®ed pay week. Although this is chosen to have minimalseasonal irregularities, nonetheless it provides only a snap-shot of the indivi-dual's earnings within the year, and, particularly for those with discontinuousemployment, may be a poor representation of earnings over the year as awhole.5

The JUVOS cohort is a 5% sample of all claims for unemployment-relatedbene®ts, again selected by reference to the claimant's NI number. The JUVOSrecords give the individual's complete unemployment history, including the

5 A further description of the NES is given in Gregory et al. (1990), and of the NESPD in Elias andGregory (1994).



number of spells of unemployment, their timing and duration, from the thirdquarter of 1983. The terminating digits on NI numbers used to select theJUVOS 5% sample include those used to select the NES 1% sample, allowingmatching of the individual's JUVOS record with the NESPD record, to givelinked information on employment and earnings histories, in conjunction withunemployment experience.6

While this matched dataset is well aligned to the question of the impact ofunemployment on future earnings, an important limitation should be noted.The JUVOS data relate to claimant unemployment. Figures being made avail-able for recent years indicate that only around half of those ceasing to claimthese bene®ts have found work. The next largest group `failed to continueclaim' for unspeci®ed reasons. Further sizeable numbers `leave' unemploy-ment for other types of bene®t, including sickness and disability. Since thelater 1980s, unemployed older men have been allowed to transfer ontoretirement bene®t, removing them from the claimant count. Thus the bound-ary between unemployment and various forms of economic inactivity hasbecome increasingly blurred. While the JUVOS data for the period studiedhere con®ne us to claimant unemployment, the shifting relationship betweenthis and other not-in-work categories should be kept in mind. Where data areavailable, extending job interruption to include economic inactivity morebroadly offers an alternative perspective (Arulampalam, this volume).

The NESPD-JUVOS data-®le for the period 1984±94 contains informationon over 150,000 men who are present in one or more years. Since our focus ison earnings, we have restricted the sample to those men for whom informationon hourly earnings is available in the relevant year and whose earnings in thesurvey period are not affected by absence. The number of men in this samplevaries with the macroeconomic cycle in overall male employment, from a lowof 66,000 in 1994 to almost 77,000 in 1988.

4. The Unemployment Experience of Men in Employment

The more common perspective in the analysis of unemployment is forward-looking, identifying the characteristics of those who become unemployed. Forpresent purposes, the relevant perspective is backward-looking: the pastunemployment experience of men in work. This section gives a brief review ofthis from our data.

As emphasised by Gregg (this volume), for young workers, unemployment isvery much a minority experience; only between 3% and just over 5% of men inemployment at any point in time have been unemployed within the previoustwelve months (Fig. 1).7 This proportion is essentially untrended over thedecade. It is, however, strongly pro-cyclical, rising with the upswing in econom-

6 A description and assessment of the NESPD-JUVOS dataset is given in Jukes (1995).7 This proportion may appear small, but it should be borne in mind that only around one-half of

men exiting from unemployment go into work.



ic activity in 1986±88, falling as the economy moved into recession in 1990±91, and rising again with the recovery in 1993±94. Since the incidence of pastunemployment is measured from the perspective of those in employment, thiscyclical pattern re¯ects the return to work of the unemployed in periods ofexpansion and the reduced exits from unemployment in recession. Fewer than1% of men in employment experience more than one spell of unemploymentwithin the preceding year, its incidence exhibiting the same cyclical pattern asdescribed above. Recent unemployment experience is highest among youngworkers (Fig. 2), and among those in lower skill occupations and the low paid(not shown). It varies strikingly across the earnings distribution (Fig. 3).Among those in the highest earnings quintile, typically fewer than 1% havebeen unemployed in the previous twelve months, never more than 2% of those

0%

1%

2%

3%

4%

5%

6%

84 85 86 87 88 89 90 91 92 93 94

Last 12 Months Repeated in Last 12 Months

Fig. 1. Men in Work Unemployed Within the Last 12 Months

,19 19–25 25–35 35–45 45–55 551

0

5

10

15

20

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84 85 86 87 88 89 90 91 92 93 94Year

Fig. 2. Men in Work Unemployed in the Last 12 Months, by Age



in the fourth quintile, but on average 10% of those in the bottom quintile; lowpaid men are ten times more likely to have experienced recent unemploymentthan men at the top of the earnings distribution. As shown in Fig. 4, theabsorption of the long-term unemployed into employment is highly sensitiveto the cyclical state of the economy, and concentrated in low-wage employ-ment.

