The Impact of Unionization Threat on Non-union Wage Rates in Canada

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The Impact of Unionization Threat on Non-union Wage Rates in Canada

  1. 1. THE IMPACT OF UNIONIZATION THREAT ON NON-UNION WAGE RATES IN CANADA BY JOEY YI ZUO OCTOBER 2007
  2. 2. iAbstractIn an effort to reduce the workers’ benefits from joining the union, employers increase wagesof their non-union workers when facing an increased threat of unionization (Rosen, 1969).This paper presents novel evidence regarding the effect of the threat of unionization on wagerates in Canada for the period between 1998 and 2006. Drawing on the insights provided bynine consecutive annual Canadian Labor Force Surveys, I find that the threat of unionizationhas a larger positive effect on the non-union wages compared to the threat’s effect on theunion wages and, hence, has an inverse effect on the union-wage gap. Further, the analysisby sectors suggests that these results hold for the private sector only and do not extend to thepublic sector. Importantly, I find that the results are sensitive to the definition of theunionization threat and to the list of explanatory variables included in the estimation model.
  3. 3. iiTABLE OF CONTENTS1. INTRODUCTION - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -12. LITERATURE REVIEW - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 33. THEORETICAL FRAMEWORK - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 54. METHODOLOGICAL APPROACH - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -74.1. Predicated Probability of Union Membership as a Measure of Unionization Threat- 74.2. Industry Union Density as a Measure of Unionization threat- - - - - - - - - - - - - - - - -94.3. Analysis by Private and Public Sector- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -94.4. Potential Problems- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - -105. DESCRIPTION OF DATA AND VARIABLES - - - - - - - - - - - - - - - - - - - - - - - - - 116. RESULTS - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 116.1. Determinants of Union Membership in Canada, 1998-2006- - - - - - - - - - - - - - - - -116.2. Effect of the Unionization Threat on Non-union Wage Rates- - - - - - - - - - - - - - - -136.3. An Analysis of Unionization Threat in Private and Public Sectors - - - - - - - - - - - - 156.4. An alternative Measure for Unionization Threat - - - - - - - - - - - - - - - - - - - - - - - - 167. CONCLUSIONS- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -168. REFERENCES - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -189. APPENDICES - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 21Appendix A Variable Description and Review of Literature on the Union Threat Effects-21Appendix B Estimation Results- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 24Appendix C Graphs- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 44
  4. 4. iiiLIST OF TABLESTable 1: Description of variables - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 21Table 2: Review of literature on the union threat effects - - - - - - - - - - - - - - - - - - - - - -22Table 3: Determinants of union membership in Canada, 1998-2006 (all sample) - - - - - 24Table 4: Effect of predicted probability of unionization on union/non-union wage rates,1998 -2006 (all sample)- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 26Table 5: Effect of union density on union/non-union wage rates, 1998-2006 (all sample)- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 29Table 6: Determinants of union membership in Canada, 1998-2006 (Public Sector)- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - 31Table 7: Effect of predicted probability of unionization on union/non-union wage rates,1998-2006 (Public Sector) - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - 33Table 8: Effect of union density on union/non-union wages, 1998- 2006 (Public Sector)- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - 36Table 9: Determinants of union membership in Canada, 1998- 2006 (Private Sector)- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -38Table 10: Effect of predicted probability of unionization on union/non-union wage rates,1998- 2006 (Private Sector) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -40Table 11: Effect of union density on union/non-union wage rates, 1998 – 2006(Private Sector) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -42
  5. 5. ivLIST OF FIGURESFigure 1: Union membership in Canada (1998-2006) - - - - - - - - - - - - - - - - - - - - - -44Figure 2: Effect of predicted probability of unionization on the non-union/union wagesand the union-wage gap, for the whole sample, by year- - - - - - - - - - - - - - - - - - - - -45Figure 3: Effect of predicted probability of unionization on the non-union/union wagesand the union-wage gap, for the public sector, by year- - - - - - - - - - - - - - - - - - - - - 46Figure 4: Effect of predicted probability of unionization on the non-union/union wagesand the union-wage gap, for the private sector, by year - - - - - - - - - - - - - - - - - - - - -47Figure 5: Effect of industry union density on the non-union/union wages and the union-wage gap, for the whole sample, by year. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -48Figure 6: Effect of industry union density on the non-union/union wages and the union-wage gap, for the private sector, by year- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 49Figure 7: Effect of industry union density on the non-union/union wages and the union-wage gap, for the public sector, by year- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -50
  6. 6. 11. Introduction Union membership in Canada has declined from approximately 55% to 30%, since the1980s, according to Blanchflower (2006). The decline can be attributed to decreasing unionmembership in the private sector. In contrast, union membership in the public sector hasincreased in the past decade. Over the last decade private firms have responded to the threatof unionization most notably by subcontracting, outsourcing, and even plant-closings. Theretail giant Wal-Mart, for example, recently closed its store in Jonquiere, Quebec after thestore became unionized (Bianco 2006). In this paper I am interested in assessing whether theunionization threat in Canada justifies such severe reactions on the part of firms. My analysis draws on the seminal work by Rosen (1969). Rosen suggests that theability of unions to negotiate higher wages increases with the extent of unionization; that is,as the proportion of employed workers who are union members in an industry or occupationincreases. In particular, Rosen notes that as the extent of unionization increases “[thepossibility] for output or product substitution against unionized firms is reduced”. Moreover,to avoid an increase in wage rates that follow unionization, employers are expected toincrease the wages of their current non-union employees in an attempt to reduce theemployees’ benefits from joining the union. This increase in non-union wages is likelygreater when the non-union employees have similar attributes to those of the union membersor in industries (occupations or cities) with substantial union presence. The literature refers tothis phenomenon as the effect of the unionization threat on non-union wages. Several studies tested these predictions (e.g., Kahn 1978; Moore et al. 1985; Podgursky1986). Despite the wide interest, the empirical evidence on the unionization threat effectremains an unsettled question. Using firm-level data, Leue and Tremblay (1993), for instance,
