Trade Unionism and Growth: A Panel Data StudyThis is the html version of the file http://www.rdg.ac.uk/Econ/Econ/workingpapers/emdp429.pdf. G o o g l e automatically generates html versions of documents as we crawl the web. To link to or bookmark this page, use the following url: http://www.google.com/search?q=cache:UQoYv9mNTVUC:www.rdg.ac.uk/Econ/Econ/workingpapers/emdp429.pdf+trade+unionism&hl=en&ie=UTF-8 Google is not affiliated with the authors of this page nor responsible for its content. These search terms have been highlighted: trade unionism Page 1 Trade Unionism and Growth: A Panel Data StudyDimitrios Asteriou*Vassilis MonastiriotisThe University of Reading London School of EconomicsAbstractThis paper investigates the long-run relationship between trade unionism andeconomic growth using a panel data set comprising of 18 OECD economies. Much ofthe existing evidence on the effects of unionism on productivity derives from micro-economic studies, with little attention to the dynamics of this relationship and theeconomy-wide effects. Using the recently developed mean group and pooled meangroup estimation techniques on cross-country panel data, the paper offers support tothe "enhancing-worker-morale face of unionism" hypothesis, revealing a positiverelationship between trade union density and labour productivity.J.E.L. Classification: C23, J51, O4Keywords:Trade unions, Economic growth, Panel data econometrics.*Corresponding Address: Dimitrios Asteriou, Department of Economics Faculty of Letters & SocialSciences, PO Box 218, Whiteknights, Reading RG6 6AA, England, Tel: (0118)9875123 (4056);Fax: (0118) 9750236; E. Mail: D.Asteriou@reading.ac.uk. We are grateful to Dr EngelbertStockhammer for making available to us the dataset used here. All responsibility for errors andomissions lies with the authors. Page 2 1. IntroductionResearch on the productivity effects of unionism over the last two decades hasbeen lively, offering new insights to the theoretical and empirical relationshipbetween these two labour market aggregates. Following the pioneering work ofBrown and Medoff (1978) and inspired by the controversial work of Freeman andMedoff (1979 and 1984), numerous empirical studies have examined the extent anddirection of the union productivity effects, mainly for the cases of the UK and theUSA. There is a rather widespread consensus in the literature about unionism having anegative impact on productivity and output, although a number of authors haveestimated positive union productivity differentials (Brown and Medoff, 1978; Clark,1980; Nickel et al., 1989; Gregg et al., 1993).It is standard in this literature to investigate productivity differentials betweenunionised vis-ŗ-vis non-unionised firms using industry or firm level data.Consequently, there is little attention on the economy-wide and dynamic effects ofunionism, while other sources of productivity differentials, like managementstrategies and production efficiency, are difficult to be accounted for anddistinguished from the direct union effects. There is comparatively little research onthe issue using aggregate national data. Among the few studies, the OECD (1997) hasfound evidence of negligible effects of unionism and the structure of wage bargainingon productivity and productivity growth. Nickel and Layard (1998) have estimated anegative union effect on growth for a panel of OECD countries. A negative impact onoutput or productivity has also been found by earlier economy-wide studies (deFina,1983; Lovell et all., 1988; Koedijk and Kremers, 1996).In this paper we investigate the economy-wide effects of unionism onproductivity and productivity growth at an aggregate level, for a large panel of data. A Page 3 long time-series (1960-1992) for 18 OECD countries allows us to investigate theshort- and long-run dynamics of unionism within an economic growth framework,while controlling for country-specific effects.1The source of the data is theComparative Welfare States Data Set (Huber et al., 1997), which includes data fromvarious sources.2In addition to traditional panel data techniques, we utilise newlydeveloped econometric methods for the estimation of dynamic panel models. Havinga set of 576 observations and using an auto-regressive distributed lags (ARDL)specification, we can identify a common-across-countries long-run coefficient for theunion productivity effects, while allowing different short-run dynamics for eachcountry. Hence, our estimates are largely unbiased