Employment Effects of Mergers and Acquisitions: A Continous Treatment Approach

Transcription

1 Employment Effects of Mergers and Acquisitions: A Continous Treatment Approach Benjamin Furlan January 30, 2014 Abstract Previous research on employment effects after merger and acquisition (M&A) activities show differing results. What they have in common is that they use a pre-defined threshold (25%, 50%, 75%) to define a successful merger. One possible factor driving this differing results is that these studies use a firm s takeover status, using M&A activities as binary treatment. We contribute to the existing literature by applying the newly developed generalised propensity score (GPS) matching technique that allows us to run our estimations based on a continuous treatment variable. Our results suggest, based on our sample of European manufacturing firms, that M&A activities have a boosting effect on employment growth in acquired firms at different levels of shares. Splitting the data set up in sub-samples, we find evidence that small and medium-sized firms benefit more than large firms. JEL Codes: F23, G34 Keywords: generalized propensity score, mergers, acquisitions, international business Department of Economics and Social Sciences, University of Salzburg. Address: Residenzplatz 9, 5010 Salzburg, Austria. Benjamin.Furlan@sbg.ac.at. 1

2 1 Introduction Merger and acquisition (M&A) activities and its effects on employment have been studied thoroughly by economists over the last years. However, the results of recent studies are contradicting and we do not get a clear answer on how M&A activities influence the changes in employment of acquired firms. 1 Not last due to the contribution of Shleifer and Summers (1988), the common belief is that hostile takeovers lead to reductions in the workforce through restructurings by the new management. Let us have a look at anecdotal evidence. Semperit, (one of the oldest and biggest firms in Austria) sold its core business, tire production, to its biggest rival Continental AG in 1985 as Semperit started struggling due to the oil price shocks in the 1970ies. Continental had to keep the employees of Semperit by contract for 10 years, and started firing people afterwards. In 2002, after 113 years in business, the whole production was outsourced into the Czech Republic, leaving only 30 employees left for deconstruction works until The Porsche Holding Salzburg, Europe s largest automotive retail company, having almost 21,000 employees in 2010, has become a 100% subsidiary of the Volkswagen AG in March In 2012, Porsche Holding AG employed 30,680 workers. Acquiring shares allows the owner to participate in the organization of the strategic focus of a firm that includes decisions regarding the labor stock. As shown by Hanson and Song (2000) negotiation power increases with higher levels of shares. In addition, especially the European Union seeks to protect minority shareholders and induced a harmonisation process of the company law. For example, European corporate laws allow stakeholders with at least 5% of all shares to call for an extraordinary general meeting (EGM). The possession of at least 25% of all shares allows minority owners to veto against decisions that intend to change the strategic focus of the firm, such as mas layoffs. The vast majority of contribtions defines M&As as a transaction where the acquiring firm takes over at least 50% of all outstanding shares. In order to avoid this restriction, our estimations are based on generalized propensity score matching techniques as proposed by Hirano and Imbens (2004). This approach allows us to estimate the whole distribution of M&A induced employment effects. We are therefore not limited to a pre-defined margin that characterises whether a transaction counts as an acquisiton (25%, 50%) 2 and results 1 significantly negative employment effects are found in Siegel and Simons (2010), Conyon et. al (2002a/2002b). Gugler and Yurtoglu (2004) compare employment effects in the United States and Europe, finding negative employment growth rates only in European firms. Increases in labor force are found by McGuckin and Nguyen (2001), Oberhofer (2013) and Stiebala and Trax (2011). 2 e.g. Gugler and Yurtoglu (2004) define a merger if at least 50% of all outstanding shares have been acquired. Stiebale and Trax (2011) use a different measure defining merger activities, namely an increase in shares from under 25% to at least 25% of the acquired firm. 1