  7. 7. 2found no significant effect of unionization threat on non-union wages. Freeman and Medoff(1981) found that in manufacturing, the unionization threat has a strong positive effect onunion wages, but no or a weak effect on non-union wages. While the literature on theunionization threat effect primarily draws on the U.S. data, Canadian studies tend to focus onidentifying the wage gap between union and non-union workers. The estimates range from9.5% in Kumar and Stengos (1985), to between 16% and 51% in MacDonald and Evans(1981), and 34.7% in Chaykowski and Slotsve (2002). To my knowledge, no study hasexamined the effect of unionization threat on wage rates in Canada. This paper attempts to fill this gap in the literature by providing evidence that pertainsto the unionization threat effect on the non-union wages and the union-wage gap. Using datafrom the Canadian Labor Force Surveys between 1998 and 2006, I build on prevalentapproach in the literature and construct industry-level union density as my proxy for thethreat of unionization. I expand on this initial approach by constructing an alternativemeasure following Farber’s (2005) methodology. This alternative measure for the threat ofunionization is constructed as the predicted probability of union membership, a function ofworker-, job-, and firm-specific attributes. I proceed to regress wages of non-union workerson these alternate unionization threat variables while controlling for a wide set of observableworker-, job-, and firm-characteristics. In addition, I extend on Farber’s (2005) study byexamining separately the effect of the threat of unionization in the private and public sectors. My results are consistent with the hypothesis that the threat of unionization is directlyrelated to an increase in non-union wages in the private sector, but not in the public sector.The magnitudes of the threat effects in my study are somewhat similar to those in the relatedliterature that draws on the U.S. data. The estimated threat effect in the non-union private
  8. 8. 3sector in Canada is between 14.9% and 21.9% (from 1998 to 2006), as compared to 20% inthe Farber’s (2005) study which draws on the U.S. data. Importantly, I find that my resultsare sensitive to the list of explanatory variables and to the definition of the unionizationthreat. In Section 2, a review of the literature on the unionization threat effect is provided.Section 3 provides a brief description of the theoretical framework that motivates myempirical analysis. Section 4 focuses on methodology and Section 5 on data description.Results are presented in Section 6. Section 7 concludes.2. Literature Review Economists have been long concerned with assessing the effect unions have not only onwages of union workers but also on wages of non-union workers. Some argued that the effectof unions on the non-union wages (i.e., the unionization threat effect) results from a desire bythe non-union employers to avoid unionization; as higher wages reduce the benefits tounionization. Rosen (1969), for instance, argued that we should observe a positiverelationship between the non-union wages and the extent of union organization (or thepercent of unionized workers in the industry) at lower levels of union organization. Thisprediction is supported by Moore et al. (1985). The authors found that the unionizationdensity in an industry with fewer union members has a positive effect on non-union wages.Corneo and Lucifora (1997) and Kahn (1978) reported similar findings. Podgursky (1986) extended the argument and found that non-union wages at medium-sized firm increase with the union threat. Pearce (1990) also suggested that the effect ofunionization threat increases with firm size in the non-union sector. Farber (2005)documented that stronger evidence in favour of the union threat effects could be found in
  9. 9. 4deregulated industries. However, some studies report that no significant threat effect is found.Freeman and Medoff (1981), for instance, found that in manufacturing, the threat has astrong positive effect on union wages, but no or weak effect on non-union wages. Leue andTremblay (1993) also claimed that no significant effect was found of the effects of either thepercentage organized or the firm-level predicted threat of unionization on non-union wages. Another strand of literature has provided findings of the unionization threat’s effect onwage dispersion. Belman and Heywood (1990), for instance, have shown that the percentageorganized in the union reduces union wage dispersion but has a weak effect on non-unionwages. According to findings in Neumark and Wachter (1995), at the industry (city) level, anincrease in the percentage organized in the union reduced (increased) the non-union industry(city) wage differential. Kahn and Curme (1987), on the other hand, found that an increase inthe percentage organized in unions decreased the dispersions of non-union wages. The reviewed papers tend to use different measures of the threat of unionization (seeAppendix A Table 2). The most common measure for the unionization threat is an industry-level or occupation-level union density. For example, Podgursky (1986) uses the proportionof production workers who are covered by union contracts in an industry as a measure of theunion threat. Kahn and Curme (1987) and Moore et al. (1985) use both industry-level andoccupation-level union membership rates. Neumark and Wachter (1995) employ the city-level union density rate as an explanatory variable in the wage regression. Farber (2005), onthe other hand, uses the predicted probability of being a union member as a measure of thethreat. Unlike the industry-level union density, Farber’s measure allows the threat ofunionization to differ not only across industries, but also across workers who are employed inthe same industry but differ in their age, attained education, marital status, gender, etc.
  10. 10. 5 In this paper, I follow Farber’s (2005) approach. I use repeated cross-sectional datafrom 1998 to 2006 for Canada to construct the predicted probability of union membership. Ialso construct an alternative measure, industry-level union density, in an attempt to infer howsensitive the results are to the definition of the union threat measure. Using both measures Ican, therefore, better understand why related literature has found remarkably differentevidence for the effect of unionization threat on non-union wages. In addition, a separateanalysis for the public sector and the private sector is provided. I am interested in the latterdistinction, since it is more likely that employers in a private sector are confronted withdecisions stipulated in a theoretical framework of profit maximization. In contrast, employersin the public sector may be pursuing other goals such as ensuring stable employment. Overall, my analysis contributes in four respects to the related literature. Namely, I: (1)estimate the effect of unionization threat on non-union and union wage rates in Canada overan extended period of time; (2) identify the threat effect on the wage gap between union andnon-union workers; (3) examine both the union and the non-union wage responses to theunionization threat separately in the public and private sectors; (4) measure the threat effectwith the predicted probability of union membership and the industry-level union density.3. Theoretical Framework Following Farber’s (2005) methodology, I let P (α , β ) denote the probability ofunionization, where α = (WU − W N ) / W N denotes the union wage gap ( 0 < α < 1 ), WU theunion wages, W N the non-union wages, and β the index of the threat of unionization for agiven union-wage gap ( 0 < β < 1 ). I assume that P > 0 , Pβ > 0 , P > 0 , P > 0 , and α αα αβ
