[1995] the Wage Curve - A Review

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ties for this simple compensating differentialstheory of wages and unemployment. First, itis not altogether clear that, in the U.S. atleast, different regions (states, cities) actuallyhave permanently higher or lower unemploy-ment rates.3 Second (and related to the

first), depending on which years are used inthe analysis, researchers have not alwaysfound a statistically significant and positiveassociation between wages and long-run un-employment rates. Perhaps more fundamen-tally, however, the compensating differentialstheory pertains to the expected unemploy-ment rate in a local market, while the wagecurve relation that occupies Blanchflowerand Oswalds attention concerns contempora-neous unemployment.

Another strand of research that is relevantto Blanchflower and Oswalds work is the lit-erature on the cyclicality of real wages.Macroeconomists generally agree that aver-age real wages are relatively stable over thebusiness cycle (see Solon, Barsky, and Parker,1994, for a summary). Nevertheless, themeasured stability of average wages may bemasked by changes in the composition of the

work force over the business cycle (AlanStockman 1983). A series of papers over thepast decade have used repeated wage obser-

vations for the same individuals to measurethe cyclicality of real wages, purged of com-positional effects. Estimates from four ofthese papers are summarized in Table 1.Each of the studies regresses the year-to-yearchange in wages for a sample of individualson the corresponding change in the aggregateunemployment rate. There is a remarkabledegree of consistency across the studies: ineach case, a 10 percent increase in unemploy-ment is estimated to reduce male wages by

just under one percent.4

A typical peak-to-

trough change in unemployment of 2.5percentage points is therefore associated

with about a 3.5 percent change in menswages. Evidence from Solon, Barsky, andParker (1994) suggests that the cyclical vari-ability of womens wages is about one-half as

large.Blanchflower and Oswalds empirical speci-

fication can be thought of as a variant on theone used by the studies in Table 1. There aretwo critical differences. First, Blanchflowerand Oswald use repeated cross-sections,rather than panel data, and therefore do notcontrol for changes in the unmeasured char-acteristics of the individuals who actually

work at different points in the business cy-cle.5 Solon, Barsky, and Parker (1994) con-clude that the use of wage data from a seriesof cross-sections, rather than from paneldata, may lead to up to a 50 percent attenu-ation in the measured elasticity of real wages

with respect to aggregate unemployment.Second, Blanchflower and Oswald use the lo-cal, rather than the aggregate unemploymentrate. If the relation between real wages andlocal unemployment is nonlinear (as Blanch-flower and Oswalds findings suggest), thenstudies such as those in Table 1 may sufferfrom aggregation bias.6 Nevertheless,

Blanchflower and Oswalds estimates of thewage curve elasticity turn out to be similar tothe estimates in Table 1. Thus, one can viewThe Wage Curve as providing new evidenceon the cyclicality of real wages that is roughlyconsistent with the existing micro-based stud-ies.

II. Major Findings

A sampling of Blanchflower and Oswaldsmajor findings is presented in Table 2. Thetable contains estimated elasticities of wages

with respect to the local unemployment ratefrom models of the form:

3John Pencavel (1994) notes that city-specificunemployment rates are highly positively corre-lated over time in Britain, but at best weakly cor-related over time in the U.S. Blanchard and Katz(1992) make a similar observation about the corre-lation of state-specific unemployment rates overtime.

4It should be pointed out that three of the pa-pers use the same data (wages for male householdheads taken from annual earnings and hours infor-mation collected in the Panel Study of Income Dy-namics).

5See Solon, Barsky, and Parker (1994, pp. 1114) for a careful discussion of the issues involvedhere.

6 Such a possibility was raised by Richard Lip-sey (1960) in his famous article on the Phillipscurve. Lipsey argued tha t aggregation biases couldaccount for counter-cyclical loops around thePhillips curve.

Card: The Wage Curve 787


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logwirt=alog Urt+bXirt+dr+ft+eirt, (1)

where wirt is the wage rate for person i ob-served in local labor market rin period t, Urtis the unemployment rate in labor marketrinperiod t, Xirt is a set of measured charac-teristics of individual i (such as gender, age,and education), dr and ft are unrestricted in-tercepts for different labor markets andtime periods, and eirt is an error term. The

estimates in columns 14 are based on U.S.annual earnings data for 19631987 fromthe March Current Population Survey (CPS).The estimates in columns 5 and 6 are basedon British weekly earnings data for 1973 to1990 from the General Household Survey(GHS).

Blanchflower and Oswald present a widerange of evidence on the appropriate func-tional form of the relation between wagesand local unemployment rates. Based onspecifications that include higher order poly-nomials of the local unemployment rate, andothers that include dummy variables for dif-ferent ranges of unemployment, they con-clude that log wages are a monotonically de-creasing and convex function of localunemployment. For most purposes, they ar-gue that the wage curve relation is well-ap-proximated by a simple log-linear function (asis assumed in equation (1)).

