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Trade and unions: Does size matter?
Economic Modelling xxx (xxxx) xxx
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Trade and unions: Does size matter?☆ Stella Capuano a , Andreas Hauptmann b , Hans-Jörg Schmerer c,b,d, ∗ a
Institute for Employment Studies (IES), UK Institute for Employment Research (IAB), Germany University of Hagen, Germany d CESifo, Germany b c
A R T I C L E
I N F O
JEL classification: F16 J51 E24 J3 Keywords: trade Unions Exports Firm level data
A B S T R A C T
Our empirical analysis builds upon the hypothesis that unions are detrimental to a firm’s efficiency. Using a rich survey of German manufacturers, we investigate firm-level determinants on the probability of collective wage bargaining with particular focus on the impact of a firm’s engagement in foreign markets. An interesting and very robust finding is that exporters are less likely to engage in union wage bargaining. This finding is in line with a pessimistic perception of unions. The negative effect of collective bargaining can be offset by efficiency gains for larger exporters, who can benefit from operation cost saving effects. Size does matter as larger firms export and may find bargaining with a single entity representing the workforce more convenient than bargaining with each worker individually. We are using firm level information on IT investment as instrument for the export dummy and successfully test for the validity of this instrument.
1. Introduction Trade unions are often suspected of being a source of firm inefficiency, for instance due to high collectively bargained wages. A first glance at the data in Fig. 1 suggests that collective bargaining firms in Germany indeed tend to pay higher wages compared to non-collective bargaining firms. The gap between both types of firms is significant and increases over time. It is often assumed that the above labor-cost gap may be responsible for the negative trend in union coverage observed in several developed economies in recent years, as documented in OECD (2017). Globalization might accelerate this process, as international competition puts
pressure on the firms to keep production costs as low as possible. As we will clarify in the next section, despite the high attention that this topic receives in the public debate, only a small part of the economic literature has tried to detect a direct link between collective agreement coverage and globalization. Motivated by the above discussion, this paper sheds light on the role of export in the firm’s choice of the bargaining regime.1 Using both parametric and nonparametric econometric techniques we are able to detect a negative link between export status and collective bargaining. On average, exporting decreases the probability that firms engage in collective bargaining. However, this negative effect disappears for
☆ Notice that an earlier version of this paper was circulated under the title “Trade and unions: Can exporters benefit from collective bargaining?”. We are grateful to two anonymous referees and the editor of the journal for helpful comments and thorough advice. We also want to thank James Cockett, Hartmut Egger, Price Fishback, David Friedman, Sebastian Galiani, Agnese Romiti and Luhang Wang as well as the participants of the 3rd Joint Workshop of Aarhus University and the IAB, AIEL XXIX National Conference of Labour Economics, ETSG (European Trade Study Group) 16th Annual Conference, Munich, 26th EALE (European Association of Labour Economists) Annual Conference, 17. Göttinger Workshop “Internationale Wirtschaftsbeziehungen”, Verein für Socialpolitik Jahrestagung 2015, Research Seminar at the University of Bayreuth, 20th EBES (European Business and Economic Society) Conference, EEFS (European Economics and Finance Society) 16th Annual Conference, The Second Annual Xiamen University International Workshop on Economic Analysis and Institutions, for helpful comments and suggestions. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. ∗ Corresponding author. Faculty of Business and Economics, Chair of International Economics, FernUniversität in Hagen, Universitätsstr. 11, D-58097, Hagen, Germany. E-mail addresses: [email protected] (S. Capuano), [email protected] (A. Hauptmann), [email protected] (H.-J. Schmerer). 1 Notice that we do not try to explain the differences in bargaining outcomes, but we use them to motivate our theoretical considerations, and to interpret our empirical results.
https://doi.org/10.1016/j.econmod.2019.03.008 Received 29 September 2017; Received in revised form 13 March 2019; Accepted 13 March 2019 Available online XXX 0264-9993/© 2019 Published by Elsevier B.V.
Please cite this article as: Capuano, S., et al., Trade and unions: Does size matter?, Economic Modelling, https://doi.org/10.1016/j.econmod.2019.03.008
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larger exporters, while it persists for small exporters. Thus, size matters for the choice of the bargaining regime for an exporting firm. The role of size can be rationalized with the existence of operation cost-saving effects in large collective bargaining firms. Assume that both individual and collective bargaining impose time and financial costs to the firm. Then, if a firm has a large number of employees, bargaining with a single entity, the union, might be more efficient than bargaining individually with each worker. The above efficiency gains tend to be higher for larger firms, and they may overcome the higher costs resulting from collective wage negotiations. This pattern should be more visible for large firms. In fact, exporters should have a stronger incentive to take advantage of the efficiency gains from collective bargaining, so as to be more competitive in the international market. Our findings contribute to explain the declining union coverage and together with soaring exports in Germany. Incumbent exporters are on average larger, and may still benefit from the efficiency gains of collective agreements. More recent waves of trade liberalization have been characterized by the presence of exporters of a smaller size, who according to our results are less likely to choose collective bargaining. This mechanisms can be characterized in a stylized search and matching model with heterogeneous firms, self-selection into export and bargaining costs.2 The trade-off between higher wages and efficiency gains from collective bargaining emerges if, for instance, bargaining costs are fixed for collective bargaining and variable for individual bargaining. This leads to a self-selection of larger firms into collective agreements. Unobserved productivity drives the export decision and the size of the firms, as more productive firms self-select into export and tend to hire more employees. Hence, size, productivity and export status are positively correlated and this correlation explains why larger exporters may prefer collective bargaining. Workers benefit from higher earnings and more job security, whereas large exporters benefit from the lower operation costs. Moreover, union wage outcomes may be regarded as a fair benchmark, which can stimulate labor productivity in unionized firms. A recent article in the The Economist (2018) argues that the decline in union coverage in developed countries might be the result of improved employment protection legislation. Nowadays, workers’ rights are part of the general labor law, and so there is less need for the unions’ negotiation activity. However, things are different in many
export markets, where employment protection standards are lower. In a globalized world, exporters have the opportunity to move production closer to foreign consumers through vertical foreign direct investments. In this context, the presence of the union at the firm level is still relevant if firms operate in the foreign market. A strong union and a works council can counteract the firm’s decision to relocate production abroad. In addition, liaising with a union can also bring positive cost-saving effects to the firms, as we argue in this paper. Related literature. A vast body of research has studied the link between unionization and economic outcomes, e.g. real wages, productivity and employment. Two prominent studies are DiNardo and Lee (2004) and Lee and Mas (2012), who find no significant effect of unions on productivity and wages for the US. Furthermore, Schmalz (2013) shows how more unionized firms increase their cash-flow balance sheet in order to insure against the higher “human capital risk” associated with collective bargaining. The studies mentioned above focus on domestic economic activities and outcomes. Economic research explicitly addressing the effect of unions on a firm’s presence on the foreign market provides also an unclear picture: for example, Carluccio et al. (2015) find that French exporters tend to negotiate firm-level contracts on top of the mandatory industry-agreements. Moreover, firm level wage agreements can mitigate the negative effects of offshoring on the of blue-collar workers. Felbermayr et al. (2014) find negative wage effects of firms’ dependency on foreign markets in the presence of collective bargaining. While most studies try to detect the causal link going from collective bargaining to economic performance, our paper takes on the opposite perspective and studies the economic determinants of the firm choice of the bargaining regime. Our paper is also related to the strand of literature studying the determinants of unionization at the firm level. Baumann and Brändle (2017) study the role of firm heterogeneity in the decision to bargain collectively at the plant or industry level. The authors show that the share of firm-level collective agreements increases in sectors characterized by high firm heterogeneity in productivity terms. Closely related to their paper, Taschereau-Dumouchel (2017) studies the role of workers’ skill-level in the decision to adopt collective bargaining agreements in the US. Wages across workers with different skills are more compressed in unionized firms. The decision within the firm depends on the vote of the workers. The higher the skill-level of the workforce in a firm, the more likely it is that the majority votes against collective bargaining. Closely related to our study, Hirsch et al. (2014) investigate the impact of firm productivity on the endogenous choice between centralized and decentralized wage formation at the firm level. The authors show that more productive firms are more likely to choose centralized bargaining. Moreover, they argue that the observed pattern may be explained through external economies of scale of the type we have clarified above. While their paper also treats collective agreement as an endogenous choice of the firm, we depart from the study by Hirsch et al. (2014) in two important respects. First of all, our focus is on the role of size and export, rather than on firm productivity. Firm productivity, size, and export status are highly correlated but the overlap between small exporters and large non-exporters is huge (see Capuano et al., 2017). Moreover, our work is consistent with an important stylized fact, i.e. the observed simultaneous increase in exports and a decline in collective bargaining.3 If larger firms are more likely to engage in collective bargaining and also show a higher probability to export, one may expect a surge in union coverage. However, in Germany a decline in union coverage goes hand in hand with soaring exports at the extensive and intensive margin. Including interactions between size and export status in our empirical models allows us to rationalize this, in so far as the effect of exports depends on firm size. Only the largest exporters tend to negotiate wages with unions, whereas smaller exporters tend to
2 In the Online Appendix we provide a more analytical treatment of such a model.
3 See Schnabel et al. (2006) for evidence on the decline in union coverage and the determinants of collective bargaining in Germany.
Fig. 1. Difference in bargaining outcomes.
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avoid collective bargaining. Thus, the parallel decline in union coverage and upswing in export intensity are consistent with our results if rising exports are driven by the behavior of the smallest firms. There is an emerging literature on the interaction between trade and unions. Heinbach and Schroepfer (2008) study the impact of trade on the probability of using opening clauses in collective bargaining agreements. The authors argue that firms more exposed to international competition must be more flexible when it comes to wage negotiation. Opening clauses allow firms to depart from the union wage only in particular situations, such as financial distress or economic downturn.4 Egger and Etzel (2012) propose a theoretical motivation of this particular channel based on a general oligopolistic equilibrium model of international trade with collective bargaining. They show that a movement from autarky to free trade with a symmetric partner country lowers union wage claims and thereby stimulates employment and raises welfare. Whether firms can extract a larger share of rents in the open economy crucially depends on the competitive environment under autarky.5 The remainder of the paper is organized as follows: the next section describes the data and provides the first descriptive evidence. Sections 3 to 5 present the findings of our empirical analysis. Section 6 contains the results of further robustness checks. Section 7 concludes.
