Dr. Heike Joebges IMK Summer School August 7th, 2009 · Neo-classical vs. Keynesian employment...

www.boeckler.de

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




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


Country comparisons:* Consumption & Exports

Total economy, 1999=100 Exports (real) Private Consumption (real)


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


Country comparisons:* GDP & domestic demand

Total economy, 1999=100Gross domestic product (real) Domestic demand (real)


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




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




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




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




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




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





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

%






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

%


Neoklassische BeschäftigungsgleichungNeo-classical employment equation



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

%





07-08-200935

Motivation

Country comparisons




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


• 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


-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


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


• 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


• 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


-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


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

Dr. Heike Joebges IMK Summer School August 7th, 2009 · Neo-classical vs. Keynesian employment...

Documents

Transcript of Dr. Heike Joebges IMK Summer School August 7th, 2009 · Neo-classical vs. Keynesian employment...