3 in a binary treatment (treated versus non-treated). By contrast, we define the amount of shares as a continuous variable (that can take any value between 0 and 100%). For the empirical analysis, we combine two data sets, both provided by Bureau van Dijk. In contrast to many previous studies, we have access to data for all M&A activities in the manufacturing sector that took place in 37 European countries. First, the ZEPHYR database includes information on M&A activities. Second, the AMADEUS database provides balance sheet information from about 8 million firms located all over Europe. Each firm is identifiable by a unique identification number. The final data set consists of almost 1,400 M&A activities during 2003 to Data and Descriptive Statistics For our empirical analysis we combine two different data sets provided by the Bureau van Dijk for the years The first data set, AMADEUS, provides balance sheet data and other financial statements. Each firm of this data set has a unique identification number which allows us to combine it with the second data base, ZEPHYR. From there we get information for business deals (e.g., acquired shares, date of transaction, price, etc.). For our estimations, we solely focus on production units (NACE Rev.2 codes: ) of unconsolidated companies. Amadeus provides information on whether a company network consist of at least one single firm. As we are interested in the employment effect of the affected firm and not on the company network as a whole, we run our estimations on unconsolidated accounts (both acquired and non-acquired). To gain useful results, we have to make some restrictions: if one firm has been a M&A target of the same firm within the same year, we treat this as a single transaction by summing up the share values. We exclude firms that have been overtaken multiple times over the years, as the effects could overlap each other. Sadly, the balance sheet data provided are hugely unbalanced and thus have many missing observations. As we need a balanced panel data set to conduct our analysis, we have to exclude all firms, for which we do not observe all relevant data over time. In a last step, we exclude a number of outliers and implausible figures. 4 This leaves us with a total of 1,369 succesful mergers. In addition to our treatet units, we need a relevant comparison group. We therefore take a random sample of 25% of all firms that have never been treated and we have full information on our covariates. The final data set thus provides a sufficient number of control units (162,989). Figure 1 shows the distribution of the final stakes after succesfull mergers. It is worth noting, that the 3 Data is available until 6/ we exclude firms with negative a negative employment stock, firms with more than 120,000 employees or unplausible growth rates (over 5000% per year). In addition, we exclude both the first and last percentil of the variables of interest. 2

4 distribution is highly skewed to the rigt, showing that 1,004 out of 1,369 acquisitions are full acquisitons. As can be seen in figure 1(b), the remaining 365 M&As are distributed over the whole magnitude. Density % Final stake (a) Density % Final stake (b) Figure 1: Distribution Final Stakes Table 1 gives a brief summary of the data-set in use. If you compare the numbers for the different samples, there are some interesting facts to mention: (i) Firms that have been acquired up to 50% are bigger (firm size is captured as the number of employees) and older than their counterparts. The same holds for capital intensity (calculated as the total assets per employee) and the productivity level (value added per employee) (ii) compared to the non-acquired firms, these firms are substantially older and bigger (iii) interestingly, we find a negative return on assets for all firms, but a even lower for the non-acquired firms. 3

5 Table 1: Summary Statistics for the full sample Variable No. of obs. Mean SD Min Max Acquired Final Stake 1, No. of Employees 1, ,375 Firm age 1, Capital Intensity 1, Productivity 1, Return on Assets 1, Acquired up to 50% Final Stake No. of Employees ,375 Firm age Capital Intensity Productivity Return on Assets Acquired above 50% Final Stake 1, No. of Employees 1, ,828 Firm age 1, Capital Intensity 1, Productivity 1, Return on Assets 1, Non-acquired Final Stake 162, No. of Employees 162, ,569 Firm age 162, Capital Intensity 162, Productivity 162, Return on assets 162,

