1. Determinantsof Output George Gazzard BournemouthUniversity - 1 What are the main determinants of wage level amongst Financial Managers within differing industries in the US - Sampling 2005-2011. George Gazzard I7953605 09/01/2015 Abstract:This paperexploresthe determinantsof HourlyWagesin differingFinancial Managerial Industriesandlooks,more specificallyatwhetherthe total output of the Financial Managerial industryisa determinantforthe premiumwage levelreceived.The basisforselectingone industry area meanswe keepeducation constant,renderingitexogenousfromthe model.Ourfindingsare that outputisinsignificantindeterminingwage levelswithinFinancial Managerial roles.Capital to Labour oursratio issignificantin negatively affectinghourly wage level,alongwith labourhours and % incapital whichpositivelyaffectHourlyWages. 2. Determinantsof Output George Gazzard BournemouthUniversity - 2 Contents Introduction.............................................................................................................................. 3 Data.........................................................................................................................................4 Variables..................................................................................................................................4 Literature Theoretical Studies.................................................................................................6 Literature Empirical Studies....................................................................................................6 Specification of Model(s)...........................................................................................................7 Model All Industrys ............................................................................................................... 7 Hypothesis............................................................................................................................... 8 Descriptive Statistics................................................................................................................. 8 Assumptions and Tests.............................................................................................................. 9 Normality Test...................................................................................................................... 9 Multi-Collinearity.................................................................................................................... 11 Hetroscedascity................................................................................................................... 13 Auto-Correlation..................................................................................................................... 13 Woolridge Serial- correlation Test............................................................................................ 14 Probability.............................................................................................................................. 14 t-Statistic................................................................................................................................ 14 Lagged Coefficient .................................................................................................................. 14 Presence of Autocorrelation.................................................................................................... 14 Unobserved Heteroeneity ....................................................................................................... 14 RedundantVariable Test...................................................................................................... 14 Hausman Test..................................................................................................................... 15 Model (1) (2) & (3) Statistical Findings...................................................................................... 15 Hourly Meanwages OLS (1) ................................................................................................. 15 Hourly Meanwages Single Fixed Effects(2)........................................................................... 16 Hourly Meanwages Double Fixed Effects (3)......................................................................... 16 Analysis.................................................................................................................................. 17 Limitations ............................................................................................................................. 18 OmittedVariable Bias:......................................................................................................... 19 Conclusion.............................................................................................................................. 19 References ............................................................................................................................. 