How do innovation and imitation change the short run impact of GDP on unemployment ? Boussemart J.P....

How do innovation and imitation change the short run impact of GDP on unemployment ?

Boussemart J.P.Briec W.Tavéra C.

Introduction

• The Okun’s law relationship as an empirical regularity (Okun, 1962) :

• Developments– Theoretical background (Prachowny 1993,…)– Empirical analysis of the dynamic effects of GDP on unemployment

(Crespo-Cuaresma 2003, Silvapulle et al. 2004) :

OLC(Expansion) < OLC (Recession)

• Okun’s law as a demand driven macroeconomic mechanism

D real GDP = +1%

D unemployment rate = -0.3 pt of %

A simple graphic version of the OL (Bureau of Economic Analysis for US datas)

Introduction

• This paper aims at : – Reexamining the supply side aspect of the OL

mechanims (distinction potential-observed real output)

– Re-visiting the OL by introducing the influence of technical change and technological distance

– Evaluating the short-run impact of technology-driven output movements on unemployment

Technical progress and catching up

• Innovation and imitation : the simple diagram

Technological leaderInnovation

Shifts of the Technological frontier

Follower countryimitation

Technical progress and catching up

Innovation and imitation as complementary proceses (Benhabib-Spiegel 994, Acemoglu-Aghion-Zilibotti, 2002)

Specification 1: an interaction-augmented-version of the OL relationship

First order effects : linear effects

Non linear effects : Squared variables Interaction terms (cross-terms)

Specification 2 : OL relationship with threshold

Threshold variable Z : Technical progress or Technological distance with the leader

Estimation method suggested by Hansen (1999) :Min square estimate of the thresholdTest for significativeness of the threshold

The measure of productivity gaps• The technological gap is measured in terms of TFP levels between any

country i and the leader (Malmquist index : Färe et al. 1994).

• At time t, the production set is defined as T = { , X can produce Y} :

• T satisfies strong disposability and convexity assumptions and we assume constant returns to scale

• The distance between country i and the world frontier can be decomposed into two components : – The time change in the technical efficiency – The geometric mean if the shift of the frontier

Technical change and productivity gaps

Productivity variation in country i

Technological gap variation (catching up)

Movement of the technological frontier (technical change)Pays i (initial)

Pays i (final)

Leader (initial)

Leader(final)

Inputs X

Output Y

Data

• Annual data , 1980-2004• 16 oecd countries : Austria, Belgium, Denmark,

Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, UK, and USA.

• 400 observations for each variable

• Equilibrium levels of output and unemployment : HP filter

Empirical results 0 : a preliminary analysis of the basic OL model

Empirical results 0 : a preliminary analysis of the basic OL model

Empirical results 1 : the interaction-augmented-version of the OL relationship

OLS pooled sample estimates Model with 𝑘 = 0 Model with 𝑘 = 1

initial equation deleted equation initial equation deleted equation Regressors 𝑌𝐺𝑖𝑡 -0.230 (8.66) -0.237 (9.17) -0.234 (8.50) -0.239 (8.84) 𝑌𝐺𝑖𝑡2 0.002 (0.71) -0.001 (0.32) 𝐷𝑡−𝑘 -0.019 (1.31) -0.023 (1.50) 𝐷𝑖𝑡−𝑘2 0.001 (0.91) 0.001 (1.61) 𝑇𝐶𝑖𝑡−𝑘 0.040 (1.02) 0.034 (0.85) 𝑇𝐶𝑖𝑡−𝑘2 0.002 (0.28) 0.018 (1.73) ሺ𝑌𝐺𝑖𝑡 ∗𝐷𝑡−𝑘ሻ -0.016 (6.97) -0.015 (7.02) -0.015 (6.50) -0.015 (6.58) ሺ𝑌𝐺𝑖𝑡 ∗𝑇𝐶𝑖𝑡−𝑘ሻ 0.026 (2.33) 0.024 (2.47) 0.035 (2.91) 0.026 (2.36) ሺ𝑇𝐶𝑖𝑡−𝑘 ∗𝐷𝑡−𝑘ሻ -0.009 (2.40) -0.007 (2.28) -0.001 (0.24) P. value F test 0.622 (a) 0.385 (b) R2 0.740 0.738 0.737 0.733 (a) F test for H0 : ሺ𝛽2,𝛽3,𝛽4,𝛽5,𝛽6ሻ= ሺ0,0,0,0,0,0ሻ. (b) F test for H0 : ሺ𝛽2,𝛽3,𝛽4,𝛽5,𝛽6,𝛽9ሻ= ሺ0,0,0,0,0,0,0ሻ