  11. 11. 6Pββ > 0 . Note that Pαβ > 0 implies that, the marginal effect of an increase in the union-wagegap on the probability of unionization increases in magnitude as the threat β increases. Let the expected wage be denoted as E (W ) . Hence, the expected wage is a weightedaverage of the union wage and the non-union wage rates with weights representing theprobability of unionization ( P ) and the probability of non-unionization ( 1 − P ), respectively.Using the above introduced notation, the expected wage can be written as: PWN (WN − WU ) E (W ) = WN + P(WU − WN ) = WN + = WN +PWN α = WN (1 + Pα ) . (1) WNEmployers who employ non-unionized workers choose WN in order to minimize E (W ) . Theoptimal W N solves the first order condition obtained by setting the derivative ofthe E (W ) with respect to WN to zero: (1 − P) − P α (1 − α ) = 0 . The effect of the threat of αunionization on the non-union wage rate can be obtained by taking the derivative of this first-order condition with respect to β : ∂WN P + P α (1 − α ) ∂WN ∂WU = β αβ + . (2) ∂β ( P + P )(1 + α ) 2 ∂WU ∂β α ααIf ∂WU / ∂β ≥ 0 , one gets: ∂WN P + P α (1 − α ) ∂WU = β αβ + ∂WU / ∂β > ≥0. (3) ∂β ( P + P )(1 + α ) α αα 2 ∂β Pβ + Pαβ α (1 − α )Since Pα > 0, Pβ > 0, Pαα > 0, Pαβ > 0, Pββ > 0 , 0 < α < 1 it follows that > 0. ( Pα + Pαα )(1 + α ) 2 The comparative statistics’ results in (2) and (3) are central to this study that aims toestimate the effect of the threat of unionization on the non-union wage. The result suggeststhat an increase in the likelihood of unionization ( β ) has: (1.) a positive effect on the non-
  12. 12. 7 ∂WNunion wage ( > 0 ); (2.) a nonnegative effect on the union wage such ∂β ∂WN ∂WUthat > ≥ 0 ; and (3.) a negative effect on the union-wage gap or the union wage ∂β ∂βpremium. This paper tests empirically these three predictions by drawing on the datacollected from nine annual labor force surveys in Canada from 1998 to 2006.4. Methodological Approach To test these predictions I use two measures for the threat of unionization ( β ). In thenext two sections I describe how these two measures are constructed. My third approach totesting the model’s prediction explores one of the model’s assumptions; i.e., that employersminimize their wage costs. While this assumption may be valid for employers in the privatesection, it may not be a good description of the employers’ decisions in the public sector. Iexplore this conjecture by examining separately the effect of unionization threat on non-union wages for workers in the private sector and for workers in the public sector.4.1. Predicated Probability of Union Membership as a Measure of Unionization Threat My first approach to estimating the unionization threat’s effect on the non-union wagerates follows Farber (2005). In the first step, I estimate the predicted probability of unionmembership by running a probit regression for each year: Prob(Unioni = 1| X i ) = φ (η X i ) . (4)In this equation, Φ ( ⋅) denotes a standard normal cumulative distribution function, η is avector of coefficients I wish to estimate, and Xi is a vector of worker and firm characteristics,
  13. 13. 8industry and province dummies (See Appendix A, Table 1 for detailed explanation ofvariables). The threat of unionization assigned to worker i in my sample is defined as follows: ∧ ∧ threat i = φ (η X i ) . (5) ∧ In the second step, I use the threat variable, threat i , as an independent variable in thewage regression. I estimate separately the wage regression for the sample that consists solelyof non-union workers and in a sample that consists solely of union workers. In particular, aneconometric specification for the union wage equation can be written as: ∧ ln( wageiU ) = δ 0U + δ1U threat iU + γ U X iU + ε iU . (6)Similarly, the wage regression I estimate for the non-union workers is: ∧ ln( wageiN ) = δ 0 N + δ1N threat iN + γ N X iN + ε iN . (7) ∧ In equations (6) and (7), threatiU is the predicted probability of being a union member ∧for a worker in the sample of union workers, and threatiN is the predicted probability ofbeing a union member for a worker in the sample of non-union workers. Hence, unionizationthreat is measured by the extent the non-union employees have similar attributes to those ofthe union members. XiU is a vector containing other explanatory variables in the union sample;XiN is a vector containing other explanatory variables in the non-union sample; γ U and γ N arevectors of estimated coefficients for the control variables; δ 0U and δ 0 N are the constants, δ 1Uand δ1N are the coefficients on the threat effects; and ε iU and ε iN are the residuals in thesample of union workers and the sample of non-union workers, accordingly. Theory suggests that non-union firms are expected to increase wage rates for their non-union workers when faced with the unionization threat. In particular, a positive correlation
  14. 14. 9between the threat of unionization and the non-union wage rates is expected. Specifically, weexpect δ1N > 0 . We also expect that δ1N > δ1U ≥ 0 , due to the results derived in (3).4.2. Industry Union Density as a Measure of Unionization Threat In my second approach, I consider an alternative measure of unionization threat,following the approach prevalent in existing literature (e.g., Podgursky 1986; Kahn andCurme 1987). This measure for the threat of unionization is the industry-level union density.In particular, this alternative measure is constructed, for each year, in the following manner: ∧ number of workers employed in industry j who are union members threat j , alt = . (8) number of workers employed in industry jIn the second step, I run a regression of the logarithmic value of hourly wage on thealternative measure of the union threat, controlling for various worker and firm specificattributes. Hence, I estimate the following equation for the sample of union workers: ∧ ln( wageiU ) = δ 0UAlt + δ1UAlt threatiUAlt + γ X iUAlt + ε iUAlt . (9)And similarly for non-union workers: ∧ ln( wageiN ) = δ 0 NAlt + δ1NAlt threatiNAlt + γ X iNAlt + ε iNAlt . (10)Theory suggests that δ1NAlt > 0 , and δ1NAlt > δ1UAlt ≥ 0 .4.3. Analysis by Private and Public Sector The motivation for my third approach stems from the assumption that is necessary togenerate the central prediction—a positive effect of the threat of unionization on non-unionwage rates. Note that in deriving the main results, I assume that employers minimize theexpected wage costs. While this assumption is most likely valid for employers in the private
  15. 15. 10sector it may not be valid for employers in the public sector. I therefore follow my firstapproach by using only the sample of workers who were employed in the private sector at thetime of a survey. I then compare the results to those obtained based on the sample of workerswho were employed in the public sector. Due to the abovementioned differences in themaximization problem across employers in the public and private sectors, I expect that therelation between the unionization threat and the non-union wages is likely to be stronger forthe sample of workers in the private sector.4.4. Potential Problems The first potential problem with the above models is the likely heteroscedasticity andautocorrelation. When the variance of regression residuals depends on the explanatoryvariables, serious consequences may occur for OLS and probit estimators. Although the OLSestimators remain unbiased, the estimated standard errors are wrong. As a result, inferencesand hypotheses tests cannot be relied on. In the probit models, “the maximum likelihoodestimators are inconsistent and the covariance matrix is inappropriate” (Green, 2003; page679). Therefore, I choose to compute and report heteroscedasticity-robust standard errors. In addition, in the probit model, the coefficients cannot be interpreted as marginaleffects. Hence, I choose to compute and report marginal effects, which are a non-linearcombination of the regression coefficients. The marginal effects are obtained by calculatingthe derivative of the outcome probability with respect to the control variables.Autocorrelation might be another problem because nine years of data is used in the secondstep. I decided to include indicator variables for each year. I also included interaction termsthat allow for the effect of various explanatory variables on wage to differ across years.