20 Appendix................................................................................................................................ 22 3. Determinantsof Output George Gazzard BournemouthUniversity - 3 Introduction Background and theory to wage levelsinFinancial Managerial roles in the US In thispaperwe aimto assesswhat determinantshave the mostsignificance in influencingwage levelsamongstFinancial Managers andfurther,the effectsLow andHighoutputindustryareashave on determiningwages, withinLowandHighoutputindustryareasof the US economyfrom2005 - 2011. Strether(2014)1 empirical studyprovideshistorical evidence thatobservedrents(surplus profits) inthe finance sectorare amongthe highestof all sectors.Philippon (2009) outlineshow relative wagesandskill intensityof the financial managerial industries hasexhibitedaU-shaped patternfrom1909 to 2006. From 1909 to 1933 the financial sectorwasa highwage,highskill industry.The 1930s saw a dramatic shift, were the sectorlostitshighhumancapital statusand its wage premiumrelativetothe otherprivate sectorindustries.The financial sectorreturnedtoits original superiorstatusthroughthe 1980s. It isimportantto provide theoryforthe existence anddeterminantsof Financial Managerswage premiumasgroundingforour study.Krueger(1988)2 explainsthe critical feature of aperfectly competitivelabourmarket,isthatlabourwhichacceptsjobsshouldexpecttoobtaincompensation equal totheiropportunitycost.Firmswill payawage that is justsufficienttoattract workersof the qualitytheydesire andnohigher. However,the deviationawayfromthe marketclearingwage to the efficientmarketequilibriumwage atprofitmaximisingequilibriuminthe FinancialManager industry,meansthe qualityof jobapplicantpoolscanbe improved,asarguedbyBulow and Summers(1986)3 . Further,the Mincerearningsfunctionthatequatesthe logof wagesas a functionof laboursupply througheducationand experience shouldindicate thatthe premiumwage rate forFinancial Managers isdeterminedby S(yearsof education).HoweverStrether(2014) like Martins(2003) suggeststhatthere islittle evidence thatthiscapital intensive educatedlabourisresponsible for determiningthe currentwage premiumthatexistsandotherfactorsare responsible suchas compensatingdifferentialsorunobservedqualitydifferences. We excludeEducationfromourmodel throughspecifyingourstudythroughonlyone industry, withapresumedskilllevel. 4 KruegerandSummers (1988), Marx (1891) and neoclassical efficiencywagestheorysuggeststhat ina perfectlycompetitive marketitisthe demandandsupplyof labourthat dictatesthe wage level. We shall thereforeexplore bothsidestodetermine variablesaffectingFinancial Managerswage levels. 1 Strether, L. (2014). Naked Capitalism. Available: http://www.nakedcapitalism.com/2014/09/finance-sector-wages-explaining-high-level-growth.html. Last accessed 09/01/2015 2 Krueger, A et al.. (1988). Efficiency Wages and the Inter Industry Wage Structure. Econometrica. 56 (2), 259-293. 3 BULOW, J., AND L. Sui:Rss (1986): "A Theory of Dual Labor Markets with Application to Industrial Policy, Discrimination, and Keynesian Unemploymnent," Journal of Labor Economics, 4, 376-414. 4 Variables Y is earnings (Yo is earnings for someone with no education + schooling) S is years of schooling, X years of potential Mincer EarningsFunction 4. Determinantsof Output George Gazzard BournemouthUniversity - 4 Data All OESdata for OLS Panel Data model hasbeencollaboratedfromBureauof LabourStatisticsand holdsfrom2005-2011.5 All data islistedasan index, flatrate amount(e.g. billionUS$ (current)) or& change.Wagesvary fromthe highestpaid SecuritiesandCommodityContractsIntermediationand Brokerage Financial ManagerstoChildDayCare Financial Managers Variables -Dependent Hourly Mean Wage (H_Mean):We will be assessingselectedindependentvariablessignificance in determiningthe HourlyMeanWage for Financial Managersacrossall 254 industryareas.As Summers(1988) studiesshow,itiscrucial to considerbothsupplyanddemandside factors determiningwage levels. -Independent(s) Multifactor productivity(MPI):measuresthe combinedeffectof predominantlytechnological advancementsandefficiency(A) improvementsonoutput,whichexistsalongside labour(L) and capital (K) withinthe Cobb-Douglasfunction.MPIiscalculatedbyassessingrelationshipvalues betweeninputsandreal outputwithinthe productionprocess.Levine(2001)6 arguesthat MFP can account forup to 60% of growth withinthe USeconomy.Anyrise inproductivitycanleadto increasesinthe marginal productof labour,thusincreasingthe demandforlabourleadingtorising wages. 7 % Output (QP):Increase inoutput,absentof a labourincrease leadstoa rise inwages.Miller (1986)8 and Tufano(1989)9 argue that financial outputcanbe measuredthroughmeasuringnew productsand innovations;however,despitethe apparentsimplicity,relationbetweenreal wages and outputremainsdeceptive theoreticallyandempirically.(Malik2000)10 5 Bureau of Labor Statistics. (2014). United States Department of Labor.Available: http://www.bls.gov/. Last accessed 08/01/205 6 W.; Levine, R. (2001). "It`s Not Factor Accumulation: Stylized Facts and Growth Models" 7 Multifactor Productivity Calculation 8 Miller, M. H. (1986): Financial Innovation: The Last Twenty Years and the Next, Journal of Financial and Quantitative Analysis, 21(4), 459471. 