if anything is unclear, please ask ! - international ... · Ædoes not cover people deciding to...
TRANSCRIPT
SUMMMARY
O
T i i
OFTRA
IN
Training summary
• Employment analysis: Concepts, instruments, NING
p y y papplications
• Labour market information systems
l j i h d d li i• Employment projections: Methods and an application
If anything is unclear, please ask !
OVERV
IEW
O iOverview
• Concepts for labour market analysisp y
• Measuring the labour market
• Labour market analysis: Approaches
• Labour market analysis: Models
• Labour market analysis: Policies
TRA
IININGOB
T i i bj ti
BJECTIVE
Training objectives
• Get an overview of commonly used employment ESy p yindicators
• Understanding broad trends
d i i l i i• Introduction in analysis instruments…
• …and application to current problems
LABO
Labour market analysis: Some basicsOURMA
• Observed employment is the result of several,
decentralised decisions RKETCO
decentralised decisionsParticipating in the labour market
Finding gainful employment ONCEPTS
Deciding how many hours to work
• In technical language we write:
( )( ) LFURHOURSETF ×−×= 1• Notation:Notation:
ETF: Total employment (full‐time equivalent)
HOURS: Hours worked per person
UR: Unemployment rate
LF: Labour force
LABO
Labour forceOURMA
• The labour force is constituted by all those who are contributing to productive employment RKET
CO
co t but g to p oduct e e p oy e tCovers both employed and job seekers
Does not cover people deciding to stay or become inactive...
h i bl t t k l t ( th
ONCEPTS
...or who are incapable to take up employment (e.g. those
with health problems)
Typically covers people above age 15.
• Inactivity can be a choice:Taxes are too high for second earners (women) to seek for
employmentemployment
Social assistance is too generous
Opportunity costs are too high (in comparison to the wage
that can be earned)
LABO
EmploymentOURMA
• Employment counts from the first hour...Employment does not indicate the number of hours worked RKET
CO
...nor the type of work carried out.
It is only a numeric head‐count indicator of all those who
contribute to a country’s productive capacity
ONCEPTS
contribute to a country s productive capacity
• Without employment covers different statuses:Job seekers, i.e. Who would like to work but can’t find
employment
Inactive, i.e. who do not or cannot work (physically or
mentally weak people)mentally weak people)
Those who would like to work but have given up to search,
i.e. discouraged workers
LABO
Types of employmentOURMA
• Several forms of employment...Dependent employment (wage earners) RKET
CO
Dependent employment (wage earners)
Self‐employment (independent workers)
Own account workers (e.g. entrepreneurs)
I f l l t ( ith t l b t t)
ONCEPTS
Informal employment (e.g. without proper labour contract)
Temporary employment
• not all of which work full time:• ... not all of which work full time:Full‐time employees (regularly work more than 30 hours per
week)
Part‐time employees (regularly work less than 30 hours per
week, sometimes very few hours: even 1 hour counts !)...
...which sometimes is involuntaryy
LABO
Working hoursOURMA
• Different aspects of working timeNormal working hours RKET
CO
Normal working hours
Over‐time working hours
Regular working hours ONCEPTS
• For labour market analysis only regular working
hours are relevantProductive capacity increases with every hour, whether it
is overtime or not
Regulation of overtime constitutes, however, important
incentives for employers wrt expansion of workforce
The marginal productivity may decline as average
working hours increaseworking hours increase...
...but the average productivity (per worker) increases in
any case
LABO
UnemploymentOURMA
• Unemployment concerns all those without a job who:Have not been working over the last week/month, not even RKET
CO
for an hour...
...are looking for a job...
and ready to take up an occupation immediately (i e no
ONCEPTS
...and ready to take up an occupation immediately (i.e. no
health problems, child care issues, etc.)
• Some job seekers are unemployed for longer spells:Typically unemployment spells above 6 months are
considered to be long‐term
People tend to loose skills (both technical and non‐technical,People tend to loose skills (both technical and non technical,
“soft” skills)
LTU are more difficult to mobilise and activate to return to
l temployment
LABO
Labour market flows IOURMA
• Most labour market surveys only cover “stocks”Current situation of interviewed person in the labour RKET
CO
Current situation of interviewed person in the labour
market
No regard to dynamic aspects: “What have you be doing
1 th/ t / ?”
ONCEPTS
1 month/quarter/year ago?”