  16. 16. 115. Description of Data and Variables My analysis draws on monthly Labor Force Surveys (LFS) conducted by StatisticsCanada. I use data collected every January from the year 1998 to 2006. The overall pooledsample consists of 432,574 observations after I drop observations because of missinginformation on union membership or control variables. Among workers in my final sample291,596 (67.4%) are non-union members and the rest 140,978 (32.6%) are union members;112,767 (26.1%) are in the public sector and 319,807 (73.9%) in the private sector. The choice of the independent variables is based on the review of the literature ondeterminants of wages. Most importantly, the human capital theory suggests that theworkers’ productivity increases with the worker’s ability and acquired skills (Becker, 1993).Wages are thereby expected to be correlated with the workers’ attained education (e.g.,number of years spent in school) and other components of the workers’ human capital (age,for instance, may measure acquired work experience). Other variables such as demographicand industry characteristics which might affect the worker’s wage are also included, assuggested by Lewis (1986). My choice of explanatory variables draws also on Belman andHeywood (1990) who used race, gender, marital status, education, employment status,location or region, and industry in their analysis of the effect of unionization on wagedispersion. Following Farber’s (2005) model, other variables which might affect the worker’sdecision to join the union, such as firm size are also included.6. Results6. 1. Determinants of Union Membership in Canada, 1998-2006 The first step to assessing the effect of the threat of unionization on wages of union and
  17. 17. 12non-union workers entails constructing the measure for the unionization threat. As describedin Section 4.1., I estimate the probit model in order to obtain the predicted probability ofbecoming a union member. The estimates are reported in Appendix B, Tables 3, 6, and 9.Table 3 presents the results for the whole sample, while Tables 6 and 9 report results forworkers employed in the public sector and those employed in the private sector, respectively.The marginal effects instead of the original coefficients are reported. The results based on the most recent survey in 2006 suggest that both worker-specificand employer-specific characteristics significantly affect the likelihood of being a unionmember. The characteristics associated with a worker that are positively associated with thelikelihood of union membership are gender (male workers are more likely to be unionmembers) and age (older workers are more likely to be union members). Workers who aremarried or have higher attained education, on the other hand, are less likely to be unionmembers. Firm characteristics also matter in terms of explaining the worker’s propensity tojoin the union. For instance, employment in the public sector increases the probability ofbeing a union member by approximately 36.7% in 2006. Also, there exists a positivecorrelation between the firm size and the probability of being a union member. The largerthe firm is, the higher the probability for the worker to become a union member. For instance,workers employed in a firm with more than five hundred employees are 35.8% more likely tobe union members compared to those in the firm with less than 20 employees in 2006. In addition, residents of Quebec, British Columbia, Manitoba, and Saskatchewan aremore likely to be union members, as compared to the Albertans. Moreover, industrydummies are all significant in determining the probability of unionization. The resultssuggest that the workers in industries that require lower skills or more labor work are more
  18. 18. 13likely to be unionized. Exceptions are the industries which are highly unionized from theearly days of unionization, for example, health care, education, and public administration. Similar results, in terms of magnitude and sign, to those found in 2006 are found acrossall nine years. However, differences are also found for some explanatory variables. Forinstance, in 2000, workers residing in New Foundland, Nova Scotia, and British Columbiawere more likely to be union members compared to Albertan workers. In 1998, marriedworkers were more likely to be unionized than the unmarried workers. After 1999, themarital status was negatively associated with the likelihood of union membership. Separate results for public and private sectors are reported in Tables 6 and 9. For theprivate sector, the estimates on major independent variables are similar, in terms of themagnitudes and the signs, to the ones obtained from the whole sample. For workers in thepublic sector, the estimates are different from those in the whole sample; for example, beingmale decreases the probability of joining the union, whereas being married increases theprobability of being a union member.6.2. The Effect of the Unionization Threat on Non-union Wage Rates I start by analyzing results of the first approach in Section 4. In particular, this approachuses the predicted probability obtained from the probit model as a measure for the threat ofunionization. The results are presented in Table 4 in Appendix B. Figure 2 in Appendix Cplots the main results reported in Table 4 based on the full sample. In particular, the Figureplots the estimated coefficients of the threat effect on both the union and non-union wages byyear, organized in the following four panels. Panel A depicts the estimated marginal effectsof the threat effect on wages of union and non-union workers from the regression without the
  19. 19. 14industry and province dummies; Panel B reports the results in which province dummies areincluded; in Panel C, controls for the industry are added; and finally, Panel D presents resultsfrom in which the province and industry dummies are included. As shown in Panel A in Figure 2, Appendix C, the estimates of marginal effects of thepredicted probability of unionization on non-union wages are approximately 30%. Thelowest estimate is at 29.5% in 1998 and the highest is at 34.9% in 2006. Compared to theeffects on non-union wage, those on the union wages are slightly higher, whereas the highesteffect is at 39.5% in 1998 and the lowest at 36% in 2001. In Panel B, in Figure 2, byincluding the province dummies to wage regression, the estimated marginal effect of thepredicted probability of unionization on the non-union wages (ranges from 29.3 to 41.8%) isstill smaller compared to the effect on the union wages (ranges from 46.5 to 56.4%); buteffects still have an increasing trend. Figure 2 Panel C adds the industry dummies to the listof explanatory variables in the wage regressions. The union threat effect on non-union wages(ranges from 15.1% to 26.9 %) is now higher than that on the union wages (ranges from -2.5to 23.2%), both showing a decreasing trend. Finally, in Panel D, Figure 2, by controlling for both province and industrycharacteristics, the threat effects are reduced. The estimates of marginal effects of theunionization threat on the non-union and union wages are decreasing over the period from27.3% to 24.8%; for instance, an increase in the probability of being a union member willincrease non-union wages by 27.3% in 1998 and by 24.8% in 2006. The threat effect onunion wages is at a lower range, decreasing from 25.9% and 13.2%. The results in both PanelC and D are consistent with the theory’s predictions.
  20. 20. 156.3. An Analysis of Unionization Threat in Private and Public Sectors Estimation results for workers in the public and private sector are presented in Tables 7and 10, and plotted in Figures 3 and 4. In the private sector, the threat effects on the non-union wage range from 21.9% in 1998 to 14.9% in 2006. The within- and between-provincevariation for the non-union wages is 39.3% to 40.6%, whereas the within- and between-province and within-industry variation is 33.1% to 12.5%. The threat effects on the unionwages are smaller after controlling for industry. Figure 3 in the Appendix C suggests thatthere is a decreasing trend in the threat effects in Panels C and D. In the public sector, with the full set of control variables, an increase in the probabilityof being a union member is estimated to increase non-union wages by 12% to 9%, and 14%to 11% increase in union wages from 1998 to 2006 (see Figure 4 and Table 7). However,with fewer controls in the wage regressions, the effects of unionization threat on the wages ofworkers in the public sector (both unionized and non-unionized) are ambiguous, because thesigns on the coefficient of the threat are both positive and negative. One explanation for theobserved pattern is as follows. Namely, the private sector aims to minimize the expectedcosts of wage payments, so the effects of unionization threat on wages are clear-cut. Themaximization problem for employers in the public sector is more ambiguous. Therefore, theeffect of unionization threat on the public workers’ wage rates cannot be determined. In conclusion, using the predicted probability of becoming a union member as ameasure of the threat of unionization, I find that the threat effects on union and non-unionwages are both positive. However, the threat effects are higher for non-union workers in theprivate sector, but not in the public sector. Importantly, I find this evidence to be sensitive tothe set of control variables that I include when estimating the wage regression.