Much of the empirical analysis in TheWage Curveis based on the brute force es-

timation of equation (1) using pooled cross-sections of micro data. The authors take somepride in the fact that the overall samples in-

volved in these exercises are large: 1.5 mil-lion observations in the case of the pooledMarch CPS data for the U.S.; and 175,000observations in the case of the GHS data forBritain. Careful consideration of equation(1), however, shows that the actual degreesof freedom involved in the estimation of the

wage curve elasticity is far less than the num-ber of individual wage observations. Indeed,the relevant dimension for the estimation ofthe unemployment coefficient is the numberof regional labor markets times the numberof time periods. Blanchflower and Oswaldsstudies of the wage curve in the U.S. andBritain use 200500 observations on locallabor markets in different years. For some ofthe other countries studied in The WageCurve, however, the number of regions ortime periods is low, leading the authors tomeasure unemployment by location and gen-der (in the case of Germany, Italy, and theNetherlands).

The realization that the unemploymentvariable in equation (1) has no i subscripthas a second important implication. Specifi-cally, individuals in the same labor marketmay share some common component of vari-ance that is not entirely attributable either totheir measured characteristics (Xirt) or to thelocal unemployment rate. In this case, the er-

TABLE 2SUMMARYOFESTIMATEDWAGECURVEELASTICITIESFORUNITEDSTATESANDUNITEDKINGDOM

U.S. (CPS data)U.K. (GHS data)

Micro Cell

Micro estimates Cell Means Estimates Means(1) (2) (3) (4) (5) (6)

1. Regional Unemployment

0.102(0.004)

0.109(0.003)

0.048(0.009)

0.082(0.013)

0.102(0.028)

2. Industry Unemployment

0.099(0.004)

0.017(0.012)

Source Table: 4.5 4.5 4.26 4.30 6.14 6.20

Notes: Standard errors in parentheses. Entries are elasticities of wages with respect to unemployment rate indi-cated in row heading.

788 Journal of Economic Literature, Vol. XXXIII (June 1995)


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ror components eirt in equation (1) will bepositively correlated across people from thesame local market, and the conventional for-mula for the estimated standard error of theunemployment effect will be significantlydownward biased (Brent Moulton 1986,

1990). This means that most of the wagecurve elasticities reported by Blanchflowerand Oswald are far less precisely estimatedthan theirt-ratios would suggest.

Blanchflower and Oswald are aware of thisdifficulty and use a very simple aggregationprocedure to study it. Taking averages overall the individuals in market r in period t,equation (1) implies:

logwrt=alog Urt+bXrt+dr+ft+ert, (2)

where log wrt represents the average logwage in the market and Xrt is a similar aver-age of the observed characteristics for all in-dividuals in that market. Equation (2) can beestimated using region-by-year cell means.Assuming that there is no correlation in theunobserved determinants of wages acrossmarkets, the residuals in equation (2) are un-correlated across observations, and conven-tional standard error formulas are valid. No-tice that the coefficients obtained by thisprocedure should in principle equal the ones

obtained by brute force estimation on themicro samples: all that should differ are thesampling errors.7 A minor difficulty withBlanchflower and Oswalds aggregate estima-tion method is that it is awkward to imple-ment with both a regional unemploymentrate and an industry unemployment rate (be-cause in this case the appropriate cells areregion x industry x year aggregates). For thisreason, Blanchflower and Oswald apply their

aggregate estimation methods separately toregion x year cells, and to industry x yearcells.

A final aspect of the specification of equa-tion (1) is the presence of year and labor mar-ket dummies. As it turns out, this is a crucial

issue in the estimation of the U.S. wagecurve, but less important for other countries.

When market dummies are excluded, the im-plicit assumption underlying the specificationis that wages respond to the transitory andpermanent components of local unemploy-ment with the same elasticity. The additionof market-specific fixed effects allows thepermanent component of wages to have anarbitrary correlation with the permanentcomponent of unemployment, and uses onlythe deviations of wages and unemploymentfrom their average values to estimate the

wage curve elasticity. For the U.S. data thismatters, because average levels of unemploy-ment across states are weakly positively cor-related with average wages, whereas transi-tory wages and unemployment rates arestrongly negatively correlated. Thus, the U.S.

wage curve elasticity tends to be smal l inmagnitude (or even positive in some subsam-ples) unless locational dummies are included,as they are in all the models reported in Ta-

ble 2. For the British data, the addition ofregion dummies rarely affects the estimated

wage curve elasticities, perhaps reflecting thegreater degree of permanence in the geo-graphic patterns of British unemploymentnoted by Pencavel (1994).

As shown by the estimates in columns 1, 2,and 5 of Table 2, Blanchflower and Oswaldsmicro-level equations with region and timedummies give elasticities of wages with re-spect to local unemployment of about

0.10very close to the micro-level esti-mates in Table 1. The estimated elasticitiesare similar for the U.S. and Britain, and alsotend to be similar when comparable proce-dures are applied to micro data sets for othercountries, including Australia, Canada, SouthKorea, and many Western European coun-tries. Perhaps surprisingly, Blanchflowerand Oswald also find that the regional un-employment elasticity for the U.S. is similar

whether or not the industry unemploymentrates is also included in the estimating

7Blanchflower and Oswalds aggregate methodcan lead to very imprecise estimates of the coeffi-cients for the individual control variables. Analternative two-step procedure would be to esti-mate equation (1) including unrestricted region x

year dummies, and then in a second stage model ,regress the region x year dummies on year effects,regional effects, and the regional unemploymentrate. This procedure uses the micro-level data toestimate the coefficients of the individual-level

variables, but leads to standard errors in the sec-ond stage that fully account for the presence ofcorrelations across people in the same market.