from any business-cycle andcountry-specific effects. Apart from the relative novelty of the applied econometricmethodology, our investigation of union productivity effects based on a large panel ofdata and controlling for short-run dynamics and country-specific effects is to ourknowledge unique.In the next section we make some theoretical considerations and derive anestimating model. Sections 3 and 4 present the empirical results. In section 3 we applytraditional econometric techniques, while in section 4 we briefly present andconsequently apply the dynamic panel data methodologies. The final sectionsummarises the results and concludes.2. A model of changes in unionisation rates and growthUnion productivity differentials can arise through a variety of mechanisms. Ata firm-level, unionism can affect the organisation and efficiency of production, the1The sample countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany,Ireland, Italy, Japan, Netherlands, Norway, New Zealand, Sweden, Switzerland, UK and USA.2The original data sources are: for employment, OECD Labour Force Statistics (various years); forunion membership figures, Visser (1996); and for GDP and investment, Penn World Tables (Mark 5.6). Page 4 pace of technological innovation and capital accumulation, training and manninglevels and so forth. At a wider level, it can impact upon average wages and wageinflation, with further effects on inflation, interest rates, investment and outputgrowth, as well as on the national comparative advantages and international trade. Atthe level of theory, negative union productivity effects can be assumed if unionsimpose rigidities in the introduction of new technologies and working practices, or ifthey reduce profitability and investment. On the other hand, unions can increaseworkers' participation and involvement and, hence, production efficiency, while theirincreasing labour costs function can foster innovation and quality-based competitionfrom the side of management.Traditionally, however, a more direct effect of unionism is assumed, as thelatter can impact directly upon the productivity of the individual workers. In this caseit is the marginal product of labour that differs between unionised vis-ŗ-vis non-unionised workers. Nevertheless, despite their theoretical distance, all thesemechanisms exhibit some practical equivalence, both in technical and empiricalterms.3In building our model we follow Brown and Medoff (1978) assuming thatboth unionised and non-unionised labour have the same coefficient in the productionfunction, with unionised labour being discounted by a factor fwhich reflectsproductivity differences:baf)*(untLLKAY+=(1)where the standard notation is used with the exception that Luis unionised labour andLnis non-unionised labour. The coefficient fcan be greater or smaller than unity,3A technical exposition of this and relevant empirical evidence can be found in Monastiriotis (2001). Page 5 implying higher or smaller productivity of unionised labour, respectively. By addingand subtracting Luin (1) and further manipulating, we get:baf)]}1(*)/(1[*{--=LLLKAYut(2)or, taking logs and using log(1+x)™x,TUDlktalygba+-++=-)1()((3)where lower case letters denote logarithms, g= b(f-1) andTUD=Lu/L.Thus,productivity growth will be a function of changes in union density:TUDlkaly+-++=-gba)1()((4)Further, we assume a standard capital accumulation process (dis depreciation rate)1)1(--+=tttKIKd(5)so that the growth of capital will bedd-=--+=----)/(/}))1({(1111ttttttttKIdKKKKIdK(6)Using Dk™dK, approximating (It/Kt-1)-dwith It/Ytand finally introducing an error termwe obtain4tititititieTUDlYIaly,,,,,)()1()/()(++-++=-gba(7)Equation (7) is our main estimating model, relating productivity growth to the shareof investment to GDP, employment growth and changes in union density. In thetraditional panel estimation techniques (section 3) the one- and two-way errorcomponent (de-meaned) and dummy variables (DVLS) transformations of thisspecification are used. In section 4 we estimate the above specification using theMean Group (MG) and Pooled Mean Group (PMG) estimators with a dynamic ECMequation that has (7) as a long-run solution.4The approximation of capital by GDP is introduced mainly to avoid data-quality problems related tothe capital series. Data availability was however an additional problem, since using the capital serieswould further shorten the time-dimension of our sample and, hence, generate problems in theapplication of the MG and PMG estimations on the ARLD specification. Page 6 3. Empirical resultsThe panel nature of the sample offers a wealth of information that can beinvestigated. Before applying a number of pooled regression methodologies, it isinteresting, therefore, to obtain first some information from the cross-sectional andtime-series dimensions.