  21. 21. 166.4. An alternative Measure for Unionization Threat In this section, I discuss the results obtained by using as an alternative measure of theunionization threat. In particular, I use as a measure for the unionization threat an industry-level union density. The results are reported in Tables 5, 8, and 11, for the full sample, forworkers in the public sector, and for workers in the private sector, respectively. The mainresults as they pertain to the unionization threat effect on the union and the non-union wagerates are depicted in Figures 5 through 7, for the full sample, the sample of workers in thepublic sector, and the sample of workers in the private sector, respectively. The alternative measure for the threat of unionization gives different results in terms ofthe magnitude and sign of the threat effects over the years, as compared to the resultsobtained when the threat measure was inferred from the probit model. The difference isparticularly pronounced for Panel D (see Figures 5 through 7 in the Appendix). For the wholesample in Panel D, the threat effects on the non-union wages are greater than the effects onthe union workers only after 2002. Moreover, the threat effects turn negative after 2002. Thisfinding suggests that with an increase in the unionization threat, the wage rates actuallydecrease. For the private sector, the threat effects on the non-union wage are greater then theeffects on the union wage only for 1998 and 1999 fro Panel D. The effects become negativeafter 1999. The threat effects on non-union wage rates of public workers are only greater thanthat on the union workers in 2000. The effects are negative throughout the nine year period.7. Conclusion Using both the predicted probability of being a union member and the industry-levelunion density as measures for the threat of unionization, I provide evidence that suggests that
  22. 22. 17the threat of unionization can have a positive impact on wages of non-union workers inCanada. In particular, an increase in the probability of being a union member is estimated toresult in a 14.9% increase in wages of non-union workers in the private sector, and 10.6% fornon-union workers in 2006 in Canada. The difference between these two percentage termsmeasures the effect of the unionization threat on the union-wage gap. The result supports the theoretical prediction that the threat of unionization has apositive effect on the non-union wages in private sectors, and a positive or close to negligibleeffect on that in public sectors. Further, the results can help explain why the threat ofunionization may tend to result in plant closures mostly in private sectors. More importantly,though, my findings are shown to be very sensitive to the list of explanatory variablesincluded in the wage regressions as well as to the definition of the threat of unionization.Upon restricting the sample to public and private sector, the results show that the threateffects on the private sector are driving the results reported for the whole sample. The results in this paper are of importance for several reasons. To my understanding ofthe literature, this paper is the first study of the union threat effects on non-union wage ratesin Canada. While a growing literature has explored the union threat effect for the UnitedStates and several European countries, studies using Canadian data have been thus farrestricted to estimating the union wage premium. The results obtained in this paper areconsistent with the theory for certain specifications but not for others. Hence, the conflictingresults reported in related literature regarding the union threat effect on the non-union wagerates are reaffirmed in this study as well. Overall, the main conclusion I draw from myanalysis is that the effects of unionization threat are not clear cut.
  23. 23. 18ReferencesBelman, D., and Heywood, J. (1990) “Union Membership, Union Organization and the Dispersion of Wages”, The Review of Economics and Statistics, Vol. 72, No. 1, pp. 148-153.Becker, G. S. (1993) Human capital: A theoretical and empirical analysis, with special reference to education, Chicago and London: University of Chicago Press. Third Edition, pp. 390.Bianco, A. (2006) “No Union Please, We Are Wal-Mart”, Business Week, Feb.13, 2006. Accessed at www.businessweek.com/magazine/content/06_07/b3971115.htm.Blanchflower, D. (2006) “A Cross-Country Study of the Union Membership”, IZA Working Paper. Accessed at http://ideas.repec.org/p/iza/izadps/dp2016.html.Chaykowski, R. P., and Slotsve, G. A. (2002) “Earnings Inequality and Unions in Canada”, British Journal of Industrial Relations, September 2002, Vol. 40, No. 3, pp. 493-519.Corneo, G., and Lucifora, C. (1997) “Wage Information Under the Union Threat Effects: Theory and Empirical Evidence”, Labor Economics, Vol. 4, No. 3, pp. 265-392.Farber, H. (2005) “Non-union Wage Rates and the Threat of Unionization”, Industrial and Labor Relations Review, Vol. 58, No. 3, pp. 335-352.Freeman, R., and Medoff, J. (1981) “The Impact of the Percentage Organized on Union and Non-union Wages”, Review of Economics and Statistics, Vol. 63, No. 4, pp. 561-572.Greene, W. H. (2003) Econometric Analysis. New Jersey: Prentice Hall. Fifth Edition.
  24. 24. 19Kahn, L., and Curme, M. (1987) “Union and Non-union Wage Dispersion”, The Review of Economics and Statistics, Vol. 69, No. 4, pp. 600-607.Kahn, L. M. (1978) “The Effect of Unions on the Earnings of Non-union Workers”, Industrial and Labor Relations Review, Vol. 31, No. 2, pp. 205-216.Kumar, P., and Stengos, T. (1985) “Measuring the Union Relative Wage Impact: A Methodological Note”, Canadian Journal of Economics, Vol. 18, No. 1, pp. 182- 189.Lewis, H. (1986) Union Relative Wage Effects: A Survey. Chicago: University of Chicago Press, 1986.Leue, C., and Tremblay, C. H. (1993) “A New Econometric Model of the Union Threat Effects”, Applied Economics, Vol. 25, No. 10, pp. 1329-1336.MacDonald, G., and Evans, J. C. (1981) “The Size and Structure of Union-Non-union Wage Differentials in Canada”, Canadian Journal of Economics, Vol. 14, No. 2, pp. 216-231.Moore, W. J., and Newman, R. J., and Cunningham, J. (1985) “The Effect of the Extent of Unionism on Union and Non-union Wages”, Journal of Labor Research, Vol. 6, No. 1, pp. 21-44.Neumark, D., and Wachter, M. (1995) “Union Effects on Non-union Wages: Evidence form Panel Data on Industries and Cities”, Industrial and Labor Relations Review, Vol. 49, No. 2, pp. 20-38.Pearce, J. (1990) “Tenure, Unions, and the Relationship between Employer Size and Wages”, Journal of Labor Economics, Vol. 8, No. 2, pp. 251-269.
  25. 25. 20Podgursky, M. (1986) “Unions, Establishment Size, and Intra-Industry Threat Effects”, Industrial and Labor Relations Review, Vol. 39, No. 2, pp. 277-284.Rosen, S. (1969) “Trade Union Power, Threat Effects and the Extent of Organization”, The Review of Economic Studies, Vol. 36, No. 2, pp. 185-196.
  26. 26. 21 Appendix A Table 1: Description of variables Variable Name Description Hourly wage before taxes and other deductions, including tips, commissions and Hrlyearn bonuses (inferred from questions 205-209 in the Labor Force Survey). Union status dummy is set to 1 if the worker is a union member and 0 otherwise Unionmbr (inferred from question 220 in the Labor Force Survey). Public sector dummy is set to 1 if the worker works in the public sector and 0 Public otherwise (inferred from question 115 in the Labor Force Survey).1 A set of age dummies indicating which age group the worker (i.e., the survey’s Age15-24, Age 25-34, respondent) belongs to at the time of the survey (inferred from question ANC_Q03 Age 35-44, Age 45-54 in the Labor Force Survey). Gender dummy is set to 1 if male and 0 otherwise (inferred from question Q01in the Male Labor Force Survey). Marital status dummy is set to 1 if married and 0 otherwise (inferred from question Married MSNC_Q01 in the Labor Force Survey). A set of dummy variables that identify the highest attained level of schooling at the time of the survey: 1 if no high school or grades<12 is the excluded group; 1 if high Hisch, Post, Univg school graduates ( Hisch), some postsecondary (Post), university graduate (Univg) (inferred from question EDQ01-04 in the Labor Force Survey) A set of provincial dummies that identify survey respondent’s residence: New Nfld, pei, ns, nb, que, Foundland, P.E.I, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, ont, man, sask, bc Saskatchewan, British Columbia. A set of dummy variables that identify the size of a firm (in number of employees) at which a survey respondent worked at the time of the survey (inferred from Firmsize question Q240 in the Labor Force Survey). The number of employees at all locations, in four categories: it is 1 if less than 20 employers, 2 if 20 to 99 employers, 3 if 100 to 500 employers, 4 if more than 500 employers. A set of industry dummy variables indicating the industry the worker works in: accordingly, the dummy variables represent Forestry, Fishing, Mining, Oil and Gas; In02, In03, In04, Utilities; Construction; Manufacturing - durables; Manufacturing - non-durables; In05, In06, In07, Wholesale Trade; Retail Trade; Transportation and Warehousing; Finance, In08, In09, In10, Insurance, Real Estate and Leasing; Professional, Scientific and Technical Services; In11, In12, In13, Management, Administrative and Other Support; Educational Services; Health Care In14, In15, In16, and Social Assistance; Information, Culture and Recreation; Accommodation and In17, In18 Food Services; Other services; Public Administration (inferred from question 115 in the Labor Force Survey).1 The public sector includes employees in public administration at the federal, provincial and municipal levels, as wellas in Crown corporations, liquor control boards and other government institutions such as schools (includinguniversities), hospitals and public libraries. The private sector comprises all other employees and self-employedowners of businesses, and self-employed persons without businesses.