• Labour market theory makes use of flowsThe extent to which employment is created depends onThe extent to which employment is created depends on
how difficult it is for an employer to find new workers
It also depends on his/her expectations regarding future
developments
Finally, it also depends on wage earners expectations and
salary requirementsy q
LABO
Labour market flows IIOURMA
• Labour market analysis needs more informationEffects of policies depend on the speed of flows more RKET
CO
Effects of policies depend on the speed of flows more
than on the impact of stocks
Additional sources of information can be used but are
t it h l f l l d i d l b f
ONCEPTS
not quite as helpful as properly designed labour force
surveys
• Some proxy indicators• Some proxy indicatorsVacancy information, help‐wanted‐index, online ads
Unemployment duration and probabilities of finding new
employment
MEA
Employment trends across countries…ASU
RING80
Employment‐to‐population ratios (2007 vs. 2010)
THELA
B
ISL
IDN
KAZ
NZL
NOR
PERRUS
SWE
THA
70
BOURM
AUS
AUT
BEL
BRA
CAN
CHL
CHN
COL
CYP
CZE
DNK
EST
FIN
FRA
DEU
IDN
IRLISR
JPNKOR
LUXMUS
NLD
PHL
POL
PRT
ROM
RUS
SVK
TWN
UKR
GBRUSAVEN
060
2010
ARKET
BEL
BUL
HRV
EST
GRCHUNITA
LVALTU
MKD
MLT
MDA
POL
SLV
ZFA
ESP
TUR
4050
MAR
30
30 40 50 60 70 802007
MEA
Employment to population ratiosASU
RINGTH
ELA
BWPOPETEPR =
BOURM
WPOP
l l
ARKET
•ET: Total employment
•WPOP: Working‐age population, i.e. all people 15 years and aboveand above
EPR: Employment‐to‐population ratio
MEA
Employment index ‐ CalculationsASU
RING
• Take a particular date as base year, e.g. 2005Take a particular date as base year, e.g. 2005 TH
ELA
B
Take a particular date as base year, e.g. 2005
Calculate the relative level of following years with
respect to that base year
BOURM
BaseYear
tBaseYeart ET
ETIndexET +×=− 100
ARKET
• Some words of cautionWhen grouping countries, add the absolute employment
BaseYear
levels first before constructing the index
Try to find a base year with a particular meaning (e.g.
peak of the cycle)p y )
MEA
…and at the regional level: Global shifts in employmentASU
RING
Employment developments (index, 2005=100)
THELA
BBOURMARKET
MEA
Country level reactions of employment during the crisisASU
RING
• G20 countries have lost up to 5 percentage points of their employment rate
• In these countries, the unemployment rate more than doubled !
THELA
B
0
ent r
ates
in p
p.)
BOURM
4-2
in e
mpl
oym
eq3
to 2
009q
3,
ARKET
-6-4
Cha
nges
(2
008q
Spai
n
nite
d S
tate
s
Can
ada
Sout
h Af
rica
Turk
ey
ed K
ingd
om Italy
Aust
ralia
n Fe
dera
tion
Fran
ce
Ger
man
y
Japa
n
Braz
il
blic
of K
orea
Mex
ico
Arge
ntin
a
Chi
na
Indo
nesi
a
U S
Uni
t
Russ
ian
Rep
ub
MEA
Temporary employment took the largest hitASU
RING
Temporary employment in the EU (%‐change year‐on‐year)
THELA
BBOURMARKET
MEA
…have accelerated during the crisisASU
RING
FinlandSweden
SwitzerlandJapan
Sectoral restructuring and house price developments
THELA
B
NetherlandsItaly
KoreaAustria
BelgiumChile
Finland
Housingdepression
BOURM
PortugalUnited States
AustraliaFranceNorwayCanada
Netherlands
ARKET
HungarySpain
GreeceNew Zealand
PolandCzech Republic
Portugal
D kUnited Kingdom
IrelandSlovak Republic
SloveniaEstonia
Hungary
Housingbubble
Q1-2001 Q1-2002 Q2-2003 Q3-2004 Q4-2005 Q1-2007 Q2-2008 Q3-2009 Q4-2010
TurkeyDenmark
MEA
Sectoral adjustmentASU
RING
• How to calculate the intensity of employment reallocation
across sectors?Si l l l i di TH
ELA
B
Simple to calculate indicator
Only one number
Most commonly used: Lilien indicator BOURM( )
2/1
1loglog ⎥
⎦
⎤⎢⎣
⎡∑ Δ−Δ=J
jt
djt
djt EEEE
Lilien
ARKET
1 ⎦⎣ =j tE• where j: sector, J: number of sectors, t: year/quarter/month, d:
i i d hi h l dj i id d (1 5time period over which sectoral adjustment is considered (1 year, 5
years, 1 quarter, etc.)