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equation (e.g., compare columns 1 and 2 of

Table 2).8

A comparison of the estimated wage curveelasticities for the U.S. based on microdataand cell-level data (e.g., columns 1 and 3) re-

veals two important facts. First, the esti-mated standard error of the wage curve elas-ticity is much larger in the (appropriate)

cell-level estimation. As expected on a priori

grounds, micro-level estimation methods(with no correction for a market-level errorcomponent) significantly overstate the preci-sion of the wage curve elasticity. A generalrule seems to be that micro-level estimatesoverstate thet-ratio of the wage curve elastic-ity by a factor of 2: a conclusion that is oflittle importance for the U.S. and U.K. analy-sis in The Wage Curve, but is more trouble-some for Blanchflower and Oswalds analysisof smaller data sets from other countries. Asecond and unexpectedfinding is that the es-

8This is potentially a puzzle, because an overallincrease in unemployment in all markets has amuch larger effect on wages in the specification incolumn 2 than in the specification in column 1.

TABLE 3ELASTICITIESOFWAGES, HOURS, ANDEARNINGSWITHRESPECTTOSTATEUNEMPLOYMENTRATES

Hourly Wage Annual Hours Annual Earnings

Actual(1)

Adjusted(2)

Actual(3)

Adjusted(4)

Actual(5)

Adjusted(6)

1. All 0.07(0.02)

0.08(0.02)

0.11(0.01)

0.12(0.01)

0.18(0.02)

0.20(0.02)

2. By Gender a. Women 0.06

(0.02)0.06(0.02)

0.08(0.02)

0.09(0.02)

0.14(0.03)

0.16(0.03)

b. Men 0.08(0.02)

0.09(0.02)

0.13(0.01)

0.15(0.01)

0.21(0.02)

0.24(0.02)

3. By Education a.


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timated U.S. wage curve elasticities aresmaller using cell-level data (compare col-umns 3 and 4 with columns 1 and 2). Indeed,based on the cell-level industry data (column4) there is no evidence of a wage curve in theU.S.!9 The British data tend to be better be-

haved: the cell-level and micro estimates ofthe wage curve elasticity are fairly close.

A troubling feature of Blanchflower andOswalds wage curves for the U.S. is that theydescribe annual, rather than hourly, earnings.Apart from a limited set of estimates for asingle 1987 cross-section (Tables 4.11 and4.18), Blanchflower and Oswald use annualearnings as a wage measure throughout theircore U.S. chapters. Because annual earningsare the product of annual hours and hourly

wages, and annual hours are highly correlatedwith contemporaneous unemployment rates,one may ask whether Blanchflower andOswald have discovered a wage curve oronly an hours curve for the United States.10

To get some evidence on this important ques-tion, I used annual earnings and annualhours data from the March CPS for 1979,1982, 1985, 1988, and 1991 to estimate the

wage curve and hours curve elasticities inTable 3.

The analysis in Table 3 uses state-by-year

data for five years and 51 states on three de-pendent variables: log hourly earnings; logannual hours; and log annual earnings. I usetwo different measures of each dependent

variable: a simple average of the appropriatevariable for all workers in the state; and aregression-adjusted average that controls forthe observable characteristics of workers ineach state (using a set of year-specific regres-sion coefficients). Like most of Blanchflowerand Oswalds U.S. specifications, the models

in Table 3 control for state effects and yeareffects. Focus for the moment on the esti-

mates in row 1 of Table 3, which pertain toall workers. These estimates reveal that theelasticity of annual earnings with respect tostate unemployment rates reflects both amodest elasticity of average hourly wages

with respect to unemployment (0.07 to

0.08), and a slightly larger elasticity of an-nual hours with respect to unemployment (0.11 to 0.12).11The implied elasticity of an-nual earnings is approximately 0.20: abouttwice as big as estimates reported by Blanch-flower and Oswald. I am unsure of thesource(s) of this discrepancy: experiments

with minor changes in the sample period andthe specification did not lead to much changein the estimates in Table 3. Perhaps a safeconclusion is that wage curve elasticities arenot quite as robust as one is led to believe bythe discussion in The Wage Curve.12

Surprisingly, however, the elasticity ofhourlyearnings in the first row in Table 3 is

just under 0.10: very similar to the majorityof the wage curve elasticities in Blanchflowerand Oswalds book. If the elasticity of hourly

wages with respect to local unemploymentrates is about 0.1, then one would expect amuch larger elasticity of annualearnings, be-cause there is an essentially mechanical cor-relation between unemployment and average

hours of work.13 In their U.S. chapters,

9Blanchflower and Oswald give no explanationfor the large discrepancy between the micro-leveland cell-level wage curve elasticities. Their discus-sion of the cell-level estimates (page 170) leavesreaders with the mistaken impression that there isa potential bias in the micro-level wage curve elas-ticities.