  27. 27. 22 Table 2: Review of literature on the union threat effectsAuthor Period Country Survey Proxy for the Threat of Unionization Findings - The union threat effect on non-union wage rate - Predicted probability of union found in private sector onlyYi Zuo 1998- Labor Force membership as a function of worker, Canada - Results are sensitive to definition of the(this paper) 2006 Surveys job, and firm characteristics unionization threat and to a set of explanatory - Industry union density variables - Predicted probability of being a union - The effect of the threat of unionization on the non- 1978-Farber U.S. CPS member as a function of worker, job, union wages is sensitive to set of explanatory 2002 and firm characteristics variables - Industry-level percentage of employed - The effect of industry-level unionization increasesPearce 1990 U.S. CPS with union membership with firm size in the non-union sector - The percentage of three-digit industry - No significant effect found of percentage organizedLeue and 1979- employment organized in unions U.S. EOPP and the predicted threat of unionization on non-Tremblay 1980 - The probability that a firm is organized union wages by a union - At the industry level, an increase in the percentage - Industry-level percentage of employed organized reduces the non-union industry wageNeumark 1973- organized in unions differential U.S. CPSand Wachter 1989 - City-level percentage of employed - At the city level, an increase in the percentage organized in unions organized in union increases the non-union city wage differential - Unionization in an industry with fewer unionMoore, - Industry-level union membership rate members has a significant positive wage effect onNewman, 1973- U.S. CPS - Occupation-level union membership non-union workersand 1979 rate - Unionization within an occupation has no wageCunningham effect on non-union workers - Large and small non-union employers tend to - Proportion of an industry’s production respond less to the union threatPodgursky 1979 U.S. CPS workers covered by union contracts - Wage at medium-sized non-union employers increases with the union threat
  28. 28. 23 Table 2 (Continued) Author Period Country Survey Proxy for the Threat of Unionization Findings - Non-union workers in highly organized markets receive Freeman 1973- - Industry-level percentage of employed higher wages than those in less unionized industries U.S. CPS and EEC and Medoff 1975 covered by collective agreement - In manufacturing, the threat has a strong positive effect on union wages, but no or a weak effect on non-union wages - For occupations which are not organized unionization - Industry-level union membership rate threat has strong impact on non-union wages Kahn 1967 U.S. SEO - Occupation-level union membership - For occupations which are highly unionized, the within rate occupation-industry union effect on non-union wages is negative - Non-union workers with below-median earnings receive Census of - Three union dummies based on the Rosen 1958 U.S. higher wages with unionization, except for managers and Manufactures percentage organized in union professionals - Non-union workers with below-median earnings receive Heywood Labor Force 1997 U.K. - Industry-level union coverage higher wages with unionization, except for managers and and Belfield Survey professionals - Threat effects are strongly correlated with union density Corneo and Fedemecanica 1990 Italy - Firm-level union density - Threat effects on wages are significant with an Lucifora Survey intermediate level of union densityAbbreviation used in Table 2: CPS - Current Population Survey; EOPP - Employment Opportunity Pilot Project; EEC - Expenditures for Employee CompensationSurveys; SEO - Survey of Economic Opportunity.
  29. 29. 24Appendix B Estimation Results Table 3: Determinants of union membership in Canada, 1998 – 2006 (all sample)Dataset: Canadian Labor Force SurveySample: All observations: Analysis by year 2006 2005 2004 2003 2002 2001 2000 1999 1998 Marginal Marginal Marginal Marginal Marginal Marginal Marginal Marginal Marginal effect effect effect effect effect effect effect effect effect (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.)Variable name: (1) (2) (3) (4) (5) (6) (7) (8) (9)Public 0.367 0.399 0.361 0.387 0.393 0.323 0.332 0.319 0.303 (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)***Male 0.045 0.030 0.046 0.035 0.046 0.050 0.052 0.060 0.051 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***Married -0.013 -0.011 0.001 -0.014 -0.005 -0.006 -0.013 0.000 0.011 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000) (0.000)***Age base category: Age 55 + … … … … … … … … … … … … … … … … … …Age between 15 and 24 -0.117 -0.098 -0.101 -0.119 -0.121 -0.130 -0.153 -0.120 -0.136 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.001)***Age between 25 and 34 -0.028 -0.047 -0.029 -0.039 -0.050 -0.050 -0.050 -0.030 -0.028 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.001)*** (0.001)***Age between 35 and 44 0.001 -0.008 -0.005 -0.012 -0.002 -0.006 -0.008 0.008 -0.001 (0.000) (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.001)*** (0.001)*** (0.001)**Age between 45 and 54 0.032 0.018 0.022 0.026 0.015 0.022 0.018 0.030 0.039 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)***High school dropout (excluded group) … … … … … … … … … … … … … … … … … …11 to 13 years of schooling/graduate -0.009 -0.020 -0.020 -0.022 -0.005 -0.022 -0.014 -0.012 -0.027 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***At least some postsecondary diploma -0.043 -0.034 -0.041 -0.043 -0.020 -0.032 -0.026 -0.036 -0.041 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***University: bachelors or graduate degree -0.122 -0.122 -0.141 -0.132 -0.123 -0.130 -0.118 -0.123 -0.139 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
