Determinants of Growth

1. Labor Market Regulation Determinantsof Growth

2. Outline – Table ofcontents • Introduction • Motivation • Question & thesis • Existingpapers • Concept & summaryofresults • Outline offurtheranalysis • Regression Analysis • Data • Model • Results • Conclusion • General conclusion • Constraints • Implications

3. Outline – Table ofcontents • Introduction • Motivation • Question & thesis • Existingpapers • Concept & summaryofresults • Outline offurtheranalysis • Regression Analysis • Conclusion

4. Motivation forthetopic The discussionaboutthe different labormarketregulationtoolstriggeredmyinterest in theconnectionwithgrowth • Public debateanddiscussionabouteffectsofregulation in thelabormarket: • Minimum wage in Germany • Lay-off protection in Spain • Obviousconnectiontounemployment • Opinion: searchfornewmechanisms

5. Motivation forthetopic The discussionaboutthe different labormarketregulationtoolstriggeredmyinterest in theconnectionwithgrowth • A numberofstudiestowardtherelationbetweenregulationandunemployment • Strong interdependence: • GDP unemployment • Effectsoflabormarket regulationunemployment efficiencylosses in production

6. Question The issueoftheinvestigationistheeffectoflabormarketregulation on growth • Question: “Doeslabormarketregulationhave a significanteffect on GDP growth?“ • Thesis: “Becauseof a tendenciallyhigherunemployment rate andtheinefficiency in productionlabormarketregulationaffectseconomicgrowthnegatively.“

7. Question‘srelevance Somepolicyinstrumentsthataretoprotectemployeesmightactually hurt generalwealthand also theirown , whileincreasingunemployment • Indicatorfortheefficiencyofappliedpolicies • In caseof a negative effect, changes in policyrecommendable • New approachesandmoreeconomicandincentivebasedapproachesnecessary

8. Existingpapers Siebert statedthatthehighandmonotonouslyincreasingunemploymentrates in Europe are a resultofthe rigid labormarkets • Labor Market Rigidities: Atthe Root ofUnemployment in Europe – Horst Siebert, 1997 • Analysis oftheunemploymenttrendsandpoliticalinstitutions in thelabormarket • Conclusion: “Thereis a sizablesectionofthelaborforceforwhichthelabormarketdoes not functionanymore.“ - Siebert

9. Calderón andChong‘sframework Calderon and Chong use a complex model forgrowth • Dependent variable: GDP growth per capita • Independent variables: • Institutional: human capital, financial depth, trade opennessandgovernance • CPI inflation rate and the real exchange rate overvaluation and termsoftradeshocks

10. Calderón andChong‘sconcept Calderón and Chong focus on the interrelation between developed and development countries and law-code and applied regulation • Fiveyeartermsstarting 1945 ending 1999 • Dividingthecountry sample intodeveloppedanddevelopment countries • Regressingthe model also for different partsoftheworld • Regressing on threeindexes: one de jure andtwo de facto indexes

11. Myconcept The basicconceptisregressing on the Solow variables and an indicatorforlabormarketrigidity • Dependent variable: GPD growth • Independent variables: physicalcapitalgrowth, laborforcegrowth, OECD-indicatorforlabormarketrigidity • Sample of 10 OECD-countries, 1985-2008

12. Summaryofresults The analysis‘ resultssuggest a negative effectoflabormarketrigidity on growth • Employmentprotectionhas a negative correlationwithgrowth in theoverall sample • Negative coefficient in regressionanalysis, significantto 90% and 95% confindencelevels • -0,3482** (0,1398) • Strong correlation also withunemployment * significant 90%, ** significant 95%

13. Outline offurtheranalysis After showingdata, frameworkandregressionresults, theresults will beinterpreted in theconclusion Regression Analysis • Data used in theregression • Framework fortheregression • Regression analysisconducted • Resultsoftheregression • Interpretation ofresults • Constraintsoftheresults • Implications - opinion Conclusion

14. Outline – Table ofcontents • Introduction • Regression Analysis • Data • Model • Results • Conclusion

15. Outline – Table ofcontents • Introduction • Regression Analysis • Data • Model • Results • Conclusion

16. Data used In total 240 datapointsarereached • Time range: 1985-2008 • Sample of 10 countries: • Canada, USA, New Zealand • Ireland, Denmark, Sweden • Germany, France, Spain, Portugal • Data points: 240

17. Data used Becauseofthefocus on OECD countries andtherecentpastthedata was not verydifficulttoobtain • Growth rates (OECD database) • Population growth rate assubstituteforlaborforce (Penn World Table) • Physicalcapitalformationaspercentageof GDP (Penn World Table) • Unemploymentrates (OECD database) • OECD-Indicatorforrigidityoflabormarket (OECD database)

