metadata: authors: 0: author: Dobbelaere,Sabien
1: author: McCormack,Grace
2: author: Prinz,Daniel
3: author: Sóvágó,Sándor
count: World
docna: 0: docna: Firm Consolidation and Labor Market Outcomes
docty: Policy Research Working Paper
abstracts: cdata!: Using rich administrative data from the
            Netherlands, this paper studies the consequences of firm
            consolidation for workers. For workers at acquired firms,
            takeovers are associated with an 8.5 percent drop in
            employment at the consolidated firm and a 2.6 percent drop
            in total labor income. These effects are persistent even
            four years later. The paper shows that the primary mechanism
            for this job loss is labor restructuring at consolidating
            firms. Workers with higher-than-expected pay relative to
            their human capital and workers with skills that are likely
            already present at acquirers are less likely to be retained.
display_title: Firm Consolidation and Labor Market Outcomes
listing_relative_url: /research/2023/06/34076736/firm-consolidation-labor-market-outcomes
topicv3: Labor Markets m1326488 855,Merger Control Policy m1327472 1746,Employment Administrative Data m1504077 2767
publishtoextweb_dt: 2023-06-08T00:00:00Z
dois: 10.1596/1813-9450-10477
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1957107
3: author_key: NA
model_extractions: Policy Research Working Paper 10477 Firm Consolidation and Labor Market Outcomes _Sabien Dobbelaere Grace McCormack Daniel Prinz Sándor Sóvágó_ Health, Nutrition and Population Global Practice June 2023
Policy Research Working Paper 10477

# **Abstract**

Using rich administrative data from the Netherlands, this paper studies the consequences of firm consolidation for workers. For workers at acquired firms, takeovers are associated with an 8.5 percent drop in employment at the consolidated firm and a 2.6 percent drop in total labor income. These effects are persistent even four years later.

The paper shows that the primary mechanism for this job loss is labor restructuring at consolidating firms. Workers with higher-than-expected pay relative to their human capital and workers with skills that are likely already present at acquirers are less likely to be retained.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dprinz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Firm Consolidation and Labor Market Outcomes<sup>_∗_</sup> Sabien Dobbelaere Grace McCormack Daniel Prinz S ́andor S ́ov ́ag ́o _Keywords:_ Takeovers, labor market outcomes, labor restructuring. _JEL classification:_ G34, J2, J3, M51.

> _∗_ Dobbelaere: Vrije Universiteit Amsterdam, Tinbergen Institute and IZA Institute of Labor Economics (sabien.dobbelaere@vu.nl). McCormack: University of Southern California (gracemcc@usc.edu). Prinz: World Bank (dprinz@worldbank.org). S ́ov ́ag ́o: University of Groningen (sovago.sandor@gmail.com). The authors are grateful to Statistics Netherlands (CBS) for providing the non-public microdata and Nathan Hendren for facilitating data access. The authors would also like to thank Eric Bartelsman, Sandra Gain, Larry Katz, Bas van der Klaauw, Hessel Oosterbeek, Roula Yazigi, and participants in the Harvard Labor and Applied Microeconomics Workshops, the University of Southern California Price School of Public Policy Workshop, and the University of Notre Dame for insightful comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1 Introduction**

Firm takeovers where one firm absorbs another firm are common phenomena in markets worldwide. Such consolidations can not only impact product competition, prices, and consumer welfare but can also be consequential for workers (Autor, Katz, Patterson and Reenen, 2020; Azar, Marinescu and Steinbaum, 2022; Benmelech, Bergman and Kim, 2022; Rinz, 2022). Understanding the shortand long-term impacts of consolidations on worker outcomes is important for effective regulation of consolidations and the design of labor market, social insurance, and safety net policies.

This paper studies how takeovers affect the labor market outcomes of workers at consolidating firms by using administrative data on takeovers in the Netherlands from 2011 to 2015. Leveraging rich individual-level information on workers at firms involved in takeovers, we find evidence of heightened job loss post-takeover for workers at acquired firms, leading to long-term negative impacts on workers’ labor market outcomes, such as earnings and total income. These negative effects are pervasive across many types of takeovers. Characterizing the types of workers affected, we find that “over-placed” workers who have higher-than-expected pay prior to the takeover and “duplicative” workers with skills that are likely already present at the acquirer are harmed the most. These results are consistent with _labor restructuring_ , where consolidating firms restructure their workforce in order to decrease labor cost and thereby increase efficiency post-takeover.

We first document the short- and long-term impacts of takeovers on incumbent workers at the firms targeted by takeovers (“target firms”). Our analysis matches target firms to control firms that have not participated in any consolidation activity during our study period and then characterizes differential worker outcomes using an event study framework. We find that takeovers can have a large and long-lasting impact on workers at target firms. While workers who are present at treated and control target firms four years prior to the takeover have similar earnings and employment trajectories prior to the takeover, our event study estimates indicate that these workers fare quite differently post-takeover. There is substantial job loss among the workers of target firms: in the four years after a takeover, workers at a target firm are 8.5% less likely to be retained at the consolidated firm compared to workers in the control firm. This lower retention rate translates into income loss: workers at target firms have e1,069.51 (2.6%) lower labor income and e789.37 (1.9%) lower total income, which includes earnings at any firm and income from various government benefits (e.g., 1
unemployment insurance benefits, social safety net payments). These effects are long-lasting and are present even in the fourth year after the takeover. We show that these effects are not driven by lower wages. Instead, we estimate a 1% reduction in the likelihood that workers are employed anywhere and no impact on overall hours worked, suggesting that workers who are not retained are not able to find employment quickly.