Table 1 Collective agreement and firm size. No CA Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
2. Data and descriptive evidence
Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
Our empirical analysis is based on the IAB Establishment panel, an annual representative sample of German plants with at least one employee, which the IAB has been carrying out since 1993 in West Germany and since 1996 in East Germany (Fischer et al., 2009). To include both Eastern and Western German firms, we keep observations from the year 1996–2016. The establishment panel has the advantage of providing high-quality information on the main variables of interest for our analysis: number of employees,6 firm export status and the type of bargaining regime in which the firm is involved. We focus on the firm collective agreement status, that we construct as a dummy variable equal to 1 if the firm recognizes a collective agreement either at the plant- or industry-level, and 0 otherwise.7 The hypothesized link between firm size and efficiency gains due to collective bargaining are expected to emerge in both firm- and industry-level collective bargaining firms.8 As to firm size, we use dummy variables for four size categories: less than 50 employees, 50 to less than 250 employees, 250 to less than 500 employees and size greater than 500.9 In the reduced form evidence presented in the next section we will include a larger set of variables. First of all, the skill composition of the workforce within each firm, which will be inserted as the share of apprenticeships, unqualified and qualified workers as well as the owner working in the firm. We will additionally account for the age of the firm, by controlling for whether the firm was founded after 1995. Furthermore, we will control for a comprehensive set of year, regional and sector fixed effects. Finally, we
1996 36.49 13.49 6.06 2.64 34.28 2000 55.99 41.41 15.86 9.89 53.93 2005 62.90 44.37 24.14 9.90 60.48 2010 71.29 54.43 29.72 11.85 68.80 2016 73.56 58.44 37.18 18.74 71.14
CA 63.51 86.51 93.94 97.36 65.72 44.01 58.59 84.14 90.11 46.07 37.10 55.63 75.86 90.10 39.52 28.71 45.57 70.28 88.15 31.20 26.44 41.56 62.82 81.26 28.86
Notes: IAB establishment panel, manufacturing industries. Descriptive statistics weighted by using inverse probability weights.
insert value-added per worker in our regressions, as a proxy for firm productivity.10 2.1. Main patterns and correlations This section presents descriptive evidence on the relationship among our main variables of interest, i.e. collective agreement, firm size and export status. Table 1 links firm collective agreement status and size. As we have pointed out in the introduction, and as our empirical analysis will explain more clearly, the gains from collective bargaining may be higher than its costs for larger firms. The above intuition finds support in Table 1, which shows the percentage frequencies of collectiveagreement firms by size-categories for five years. Considering the latest year, i.e. 2016, the share of firms engaging in collective agreement increases monotonically with size, ranging from 26.44 percent among the smallest firms to 81.26 percent among the largest ones. Importantly, Table 1 also shows that the total share of collective agreement firms has decreased over time, ranging from 46.07 percent in 2000 to around 28.86 percent in 2016, which is in accordance with the welldocumented tendency to the decentralization of the bargaining process in recent years (Hirsch and Schnabel, 2014). A more careful look at Table 1 reveals that small and medium-sized firms drive such a tendency. Indeed, the percentage of collective agreement firms in the first two size-categories (i.e. firms with less than 50 and with 50–250 employees) has decreased by more than 17 percentage points from 2000 to 2016, while it has only declined by less than 9 percentage points among firms in the largest size category. In absolute terms the share of collective bargaining firms in the largest size category is still more than 80 percent. Hence, the overall message delivered
4 See also Brändle and Heinbach (2013) for another study on the effects of opening clauses. 5 The mechanism outlined in their paper finds empirical support by Gürtzgen (2009). Fanti and Gori (2013) study the role of collective bargaining in a Cournot duopoly. 6 Two types of information on firm size are included in the data: total number of employees and employees subject to social security contribution. Our main analysis is based on the former definition, but results of robustness checks based on the latter are qualitatively similar. 7 We use the terms “firm” and “plant” interchangeably throughout the paper, because a large majority of plants in our data are single unit firms. 8 Notice that the main results can be replicated when running separate regressions for both type of collective bargaining firms. Additional results are available upon request. 9 We also tried using log revenue as size measure. Results are comparable to the results obtained from our benchmark specification.
10 Summary statistics on the estimation sample are provided in the Online Appendix.
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Table 2 Export status and firm size.
Table 3 Probit marginal effects (without dummies). Domestic firms
Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
1996 87.63 35.51 13.85 8.86 82.54 2000 85.70 40.25 17.48 11.31 80.39 2005 79.32 30.08 15.54 11.50 73.86 2010 76.39 29.04 14.37 14.54 70.90 2016 72.91 26.72 14.58 17.47 67.30
Exporting firms
Dependent variable: Collective agreement (dummy)
12.37 64.49 86.15 91.14 17.46
Export (dummy)
(1)
(2)
(3)
0.137∗∗∗ (0.010)
−0.070∗∗∗
−0.126∗∗∗
(0.010) 0.265∗∗∗ (0.012) 0.490∗∗∗ (0.016) 0.642∗∗∗ (0.012)
No No No 54,711
No No No 54,711
(0.009) −0.001 (0.012) 0.106∗∗∗ (0.021) 0.279∗∗∗ (0.023) 0.039∗∗∗ (0.005) −0.111∗∗∗ (0.010) 0.356∗∗∗ (0.010) No No No 54,711
Between 50 and 250 employees Between 250 and 500 employees
14.30 59.75 82.52 88.69 19.61
More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy)
20.68 69.92 84.46 88.50 26.14
Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
23.61 70.96 85.63 85.46 29.10
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. In Column (3) we additionally control for the share of workers with different qualification levels in each firm.
27.09 73.28 85.42 82.53 32.70
in Table 3 does not include any type of fixed effects, while the one in Table 4 includes year, sector and regional dummies. In the first column of both tables we use the export status as the only explanatory variable. When fixed effects are not included (i.e. the first column of Table 3), the estimated marginal effect suggests that being an exporter increases the probability of engaging in collective agreements by about 14 percentage points. However - and not surprisingly - this effect is slightly reduced as soon as we allow fixed effects to capture all those time, sectoral and regional factors that might influence the collective agreement status.11 Regional disparities on the provincial level can be explained by the reunification in Germany. Schnabel (2016) shows that labor markets in the East and the West of Germany are very unequal. Those differences are systematic and must be taken into consideration when estimating the effects of exporters’ choice of the wage bargaining regime. Schnabel (2016) argues that differences in the union coverage and work councils are among the main drivers behind inequality between East and West Germany. We argue that the catching up process varies among the different provinces in East Germany, which explains why we do not solely rely on the inclusion of an East-West dummy. Though smaller, the effect of collective agreement on the probability of exporting remains positive if we do not add any other explanatory variable, and equal to 0.1 (column 1 of Table 4). The estimated positive correlation between export and collective agreement statuses is likely to be spurious in the above regressions because we are still excluding some important firm characteristics that may influence export status and be correlated with collective agreement. If this is the case, the export dummy will capture also the effect of those omitted variables. As we have discussed, one of the factors that co-varies both with export status and collective agreement is the size of the firm. Thus, in the second column of Tables 3 and 4 we additionally control for firm size categories. Interestingly, the marginal effect of export on the probability of engaging in collective agreement now turns negative: exporting firms seem to have a ceteris paribus lower probability of collective agreements. The marginal effect of export status on collective agreement is either −0.070 or −0.047 depending on whether fixed effects are included or not in the specification. Firm size has a considerable positive influence on the probability of collective agree-
Notes: IAB establishment panel, manufacturing industries. Descriptive statistics weighted by using inverse probability weights.
by this simple cross tabulation is that, while still in the early 2000s most German firms were engaging in collective bargaining, in later years an increasing fraction of small and medium-sized firms started not to bargain collectively with their workers, whereas larger firms still continue to do so. As a second exploratory look at the data we focus on the distribution of firms according to size-class and export status. As shown in Table 2, and as predicted by Melitz-type models, firms clearly sort into the exporting regime according to size. Comparing Tables 1 and 2, one immediately realizes that size drives both the export status and the adopted bargaining regime. Indeed, in 2016 around 82.53 percent of the largest firms produce for the foreign market and almost the same percent of them adopt collective agreements. We also observe that the share of exporting firms within the smaller size categories has increased by at least 10 percentage points between 2000 and 2016. Importantly, the share of exporting firms has increased much more among small and medium-sized firms than among the largest ones (among those, actually, it has slightly declined). We have now showed that size is an important factor driving both collective agreement and export but export at the extensive margin has been driven by small and medium sized firms entering the foreign market (Table 2). These firms are the ones among which collective agreement has more sharply declined (Table 1). In what follows we will further elaborate on this intuition, examining in detail the interplay among our main variables of interest. 3. Baseline model and results To test the relationships among export status, collective agreement and size, we first estimate simple probit regressions where the dependent variable is firm collective bargaining. We focus on export status and firm size as the main variables of interest. Tables 3 and 4 report the marginal effects from this first set of regressions. The specification
11 The sectoral dummies distinguish between different economic activities on a very broad level. The regional dummies are at the province level.
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Table 4 Probit marginal effects.
Table 5 Probit marginal effects of the model with interactions.