(i) Cross-sectional and time-series analysesA first impression about the relation between productivity growth and changesin unionisation rates can be obtained by looking at scatter plots. Hence, we plottedthese two aggregates against each other once for each of our sample years and oncefor each of our sample countries. The general pattern was surprisingly one of apositive relationship, although the graphs were often very sensitive to the inclusion ofsome observations. Moreover, the scatter-plots revealed some cyclicality, withpositive and negative slopes alternating every around five years. These two pieces ofinformation seem to suggest that the relationship under investigation has not beenstable over the years and across countries in the period of our focus. This furthersuggests that country and time specific effects must be significant.To get some more formal indication for this, we also run cross-country andtime-series regressions based on the model derived earlier. The coefficients obtainedfor the unionisation variable were quite stable in the cross-country regressions (overthe years), but less so in the time-series regressions (among countries). Moreover, thecoefficients from the cross-country regressions also seemed to follow a time trend.Figure 1 plots these estimates over time. Page 7 As it can be seen, the estimated union effects have been rather volatile with amean value close to zero, especially during the 1970s. The logarithmic trend fitted,suggests an original negative union effect which in the 1980s turns positive. Overall,however, the results obtained are not statistically significant, as Table 1 reveals.As the overall performance of the estimated regressions is not satisfactory, thisis as much of inferences as we can make from the uni-dimensional regressions. Thenext step is to look at the panel dimension, by pooling the data together.(ii) Traditional panel data analysisIn order to get as much information as possible from the panel, we ran allpossible regression specifications, both for productivity growth and for output growth.The results are presented in Table 2. The union coefficient is significant in all casesfor both the output and productivity growth regressions, with the exception of the timerandom effects model (second last column), which also performs very poorly, with theimplication that the time effects are fixed and not random. However, all specificationtests show both time and country effects to be significant, suggesting that the correctspecification is a two-way error component model (last column).This last model suggests that the net effect of unionism on productivity,controlling for time and country specific effects, has been positive in the threedecades and for the 18 countries of our sample. The interpretation of the theoreticalmodel in (7) suggests that unionised labour has -other things equal- been by around19% (=0.214/1.115) more productive than non-unionised labour. The coefficients onthe capital and labour variables have the expected signs and are significant. Moreover,the results are very stable across the different specifications. The results from the Page 8 traditional analysis on the panel seem to suggest that unionism enhances theproductivity of labour.On the other hand, the country specific effects are always highly significant. Itis interesting to investigate the determinants of these effects. One possible explanationis that the productivity effects of unionism might differ among countries depending onthe strategies employed by the national trade unions and the structure of wagebargaining (centralisation and co-ordination) in each country. To test for that, weexamined the relationship between the estimated country-fixed effects and someindicators of union co-ordination and centralisation of wage bargaining, produced bythe OECD (1997). Correlation analysis between these variables returned a statisticallysignificant correlation coefficient of 0.34, suggesting that the estimated positive unionproductivity effect is weaker in countries with more rigid wage bargaining structures.4. Robustness of the empirical findingsThe unconventional result obtained above, of a positive relationship betweenunionism and productivity requires further investigation, since the data used here havecomplex dynamics and are characterised by strong trends and non-stationarity. Theidentified time and country effects could be possibly capturing country specific long-or short-run dynamics which the traditional pooled estimators, such as the fixed andrandom effects, cannot estimate. Therefore, these methods might not be appropriate inour case. New estimation techniques are now available in the literature that allow sucheffects to be controlled for and measured.