dr. heike joebges imk summer school august 7th, 2009 · neo-classical vs. keynesian employment...
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Labor demand in a macro-econometric model: Neo-classical vs. Keynesian specifications
Dr. Heike Joebges IMK Summer School August 7th, 2009
07-08-20092
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?
Why do we care about the labor demand?
07-08-20093
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,…)
Do high wages/wage increases cause unemployment?
07-08-20094
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.
Do high wages/wage increases cause unemployment?
07-08-20095
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-20096
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-2009*Selected countries with similar labor costs (Joebges et al. 2008)7
Country comparisons:* Labor market
Total economy, 1999=100Compensation of employees per hour Employment (in hours)
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain1 national currencyQuelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
100
110
120
130
140
150
1999 2001 2003 2005 2007
AT
DE
FR
UK1
NL
96
98
100
102
104
106
1999 2001 2003 2005 2007
DE
NL
FR
AT
UK1
07-08-2009*Selected countries with similar labor costs (Joebges et al. 2008)8
Country comparisons:* Consumption & Exports
Total economy, 1999=100 Exports (real) Private Consumption (real)
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain1 national currencyQuelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
100
120
140
160
180
200
220
1999 2001 2003 2005 2007
DENL
FR
AT
UK1
100
110
120
130
1999 2001 2003 2005 2007
DE
NL
FR
AT
UK1
07-08-2009*Selected countries with similar labor costs (Joebges et al. 2008)9
Country comparisons:* GDP & domestic demand
Total economy, 1999=100Gross domestic product (real) Domestic demand (real)
AT = Austria, DE = Germany, FI = Finland, FR = France, NL = Netherlands, UK = Great Britain1 national currencyQuelle: Reuters EcoWin (Eurostat-national accounts); IMK-calculations.
100
105
110
115
120
125
130
1999 2001 2003 2005 2007
DE
NL
FR
ATUK1
95
100
105
110
115
120
125
130
1999 2001 2003 2005 2007
FR
AT
DE
NL
UK1
07-08-200911
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-200912
How to specify the employment equation?
How does the employment equation fit in the macro- econometric model?
Macro-model sets the frame for the equation!
Econometric analysis of labor demand
07-08-200913
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.
Overview: Macro-econometric Models
07-08-200914
Short- to medium-term macroeconomic forecasts
Analysis of different macroeconomic policies
Structural model (47 stochastic equations)
Equations guided by economic theory, but good data- fit is necessary
No calibration
Same equations for forecasting and for policy simulations
National Accounts Statistics raw data
IMK-Model for Germany Focus
07-08-200915
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
IMK-Model for Germany „Philosophy“
07-08-200916
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)
IMK-Model for Germany Estimation approach
07-08-200917
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,…)
IMK-Model for Germany Data
07-08-200918
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-200919
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 capitalor: relative labor to capital costs(the ratio of real wage costs and real user costs of capital)Wage moderation increases employment
I) “Neo-classical” equation: Theory
07-08-2009*including: income tax & social security taxes of both employers & employees20
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)
I) “Neo-classical” equation: Variables
07-08-2009*Hinz/Logeay 2006; **in constrast to Barrell et al. 199621
… 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)
I) “Neo-classical” equation: Results
07-08-200922
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-200923
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
II) “Keynesian” equation: Theory
07-08-200924
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)
II) “Keynesian” equation: Variables
07-08-200925
Real GDP-elasticity can be restricted to 1
Capital stock elasticity is significantly negative
point estimate: -0,5
II) “Keynesian” equation: Results
07-08-200926
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
Comparing the employment equations:
07-08-200927
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!
Comparing the employment equations:
07-08-200928
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-2009*compensation per employee; **differences to baseline scenario in percent 29
Simulation: Negative wage* shock**
-10
-8
-6
-4
-2
0
2
1 2 3 4 5 6 7 8 9 10
%
Neoklassische Beschäftigungsgleichung
Keynessche Beschäftigungsgleichung
Neo-classical employment equation
Keynesian employment equation
07-08-2009*differences to baseline scenario in percent 30
Simulation: … effect on employment*
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
1 2 3 4 5 6 7 8 9 10
%
Neoklassische Beschäftigungsreaktion
Keynesianische Beschäftigungsreaktion
Neo-classical employment equation
Keynesian employment equation
07-08-2009*differences to baseline scenario in percent 31
Simulation: … effect on real GDP*
-1,5
-1,0
-0,5
0,0
0,5
1 2 3 4 5 6 7 8 9 10
%Neoklassische Beschäftigungsgleichung
Keynessche Beschäftigungsgleichung
Neo-classical employment equation
Keynesian employment equation
07-08-2009*differences to baseline scenario in percent 32
Simulation: …effect on real private consumption*
-4,5
-4,0
-3,5
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
1 2 3 4 5 6 7 8 9 10
%
Neoklassische Beschäftigungsgleichung
Keynessche Beschäftigungsgleichung
Neo-classical employment equation
Keynesian employment equation
07-08-2009*differences to baseline scenario in percent 33
Simulation: … effect on real exports*
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
1 2 3 4 5 6 7 8 9 10
%
Keynessche Beschäftigungsgleichung
Neoklassische BeschäftigungsgleichungNeo-classical employment equation
Keynesian employment equation
07-08-2009*differences to baseline scenario in percent 34
Simulation: … effect on consumption deflator*
-3,5
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1 2 3 4 5 6 7 8 9 10
%
Keynessche Beschäftigungsgleichung
Neoklassische Beschäftigungsgleichung
Keynesian employment equation
Neo-classical employment equation
07-08-200935
Motivation
Country comparisons
Macro-econometric analysis
“Neo-classical” employment equation
“Keynesian” employment equation
Simulation results
Summary
Outline
07-08-200936
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)
Summary
07-08-2009*...neither with labor market institutions (Bassanini et al. 2006)37
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”?