Dependent variable: Collective agreement (dummy)
Export (dummy)
(1)
(2)
(3)
0.100∗∗∗ (0.009)
−0.047∗∗∗
−0.088∗∗∗
(0.009) 0.216∗∗∗ (0.011) 0.404∗∗∗ (0.017) 0.545∗∗∗ (0.016)
(0.009) 0.000 (0.012) 0.091∗∗∗ (0.019) 0.228∗∗∗ (0.023) 0.026∗∗∗ (0.004) −0.058∗∗∗ (0.010) 0.309∗∗∗ (0.010) Yes Yes Yes 54,711
Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
Yes Yes Yes 54,711
1. Marginal effects of the export dummy for different firm-size categories Less than 50 employees −0.165∗∗∗ (0.015) −0.060∗∗∗ Between 50 and 250 employees (0.020) −0.134∗∗∗ Between 250 and 500 employees (0.047) −0.000 More than 500 employees (0.070) 2. Marginal effect of size for exporters Between 50 and 250 employees Between 250 and 500 employees More than 500 employees
3. Marginal effect of size for non-exporters Between 50 and 250 employees
Significance levels: < < < 0.01. Standard errors clustered at the firm level in parentheses. In Column (3) we additionally control for the share of workers with different qualification levels in each firm. ∗p
0.10, ∗∗ p
Yes Yes Yes 54,711
Dependent variable: Collective agreement (dummy)
0.05, ∗∗∗ p
Between 250 and 500 employees More than 500 employees Observations
ments, which increases monotonically with firm size. Indeed, relative to firms with less than 50 employees (the base category) firms with more than 500 employees have between 64 and 55 percentage points higher probability of engaging in collective agreements (second column of Tables 3 and 4, respectively). The relative difference between the probability of collective bargaining across the different categories remains stable even after the inclusion of fixed effects. The negative association between collective agreement and export status is robust to the inclusion of other possibly important firm characteristics, such as the age of the firm, and the presence of a works council, as it is shown in the last column of Tables 3 and 4. This first set of results suggests that exporters tend to refrain from collective agreement. One obvious explanation for this result is that, given the higher competition from abroad, exporters will tend to choose the bargaining regime that is associated with the lower cost. However, the coefficient of the export dummy in the above regressions has to be interpreted as an average effect across all firms. Given the descriptive evidence we have examined in the previous section, it is natural to ask how the estimated effect of the export status changes across firm size categories. Hence, we include interaction terms between export status and size in the previous probit regressions. Table 5 shows the marginal effect computed using the coefficient from this estimation. The first panel of Table 5 shows the marginal effect of export status for every category of firm size. While the marginal effect is negative and significant for all size categories up to 500 employees, it considerably decreases non-monotonically in magnitude and turns insignificant for the largest firms. This indicates that smaller exporters are on average less likely to choose collective bargaining, while entering the international market does not seem to change the collective agreement status of the largest firms. We argue that the largest firms were exporters already and that those firms benefit from the operation cost advantage outlined in the introduction. The last two panels of Table 5 display the marginal effects of size for exporters and non exporters separately. Interestingly, the probability of collective bargaining is 34 percentage points larger for the largest exporting firms than for the smallest exporting firms (second panel of Table 5). Moreover, the correlation between size and collective agreement is stronger for exporters than for non-exporters, which is evident from the comparison of the magnitudes of the marginal effects of size for the two categories of firms (second and third panel of Table 5).
0.044∗∗ (0.018) 0.146∗∗∗ (0.027) 0.342∗∗∗ (0.029)
−0.061∗∗∗ (0.021) 0.115∗∗ (0.046) 0.177∗∗ (0.070) 54,711
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. The marginal effects of the size categories at points 2. and 3. are the discrete change of the predicted probability of engaging in collective agreement with respect to the base firm size category (i.e. a firm with less than 50 employees). All other control variables are evaluated at their average value in the estimation sample.
Overall, the above results can be summarized as follows: while exposure to international competition does not have any effect on the choice of the bargaining regime for large firm, it might instead induce small and medium-sized firms to refrain from collective bargaining. However, the larger the exporters get, the more likely it is that they will choose collective agreement. As discussed in the introduction, this empirical finding might be easily explained with the presence of external economies of scales accruing to large firms when they negotiate wages collectively. Moreover, firms that are exposed to international competition should be more induced to exploit the efficiency gains from collective bargaining, so as to be more competitive in the foreign market. Hence, it is intuitive to find that exporters show a stronger tendency towards collective bargaining than non-exporters (of any size). 4. A bivariate probit model for collective agreement and export status As we have just seen, including important observable characteristics in the probit equation for collective bargaining helps eliminate the initial bias of the effect of the export status. However, omitted variables may not to be the only source of bias in the previous models. In fact, not only is there self-selection into export, but the results we find may also be just driven by unobservable firm characteristics that affect both export status and collective agreement. In order to address the above concern, we follow common practice in the literature dealing with binary response models with endogenous binary regressors (for a recent application in the trade literature see, for example, Egger et al., 2011), and we estimate a bivariate probit model for collective agreement and export status. The intuition behind this approach is straightforward: we specify a two-equation model, one for the probability of collective bargaining, and the other one for the probability of export. 5
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Table 7 Check of the exclusion restrictions of the Bivariate probit model (II).
Table 6 Check of the exclusion restrictions of the Bivariate probit model (I). Dependent variable: Collective agreement (dummy)
Dependent variable: Export (dummy)
Variable of interest: Investment in IT (dummy)
Investments in IT (dummy) Export (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Constant Year-dummies Sector-dummies Region-dummies Observations R2
Variable of interest: Investment in IT (dummy)
(1) OLS
(2) Probit
−0.001
0.003 (0.021) −0.323∗∗∗ (0.031) −0.000 (0.041) 0.306∗∗∗ (0.063) 0.774∗∗∗ (0.076) 0.093∗∗∗ (0.016) −0.210∗∗∗ (0.037) 1.124∗∗∗ (0.041) −1.986∗∗∗ (0.181) Yes Yes Yes 54,711 0.289
(0.006) −0.089∗∗∗ (0.008) −0.002 (0.012) 0.092∗∗∗ (0.018) 0.184∗∗∗ (0.017) 0.025∗∗∗ (0.004) −0.060∗∗∗ (0.010) 0.382∗∗∗ (0.013) −0.086∗ (0.049) Yes Yes Yes 54,711 0.347
Investments in IT (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Constant Year-dummies Sector-dummies Region-dummies Observations R2
(1) OLS
(2) Probit
0.078∗∗∗ (0.006) 0.258∗∗∗ (0.013) 0.296∗∗∗ (0.017) 0.307∗∗∗ (0.017) 0.070∗∗∗ (0.005) 0.014 (0.011) 0.095∗∗∗ (0.013) −0.378∗∗∗ (0.052) Yes Yes Yes 54,711 0.360
0.257∗∗∗ (0.021) 0.700∗∗∗ (0.039) 0.885∗∗∗ (0.062) 0.989∗∗∗ (0.069) 0.243∗∗∗ (0.017) 0.031 (0.038) 0.272∗∗∗ (0.040) −2.907∗∗∗ (0.193) Yes Yes Yes 54,711 0.304
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. We additionally control for the share of workers with different qualification levels in each firm. In the probit regression in column (2) coefficients are reported and the R2 value is the pseudo R2 .
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. We additionally control for the share of workers with different qualification levels in each firm. In the probit regression in column (2) coefficients are reported and the R2 value is the pseudo R2 .
marginal effect of the export dummy in Column 1 has the usual interpretation of the average change in the probability of collective bargaining when firms switch from the domestic to the exporting regime. As we can see, the effect of exporting on collective bargaining remains negative and significant. Exporters seem to be on average 17 percentage points less likely to bargain collectively with their workers. Columns (2) and (3) of the same table decompose the total marginal effect into two components, which can be interpreted as the exporters’ and the non-exporters’ contributions to the probability of engaging in collective agreements. Intuitively, the results of Table 8 for the marginal effect of the export dummy could be read as follows: ceteris paribus, collective agreements decrease the average probability of exporting by 17 percentage points. This is the result of two effects. On the one hand, exporters have an 8.6 percentage points lower probability of collective agreement with respect to those who don’t export. On the other hand, domestic firms would lower their probability of collective agreement by 7.9 percentage points if they started to export. The marginal effects of the other covariates have the same sign and partly the same magnitude as the simple probit regressions in Table 4. The correlation between the error terms in the export and the collective bargaining equations (𝜌) is positive but insignificant. Hence, even if our concern for the endogeneity of the export dummy was reasonable, the data do not show statistical support for this hypothesis.
The export status dummy will appear as an explanatory variable in the former equation, and as the dependent variable in the latter. The key issue is that the error terms of the two equations are allowed to be correlated, and the correlation between the error terms will be one of the estimated parameters of the model. In this way we explicitly take into account the potential source of endogeneity we mentioned above (i.e. correlation in unobservables) and we are able to obtain unbiased estimates of the marginal effect of export status on collective bargaining. It is advisable to have at least one variable which appears in the equation explaining export status which is not included in the one for collective agreement (Monfardini and Radice, 2008). Following Hauptmann and Schmerer (2013) we use a dummy variable indicating whether the firm has invested in IT in the previous year as an instrument.12 Indeed, both OLS and probit regressions of collective bargaining and export dummies on Investment in IT confirm this hypothesis. As we show in Tables 6 and 7, investment in IT is highly significant in the regressions featuring export as a dependent variable, and insignificant in the regressions with collective bargaining as dependent variable. Results. Table 8 reports the marginal effects of the estimated bivariate probit model. Column 1 shows the marginal effects on the unconditional probability of collective agreement. These should be compared to the marginal effects in Table 4 commented earlier. The estimated
5. Coarsened exact matching Our analysis so far has documented the existence of a negative link between collective bargaining and export. Moreover, the bivariate probit model estimated in the previous section suggests that the negative effect of export on collective bargaining may be causal. Although standard test statistics support our exclusion restrictions and hence the validity of our results, the empirical models presented so far hinge on strong parametric assumptions, e.g. the distribution of the error term. To check whether the effect found is robust to model specification, we
12
As argued in Hauptmann and Schmerer (2013), this variable is likely to be correlated with export, as exporters may invest in communication technology in order to ease their international activities (see also Bertschek et al., 2015) but there are instead no obvious reasons why investments in IT may be correlated with collective bargaining. For a detailed discussion on the cost-saving aspects of IT investment and its effect on overall competition see Mallick and Ho (2008) and Ho and Mallick (2010). 6
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Table 8 Bivariate probit model (marginal effects).
Table 9 Balance condition in 2016.
