Download Labor demand in a macro

Labor demand
in a macro-econometric model:
Neo-classical vs. Keynesian specifications
Dr. Heike Joebges
IMK Summer School
August 7th, 2009
www.boeckler.de
Why do we care about the
labor demand?
Policy implications!
 What is the best response to the current crisis for
Germany?
Wage moderation and further labor market reforms or
wage increases coupled with minimum wage
introduction etc.?
 What are the implications for the euro area?
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07-08-2009
Do high wages/wage increases
cause unemployment?
YES! – The prevailing view in Germany –
 Up to the current crisis: praise of past wage
moderation & past labor market reforms
(e.g. Deutsche Bundesbank (2007), SVR (2007), European
Commission (2007), OECD (2008))
 Since the current crisis: growing calls for more wage
moderation in order to save employment
(e.g. organization of employers, research institutes,…)
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07-08-2009
Do high wages/wage increases
cause unemployment?
NO! – Looking at case studies for “big” countries –
Relatively higher wage increases go in line with
 … higher employment growth
 … higher consumption & domestic demand growth
 … higher GDP growth
 yet: less export growth
Only for small countries, the reverse holds.
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07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
5
07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
6
07-08-2009
Country comparisons:*
Labor market
Total economy, 1999=100
Compensation of employees per hour
106
150
UK
140
104
FR
120
AT
110
DE
2001
2003
2005
AT
1
NL
130
100
1999
Employment (in hours)
2007
NL
UK
102
1
FR
100
DE
98
96
1999
2001
2003
2005
2007
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain
1
national currency
Quelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
7
*Selected countries with similar labor costs (Joebges et al. 2008)
07-08-2009
Country comparisons:*
Consumption & Exports
Total economy, 1999=100
Exports (real)
Private Consumption (real)
220
130
NL
200
FR
120
AT
160
UK
140
1
DE
180
AT
1
110
NL
FR
120
100
1999
UK
DE
2001
2003
2005
2007
100
1999
2001
2003
2005
2007
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain
1
national currency
Quelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
8
*Selected countries with similar labor costs (Joebges et al. 2008)
07-08-2009
Country comparisons:*
GDP & domestic demand
Total economy, 1999=100
Gross domestic product (real)
Domestic demand (real)
130
130
UK
125
AT
125
120
120
FR
115
NL
115
AT
110
110
DE
105
100
1999
1
UK
FR
1
NL
2001
2003
2005
2007
105
DE
100
95
1999
2001
2003
2005
2007
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain
1
national currency
Quelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
9
*Selected countries with similar labor costs (Joebges et al. 2008)
07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
11
07-08-2009
Econometric analysis
of labor demand
 How to specify the employment equation?
 How does the employment equation fit in the macroeconometric model?
Macro-model sets the frame for the equation!
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07-08-2009
Overview:
Macro-econometric Models
 Theory based models for policy simulations
(Quest II, Multimod): sound theoretical foundation;
partly calibrated coefficients;
 Data based models for short-term forecasting
(OFCE, Fair): try to fit data as well as possible,
theoretical foundation is secondary;
 Forecasting and policy simulations (NIGEM, OEF):
different equations for different purposes, i.e.
estimated and calibrated coefficients.