(i) The MG and PMG estimation methodology Page 9 It has become conventional to view long-run parameters as reflectingcointegrating relationships among a set of I(1) variables. The standard methodology insuch cases first establishes the order of integration of the variables in question, andthen - having established that the variables are of the same order of integration - testswhether there is at least one linear relationship among these variables.Our analysis follows a different approach. This can be justified by two facts.First, there are only a few (and even fewer statistically satisfactory) tests ofcointegration in a panel data context, while it is also well known that tests of order ofintegration in panel data do not reliably distinguish between series that contain a unitroot and those that are stationary with a "near-unit root". Second, long-runparameters may be consistently estimated using the traditional autoregressive-distributed lag (ARDL) approach (Pesaran and Shin, 1998). Moreover, as Pesaran,Shin and Smith (1999) have shown, this approach yields consistent andasymptotically normal estimates of the long-run coefficients irrespective of whetherthe underlying regressors are I(1) or I(0).5Further, it compares favourably in MonteCarlo experiments with conventional methods of cointegration analysis.Therefore, our estimates were obtained using two recently developed methodsfor the statistical analysis of dynamic panel data: the Mean Group (MG) and thePooled Mean Group (PMG) estimation. These methods are particularly suited to theanalysis of panels with large time and cross-section dimensions.6MG estimationderives the long-run parameters for the panel from an average of the long-runparameters from ARDL models for individual countries (see Pesaran and Smith,1995). For example, if the ARDL is the following5In our analysis the GDP, Investment, Employment and Union Density variables are clearly trendedfor all countries and can be assumed to be I(1), hence become stationary after first differencing(productivity and employment growth, changes in union density) or taking their ratio (investmentshare). Page 10 ititiitiitiezdxLbyLa++=)()((8)for countrly i,where i=1,....,N,then the long-run parameter for country iis)1()1(iiidb=q(9)and the MG estimator for the whole panel will be given by==NiiN11qq(10)It can be shown that MG estimation with sufficiently high lag orders yieldssuper-consistent estimators of the long-run parameters even when the regressors areI(1) (see Pesaran, Shin and Smith, 1999).The PMG method of estimation, introduced by Pesaran, Shin and Smith(1999) occupies an intermediate position between the MG method, in which both theslopes and the intercepts are allowed to differ across country, and the standard fixedeffects method, in which the slopes are fixed and the intercepts are allowed to vary. InPMG estimation, only the long-run coefficients are constrained to be the same acrosscountries, while the short-run coefficients are allowed to vary.Setting this out more precisely, the unrestricted specification for the ARDLsystem of equations for t=1,2,...Ttime periods and i=1,2,...Ncountries for thedependent variable yisitmjinjjtiijjtiijitxyyemdl+++===--10,,(11)where xijis the (k•1)vector of explanatory variables for group iand mirepresents thefixed effects. In principle the panel can be unbalanced and mand nmay vary acrosscountries. This model can be re-parameterised as a VECM system6Quah (1993) has referred to such data sets as "data fields". Page 11 itmjinjjtiijjtiijtiitiiitxyxyyemggbq++++-=-=-=----1110,,1,1,)((12)where bis are the long-run parameters and qis are the error correction parameters.The pooled group restriction is that the elements of bare common across countries, sothatitmjinjjtiijjtiijtitiiitxyxyyemggbq++++-=-=-=----1110,,1,1,)((13)All the dynamics and the ECM terms are free to vary. Estimation of this model is bymaximum likelihood. Again it is proved that under some regularity assumptions, theparameter estimates of this model are consistent and asymptotically normal for bothstationary and non-stationary I(1) regressors. Both MG and PMG estimations requireselecting the appropriate lag length for the individual country equations. Thisselection was made using the Schwarz Bayesian Criterion.