We next turn to heterogeneity analyses to better understand which workers are affected by takeovers the most. We find that a dominant mechanism driving job loss in this setting is labor restructuring, changes in the consolidated firm’s production process. We highlight two types of workers affected by restructuring activities: “over-placed” workers who are compensated at higher rates than what would be expected based on their human capital and “duplicative” workers who are likely to be made redundant by the consolidation.

We present two pieces of evidence consistent with the importance of labor restructuring. First, we show that labor restructuring leads to more job loss among “over-placed” workers. As in Schmieder, von Wachter and Heining (2022) and Lagaras (2023), we characterize over-placed workers as workers who have secured employment at a firm with a higher wage premium than is expected for workers’ skills that are valued equally across employers, estimated using a standard Abowd, Kramarz and Margolis (1999) decomposition. We find that over-placed workers are 3.3 times more likely to be separated from the consolidated firm post-takeover compared to workers who are not over-placed, consistent with firms targeting restructuring activities at more generously compensated workers.

Second, we use worker-level skill data to create a measure of human capital relatedness (HCR) between acquirer and target firms participating in a takeover (Lee, Mauer and Xu, 2018). We find that high HCR takeovers, that is, takeovers where the target and acquirer workers have a more similar skill distribution, generate larger employment reductions at the consolidated firm in terms of both retention and hours worked. We then exploit our rich individual-level data to study the outcomes of specific workers who have similar skills to the workers at acquirers and thus are most likely to serve similar roles. We find that for a worker of a specific skill level, being acquired by a firm with a larger share of workers with that same skill level increases the likelihood of separation. For a given worker, being acquired by a firm where over two-thirds of the workforce has a similar skill set leads to a 18.9% reduction in retention compared to 9.4% for workers being acquired by where fewer than a third of workers have the same skills.

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We finally investigate two alternative mechanisms that could explain our results: increasing labor market concentration and increasing product market concentration as consequences of takeovers. First, we find that virtually none of the takeovers in our sample has a meaningful impact on labor market concentration (see also Arnold, 2021). Therefore, we conclude that takeover-induced changes in labor market concentration cannot drive our job loss results. Second, we also find that takeovers that are more likely to increase product market concentration (e.g., horizontal takeovers between firms in the same narrowly defined industry) do not induce larger job loss.

Our work contributes to the recent literature on the consequences of mergers and acquisitions for workers (Furlan, 2015; Geurts and Van Biesebroeck, 2019; Arnold, 2021; Todd and Heining, 2020; Prager and Schmitt, 2021). We make several contributions to this literature. First, we are able to study administratively identified takeovers, rather than relying on worker flows between firms as some prior work did, potentially missing the mergers of smaller firms. Second, we have comprehensive administrative data on a variety of worker outcomes, including employment outcomes, earnings, and benefit uptake. These data allow us to comprehensively characterize the consequences of takeovers. Third, using detailed data on wages and skills, we are able to examine potentially important mechanisms, including labor market concentration and labor restructuring behind takeover-induced job loss. In this latter aspect, the most similar papers to ours are Lagaras (2017) which uses micro-data from Brazil to characterize how workers at target firms are affected differently based on their occupational relatedness with the acquirer and Lagaras (2023) which studies long-term earnings of workers separated from target firms. In another related paper, Lee, Mauer and Xu (2018) use aggregate data in the United States to study how post-consolidation job loss is increasing in the human capital relatedness of consolidating firms. Similar to Lagaras (2017) and Lee, Mauer and Xu (2018), we find that job loss is higher among target workers at firms that have higher human capital relatedness (and therefore are more likely to have efficiency gains) and further find that this job loss is higher for the specific types of workers whose skills are replicated in the acquirer. We further are able to complement this work by demonstrating through our comprehensive income measures (including non-labor income) that even in the generous social safety net environment of the Netherlands, affected individuals tend to be underemployed and have lower total income, even four years after their firm is acquired.

We also contribute to the literature on the longer-term impacts of job loss (e.g., Couch and 3
Placzek, 2010; Farber, 2017; Goldschmidt and Schmieder, 2017; Schmieder, von Wachter and Heining, 2022). This literature typically compares workers who lost their jobs to workers who did not lose their jobs and uses mass layoff events for identification. Our work uses another source of exogenous variation in job loss, namely firm takeovers. Firm takeovers substantially increase the probability of job loss and job loss has longer-term negative consequences for workers.