• The indicator will change depending on:the sectoral detail (number of sectors J)
the period over which change is considered (i.e. d)
MEA
Unemployment developments,ASU
RING
200
Evolution of unemployment (2005‐2010, 2005q4 = 100)
THELA
B
150
BOURM
100
ARKET50
Western Europe Eastern Europe and CIS
Southern Europe North America and Oceania
Asia Latin America
MEA
…long‐term unemployment, …ASU
RING180
Percentage increase in numbers of long‐term unemployed, Q1 2009–Q1 2010
THELA
B90
120
150
BOURM
0
30
60
ARKET
‐30
0
huania
nmark
stonia
reland
Cyprus
Latvia
States
Spain
orway
Finland
weden
ngdom
ovakia
ortugal
ulgaria
epublic
ovenia
Greece
Turkey
France
ungary
Italy
Japan
rlands
elgium
Poland
Malta
Brazil
mbourg
Africa
Austria
omania
rmany
ia, FYR
Croatia
Lith
De E I C
United N F Sw
United Kin Slo
Po BuCzech Re Slo G T F
Hu
Nethe Be P
Luxem
South ARo Ge
Macedoni C
MEA
…and inactivity increased during the crisis...ASU
RING
Inactive population (in % of working‐age‐population)
THELA
BBOURMARKET
MEA
…against the background of large scale under‐employmentASU
RINGTime‐related under‐employment (in % of labour force), latest year available
THELA
B
20.0
25.0
BOURM
10.0
15.0
ARKET
0 0
5.0
0.0
MEA
Unemployment flows IASU
RING
Unemployment in‐ and outflows in US, Canada, Japan and UK
35% 2.5%
THELA
B
30%
Outflow
BOURM25% 2.0%
ARKET20%
Inflow
15%
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
*
1.5%
Inflow
MEA
Unemployment flows IIASU
RING
9% 0 8%
Unemployment in‐ and outflows in France, Germany and Italy
THELA
B
9% 0.8%
BOURM
7%
0.6%
Outflow
ARKET
5%
Inflow
4%
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
*
0.4%
EMP
Questions for employment analysisPLO
YMEN
• How to stimulate employmentIs employment growing in line with output/productivity?
Are certain categories benefiting more/less from output
NTANALY
Are certain categories benefiting more/less from output
growth?
Which sectors contribute to employment most?
YSIS• How to improve income generationCan wages increase without creating unemployment?
Do policies need to adjust to stimulate/restrict wage growth?p j / g g
Is the economy gaining/loosing competitiveness?
• How to enhance labour market chancesCan workers switch easily between jobs/firms/sectors?
Do job seekers find quickly re‐employment?
How do policies need to adjust to stimulate labour market
reactivity
EMP
How to analyse employment developmentsPLO
YMEN
• Three levels of analysisOver the medium run: Link between employment and
h/ d i i
NTANALY
growth/productivity
Over the short run: Trade‐off between higher wages and
lower unemployment YSISDynamic analysis: Understanding labour market flows
• The (appropriate) level of analysis depends on the availability of data:
Key Indicators of the Labour Market: Contains
information on employment and growth for all 182 ILOinformation on employment and growth for all 182 ILO
member countries
KILM also has information for wages but with less
coverage
So far only limited information available for flows
EMP
Medium‐run analysis: The Okun’s curve IPLO
YMEN
• Traditional approach to labour market analysis:Statistical relationship between output growth and N
TANALY
Statistical relationship between output growth and
unemployment
Alternative: Elasticity between growth and employment
All t i htf d l l ti f l t
YSIS: OK
Allows straightforward calculation of employment
developments once GDP estimates have been carried out
KUN’S
CUURV
E
EMP
Medium‐run analysis: The Okun’s curve IIPLO
YMEN
• Underlying approach to the GET (Global employment
Trends) NTANALY
e ds)
• Comes in two varietiesIdentify different elasticities depending on whether we are in YSIS: O
K
a recession or a boom
OR: Use historical elasticities
• Bottom‐up approach: Use sectoral elasticities and KUN’S
CU
Bottom up approach: Use sectoral elasticities and
aggregate
• Based on annual data URV
E
EMP
Introduction to employment elasticities IPLO
YMEN
• Quantitative measure for measuring the “employment‐
intensity” of growth NTANALY
te s ty o g o t%‐change in employment given a 1‐percentage point change
in economic growth
E l t d l dd d ( t t) d d i t
YSIS: OK
Employment and value‐added (output) are needed input
variables
Examine how growth in output and employment evolve KUN’S
CU
together over time
Can examine for population subsets – e.g. women, men,
youth URV
E
youth
Can be applied at sectoral level as well, e.g. change in
aggregate output in relation to employment by sector
EMP
Introduction to employment elasticities IIPLO
YMEN
• When value‐added and employment data correspond