Dependent variable: Collective agreement (dummy) Variable of interest: Export (dummy) Founded before 1995 Size class 1 Size class 2 Size class 3 Value added West Sector 1 Sector 2 Sector 3
Exclusion restrictions: Investment in IT (dummy) (1)
(2)
(3)
−0.165∗∗∗
−0.086∗∗∗
−0.079∗∗∗
(0.049) 0.023 (0.019) 0.118∗∗∗ (0.026) 0.255∗∗∗ (0.027) 0.031∗∗∗ (0.006) −0.056∗∗∗ (0.010) 0.313∗∗∗ (0.010) Yes Yes Yes 54,711 0.169 0.108 2.475
(0.027) 0.104∗∗∗ (0.011) 0.190∗∗∗ (0.017) 0.299∗∗∗ (0.021) 0.043∗∗∗ (0.004) −0.026∗∗∗ (0.007) 0.194∗∗∗ (0.008) Yes Yes Yes 54,711
(0.023) −0.081∗∗∗ (0.010) −0.072∗∗∗ (0.014) −0.045∗∗∗ (0.016) −0.012∗∗∗ (0.003) −0.030∗∗∗ (0.006) 0.119∗∗∗ (0.007) Yes Yes Yes 54,711
𝜕 P(CA) 𝜕X
Export (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
𝜌 𝜌 (std. err.) 𝜒 2 (Wald test𝜌 = 0)
𝜕 P(CA=1,Exp=1) 𝜕X
𝜕 P(CA=1,Exp=0) 𝜕X
Matched
Raw
Matched
−0.119 −0.913
0.013 −0.011 0.007 0.007 0.034 0.000 0.000 0.000 0.000
0.909 2.108 2.285 6.308 1.310 1.091 0.230 0.800 1.117
1.011 1.008 1.007 1.029 0.981 1.000 1.000 1.000 1.000
0.730 0.335 0.636 0.248 −0.407 −0.069 0.136
a matching technique called “coarsened exact matching” (CEM) introduced by Iacus et al. (2011). This method has the advantage of being independent of model assumptions, except the usual “ignorability” condition (i.e. no omitted matching variables).13 CEM proceeds in three steps. First, the matching variables are coarsened into broader categories. Then, the observational sample is divided in strata, each characterized by the same value of the coarsened matching variables. Third, the strata that do not contain at least one treated and one control unit are discarded. After obtaining a CEM-matched sample, one can compute the ATE and ATET using any other parametric or nonparametric model (Blackwell et al., 2009). After forming the matched sample, we run nearest neighbor matching (NNM) to compute the ATE and the ATET (Abadie and Imbens, 2006). NNM uses an average of the outcomes of the nearest observations to impute the counterfactuals. The “nearest” observations are determined using a weighted function of the covariates for each observation (called Mahalanobis distance). The difference between the observed outcome and the imputed counterfactual for each firm is an estimate of the firm-level treatment effect. The ATE will be the average of the firm-level effects over all firms, while the ATET will be the average of the firm-level treatment effects taken only over the exporters. The resulting treatment effects have a casual interpretation under two conditions. First, as we have already mentioned, the matched exporting and domestic firms do not have to differ systematically in the matching variables. Secondly, self-selection into export should be only driven by observable firm characteristics.14 For consistency with our previous analysis we match exporters and non-exporters on the same variables that we have used as controls in the previous models.15 Checking the balance in the matched sample. The CEM algorithm performs exact matching (either on coarsened or actual data) and hence it should achieve by construction a high degree of balance between the treated and control samples. The next two tables confirm our hypothesis. Table 9 reports two balance measures: the standardized difference of the variables, which compares the means of the matching variables
estimate the effect of export on collective bargaining using nonparametic techniques. Following the programme evaluation literature, our effects of interest are the “Average Treatment Effect on the Treated” (ATET) and the “Average Treatment Effect” (ATE). In our context, the ATET compares the observed probability of collective bargaining among exporters with the probability of collective bargaining that the same exporters would have if they were domestic. The expression for the ATET is: ATET = P(CA = 1 ∣ EXP = 1)EXP=1 (1)
The ATE combines the ATT with the same effect among domestic firms, i.e. the difference between the probability of collective bargaining for domestic firms if they were exporters and their observed probability of collective bargaining. Formally, ATE = ATET + P(CA = 1 ∣ EXP = 1)EXP=0
− P(CA = 1 ∣ EXP = 0)EXP=0
Variance ratio
Raw
The table reports the standardized differences and variance ratios for the last year in the sample, i.e. 2016. We present the balance measures for both the raw and matched sample. If exporting and non-exporting firms are perfectly matched according to a variable, the standardized difference is zero and the variance ratio is one.
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. All specifications additionally control for the share of workers with different qualification levels in each firm.
− P(CA = 1 ∣ EXP = 0)EXP=1
Standardized differences
(2)
The critical point for the unbiased estimation of the ATE and ATET is the evaluation of the counterfactuals, i.e. the probability of collective bargaining if a firm’s export status was different from the one directly observed. In general, using the observed outcomes for domestic firms as counterfactuals for exporting firms (and vice-versa) is not recommendable, because being an exporter is not a randomly assigned characteristic to the firms, but exporting and domestic firms typically differ along several dimensions. Finding the right counterfactual in nonexperimental data is the main aim of any matching technique. The basic idea behind matching is to trim the data so as to create a “balanced sample” of observations. In our application, this would be a sample where the distribution of the matching variables (i.e. the main drivers of a firm’s export behavior) among exporters is as similar as possible to the distribution of the matching variables among non-exporters. We use
13 For a recent application of CEM in the trade literature, see for example de Rassenfosse et al. (2016). 14 The insignificance of the correlation of the error terms in the bivariate probit model hints that observable characteristics may indeed be the only drivers of a firm’s export behavior. 15 For dummy variables, which do not need any coarsening, the CEM algorithm will find exact matches based on the actual variables. For continuous variables, e.g. value-added per worker, the exact matching will be based on the coarsened variables.
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Table 10 Balance condition: matched variance ratio 1996–2016. Year
1996
1997
1998
1999
2000
2001
2002
Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods Year Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods Year Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods
. 0.989 0.977 1.059 1.240 1.000 1.000 1.000 1.000 2003 0.979 1.001 1.000 0.998 1.020 1.000 1.000 1.000 1.000 2010 0.964 1.010 1.024 1.000 0.984 1.000 1.000 1.000 1.000
. 1.000 0.999 1.016 1.101 1.000 1.000 1.000 1.000 2004 0.915 1.003 1.009 1.000 1.032 1.000 1.000 1.000 1.000 2011 0.974 1.007 1.005 1.070 0.973 1.000 1.000 1.000 1.000
0.945 0.980 1.034 1.031 1.121 1.000 1.000 1.000 1.000 2005 0.954 1.003 1.010 1.000 0.995 1.000 1.000 1.000 1.000 2012 0.997 1.003 1.002 1.022 1.032 1.000 1.000 1.000 1.000
0.992 1.000 1.000 1.100 0.950 1.000 1.000 1.000 1.000 2006 0.993 1.001 1.000 1.041 1.066 1.000 1.000 1.000 1.000 2013 1.027 1.003 1.008 0.998 1.062 1.000 1.000 1.000 1.000
0.945 1.004 1.000 1.121 0.989 1.000 1.000 1.000 1.000 2007 0.999 1.002 1.002 1.033 1.045 1.000 1.000 1.000 1.000 2014 0.997 1.008 1.014 0.994 1.250 1.000 1.000 1.000 1.000
0.993 1.001 1.004 1.002 1.105 1.000 1.000 1.000 1.000 2008 1.004 1.013 1.050 1.032 1.039 1.000 1.000 1.000 1.000 2015 1.022 1.005 1.003 1.014 1.072 1.000 1.000 1.000 1.000
1.010 1.001 1.005 0.998 1.019 1.000 1.000 1.000 1.000 2009 0.981 1.008 1.013 1.054 1.113 1.000 1.000 1.000 1.000 2016 1.011 1.008 1.007 1.029 0.981 1.000 1.000 1.000 1.000
for exporters and non-exporters,16 and the ratios between the variance of the matching variables for exporters and non-exporters. In a perfectly balanced sample standardized differences are equal to zero, and variance ratios are equal to one. We show the details for the most recent year of the panel, i.e. 2016, and present the matched variance ratio for the whole sample period.17 In 2016 the standardized differences are close to zero for all variables and the variance ratios are close to one. Moreover, comparing the raw and the matched sample shows a strong impact of the matching procedure in eliminating the initial imbalance. Table 10 shows a similar picture for the variance ratios for all years. Table A1 in the Online Appendix provides an additional summary statistic that contains the number of observations and the number of matches in the raw and the matched samples for all the different years. Treatment effects. Figs. 2 and 3 present the ATE and ATET for each year. The ATE are always negative and significant in more recent years of our sample. The ATET, i.e. the effects of export on collective bargaining computed only for exporters are less precisely estimated especially at the beginning and the end of the sample periods, while they are negative and significant in the middle periods. Overall, the analysis supports the negative effect of export on collective bargaining in most but not all years. 16
d=
6. Robustness checks As robustness checks, we re-run our analysis on different subsamples of observations. Our subgroups are: 1) Eastern German firms; 2) Western German firms; 3) firms producing for the food industry; 4) firms producing consumer goods; 5) firms in the producer goods sector and 6) firms in the investment goods sector. The results of these robustness checks are reported in the Online Appendix. Table A2 shows the baseline probit regressions. For comparative purposes, the first column of the table reports the benchmark results in the main text. The estimated coefficient of the export dummy hardly changes across subgroups and it is significant and negative. Tables A3–A8 report the coefficients of the bivariate probit model estimated on the different subsamples. The export-dummy is negative and significant for Western German firms but insignificant in the regressions for Eastern Germany. Different results for firms located in the West and firms located in the East of Germany are not too surprising and in line with other authors’ findings. See Schnabel (2016) for the differences between the labor market in East- and West-Germany. In the regressions based on the different sectors we find a negative and significant effect of export on collective bargaining only for firms producing consumer and producer goods, but not for firms in the other two sectors. Finally, Figs. A1 and A2 present the robustness checks on the treatment effects. Here the export-dummy becomes insignificant in some years. However, once the sample is split in several subsamples, the number of firms that can be matched each year is very small. Hence, these results must be interpreted with caution.
The expression for the standardized difference for a continuous variable is: x −x √t c , where x t and xc are the sample means of variable x in the treated 2 2 s −sc t 2
and control group respectively and s2t and s2c are the standard deviations of x in the treated and control groups respectively. The standardized difference for ̂p −̂ p a dummy variable is d = √ ̂p (1−̂tp )+̂pc (1−̂p ) , where ̂ pt and ̂ pc denote the mean of t
t
c
c
2
the dummy variable in the treated and control groups respectively (Flury and Riedwyl, 1986). The standardized differences do not depend on sample size, hence they can be used to compare the balance in the matched sample with that in the unmatched sample (Austin, 2009). 17 Other statistical tests for the balance are available from the authors upon requests. 8
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Fig. 2. Average treatment effects.
Fig. 3. Average treatment effect on the treated.
7. Summary and conclusions
a single entity (the union), instead of individually with each of their workers. Furthermore, large exporters should be more likely to exploit the efficiency gains for collective bargaining, so as to be more productive and competitive in the international market. This is exactly what our empirical evidence has shown: large exporters choose the collective agreement regime more often than non-exporting firms of any size.
In this paper we have provided empirical evidence on the link between export and collective agreement. Exposure to international competition is sometimes believed to put the existence of historical labor market institutions in Western Europe at risk, such as employment protection and collective agreement. However, the evidence presented in this paper does not totally support this fear: larger exporting firms do not seem to show a tendency towards abandoning collective agreements. We have developed a straightforward theoretical explanation for this result: under the assumption that bargaining is costly, large firms might be better off by collectively bargaining with
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.econmod.2019.03.008.