9 Tufano, P. (1989): Financial innovation and first-mover advantages, Journal of Financial Economics, 25, 213240. 10 Malik, A, 2000. The Relationship between Real Wages and Output: Evidence from Pakistan. Pakistan Development Review, 4, 1111-1126. [Accessed 11/01/2015]. Cobb-DouglasFunction MultifactorProductivity Calculation 5. Determinantsof Output George Gazzard BournemouthUniversity - 5 % Capital (KP):Marx (1847)11 work highlightsthe diametricallyopposedrelationshipbetween capital and wage-labour.FastgrowthinCapital andprofitshasa parallel relation.Profitgrowthis fast,onlywhenprice of labour(relative wages) decrease atthe same rate. Capital to labour hours ratio (KLI):The use of an Isoquantsmapdemonstratesthe relationshipin combinationsof Kand L withinthe ProductionFunction(See below).Chiang(1984) arguesitshows the Trade-off betweencapital andlabour.Itdevelopsthatmore labour,lowersthe marginal product of labourandleadsto lowerwages. Labour Hours (LI):We make an assumptionthatlabourhoursreflectapositive correlationwith labourproductivity. Sharpe (2008) statesreal wagesare determinedthroughlaborproductivity growth.However,Bruce (2002)12 studyof Canadaoutlinesthere tobe norelationshipbetween Labour Hours andWages. Intermediate cost: (ICV) Price/wage spiralsmayexistwithin Financial Managerial roleswhere supplyshocksaffectcostof production.Hazlitt(2009) statespartiesraise price to protectprofits marginsfromrisingcosts.Labour attemptstopush nominal wagesupwardtocatch up withrising prices,topreventfall inreal wages.13 Implyingthe effectsICV mayhave onWages. 11 Marx, K, 1847. Wage Labor and Capital. Wage Labor and Capital, The Original, 1, 5-8. [Accessed 11 January 2015]. 12 Bruce, C, 2002. The Connection between Labour productivity and Wages. Expert Witness, 1, 2-3. Available from: http://www.economica.ca/ew07_2p1.htm [Accessed 11 January 2015]. 13 Henry Hazlitt. "What You Should Know About Inflation", Mises Institute, referenced 2009-06-07. IsoquantMap 6. Determinantsof Output George Gazzard BournemouthUniversity - 6 Literature Theoretical Studies Consistentwiththe originalPhilipscurve,Fisher(1973) hasoutlinedthe inverserelationship betweenunemploymentrate andcorrespondinginflationrate may have aninfluence onwage levels due to the downwardnominal wage rigidity.14 Simplyput,Phillipsindicatesthatwagestendtorise fasterwhenunemploymentislow. Asaclimate of lowerunemploymentrateswillcause employers to bidwagesup inattemptto lure higherqualityemployeesawayfromothercompanies.15 Martins (2003)16 exploresthe differinginter-industrywage structures,wherespecificindustriespay workershigher,clearlycontradictingthe law of one price,thussuggestingthatlabourmarketsare not alwaysappropriatelydescribedbyacompetitiveframework. Alternate theoriessuchasStiglitz(1986)17 efficiencyof wage formulations;implythatjobattributes havingnoeffectwiththe utilityworkersreceive withinthe jobshouldhave systematiceffectson wage levelsbecausetheycaninfluence the optimalwage forfirmstochoose.AsStiglitz(1986) BulowandSummers(1986)18 andmany othertheoristshave argued efficiencywage theoriesshow positive andnormative implicationssignificantly differentfromthose of more standardcompetitive models. Literature Empirical Studies Acknowledgementof the MincerWage Equation andsignificance of education wagesisessentialto any empirical studyof laboureconomics.Previousstudiesinvestigatingthe returnsoneducation such as Card (2001), developsthe notionthatexistenceof strongersupplysidefactors,inthiscase highereducation,leadtolargerwage ratesandreturnson education.19 Borjasand Ramey(1995)20 argue thatthe increasedglobalisationhaspressuredreductionsinlow skilledFinancial Manageriallevels,leadingtoarelative reductionindemandforlesseducatedlabour and thusreducingwage levelsinsome of these specificindustriesdue toa decrease in competitivenessanddemandandsubsequentincrease of supply. Smith(1904)21 emphasizesthatwage differentialsare determinedbycompetitive factorsE.g. differencesincostsof trainingandfurthernon-competitive factorsresultingfromLawsof Europe, e.g.restrictedlabourmobility.Smithsanalysisbetweenthe rolesof demandandsupplyfactorsas well asinstitutional forcesstill holdsasakeytheme inanywage determinantresearch. Throughthe 1950s, Levinson(1960) was one economistwhodeterminedwage differentialsthrough demandside factorssuchas: industrial concentrationandvariationsinprofitsasdeterminantsof 14 Irving Fisher. The Journal of Political Economy, Vol. 81, No. 2, Part 1 (Mar. - Apr., 1973), pp. 496-502. Reprint of 1926 article by Irving Fisher 15 The Editors of Encyclopdia Britannica. (2014). The Philips Curve.Available: http://www.britannica.com/EBchecked/topic/456596/Phillips-curve. Last accessed 08/01/2015 16 Martins, P. (2003). Economic Letters. Industry wage premia: evidence from the wage distribution. 83 (1), 157-163. 17 Stiglitz, J. (1974). Alternative Theories of Wage Determination and Unemployment in LDC's: The Labor Turnover Model. The Quarterly Journals of Economics. 