to precisely the same group: NTANALY
to p ec se y t e sa e g oup:%‐change in labour productivity given a 1‐percentage point
change in economic growth
S t d i d t l l t d i
YSIS: OK
Sector and industry‐level trends in an economy
•Analysing structural changes in employment: KUN’S
CU
Movement from agriculture to higher value added sectors
Labour absorbing versus labour shedding industries
URV
E
EMP
Calculating employment elasticitiesPLO
YMEN
• Two main methods:“Arc”‐elasticity (spreadsheet calculation): N
TANALY
( )( ) =
−⎛⎝⎜
⎞⎠⎟
εii i iE E E
Y Y Y1 0 0/
/ YSIS: OK“Point”‐elasticity (using econometric regressions):
( )−⎝ ⎠i i iY Y Y1 0 0/
KUN’S
CU
YE lnln βα +=1:
URV
E
2: ββ =⎟⎠⎞
⎜⎝⎛
∂∂
→⎟⎠⎞
⎜⎝⎛ ∂
=∂
EY
YE
YY
EE
⎠⎝∂⎠⎝ EYYE
EMP
Which method should I use?PLO
YMEN
• The “arc” method is preferred when there are very few
data (e.g. year‐over year comparison): NTANALY
data (e.g. yea o e yea co pa so ):Computationally simple (can be worked out by hand or in a
spreadsheet)
L d t l til lt
YSIS: OK
Leads to volatile results
• The “point” method is preferred when there are KUN’S
CU
several observations:Provides more stable results
Gi l i hi b h i bl h
URV
E
Gives average relationship between the variables over the
period in question, instead of relationship between start‐ and
end‐points
EMP
Relationship between elasticities, productivity and employmentPLO
YMEN
• When output and employment correspond to the same
group, there is a special relationship: NTANALY
g oup, t e e s a spec a e at o s p:
Y = E Pi i i× YSIS: OKΔ Δ ΔY = E Pi i i+ KU
N’SCU
where ∆ represents the growth rate of a particular variable
•We then have: URV
E
We then have:
ε = 1 − Δ P
ε =Δ E
ε = 1 Δ Y
ε =Δ Y
EMP
Elasticities, productivity and employment: numeric examplePLO
YMENN
TANALYGDP Growth Difference
Arc Employment Elasticity
+ Arc YSIS: OK
GDP Growth
Employment growth
Productivity growth
(Productivity+Employment)
in GDP growth
Employment Elasticity
Productivity Elasticity
Productivity Elasticity
1985 8.7 0.8 7.8 8.7 -0.1 0.09 0.90 0.99
1986 6 3 0 2 6 5 6 4 0 0 0 03 1 03 1 00
KUN’S
CU
1986 6.3 -0.2 6.5 6.4 0.0 -0.03 1.03 1.00
1987 5.8 5.7 0.1 5.8 0.0 0.99 0.01 1.00
1988 6.4 0.5 5.9 6.4 0.0 0.08 0.92 1.00 URV
E
1989 4.8 2.7 2.1 4.7 -0.1 0.55 0.44 0.99
1990 4.6 4.1 0.4 4.6 0.0 0.90 0.10 1.00
EMP
Interpreting employment elasticitiesPLO
YMEN
G
NTANALY
GDP growth
Employmentelasticity Positive GDP growth Negative GDP growth YSIS: O
K
elasticity
ε < 0 (-) employment growth(+) productivity growth
(+) employment growth(-) productivity growth KU
N’SCU
0 ≤ ε ≤ 1 (+) employment growth(+) productivity growth
(-) employment growth(-) productivity growth
(+) employment growth ( ) employment growth
URV
E
ε > 1 (+) employment growth(-) productivity growth
(-) employment growth(+) productivity growth
EMP
Employment may be more elastic even with lower GDP growthPLO
YMEN
Employment elasticities versus GDP growth in Asia (200‐2004)
9
10
NTANALY7
8
9
%)
China
Vietnam YSIS: OK5
6
ual G
DP g
row
th (%
India
BangladeshThailand
K
KUN’S
CU3
4
Aver
age
annu
Sri Lanka
Singapore
Malaysia PhilippinesPakistanKorea
Indonesia
URV
E
1
2
00 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Employment elasticity
EMP
Sectoral elasticities in Pakistan, 1992‐2004 PLO
YMEN
Agriculture Value Added
Industry Value Added
Services Value Added
NTANALY
Agriculture Industry Services
Added Growth
(average annual %)
Added Growth
(average annual %)
Added Growth
(average annual %)
YSIS: OK
Pakistan 0.19 1.31 1.18 2.8 4.3 4.3
• With sufficient sectoral information elasticities can be
KUN’S
CU
• With sufficient sectoral information, elasticities can be
split:Information on employment elasticity by sector U
RVEBut: No information about cross elasticities:
“How much employment in sector 1 is modified with a 1%‐
change in value added in sector 2”change in value added in sector 2
EMP
Main characteristics of Okun‘s elasticitiesPLO
YMEN
• Backward‐looking• Dynamic N
TANALY
y a c
• Volatile• No “ideal” figure YSIS: O
K
• Misleading when used in isolation
• Complementary when used with GDP growth, KUN’S
CU
unemployment rates and other LMI
URV
E
EMP
Problems with the Okun’s curvePLO
YMEN
• Problems:Non‐stable relationship: Intensity of job creation varies over N
TANALY
the business cycle
Non‐stable relationship II: Intensity of job creation changes
with sectoral adjustment YSIS: OK
with sectoral adjustment
Problems of statistical identification: Growth‐employment
elasticity differs depending on the time horizon at which to KUN’S
CU
look What is the right horizon?