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07-08-2009
IMK-Model for Germany
Focus
 Short- to medium-term macroeconomic forecasts
 Analysis of different macroeconomic policies
 Structural model (47 stochastic equations)
 Equations guided by economic theory, but good datafit is necessary
 No calibration
 Same equations for forecasting and for policy
simulations
 National Accounts Statistics raw data
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07-08-2009
IMK-Model for Germany
„Philosophy“
 Based on Keynesian/New-Keynesian elements
 Crucial difference between short- and long-term
 Short-term: prices and wages only partly flexible;
 Long-term: adjustment mechanism towards steady
state (adjustment of prices, wages, …)
 Real effects of economic policy
 Existence of unemployment in the long run
 Existence of nominal rigidities
 Market spillovers
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07-08-2009
IMK-Model for Germany
Estimation approach
 Analysis of time series properties
 Single error correction equations
 Tests for serial correlation
 Stability tests
 Evaluation of the forecasting quality of the stochastic
equation (dynamic in-sample and out-of-sample
forecast)
 Evaluation of the behavior of the stochastic equation
inside the model (ex post simulation)
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07-08-2009
IMK-Model for Germany
Data
Data
 All data in logs (excluding: rates, ratios, dummies)
 Quarterly data
 Raw data, not seasonally adjusted
 All data from national accounts, starting 1980 Q1
 Almost all variables are I(1)
 Structural breaks! (Reunification, European Monetary
Union,…)
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07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
18
07-08-2009
I) “Neo-classical” equation:
Theory
In the short run, employment is driven by demand
factors, but in the long run, only by supply factors:
 Real output
 Real wage costs
 Real user costs of capital
or: relative labor to capital costs
(the ratio of real wage costs and real user costs of
capital)
Wage moderation increases employment
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07-08-2009
I) “Neo-classical” equation:
Variables
Employment:
 Persons employed
[Hinz/Logeay (2006): hours worked]
Real output:
 Real GDP
Real wage costs:
 Compensation of employees per hours worked*
 Proxy for producer prices: GDP-deflator
Real user costs of capital [Barrel et al. 1996]:
 Deflator of non-residential private investment
 Real interest rates (short-term: 3m; long-term: 10y)
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*including: income tax & social security taxes of both employers & employees
07-08-2009
I) “Neo-classical” equation:
Results
… for the co-integration relation:
 Real GDP elasticity can be restricted to 1
 Real wage elasticity is significantly negative,
point estimate about -0,3 for persons employed [-0,6 for hours worked]*
 No substitution effect** (relative factor price elasticity
is not significant for reunified Germany)
 System approach (VECM) confirms the elasticiy
estimates and the single equation approach (weak
exogeneity of real wages and real output)
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*Hinz/Logeay 2006; **in constrast to Barrell et al. 1996
07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
22
07-08-2009
II) “Keynesian” equation:
Theory
 Aggregate demand for goods and services
determines supply and thereby employment
 In the short-run, labor is the only mobile production
factor; in the medium-run, capital stock adjustment
 Employment demand depends on total demand and
the capital stock
 Unemployment is the result of insufficient demand,
which could be raised by economic policy
Expansionary economic policy increases employment
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07-08-2009
II) “Keynesian” equation:
Variables
Employment:
 Persons employed
Real output:
 Real GDP
Real capital stock:
 Real capital stock (last period)
(Indicator construction: start value (DESTATIS) plus real investment
excluding construction investment & real depreciation)
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07-08-2009
II) “Keynesian” equation:
Results
 Real GDP-elasticity can be restricted to 1
 Capital stock elasticity is significantly negative
point estimate: -0,5
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07-08-2009
Comparing the
employment equations:
Co-integration relation
“Neo-classical” equation:
Employment = real GDP -0,3*real wage + trend + c
“Keynesian” equation:
Employment = real GDP -0,5*real capital stock +trend+c
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07-08-2009
Comparing the
employment equations:
Which equation performs better?
 Hard to discriminate between Keynesian/
Neo-classical employment equations
 Both equations perform well with regards to test
statistics, robustness, & out-of-sample performance!
 Slightly better out-of-sample performance for the
Keynesian equation, esp. starting 2001
 Yet: implications for employment differ enormously!
