5.6 Tratamiento
5.6.2 Tratamiento propuesto
5.6.2.4 Servicios incluidos en el tratamiento
Figure14: Model II, Responses of RelativeEmployment
Whenwe onsiderthee e tsofasymmetri te hnologysho ksonthewagespread
( gure15)weobserve,at rst,thataskillbiasedte hnologysho kleadstothe
high-est response of the wage spread. Furthermore, the response of the wage spread is
morepersistentthaninthemodelwithperfe tlabormarketswherethewagespread
is returned to its steady state level after 23 quarters. For a low skill biased sho k
the results are similar as in the model with perfe t labor markets ( ompare gure
11). In ontrast tothe rst model,a persistentin rease inthe wage spreadis found
for a neutral produ tivity sho k ( gure15, solid line). In parti ular, su h behavior
is found empiri allyfor the German data (see gure 7 right).
-0,015 -0,010 -0,005 0,000 0,005 0,010 0,015 0,020 0,025
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Quarters
% d e v . fr o m s te a d y s ta te
neutral skill augmenting low skill augmenting
Figure 15: Model II, Responses of Wage Inequality
tern of low skilled workers, the re ent model displays the highest response of this
variableafteraneutralprodu tivity sho k whi h in reases the produ tivity of both
types of workers. A skill biased te hnology sho k does lead to a rather small
re-sponseoflowskilledemployment,only. Atleasttheresponsesofaskillbiasedsho k
is more onsistent with the empiri al ndings for ontinental European ountries,
where rather lowpositiveresponses of the employment patternof lowskilled
work-ers is found during the so- alled IT revolution. Furthermore, within the assumed
sear hand mat hingframework, rms de idetohire workers whentheir
produ tiv-ityex eedsthe rm'ssear h osts. In the ases ofskillorlowskillbiasedsho ks the
produ tivitygainsare nothighenoughtorea hasimilarresponseasafteraneutral
sho k.
-0,02 0,00 0,02 0,04 0,06 0,08 0,10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Quarters
% d e v . fr o m s te a d y s ta te
neutral skill augmenting low skill augmenting
Figure16: Responses of low skill employment
Ina furtherstep weraise the question whetherthe models are apableto
repro-du e basi fa tsof the business y le. Table 6belowreports the empiri al ndings
for the U.S. and Germany.
U.S.,1964.1-1999.1
relative CorrelationofobservedVariables
Volatility y i n
s n
u n
s
=n
u w
s w
u w
s
=w
u
y | 1.00 0.81 0.89 0.05 -0.22 0.23 0.37 0.33 -0.04
0.77 1.00 0.75 -0.01 -0.21 0.51 0.61 0.41 -0.27
i 2.44 1.00 0.02 -0.13 0.13 0.39 0.20 0.02
ns 0.55 1.00 0.16 0.29 0.10 -0.12 0.27
nu 1.22 1.00 -0.90 -0.27 -0.54 0.44
n
s
=n
u
1.25 1.00 0.31 0.47 -0.31
w
s
0.95 1.00 0.71 0.15
w
u
1.16 1.00 -0.59
ws=wu 0.82 1.00
Germany,1973.1-2000.1
y | 1.00 0.78 0.73 -0.28 -0.24 0.13 0.42 0.51 -0.22
1.47 1.00 0.62 -0.23 -0.19 0.10 0.27 0.18 0.09
i 2.24 1.00 -0.17 -0.13 0.06 0.33 0.33 -0.06
n
s
0.70 1.00 0.86 -0.45 -0.20 -0.11 -0.02
n
u
1.16 1.00 -0.85 -0.23 -0.18 0.01
n
s
=n
u
0.67 1.00 0.20 0.17 -0.14
ws 0.22 1.00 0.76 0.27
wu 0.24 1.00 -0.38
ws=wu 0.16 1.00
Ingeneralweobserveforboth ountries aratherlowvolatilityofskilledworkers
(around 2/3 ofthe volatility ofthe GDP)and a ratherhigh volatilityof low skilled
workers. Furthermore, wages in Germany are rather low volatile ompared to the
U.S. (:40 <:90). An important di eren e is observed for the volatility of the wage
spread for Germany a rather stable wage spread is reported whereas we observe a
volatile variablefor the U.S..
The simulationresults of the twomodels are reportedin table 7below.