URV
E
EMP
Applying the Okun‘s curve to current problems: G20PLO
YMEN
• Estimating employment elasticities using the “Point”‐
elasticity:
NTANALY
elasticity:Problem: Often time series are very short, especially for
employment YSIS: OK
Missing data
• Solution: KUN’S
CU
Using panel data
Group data from different countries
Calculate panel‐wide elasticity on a larger sample URV
E
EMP
Applying the Okun‘s curve to current problems: G20PLO
YMEN
• But:How to take into account differences across countries? N
TANALY
How to take into account differences across countries?
Are the country elasticities the same ?
• Solution: YSIS: OK
Use fixed effects for level differences across countries
Differentiate coefficients across countries
Group data to improve efficiency of estimation
KUN’S
CU
Group data to improve efficiency of estimation
GDPET β
URV
Eititiiit GDPET εβα ++=
EMP
Applying the Okun‘s curve to current problems: G20PLO
YMEN
Country Country‐specific coefficient
Argentina 0.31 NTANALY
Australia 0.57Brazil 0.23Canada 0.57China 0.02F 0 35
YSIS: OK
France 0.35Germany 0.29India 0.04Indonesia 0.12Italy 0.31 KU
N’SCU
yJapan 0.28Korea 0.34Mexico 0.13Russia 0.32 U
RVE
Saudi Arabia 0.24South Africa 0.77Spain 1.18Turkey 0.32United Kingdom 0 47United Kingdom 0.47USA 0.59
EMP
Applying the Okun‘s curve to current problems: Fiscal multiplier IPLO
YMEN
• Okun’s elasticities can also be used for policy analysis:How much employment can be generated from a 1%‐ N
TANALY
How much employment can be generated from a 1%
increase in public spending
How should public spending and taxation evolve over the
b i l ? P /A /C t li l?
YSIS: OK
business cycle? Pro‐/A‐/Counter‐cyclical?
• Extended Okun’s curve estimation:Add an estimate of the impact of public spending/deficit on
KUN’S
CU
Add an estimate of the impact of public spending/deficit on
GDP to the employment elasticity
1
URV
E
2
1ititiiit
diP bli SGDP
GDPET
δ
εβα
++
++=2ititiiit dingPublicSpenGDP εδγ ++=
EMP
Applying the Okun‘s curve to current problems: Fiscal multiplier IIPLO
YMEN1.4
1.6
1.8
2.0
ultip
liers
Emerging economies
NTANALY0.4
0.6
0.8
1.0
1.2
Em
ploy
men
t mu
YSIS: OK
1.61.82.0
rs
Advanced economies0.0
0.2
Arge
ntin
a
Sout
hAfri
ca
Mex
ico
Bots
wan
a
Keny
a
Chi
na
Short-term multiplier Long-term multiplier KUN’S
CU
0 60.81.01.21.4
ploy
men
t mul
tiplie
rURV
E
0.00.20.40.6
erla
nd
Japa
n
stat
es
nite
d gd
om
ranc
e
stra
lia
man
y
Italy
Em
p
Switz
e J
Uni
ted
s
Un
King Fr
Aus
Ger
m
Short-term multiplier Long-term multiplier
EMP
Applying the Okun‘s curve to current problems: Fiscal multiplier IIIPLO
YMEN over the degree of pro-cycality of government spending (1991-2008)
Net employment creation in Sub‐Saharan African countries
NTANALY0
0.1
YSIS: OK
0.1
0.
ymen
t gro
wth
% p
.a.)
KUN’S
CU-0.2
-0
Net
em
ploy
(in
%
URV
E
-0.3
Lowprocycality
Mediumprocycality
Highprocycality
THEShort‐term analysis: The Phillips curvePHILLIPS
• Driving question:How do wages and prices react to unemployment changes? S
CURV
E
How do wages and prices react to unemployment changes?
Can unemployment be lowered without driving up prices?
How does employment react to a macroeconomic shock?
• Statistical observation:Higher unemployment rates are correlated with lower rates
of price inflationof price inflation
As the unemployment rate goes down, inflation starts to
accelerate
Question: Can this relationship be exploited by policy
makers?