27
07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
28
07-08-2009
Simulation: Negative wage*
shock**
2
0
-2
%
-4
Neo-classical employment
Neoklassische
equation
Beschäftigungsgleichung
-6
-8
-10
Keynessche
Keynesian
Beschäftigungsgleichung
employment equation
1
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2
3
4
5
6
7
8
*compensation per employee; **differences to baseline scenario in percent
9
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07-08-2009
Simulation: … effect on
employment*
2,0
1,5
1,0
Neoklassische
Neo-classical
Beschäftigungsreaktion
employment equation
0,5
%
0,0
-0,5
Keynesian
employment
Keynesianische
Beschäftigungsreaktion
equation
-1,0
-1,5
1
30
2
3
4
*differences to baseline scenario in percent
5
6
7
8
9
10
07-08-2009
Simulation: … effect on
real GDP*
0,5
0,0
%
Neo-classical
Neoklassische
employment
equation
Beschäftigungsgleichung
-0,5
-1,0
Keynessche
Keynesian
Beschäftigungsgleichung
employment equation
-1,5
1
31
2
3
4
*differences to baseline scenario in percent
5
6
7
8
9
10
07-08-2009
Simulation: …effect on
real private consumption*
0,0
-0,5
-1,0
-1,5
Neo-classical
Neoklassische
Beschäftigungsgleichung
employment
equation
-2,0
%
-2,5
-3,0
Keynesian
employment
equation
-3,5
Keynessche
Beschäftigungsgleichung
-4,0
-4,5
1
32
2
3
4
5
*differences to baseline scenario in percent
6
7
8
9
10
07-08-2009
Simulation: … effect on
real exports*
3,5
Keynessche
Keynesian
Beschäftigungsgleichung
employment
equation
3,0
2,5
2,0
Neoklassische
Beschäftigungsgleichung
%
Neo-classical
employment
equation
1,5
1,0
0,5
0,0
1
2
3
4
5
6
7
8
9
10
-0,5
33
*differences to baseline scenario in percent
07-08-2009
Simulation: … effect on
consumption deflator*
0,5
0,0
1
2
3
4
5
6
7
8
9
10
-0,5
-1,0
% -1,5
Neo-classical
employment
Neoklassische
Beschäftigungsgleichung
equation
-2,0
-2,5
-3,0
Keynesian
Keynessche
employment
Beschäftigungsgleichung
equation
-3,5
34
*differences to baseline scenario in percent
07-08-2009
Outline
 Motivation
 Country comparisons
 Macro-econometric analysis
 “Neo-classical” employment equation
 “Keynesian” employment equation
 Simulation results
 Summary
35
07-08-2009
Summary
Neo-classical vs. Keynesian employment equation:
 Both equations perform equally well with regards to
test statistics, robustness, & out-of-sample
performance!
 Hard to discriminate between the two equations
 Out-of-sample performance is slightly better for
Keynesian equation, esp. from 2001 onwards
 Reaction to shocks is similar, if oil price or demand
shocks are modeled; only wage shocks make a huge
difference (and only for employment)
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07-08-2009
Summary (continued)
Arguments in favor of the Keynesian equation:
 Stagnating employment after years of wage
moderation cannot be explained with the neoclassical
employment equation*
 Model is better fitting the data
Future research:
 Better discrimination, if equation is specified for
“hours worked” instead of “persons employed”?
37
*...neither with labor market institutions (Bassanini et al. 2006)
07-08-2009
Thank you !
38
07-08-2009
Neoclassical employment
equation
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Dependent Variable: DLOG(DE_EE-DE_RES_EE)
Method: Least Squares
Date: 11/14/06 Time: 13:17
Sample (adjusted): 1981Q2 2005Q4
Included observations: 99 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LOG(DE_EE(-1)-DE_RES_EE(-1))-LOG(DE_GDP00(-1))
LOG(DE_COEE(-1))-LOG(DE_PGDP00(-1))
C
@TREND
S91Q1
I91Q1
DLOG(DE_EE(-3)-DE_RES_EE(-3))
DLOG(DE_EE(-4)-DE_RES_EE(-4))
DLOG(DE_GDP00(-1))+DLOG(DE_GDP00(-2))
DLOG(DE_GDP00)
I91Q1(-3)
I91Q1(-4)
-0.220436
-0.064728
1.246657
-0.000601
0.015363
0.282305
-0.098304
0.515623
-0.050927
0.095358
0.031576
-0.164672
0.021937
0.013277
0.132436
7.52E-05
0.002674
0.005508
0.029583
0.057450
0.009478
0.018839
0.009713
0.018881
-10.04837
-4.875031
9.413243
-7.995935
5.745648
51.25275
-3.322965
8.975184
-5.372937
5.061848
3.250836
-8.721483
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0013
0.0000
0.0000