5. Estimating the Impact of Unemployment on Wages

Our empirical framework is a wage equation of standard form, involvinghuman capital measures and other controls, augmented by measures of theindividual's past unemployment experience. The equation is estimated on thefull sample of men in employment, with and without past unemployment,

02468

1012141618

Per

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1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Year

Fig. 3. Men in Work Unemployed in the Last 12 Months by Earnings Quintile

0.0

0.5

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2.0

2.5

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3.5

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Per

cent

age

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84 85 86 87 88 89 90 91 92 93 94

Year

1st Quintile 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile

Fig. 4. Men in Work with over 365 Days of Recent Unemployment, by Earnings Quintile



setting the impact of unemployment against the general evolution of maleearnings.

Three dimensions of previous unemployment experience have been identi-®ed as potential in¯uences on subsequent earnings: occurrence (incidence),duration of the spell and the elapsed time between the end of the spell and thewage observation. We measure incidence and timing jointly through the dummyvariable UN ÿ Qk. This refers to the individual's most recent spell of unemploy-ment prior to the wage observation; in coding the data UN ÿ Qk takes a unitvalue when this exit took place k quarters prior to the wage observation. Sincethe dataset gives a wage observation only once in the year, we cannot trace thequarterly wage evolution directly for the individual. Instead, the pro®le isderived from the separate groups who have exited unemployment in each ofthe k successive quarters prior to the wage observation. So those individuals wholast left unemployment in the three months prior to the wage observation aregiven a unit value for UN ÿ Q1; those who most recently exited unemploymentbetween three and six months previously are given a unit value for UN ÿ Q2, andsimilarly for earlier dates of exit. Where these individuals remain in continuousemployment for more than one year their next wage observation again picks uptheir prior unemployment experience; so, for example, those whose wages wereobserved one quarter after their exit from unemployment (UN ÿ Q1) will havetheir next wage observation linked with UN ÿ Q5. The length of the time-frameforÿk, the period of in¯uence of unemployment on future wages, as well as thesize of the wage impact, are to be determined by the data. Depression of wagesprior to unemployment is tested through the variable UN � Qk which takes a unitvalue if the individual is to become unemployed k quarters after the wageobservation, with the span of �k again to be determined by the data. Thepotential impact of the length of the most recent unemployment spell isrepresented through its duration measured in days, DURATION. As a further testof scarring due to earlier unemployment spells two variables are used: SPELLS,measuring the total number of prior spells, and PRIOR-DURN, the cumulatedduration, in days, of these earlier spells.

The form of the earnings equation is:

ln wit � f (INDIV i , EXPERit , TENURE it , AGE it , UN ÿ Q kit , DURATION it , SPELLS it ,

PRIOR-DURN it , UN � Q kit , PARTTIME it , PUBLIC it , OCC it , INDit , REGION it , TIME t) (1)

where the subscript it denotes individual i in year t, w is hourly earnings,excluding overtime pay and overtime hours, de¯ated to 1994 prices by theretail prices index, and INDIV are the time-invariant individual characteristics.Work experience, EXPER , is measured as the number of years of employmentwhich the individual has accumulated, including in his present job, less totaltime spent unemployed. From 1975, the number of years in employment aretaken as the number of years in which the individual is present in the NESPD.For those older workers who were in employment prior to 1975, the extra yearsof work experience are estimated as his age in 1975 less 17 years for time ineducation prior to starting work. From 1984, time spent unemployed is taken



directly from JUVOS. Prior to that year, an imputation is made, but only forthose individuals with a subsequent unemployment record in JUVOS. Theseindividuals are attributed the average unemployment experience of their age-by-occupation group for each year between 1975 and 1982 in which they are inemployment. TENURE in the current job is measured by cumulating responsesover successive years to the question in the NES whether the employee hasbeen in his present job with his current employer for more than twelvemonths. It is at least plausible that the individual's age (AGE, measured inintervals) has an in¯uence on earnings in addition to the effects of experienceand tenure; increasing personal maturity may enhance productivity anddiminishing energy or ¯exibility subsequently diminish it. For each age group,it will also capture any cohort effects. A series of dummy variables control forother in¯uences on the individual's earnings: occupation (OCC); industry(IND); region of workplace (REGION); working less than 30 hours work per week( PARTTIME); employment in the central or local government, or a publiccorporation ( PUBLIC). Time dummies control for economy-wide real wagegrowth and the effects of the macroeconomic cycle.