10This is less of a problem for their studies ofother countries, which typically rely on weeklyearnings.

11Because log(annual earnings) = log(annualhours) + log(hourly wage), the elasticity of annualhours with respect to unemployment is the sum ofthe elasticities of annual hours and hourly wages

with respect to une mployment.12Conceptually, the estimates in Table 3 are

comparable to Blanchflower and Oswalds esti-mates based on regional cell means (like the oneshown in column 3 of Table 2). As noted earlier itis unclear why Blanchflower and Oswalds esti-mates from this procedure are so small in magni-tude.

13Note that (in steady state) the unemploymentrate is a measure of the fraction of annual weeksspent in the labor force but not employed. Thus

weeks of work equals LF(1 U), where LF repre-sents weeks in the labor force and Uis the retro-spective unemployment rate. If Edenotes annualearnings, w denotes average hourly wages, and hdenotes hours per week, then

log(E) = log(w) +log(h) +log(LF) U,

and the derivative of log annual earnings with respect tounemployment is the sum of four effects: the derivativeof hourly wages with respect to unemployment (thepure wage curve effect); the derivative of hours per



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Blanchflower and Oswald argue that the wagecurve elasticity is the same for hourly,

weekly, and annual earnings. Conceptually,this is extremely unlikely, and as shown inTable 3, it is also empirically suspect. A ma-

jor weakness of The Wage Curve is theauthors failure to consider this point morecarefully.

Another disappointing feature of The WageCurve is the absence of any discussion aboutthe potential effects of selection bias on theslope of the wage curve. Economists have ar-gued for the past decade that compositionbias is a potentially important factor in un-

week with respect to unemployment; the derivative oflabor force participation with respect to unemployment;and a purely mechanical 1 term.

TABLE 4SUMMARY OF ESTIMATEDWAGE CURVE ELASTICITIES BY TYPE OFWORKER AND SECTOR,

FOR UNITED STATES, UNITED KINGDOM, CANADA, AND AUSTRALIA

By Gender By Education By Age By Union Status By Sector

All

(1)

Males

(2)

Females

(3)

Low

(4)

High

(5)

Young

(6)

Old

(7)

Union

(8)

Nounion

(9)

Private

(10)

Public

(11)

1. U.S. 196387 March CPS (annual earnings) Table 4.17

0.098(0.004)

0.119(0.005)

0.064(0.006)

0.155(0.008)

0.064(0.006)

0.192(0.008)

0.063(0.008)

2. U.S. 1987 monthly CPS (weekly earnings) Table 4.18

0.112(0.007)

0.121(0.009)

0.106(0.011)

0.131(0.010)

0.079(0.011)

0.120(0.008)

0.106(0.015)

0.108(0.013)

0.107(0.008)

3. U.S. 198388 March CPS

(annual earnings) Table 4.19

0.114(0.023)

0.070(0.045)

0.119(0.025)

4. U.S. 196387 March CPS (annual earnings) Page 160

0.100(0.004)

0.047(0.008)

5. U.K. 198389 BSAS (weekly earnings) Table 6.7

0.123(0.023)

0.216(0.028)

0.040(0.046)

0.167(0.041)

0.008(0.050)

0.213(0.051)

0.098(0.053)

0.014(0.035)

0.188(0.032)

0.156(0.030)

0.138(0.048)

6. U.K. 197390 GHSS

(weekly earnings) Table 6.15

0.082(0.013)

0.094(0.015)

0.074(0.023)

0.065(0.018)

0.053(0.040)

0.133(0.026)

0.070(0.026)

7. Canada 197287 SCF (annual earnings) Table 8.4

0.095(0.016)

0.223(0.046)

0.065(0.040)

0.169(0.049)

0.095(0.028)

8. Australia 1986 (weekly earnings) Table 8.7

0.195(0.034)

0.210(0.035)

0.200(0.072)

0.130(0.060)

0.307(0.087)

0.139(0.069)

0.201(0.104)

Notes:Standard errors in parentheses. All entries represent the elasticity of wages with respect to the regional unemploy-ment rate.



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derstanding how measured wages respond tochanges in unemployment. It seems likelythat similar composition biases affect themeasured elasticity of wages with respect tolocal unemployment rates.14 Blanchflowerand Oswald could have easily used their

March CPS data sets to give some indicationof the likely magnitude of such biases in theU.S. labor market. In particular, consecutiveMarch surveys can be matched to form two-

year panels, permitting a comparison of theeffects of local unemployment on the averagechanges in wages for individuals who work inthe same market in two consecutive years,

versus the effects on the changes in averagewages for all workers in the market. This ex-ercise should be a top priority for any further

work on the wage curve.