88 (2), 194-227 18 BULOW, J., AND L. Sui:Rss (1986): "A Theory of Dual Labor Markets with Application to Industrial Policy, Discrimination, and Keynesian Unemploymnent," Journal of Labor Economics, 4, 376-414. 19 Card, D. (2001). Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems. Econometrica. 69 (5), 1127- 1160problems." Econometrica 69.5 (2001): 1127-1160. 20 Borjas, George J. and Valerie Ramey (1995) Foreign Competition, Market Power and Wage Inequality, Quarterly Journal of Economics 110:1075-1110 21 Smith, A. (1904). An Inquiry into the Nature and Causes of the Wealth of Nations. London: Methuen, 5 edition. url www.econlib.org/library/s mith/ smWNtoc.html. 7. Determinantsof Output George Gazzard BournemouthUniversity - 7 wage differentialsthroughoutindustries.22 Levinsonanalysedthe structure of anindustryonwage determination. Thurow(1976)23 researchprovesthatindustryandgeographicvariablesare significantto individual earningsfunctions(wages).Wage levelswithinamarginal productivityworldare supposedtobe givenonthe basisof skillssuppliedandnotdependentuponthe regionorindustryof use.Thurows findingsinthe significance of industryandgeographicvariablesconstitute foradeviationfrom normsof a competitive market. Yellen(1984) exploresthe efficiencywage hypothesis,whichsuggeststhatthere isarelationship withthe productivityof labourandthe wage rate.Where in line withthe hypothesispresentedin thisstudy,wage cuts harm productivityof labour.Yellenarguesthatthese cutsmayendupraising labourcosts.24 Specification of Model(s) I have splitall 254 IndustriesintoHighOutputandLow Outputin orderinvestigationwhetheroutput has the same effectsforLowand HighOutputindustries. Ihave ensuredrobuststandarderrors throughmy modelsbyusingthe white test.The three models thatwill be createdrun usingOLSno effects,single effectsanddoubleeffects: 1. All Industrys1778 Observations 2. HighOutput(UpperPercentile) 896 Observations 3. Low Output(LowerPercentile) 886 Observations Model All Industrys : = 0 + 1 + 2 + 3 + 4 + 6 + 7 + , : = 0 + 1 + 2 + 3 + 4 + 6 + 7 + , : = 0 + 1 + 2 + 3 + 4 + 6 + 7 + The modelspresentedmeasure the industrylevelof output,differingfromthe traditional earnings function.The modelsinclude demandandsupplyside variables. Where; 0 = The unknownpopulationparameterof the constant. = The unknownpopulationparameterof the coefficientof variable i. 22 2Levinson (1960), pp. 1-22, Segal (1961), and Ross and Goldner (1950). 23 THUROW, L. (1976): Generating Inequality. New York: Basic Books. l24 Yellen, J. (1984). Efficiency Wage Models of Unemployment. .. 74 (2), 200-203. 8. Determinantsof Output George Gazzard BournemouthUniversity - 8 = Error term Randomcomponentnotexplainedbymodel. Hypothesis We propose ourthe expectationsof ourindependents basedonmyliterature review asthe following: : 1 > 0 : 2 > 0 : 3 < 0 : 4 > 0 : 5 > 0 : 6 > 0 We propose ourNull hypothesiswherenone of the coefficientshave explanatory power: 0: = 0 Alternative Hypothesis: 1: 1 0 1: 2 0 1: 3 0 1: 4 0 1: 5 0 1: 6 0 Descriptive Statistics Financial Managerial Output HourlyMean Wages ($) All Output (1) High Output (2) Low Output (3) Mean 49.60635 50.62894 48.53963 Median 48.78 50.28 48.235 Maximum 88.71 88.71 74.84 Minimum 27.08 27.08 33.45 Std. Dev. 7.866209 8.989569 6.318814 Observations 1776 895 880 Financial Output(QP) Mean 0.4824 2.665203 1.718481 9. Determinantsof Output George Gazzard BournemouthUniversity - 9 The Descriptive statisticsforall three models (1)(2)&(3) provideinsightful feedbackastheysupport my hypothesis.Theyshowthe positive correlationbetweenmean wagesandmeanoutput.For example,Financial Managerialindustrieswithahighoutput(2.66 Output) have highermeanwages (2.66 Wages) andFinancial Managerswithlow output(1.72) have a lowermeanwages(48.53). This supportsMiller(1986)25 findingsinthe relationshipwagesandoutputshare. Itisinterestingtonote the spreadin Std.Deviationsinhighoutputagainstlow outputislargerinHighOutputmodel,upon lookingatour data we see the upperextremities/tail of HighOutput datatolie furtheroutthenthe lower.ThissupportsStrether(2014)26 in outliningthe highestoutputindustriesreceivingpremium wages Assumptions and Tests Normality Test The Normalitytestsforbothmy panel single effects residuals anddependentvariables are visually normallydistributed.(See Appendixforall Histograms).However,the Jarque- Bera(afunction developedon Skewness &Kurtosis) comesinveryhighwhichindicatesthatthe residualsare not normallydistributed.Thiswill affectthe biasof mymodels. DependentNormalityplotsare below: My residualsalsohave ameanof near zeroshowingmydisturbance termsare unbiased. Anydata whichwasnot normallydistributedwaslogged. ( ) 0 25 Miller, M. H. (1986): Financial Innovation: The Last Twenty Years and the Next, Journal of Financial and Quantitative Analysis, 21(4), 459471. 