Estimation of (1) presents two econometric issues. The ®rst is unobservedheterogeneity. Educational attainment is in principle observable and highlyrelevant to earnings, but the NESPD, based on the employer's payroll records,does not record this. This, and other time-invariant dimensions of individualheterogeneity including work attitudes, motivation and ability are controlledfor through within-group estimation. However, the within-group estimatorcontrols only for individual ®xed effects. Other time-varying in¯uences onearnings not available in the data set have to be omitted. These includedemographic characteristics which may affect the individual's ®nancial respon-sibilities, along with characteristics of the establishment (eg number of employ-ees) and of the job, other than those captured through the industry andoccupational dummies. Similarly, potentially important local labour marketeffects, such as those relating to inner cities or rural areas, will not beadequately captured through the regional dummies. To the extent that any ofthese omitted variables are correlated with the included variables, thereremains a risk of bias in the estimated coef®cients.

A further issue is sample selection. For an individual's wage to be observed hemust (a) be in employment, (b) be located for the NESPD survey and (c) reporthourly earnings which are not affected by absence. Each of these requirementsmay give rise to non-randomness in the sample of wage observations.

(a) Of those not present in the NESPD in any year, JUVOS identi®es thosewho are unemployed. An important further category, however, are neither inwork nor unemployed, i.e. are economically inactive. Like unemployed men,this missing group come disproportionately from older, low-skilled and poten-tially low-paid men.

(b) The main group whom the sample fails to locate are recent job-changers. These are disproportionately, but by no means exclusively, youngworkers, low-paid, and from certain high-turnover industries and occupations.



(c) Sickness rates are higher among manual than non-manual men, andalso vary across industries. Loss of pay due to absence is more likely whereearnings are based on hourly or work-related rates than in jobs with a greateradministrative component.

We aim to correct for this potential non-randomness by running a probit onpresence in the wage sample and including the associated Heckman correctionterm in the wage equation. Since the NESPD is a survey of wages, all theinformation contained in it is relevant to the wage equation. The additionalvariables included in the probit are the number of prior unemployment spellsand their cumulated duration (re¯ecting their impact on subsequent inactivityor job instability) at longer lags than those used in the wage equation. Tostrengthen identi®cation, age is entered as quadratic, where it is in piece-wiseinterval form in the wage equation. A further contribution to identi®cationderives from the nonlinear functional form of the probit. Although thecorrection term itself is signi®cant, its inclusion leaves the estimated coef®-cients in the wage equation essentially unchanged (Table 1). There are twopossible interpretations for this result. The ®rst is that, in spite of the a priori

Table 1Within-group Estimates of Hourly Earnings: Unemployment Variables, Full Sample

Dependent variable: ln hourly earnings

With Without With WithoutHeckman Heckman Heckman Heckman

Variable correction correction Variable correction correction

UN ÿ Q 1 ÿ0.116(23.1)

ÿ0.109(24.0)

DURATION/100 ÿ0.026(28.10)

ÿ0.026(24.9)

UN ÿ Q 2 ÿ0.098(21.2)

ÿ0.098(23.3)

DURATIONSQ /10000 0.001(18.77)

0.000(17.1)

UN ÿ Q 3 ÿ0.107(24.1)

ÿ0.114(28.4)

UN ÿ Q 4 ÿ0.083(18.2)

ÿ0.076(18.6)

PRIOR-DURATION/100 ÿ0.037(43.52)

ÿ0.029(36.7)

UN ÿ Q 5 ÿ0.076(17.5)

ÿ0.064(16.3)

PRIOR-DURATIONSQ /10000 0.001(25.02)

0.002(6.9)

UN ÿ Q 6 ÿ0.072(15.7)

ÿ0.066(15.8)

UN ÿ Q 7 ÿ0.073(16.7)

ÿ0.076(19.0)

UN � Q 0 ÿ0.027(9.6)

ÿ0.025(9.8)

UN ÿ Q 8 ÿ0.055(12.4)

ÿ0.044(10.6)

UN � Q 1 ÿ0.020(7.9)