18. OECD-Indicator The indicatorallowscomparisons on therigidityofthelaborcodeof different countries • Overall employmentprotection • Consistsof 21 items • Individual dismissal • Costsofcollectivedismissal • Regulation oftemporarycontracts • Quantifiestheflexibilityorrigidityof a country‘slabormarket • Value range: 0-5, average: 2,03

19. Data overviewscreen

20. Growth rates The growthratesdifferslightlywhiledecliningover time • Average growth rate: 2.81% • Overtime decline: r=-0.0847

21. Interdependences Group averagespointtowardstherelationstated in thethesis, eventhoughtherelationheredoes not seemtobe linear

22. Interdependences Group averagespointtowardstherelationstated in thethesis, eventhoughtherelationheredoes not seemtobe linear

23. Outline – Table ofcontents • Introduction • Regression Analysis • Data • Model • Results • Conclusion

24. Model In mygrowth model the simple OECD-Indicatorisaddedtotheequation • Solow-Growth-Model: • Model includingindicator:

25. Regression framework Onenewcoefficienttotracktheindicator‘seffectisintroduced • Solow-Growth-Model forregression: • Addeddeterminant:

26. Potential problemsofthe model Shortcomingsoftheused model couldbetheitssimplicityandtheproblemofcomparabilityofthe different countries anddecades • Simplicity – mostregressionsbased on a complexsystemof variables • Interdependencesandbias – the different countries anddecadesarehardlycomparable • Robustness check via introductionof a dummy

27. Model to check robustness Including a dummy variable mighteliminatecountryor time specificeffects • Introductionoftheyearas a dummy variable: • Introductionof a dummyforoneofthegroups:

28. Outline – Table ofcontents • Introduction • Regression Analysis • Data • Model • Results • Conclusion

29. Regression results The coefficientsofdeterminationsuggeststheemployed model hassomeexplanatory power

30. Regression results The coefficientsforcapitalandtheemploymentprotectionindexaresignificant, whereasthepopulationis not * significant 90%, ** significant 95%

31. Regression results The constant is neither significant nor especially relevant, and might not even be valid * significant 90%, ** significant 95%

32. Correlationmatrix Surprisinglythecorrelationbetweenunemploymentandgrowthis positive in the sample

33. Regression forunemployment The GDP growth rate ofthecurrentyearhasinsignificantinfluence * significant 90%, ** significant 95%

34. Robustness check Toeliminateunwantedinfluencesthe model ischeckedusing different dummy variables • Introductionoftheyearas a dummy variable: • Introductionof a dummyforoneofthegroups:

35. Robustness check Whiletheyear‘sinfluenceisinsignificant, thereisvariationamongthe different groups

36. Robustness check Whiletheyear‘sinfluenceisinsignificant, thereisvariationamongthe different groups * significant 90%, ** significant 95%

37. Robustness check Whiletheyear‘sinfluenceisinsignificant, thereisvariationamongthe different groups * significant 90%, ** significant 95%

38. Outline – Table ofcontents • Introduction • Regression Analysis • Conclusion • General conclusion • Constraints • Implications

39. Conclusion The thesisismostlikelytobetruejudgingby all oftheresults • Impact on growthexists withrespectto OECD-countries • Reasonsare • Effects on longtermunemployment • Frictions on thelabormarketconstraining an efficientallocation

40. Constraintsoftheconclusion The effectisdifficulttoquantifyandtogeneralize • Relation might not be linear • Actualconherencydifficulttodistinguish • Different factorswithrespecttoeachcountry

41. Weaknessesofthe model Simplicityandweaksubstitutesforthe variables arethemodel‘sshortcomings • Simplicity of the model reduces the explanatory power • Insignificant coefficient for population i.e. labor force population a weak substitute

42. Weaknessesoftheanalysis The numberofdatapointsandthenumberof different countries couldbehigher • Limited setof countries • Countries also chosenbydataavailability • High weightforeachofthe countries • Cure: datapoints per year > 20

43. Implications European countries shouldreevaluatetheirlabormarketpolicies • OECD-countries with • rigid employmentprotection • lowgrowth • highunemployment shouldconsiderderegulation • Alternative approachestoprotectworkersfromthetroublesofdismissalanduncertaintyexist • Netherlands: 15% boostoftemporaryemploymentincome • Transfers thatdecreasewhilestayingunemployed

44. Thankseveryoneforyourattention!