A. Variation in the Slope of the Wage Curve

One of the questions that probably occursto most economists when they encounter aspecification like equation (1) is whether achange in contemporaneous unemploymenthas the same effect on wages of differentkinds of workers, or in different industries orsectors. Blanchflower and Oswald present a

variety of evidence on this issue from theU.S., Britain, Canada, and Australia, which Ihave attempted to summarize in Table 4.15

There are several informal hypotheses inthe labor economics literature that mightshed light on the relative slope of the wagecurve across groups. One of these is the no-tion that wages in internal labor markets are

isolated from cyclical shocks. According tothis view, wages of more senior workers areless likely to vary with current labor marketconditions, whereas the wages of recent hiresare more closely linked to external marketforces.16 A second and related idea is that

better-educated workers have greater levelsof firm-specific human capital (Walter Oi1962). The presence of firm-specific capitalintroduces a wedge between the level of pro-ductivity at the current employer and outsideopportunity wages, and might allow employ-ers to smooth wages over the business cy-cle.17 If this notion is correct, then wages ofbetter-educated workers may be less respon-sive to local unemployment rates. A thirdidea, associated with H. Gregg Lewis (1963),is that unionized wages tend to be less sensi-tive to business cycle conditionsin part be-cause, in North America at least, union wagesare set in multi-year contracts. Finally,Lawrence Katz and Alan Krueger (1991) haveargued that public sector wagesespeciallythe wages of federal workersare likely to berelatively insensitive to local labor marketconditions.18

When the wage is measured by annualearnings (as in most of Blanchflower andOswalds work for the U.S.) differences in the

cyclical sensitivity of hoursmay also contrib-ute to differences across groups in the esti-mated wage curve elasticity. To illustrate thepotential importance of this phenomenon,rows 25 of Table 3 present estimated elas-ticities of hourly wages, annual hours, and an-nual earnings for various subgroups of U.S.

workers, using 19791991 CPS data. Withthese estimates it is possible to calculate dif-ferences across groups in the fraction of theelasticity of annual earnings that is attribut-

able to the cyclical variability of hourly wagesas opposed to the cyclicality of hours.

14Note that the extent of composition bias islikely to vary by whether the wage measure isbased on earnings in a short period (such as a

week or month) or earnings over a longer period,such as a year.15One distracting feature of The Wage Curveis

the authors lack of consistency across specifica-tions and data sets in designating different groups.For example, in their two main U.S. tables, theydivide workers into very different age and educa-tion categories. For this reason, and for interna-tional comparability, somewhat ambiguous cate-gory labels are needed in Table 4. Blanchflowerand Oswalds analysis for South Korea is based onindustry unemployment and is not necessarilycomparable to the regional wage curve elasticitiesin Table 4.

16This idea can be formalized in a variety ofways. See Paul Beaudry and Joh n DiNardo (1991)for one example.

17Technically, of course, employers shouldsmooth total earnings rather than the hourly wagerate (Abowd and Card 1987).

18 Some observers might also argue that publicsector wages are set by political as well as marketforces, and are therefore unlikely to fully reflectcurrent labor market conditions.



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There are several consistent patterns thatemerge from Tables 3 and 4. For example,the wages of men are uniformly more respon-sive to local unemployment rates than the

wages of women. As shown in row 2 of Table3, the hourly wages of U.S. men vary more

with local unemployment than the hourlywages of women, and their annual hours arealso more cyclically variable. Thus, mens to-tal annual earnings are considerably moreelastic with respect to local unemploymentthan womens. Similarly, hourly, weekly, andannual wages of younger workers vary more

with local unemployment rates than thewages of older workers. Patterns by educa-tion are less clear cut. For the most part, an-nual earnings of high-education workers ap-pear to be less variable than those oflow-education workers. The evidence forBritain differs between the two data sets usedby Blanchflower and Oswald, however, and inAustralia more-educated workers have a sig-nificantly higher wage elasticity.

With respect to union-nonunion differ-ences, the two U.S. data sets give slightly dif-ferent answers: the 1987 cross-section showsabout equally variable union and nonunion

weekly wages, while the 198388 data on an-nual earnings show more elasticity in the

nonunion sector.19 In Britain, union mem-bers wages appear to be totally unresponsiveto local unemployment. Public sector wagesare less variable with respect to local unem-ployment in the United States, but the differ-ences in Britain are slight. Finally, the esti-mates in row 5 of Table 3 present some crudeevidence on the question of whether the

wages of new-hires are more responsive to lo-cal unemployment rates than the wages of

workers with continuing seniority. The hourly

wages of recent job switchers appear to varymore with local unemployment, but even thewages of those who maintain a stable employ-ment relation over the year vary significantly

with cyclical conditions. Some related evi-dence on this issue is presented by Blanch-flower and Oswald for South Korea, using in-

dustry rather than regional unemploymentrates. There, they find that the wage curveelasticity is monotonically decreasing with

job tenure, with relatively high unemploy-ment elasticities for new hires (those withone year or less of seniority).