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Economic Modelling journal homepage: www.journals.elsevier.com/economic-modelling
Trade and unions: Does size matter?☆ Stella Capuano a , Andreas Hauptmann b , Hans-Jörg Schmerer c,b,d, ∗ a
Institute for Employment Studies (IES), UK Institute for Employment Research (IAB), Germany University of Hagen, Germany d CESifo, Germany b c
A R T I C L E
I N F O
JEL classification: F16 J51 E24 J3 Keywords: trade Unions Exports Firm level data
A B S T R A C T
Our empirical analysis builds upon the hypothesis that unions are detrimental to a firm’s efficiency. Using a rich survey of German manufacturers, we investigate firm-level determinants on the probability of collective wage bargaining with particular focus on the impact of a firm’s engagement in foreign markets. An interesting and very robust finding is that exporters are less likely to engage in union wage bargaining. This finding is in line with a pessimistic perception of unions. The negative effect of collective bargaining can be offset by efficiency gains for larger exporters, who can benefit from operation cost saving effects. Size does matter as larger firms export and may find bargaining with a single entity representing the workforce more convenient than bargaining with each worker individually. We are using firm level information on IT investment as instrument for the export dummy and successfully test for the validity of this instrument.
1. Introduction Trade unions are often suspected of being a source of firm inefficiency, for instance due to high collectively bargained wages. A first glance at the data in Fig. 1 suggests that collective bargaining firms in Germany indeed tend to pay higher wages compared to non-collective bargaining firms. The gap between both types of firms is significant and increases over time. It is often assumed that the above labor-cost gap may be responsible for the negative trend in union coverage observed in several developed economies in recent years, as documented in OECD (2017). Globalization might accelerate this process, as international competition puts
pressure on the firms to keep production costs as low as possible. As we will clarify in the next section, despite the high attention that this topic receives in the public debate, only a small part of the economic literature has tried to detect a direct link between collective agreement coverage and globalization. Motivated by the above discussion, this paper sheds light on the role of export in the firm’s choice of the bargaining regime.1 Using both parametric and nonparametric econometric techniques we are able to detect a negative link between export status and collective bargaining. On average, exporting decreases the probability that firms engage in collective bargaining. However, this negative effect disappears for
☆ Notice that an earlier version of this paper was circulated under the title “Trade and unions: Can exporters benefit from collective bargaining?”. We are grateful to two anonymous referees and the editor of the journal for helpful comments and thorough advice. We also want to thank James Cockett, Hartmut Egger, Price Fishback, David Friedman, Sebastian Galiani, Agnese Romiti and Luhang Wang as well as the participants of the 3rd Joint Workshop of Aarhus University and the IAB, AIEL XXIX National Conference of Labour Economics, ETSG (European Trade Study Group) 16th Annual Conference, Munich, 26th EALE (European Association of Labour Economists) Annual Conference, 17. Göttinger Workshop “Internationale Wirtschaftsbeziehungen”, Verein für Socialpolitik Jahrestagung 2015, Research Seminar at the University of Bayreuth, 20th EBES (European Business and Economic Society) Conference, EEFS (European Economics and Finance Society) 16th Annual Conference, The Second Annual Xiamen University International Workshop on Economic Analysis and Institutions, for helpful comments and suggestions. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. ∗ Corresponding author. Faculty of Business and Economics, Chair of International Economics, FernUniversität in Hagen, Universitätsstr. 11, D-58097, Hagen, Germany. E-mail addresses: [email protected] (S. Capuano), [email protected] (A. Hauptmann), [email protected] (H.-J. Schmerer). 1 Notice that we do not try to explain the differences in bargaining outcomes, but we use them to motivate our theoretical considerations, and to interpret our empirical results.
https://doi.org/10.1016/j.econmod.2019.03.008 Received 29 September 2017; Received in revised form 13 March 2019; Accepted 13 March 2019 Available online XXX 0264-9993/© 2019 Published by Elsevier B.V.
Please cite this article as: Capuano, S., et al., Trade and unions: Does size matter?, Economic Modelling, https://doi.org/10.1016/j.econmod.2019.03.008
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larger exporters, while it persists for small exporters. Thus, size matters for the choice of the bargaining regime for an exporting firm. The role of size can be rationalized with the existence of operation cost-saving effects in large collective bargaining firms. Assume that both individual and collective bargaining impose time and financial costs to the firm. Then, if a firm has a large number of employees, bargaining with a single entity, the union, might be more efficient than bargaining individually with each worker. The above efficiency gains tend to be higher for larger firms, and they may overcome the higher costs resulting from collective wage negotiations. This pattern should be more visible for large firms. In fact, exporters should have a stronger incentive to take advantage of the efficiency gains from collective bargaining, so as to be more competitive in the international market. Our findings contribute to explain the declining union coverage and together with soaring exports in Germany. Incumbent exporters are on average larger, and may still benefit from the efficiency gains of collective agreements. More recent waves of trade liberalization have been characterized by the presence of exporters of a smaller size, who according to our results are less likely to choose collective bargaining. This mechanisms can be characterized in a stylized search and matching model with heterogeneous firms, self-selection into export and bargaining costs.2 The trade-off between higher wages and efficiency gains from collective bargaining emerges if, for instance, bargaining costs are fixed for collective bargaining and variable for individual bargaining. This leads to a self-selection of larger firms into collective agreements. Unobserved productivity drives the export decision and the size of the firms, as more productive firms self-select into export and tend to hire more employees. Hence, size, productivity and export status are positively correlated and this correlation explains why larger exporters may prefer collective bargaining. Workers benefit from higher earnings and more job security, whereas large exporters benefit from the lower operation costs. Moreover, union wage outcomes may be regarded as a fair benchmark, which can stimulate labor productivity in unionized firms. A recent article in the The Economist (2018) argues that the decline in union coverage in developed countries might be the result of improved employment protection legislation. Nowadays, workers’ rights are part of the general labor law, and so there is less need for the unions’ negotiation activity. However, things are different in many
export markets, where employment protection standards are lower. In a globalized world, exporters have the opportunity to move production closer to foreign consumers through vertical foreign direct investments. In this context, the presence of the union at the firm level is still relevant if firms operate in the foreign market. A strong union and a works council can counteract the firm’s decision to relocate production abroad. In addition, liaising with a union can also bring positive cost-saving effects to the firms, as we argue in this paper. Related literature. A vast body of research has studied the link between unionization and economic outcomes, e.g. real wages, productivity and employment. Two prominent studies are DiNardo and Lee (2004) and Lee and Mas (2012), who find no significant effect of unions on productivity and wages for the US. Furthermore, Schmalz (2013) shows how more unionized firms increase their cash-flow balance sheet in order to insure against the higher “human capital risk” associated with collective bargaining. The studies mentioned above focus on domestic economic activities and outcomes. Economic research explicitly addressing the effect of unions on a firm’s presence on the foreign market provides also an unclear picture: for example, Carluccio et al. (2015) find that French exporters tend to negotiate firm-level contracts on top of the mandatory industry-agreements. Moreover, firm level wage agreements can mitigate the negative effects of offshoring on the of blue-collar workers. Felbermayr et al. (2014) find negative wage effects of firms’ dependency on foreign markets in the presence of collective bargaining. While most studies try to detect the causal link going from collective bargaining to economic performance, our paper takes on the opposite perspective and studies the economic determinants of the firm choice of the bargaining regime. Our paper is also related to the strand of literature studying the determinants of unionization at the firm level. Baumann and Brändle (2017) study the role of firm heterogeneity in the decision to bargain collectively at the plant or industry level. The authors show that the share of firm-level collective agreements increases in sectors characterized by high firm heterogeneity in productivity terms. Closely related to their paper, Taschereau-Dumouchel (2017) studies the role of workers’ skill-level in the decision to adopt collective bargaining agreements in the US. Wages across workers with different skills are more compressed in unionized firms. The decision within the firm depends on the vote of the workers. The higher the skill-level of the workforce in a firm, the more likely it is that the majority votes against collective bargaining. Closely related to our study, Hirsch et al. (2014) investigate the impact of firm productivity on the endogenous choice between centralized and decentralized wage formation at the firm level. The authors show that more productive firms are more likely to choose centralized bargaining. Moreover, they argue that the observed pattern may be explained through external economies of scale of the type we have clarified above. While their paper also treats collective agreement as an endogenous choice of the firm, we depart from the study by Hirsch et al. (2014) in two important respects. First of all, our focus is on the role of size and export, rather than on firm productivity. Firm productivity, size, and export status are highly correlated but the overlap between small exporters and large non-exporters is huge (see Capuano et al., 2017). Moreover, our work is consistent with an important stylized fact, i.e. the observed simultaneous increase in exports and a decline in collective bargaining.3 If larger firms are more likely to engage in collective bargaining and also show a higher probability to export, one may expect a surge in union coverage. However, in Germany a decline in union coverage goes hand in hand with soaring exports at the extensive and intensive margin. Including interactions between size and export status in our empirical models allows us to rationalize this, in so far as the effect of exports depends on firm size. Only the largest exporters tend to negotiate wages with unions, whereas smaller exporters tend to
2 In the Online Appendix we provide a more analytical treatment of such a model.
3 See Schnabel et al. (2006) for evidence on the decline in union coverage and the determinants of collective bargaining in Germany.
Fig. 1. Difference in bargaining outcomes.
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avoid collective bargaining. Thus, the parallel decline in union coverage and upswing in export intensity are consistent with our results if rising exports are driven by the behavior of the smallest firms. There is an emerging literature on the interaction between trade and unions. Heinbach and Schroepfer (2008) study the impact of trade on the probability of using opening clauses in collective bargaining agreements. The authors argue that firms more exposed to international competition must be more flexible when it comes to wage negotiation. Opening clauses allow firms to depart from the union wage only in particular situations, such as financial distress or economic downturn.4 Egger and Etzel (2012) propose a theoretical motivation of this particular channel based on a general oligopolistic equilibrium model of international trade with collective bargaining. They show that a movement from autarky to free trade with a symmetric partner country lowers union wage claims and thereby stimulates employment and raises welfare. Whether firms can extract a larger share of rents in the open economy crucially depends on the competitive environment under autarky.5 The remainder of the paper is organized as follows: the next section describes the data and provides the first descriptive evidence. Sections 3 to 5 present the findings of our empirical analysis. Section 6 contains the results of further robustness checks. Section 7 concludes.