ÿ0.038(15.6)

UN ÿ Q 9 ÿ0.037(10.52)

ÿ0.026(8.2)

UN � Q 2 ÿ0.010(3.8)

ÿ0.017(6.9)

PU ÿ0.019(10.67)

ÿ0.020(4.5)

LAMBDA ÿ0.091(16.6)

R2 0.238 0.208Adjusted R2 0.237 0.208Standarderror

0.157 0.167

No. ofobservations

57,5938 57,5938

Equations include EXPER, TENURE, AGE, PARTTIME, CENTRALGOV, LOCALGOV, PUBLIC, OCC, DIV, REGION, YEAR.



arguments given above, non-presence in the NESPD is close to random.Alternatively, the identi®cation achieved may be weak. However, Arulampalam(this volume), who can include a further range of personal and householdcharacteristics in the probit, also ®nds that the inclusion of the correctionterm does not change the estimated coef®cients on the unemployment termsin the wage equation, suggesting that, in this area, selection correction doesnot have a major role to play.

6. Estimation Results

Since our focus is on the effects of unemployment on the wage, the resultsreported in Table 1 concentrate on these variables; the full set of estimatedcoef®cients is given in Appendix Table A. Looking ®rst at the impact of jobinterruption, separately from its duration, a spell of unemployment whichended in any of the immediately preceding three quarters (UN ÿ Q1, UN ÿ Q2,UN ÿ Q3) carries an earnings penalty of around 10%. Given continuous subse-quent employment, the impact diminishes as the interruption recedes furtherinto the past (UN ÿ Q4 to UN ÿ Q9). After two years, the most recent completedspell still depresses earnings, but the effect has fallen to 3.7% (UN ÿ Q9).Thereafter, quarterly coef®cients become erratic and poorly de®ned. Thesubsequent time pro®le has therefore been captured by switching in a marker,PU, for all subsequent periods; its coef®cient measures the effect of unemploy-ment incidence beyond the detailed two-year pro®le ± the permanent scar.This indicates a permanent earnings loss, beyond the two-year period, of 1.9%.Taken together, the variables UN ÿ Qk and PU suggest that job interruptioninvolving intervening unemployment has a substantial impact on subsequentearnings; however, this effect is mostly temporary, largely disappearing overtwo years of continuous subsequent employment.

In addition to the earnings penalty from the occurrence of an unemploy-ment spell, a further decrease in future earnings can be identi®ed arising fromthe duration of the most recent unemployment spell (DURATION). An unem-ployment spell of 30 days reduces earnings by a further 0.8%, rising to 5.1%for a six-month spell, and to 11.1% for a one-year spell. A one-year spell ofunemployment thus doubles the impact of job interruption on ®rst-year earn-ings after re-employment. Combining these two effects from incidence andspell length, a worker re-entering employment after a year's unemploymentwill, ceteris paribus, experience an initial reduction in earnings of 20% from thelevel expected without the unemployment spell. Subsequent continuous em-ployment reduces the earnings loss from the employment interruption, butnot from its duration. The overall setback remains around 13% even after twoyears, and continues at that level thereafter.

The quadratic term on length of spell is well determined and offsetting. Alonger spell brings greater earnings loss, but at a diminishing rate. Thiscontradicts the hypothesis of accelerating loss of human capital with theduration of unemployment.

Further prior spells of unemployment have an additional depressive effect



on earnings, whether measured by their cumulated duration or by number ofspells. Unlike the most recent spell, it is not possible to obtain separatecontributions for cumulated duration as against cumulated number of spells.Cumulated duration performs rather better, and is the measure which isreported ( PRIOR±DURN).8 The impact of unemployment duration in early spellsis marginally greater than the impact from the most recent spell.

Future unemployment ( UN � Qk) is indeed foreshadowed in present earn-ings. When separation will take place within the current quarter (this isdesignated UN � Q0 as the earnings survey is taken around week 3 of the 13-week quarter) or in one of the subsequent two quarters (UN � Q1, UN � Q2),earnings are already depressed by between 1% and 2.7%. Beyond this time-horizon, any effect is small and usually insigni®cant. This modest and relativelyshort-run effect fails to con®rm the evidence of three-year lead times andsubstantial pre-separation earnings losses found for displaced workers in theUnited States.