In principle, one could imagine using thepatterns of variation in the slope of the wagecurve across sectors and types of workers as ameans of choosing between competing theo-ries of the wage curve. Although they presenta wide range of wage curve estimates for dif-ferent subgroups of workers, Blanchflowerand Oswald do not pursue any kind of formaltesting (see Section IV for more on theirtheoretical models and insights). Several cutsat the data also remain unexplored. For ex-ample, they dont estimate wage curves forself-employed workers, although such an ex-ercise would be feasible with their U.S. dataand might be helpful in distinguishing be-tween theories. Furthermore, they concen-trate almost exclusively on the effect of theoverall unemployment rate.20 An interestingquestion is whether the wages of a specificgroup of workers are more strongly related tothe groupsunemployment rate, or to a sum-mary measure of market conditions like theoverall unemployment rate. If the correct

specification of the wage curve uses a group-specific unemployment rate, then one simpleexplanation for the differences in the wagecurve slopes in Tables 3 and 4 is that group-specific unemployment rates are related tothe overall unemployment rate with differentelasticities.21 Another explanation for differ-ences in the slope of the wage curve acrossgroups is differences in the extent of cyclical

19It should be noted that there i s slippage inthe identification of union status in U.S. data onannual earnings, because union status refers to thesurvey week in March of each year, while earningsare for the previous year.

20One exception i s a consideration of the hy-

pothesis that a change in the average duration ofunemployment has a differential effect on wages(see Table 6.22).

21Specifically, suppose the correct specificationof the wage curve is

log (wijrt)= alog Ujrt+Xijrtb+. . .

where wijrt is the wage of the ith person in groupj inlabor market rand period t, and Ujrt is the unemploy-ment rate of the group in market r. Suppose further that

log Ujrt=djt+ejlog Urt,

where Urtis the overall unemployment rate in market r.Then the wage curve elasticity for group j using theaggregate unemployment rate is aej.



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composition bias. As noted earlier, this entiretopic remains unexplored in The Wage Curve.

III. What the Wage Curve Is Not

Before turning to the theoretical models

that Blanchflower and Oswald offer for theexistence of a stable relation between localunemployment and wages, it is useful to layout their conclusions as to what the wagecurve is not . Specifically, Blanchflower andOswald argue that the wage curve is neither aPhillips curve, nor a labor supply function.For many readers, these arguments may bethe most problematic aspect of their work.

A. Not the Phillips Curve

The Phillips curve relationship is one ofthe most durableand controversialhy-potheses in post-World War II economics.Based on patterns of aggregate labor marketdata for the U.K. spanning almost 100 years,Alban Phillips (1958) postulated the existenceof negative relation between the rate ofchange of wages and the contemporaneousunemployment rate.22 Lipsey (1960) showedthat such an aggregate relation could be de-rived from a model describing wage adjust-ments at the individual market level. While

this idea bears a superficial resemblance tothe wage curve, Lipseys specification impliesthat unemployment determines the rate ofchange of wages, whereas Blanchflower andOswalds specification (equation (1)) impliesthat unemployment determines the level of

wages (adjusted for permanent market-spe-cific differentials).

Blanchflower and Oswald test betweenthese competing specifications by estimatingan augmented version of equation (2):

logwrt=alog Urt+bXrt

+logwrt1+dr+ft+ert. (3)

They argue that a test of = 0 versus thealternative of = 1 is an appropriate test ofthe wage curve versus the Phillips curve.Because of technical problems associated

with the presence of both a lagged dependentvariable and a regional fixed effect in (3), and

possible serial correlation in the market errorterm ert, a better test might be to considera first-differenced version of equation (2):

logwrt=a1log Urt+a2log Urt1

+b1Xrt+b2Xrt1

+gt+ert,

(4)

where gt is a renormalized time effect, andfirst-differencing within markets has elimi-nated the region fixed effects. The wagecurve hypothesis implies thata2 =a1, whilethe Phillips curve hypothesis implies that a2= 0. Blanchflower and Oswald base all theirinferences on (3), whereas I believe that (4)is a more appropriate statistical frameworkfor testing between the wage curve and thePhillips curve.

For what they are worth, Blanchflower andOswalds empirical results based on the esti-mation of equation (3) on U.S. and U.K. dataare strongly supportive of the wage curve,rather than the Phillips curve. Estimates ofthe coefficient from equation (8) are gener-ally close to zero and statistically insignificant(see their Tables 4.27 and 6.20, for example).From this evidence, Blanchflower andOswald conclude that the Phillips curve isdead, and that the proper specification of the

relation between unemployment and wagesexpresses the level of wages as a function ofthe level of unemployment. Nevertheless, Isuspect that reports of the death of the Phil-lips curve are premature. More evidence onthe dynamic relation between wages and un-employment will probably be required beforeeconomists disavow Phillips hypothesis.

B. Not a Labor Supply Function

At least since the publication of Robert Lu-

cas and Leonard Rappings (1969) famousstudy of aggregate labor markets, someeconomists have argued that variation in un-employment should be interpreted as laborsupply behavior. Because short run changesin employment and unemployment are ap-proximately mirror images, a finding that

wages rise with contemporaneous reductionsin unemployment may simply reflect move-ments along an upward-sloping labor supplyfunction. In order to test this interpretation,Blanchflower and Oswald estimate a series of

22Phillips (1958) was careful to abstract fromdifferences in the rate of price inflation.