Table 1 Collective agreement and firm size. No CA Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
2. Data and descriptive evidence
Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
Our empirical analysis is based on the IAB Establishment panel, an annual representative sample of German plants with at least one employee, which the IAB has been carrying out since 1993 in West Germany and since 1996 in East Germany (Fischer et al., 2009). To include both Eastern and Western German firms, we keep observations from the year 1996–2016. The establishment panel has the advantage of providing high-quality information on the main variables of interest for our analysis: number of employees,6 firm export status and the type of bargaining regime in which the firm is involved. We focus on the firm collective agreement status, that we construct as a dummy variable equal to 1 if the firm recognizes a collective agreement either at the plant- or industry-level, and 0 otherwise.7 The hypothesized link between firm size and efficiency gains due to collective bargaining are expected to emerge in both firm- and industry-level collective bargaining firms.8 As to firm size, we use dummy variables for four size categories: less than 50 employees, 50 to less than 250 employees, 250 to less than 500 employees and size greater than 500.9 In the reduced form evidence presented in the next section we will include a larger set of variables. First of all, the skill composition of the workforce within each firm, which will be inserted as the share of apprenticeships, unqualified and qualified workers as well as the owner working in the firm. We will additionally account for the age of the firm, by controlling for whether the firm was founded after 1995. Furthermore, we will control for a comprehensive set of year, regional and sector fixed effects. Finally, we
1996 36.49 13.49 6.06 2.64 34.28 2000 55.99 41.41 15.86 9.89 53.93 2005 62.90 44.37 24.14 9.90 60.48 2010 71.29 54.43 29.72 11.85 68.80 2016 73.56 58.44 37.18 18.74 71.14
CA 63.51 86.51 93.94 97.36 65.72 44.01 58.59 84.14 90.11 46.07 37.10 55.63 75.86 90.10 39.52 28.71 45.57 70.28 88.15 31.20 26.44 41.56 62.82 81.26 28.86
Notes: IAB establishment panel, manufacturing industries. Descriptive statistics weighted by using inverse probability weights.
insert value-added per worker in our regressions, as a proxy for firm productivity.10 2.1. Main patterns and correlations This section presents descriptive evidence on the relationship among our main variables of interest, i.e. collective agreement, firm size and export status. Table 1 links firm collective agreement status and size. As we have pointed out in the introduction, and as our empirical analysis will explain more clearly, the gains from collective bargaining may be higher than its costs for larger firms. The above intuition finds support in Table 1, which shows the percentage frequencies of collectiveagreement firms by size-categories for five years. Considering the latest year, i.e. 2016, the share of firms engaging in collective agreement increases monotonically with size, ranging from 26.44 percent among the smallest firms to 81.26 percent among the largest ones. Importantly, Table 1 also shows that the total share of collective agreement firms has decreased over time, ranging from 46.07 percent in 2000 to around 28.86 percent in 2016, which is in accordance with the welldocumented tendency to the decentralization of the bargaining process in recent years (Hirsch and Schnabel, 2014). A more careful look at Table 1 reveals that small and medium-sized firms drive such a tendency. Indeed, the percentage of collective agreement firms in the first two size-categories (i.e. firms with less than 50 and with 50–250 employees) has decreased by more than 17 percentage points from 2000 to 2016, while it has only declined by less than 9 percentage points among firms in the largest size category. In absolute terms the share of collective bargaining firms in the largest size category is still more than 80 percent. Hence, the overall message delivered
4 See also Brändle and Heinbach (2013) for another study on the effects of opening clauses. 5 The mechanism outlined in their paper finds empirical support by Gürtzgen (2009). Fanti and Gori (2013) study the role of collective bargaining in a Cournot duopoly. 6 Two types of information on firm size are included in the data: total number of employees and employees subject to social security contribution. Our main analysis is based on the former definition, but results of robustness checks based on the latter are qualitatively similar. 7 We use the terms “firm” and “plant” interchangeably throughout the paper, because a large majority of plants in our data are single unit firms. 8 Notice that the main results can be replicated when running separate regressions for both type of collective bargaining firms. Additional results are available upon request. 9 We also tried using log revenue as size measure. Results are comparable to the results obtained from our benchmark specification.
10 Summary statistics on the estimation sample are provided in the Online Appendix.
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Table 2 Export status and firm size.
Table 3 Probit marginal effects (without dummies). Domestic firms
Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total Less than 50 employees Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Total
1996 87.63 35.51 13.85 8.86 82.54 2000 85.70 40.25 17.48 11.31 80.39 2005 79.32 30.08 15.54 11.50 73.86 2010 76.39 29.04 14.37 14.54 70.90 2016 72.91 26.72 14.58 17.47 67.30
Exporting firms
Dependent variable: Collective agreement (dummy)
12.37 64.49 86.15 91.14 17.46
Export (dummy)
(1)
(2)
(3)
0.137∗∗∗ (0.010)
−0.070∗∗∗
−0.126∗∗∗
(0.010) 0.265∗∗∗ (0.012) 0.490∗∗∗ (0.016) 0.642∗∗∗ (0.012)
No No No 54,711
No No No 54,711
(0.009) −0.001 (0.012) 0.106∗∗∗ (0.021) 0.279∗∗∗ (0.023) 0.039∗∗∗ (0.005) −0.111∗∗∗ (0.010) 0.356∗∗∗ (0.010) No No No 54,711
Between 50 and 250 employees Between 250 and 500 employees
14.30 59.75 82.52 88.69 19.61
More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy)
20.68 69.92 84.46 88.50 26.14
Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
23.61 70.96 85.63 85.46 29.10
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. In Column (3) we additionally control for the share of workers with different qualification levels in each firm.
27.09 73.28 85.42 82.53 32.70
in Table 3 does not include any type of fixed effects, while the one in Table 4 includes year, sector and regional dummies. In the first column of both tables we use the export status as the only explanatory variable. When fixed effects are not included (i.e. the first column of Table 3), the estimated marginal effect suggests that being an exporter increases the probability of engaging in collective agreements by about 14 percentage points. However - and not surprisingly - this effect is slightly reduced as soon as we allow fixed effects to capture all those time, sectoral and regional factors that might influence the collective agreement status.11 Regional disparities on the provincial level can be explained by the reunification in Germany. Schnabel (2016) shows that labor markets in the East and the West of Germany are very unequal. Those differences are systematic and must be taken into consideration when estimating the effects of exporters’ choice of the wage bargaining regime. Schnabel (2016) argues that differences in the union coverage and work councils are among the main drivers behind inequality between East and West Germany. We argue that the catching up process varies among the different provinces in East Germany, which explains why we do not solely rely on the inclusion of an East-West dummy. Though smaller, the effect of collective agreement on the probability of exporting remains positive if we do not add any other explanatory variable, and equal to 0.1 (column 1 of Table 4). The estimated positive correlation between export and collective agreement statuses is likely to be spurious in the above regressions because we are still excluding some important firm characteristics that may influence export status and be correlated with collective agreement. If this is the case, the export dummy will capture also the effect of those omitted variables. As we have discussed, one of the factors that co-varies both with export status and collective agreement is the size of the firm. Thus, in the second column of Tables 3 and 4 we additionally control for firm size categories. Interestingly, the marginal effect of export on the probability of engaging in collective agreement now turns negative: exporting firms seem to have a ceteris paribus lower probability of collective agreements. The marginal effect of export status on collective agreement is either −0.070 or −0.047 depending on whether fixed effects are included or not in the specification. Firm size has a considerable positive influence on the probability of collective agree-
Notes: IAB establishment panel, manufacturing industries. Descriptive statistics weighted by using inverse probability weights.
by this simple cross tabulation is that, while still in the early 2000s most German firms were engaging in collective bargaining, in later years an increasing fraction of small and medium-sized firms started not to bargain collectively with their workers, whereas larger firms still continue to do so. As a second exploratory look at the data we focus on the distribution of firms according to size-class and export status. As shown in Table 2, and as predicted by Melitz-type models, firms clearly sort into the exporting regime according to size. Comparing Tables 1 and 2, one immediately realizes that size drives both the export status and the adopted bargaining regime. Indeed, in 2016 around 82.53 percent of the largest firms produce for the foreign market and almost the same percent of them adopt collective agreements. We also observe that the share of exporting firms within the smaller size categories has increased by at least 10 percentage points between 2000 and 2016. Importantly, the share of exporting firms has increased much more among small and medium-sized firms than among the largest ones (among those, actually, it has slightly declined). We have now showed that size is an important factor driving both collective agreement and export but export at the extensive margin has been driven by small and medium sized firms entering the foreign market (Table 2). These firms are the ones among which collective agreement has more sharply declined (Table 1). In what follows we will further elaborate on this intuition, examining in detail the interplay among our main variables of interest. 3. Baseline model and results To test the relationships among export status, collective agreement and size, we first estimate simple probit regressions where the dependent variable is firm collective bargaining. We focus on export status and firm size as the main variables of interest. Tables 3 and 4 report the marginal effects from this first set of regressions. The specification
11 The sectoral dummies distinguish between different economic activities on a very broad level. The regional dummies are at the province level.
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Table 4 Probit marginal effects.
Table 5 Probit marginal effects of the model with interactions.
Dependent variable: Collective agreement (dummy)
Export (dummy)
(1)
(2)
(3)
0.100∗∗∗ (0.009)
−0.047∗∗∗
−0.088∗∗∗
(0.009) 0.216∗∗∗ (0.011) 0.404∗∗∗ (0.017) 0.545∗∗∗ (0.016)
(0.009) 0.000 (0.012) 0.091∗∗∗ (0.019) 0.228∗∗∗ (0.023) 0.026∗∗∗ (0.004) −0.058∗∗∗ (0.010) 0.309∗∗∗ (0.010) Yes Yes Yes 54,711
Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
Yes Yes Yes 54,711
1. Marginal effects of the export dummy for different firm-size categories Less than 50 employees −0.165∗∗∗ (0.015) −0.060∗∗∗ Between 50 and 250 employees (0.020) −0.134∗∗∗ Between 250 and 500 employees (0.047) −0.000 More than 500 employees (0.070) 2. Marginal effect of size for exporters Between 50 and 250 employees Between 250 and 500 employees More than 500 employees
3. Marginal effect of size for non-exporters Between 50 and 250 employees
Significance levels: < < < 0.01. Standard errors clustered at the firm level in parentheses. In Column (3) we additionally control for the share of workers with different qualification levels in each firm. ∗p
0.10, ∗∗ p
Yes Yes Yes 54,711
Dependent variable: Collective agreement (dummy)
0.05, ∗∗∗ p
Between 250 and 500 employees More than 500 employees Observations
ments, which increases monotonically with firm size. Indeed, relative to firms with less than 50 employees (the base category) firms with more than 500 employees have between 64 and 55 percentage points higher probability of engaging in collective agreements (second column of Tables 3 and 4, respectively). The relative difference between the probability of collective bargaining across the different categories remains stable even after the inclusion of fixed effects. The negative association between collective agreement and export status is robust to the inclusion of other possibly important firm characteristics, such as the age of the firm, and the presence of a works council, as it is shown in the last column of Tables 3 and 4. This first set of results suggests that exporters tend to refrain from collective agreement. One obvious explanation for this result is that, given the higher competition from abroad, exporters will tend to choose the bargaining regime that is associated with the lower cost. However, the coefficient of the export dummy in the above regressions has to be interpreted as an average effect across all firms. Given the descriptive evidence we have examined in the previous section, it is natural to ask how the estimated effect of the export status changes across firm size categories. Hence, we include interaction terms between export status and size in the previous probit regressions. Table 5 shows the marginal effect computed using the coefficient from this estimation. The first panel of Table 5 shows the marginal effect of export status for every category of firm size. While the marginal effect is negative and significant for all size categories up to 500 employees, it considerably decreases non-monotonically in magnitude and turns insignificant for the largest firms. This indicates that smaller exporters are on average less likely to choose collective bargaining, while entering the international market does not seem to change the collective agreement status of the largest firms. We argue that the largest firms were exporters already and that those firms benefit from the operation cost advantage outlined in the introduction. The last two panels of Table 5 display the marginal effects of size for exporters and non exporters separately. Interestingly, the probability of collective bargaining is 34 percentage points larger for the largest exporting firms than for the smallest exporting firms (second panel of Table 5). Moreover, the correlation between size and collective agreement is stronger for exporters than for non-exporters, which is evident from the comparison of the magnitudes of the marginal effects of size for the two categories of firms (second and third panel of Table 5).