26 Strether, L. (2014). Naked Capitalism. Available: http://www.nakedcapitalism.com/2014/09/finance-sector-wages-explaining-high-level-growth.html. Last accessed 09/01/2015 Normality Test Mean Jarque-Bera Visual Inspection NormallyDistributed OLS 1 1.78e 126.72 Pass OLS 2 7.11e 38.67 Pass OLS 3 4.00e 27.13 Pass PanelSingle 1 2.66e 126.72 Pass PanelSingle 2 2.10e 38.67 Pass PanelSingle 3 -3.82e 27.13 Pass PanelDouble 1 -2.63e 311.95 Pass PanelDouble 2 7.20e 84.40 Pass PanelDouble 3 1.34e 207.09 Pass Dependent 1 49.60 179.60 Pass Dependent 2 50.62 35.96 Pass Dependent 3 48.53 77.18 Pass 10. Determinantsof Output George Gazzard BournemouthUniversity - 10 DependentNormality Plots (1)(2)& (3) (1) (2)&(3) .00 .01 .02 .03 .04 .05 .06 20 30 40 50 60 70 80 90 100 Density H_MEAN .00 .01 .02 .03 .04 .05 20 30 40 50 60 70 80 90 100 Density H_MEAN .00 .01 .02 .03 .04 .05 .06 .07 .08 25 30 35 40 45 50 55 60 65 70 75 80 Density H_MEAN 11. Determinantsof Output George Gazzard BournemouthUniversity - 11 Multi-Collinearity Resultsbelowshowthe correlationbetweenindependentvariables.Model (3) doesinfactexhibita multi-collinearityproblembetween %change Capital andLabourHours inwith (0.621974). The presence of thiscan be arguablyputdownto the factboth variablescanbe seentofall underthe MultifactorProductivityfunctiontogethersoshare a positive relationship. Further,anyscore more than 10 on the centredVIFwill indicatemulti-collinearity. MyVIFscoresare all below 10 so I donot thinkthere isanycollinearrelationship betweenindependents. All Output KLI MPI KP QP LI ICV KLI 1.000000 0.002008 0.052831 -0.170507 -0.558541 -0.079147 MPI 0.002008 1.000000 -0.042846 0.154122 -0.083061 0.033296 KP 0.052831 -0.042846 1.000000 0.293446 0.395330 -0.106466 QP -0.170507 0.154122 0.293446 1.000000 0.265208 -0.001518 LI -0.558541 -0.083061 0.395330 0.265208 1.000000 -0.117290 ICV -0.079147 0.033296 -0.106466 -0.001518 -0.117290 1.000000 The Use of a Scatter box matrix withline of bestfithelpsusunderstandthe relationshipof the residualsanddependentvariables. Itprovidesuswithastrongunderstandingastohow to Independentswill affectthe dependent. High Output KLI MPI KP QP LI ICV KLI 1.000000 0.027373 0.244204 -0.107253 -0.592909 -0.127273 MPI 0.027373 1.000000 -0.138883 0.095893 -0.170429 0.189337 KP 0.244204 -0.138883 1.000000 0.227124 0.144785 -0.139973 QP -0.107253 0.095893 0.227124 1.000000 0.151138 0.064229 LI -0.592909 -0.170429 0.144785 0.151138 1.000000 -0.121309 ICV -0.127273 0.189337 -0.139973 0.064229 -0.121309 1.000000 Low Output KLI MPI KP QP LI ICV KLI 1.000000 -0.028474 -0.185887 -0.241677 -0.568003 -0.118406 MPI -0.028474 1.000000 -0.078993 0.175277 -0.212037 0.055322 KP -0.185887 -0.078993 1.000000 0.220778 0.621974 -0.029235 QP -0.241677 0.175277 0.220778 1.000000 0.242947 0.053723 LI -0.568003 -0.212037 0.621974 0.242947 1.000000 -0.037899 ICV -0.118406 0.055322 -0.029235 0.053723 -0.037899 1.000000 12. Determinantsof Output George Gazzard BournemouthUniversity - 12 0 100 200 300 400 KLI 0 100 200 300 400 MPI -20 -10 0 10 20 KP -80 -40 0 40 80 QP 0 100 200 300 CII 0 100 200 300 LI 0 200,000 400,000 600,000 800,000 0 100 200 300 400 KLI ICV 0 100 200 300 400 MPI -20 -10 0 10 20 KP -80 -40 0 40 80 QP 0 100 200 300 CII 0 100 200 300 LI 0 200,000 600,000 ICV Variance Inflation Factors Date: 01/08/15 Time: 20:17 Sample: 1 1778 Included observations: 1769 Coefficient Uncentered Centered Variable Variance VIF VIF C 5.573142 197.0937 NA KLI 8.66E-05 43.12665 1.832054 MPI 0.000107 42.46222 1.128138 KP 0.004046 1.934681 1.626947 QP 0.000545 1.272782 1.268060 CII 0.000208 85.84716 2.493916 LI 0.000142 54.01381 2.880296 ICV 2.24E-11 1.143315 1.047189 13. Determinantsof Output George Gazzard BournemouthUniversity - 13 Heteroscedascity For standarderror termsto be accurate, myresidualsvariance mustremainconsistentthroughout our distribution.The assumption standsat: ( ) = 2 Model 1, 2, 3 HeteroskedasticityTest:Breusch-Pagan-Godfrey F-statistic 7.891779 Prob. F(6,1762) 0.0000 Obs*R-squared 46.29470 Prob. Chi-Square(6) 0.0000 Scaled explained SS 63.89384 Prob. Chi-Square(6) 0.0000 Our Breusch- Pagan- Godfreyformodels(1) (2) and (3) providesF-statisticresultsof (7.891779)(6.775513) and(12.11185) respectively.Therefore,myresultsforall three modelsare hetroscedasticwhichmaycause amisspecificationinthe model asthe variance isnotconsistent withinthe models.WhenIrunmymodel I will ensure robuststandarderrorsusingthe white heteroscedascity- consistentstandarderrorsand covariance. Furtherthe White test will testboth heteroscedascityandspecificationbias. Auto-Correlation As Durbin-Watsonstatisticisnotappropriate forPanel Dataautocorrelationtests, we shall lookat Wooldridge (2002)27 test. Woolridge proposesamanual meanstoprove no autocorrelationthrough OLS panel data.The model is: yit = + Xit 1 + Zi2 + i + it i {1, 2, . . . , N }, t {1, 2, . . . , Ti} Havingexperimentedwiththis manual model andregressedresidualsvlaggedresiduals, are findings are that we will accept1: First Order Correlation due to our findings (0.0000). The error terms are thennot independentlydistributedacrossthe observations. Thiswill limit our coefficient estimates and deviate away from producing a BLUE model. 