Perfe tLaborMarkets
relative CorrelationofsimulatedVariables
Volatility y i n
s n
u n
s
=n
u w
s w
u w
s
=w
u
y | 1.00 0.72 0.91 0.94 0.93 0.94 0.98 0.92 -0.89
0.49 1.00 0.38 0.45 0.38 0.36 0.75 0.78 0.35
i 0.69 1.00 0.95 0.85 0.73 0.87 0.77 0.46
ns 0.06 1.00 0.68 0.89 0.93 0.69 0.69
nu 0.02 1.00 0.27 0.66 0.88 -0.06
n
s
=n
u
0.07 1.00 0.81 0.37 0.94
w
s
0.08 1.00 0.84 0.61
w
u
0.07 1.00 0.08
ws=wu 0.01 1.00
LaborMarketFri tions
y | 1.00 0.78 0.90 0.80 0.81 -0.07 0.99 0.99 0.30
0.49 1.00 0.18 -0.04 0.03 0.05 0.68 0.65 0.30
i 0.77 1.00 0.96 0.98 -0.06 0.83 0.85 0.19
n
s
0.05 1.00 0.95 0.07 0.71 0.69 0.28
n
u
0.05 1.00 -0.16 0.72 0.77 0.01
n
s
=n
u
0.19 1.00 0.03 -0.13 0.63
ws 0.07 1.00 0.97 0.42
wu 0.06 1.00 0.19
ws=wu 0.01 1.00
Comparingthereported orrelationswiththeempiri al ndings,we ndthatthe
output orrelation ofthe employment andwages are mu hhigherthan found inthe
data, although when labor market fri tions are taken into a ount the orrelation
between output and employment islowerthan in amodel withoutfri tions.
Furthermore,we nd anegative orrelation between outputand low skilled
em-ployment in the data (table 6) whi h is not reprodu ed by the simulations results,
also the a negative orrelation between employment and wages is not found in the
model. However, the high orrelation of lowand high skilled employment whi h is
reportedbytheGermandataisreprodu edinbybothmodels. Also,themodelwith
sear hfri tionsreprodu esthe negative orrelation between te hnology andrelative
employmentfound inthe German data.
However, when omparingdynami orrelation oeÆ ientsbetweenoutput,wage
inequalityandtherelativeemploymentratio,weobservethatthe modelwithsear h
fri tions displays a delayed orrelation between output and wage inequality and
the relative employment position (see table 8 below). In parti ular, this delayed
orrelation is evident for the U.S. and Germany.
WageInequality
t 1 t t+1 t+2 t+4
U.S. -0.14 -0.04 0.09 0.20 0.23
Germany -0.29 -0.22 -0.05 0.17 0.41
RBC -0.86 -0.89 -0.81 -0.74 -0.66
Sear h 0.28 0.30 0.31 0.31 0.30
RelativeEmployment
t 1 t t+1 t+2 t+4
U.S. 0.29 0.23 0.13 0.01 -0.22
Germany -0.07 0.13 0.16 0.14 -0.06
RBC 0.91 0.95 0.88 0.81 0.68
Sear h -0.11 -0.07 0.06 0.10 0.08
Allinalltheabilityofthemodelstoreprodu esomefa tsofthe business y leis
mixed. Themodelwithperfe tlabormarkets parti ularlyoverstates the orrelation
betweenvariableswhereasthemodelwithsear hfri tionsunderstatesthevariability
aswellasthe orrelationofsomevariables. However, themodelwithsear hfri tions
is, ingeneral,able toreprodu ea delayed orrelation between output andthe wage
spread and the relative employment position.
7 Con luding Remarks
Although the apability of the analyzed models to reprodu e business y le fa ts
has tobeimproved,importantinsights on erning thetransmissionpro ess of
te h-nologi al hange under the assumption of labormarketfri tions and the e e ts on
employmentand wages ould bederived.
In parti ular it ould be shown by the omparison of the two models, that
rea-sonable impulse responses, i.e. the delayed response of labor market variables due
to te hnologi alinnovations, require a ertaindegree of labormarket imperfe tion.
In parti ular, labor market institutions prevent the adjustment of wages whi h led
to the persistent response of wage inequality inthe model with sear h fri tions.
Con erning the unemployment pattern of low skilled workers, the impli ations
of the models are twofold. First, the demand for low skilled labor depends on the
produ tivityofskilledworkers aswellasthee onomi positionofthe e onomy.