THEOrigins of the Phillips curvePHILLIPS4 00
5.00
SCU
RVE
2000Q4
2004Q4
2005Q3
3.00
4.00
n rate
2003Q22006Q42.00
Inflatio
2002Q1
0.00
1.00
2 00 1 00 0 00 1 00 2 00‐2.00 ‐1.00 0.00 1.00 2.00
Unemployment gap
• Negative slope when plotting unemployment against
inflation rate over a full business cycle (here: USA)
THETraditional Phillips curvePHILLIPS
• Traditional Phillips curve:First observed by William Phillips for the United Kingdom S
CURV
E
First observed by William Phillips for the United Kingdom
Statistical relationship between inflation rate and
unemployment
W l fi d f th t iWas also confirmed for other countries
But: Did not remain constant for longer time periods
• In the 1970s the relationship broke down• In the 1970s the relationship broke downUnemployment remain high despite accelerating inflation
Stagflation
The entire Phillips curve seemed to have shifted upwards
Short‐ and long‐run Phillips curve
• New labour market concept: the structural
unemployment rate (the long‐run Phillips curve)u e p oy e t ate (t e o g u ps cu e)Unemployment rate at which inflation is neither accelerating
nor decelerating (NAIRU)
At th t t th i i t f ll d ith tAt that rate the economy is running at full speed without
overheating nor deflating
Bringing unemployment rate further down is unsustainable
over the long‐run
• Question: What affects the structural unemployment
rate?rate?Depends on the indicator: HP‐filter vs. structural model
Unionized wage bargaining, employment protection
Lack of product market competition
THEModern formulation of the Phillips curvePHILLIPS
• Inflation expectations play an important role:Price changes only in reaction to anticipated unemployment S
CURV
E
Price changes only in reaction to anticipated unemployment
gaps
Can be combined with backward looking elements: Inflation
i t d t l k f i f ti l l h i h bitpersistence due to lack of information or slowly changing habits
• Inflation as a weighted average of past and (expected) f t i fl tifuture inflation:
GU lEβ tttt ntGapUnemploymeE ++= +− 11 πβαππ
THEPhillips curve vs. wage curvePHILLIPS
• The Phillips curve is a short‐cut for a more elaborate
model of the labour market SCU
RVE
ode o t e abou a etPrices are influenced by wages and capacity constraints at
the firm level
W i fl d b i d th l tWages are influenced by prices and the unemployment gap
Wage‐price spiral determined simultaneously by demand
conditions on both labour and product markets
( )NAIRUUEw twutBwtFwt −+Δ+Δ=Δ −+ βπβπβ ππ 11
( )YYwwE tpxtpwBtpwFt −+Δ+Δ=Δ −+ βββπ 11
THEPhillips curve examplePHILLIPS
Employment reaction to a temporary real wage shock • Phillips curve models allow
full specification of the
economic dynamics SCU
RVE
economic dynamics
Employment reaction to a temporary technology shock
• They can take into account y
differences in structural
characteristics of the labour
k t ( LM fl ibilit )market (e.g. LM flexibility)
MAT
Modern labour market analysis: A primerTCH
INGA
• Starting point: The Beveridge curve
At every point in time there is co‐existence of open job vacancies and
unemployed job seekers ANDUNE
p y j
Depending on the position in the cycle an economy moves up and down the
curve
Sometimes the entire curve moves, due to structural and policy changes EMPLO
Y
, p y g
MEN
TFLLO
WS
MAT
Understanding labour market flows ITCH
INGA
Decomposing unemployment dynamics
ttttt OUTINELU −=Δ−Δ=Δ ANDUNE
ttttt
Labour force growth as a function of history and incentives
TLL βββ ΔΔΔ
EMPLO
Y
LttLtLtLLt TaxuLL εβββα ++Δ+Δ+=Δ −− 31211
MEN
TFL
• Labour force growth determined byHistorical trends (persistence)
Di d k ff (i β lik l b i )
LOWS
Discouraged worker effect (i.e. βL2 likely to be negative)
Tax incentives (and other non‐tax measures such as child
care provisions, etc.)