27 Wooldridge,J. M. (2002). Econometric analysis of cross section and panel data. The MIT press. HeteroskedasticityTest:Breusch-Pagan-Godfrey F-statistic 6.775513 Prob. F(6,881) 0.0000 Obs*R-squared 39.16868 Prob. Chi-Square(6) 0.0000 Scaled explained SS 46.29434 Prob. Chi-Square(6) 0.0000 HeteroskedasticityTest:Breusch-Pagan-Godfrey F-statistic 12.11185 Prob. F(6,873) 0.0000 Obs*R-squared 67.62455 Prob. Chi-Square(6) 0.0000 Scaled explained SS 99.84394 Prob. Chi-Square(6) 0.0000 14. Determinantsof Output George Gazzard BournemouthUniversity - 14 Wooldridge Serial- correlationTest Probability t-Statistic Lagged Coefficient Presence of Autocorrelation All Output(1) 0.0000 4.829428 0.139283 Yes Unobserved Heterogeneity RedundantVariableTest The redundantvariable test,willshowwhetherwe shouldomitstatvariable ornotand demonstrateswhetherwe have any explanatory variables.The probabilityformy redundantvariable testsinmodels(1) (2) and(3) equatestozerofor all models.The testshowsthere isunobserved heterogeneitywithinmymodel. All Output RedundantFixed Effects Tests Equation:Untitled Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 25.271341 (252,1510) 0.0000 Cross-section Chi-square 2922.409912 252 0.0000 High Output RedundantFixed Effects Tests Equation:Untitled Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 36.837978 (126,755) 0.0000 Cross-section Chi-square 1746.521871 126 0.0000 Low Output RedundantFixed Effects Tests Equation:Untitled Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 14.431846 (125,748) 0.0000 Cross-section Chi-square 1079.955463 125 0.0000 Wooldridge Serial-correlation 20.22819927 T- Test Accept Null Hypothesis 15. Determinantsof Output George Gazzard BournemouthUniversity - 15 HausmanTest The Hausman testallowsme toidentifywhetherafixedeffectsorrandomeffectspanelmodel is more appropriate formy data.I conclude thatthe fixedeffectsmodel more appropriate. Test:Heteroscedascity 2 Probability Decision All Output (1) 60.244566 0.0000 FixedEffects High Output (2) 27.682378 0.0001 FixedEffects Low Output (3) 67.923839 0.0000 FixedEffects WaldTest The Waldtestis usedto testthe true value of the parameterbasedonthe sample estimate. The cross sectionresultforChi-square meansIrejectmy 1andagainaccept the decisiontouse a fixedeffectsmodelbasedonthe results forall models.. Model (1) (2) & (3) Statistical Findings HourlyMeanwages OLS (1) All Output (1) High Output (2) Low Output (3) IndependentVariables Constant 17.82*** (2.20) 16.33*** (3.00) 35.16*** (4.57) Multifactor Productivity 0.01*** (0.02) 0.07*** (0.01) 0.04 (0.02) % change Output -0.22 (0.06) -0.04 (0.04) 0.002 (0.03) % change Capital 0.08*** (0.01) -0.26*** (0.09) -0.41*** (0.09) Capital to Labours Hours Ratio 0.15*** (0.01) 0.19*** (0.01) 0.06*** (0.01) Labour hours 0.06*** (0.01) 0.06*** (0.01) 0.02 (0.02) Intermediate costs 0.0000239*** (0.00000477) 0.0000781*** (0.00000193) 0.0000211*** (0.00000426) R-Squared 0.19 0.27 0.09 F Statistic 66.79 55.42 15.05 No of Observations 1769 888 880 Log Likelihood -5979.96 -3070.39 -2827.172 16. Determinantsof Output George Gazzard BournemouthUniversity - 16 28HourlyMean wagesSingleFixed Effects(2) All Output (1) High Output (2) Low Output (3) IndependentVariables Constant -3.07 (2.51) -6.00 (6.64) 0.38 (10.35) Multifactor Productivity 0.04** (0.01) 0.08 (0.02) -0.03 (0.05) % change Output 0.01 (0.01) -0.03** (0.03) 0.04 (0.03) % change Capital -0.35)*** (0.04) -0.39*** (0.10) -0.28*** 0.05 Capital to Labours Hours Ratio 0.26*** (0.01) 0.24*** (0.03) 0.18*** (0.05) Labour hours 0.18*** (0.02) 0.19*** (0.02) 0.18** (0.05) Intermediate costs 0.000038** (0.0000012) 0.0000231** (0.00000103) 0.0000309*** (0.00000767) R-Squared 0.84 0.90 0.07 F Statistic 31.63 50.59 15.79 No of Observations 1769 888 880 Log Likelihood -4518.755 -2197.127 -2287.194 HourlyMeanwages DoubleFixedEffects (3) All Output (1) High Output (2) Low Output (3) IndependentVariables Constant 36.79*** (1.61) 39.34*** (2.74) 23.77*** (4.88) Multifactor Productivity 0.02*** (0.01) 0.02** (0.01) 0.02 (0.02) % change Output -0.01 (0.01) -0.005 (0.01) -0.02 (0.01) % change Capital -0.06)** (0.03) -0.10*** (0.03) -0.06 (0.02) Capital to Labours Hours Ratio 0.04*** (0.01) 0.05*** (0.01) 0.08*** (0.01) Labour hours 0.05*** (0.005) 0.03** (0.01) 0.15*** (0.03) Intermediate costs 0.00000834 (0.00000775) 0.0000227 (0.0000426) 0.00000381 (0.00000739) R-Squared 0.92 0.94 0.86 F Statistic 62.67 91.77 33.60 No of Observations 1769 888 880 28 * 90% significant **95% significant ***99% significant 17. Determinantsof Output George Gazzard BournemouthUniversity - 17 Log Likelihood -3963.263 -1931.483 -2001.598 29 Analysis Lookingat the R Squared,(the percentage of the response variable variationthatisexplainedbyour linearmodels) we candetermine that acrossall ourregressionsinmodels(1) (2) &(3) our R squared valuesof ((0.19), (0.27), (0.09)), ((0.84) (0.90) (0.07)) and ((0.92) (0.94) (0.86)) respectively,indicate higherR Squaredare presentin HighOutputindustries.The resultsformodel (1) (2) & (3) support my original hypothesis 1>0.These findingsforournoeffects,singleanddouble effectsmodel with highoutput(888 observations),supportMalik(2000)30 findingsinindicatingthe outputlevel(QP) of Financial Managerindifferingindustries,doesinfacthave