Empirical results 1 : the interaction-augmented-version of the OL relationship• First order linear approximation of the impact of GDP on unemployment rate :

-0.237 – 0.015 D + 0.024 TC with k = 0-0.239 – 0.015 D + 0.026 TC with k = 1

• The total effect of a 1% rise in output on unemployment variation is twice the first order effect for a technological distance close to 16%

• The impact of a 1% rise in output on unemployment variation is zero when technical change is close to 9.2% - 9.9 %

• Very rapid increases in the rhythm of technical change can thus lead to a reversal of the traditional effect on unemployment movements in the short run

Empirical results 2 : the threshold version of the OL relationship

Empirical results 2 : the threshold version of the OL relationship

Empirical results 2 : the threshold version of the OL relationship

Empirical results 2 : the threshold version of the OL relationship• The short run impact (in absolute value) of GDP

movements on unemployment rate is :

larger when the technological distance is large (imitation)

close to zero for countries close to the technological frontier

smaller when the size of technical progress is large (innovation)

Some concluding remarks

• The OL relationship does not contain only demand induced macroeconomic mechanims

• The origins of variations in TFP matter for determining the total impact of GDP movemements on unemployment, even in the short run

• Imitation and innovation generate second order non linear mechanisms that can boost or mitigate the traditional first order OLC

• Our results lend suppport to recent empirical papers which show that the ouput-unemployment relationship might be dominated by permanent shocks rather than by temporary shocks only (Sinclair 2009)

How many true values are there for the Okun’s Law coefficient? One or Two ?

A meta-analysis of empirical results

Roger Perman(a) - Gaetan Stephan(b) - Christophe Tavéra(b)

University of StrathclydeCREM, CNRS – Université de Rennes 1

Loi d’Okun

Exemple : Etats-Unis, 1970-2011, données trimestrielles, Coefficient moyen = -0.41

Objectif / Methode

• Objectif : estimer le coefficient d’Okun

• Méthode : – Ne pas utiliser un nouvelle base de données– Utiliser les estimations obtenues dans la littérature

et les caractéristiques des analyses économétriques correspondantes

Les catégories de modélisations

• Les modèles ad-hoc

• La fonction de production avec

Méthode d’échantillonnage : Etape 1

• Recherche d’articles dans Econlit avec critères : – mots clés : Okun’s Law – Output-unemployment

relationship– Presence d’un abstract (verification estimation

présente)– Publication après 1980– Presence dans Econlit en décembre 2010

• Papiers identifiés : 97

Méthode d’échantillonnage : Etape 2

• Exclusion des articles – Ne contenant pas une estimation originale de la

loi d’Okun– Ne précisant pas suffisamment les caractéristiques

de l’estimation (période, etc.)– Contenant des estimations de modèles non

linéaires de la loi d’Okun

• Papiers retenus : 30

Cycle de vie de la publication

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Homogénéisation des estimations

• Réécriture des équations estimées sous la forme :

Caractéristiques statistiques de l’échantillon

Minimum Maximum Mean Standard deviation

Median

OLC -3.22 0.17 -0.77 0.71 -0.58 Number of observations 21 408 50,4 46.54 41 First year 1948 1990 1968.2 10.75 1970 Last year 1985 2006 1999.2 4.61 1999 Proportion of OLC estimators with the following features (%) Time series data base 98.9 Country 74.4 Panel data base 1.1 Region 26.0 Yearly frequency 68.5 European countries 74.7 Frequency higher than year 31.5 Unites States 7.7 Endogenous variable : Unemployment rate 41.8 Rest of the world 17.9 Endogenous variable : Real output 58.2 Static model 53.8 Model in level 9.2 Dynamic model 39.9 Model in first difference 14.7 Cointegrated model 6.6 Equilibrium values of real output and unemployment from filtering procedure 76.1