These results relate to the `average' effect of unemployment on subsequentearnings, effects which may be expected to vary with worker characteristics.The same estimation across age groups, reported in Table 2, shows a strikingrelationship with age. For workers under 21, none of the coef®cients on

8 A further possible interpretation of the negative relation between earnings and unemploymentduration has been suggested by a referee. Longer durations occur in areas of high unemployment,which also tend to have lower levels of earnings (Blanch¯ower and Oswald, 1994).

Table 2Effects of Unemployment on Hourly Earnings by Age-Group


Age under 19 19±20 21±24 25±34 35±44 45±54 55±64

UN ÿ Q 1 ÿ0.054 ÿ0.013 ÿ0.071� ÿ0.101� ÿ0.150� ÿ0.176� ÿ0.172�UN ÿ Q 2 ÿ0.066 0.011 ÿ0.037 ÿ0.084� ÿ0.130� ÿ0.180� ÿ0.176�UN ÿ Q 3 ÿ0.084 0.000 ÿ0.050� ÿ0.104� ÿ0.132� ÿ0.184� ÿ0.180�UN ÿ Q 4 ÿ0.048 0.020 ÿ0.006 ÿ0.064� ÿ0.122� ÿ0.160� ÿ0.157�UN ÿ Q 5 ÿ0.074 0.027 ÿ0.004 ÿ0.052� ÿ0.114� ÿ0.127� ÿ0.130�UN ÿ Q 6 ÿ0.066 0.035 ÿ0.006 ÿ0.060� ÿ0.086� ÿ0.129� ÿ0.131�UN ÿ Q 7 ÿ0.115 ÿ0.001 ÿ0.013 ÿ0.053� ÿ0.090� ÿ0.122� ÿ0.124�UN ÿ Q 8 ÿ0.065 0.006 0.017 ÿ0.042� ÿ0.078� ÿ0.089� ÿ0.091�UN ÿ Q 9 ÿ0.018 0.021 0.026 ÿ0.024� ÿ0.062� ÿ0.062� ÿ0.064�

DURN/100 ÿ0.034 ÿ0.042� ÿ0.027� ÿ0.018� ÿ0.021� ÿ0.024� ÿ0.024�DURNSQ /100 ÿ0.003 0.002 0.001� 0.001� 0.001� 0.001� 0.001�PRIOR/100 ÿ0.069� ÿ0.067� ÿ0.046� ÿ0.030� ÿ0.028� ÿ0.033� ÿ0.032�PRIORSQ /10,000 0.003 0.003� 0.001� 0.001� 0.001 0.001� 0.001�PU ÿ0.138� ÿ0.005 0.008 ÿ0.005 ÿ0.033� ÿ0.043� ÿ0.044�

UN � Q 1 ÿ0.081� ÿ0.021 ÿ0.037� ÿ0.037� ÿ0.023� ÿ0.030� ÿ0.029�UN � Q 2 ÿ0.067� ÿ0.028 ÿ0.050� ÿ0.037� ÿ0.025� ÿ0.004 ÿ0.004UN � Q 3 0.006 0.001 ÿ0.037� ÿ0.024� ÿ0.013 0.001 0.002

Equations include EXPERIENCE, TENURE, AGE, PART TIME, CENTRALGOV, LOCALGOV, PUBLIC CORP, OCC, DIV,REGION, YEAR and Heckman correction.� denotes signi®cant at the 1% level.



UN ÿ Qk are signi®cant, indicating that job interruption itself has a negligibleimpact on future earnings. Cumulated time in past unemployment, on theother hand, has a substantial depressive effect on future earnings, and thepermanent marker PU is also strongly signi®cant. A spell of unemployment as ayoung man does not damage future earnings; but sustained spells of unem-ployment do so, and leave a permanent scar. This is consistent with theevidence of Gregg (this volume). By age 21±4, the effect of duration inunemployment is even stronger, and the effects of employment interruptionbegin to emerge, although the initial earnings penalty is only 7% anddisappears within the year. Unemployment still does not seriously penalise, butits cumulated duration does. It is among prime-age and older men that theeffects of job interruption become most sharply de®ned. Between ages 25 and64, the earnings setback from dislocation is pronounced, reaching its peakamong the over-45 age group. For them, re-employment implies earningsaround 18% lower in the ®rst year, still 9% lower after two years, and over 4%lower in the long-term. In all cases, unemployment duration adds a furtherpenalty.