6 3 Econometric Methodology For the econometric analysis, we apply a continuous treatment approach as developed by Imbens (2000) and Hirano and Imbens (2004). The method of the generalized propensity score (GPS) allows the treatment variable to be continous, compared to the binary treatment in the propensity score methodology as derived by Rosenbaum and Rubin (1983). This section will provide a detailed description on the methodology as we follow the implications of Fryges and Wagner (2008). The idea behind this technique follows a threefold path, namely (i) calculation of the conditional distribution of the treatment variable given a set of observable firm-specific characteristics (ii) estimation of the conditional expectation of the outcome and (iii) the dose-response function (average treatment effect). We will use the following notation: statistical units are indexed by i = 1,..., N and Y i (d) refers to the potential outcome that is dependent on the level of the treatment d D, whereas the treatment is a continuous variable D = [d 0, d 1 ]. In our case, the treatment variable are the final stakes after M&A activities, ranging from 0-100%. In the first step, as suggested by Hirano and Imbens (2004), we estimate the conditional distribution of the treatment variable given a set of explanatory variables: E(D i X i ) = F (X i β), (1) with 0 < F (X i β) < 1 for all X i β R making sure that the predicted values of D i lie in the interval (0,1), even for the limit observations zero or one. Papke and Wooldridge (1996) propose a quasi-maximum likelihood estimator of β. As can be seen in figure 1, the distribution of the final shares is highly skewed, such that more than half of the observations are 100% acquisitions. We therefore follow Wagner (2001, 2003) and apply a fractional logit model as developed by Papke and Wooldridge (1996) to estimate the share intensity of the regarding firms in our data set. This estimation procedure maximises the Bernoulli log-likelihood function given by l i (β) D i log[λ(x i β)] + (1 D i )log[1 Λ(X i β)] (2) using a generalised linear models (GLM) framework as established by McCullagh and Nelder (1989). The estimated GPS ˆR i is therefore given by: ˆR i = [Λ(X i ˆβ)] D i [1 Λ(X i ˆβ)] (1 D i ). (3) 5

7 In the second stage we model the conditional expectation of our outcome variable Y i (in our case: employment growth) as a function of our treatment variable D i and the estimated propensity score ˆR i. Following Hirano and Imbens, we chose a quadratic approximation for the conditional expectation of Y i : E[Y i D i, ˆR i ] = α 0 + α 1 D i + α 2 D 2 i + α 3 ˆRi + α 4 ˆR2 i + α 5 D i ˆRi (4) that is simply estimated with OLS. In the last step we estimate the average expected outcome at any treatment level d by using the regression coefficients obtained in step 2: Ê[Y (d)] = 1 N N (ˆα 0 + ˆα 1 d + ˆα 2 d 2 + ˆα 3ˆr(d, X i ) + ˆα 4ˆr(d, X i ) 2 + ˆα 5 dˆr(d, X i ). (5) i=1 where N indicates the number of observations in our data set. The confidence intervals are determined via bootstrapping (500 replications). To calculate the entire dose-response function, we divide the density d in 10%-cohorts. We then run the calculations of equation (5) for the cohorts. Due to the fact, that the majority of the M&As are 100% takeovers, the last cohort consists only of full acquisitions. 5 Last, we check whether the balancing property is fulfilled, meaning that observational units with the same propensity score have the same distribution of observable characteristics independent of their treatment status. 5 the cohorts are therefore: [0, 10[, [10, 20[, [20, 30[, [30, 40[, [40, 50[, [50, 60[, [60, 70[, [70, 80[, [80, 90[, [90, 100[ and [100] 6

8 4 Results In this section we will provide the estimated dose-response functions that can be interpreted as the average treatment effect of M&A activities on the employment growth rate. Given the data set from we evaluate the impact as the mean employment growth rate of the overtaken firm from the year of the treatment t to t + 2. The dose response functions are based on the pooled data set. Following Hirano and Imbens (2004) we calculate the conditional distribution of our treatment variable given a set of covariates. As mentioned in the section above, we apply a fractional logit model, developed by Papke and Wooldridge (1996) in order to estimate the share density of M&As. The firm-specific explanatory variables include the size of firm (measured as the logarithmic number of employees), the age of the firm, an interaction term of these two variables, the capital intensity, the return on assets and the productivity level of each firm. We furthermore use industry (1-digit level) and year dummies to capture these time-fixed effects. Recent empirical works have either used either only treated units (see, e.g. Hirano and Imbens, 2004) or both treated and non-treated as done e.g. by Fryges and Wagner (2008). We follow a more comprehensive approach and provide results for either case. In addition, we use the following sub-samples: (i) only treated observational units, (ii) firms that are located in richer 6 or poorer European countries, respectively, (iii) small or big firms 7. In table 2 we report the parameter estimates as well as the marginal effects of the fractional response model for all samples, as mentioned above. We observe a significant positive effect of firm size (measured as number of employees) on the amount of shares bought for the full sample as well as for all sub-samples (only the sample representing big firms shows no significance). This indicates, that the probability to become a M&A target increases with firm size, although this is not a necessity for big firms (sample 5). The results of the fractional response share model also suggests that older firms are more likely to become a target of M&As. Both positive effects, firm size and age of the firm, are diminished by the negative interaction term of these two covariates. Interestingly, capital intensity is negative for most groups, only showing a positive effect for the rich EU countries. By contrast, return on assets increases the probability for being taken over significantly, as well as productivity. When we compare the different groups, two 6 we refer rich as countries that have a higher GDP per capita than the EU-27 average. Given our data-set, these countries are: Luxembourg, Austria, Ireland, Netherlands, Sweden, Denmark, Germany, Belgium, Finland and France 7 The definition for micro-, small-, medium- and large-sized enterprises is taken by the European Commission (EU recommendation 2003/361): Micro entities are defined as up to 10, small firms up to 50, medium companies up to 250 and large firms over 250 employees 7