0.0000
0.0016
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.994099
0.993353
0.002700
0.000634
451.4538
1.815085
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.003879
0.033119
-8.877855
-8.563295
1332.462
0.000000
07-08-2009
Neoclassical employment
equation
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Prognoseguete (dynamic, in-sample, Sample (adjusted): 1981Q2 2005Q4)
Root Mean Squared Error
166.7584
Mean Absolute Error
123.8024
Mean Absolute Percentage Error
Theil Inequality Coefficient
0.002712
Bias Proportion
0.001645
Variance Proportion
0.006768
Covariance Proportion
0.991587
0.391491
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 2001Q4)
Root Mean Squared Error
170.4378
Mean Absolute Error
148.4559
Mean Absolute Percentage Error
0.428285
Theil Inequality Coefficient
0.002451
Bias Proportion
0.532123
Variance Proportion
0.002521
Covariance Proportion
0.465356
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 2000Q4)
Root Mean Squared Error
433.1084
Mean Absolute Error
409.7640
Mean Absolute Percentage Error
1.178743
Theil Inequality Coefficient
0.006181
Bias Proportion
0.895106
Variance Proportion
0.008998
Covariance Proportion
0.095896
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 1999Q4)
Root Mean Squared Error
231.9727
Mean Absolute Error
191.9230
Mean Absolute Percentage Error
0.551352
Theil Inequality Coefficient
0.003317
Bias Proportion
0.382732
Variance Proportion
0.028472
Covariance Proportion
0.588795
07-08-2009
Neoclassical employment
equation
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1 .2
1 .0
20
0 .8
10
0 .6
0
0 .4
-1 0
0 .2
-2 0
0 .0
-3 0
-0 .2
84
86
88
90
92
94
96
98
00
02
04
CU SU M
5 % S ig n ifica n ce
In -S a m p le P ro g n o s e n "eq _ d e _ e e _ 5 "
84
86
88
90
92
94
96
98
00
02
04
C U S U M o f S q u a re s
5 % S ig n ifica n ce
36000
35600
35200
34800
34400
34000
33600
33200
32800
95
41
96
97
98
99
00
01
02
03
YH A T
YH A T+1 . 9 6 *Y H ATS E
YH A T-1 .9 6 * YH ATS E
d e _ e e Ab h . B e s ch a e f tig te , In l .
04
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07-08-2009
Neoclassical employment
equation
O u t-O f-S a m p le P ro g n o s e n "e q _ d e_ e e _ 5 "
Ou t-O f-S a m p le P ro gn o s e n (2) "e q _ de _ e e _ 5 "
36400
36000
36000
35500
35600
35000
35200
34800
34500
34400
34000
34000
33500
33600
33000
33200
95
96
97
98
99
00
01
02
03
04
05
95
O u t-O f-S a m p le P ro g n os e n (3 ) "e q _ d e _e e _ 5 "
96
97
98
99
00
01
02
03
04
05
Ou t-O f-S a m p le P ro gn o s e n (4) "e q _ de _ e e _ 5 "
36400
36000
36000
35500
35600
35000
35200
34800
34500
34400
34000
34000
33500
33600
33000
33200
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95
96
97
98
99
00
01
02
03
04
05
95
96
97
98
99
00
01
02
03
04
05
07-08-2009
Keynesian employment
equation
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Dependent Variable: DLOG(DE_EE-DE_RES_EE)
Method: Least Squares
Date: 11/20/06 Time: 15:21
Sample (adjusted): 1981Q2 2005Q4
Included observations: 99 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
LOG(DE_EE(-1)-DE_RES_EE(-1))-LOG(DE_GDP00(-1))
-0.212295
0.022403
-9.476184
C
1.872430
0.207688
9.015596
LOG(DE_CSTOCK00(-1))
-0.109144
0.012811
-8.519472
S91Q1
0.031735
0.004130
7.684436
I91Q1
0.274198
0.006119
44.80760
DLOG(DE_EE(-4)-DE_RES_EE(-4))
0.362250
0.068722
5.271250
DLOG(DE_GDP00(-1))+DLOG(DE_GDP00(-2))
-0.029472
0.011703
-2.518239
DLOG(DE_GDP00)
0.116759
0.027073
4.312782
I91Q1(-4)
-0.113091
0.022755
-4.969905
Z1
-0.009537
0.003208
-2.973324
Z2
0.005531
0.001631
3.391565
Z3
0.001819
0.001777
1.023465
S91Q1*Z1
-0.002478
0.002278
-1.087990
S91Q1*Z2
-0.004771
0.001644
-2.902363
S91Q1*Z3
-0.003815
0.001968
-1.938080
DLOG(DE_CSTOCK00)+DLOG(DE_CSTOCK00(-1))+DLOG(DE_CSTOCK00(-2))+DLOG(DE_CSTOCK00(-3))
0.096677
0.016744
5.773985
R-squared 0.995102
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Mean dependent var
0.994217
S.D. dependent var
0.002519
Akaike info criterion
0.000526
Schwarz criterion
460.6736
F-statistic
1.727360
Prob(F-statistic)
Prob.