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augmented versions of the regionally aggre-gated wage curveequation (2). Specifically,they include either the employment-popula-tion rate in the region, or the regional laborforce participation rate, as an additional ex-planatory variable. If the wage curve is actu-

ally an inverted labor supply function, theyargue that either actual employment, or no-tional employment (i.e., employment plusunemployment) should do a better job of ex-plaining local variation in wage levels thanthe unemployment rate.

Their empirical analysis shows that onceregional fixed effects are taken into account,local participation rates or local employmentrates have no significant effect on wages,

while local unemployment rates continue toexert a systematic effect on wages. Blanch-flower and Oswalds results for both the U.S.and Britain strongly support the view that itis local unemployment, rather than local em-ployment or the size of the local labor force,that effects wages. To the extent that one be-lieves the labor supply function should ex-press the total quantity of labor as a functionof wages, these findings seem to be inconsis-tent with a labor supply interpretation.

As Blanchflower and Oswald point out, an-other aspect of their empirical findings is in-

consistent with conventional beliefs about la-bor supply. Specifically, the results in Tables3 and 4 show that the wage curve is muchsteeper for younger workers. Under a laborsupply interpretation, the slope of the wagecurve is related to the inverse market supplyelasticity. Most analysts would probably ex-pect a higher supply elasticity for younger

workers, implying a flatter wage curve forthese workers. On the other hand, the factthat the wage curve is flatter for women is

consistent with the conventional view thatthe labor supply of women is more elasticthan that of men.

IV. Theoretical Interpretations

Having rejected both the Phillips curveand labor supply as potential explanations forthe wage curve, Blanchflower and Oswald ar-gue that it represents something new. Ratherthan laying out a single theoretical explana-tion, however, they present a series of three

alternative models that are consistent with astable negative relationship between real

wages and local unemployment rates: a modelof regionally based implicit contracts; an effi-ciency wage model; and a bargaining model.Curiously, they choose to present these mod-

elsbeforethe core empirical chapters of theirbook, although it seems clear that the models

were derived after most of the data analysiswas completed, and after considerable intro-spection about the nature of the wage curverelation. It is almost as if the theoreticalmodels are placed in the third chapter to

ward off charges of measurement beforetheoryalthough the authors readily admittheir crime in the preface.

Blanchflower and Oswalds first model pos-its a set of spatially isolated employers, eachof which offers a standard Azariadis-Baily-Gordon wage/employment contract to poten-tial employees. In this model, places differ intheir amenity values, and these differencesgenerate differences in wages and expectedemployment/unemployment rates across loca-tions. Under the critical assumption that theunemployment benefit paid to workers in pe-riods of low demand is constant across re-gions, Blanchflower and Oswald show that re-gions with higher amenity values will offer

contracts with lower wages, lower expectedemployment probabilities, and higher unem-ployment.23 Thus wages and unemploymentrates will be negatively correlated across re-gions.

Two aspects of this model are especiallyrelevant in light of Blanchflower andOswalds empirical work. First, the contrac-tual model is fundamentally one of wages andemployment: unemployment is residually de-termined and only indirectly correlated with

wages. Having dismissed the labor supply ex-planation precisely because it posits a rela-tion between wages and employment, rather

23The intuition for this result is as follows. If ahigher wage is paid to employed workers in a low-amenity region, but the level of the unemploy-ment benefits is constant across regions, then theoptimal contract will lead to a higher level of con-tractual employment (for each realization of thedemand shock) in the low-amenity region, in orderto offset partially the higher income risk in thisregion.



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than between wages and unemployment, Ifind it hard to understand the authors enthu-siasm for the contracting model! Second, therelation between wages and unemploymentgenerated by the contractual model is attrib-utable to permanent region characteristics

(amenity values). Wages are negatively re-lated to permanent unemployment rates, al-though they may be positively or negativelyrelated to unemployment rates in any par-ticular period. In fact, the empirical findingsfor the U.S. suggest that almost the oppositeis true: wages are (weakly) positively corre-lated with permanent unemployment rates,but strongly negatively correlated with con-temporaneous unemployment rates. Again, itseems to me that the contracting model fallsshort.

Blanchflower and Oswalds second modelis a version of Carl Shapiro and JosephStiglitzs (1984) efficiency wage model. As inthe contracting model, they assume that em-ployers in different regions offer pay pack-ages of equal expected utility. In the effi-ciency wage model, however, firms mustoffer a net wage that exceeds the value ofunemployment; otherwise employees willshirk. Because the penalty for shirking isgreater if it takes a longer time to find a new

job, firms can offer a lower wage premium ifunemployment rates are higher. Thus, withinregions, there is a negatively sloped wagecurve representing the locus of wages andcontemporaneous unemployment rates thatsatisfy a no-shirking constraint. Across re-gions with different amenity values, however,Blanchflower and Oswald argue that there islikely to be a positive long run association be-tween expected wages and expected unem-ployment, as in the Harris-Todaro frame-

work . The differing implications of theefficiency wage model for the correlation ofwages with contemporaneous and permanentunemployment is an attractive feature of thisclass of model.