9 interesting results emerge: First, we find almost no evidence, that big firms become M&A targets. Second, the amount of shares bought depends on whether the target firm is located in the richer EU regions or not. We include both year and industry dummies (based on the 2-digit NACE code) to control for economic and/or industry-specific trends. A high McFadden-R 2 between 0.6 and 0.7 shows a very good fit of our model. Given the results from the fractional response share model, we can calculate the dose-response function for our outcome variable for all cohorts. The dependent variable employment growth is measured as the mean growth rate of the two years after a successful merger. This allows us to make sure that we do not measure short-run effects in the employment stock. Figure 1 shows the pooled results for the full sample (the estimation coefficients and the bootsrapped standard errors are reported in the Appendix) Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 2: dose-response functions Full sample The results show a significant positive effect on employment growth for M&As up to 50% takeovers. The overtaken targets in this range show a higher employment growth of about 3.4 percentage points compared to the counterfactual scenario in which they would not have been a target of M&A. Moreover, we find an ongoing positive, although not statistically significant effect for takeovers over 50%. For this sample, we finally find a significant growth rate of about 1% for 100% acquisitions. Following Hirano and Imbens (2004) who use only treated observational units in their work, figure 2 provides the average treatment effect only for firms that have been targets to M&A activities. It 8

10 is noticeable that firm, that have been acquired up to a stake of 50% show a positive and significant treatment effect, although it is slightly smaller (3.2%) than in the full sample. To make this effect clear, one can observe the coefficient of the 10-20% cohort, namely 4.6%. This coefficient states that companies that have been acquired between 10 and 20% have a mean growth over the following two years that is 4.6% higher than the employment growth of firms, that have been traded on a different level of shares. Moreover, we find negative dose-response functions beginning with acquisitions of at least 50%. These effects are not statistically significant though. Like in the full sample, 100% M&As have a small significant positive effect on employment growth. Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 3: dose-response functions only treated To deepen the analysis, we divide our full sample in two other samples. The sample presented in figure 3 consists of overtaken firms that are located in countries that have a higher GDP per capita than the EU-27 mean. Therefore, figure 4 covers M&A activities that took place in the poorer regions. In comparison to the previous results, it is notable that we observe a constant positive effect on employment growth. This effect is much more pronounced for firms in poor countries, where we find a positive outcome between 20% and 40% and for 100% of acquired shares. For the counterpart, there is almost no significant effect, mostly positive though (except for the cohort between 70-90% final shares) 9

11 Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 4: dose-response functions rich countries Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 5: dose-response functions rich countries Finally, we run our estimations, splitting our sample in another two sub-groups to test whether small firms profit more from being acquired than big firms. Due to limitations in our dataset, we combine micro-, small- and medium-sized firms (up to 250 employees) into one sub-set and big firms (more than 250 employees). We cannot divide our dataset 10

12 further, as we do not have a sufficient number of treated units for this case. Figures 5 and 6 therefore represent the dose reponse functions for the differentiation in firm size Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 6: dose-response functions big firms Employment Growth in % Final Shares Employment Growth Lower Bound 95% Confidence Interval Upper Bound 95% Confidence Interval Figure 7: dose-response functions small/medium firms The distinction between small and big firms shows some interesting facts. First, we do not observe any significant positive effect for big firms, but a negative significant em- 11