(ii) The MG and PMG estimation resultsInitially we estimated the model given in (7) assuming that all of the long runcoefficients are the same across countries. The estimation results from the MG andPMG methods are presented in Table 3. The PMG estimates provide further evidenceto our previous finding of a strong positive relationship between changes inunionisation and productivity growth, while the MG results are in the same line butless strongly so. The capital growth variable (investment share) has the expected sign,which for the PMG model is highly significant. The growth of employment has againa positive estimated effect, but this is insignificant in the PMG estimation, with theimplication that labour productivity is constant across different employment levels.Although the Hausman test for the poolability of this coefficient is rejected, for boththe unionism variable and investment as a share of GDP, the pooling restrictions Page 12 cannot be rejected (p-values 0.31 and 0.34 respectively). Moreover, the jointHasuman test suggests that the PMG results are more appropriate than the MG ones.Overall, the results obtained from the ARDL specifications are highly consistent tothe ones derived from the more traditional methods. The estimated effect ofunionisation changes on productivity growth suggests that discount factor forunionised labour, f, is equal to 1.19, or that unionised labour is 19% more productivethan non-unionised labour. This result is identical to the one obtained from the two-way error component model of Table 2. The estimated returns to capital are also verysimilar to the ones obtained earlier.7Further, restricting the coefficient of unionism tobe the same in all countries but removing this restriction for the other two coefficientsdoes not affect significantly our main conclusions (Table 4). Again the unionismcoefficient is positive and significant for the PMG estimates (which are againapproved by the Hausman test for poolability), while now both coefficients forinvestment share and employment growth become insignificant. Alternative estimates≠ not reported here for economy of space ≠ restricting subsets of the long-runcoefficients gave similar results with those initially obtained.8The employmentgrowth coefficient verifies the constant returns to scale long run effect of employmenton labour productivity, which was not captured in the traditional estimation methodsthat didn't control for the short-run dynamics.9For capital growth, the insignificantresult strengthens our earlier conclusion about the poolability of this coefficient. Thistime however, the union productivity effect increases further, to around 23%.7The estimated coefficient in all specifications is lower than theory would suggests, but this is largelydue to a scaling effect caused by our approximation of capital growth with the investment share.8Tables and results are available from authors upon request.9The short-run dynamics are very different from the fixed country-specific effects estimated before. Acorrelation analysis of the impact of the wage bargaining structure on these dynamics returned asignificant correlation coefficient of -0.45, which implied that in countries with more rigid wagebargaining structures unionism had also a positive short-run effect. Page 13 This figure is at the margin of plausibility if one assumes that the unionproductivity effects are solely activated through workers' performance (Brown andMedoff, 1978). Hence, an explanation suggesting that union productivity effects areactivated through a number of plausible mediating factors (production efficiency,capacity utilisation and the extent of labour hoarding being but a few) cannot be ruledout. In any case, the overall productivity effect of unionism is robustly found to havebeen positive in the three decades and for the 18 countries of our sample.5. ConclusionsNumerous studies at the firm and industry levels have provided evidence of anegative productivity effect of unionism, although there are cases where a positiveunion productivity effect has been estimated. Most of the empirical literature