A fundamental property of the efficiencywage model is that wages of a given group ofworkers are related to the group-specific un-employment rate. Higher unemployment forunskilled workers, for example, should haveno effect on the wages of skilled workers,once their own unemployment is taken into

account. The absence of any empirical evi-dence on such questions, noted earlier, is dis-appointing. It is also somewhat disappointingthat Blanchflower and Oswald did not givemore thought to developing the implicationsof an efficiency wage model for differences in

the slope of the wage curve across differentgroups of workers. As it stands, I suspect thatmany readers will find the efficiency wagemodel a leading contender for explaining the

wage curve, yet they will come away from thebook with no real evidence in favor of themodel, other than the wage curve itself.

The third model presented by Blanch-flower and Oswald is a conventional unionbargaining model, similar to one studied byGeorge de Menil (1971), and in an earlierbook by Oswald and Alan Carruth (1989).This model generates a wage equation of theform:

w=a+s(/n),

where w is the negotiated wage, a is an al-ternative wage available to workers in theevent of a dispute, /nrepresents the level ofprofits per worker, andsis a relative bargain-ing power parameter (perhaps depending onrelative discount rates). Blanchflower andOswald assume that unemployment affects

wages by lowering the alternative wage a. Inthe bargaining model, then, the wage curvearises because higher unemployment lowers

workers threat point. This model may be lessattractive as a formal underpinning for the

wage curve in countries where the level ofunionization is low (such as the U.S.), or incountries where union wage negotiations oc-cur at a national level (such as Sweden). It isalso slightly puzzling that the slope of the

wage curve is lower for union than nonunion

workersespecially in Britain.Unlike the other theoretical models,Blanchflower and Oswald present some di-rect empirical evidence on the bargainingmodel, based on estimates of the wage curveacross 19 U.S. manufacturing industries. Spe-cifically, they estimate an industry-aggre-gated wage equation that includes the indus-try unemployment rate, controls for thecharacteristics of workers in the industry, anda measure of profits per employee in the in-dustry. The results suggest that profits exert a



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reasonably large positive effect on annualearnings of employees in an industry, control-ling for permanent industry effects and year-effects.24 As noted earlier, it is unfortunatethat the authors use annual earnings as a

wage measure, rather than a standardized

time wage, because critics might argue thatemployee hours and profits are related forreasons other than bargaining. Nevertheless,the findings may stimulate more work on theissue of whether wages are affected by rela-tively short run changes in employer profit-ability.

V. Conclusions

There is a wage curve. The wages of indi-viduals who work in labor markets with

higher unemployment are lower than those ofsimilar workers in markets with lower unem-ployment. Furthermore, the tendency for the

wage curve to show up for different kinds ofworkers, in different economies, and at dif-ferent times, suggests that the wage curvemay be close to an empirical law of econom-ics. Even if the question of why local unem-ployment rates affect pay remains unsettled,as I believe it does, the existence of a wagecurve relation is an important addition to our

knowledge about the modern labor market.One can imagine future research that usesthe negative correlation between unemploy-ment and wages as a means to study otherphenomena. One can also imagine a growingbody of work that follows The Wage Curveslead in using the diverse experiences of locallabor markets as an intermediate-level labo-ratory for economic researchpart way be-tween the individualistic focus of traditionalmicroeconometric research, and the aggre-gate focus of traditional macroeconometrics.More than any other lesson, this may be thelong-run contribution of The Wage Curve.

A book with the ambitious agenda of TheWage Curve is bound to garner championsand critics. Many readers will be stimulatedby the conclusion that the wage curve issomething new: a surrogate supply functionthat can be combined with a simple demand

curve to yield interesting models of the labormarket; a challenge to orthodox theories ofsupply and demand. Others will find the bookmildly infuriating: slightly oversold in parts,and too quick to jump to strong conclusions.Much of the book is carefully considered, re-

vealing the insights that can only come fromyears of tedious research. Other parts aremore hastily conceived. The authors havetaken to heart the advice to let the dataspeak. Indeed, with data sets from a dozencountries, and some 200 tables and graphs,readers run the risk of permanent deafness.Nevertheless, most economists will be able topick up the book and read it. Despite someobvious glitches in organization, it is gener-ally well written, and the level of analysis isrelatively nontechnical.

Does anyone need to read it? Certainlythose who have followed the authors pre-

vious articles on the wage curve will findsome repetition of the themes that Blanch-flower and Oswald have been developing forclose to a decade. But there is much that isnew here, as well. A very careful analysis ofover 25 years worth of U.S. data forms thecore of the book, and is new. Similarly, muchof the detailed analysis of British data is newand worthwhile reading. Applied economists

who are interested in the issues of unemploy-ment and wage determination will find thebook an excellent investment.

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[1995] the Wage Curve - A Review