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

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,…)

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

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

    Boussemart J.P. Briec W. Tavéra C.
  2. Introduction The Okun’slawrelationship as an empiricalregularity (Okun, 1962) : Developments Theoretical background (Prachowny 1993,…) Empiricalanalysis of the dynamiceffects of GDP on unemployment (Crespo-Cuaresma 2003, Silvapulle et al. 2004) : OLC(Expansion) < OLC (Recession) Okun’slaw as a demanddrivenmacroeconomicmechanism unemployment rate = -0.3 pt of % D real GDP = +1%
  3. A simple graphic version of the OL (Bureau of EconomicAnalysis for US datas)
  4. Introduction This paperaimsat : Reexamining the supplyside 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
  5. Technicalprogress and catching up Innovation and imitation : the simple diagram Shifts of the Technologicalfrontier Technological leader Innovation Follower country imitation
  6. Technicalprogress and catching up Innovation and imitation as complementaryproceses (Benhabib-Spiegel 994, Acemoglu-Aghion-Zilibotti, 2002)
  7. Specification 1: an interaction-augmented-version of the OL relationship First ordereffects : lineareffects Non lineareffects: Squared variables Interaction terms (cross-terms)
  8. Specification 2 : OL relationship with threshold Threshold variable Z : Technicalprogress or Technological distance with the leader Estimation methodsuggested by Hansen (1999) : Min square estimate of the threshold Test for significativeness of the threshold
  9. The measure of productivity gaps The technological gap ismeasured in terms of TFP levelsbetweenany country i and the leader (Malmquist index : Färe et al. 1994). At time t, the production set isdefined as T = { , X canproduce Y} : T satisfiesstrongdisposability and convexityassumptions and we assume constant returns to scale The distance between country i and the world frontiercanbedecomposedintotwo components : The time change in the technicalefficiency The geometricmean if the shift of the frontier
  10. Technical change and productivity gaps Output Y Productivity variation in country i Leader(final) Technological gap variation (catching up) Pays i (final) Leader (initial) Movement of the technologicalfrontier (technical change) Pays i (initial) Inputs X
  11. 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 Equilibriumlevels of output and unemployment : HP filter
  12. Empiricalresults 0 : a preliminaryanalysis of the basic OL model
  13. Empiricalresults 0 : a preliminaryanalysis of the basic OL model
  14. Empiricalresults 1 : the interaction-augmented-version of the OL relationship
  15. Empiricalresults 1 : the interaction-augmented-version of the OL relationship First orderlinear approximation of the impact of GDP on unemployment rate : -0.237 – 0.015 D + 0.024 TC withk = 0 -0.239 – 0.015 D + 0.026 TC withk = 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
  16. Empiricalresults 2 : the threshold version of the OL relationship
  17. Empiricalresults 2 : the threshold version of the OL relationship
  18. Empiricalresults 2 : the threshold version of the OL relationship
  19. Empiricalresults 2 : the threshold version of the OL relationship The short run impact (in absolute value) of GDP movements on unemployment rate is : largerwhen the technological distance is large (imitation) close to zero for countries close to the technologicalfrontier smallerwhen the size of technicalprogressis large (innovation)
  20. Someconcludingremarks The OL relationshipdoes not containonlydemandinducedmacroeconomicmechanims The origins of variations in TFP matterfor determining the total impact of GDP movemements on unemployment, even in the short run Imitation and innovation generatesecond order non linearmechanismsthatcanboost or mitigate the traditional first order OLC Our resultslendsuppport to recentempiricalpaperswhich show thatthe ouput-unemployment relationshipmightbedominated by permanent shocksratherthan by temporaryshocksonly (Sinclair 2009)

  21. 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 Strathclyde CREM, CNRS – Université de Rennes 1
  22. Loi d’Okun Exemple : Etats-Unis, 1970-2011, données trimestrielles, Coefficient moyen = -0.41
  23. 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
  24. Les catégories de modélisations Les modèles ad-hoc La fonction de production avec
  25. Méthode d’échantillonnage : Etape 1 Recherched’articlesdansEconlit avec critères : mots clés : Okun’s Law – Output-unemployment relationship Presence d’un abstract (verification estimation présente) Publication après 1980 Presence dansEconlit en décembre 2010 Papiersidentifiés : 97
  26. 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
  27. Cycle de vie de la publication
  28. Homogénéisation des estimations Réécriture des équations estimées sous la forme :
  29. Caractéristiques statistiques de l’échantillon
  30. Meta régression : Biais et tests
  31. Meta régression : tests de biais Test de Stanley(Test de biais de Type 1) Remarque : = trueeffect Galbraith plot (Test de Type 2) Diagramme croisé : (précision des estimateurs – t statistiques correspondants)
  32. Meta régression multuvariée Principales dummies retenues pour la régression multivariée
  33. Quelques résultats sur les biais Test d’absence de biais de type 1
  34. Quelques résultats de la méta régression multivariée
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