usesAnglo-Saxon data and there are few cross-country studies. Further, time-seriesanalysis on the issue is rather scarce, with the implication that the dynamics of therelationship at question have been relatively overlooked.Attempting to partially fill this gap, in this paper we examined the long- andshort-run relationship between unionism and productivity using a panel of 18 OECDcountries over a 32-year period. The MG and PMG estimation techniques that weused together with more traditional methods are at the forefront of panel dataeconometrics. Our time-series and cross-country analyses revealed that thisrelationship has been different among countries and over time. Controlling forpossible time and country-specific effects, the panel data analyses allowed theestimation of a common across countries long-run coefficient. The good performanceof our regressions and the stability of our results, we interpret as evidence in supportof the appropriateness of the econometric method we employed. Page 14 Our basic results provide robust evidence of a positive impact of unionism onproductivity. Both the long- and short-run effects are positive and statisticallysignificant, although we also offer some evidence suggesting that country-specificfactors, like the strategies employed by the national trade unions and the degree of co-ordination among them and between them and the employers, might play an importantrole at the short-run. Page 15 References1. Brown C. and J. Medoff (1978), "Trade unions in the production process",Journal of Political Economy, Vol.86, pp.355-378.2. Clark K. (1980), "The impact of unionisation on productivity: a case study",Industrial and Labour Relations Review, Vol.33, pp.451-469.3. DeFina R. (1983), "Unions, relative wages and economic efficiency", Journal ofLabor Economics, Vol.1, No4, pp.408-429.4. Freeman R. and J. Medoff (1979), "The two faces of unionism?", Public Interest,No57, pp.69-93.5. Freeman R. and J. Medoff (1984), What do Unions do?, Basic Books, NY.6. Gregg P., S. Machin and D. Metcalf (1993), "Signals and cycles: productivitygrowth and changes in union status in British companies, 1984-1989", EconomicJournal, Vol.103, pp.894-907.7. Huber E., C. Ragin and J.D. Stephens (1997), "Comparative Welfare States DataSet", Northwestern University and University of North Carolina.8. Koedijk K. and J. Kremers (1996), "Deregulation: a political economy analysis",Economic Policy, Vol.26, pp.443-467.9. Lovell C., R. Sickles and R. Warren Jr (1988), "The effects of unionisation on labourproductivity: some additional evidence", Journal of Labor Research, vol.9, pp.55-63.10. Monastiriotis V. (2000) "Trade unions and productivity: model specification andempirical evidence", unpublished manuscript, Department of Geography andEnvironment, LSE, UK.11. Nickel S. and R. Layard (1998), "Labour market institutions and economicperformance", CEP Discussion Paper No. 407, LSE. Page 16 12. Nickell S., S. Wadhwani and M. Wall (1989), "Unions and productivity growth inBritain, 1974-86: evidence from Company Accounts data", CLE Working Paper No.1149, LSE.13. OECD (1997), Economic performance and the structure of collective bargaining,ch.3 in Employment Outlook, OECD, Paris.14. Pesaran M.H. and Y. Shin (1998), "An Autoregressive Distributed Lag ModellingApproach to Cointegration Analysis", in S. Strom, A. Holly and P. Diamond(eds.), Centennial Volume of Ragnar Frisch, Cambridge University Press.15. Pesaran M.H., Y. Shin and R.P. Smith (1999) "Pooled Mean Group Estimation ofDynamic Heterogeneous Panels" Journal of American Statistical Association,Vol. 94, pp. 621-634.16. Pesaran M.H. and R.P. Smith (1995) "Estimation of long-run Relationships fromDynamic Heterogeneous Panels" Journal of Econometrics, 68, pp. 79-113.17. Quah D. (1993) "Empirical Cross-Section Dynamics in Economic Growth"European Economic Review, 37, pp. 326-434.18. Visser J. (1996), "Unionisation Trends: The OECD Countries Union MembershipFile", University of Amsterdam, Centre for Research of European Societies andLabour Relations CESAR. Page 17 Table 1: Estimated union effect on growth (cross-country and time-series regressions)Year Impact ofTUD Year Impact ofTUD Year Impact ofTUDYear/Country Impact ofTUDCountryImpact