  30. 30. 25 Table 3 (Continued) Dataset: Canadian Labor Force Survey Sample: All observations: Analysis by year 2006 2005 2004 2003 2002 2001 2000 1999 1998 Marginal Marginal Marginal Marginal Marginal Marginal Marginal Marginal Marginal effect effect effect effect effect effect effect effect effect (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) Variable name: (1) (2) (3) (4) (5) (6) (7) (8) (9) Firm with less than 20 employees (base) … … … … … … … … … … … … … … … … … … Firm with 20-99 employees 0.187 0.226 0.221 0.208 0.228 0.207 0.216 0.233 0.234 (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** Firm with 100-500 employees 0.343 0.368 0.382 0.376 0.351 0.364 0.384 0.385 0.371 (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** Firm with more than 500 employees 0.358 0.374 0.383 0.394 0.394 0.382 0.399 0.409 0.396 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** Additional control variables: Industry X X X X X X X X X Province X X X X X X X X X Observations 50085 49634 46846 49461 48268 48485 46963 47036 45798 Pseudo R-squared 0.316 0.316 0.313 0.323 0.326 0.314 0.303 0.318 0.305Robust standard errors in parentheses*significant at 10%; ** significant at 5%; *** significant at 1%
  31. 31. 26 Table 4: Effect of predicted probability of unionization on union/non-union wage rates, 1998 – 2006 (all sample)Dataset: Canadian Labor Force Survey Panel D-Control for Panel A-No control for Panel B-Control for Panel C-Control for both industry and industry or province province industry provinceSample: Non-union Union Non-union Union Non-union Union Non-union Union workers workers workers workers workers workers workers workers Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.)Variable name: (1) (2) (3) (4) (5) (6) (7) (8)The threat of unionization in 2006 0.349 0.376 0.418 0.564 0.151 -0.025 0.248 0.132 (0.017)*** (0.019)*** (0.017)*** (0.021)*** (0.022)*** (-0.026) (0.025)*** (0.049)***The threat of unionization in 2005 0.338 0.375 0.398 0.548 0.182 0.003 0.264 0.142 (0.015)*** (0.016)*** (0.016)*** (0.018)*** (0.019)*** (-0.022) (0.021)*** (0.040)***The threat of unionization in 2004 0.370 0.394 0.415 0.553 0.210 0.041 0.278 0.165 (0.014)*** (0.015)*** (0.014)*** (0.017)*** (0.017)*** (0.019)** (0.019)*** (0.035)***The threat of unionization in 2003 0.326 0.376 0.360 0.514 0.197 0.068 0.249 0.171 (0.013)*** (0.014)*** (0.013)*** (0.015)*** (0.015)*** (0.017)*** (0.016)*** (0.029)***The threat of unionization in 2002 0.315 0.366 0.341 0.486 0.214 0.085 0.257 0.168 (0.013)*** (0.013)*** (0.013)*** (0.014)*** (0.015)*** (0.016)*** (0.016)*** (0.027)***The threat of unionization in 2001 0.321 0.360 0.345 0.480 0.236 0.112 0.274 0.192 (0.013)*** (0.014)*** (0.013)*** (0.015)*** (0.015)*** (0.017)*** (0.016)*** (0.029)***The threat of unionization in 2000 0.329 0.380 0.340 0.485 0.261 0.161 0.283 0.222 (0.014)*** (0.015)*** (0.014)*** (0.016)*** (0.017)*** (0.019)*** (0.018)*** (0.035)***The threat of unionization in 1999 0.324 0.374 0.333 0.456 0.268 0.193 0.287 0.235 (0.015)*** (0.016)*** (0.015)*** (0.018)*** (0.018)*** (0.022)*** (0.020)*** (0.040)***The threat of unionization in 1998 0.295 0.395 0.293 0.465 0.269 0.232 0.273 0.259 (0.016)*** (0.019)*** (0.016)*** (0.021)*** (0.021)*** (0.026)*** (0.023)*** (0.049)***Public sector -0.003 -0.099 0.024 -0.092 -0.026 -0.047 -0.003 -0.026 -0.009 (0.008)*** (0.009)*** (0.009)*** (0.011)** (0.011)*** (-0.012) (-0.019)Male 0.250 0.174 0.252 0.170 0.217 0.137 0.219 0.138 (0.003)*** (0.004)*** (0.003)*** (0.004)*** (0.003)*** (0.005)*** (0.003)*** (0.005)***Married 0.099 0.045 0.105 0.048 0.085 0.038 0.091 0.042 (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)***Age base category: Age 55 + … … … … … … … … … … … … … … … …
  32. 32. 27 Table 4 (Continued)Dataset: Canadian Labor Force Survey Panel D-Control for Panel A-No control for Panel B-Control for Panel C-Control for both industry and industry or province province industry provinceSample: Non-union Union Non-union Union Non-union Union Non-union Union workers workers workers workers workers workers workers workers Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.)Variable name: (1) (2) (3) (4) (5) (6) (7) (8)Age between 15 and 24 -0.367 -0.39 -0.358 -0.364 -0.321 -0.374 -0.313 -0.358 (0.008)*** (0.012)*** (0.008)*** (0.012)*** (0.008)*** (0.011)*** (0.008)*** (0.013)***Age between 25 and 34 -0.105 -0.123 -0.095 -0.110 -0.103 -0.129 -0.094 -0.118 (0.008)*** (0.008)*** (0.007)*** (0.008)*** (0.007)*** (0.008)*** (0.007)*** (0.008)***Age between 35 and 44 0.017 -0.033 0.025 -0.022 0.008 -0.039 0.016 -0.030 (0.008)** (0.007)*** (0.007)*** (0.007)*** (-0.007) (0.007)*** (0.007)** (0.007)***Age between 45 and 54 0.042 -0.001 0.050 0.005 0.037 -0.003 0.045 0.004 (0.008)*** (-0.007) (0.008)*** (-0.007) (0.008)*** (-0.007) (0.007)*** (-0.007)High school dropout (excluded group) … … … … … … … … … … … … … … … …11 to 13 years of schooling/graduate 0.153 0.123 0.141 0.111 0.136 0.123 0.125 0.109 (0.005)*** (0.006)*** (0.004)*** (0.006)*** (0.004)*** (0.006)*** (0.004)*** (0.006)***At least some postsecondary diploma 0.251 0.206 0.241 0.203 0.217 0.203 0.209 0.197 (0.004)*** (0.006)*** (0.004)*** (0.005)*** (0.004)*** (0.006)*** (0.004)*** (0.006)***University: bachelors or graduate degree 0.554 0.463 0.531 0.458 0.495 0.453 0.476 0.443 (0.006)*** (0.007)*** (0.006)*** (0.007)*** (0.007)*** (0.008)*** (0.007)*** (0.011)***Firm with less than 20 employees (base) … … … … … … … … … … … … … … … …Firm with 20-99 employees 0.067 -0.081 0.058 -0.092 0.062 -0.029 0.053 -0.031 (0.005)*** (0.012)*** (0.005)*** (0.012)*** (0.005)*** (0.013)** (0.005)*** (0.015)**Firm with 100-500 employees 0.087 -0.094 0.075 -0.113 0.072 -0.029 0.062 -0.033 (0.006)*** (0.013)*** (0.006)*** (0.013)*** (0.006)*** (0.014)** (0.007)*** (-0.021)Firm with more than 500 employees 0.123 -0.057 0.107 -0.09 0.114 0.012 0.100 -0.001 (0.005)*** (0.013)*** (0.005)*** (0.013)*** (0.006)*** (-0.015) (0.007)*** (-0.025)