Summary (continued)
07-08-200938
Thank you !
07-08-200939
Neoclassical employment equation
• 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)) -0.220436 0.021937 -10.04837 0.0000• LOG(DE_COEE(-1))-LOG(DE_PGDP00(-1)) -0.064728 0.013277 -4.875031 0.0000• C 1.246657 0.132436 9.413243 0.0000• @TREND -0.000601 7.52E-05 -7.995935 0.0000• S91Q1 0.015363 0.002674 5.745648 0.0000• I91Q1 0.282305 0.005508 51.25275 0.0000• DLOG(DE_EE(-3)-DE_RES_EE(-3)) -0.098304 0.029583 -3.322965 0.0013• DLOG(DE_EE(-4)-DE_RES_EE(-4)) 0.515623 0.057450 8.975184 0.0000• DLOG(DE_GDP00(-1))+DLOG(DE_GDP00(-2)) -0.050927 0.009478 -5.372937 0.0000• DLOG(DE_GDP00) 0.095358 0.018839 5.061848 0.0000• I91Q1(-3) 0.031576 0.009713 3.250836 0.0016• I91Q1(-4) -0.164672 0.018881 -8.721483 0.0000••• R-squared 0.994099 Mean dependent var 0.003879• Adjusted R-squared 0.993353 S.D. dependent var 0.033119• S.E. of regression 0.002700 Akaike info criterion -8.877855• Sum squared resid 0.000634 Schwarz criterion -8.563295• Log likelihood 451.4538 F-statistic 1332.462• Durbin-Watson stat 1.815085 Prob(F-statistic) 0.000000•
07-08-200940
Neoclassical employment equation
• Prognoseguete (dynamic, in-sample, Sample (adjusted): 1981Q2 2005Q4)•• Root Mean Squared Error 166.7584• Mean Absolute Error 123.8024• Mean Absolute Percentage Error 0.391491• Theil Inequality Coefficient 0.002712• Bias Proportion 0.001645• Variance Proportion 0.006768• Covariance Proportion 0.991587•• 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••
07-08-200941
Neoclassical employment equation
-30
-20
-10
0
10
20
30
84 86 88 90 92 94 96 98 00 02 04
C U SU M 5% S ign ificance
-0 .2
0 .0
0 .2
0 .4
0 .6
0 .8
1 .0
1 .2
84 86 88 90 92 94 96 98 00 02 04
C U SU M o f Squ ares5 % Sign ifica nce
32800
33200
33600
34000
34400
34800
35200
35600
36000
95 96 9 7 98 99 00 01 02 03 04 0 5
YH ATYH AT+1 .96*YH ATS EYH AT-1 .9 6*YH ATSEde_ ee Ab h . Bes chaef tig te , In l .