13 ployment effect for firms that are held by a different company by at least 70%. Regarding the dose response functions, we observe a decrease in the outcome variable of around 3%. Second, small and medium-sized firms take a big profit from being overtaken up to 50% of their outstanding shares. In contrast to the results explained above, this subgroup shows the biggest amplitude with a considerable treatment effect for the first five cohorts of around 6.7%. 12

14 Table 2: Determinants of the Employment Growth Rate - Fractional Response Share Model full sample treated (1) (2) Parameter Marginal Parameter Marginal Variable Estimates Effects Estimates Effects Number of employees (0.051) (0.000) (0.227) (0.023) Age of firm (0.098) (0.001) (0.365) (0.037) Age * employees (0.016) (0.000) (0.066) (0.007) Capital intensity (0.055) (0.000) (0.115) (0.012) Return on assets (0.037) (0.000) (0.066) (0.007) Productivity (0.077) (0.001) (0.127) (0.013) Time effects a Industry effects b McFadden-R N 164,358 1,369 above EU 27 below EU 27 (3) (4) Parameter Marginal Parameter Marginal Estimates Effects Estimates Effects Number of employees (0.083) (0.000) (0.067) (0.000) Age of firm (0.146) (0.001) (0.132) (0.001) Age * employees (0.025) (0.000) (0.021) (0.000) Capital intensity (0.094) (0.000) (0.079) (0.000) Return on assets (0.064) (0.000) (0.049) (0.000) Productivity (0.169) (0.001) (0.110) (0.001) Time effects a Industry effects b McFadden-R N 38, ,995 big firms small firms (5) (6) Parameter Marginal Parameter Marginal Estimates Effects Estimates Effects Number of employees (0.197) (0.004) (0.153) (0.001) Age of firm (0.413) (0.009) (0.219) (0.000) Age * employees (0.060) (0.001) (0.050) (0.000) Capital intensity (0.114) (0.002) (0.064) (0.000) Return on assets (0.060) (0.001) (0.045) (0.000) Productivity (0.136) (0.003) (0.091) (0.001) Time effects a Industry effects b McFadden-R N 13, ,825 Note: Standard errors are reported in parantheses.,, denote 10%, 5% and 1% significance levels, respectively. a Tests for joint significance are based on chi 2 -tests 6 degrees of freedom. b Tests for joint significance are based on chi 2 -tests with 2 degrees of freedom for 1-digit NACE codes. 13

15 5 Conclusion The aim of this paper is to give a deeper understanding on how M&A activities influence the labor stock of the overtaken firm. Previous studies focus on a pre-defined definition of M&A. Furthermore, most of these studies have only access to data for a specific country. Therefore authors find either positive or negative effects that are caused by M&A activities. We contribute such, that we do not focus on a certain margin that has to be reached in order to be a merger, but run our estimations on any level of stakes. We thus apply a continous treatment approach. This threefold technique allows us to calculate the average treatment effect on any amount of shares sold. Our findings add some interesting results to the existing findings: First, we cannot confirm a general negative employment effect as found in previous studies (e.g.: Siegel and Simons, 2010) for European firms. Second, we can show that the effect on labour stock of the overtaken firm crucially depends on the amount of stakes acquired. We find higher positive employment effects for firms overtaken up to 50% that show an employment growth rate of around 3.4% compared to a situation in which these firms would not have been acuqired. This effect diminishes with the amount of stakes bought. Third, we split the whole sample into several sub-groups. We run our estimations for firms that are either located in the wealthier EU region or not. This distinction shows that firms in poorer countries benefit more than their coutnerparts of rich EU member coutries. In fact, these firms hardly profit from being overtaken. In a last step, we calculate the dose-response functions both for firms up to 250 employees and big firms (more than 250 employees). Interestingly, we find the biggest employment growth rate for small firms, whereas big firms even tend to reduce their labor stock if they have been acquired with at least 70& of all outstanding stocks. Summing up, this paper shows, that it is not sufficient to observe employment effects after M&A activities at a certain margin (25, 50 or 75%). Furthermore, we find strong evidence that the effect varies among firm size and countries these firms are located in. 14

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