Meta régression : Biais et tests

Biais TestsType 1 Test FAT de StanleyType 2 Galbraith Plot

Meta régression : tests de biais

• Test de Stanley (Test de biais de Type 1)

Remarque : = true effect

• Galbraith plot (Test de Type 2)– Diagramme croisé :

(précision des estimateurs – t statistiques correspondants)

Meta régression multuvariée

Divers Sample Frequency Country Model Endogenous

Filter

FirstyearLastyearPubyear

SampTSSampPA

FreqYFreqSQ

CountDEDCountDINGCountReg

ModSTAModDYNOthexoNoothexoNeq1NeqN

EndYEndU

LevelDeltaFiltLTFiltHPFiltBKFiltBNFiltUCFiltMOD

Principales dummies retenues pour la régression multivariée

Quelques résultats sur les biaisTest d’absence de biais de type 1

Dependent variable = t-statistic on the OL coefficient OLS estimator IRLS estimator Obs. 𝛽

(bias) 𝛼

(precision effect)

R2

𝛽 (bias)

𝛼 (precision

effect)

R2

Whole sample 263 -3.531 (-7.23)

-0.200 (-12.01)

0.36 -3.69 (-6.98)

-0.187 (-3.26)

0.37

Output sub-sample 151 -2.164 (-5.67)

-0.620 (-12.45)

0.51 -2.039 (-6.72)

-0.605 (-11.65)

0.47

Unemployment sub-sample

112 0.171 (0.12)

-0.265 (-8.39)

0.39 -0.125 (-0.06)

-0.253 (-3.11)

0.39

Quelques résultats de la méta régression multivariée

Whole sample Unemployment sub-sample

Output sub-sample

OLS STEPWISEprocedure

IRLSprocedure

STEPWISE then IRLS

STEPWISE then IRLS

Constant

-240,409 (-2,01) -189,478 (-1,73) -194,449 (-3.00)

-278.497 (-4.23)

Precision

-0,400 (-3,08) -0,454 (-10,23) -0,528 (-9,44)

-0.400 (-9.09) -1.018 (-13.79)

SAMPPA

-0,261 (-1,74) -0,292 (-2,06) -0,174 (-1,80)

FREQSQ 0,152 (1,37) 0,149 (4,95) 0,186 (4,38) 0.203 (5.40) -1.671 (-11.56)

COUNTDING 0,188 (3,83) 0,193 (4,40) 0,225 (4,83) 0.191 (7.44)

REG 0,334 (2,67) 0,333 (2,76) 0,293 (3,71) 0.206 (2.86)

MODDYN 0,117 (2,36) 0,151 (3,55) 0,145 (2,96) 1.276 (10.07)

OTHEXO 0,138 (2,16) 0,186 (3,48) 0,218 (5,54) -0.780 (-5.74)

NEQN -0,057 (-1,65) -0,058 (-1,88)

ENDY

-0,437 (-3,35) -0,455 (-5.00) -0,390 (-6,22)

LEVEL -0,124 (-1,71) -0.227 (-7.48) 1.274 (8.69)

FILTLT -0,153 (-1,09)

FILTHP -0,031 (-0,54)

FILTBK -0,160 (-1.00) 0.166 (2.58)

FILTBN -0,300 (-1,20)

FILTUC -0,019 (-0,16)

FILTMOD 0,545 (0,88)

AVGYEAR 0,120 (1,99) 0,095 (1,71) 0,097 (2,96) 0.140 (4.20)

LOGNOBS

R2 0,652 0,643 0,608 0.58 0.79

F-test (P. value)

Reset test (P. value) 0.061 (0.80) 0.691 (0.41) 0.024 (0.87) 0.936 (0.33) 0.557 (0.46)