a positive influence onourdependent variable,HourlyWage,(H_Mean). Malik (2000) findingsare furthersupportedbythe factour Low Outputmodels,have lowerR Squaredvaluesat(0.09)(0.07)(0.86) respectively,whichissubsequentlylowerthenAll Output models(1)(2) &(3). Thisisobviouslydue tothe fact All outputmodel includesHighoutputFinancial Managers withinit.Further,itisinterestingtonote thatthe standard deviationswithinHourly WageswithinourHigh OutputModel (8.999) andour Low OutputModel (6.318) differtoa significantdegree evenwiththe amountof observationswithineachmodel beingverysimilar(888) & (880). ThissupportsMarkovich(2014)31 inoutliningthe distributionof income orStandard deviationsincreaseswithinthe highoutputfinancialindustriescomparedwiththe low output.Thus outliningthe presence of risingincomeinequalityfrom2005-2011, within the Financial Managerial industriesinthe US. Our coefficientsshowthatMultifactorproductivityandlabourhoursbothacceptmy 1that theyare statisticallysignificantindetermininghourlywage levels,inAll outputandHighoutputindustries, howeverhave nosignificance indeterminingwageswithinLow outputindustrys(3). Isthisdue to the lack of technological advancementsandefficiency withinsmall outputfirms Levine (2001)32 ?(It isimportantto outline thatMultifactorproductivityisnotsignificantfordeterminingwagesinHigh Outputindustryunderthe Single FixedEffectsmodel.Therefore we mayconclude Multifactor productivitytobe insignificant todeterminingwages asPanel DataSingle Effectsincludestime series so may make a more credible assumption.) It isinterestingtonote the negative significance% change incapital has ondeterminingwages withinHighoutputandLow output contradictive of the CobbDouglasfunction andacceptingof my 1 . IntermediatecostsandCapital toLabours Hours ratioboth show positive significantresults inNo effectsandSingle effects,Howevernosignificance withinanyOutputareawithinourDouble FixedEffectsmodel forindependentvariable Intermediatecosts,withresultsshowinghigher coefficients inthe HighOutput industry areaswhere significant initiallyacceptingour 1 . However, 29 * 90% significant **95% significant ***99% significant 30 Malik, A, 2000. The Relationship between Real Wages and Output: Evidence from Pakistan. Pakistan Development Review, 4, 1111-1126. [Accessed 11/01/2015]. 31 Markovich, S. (2014). The Income inequality debate. Available: http://www.cfr.org/united-states/income-inequality-debate/p29052. Last accessed 14/01/2015. 32 W.; Levine, R. (2001). "It`s Not Factor Accumulation: Stylized Facts and Growth Models" 18. Determinantsof Output George Gazzard BournemouthUniversity - 18 % change inoutputhad no significance indeterminingWages inanymodel orOutputtype other than HighOutputfor Single FixedEffects whichmayleadusintodeeminganalysisof Outputs R squaredvalue,invaluable.More,the Fstatisticat 0 for all 3 modelsindicateseachmodel tobe significant. Thismeanswe acceptournull hypothesis 0foroutput. Financial Managerial sector is deemed as a high skilled service sector. Our positive coefficients for Capital to Labours Hours ratio show this variable to be significantin determining hourly wages within Financial Managerial roles for all modelswith all output sectors. Thiswould normally be expected for manufacturing industries and not service sectors. Although these findings accept my hypothesis 1 and support Chiang (1984) findings, it comes as a surprise that it is the only independent variable significantthrougheveryoutputlevel. Labour Hours and % capital within our multi collinearity matrices indicate a positive linear relationship. This is contrary to our statistical findings which indicate a negative correlation with coefficientsforlabourhoursat(0.05) (0.03) (0.15) & for change incapital at (-0.06) (-0.10) (-0.06) Robustnessofmodels The discrepancyfromstatistical findingsfromnoeffects,single,anddouble fixedeffectsfrommodel to model indicateslimitrobustnesstoourmodels.The presence of serial correlationand heterogeneitylimitsthe qualityof ourmodelsbeingBLUEand adds more bias. We take ourSingle FixedEffectsPanel Model asourmostaccurate model,although,itonlysupportsmy 1inone scenarioinHighOutputindustryareas. The presence of autocorrelationwithinmymodel mayhave beendue tothe time period(2005-2011) whichexhibitedfinancial turmoil.Creatingadummy variable forthisperiodmayhave removedautocorrelationinthe single fixedeffectspanel model only,as double fixedisalreadyincludesdummyforthe time series. Limitations The limitationspresentwithincreatingourmodelsfordeterminingimpactof outputonwagesare as follows:We have omittedvariable biaswhichcanbe seeninthe sub headerbelow. Further,aswe have a limitednumberof observationsN,we have Nickellbias,whicharisesinthe staticestimate throughcalculationof the autoregressive terms. More,the sample (OESData) maynot provide afair reflectionof the specificareaunderinvestigation.Itcanbe notedthat there have beenchangesin the methodused forcalculatingmeanwagesforoccupationswithanyworkersearningabove $70 perhour inattemptto remove downwardbias.Resultantly,thischange maysee highermeanwage estimatesforsome occupations,although,medianandpercentilewage estimateswill notbe affected. Unfortunately,assessingfinancial innovationisjustascomplicatedasobtainingsensible measures for financial productivity. These difficultiesinmeasuringfinancial outputlimit ourunderstandingof financial development.Whileitis well establishedthatfinancial developmentmattersforgrowth (Levine 2005)33 33 Levine, R. (2005): Finance and Growth: Theory and Evidence, in Handbook of Economic Growth, ed. by P. Aghion, and S. N. Durlauf, vol. 1A, pp. 865934. Elsevier, Amsterdam. 