  33. 33. 28 Table 4 (Continued) Dataset: Canadian Labor Force Survey Panel D-Control for Panel A-No control for Panel B-Control for Panel C-Control for both industry and industry or province province industry province Sample: Non-union Union Non-union Union Non-union Union Non-union Union workers workers workers workers workers workers workers workers Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) Variable name: (1) (2) (3) (4) (5) (6) (7) (8) Additional control variables: Industry X X X X Province X X X X Year X X X X X X X X Interaction terms with year X X X X X X X X Constant 2.039 2.453 2.082 2.464 2.001 2.403 2.043 2.425 (0.007)*** (0.012)*** (0.008)*** (0.013)*** (0.013)*** (0.038)*** (0.013)*** (0.039)*** Observations 291596 140978 291596 140978 291596 140978 291596 140978 Adjusted R-squared 0.412 0.328 0.443 0.362 0.472 0.375 0.499 0.407Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%
  34. 34. 29 Table 5: Effect of union density on union/non-union wage rates, 1998 – 2006 (All sample)Dataset: Canadian Labor Force Survey Panel A Panel B Panel C Panel DSample: Non-union Union Non-union Union Non-union Union Non-union Union workers workers workers workers workers workers workers workers Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.)Variable name: (1) (2) (3) (4) (5) (6) (7) (8)Industry-level union density in 2006 0.234 0.373 0.279 0.337 -0.167 -0.206 -0.132 -0.204 (0.001)*** (0.001)*** (0.013)*** (0.017)*** (0.153) (0.145) (0.148) (0.142)Industry-level union density in 2005 0.251 0.340 0.291 0.320 -0.101 -0.153 -0.071 -0.145 (0.001)*** (0.001)*** (0.013)*** (0.015)*** (0.127) (0.117) (0.124) (0.114)Industry-level union density in 2004 0.271 0.331 0.313 0.325 -0.069 -0.118 -0.046 -0.107 (0.001)*** (0.001)*** (0.012)*** (0.016)*** (0.103) (0.101) (0.100) (0.098)Industry-level union density in 2003 0.235 0.350 0.277 0.301 -0.065 -0.070 -0.046 -0.060 (0.001)*** (0.001)*** (0.012)*** (0.014)*** (0.082) (0.081) (0.080) (0.079)Industry-level union density in 2002 0.224 0.324 0.282 0.308 -0.023 -0.020 -0.009 -0.009 (0.001)*** (0.001)*** (0.012)*** (0.014)*** (0.064) (0.069) (0.062) (0.066)Industry-level union density in 2001 0.235 0.331 0.299 0.286 0.016 0.020 0.028 0.035 (0.001)*** (0.001)*** (0.012)*** (0.015)*** (0.055) (0.065) (0.052) (0.062)Industry-level union density in 2000 0.260 0.326 0.297 0.317 0.034 0.057 0.038 0.084 (0.001)*** (0.001)*** (0.012)*** (0.016)*** (0.055) (0.074) (0.052) (0.071)Industry-level union density in 1999 0.273 0.284 0.296 0.239 0.077 0.086 0.081 0.108 (0.001)*** (0.001)*** (0.012)*** (0.016)*** (0.069) (0.084) (0.066) (0.081)Industry-level union density in 1998 0.254 0.347 0.270 0.295 -0.167 -0.206 -0.132 -0.204 (0.001)*** (0.001)*** (0.013)*** (0.016)*** (-0.088) (0.103)* (-0.085) (0.099)**Public sector 0.020 -0.002 0.039 0.005 0.068 0.034 0.087 0.056 (0.000)*** (0.000)*** (0.007)*** -0.005 (0.008)*** (0.006)*** (0.008)*** (0.006)***Male 0.237 0.183 0.262 0.192 0.228 0.152 0.230 0.153 (0.000)*** (0.000)*** (0.003)*** (0.004)*** (0.003)*** (0.004)*** (0.003)*** (0.004)***Married 0.091 0.049 0.104 0.050 0.086 0.037 0.092 0.042 (0.000)*** (0.000)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)***Age base category: Age 55 + … … … … … … … … … … … … … … … …Age between 15 and 24 -0.399 -0.432 -0.381 -0.438 -0.351 -0.412 -0.342 -0.400 (0.000)*** (0.001)*** (0.008)*** (0.011)*** (0.007)*** (0.011)*** (0.007)*** (0.010)***Age between 25 and 34 -0.107 -0.137 -0.103 -0.129 -0.113 -0.138 -0.103 -0.129 (0.000)*** (0.001)*** (0.007)*** (0.008)*** (0.007)*** (0.008)*** (0.007)*** (0.007)***
  35. 35. 30 Table 5 (Continued) Panel A Panel B Panel C Panel D Sample: Non-union Union Non-union Union Non-union Union Non-union Union workers workers workers workers workers workers workers workers Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) (S.E.) Variable name: (1) (2) (3) (4) (5) (6) (7) (8) Age between 35 and 44 0.020 -0.028 0.024 -0.024 0.007 -0.039 0.015 -0.029 (0.000)*** (0.000)*** (0.007)*** (0.007)*** -0.007 (0.007)*** (0.007)** (0.007)*** Age between 45 and 54 0.046 0.014 0.055 0.017 0.044 0.006 0.051 0.012 (0.000)*** (0.000)*** (0.008)*** (0.007)** (0.008)*** (-0.007) (0.007)*** (0.007)* High school dropout (excluded group) … … … … … … … … … … … … … … … … 11 to 13 years of schooling/graduate 0.161 0.117 0.136 0.098 0.132 0.117 0.121 0.104 (0.000)*** (0.000)*** (0.004)*** (0.006)*** (0.004)*** (0.006)*** (0.004)*** (0.006)*** At least some postsecondary diploma 0.255 0.196 0.232 0.188 0.211 0.194 0.204 0.188 (0.000)*** (0.000)*** (0.004)*** (0.006)*** (0.004)*** (0.006)*** (0.004)*** (0.005)*** University: bachelors or graduate degree 0.524 0.385 0.496 0.384 0.467 0.416 0.449 0.404 (0.000)*** (0.000)*** (0.006)*** (0.007)*** (0.006)*** (0.007)*** (0.006)*** (0.007)*** Firm with less than 20 employees (base) … … … … … … … … … … … … … … … … Firm with 20-99 employees 0.083 -0.034 0.080 -0.017 0.087 0.014 0.079 0.016 (0.000)*** (0.001)*** (0.004)*** (-0.012) (0.004)*** (-0.012) (0.004)*** (-0.011) Firm with 100-500 employees 0.129 -0.013 0.124 0.018 0.124 0.046 0.115 0.048 (0.000)*** (0.001)*** (0.005)*** (-0.011) (0.005)*** (0.011)*** (0.005)*** (0.011)*** Firm with more than 500 employees 0.193 0.055 0.180 0.075 0.186 0.108 0.172 0.103 (0.000)*** (0.001)*** (0.004)*** (0.010)*** (0.004)*** (0.011)*** (0.004)*** (0.010)*** Additional control variables: Industry X X X X Province X X X X Year X X X X X X X X Interaction terms with year X X X X X X X X Constant 2.06 2.399 2.029 2.358 2.004 2.362 2.031 2.346 (0.000)*** (0.001)*** (0.009)*** (0.014)*** (0.013)*** (0.038)*** (0.014)*** (0.038)*** Observations 291596 140978 291596 140978 291596 140978 291596 140978 Adjusted R-squared 0.403 0.340 0.446 0.360 0.472 0.375 0.498 0.406Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

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