0.044∗∗ (0.018) 0.146∗∗∗ (0.027) 0.342∗∗∗ (0.029)
−0.061∗∗∗ (0.021) 0.115∗∗ (0.046) 0.177∗∗ (0.070) 54,711
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. The marginal effects of the size categories at points 2. and 3. are the discrete change of the predicted probability of engaging in collective agreement with respect to the base firm size category (i.e. a firm with less than 50 employees). All other control variables are evaluated at their average value in the estimation sample.
Overall, the above results can be summarized as follows: while exposure to international competition does not have any effect on the choice of the bargaining regime for large firm, it might instead induce small and medium-sized firms to refrain from collective bargaining. However, the larger the exporters get, the more likely it is that they will choose collective agreement. As discussed in the introduction, this empirical finding might be easily explained with the presence of external economies of scales accruing to large firms when they negotiate wages collectively. Moreover, firms that are exposed to international competition should be more induced to exploit the efficiency gains from collective bargaining, so as to be more competitive in the foreign market. Hence, it is intuitive to find that exporters show a stronger tendency towards collective bargaining than non-exporters (of any size). 4. A bivariate probit model for collective agreement and export status As we have just seen, including important observable characteristics in the probit equation for collective bargaining helps eliminate the initial bias of the effect of the export status. However, omitted variables may not to be the only source of bias in the previous models. In fact, not only is there self-selection into export, but the results we find may also be just driven by unobservable firm characteristics that affect both export status and collective agreement. In order to address the above concern, we follow common practice in the literature dealing with binary response models with endogenous binary regressors (for a recent application in the trade literature see, for example, Egger et al., 2011), and we estimate a bivariate probit model for collective agreement and export status. The intuition behind this approach is straightforward: we specify a two-equation model, one for the probability of collective bargaining, and the other one for the probability of export. 5
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Table 7 Check of the exclusion restrictions of the Bivariate probit model (II).
Table 6 Check of the exclusion restrictions of the Bivariate probit model (I). Dependent variable: Collective agreement (dummy)
Dependent variable: Export (dummy)
Variable of interest: Investment in IT (dummy)
Investments in IT (dummy) Export (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Constant Year-dummies Sector-dummies Region-dummies Observations R2
Variable of interest: Investment in IT (dummy)
(1) OLS
(2) Probit
−0.001
0.003 (0.021) −0.323∗∗∗ (0.031) −0.000 (0.041) 0.306∗∗∗ (0.063) 0.774∗∗∗ (0.076) 0.093∗∗∗ (0.016) −0.210∗∗∗ (0.037) 1.124∗∗∗ (0.041) −1.986∗∗∗ (0.181) Yes Yes Yes 54,711 0.289
(0.006) −0.089∗∗∗ (0.008) −0.002 (0.012) 0.092∗∗∗ (0.018) 0.184∗∗∗ (0.017) 0.025∗∗∗ (0.004) −0.060∗∗∗ (0.010) 0.382∗∗∗ (0.013) −0.086∗ (0.049) Yes Yes Yes 54,711 0.347
Investments in IT (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Constant Year-dummies Sector-dummies Region-dummies Observations R2
(1) OLS
(2) Probit
0.078∗∗∗ (0.006) 0.258∗∗∗ (0.013) 0.296∗∗∗ (0.017) 0.307∗∗∗ (0.017) 0.070∗∗∗ (0.005) 0.014 (0.011) 0.095∗∗∗ (0.013) −0.378∗∗∗ (0.052) Yes Yes Yes 54,711 0.360
0.257∗∗∗ (0.021) 0.700∗∗∗ (0.039) 0.885∗∗∗ (0.062) 0.989∗∗∗ (0.069) 0.243∗∗∗ (0.017) 0.031 (0.038) 0.272∗∗∗ (0.040) −2.907∗∗∗ (0.193) Yes Yes Yes 54,711 0.304
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. We additionally control for the share of workers with different qualification levels in each firm. In the probit regression in column (2) coefficients are reported and the R2 value is the pseudo R2 .
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. We additionally control for the share of workers with different qualification levels in each firm. In the probit regression in column (2) coefficients are reported and the R2 value is the pseudo R2 .
marginal effect of the export dummy in Column 1 has the usual interpretation of the average change in the probability of collective bargaining when firms switch from the domestic to the exporting regime. As we can see, the effect of exporting on collective bargaining remains negative and significant. Exporters seem to be on average 17 percentage points less likely to bargain collectively with their workers. Columns (2) and (3) of the same table decompose the total marginal effect into two components, which can be interpreted as the exporters’ and the non-exporters’ contributions to the probability of engaging in collective agreements. Intuitively, the results of Table 8 for the marginal effect of the export dummy could be read as follows: ceteris paribus, collective agreements decrease the average probability of exporting by 17 percentage points. This is the result of two effects. On the one hand, exporters have an 8.6 percentage points lower probability of collective agreement with respect to those who don’t export. On the other hand, domestic firms would lower their probability of collective agreement by 7.9 percentage points if they started to export. The marginal effects of the other covariates have the same sign and partly the same magnitude as the simple probit regressions in Table 4. The correlation between the error terms in the export and the collective bargaining equations (𝜌) is positive but insignificant. Hence, even if our concern for the endogeneity of the export dummy was reasonable, the data do not show statistical support for this hypothesis.
The export status dummy will appear as an explanatory variable in the former equation, and as the dependent variable in the latter. The key issue is that the error terms of the two equations are allowed to be correlated, and the correlation between the error terms will be one of the estimated parameters of the model. In this way we explicitly take into account the potential source of endogeneity we mentioned above (i.e. correlation in unobservables) and we are able to obtain unbiased estimates of the marginal effect of export status on collective bargaining. It is advisable to have at least one variable which appears in the equation explaining export status which is not included in the one for collective agreement (Monfardini and Radice, 2008). Following Hauptmann and Schmerer (2013) we use a dummy variable indicating whether the firm has invested in IT in the previous year as an instrument.12 Indeed, both OLS and probit regressions of collective bargaining and export dummies on Investment in IT confirm this hypothesis. As we show in Tables 6 and 7, investment in IT is highly significant in the regressions featuring export as a dependent variable, and insignificant in the regressions with collective bargaining as dependent variable. Results. Table 8 reports the marginal effects of the estimated bivariate probit model. Column 1 shows the marginal effects on the unconditional probability of collective agreement. These should be compared to the marginal effects in Table 4 commented earlier. The estimated
5. Coarsened exact matching Our analysis so far has documented the existence of a negative link between collective bargaining and export. Moreover, the bivariate probit model estimated in the previous section suggests that the negative effect of export on collective bargaining may be causal. Although standard test statistics support our exclusion restrictions and hence the validity of our results, the empirical models presented so far hinge on strong parametric assumptions, e.g. the distribution of the error term. To check whether the effect found is robust to model specification, we
12
As argued in Hauptmann and Schmerer (2013), this variable is likely to be correlated with export, as exporters may invest in communication technology in order to ease their international activities (see also Bertschek et al., 2015) but there are instead no obvious reasons why investments in IT may be correlated with collective bargaining. For a detailed discussion on the cost-saving aspects of IT investment and its effect on overall competition see Mallick and Ho (2008) and Ho and Mallick (2010). 6
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Table 8 Bivariate probit model (marginal effects).
Table 9 Balance condition in 2016.