The effects of employment interruption and spell duration are combined inTable 3, which evaluates the effect of a six-month unemployment spell on theearnings of prime-age men. The age pro®le is striking. In each case, thepenalty diminishes but, beyond age 35, it remains substantial.

To give a further intuitive illustration of the impact of unemployment onfuture earnings, Table 4 presents the estimated pro®les for selected workertypes and unemployment experience. Two principal categories of losersemerge. Those who were highest paid and/or in higher occupational groups,such as managers and professionals, experience large and sustained losses inearnings, with percentage losses at least double those for manual workers.However, for this group, unemployment ± particularly long-term unemploy-ment ± is a relatively unusual occurrence. Among skilled, semi-skilled andunskilled manual workers, the earnings losses are broadly similar; in each case,long-term unemployment is the major in¯uence, most notably for skilledcraftsmen. For all groups, the two gradients are clear: the earnings lossesdiminish as the unemployment spell recedes further into the past, but its effectnever disappears. Longer duration unemployment always damages futureearnings.

Table 3The Wage Penalty for Prime Age Men after a 6-Month Unemploy-

ment Spell (%)

In the ®rst year After 2 years After 3 years

AGED 25±34 13.3 7.1 5.1AGED 35±44 16.7 11.3 8.0AGED 45±54 22.4 12.9 9.9



7. Conclusion

This study has estimated the effects of unemployment on the subsequentearnings of British men over the decade 1984±94. We ®nd consistent evidenceof scarring: unemployment imposes further and lasting costs through thedepression of subsequent earnings. The impact of this occurs in two parts,relating to incidence and duration. Unemployment incidence itself gives riseto an earnings penalty, but this is largely temporary, most of it being elimi-nated after two years of continuous re-employment. The effect of duration, onthe other hand, is permanent and proportional to the length of the spell. The®nding on incidence is consistent with the loss of human capital implied in jobseparation, regardless of the length of spell. The impact of duration can beidenti®ed in addition to the lower level of work experience accumulated dueto the unemployment. This effect is consistent with irrecoverable deteriorationof skills. While underlining the adverse effects of long-term unemployment, itdoes not, though, support the stronger view that the deterioration of humancapital accelerates as time out of work lengthens. However, these results on thelargely temporary nature of the earnings setback from unemployment inci-dence relate to continuous subsequent employment for two years and more.Repeat spells not only build up cumulated duration but interrupt the recoveryprocess from the previous dislocation.

The effect of unemployment on earnings is not evenly spread. Among youngworkers, employment interruption has no direct effect on future earnings,although effects do develop from extended durations. Older workers, on theother hand, those over 35 and particularly those over 45, are signi®cant losers,as are those in higher-level occupations. A similarly clear-cut result relates tothe unemployed person's previous position in the earnings distribution. The

Table 4The Wage Penalty: Pro®les by Occupation and Unemployment Duration

(%)

In the ®rst year After 2 years After 3 years

ManagerUnemployed for 3 months 18.1 10.9 9.0Unemployed for 6 months 21.2 14.0 13.1Unemployed for 12 months 26.2 19.0 20.1

Skilled craftmanUnemployed for 3 months 7.6 5.4 3.8Unemployed for 6 months 10.0 7.8 6.7Unemployed for 12 months 14.2 12.0 12.0

Machine operativeUnemployed for 3 months 8.8 3.1 2.9Unemployed for 6 months 10.3 4.6 4.6Unemployed for 12 months 12.7 7.0 7.6

Unskilled occupationUnemployed for 3 months 6.3 4.9 2.5Unemployed for 6 months 7.1 5.7 3.7Unemployed for 12 months 8.3 6.9 5.6



higher the previous position, the greater the subsequent earnings loss. For thehigh-paid, the loss of potential earnings is severe and long-lasting. The low-paid lose little in subsequent earnings from unemployment. The impact ofunemployment on subsequent earnings therefore partially re-balances prone-ness to unemployment. Its impact is least among the young and the low-paid,the groups most likely to be affected by unemployment, and greatest amongolder, high-earning managers and professionals, where unemployment experi-ence is least common. In general, unemployment brings an earnings set-back,but continuous subsequent employment brings a substantial recovery. Theirrecoverable dimension arises from its duration.