19. Determinantsof Output George Gazzard BournemouthUniversity - 19 Omitted VariableBias: From preliminaryanalysisintoourFinancial Managerdata,there seemstobe large differencesin wage earningsbasedonlocation.Stiglitz(1974) arguesthat there are large wage differentials betweenurbanandrural sectorswhichwill inevitablyleadtomigration.Therefore,the basisfor differentFinancial Managersinhabitingrural orurban areasmay dictate theirownwage levels.The exclusionof locationfromourmodel meansthere isanomittedvariable bias.34 Philippon(2009) researchhighlightsthatoverthe past10 years30% to 50% of wage differentials observedforFinancial Managersthroughdifferentindustriescanbe attributedtorent (surplus profits.35 The profitlevelsof the firmsthateach financial managerisworkingin, ournotincluded withinourmodel. Conclusion In answeringourmainresearchinvestigationquestions:The maindeterminantsof hourlywages withinFinancial Managerialindustriesandthe effectshighlevelsof outputhave ondetermining wage levelswe come totwoconclusions. Intermediate costs(ICV) are significantondetermining wagesthroughall Output industries withinourNoeffectsandSingleEffectsmodel.LabourHours(LI) and % Change Capital are significantinall modelsatall outputlevelswithinFinancialManagerial industries,%Change Capital hasanegative effectonhourlywages .Outputhasno significance on hourlywagesof financial managerswhich rejectsmy1.However,inline withmytheoretical understandingandpreviousliterature Istill believeOutputhasaneffectwithinpremiumwage sectorssuch as Financial Managerial industriesdespite myfindings. 34 Stiglitz, J. (1974). Alternative Theories of Wage Determination and Unemployment in LDC's: The Labor Turnover Model. The Quarterly Journals of Economics. 88 (2), 194-227 35 Philippon, T. (2009). 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Variables Y is earnings (Yo is earnings for someone with no education + schooling) S is years of schooling, X years of potential W. Levine, R. (2001). "It`s Not Factor Accumulation: Stylized Facts and Growth Models" Multifactor Productivity Calculation Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. The MIT press. Yellen, J. (1984). Efficiency Wage Models of Unemployment. .. 74 (2), 200-203. 22. Determinantsof Output George Gazzard BournemouthUniversity - 22 0 20 40 60 80 100 120 -20 -10 0 10 20 30 Series: Residuals Sample 1 895 Observations 888 Mean 4.00e-15 Median -0.362123 Maximum 30.62997 Minimum -23.98705 Std. Dev. 7.568628 Skewness 0.401652 Kurtosis 3.296578 Jarque-Bera 27.13045 Probability 0.000001 0 10 20 30 40 50 60 70 -15 -10 -5 0 5 10 15 20 Series: Standardized Residuals Sample 2005 2011 Observations 880 Mean 2.10e-14 Median -0.668825 Maximum 22.16905 Minimum -14.38553 Std. Dev. 5.882892 Skewness 0.465936 Kurtosis 3.431883 Jarque-Bera 38.67998 Probability 0.000000 0 40 80 120 160 200 240 280 -20 -10 0 10 20 30 Series: Standardized Residuals Sample 2005 2011 Observations 1769 Mean 2.66e-14 Median -0.650921 Maximum 32.52855 Minimum -19.68385 Std. Dev. 7.058561 Skewness 0.537926 Kurtosis 3.749520 Jarque-Bera 126.7222 Probability 0.000000 0 20 40 60 80 100 120 -20 -10 0 10 20 30 Series: Standardized Residuals Sample 2005 2011 Observations 888 Mean -3.82e-14 Median -0.362123 Maximum 30.62997 Minimum -23.98705 Std. Dev. 7.568628 Skewness 0.401652 Kurtosis 3.296578 Jarque-Bera 27.13045 Probability 0.000001 0 50 100 150 200 250 300 350 400 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 Series: Standardized Residuals Sample 2005 2011 Observations 1769 Mean -2.63e-16 Median -0.077840 Maximum 12.36958 Minimum -9.260541 Std. Dev. 2.274458 Skewness 0.301389 Kurtosis 4.966962 Jarque-Bera 311.9543 Probability 0.000000 0 20 40 60 80 100 120 -8 -6 -4 -2 0 2 4 6 8 10 Series: Standardized Residuals Sample 2005 2011 Observations 888 Mean 7.20e-17 Median -0.063560 Maximum 10.66483 Minimum -8.308177 Std. Dev. 2.131271 Skewness 0.228446 Kurtosis 4.439591 Jarque-Bera 84.40334 Probability 0.000000 Appendix Normal distributionofresiduals(1)(2)&(3) 0 40 80 120 160 200 240 280 -20 -10 0 10 20 30 Series: Standardized Residuals Sample 2005 2011 Observations 1769 Mean 1.78e-14 Median -0.650921 Maximum 32.52855 Minimum -19.68385 Std. Dev. 7.058561 Skewness 0.537926 Kurtosis 3.749520 Jarque-Bera 126.7222 Probability 0.000000 0 10 20 30 40 50 60 70 -15 -10 -5 0 5 10 15 20 Series: Residuals Sample 1 882 Observations 880 Mean 7.11e-15 Median -0.668825 Maximum 22.16905 Minimum -14.38553 Std. Dev. 5.882892 Skewness 0.465936 Kurtosis 3.431883 Jarque-Bera 38.67998 Probability 0.000000