MAT
Understanding labour market flows IITCH
INGA
Decomposing employment creation
ttt tionJobDestrucHiringET −=Δ ANDUNE
ttt g
Hiring as a function of the matching rate EMPLO
Y
between vacancies and job seekers
( )ttt UVmHiring ,=
MEN
TFL
• Change in employment determined byHiring intensity LO
WS
Rate of job destruction
The facility with which new vacancies V are matched with
job seekers Ujob seekers U
MAT
Understanding labour market flows IIITCH
INGA
• Hiring depends on incentives to open vacancies (i.e. job creation) A
NDUNE
Demand factors: Investment, private consumption, external demand
Persistence effects: Past employment rates
Relative prices: Wages, user cost of capital EMPLO
Y
p g , p
Financial markets: Real share prices
Demand pressure on the labour market (labour market tightness)
MEN
TFLADwETnJobCreatio βββα +++
Job creation as a function of demand and supply factors
LOWS
JCttt
tttJCt
rVUADwETnJobCreatioεβββ
βββα++++
+++=
−
−
1654
3211
MAT
Understanding labour market flows IIITCH
INGA
•Job destruction is a function ofRelative prices: Wages, real interest rates, tax wedge
Schumpeter effect: TFP import competition
ANDUNE
Job destruction determined by technological and competitive forces
REERrTFPtionJobDestruc βββα +++=
Schumpeter effect: TFP, import competition
EMPLO
YJDttJDtJDtJD
tJDtJDtJDJDt
ADwIMPREERrTFPtionJobDestruc
εββββββα
+++++++=
654
321
MEN
TFL
•WagesNegotiation (Nash bargaining) between firms and workers
Distribution of producer rent (matching rent)
LOWS
Distribution of producer rent (matching rent)
Wages depend on reservation wages and bargaining power
( ) ( )ttttt UVUBw ,1 Π+−= γγ
MAT
Data and methodologyTCH
INGA
• Data:Macro data from OECD A
NDUNE
Unemployment flow estimates by Elsby et al. (2008)‐Estimated flows based on LFS information on unemployment
duration EMPLO
Y
duration
‐Match job creation/destruction rates under certain assumptions
• Methodology: MEN
TFL
Start with single‐equation identification
The estimate system of equations
Full macro‐model on the basis of GMM LOWS
Full macro‐model on the basis of GMM
MAT
Determinants of unemployment outflows IITCH
INGA
• Decomposition of unemployment outflows shows that:Demand components play an important role (>40%)
d f f l l ff ( )
ANDUNE
Indication for some financial accelerator effect (>30%)
Relative prices (wages) more moderate role (<20%)
EMPLO
YMEN
TFLLO
WS
MAT
Determinants of unemployment inflows IITCH
INGA
• Decomposition of unemployment inflows shows:No Schumpeterian effect from import penetration (strong
d d ff )
ANDUNE
demand effect)
Job churning due to changes in interest rates and TFP growth
EMPLO
YMEN
TFLLO
WS
MAT
A simple macro framework ITCH
INGA
• Unemployment flows influence each other:Higher job destruction rates increases unemployment pool...
h k h f f f ll
ANDUNE
This makes it cheaper for firms to fill vacancies...
This increases hiring and job creation rates...
...which makes it more difficult for other firms to find new EMPLO
Y
labour...
which lowers job creation rates, etc....
•An aggregate supply curve to understand interest rates: MEN
TFL
•An aggregate supply curve to understand interest rates:The basic labour flow model assumes fixed interest rates and
productivity LOWS
To analyse macroeconomic employment dynamics we need
an aggregate supply curve
MAT
A simple macro framework IITCH
INGA
• First step: Only fiscal policy reaction curveMutual dependence of unemployment flows on each other
l f h f h l b k
ANDUNE
A policy reaction function to the state of the labour market
Long‐term interest rate purely determined by changes in
government debt EMPLO
Y
No considerations to short‐term variations in private savings
(assuming historical trend)
Considering different fiscal and labour market policies MEN
TFLtitijttttt PolicyLMMacroOutflowsInflows ,,1 εα +++++= −
Considering different fiscal and labour market policies
individually
LOWS
tptpjttt
totojttttt
j
InflowsOutflowsPolicy
PolicyLMMacroInflowsOutflows ,,1
,,
εα
εα
+++=
+++++= −
trtrjtttt
tptpjttt
SavingsDebtPolicyRIRL
ffy
,,
,,
εα ++++=
MAT
Assessing policy effectiveness: Job destructionTCH
INGA9.5
10Labour market spending: Contributions to job destruction
(short- vs. long-term)
ANDUNE
6.1
0.8
51
n %
)
EMPLO
Y-3.9
0.8
-1.6
-50
ntrib
utio
ns (i
MEN
TFL
-5.7 -5.7-6.7
12 9
-6.6
5-1
0C
on
LOWS
-12.9
-15
Trainingexpenditures
Public employmentservices
Hiringincentives
Unemploymentbenefits
Direct jobcreation
Short-term effecttfl
Long-term effecttflon outflows on outflows
MAT
Assessing policy effectiveness: Job creationTCH
INGA39.2
40Labour market spending: Contributions to job creation
(short- vs. long-term)
ANDUNE25.7
304
n %
)
EMPLO