Our analysis of the unemployment history of men in work revealed thecyclical sensitivity of re-employment; a macroeconomic upswing, as occurredin the late 1980s, is very effective in getting the unemployed back into work,most notably the long-term unemployed. Since prevention is better than cure,a ®rst contribution to enhancing the re-employment prospects of the unem-ployed, reducing durations, would be a high level of economic activity. Ourestimates show that scarring is particularly pronounced in the case of oldermen and the long-term unemployed. Projects such as RESTART, targeted atthe prevention of long-term unemployment, and the New Deal, targeted atolder workers and the long-term unemployed, should be welcomed. In thesame way that the effect of scarring should be added into the costs ofunemployment, its elimination should be enumerated among the bene®tswhen these programmes are evaluated. The dislocation costs of unemploymentare particularly high for prime age and older men who are higher earners.While the numbers involved are not large, the size of the penalties indicatesthat active policies towards job ®nding for displaced men in these categorieswould be ef®cient. With suitable incentives, this could be provided within theprivate sector. Much of the brunt of unemployment is concentrated onparticular groups in unemployment blackspots, such as some inner cities. Theevidence on scarring reinforces the role for policies to tackle unemploymentat all points.

St. Hilda's College, Oxford

Queen Mary and West®eld College, London



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Table AWithin-group Estimates of Hourly Earnings: Further Variables; Full Sample


Variable coef®cient t-value Variable coef®cient t-value

EXPERIENCE 1±2 ÿ0.032 3.1 AGE , 19 baseEXPERIENCE 2±5 0.031 3.0 AGE 19±21 0.182 68.3EXPERIENCE 5±10 0.062 6.1 AGE 22±25 0.217 61.8EXPERIENCE 10±15 0.058 5.6 AGE 26±30 0.219 50.3EXPERIENCE 15±20 0.053 5.0 AGE 31±35 0.217 42.7EXPERIENCE 25±30 0.045 4.1 AGE 36±40 0.210 36.5EXPERIENCE 30±35 0.048 4.3 AGE 41±45 0.195 30.9EXPERIENCE 35� 0.053 4.5 AGE 46±50 0.180 26.1TENURE 1 0.008 8.6 AGE 51±55 0.161 21.6TENURE 2 0.010 9.3 AGE 56±60 0.158 19.6TENURE 3 0.009 7.7 AGE 61±65 0.176 20.2TENURE 4 0.010 7.5 AGRICULTURE ÿ0.047 6.6TENURE 5 0.008 5.7 ENERGY 0.133 27.0TENURE 6 0.011 7.3 MINERALS 0.045 12.4TENURE 7 0.012 7.2 METAL GOODS 0.031 10.7TENURE 8 0.013 7.4 OTHER MANUF 0.030 9.4TENURE 9 0.011 5.9 CONSTRUCTION 0.009 2.8TENURE 10±14 0.005 2.3 DISTRIBUTION ÿ0.025 8.3TENURE 15� 0.014 2.6 TRANSPORT 0.019 5.4PART TIME ÿ0.002 1.0 BANKING 0.019 6.5CENTRAL GOVERNMENT ÿ0.019 5.1 OTHER SERVICES baseLOCAL GOVERNMENT 0.053 18.0 GREATER.LONDON ÿ0.062 25.1PUBLIC CORPORATION 0.016 8.1 EAST ANGLIA ÿ0.099 18.7PRIVATE SECTOR base SOUTH WEST ÿ0.093 23.0MANAGER 0.134 41.5 W. MIDLANDS ÿ0.113 26.7PROFESSIONAL 0.119 34.7 E. MIDLANDS ÿ0.107 25.3ASSOC PROFESSIONAL 0.087 32.0 YORKS HUMBERSIDE ÿ0.101 22.8CLERICAL 0.017 6.7 NORTH WEST ÿ0.111 26.6CRAFT 0.034 14.8 NORTH ÿ0.141 25.3PERSONAL SERVICES ÿ0.002 0.7 WALES ÿ0.127 22.2SALES 0.060 15.9 SCOTLAND ÿ0.110 20.2MACHINE OPERATIVE 0.023 10.7 SOUTH EAST baseOTHER OCCUPATIONS base (EXCL LONDON)

R2 0.238Adjusted R2 0.131Standard error 0.157No. of observations 575 938



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Unemployment and Subsequent Earnings: Estimating Scarring Among British Men 1984–94

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