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0137
0.0000
0.0000
0.0039
0.0011
0.3091
0.2797
0.0047
0.0560
0.0000
0.003879
0.033119
-8.983306
-8.563893
1124.202
0.000000
07-08-2009
Keynesian employment
equation
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44 ••
Prognoseguete (dynamic, in-sample, Sample (adjusted): 1981Q2 2005Q4)
Root Mean Squared Error
154.4936
Mean Absolute Error
116.9241
Mean Absolute Percentage Error
Theil Inequality Coefficient
0.002513
Bias Proportion
0.000206
Variance Proportion
0.000027
Covariance Proportion
0.999767
0.380390
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 2001Q4)
Root Mean Squared Error
122.8920
Mean Absolute Error
95.22245
Mean Absolute Percentage Error
0.273853
Theil Inequality Coefficient
0.001770
Bias Proportion
0.000991
Variance Proportion
0.030811
Covariance Proportion
0.968198
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 2000Q4)
Root Mean Squared Error
297.7411
Mean Absolute Error
269.7591
Mean Absolute Percentage Error
0.774579
Theil Inequality Coefficient
0.004258
Bias Proportion
0.809648
Variance Proportion
0.005017
Covariance Proportion
0.185335
Prognoseguete (dynamic, out-of-sample, Sample (adjusted): 1981Q2 1999Q4)
Root Mean Squared Error
214.4531
Mean Absolute Error
176.8086
Mean Absolute Percentage Error
0.507052
Theil Inequality Coefficient
0.003067
Bias Proportion
0.404633
Variance Proportion
0.000054
Covariance Proportion
0.595313
07-08-2009
Keynesian employment
equation
30
1 .2
1 .0
20
0 .8
10
0 .6
0
0 .4
-1 0
0 .2
-2 0
0 .0
-3 0
-0 .2
86
88
90
92
94
96
98
00
02
04
CU SU M
5 % S ig n ific a n c e
In -S a m p le P ro g n o s e n "e q _ d e _ e e _ 1 0 "
86
88
90
92
94
96
98
00
02
04
C U S U M o f S q u a re s
5 % S ig n ific a n ce
36500
36000
35500
35000
34500
34000
33500
33000
32500
95
45
96
97
98
99
00
01
02
03
YH A T
YH A T+ 1 . 9 6 * Y H ATS E
YH A T-1 .9 6 * YH ATS E
d e _ e e Ab h . B e s ch a e f tig te , In l .
04
05
07-08-2009
Keynesian employment
equation
O u t-O f-S a m p le P ro g n o s e n "e q_ d e _ e e _ 1 0 "
O u t-O f-S a m p le P ro g n o s e n (2 ) "e q _ d e _ e e _ 1 0 "
36400
36000
36000
35500
35600
35000
35200
34800
34500
34400
34000
34000
33500
33600
33000
33200
95
96
97
98
99
00
01
02
03
04
05
95
O u t-O f-Sa m p le Pro g n o s e n (3 ) "e q _ d e _ e e _ 1 0 "
96
97
98
99
00
01
02
03
04
05
O u t-O f-S a m p le P ro g n o s e n (4 ) "e q _ d e _ e e _ 1 0 "
36400
36000
36000
35500
35600
35000
35200
34800
34500
34400
34000
34000
33500
33600
33000
33200
46
95
96
97
98
99
00
01
02
03
04
05
95
96
97
98
99
00
01
02
03
04
05
07-08-2009
Literature
Barrel, R./Pain, N./Young, G. (1996): A cross-country comparison of the demand
for labour in Europe, Weltwirtschaftliches Archiv 132 (4): 638-650.
Bassanini, A./Duval, R. (2006): Employment Patterns in OECD countries:
Reassessing the Role of Policies and Institutions, OECD Economics
Department Working Paper no. 486.
Deutsche Bundesbank (2007): Monthly Bulletin, August, S. 47-48.
Hinz, D./Logeay, C. (2006): Forecasting Employment for Germany, IMK Working
Paper No. 1.
Joebges, H./Logeay, C./Peters, D./Stephan, S./Zwiener, R. (2008): Deutsche
Arbeitskosten steigen im europäischen Vergleich nur gering, IMK Report Nr.
34, November.
Sachverständigenrat (SVR 2007): Jahresgutachten.
European Commission (2007): Raising Germany’s Growth Potential, DG ECFIN
Occasional Paper Nr. 28, February.
OECD 2008: Economic Surveys, Vol. 7, April.
47
07-08-2009