ofTUD19612.873(2.09)1971-0.347(-1.10)19810.731(0.95)1991-0.009(-0.01)IRE1.848(2.25)1962-1.180(-2.21)19720.101(0.33)1982-0.254(-0.29)19920.648(2.70)ITA-0.343(-0.86)1963-0.331(-0.274)1973-0.085(-0.15)19830.753(1.31)AUL-1.690(-2.50)JPN4.024(2.11)19640.446(0.36)1974-0.018(-0.09)19840.373(1.41)AUS-0.547(-0.38)NET-0.580(-1.24)19650.388(0.55)19750.280(1.89)19850.240(0.43)BEL-0.370(-0.71)NOR-0.429(-0.99)1966-0.945(-1.15)19760.481(0.69)1986-0.247(-0.72)CAN-0.475(-0.77)NZL0.143(0.87)1967-0.547(-0.58)1977-0.693(-0.78)1987-0.466(-1.30.)DEN0.237(0.51)SWE0.095(0.24)1968-0.058(-0.05)19780.017(0.03)1988-0.671(-1.69)FIN0.777(1.63)SWZ-0.820(-0.51)19690.141(0.32)19790.79(0.20)1989-0.344(-0.54)FRA0.970(0.92)UKM-0.475(-1.10)19700.589(1.37)19800.103(0.16)1990-0.397(-0.59)FRG0.312(0.30)USA0.006(0.01)Notes: All regressions have been estimated by OLS. t-statistics are in parentheses.Figure 1: The effect of changes in unionism on productivity growth (cross-country regressions)-1.5-1-0.500.511962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992yeareffect Page 18 Table 2: Pooled regressions on productivity and output growthModelNECFE (1) CFE (2)TFECRETRE(C/T)FEProductivity growthInvestment share0.145(5.71)0.171(4.22)0.171(4.22)0.127(5.49)0.157(4.78)0.135(2.31)0.129(3.11)Employment growth0.149(2.43)0.222(3.57)0.222(3.57)0.034(0.62)0.201(3.30)-0.774(-2.10)0.115(2.08)Change in union density0.166(1.86)0.191(2.13)0.191(2.13)0.168(2.05)0.186(2.10)-0.428(-0.74)0.214(2.67)Constant-0.015(-2.28)--0.023(-2.15)--0.019(-2.15)-0.003(-0.22)-Brausch-Pagan test--4.19#-59.17--Hausman test----11.07--F-test (year dummies)---7.14--7.60F-test (country dummies)-4.28----5.29F-test ( y+c dummies)------7.13R-squared0.080.500.080.600.080.000.66Output growthInvestment share0.152(7.38)0.220(6.79)0.220(6.79)0.116(6.26)0.185(6.92)0.118(2.42)0.124(3.75)Employment growth0.377(7.65)0.415(8.33)0.415(8.33)0.277(6.28)0.410(8.36)-0.341(-1.11)0.337(7.69)Change in union density0.226(3.13)0.229(3.18)0.229(3.18)0.146(2.42)0.235(3.30)-0.280(-0.58)0.179(2.80)Constant-0.018(-3.34)--0.037(-4.32)--0.027(-3.80)-0.002(-0.16)-Brausch-Pagan test--4.75#-69.90--Hausman test----17.75--F-test (year dummies)---8.00--8.31F-test (country dummies)-5.17----5.58F-test ( y+c dummies)------7.91R-squared0.200.630.190.710.190.000.75Notes:t-statistics are in parentheses. The Brausch-Pagan and Hausman tests are 2tests for the significance ofrandom effects (against no effects and fixed effects, respectively). The various F-tests refer to thesignificance of the corresponding dummies. The abbreviations in the head of the Table are as follows(estimation method in parenthesis): NE, pooled regressions with no controls for any effects (OLS);CFE (1), country fixed effects (DVLS); CFE (2), country fixed effects (GLS); TFE, time fixed effects(DVLS); CRE, country random effects (GLS); TRE, time random effects (GLS); and (C/T)FE, two-way error component model with both time and country fixed effects (DVLS).#: Instead of the B-P test, an F-test for zero variance of the random effects is used in the CFE (2) model. Page 19 Table 3: Pooled Mean Group and Mean Group Estimates(Dependent Variable: DDyit)PMG EstimatesMG EstimatesHausman TestCoef.s.e.t-ratioCoef.s.e.t-ratioHp-val(I/Y)it0.1240.0216.0060.670.0631.0610.910.34DDlit0.0360.0430.8540.2470.1052.3614.860.03DDTUDit0.1900.0672.8460.4350.2521.7261.010.31Joint Hausman test:5.420.14Error Correction Coefficientsff-0.9790.021-46.791 -0.9880.012 -82.313Short-Run Coefficients not reported for economy of spaceNotes:The maximum number of time periods and groups are: 32 18SBC (Schwarz) has been used to select the lag orders for each group.All the long-run parameters have been restricted to be the same across groups.The mean group estimates have been used as initial estimate(s) of the long-run parameter(s) for thepooled maximum likelihood estimation.Table 4: Pooled Mean Group and Mean Group Estimates(Dependent Variable: DDyit)PMG EstimatesMG EstimatesHausman TestCoef.s.e.t-ratioCoef.s.e.t-ratiohp-valLong-Run Coefficients Restricted to be the Same Across all GroupsDDTUDit0.2310.0643.5820.4530.2521.7260.700.40Unrestricted Long-Run CoefficientsDDlit0.0540.0660.8180.0670.0631.061(I/Y)it0.1210.01771.0350.2470.1052.361Error Correction Coefficientsff-0.9880.012-80.809 -0.9880.012 -82.313Short-Run Coefficients not reported for economy of spaceNotes:The maximum number of time periods and groups are: 32 18SBC (Schwarz) has been used to select the lag orders for each group.All the long-run parameters have been restricted to be the same across groups.The mean group estimates have been used as initial estimate(s) of the long-run parameter(s) for thepooled maximum likelihood estimation.
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