In -Sam p le Progn os en "eq _de_ee_5"
07-08-200942
Neoclassical employment equation
3 3 2 0 0
3 3 6 0 0
3 4 0 0 0
3 4 4 0 0
3 4 8 0 0
3 5 2 0 0
3 5 6 0 0
3 6 0 0 0
3 6 4 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-S a m p le P ro g n o s e n "e q _ d e_ e e _ 5 "
3 3 0 0 0
3 3 5 0 0
3 4 0 0 0
3 4 5 0 0
3 5 0 0 0
3 5 5 0 0
3 6 0 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-S a m p le P ro gn o s e n (2) "e q _ de _ e e _ 5 "
3 3 2 0 0
3 3 6 0 0
3 4 0 0 0
3 4 4 0 0
3 4 8 0 0
3 5 2 0 0
3 5 6 0 0
3 6 0 0 0
3 6 4 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-O f-S a m p le P ro g n os e n (3 ) "e q _ d e _e e _ 5 "
3 3 0 0 0
3 3 5 0 0
3 4 0 0 0
3 4 5 0 0
3 5 0 0 0
3 5 5 0 0
3 6 0 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-S a m p le P ro gn o s e n (4) "e q _ de _ e e _ 5 "
07-08-200943
Keynesian employment equation
• 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 Prob.••• LOG(DE_EE(-1)-DE_RES_EE(-1))-LOG(DE_GDP00(-1)) -0.212295 0.022403 -9.476184 0.0000• C 1.872430 0.207688 9.015596 0.0000• LOG(DE_CSTOCK00(-1)) -0.109144 0.012811 -8.519472 0.0000• S91Q1 0.031735 0.004130 7.684436 0.0000• I91Q1 0.274198 0.006119 44.80760 0.0000• DLOG(DE_EE(-4)-DE_RES_EE(-4)) 0.362250 0.068722 5.271250 0.0000• DLOG(DE_GDP00(-1))+DLOG(DE_GDP00(-2)) -0.029472 0.011703 -2.518239 0.0137• DLOG(DE_GDP00) 0.116759 0.027073 4.312782 0.0000• I91Q1(-4) -0.113091 0.022755 -4.969905 0.0000• Z1 -0.009537 0.003208 -2.973324 0.0039• Z2 0.005531 0.001631 3.391565 0.0011• Z3 0.001819 0.001777 1.023465 0.3091• S91Q1*Z1 -0.002478 0.002278 -1.087990 0.2797• S91Q1*Z2 -0.004771 0.001644 -2.902363 0.0047• S91Q1*Z3 -0.003815 0.001968 -1.938080 0.0560• DLOG(DE_CSTOCK00)+DLOG(DE_CSTOCK00(-1))+DLOG(DE_CSTOCK00(-2))+DLOG(DE_CSTOCK00(-3))
0.096677 0.016744 5.773985 0.0000••• R-squared 0.995102 Mean dependent var 0.003879• Adjusted R-squared 0.994217 S.D. dependent var 0.033119• S.E. of regression 0.002519 Akaike info criterion -8.983306• Sum squared resid 0.000526 Schwarz criterion -8.563893• Log likelihood 460.6736 F-statistic 1124.202• Durbin-Watson stat 1.727360 Prob(F-statistic) 0.000000•
07-08-200944
Keynesian employment equation
• Prognoseguete (dynamic, in-sample, Sample (adjusted): 1981Q2 2005Q4)•• Root Mean Squared Error 154.4936• Mean Absolute Error 116.9241• Mean Absolute Percentage Error 0.380390• Theil Inequality Coefficient 0.002513• Bias Proportion 0.000206• Variance Proportion 0.000027• Covariance Proportion 0.999767•• 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••
07-08-200945
Keynesian employment equation
-3 0
-2 0
-1 0
0
1 0
2 0
3 0
8 6 8 8 9 0 9 2 9 4 9 6 9 8 0 0 0 2 0 4
C U S U M 5 % S ig n ifica n ce
-0 .2
0 .0
0 .2
0 .4
0 .6
0 .8
1 .0
1 .2
8 6 8 8 9 0 9 2 9 4 9 6 9 8 0 0 0 2 0 4
C U SU M o f Sq u a re s5 % S ig n ifica n ce
3 2 5 0 0
3 3 0 0 0
3 3 5 0 0
3 4 0 0 0
3 4 5 0 0
3 5 0 0 0
3 5 5 0 0
3 6 0 0 0
3 6 5 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
YH A TYH A T+1 .9 6 *YH ATS EYH A T-1 .9 6 *YH ATS Ed e _ e e Ab h . B e s ch a e f tig te , In l .
In -Sa m p le P ro g n o s e n "e q _ d e _ e e _ 1 0 "
07-08-200946
Keynesian employment equation
3 3 2 0 0
3 3 6 0 0
3 4 0 0 0
3 4 4 0 0
3 4 8 0 0
3 5 2 0 0
3 5 6 0 0
3 6 0 0 0
3 6 4 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-Sa m p le P ro g n o s e n "e q_ d e _ e e _ 1 0 "
3 3 0 0 0
3 3 5 0 0
3 4 0 0 0
3 4 5 0 0
3 5 0 0 0
3 5 5 0 0
3 6 0 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-Sa m p le P ro g n o s e n (2 ) "e q _ d e _ e e _ 1 0 "
3 3 2 0 0
3 3 6 0 0
3 4 0 0 0
3 4 4 0 0
3 4 8 0 0
3 5 2 0 0
3 5 6 0 0
3 6 0 0 0
3 6 4 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-O f-Sa m p le Pro g n o s e n (3 ) "e q _ d e _ e e _ 1 0 "
3 3 0 0 0
3 3 5 0 0
3 4 0 0 0
3 4 5 0 0
3 5 0 0 0
3 5 5 0 0
3 6 0 0 0
9 5 9 6 9 7 9 8 9 9 0 0 0 1 0 2 0 3 0 4 0 5
Ou t-Of-Sa m p le P ro g n o s e n (4 ) "e q _ d e _ e e _ 1 0 "
07-08-200947
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.
Literature