Se -ond, theemploymentstatusoflowskilledworkers an beenhan ed duetoadvan es
in low-skillaugmenting te hnology (as well as better s hooling, et .), however, the
impa tof su hapoli yisa e tedby labormarketfri tions. Theresults show, that
status of this groupin a fri tionlesse onomy, whereas under labormarketfri tions
the e e ts are rather low.
Although, a detailed onsideration of rigid institutions due to high reservation
wages orgenerous so ialbene tsystems isleftforfuture resear hthe resultsof this
papershowapossibleway toexaminetheout omesofte hnologi aladvan esunder
the existen e of labor marketfri tions.
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The U.S. unemployment data ( gure 2)are taken from the Bureau of labor
statisti s (www.bls.gov) and are based on monthly observation. The
Ger-man data are taken from the \Zahlen-Fibel" published by the Institut fur
Arbeitsmarkt und Berufsfors hung (IAB) (www.iab.de) and are based on
an-nual observations. In the latter ase the quarterly data are obtained from
linear interpolation. Forboth ountries the quarterly real GDP is taken from
the OCED MainE onomi Indi ators.
Employmentof high and lowskilledworkers:
Basedonannualdataforthe U.S.and Germany whi hare linearinterpolated
in order toobtain quarterly data. Forthe U.S., the data are taken fromU.S.
Bureau of the Census (1998),Measuring 50 Years of E onomi Change Using
the Mar h Current Population Survey, Current Population Reports P60-203,
Washington DC, September 1998. and U.S. Bureau of the Census (2000),
Current Population Reports P60-209, Money In ome in the United States:
1999, U.S.Government Printing OÆ e,Washington D.C.
For Germany,the data are taken from
FederalStatisti alOÆ eGermany,Fa hserie1, BevolkerungundErwerbstatigkeit,
Reihe 4.2.1, Struktur der Arbeitnehmer, Metzler - Poes hel, Wiesbaden,
var-ious issues sin e 1978 and Fa hserie 16, Lohne und Gehalter, Reihe 2.2 und
2.1,Metzler-Poes hel, Wiesbaden,variousissuessin e 1978. SeealsoGreiner
et al.(2004).
tertiary edu ation:
The values for 1980 / 1989 are measured as the proportion of the
popula-tion with a university degree ( f. OECD (1993): 172). The 2002 values are
measured as per entage of population (age group 25-64) that has attained a
tertiary typeA oranadvan edresear hprogramin2001(Cf. OECD(2003)).
wage spread:
Note that the German data refer to the West German manufa turingse tor,
only. However, a similar behavior of aggregate wage data is found by
Fitzen-berger (1999). For the U.S. the Data are taken from the CPS and show the
ratio of wages for workers whi h some ollege degree to workers with a high
s hool degree. Forfurther details see Greineret al.(2004).
Table 9: Testing for UnitRoots
U.S.,1972.1-1998.4 Germany,1975.1-2000.1
Deterministi ADF Deterministi ADF
Variable Terms Lags Test Statisti Terms Lags TestStatisti
w
s
=w
u
onstant,trend 2 -2.3544 onstant,trend 2 -3.0549
w
s
=w
u
onstant 1 -4.2355 onstant 1 -2.3139
n
s
=n
u
onstant,trend 2 -4.3566 onstant,trend 2 -2.8551
n
s
=n
u
onstant 1 -6.1377 onstant 1 -4.5677
LP onstant,trend 2 -2.5671 onstant,trend 2 -2.3649
LP onstant 1 -5.3564 onstant 1 -8.4994
v=u onstant,trend 2 -3.3264 onstant,trend 2 -20.5764
v=u onstant 1 -4.8278 onstant 1 -8.1193
M KinnonCriti alValues:
1% 5% 10%
levels -3.96 -3.41 -3.13
1st.di . -3.43 -2.86 -2.57
The lag length of the VAR models for the U.S. and German e onomies are
deter-mined by using the generalinformation riteria.
26
Table 10: VAR Spe i ations
Variables(inter eptandlineartimetrendin luded)
U.S.,1970.1-1998.4 Germany,1973.1-2000.1
Information ws=wu ws=wu
riteria ns=nu ns=nu
LP LP
AIC 10 2
FPE 10 2
HQ 2 2
SC 2 2
AIC:AkaikeInformationCriterion;FPE:Fore astPredi tionError;
HQ:Hennan-Quinn;SC:S hwarzCriterion
26
Adetailed des riptionofthespe i ationtests an befoundinLutkepohl(2004):110 ..