Y15.7 15.6
20nt
ribut
ions
(in
MEN
TFL
5.3 4.0 3.5 3.5
7.5
2.8
10C
on
LOWS
0
Unemploymentbenefits
Hiringincentives
Trainingexpenditures
Public employmentservices
Direct jobcreation
Short-term effecton outflows
Long-term effecton outflowson outflows on outflows
MAT
Policy effectiveness depends on macro environment: Public debtTCH
INGA
Low Intermediate HighPublic debt level
Government consumption
Low Intermediate HighPublic debt level
Non-wage government consumption
Employment multipliers at different levels of public debt
ANDUNE4
68
nt e
stim
ate
Low Intermediate High
510
15nt
est
imat
e
Low Intermediate High
EMPLO
Y-20
2C
oeffi
cien
Note: Iterated estimates
-50
5C
oeffi
cien
Note: 1-stage estimates MEN
TFL5
10st
imat
e
Wage government consumption
015
020
0st
imat
e
Spending on public employment services
LOWS
-50
Coe
ffici
ent e
s
-50
050
100
Coe
ffici
ent e
s
Median of coefficient 5% confidence interval
Low Intermediate HighPublic debt level
Note: 1-stage estimates
Low Intermediate HighPublic debt level
Note: Iterated estimates
MAT
Policy effectiveness depends on environment: Structural unemployment
Hiring incentives
TCHINGA10
015
0st
imat
e
Hiring incentives
ANDUNE
050
Coe
ffici
ent e
sEM
PLOY
-50
Low Intermediate HighStructural unemployment rate
Note: Iterated estimates
Training expenditures MEN
TFL
4060
estim
ate
g p
LOWS
200
20C
oeffi
cien
t e-2
Low Intermediate HighStructural unemployment rate
Note: Iterated estimates
MAT
Policy effectiveness depends on environment: Financial crisis timesTCH
INGA15
Low Intermediate HighFinancial stress tercile
Government consumption
50
Low HighFinancial stress tercilePublic employment
ANDUNE0
510
effic
ient
est
imat
e
3040
effic
ient
est
imat
e
EMPLO
Y
-5C
oe
Note: Iterated estimates
20C
oe
Note: Iterated estimates
Direct job creation Unemployment benefits MEN
TFL30
040
0m
ate
Low Intermediate HighFinancial stress tercile
010
0m
ate
Low Intermediate HighFinancial stress tercile
LOWS
00
100
200
Coe
ffici
ent e
sti
050
Coe
ffici
ent e
sti
-100
Note: Iterated estimates
-50
Note: Iterated estimates
MAT
A simple macro framework IIITCH
INGA
• Second step: Endogenous short‐term interest ratesTaylor rule for interest rates
fl d
ANDUNE
New Keynesian inflation determination
Aggregate demand determined by state of the labour market
EMPLO
YMEN
TFLLO
WS
MAT
Using second‐step macro model for policy simulationTCH
INGA
• Estimation and simulationUsing GMM method to estimate the full model using panel
d
ANDUNE
data
Simulating the resulting model for the “average G20” country
Shock the model with the 2009 unemployment shock, i.e. the EMPLO
Y
baseline scenario should yield the average decline in
employment growth
Create three counter‐factuals: One austerity scenario and two MEN
TFL
Create three counter factuals: One austerity scenario and two
public deficit scenarios (spending vs. tax reduction)
Here: Only two alternative scenarios depicted LOWS
MAT
Employment recovery: The baseline scenarioTCH
INGAANDUNEEM
PLOYM
ENTFLLO
WS
• Baseline scenarioBaseline scenarioRecovery in employment by 2017 to pre‐crisis trend growth
rates
MAT
Employment recovery: Additional stimulusTCH
INGAANDUNEEM
PLOYM
ENTFLLO
WS
• Additional stimulus pushes employment up
MAT
Employment recovery: Austerity measuresTCH
INGAANDUNEEM
PLOYM
ENTFLLO
WS
• Austerity worsens employment outlook
EXER
Exercise 1: Labour market indicatorsRCISES
• Calculate labour market information from basic data for
TrinidadEmployment‐to‐population ratio
Unemployment rate
Sectoral employment ratesp y
Sectoral adjustment (annual frequency)
Average hours worked
EXER
Exercise 2: Employment elasticitiesRCISES
• Calculating employment elasticities for TrinidadCalculate annual GDP and employment growth rates
l l l l l ( h d)Calculate yearly employment elasticities (Arc‐method)
Calculate two 5‐year elasticities
Calculate the Point‐elasticity
Examine results
•On the basis of these employment elasticities…how big is the jobs gap between current and pre crisis…how big is the jobs gap between current and pre‐crisis
employment developments?
…how big would the jobs gap be next year with a GDP growth
rate that is only half as big as the current trend?
EXER
Exercise 3: Phillips curve for TrinidadRCISES
• Construct a structural unemployment rate for ToT:Use an historical average
l l f l dCalculate an HP filtered version
• Extract price inflation informationEstimate a basic Phillips curvep
How does the Phillips curve change with different estimates
for the structural unemployment rate
How much more unemplyoment is being generated byHow much more unemplyoment is being generated by
bringing the inflation rate down by 1 percentage point?