Dependent variable: Collective agreement (dummy) Variable of interest: Export (dummy) Founded before 1995 Size class 1 Size class 2 Size class 3 Value added West Sector 1 Sector 2 Sector 3
Exclusion restrictions: Investment in IT (dummy) (1)
(2)
(3)
−0.165∗∗∗
−0.086∗∗∗
−0.079∗∗∗
(0.049) 0.023 (0.019) 0.118∗∗∗ (0.026) 0.255∗∗∗ (0.027) 0.031∗∗∗ (0.006) −0.056∗∗∗ (0.010) 0.313∗∗∗ (0.010) Yes Yes Yes 54,711 0.169 0.108 2.475
(0.027) 0.104∗∗∗ (0.011) 0.190∗∗∗ (0.017) 0.299∗∗∗ (0.021) 0.043∗∗∗ (0.004) −0.026∗∗∗ (0.007) 0.194∗∗∗ (0.008) Yes Yes Yes 54,711
(0.023) −0.081∗∗∗ (0.010) −0.072∗∗∗ (0.014) −0.045∗∗∗ (0.016) −0.012∗∗∗ (0.003) −0.030∗∗∗ (0.006) 0.119∗∗∗ (0.007) Yes Yes Yes 54,711
𝜕 P(CA) 𝜕X
Export (dummy) Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker (ln) Founded after 1995 (dummy) Works council (dummy) Year-dummies Sector-dummies Region-dummies Observations
𝜌 𝜌 (std. err.) 𝜒 2 (Wald test𝜌 = 0)
𝜕 P(CA=1,Exp=1) 𝜕X
𝜕 P(CA=1,Exp=0) 𝜕X
Matched
Raw
Matched
−0.119 −0.913
0.013 −0.011 0.007 0.007 0.034 0.000 0.000 0.000 0.000
0.909 2.108 2.285 6.308 1.310 1.091 0.230 0.800 1.117
1.011 1.008 1.007 1.029 0.981 1.000 1.000 1.000 1.000
0.730 0.335 0.636 0.248 −0.407 −0.069 0.136
a matching technique called “coarsened exact matching” (CEM) introduced by Iacus et al. (2011). This method has the advantage of being independent of model assumptions, except the usual “ignorability” condition (i.e. no omitted matching variables).13 CEM proceeds in three steps. First, the matching variables are coarsened into broader categories. Then, the observational sample is divided in strata, each characterized by the same value of the coarsened matching variables. Third, the strata that do not contain at least one treated and one control unit are discarded. After obtaining a CEM-matched sample, one can compute the ATE and ATET using any other parametric or nonparametric model (Blackwell et al., 2009). After forming the matched sample, we run nearest neighbor matching (NNM) to compute the ATE and the ATET (Abadie and Imbens, 2006). NNM uses an average of the outcomes of the nearest observations to impute the counterfactuals. The “nearest” observations are determined using a weighted function of the covariates for each observation (called Mahalanobis distance). The difference between the observed outcome and the imputed counterfactual for each firm is an estimate of the firm-level treatment effect. The ATE will be the average of the firm-level effects over all firms, while the ATET will be the average of the firm-level treatment effects taken only over the exporters. The resulting treatment effects have a casual interpretation under two conditions. First, as we have already mentioned, the matched exporting and domestic firms do not have to differ systematically in the matching variables. Secondly, self-selection into export should be only driven by observable firm characteristics.14 For consistency with our previous analysis we match exporters and non-exporters on the same variables that we have used as controls in the previous models.15 Checking the balance in the matched sample. The CEM algorithm performs exact matching (either on coarsened or actual data) and hence it should achieve by construction a high degree of balance between the treated and control samples. The next two tables confirm our hypothesis. Table 9 reports two balance measures: the standardized difference of the variables, which compares the means of the matching variables
estimate the effect of export on collective bargaining using nonparametic techniques. Following the programme evaluation literature, our effects of interest are the “Average Treatment Effect on the Treated” (ATET) and the “Average Treatment Effect” (ATE). In our context, the ATET compares the observed probability of collective bargaining among exporters with the probability of collective bargaining that the same exporters would have if they were domestic. The expression for the ATET is: ATET = P(CA = 1 ∣ EXP = 1)EXP=1 (1)
The ATE combines the ATT with the same effect among domestic firms, i.e. the difference between the probability of collective bargaining for domestic firms if they were exporters and their observed probability of collective bargaining. Formally, ATE = ATET + P(CA = 1 ∣ EXP = 1)EXP=0
− P(CA = 1 ∣ EXP = 0)EXP=0
Variance ratio
Raw
The table reports the standardized differences and variance ratios for the last year in the sample, i.e. 2016. We present the balance measures for both the raw and matched sample. If exporting and non-exporting firms are perfectly matched according to a variable, the standardized difference is zero and the variance ratio is one.
Significance levels: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Standard errors clustered at the firm level in parentheses. All specifications additionally control for the share of workers with different qualification levels in each firm.
− P(CA = 1 ∣ EXP = 0)EXP=1
Standardized differences
(2)
The critical point for the unbiased estimation of the ATE and ATET is the evaluation of the counterfactuals, i.e. the probability of collective bargaining if a firm’s export status was different from the one directly observed. In general, using the observed outcomes for domestic firms as counterfactuals for exporting firms (and vice-versa) is not recommendable, because being an exporter is not a randomly assigned characteristic to the firms, but exporting and domestic firms typically differ along several dimensions. Finding the right counterfactual in nonexperimental data is the main aim of any matching technique. The basic idea behind matching is to trim the data so as to create a “balanced sample” of observations. In our application, this would be a sample where the distribution of the matching variables (i.e. the main drivers of a firm’s export behavior) among exporters is as similar as possible to the distribution of the matching variables among non-exporters. We use
13 For a recent application of CEM in the trade literature, see for example de Rassenfosse et al. (2016). 14 The insignificance of the correlation of the error terms in the bivariate probit model hints that observable characteristics may indeed be the only drivers of a firm’s export behavior. 15 For dummy variables, which do not need any coarsening, the CEM algorithm will find exact matches based on the actual variables. For continuous variables, e.g. value-added per worker, the exact matching will be based on the coarsened variables.
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Table 10 Balance condition: matched variance ratio 1996–2016. Year
1996
1997
1998
1999
2000
2001
2002
Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods Year Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added per-worker West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods Year Founded before 1995 Between 50 and 250 employees Between 250 and 500 employees More than 500 employees Value added West (dummy) Consumer goods manufacturing Producer goods manufacturing Investment goods
. 0.989 0.977 1.059 1.240 1.000 1.000 1.000 1.000 2003 0.979 1.001 1.000 0.998 1.020 1.000 1.000 1.000 1.000 2010 0.964 1.010 1.024 1.000 0.984 1.000 1.000 1.000 1.000
. 1.000 0.999 1.016 1.101 1.000 1.000 1.000 1.000 2004 0.915 1.003 1.009 1.000 1.032 1.000 1.000 1.000 1.000 2011 0.974 1.007 1.005 1.070 0.973 1.000 1.000 1.000 1.000
0.945 0.980 1.034 1.031 1.121 1.000 1.000 1.000 1.000 2005 0.954 1.003 1.010 1.000 0.995 1.000 1.000 1.000 1.000 2012 0.997 1.003 1.002 1.022 1.032 1.000 1.000 1.000 1.000
0.992 1.000 1.000 1.100 0.950 1.000 1.000 1.000 1.000 2006 0.993 1.001 1.000 1.041 1.066 1.000 1.000 1.000 1.000 2013 1.027 1.003 1.008 0.998 1.062 1.000 1.000 1.000 1.000
0.945 1.004 1.000 1.121 0.989 1.000 1.000 1.000 1.000 2007 0.999 1.002 1.002 1.033 1.045 1.000 1.000 1.000 1.000 2014 0.997 1.008 1.014 0.994 1.250 1.000 1.000 1.000 1.000
0.993 1.001 1.004 1.002 1.105 1.000 1.000 1.000 1.000 2008 1.004 1.013 1.050 1.032 1.039 1.000 1.000 1.000 1.000 2015 1.022 1.005 1.003 1.014 1.072 1.000 1.000 1.000 1.000
1.010 1.001 1.005 0.998 1.019 1.000 1.000 1.000 1.000 2009 0.981 1.008 1.013 1.054 1.113 1.000 1.000 1.000 1.000 2016 1.011 1.008 1.007 1.029 0.981 1.000 1.000 1.000 1.000
for exporters and non-exporters,16 and the ratios between the variance of the matching variables for exporters and non-exporters. In a perfectly balanced sample standardized differences are equal to zero, and variance ratios are equal to one. We show the details for the most recent year of the panel, i.e. 2016, and present the matched variance ratio for the whole sample period.17 In 2016 the standardized differences are close to zero for all variables and the variance ratios are close to one. Moreover, comparing the raw and the matched sample shows a strong impact of the matching procedure in eliminating the initial imbalance. Table 10 shows a similar picture for the variance ratios for all years. Table A1 in the Online Appendix provides an additional summary statistic that contains the number of observations and the number of matches in the raw and the matched samples for all the different years. Treatment effects. Figs. 2 and 3 present the ATE and ATET for each year. The ATE are always negative and significant in more recent years of our sample. The ATET, i.e. the effects of export on collective bargaining computed only for exporters are less precisely estimated especially at the beginning and the end of the sample periods, while they are negative and significant in the middle periods. Overall, the analysis supports the negative effect of export on collective bargaining in most but not all years. 16
d=
6. Robustness checks As robustness checks, we re-run our analysis on different subsamples of observations. Our subgroups are: 1) Eastern German firms; 2) Western German firms; 3) firms producing for the food industry; 4) firms producing consumer goods; 5) firms in the producer goods sector and 6) firms in the investment goods sector. The results of these robustness checks are reported in the Online Appendix. Table A2 shows the baseline probit regressions. For comparative purposes, the first column of the table reports the benchmark results in the main text. The estimated coefficient of the export dummy hardly changes across subgroups and it is significant and negative. Tables A3–A8 report the coefficients of the bivariate probit model estimated on the different subsamples. The export-dummy is negative and significant for Western German firms but insignificant in the regressions for Eastern Germany. Different results for firms located in the West and firms located in the East of Germany are not too surprising and in line with other authors’ findings. See Schnabel (2016) for the differences between the labor market in East- and West-Germany. In the regressions based on the different sectors we find a negative and significant effect of export on collective bargaining only for firms producing consumer and producer goods, but not for firms in the other two sectors. Finally, Figs. A1 and A2 present the robustness checks on the treatment effects. Here the export-dummy becomes insignificant in some years. However, once the sample is split in several subsamples, the number of firms that can be matched each year is very small. Hence, these results must be interpreted with caution.
The expression for the standardized difference for a continuous variable is: x −x √t c , where x t and xc are the sample means of variable x in the treated 2 2 s −sc t 2
and control group respectively and s2t and s2c are the standard deviations of x in the treated and control groups respectively. The standardized difference for ̂p −̂ p a dummy variable is d = √ ̂p (1−̂tp )+̂pc (1−̂p ) , where ̂ pt and ̂ pc denote the mean of t
t
c
c
2
the dummy variable in the treated and control groups respectively (Flury and Riedwyl, 1986). The standardized differences do not depend on sample size, hence they can be used to compare the balance in the matched sample with that in the unmatched sample (Austin, 2009). 17 Other statistical tests for the balance are available from the authors upon requests. 8
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Fig. 2. Average treatment effects.
Fig. 3. Average treatment effect on the treated.
7. Summary and conclusions
a single entity (the union), instead of individually with each of their workers. Furthermore, large exporters should be more likely to exploit the efficiency gains for collective bargaining, so as to be more productive and competitive in the international market. This is exactly what our empirical evidence has shown: large exporters choose the collective agreement regime more often than non-exporting firms of any size.
In this paper we have provided empirical evidence on the link between export and collective agreement. Exposure to international competition is sometimes believed to put the existence of historical labor market institutions in Western Europe at risk, such as employment protection and collective agreement. However, the evidence presented in this paper does not totally support this fear: larger exporting firms do not seem to show a tendency towards abandoning collective agreements. We have developed a straightforward theoretical explanation for this result: under the assumption that bargaining is costly, large firms might be better off by collectively bargaining with
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.econmod.2019.03.008.
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