Our findings have important implications for assessing welfare effects of firm consolidations. First, they suggest that takeovers can have negative, long-lasting impacts on target workers’ labor market outcomes. Second, we show evidence that labor restructuring is an important determinant of job loss: workers who are over-placed or workers in acquisitions where the acquirer is more likely to have workers already serving a given role experience higher rates of separation. These findings suggest that even consolidations that are typically viewed favorably from a consumer welfare perspective, those that increase efficiency without likely increasing product market concentration, can still have large, long-term welfare impacts for target workers through labor restructuring.

The remainder of this paper proceeds as follows. In Section 2, we describe the data and our empirical approach. In Section 3, we show that takeovers have long-lasting, negative impacts on incumbent worker outcomes. In Section 4, we demonstrate that labor restructuring is the primary mechanism driving these results through a series of heterogeneity analyses. Finally, in Section 5, we conclude.

# **2 Data and Methods**

## **2.1 Data Sources**

We study the impact of takeovers on labor demand and the labor market outcomes of workers of acquirer and acquired firms using several administrative datasets from Statistics Netherlands (CBS). We begin with an administrative dataset which identifies various “firm events”, such as when firms are established, closed down, or taken over. A takeover is defined as an event where one business takes over one or more other businesses in the sense that the production factors of the acquirer continue in the same manner. The business which is taken over will cease to exist in its previous form. In this dataset, we can observe the date of the takeover and the different firms involved. A unique advantage of this dataset is that, in contrast to previous papers which relied on worker flows 4
to identify takeovers (e.g., Todd and Heining, 2020), we are able to identify all takeover events. Having data on takeovers means that we can accurately capture these events even for small firms. Appendix A provides details on specific administrative datasets that were used to construct our sample.

We combine information on takeovers with a matched employer-employee dataset for all Dutch firms and workers covering the 2006–2019 period. This dataset contains each employment spell at the monthly level, including information on earnings, hours, and contract types. To assess the labor market outcomes of workers, we also link to an administrative dataset covering income from various government benefits, including unemployment insurance benefit and social safety net payments.<sup>1</sup> The final employee-level dataset used is an education database, which contains education information for a subsample of workers. While coverage of this dataset is increasing over time (from 47% in 2006 to 71% in 2019), its sample is skewed towards younger, more educated workers. We use an inverse Mincer equation to impute education levels for workers for whom education information is missing. For the full procedure, we refer to Appendix B.

# **2.2 Sample Construction and Matching Procedure**

We focus on takeovers that happened between 2011 and 2015. We make a number of sample restrictions. First, we restrict the sample to takeovers that involve firms that are active in the four years prior to the takeover and that do not experience any firm event (e.g., takeover, merger, etc.) in those intervening years. Second, because we are interested in employee outcomes, we require that both consolidating firms have at least three employees in the year prior to the takeover. This restriction ensures that we eliminate firms with only self-employed individuals and owners. Third, we restrict the sample to takeovers in which both consolidating firms only have one business unit prior to the takeover.<sup>2</sup> Finally, because our empirical strategy uses matching, we require that the variables used in the matching process are non-missing.

For our main analysis, we focus primarily on effects for _incumbent workers_ who were present at the target firm four years prior to the takeover. While this restriction prevents us from looking at results for workers with shorter tenures, it also allows us to avoid conditioning our sample on

> 1Our administrative dataset covers all Dutch residents and firms, therefore selective exit from our sample does not affect results related to retention and compensation at workers’ original firms.

> 2Appendix A provides more details on our definition of a firm vis-`a-vis Dutch categorization and firm hierarchies.

5
a potentially endogenous outcome, retention prior to the takeover. Thus, we are able to test for potential pre-trends and anticipation effects in retention and other individual-level outcomes.

The Netherlands has various employment arrangements that imply different contract lengths and legal protections. We restrict our sample to permanent and fixed-term contracts, arrangements that are comparable to US employment relationships.<sup>3</sup> Appendix A provides more details on these restrictions.

Our analysis uses an event-study framework to study the effect of takeovers on workers’ labor market outcomes. We match firms that were targeted by a takeover to control firms that do not participate in a takeover during the period of observation. To make the control firms comparable to target firms, we use coarsened exact matching based on firm characteristics four years prior to the takeover.<sup>4</sup> We create coarse cells based on year of takeover, 3-digit industry code, province, firm-level employment (seven categories), number of establishments (three categories), firm age (five categories), average worker tenure (three categories), and average earnings of workers from that specific firm (four categories).<sup>5</sup> Our procedure then matches each target firm to a control firm that is in the same coarse cell of firm-level characteristics. If there is more than one potential control firm in a cell, we then use propensity score matching within these cells. To construct propensity scores, we estimate a linear probability model of participating in a takeover using polynomials of employment, number of establishments, and municipality fixed effects.<sup>6</sup> We estimate this propensity score separately for each year. A firm that is targeted by a takeover is assigned to a control firm with the closest propensity score within the cell. At the end of the matching procedure, each target firm is matched to a control firm that had similar observable characteristics four years prior to the takeover.

> 3In the Netherlands, companies can offer employees permanent (“open-ended”) or fixed-term (“temporary”) contracts. Employment protection legislation including protection against dismissal in the Netherlands is very strong for the former but not for the latter (OECD, 2019). The incidence of temporary contracts in the Netherlands is high at around 20% of employment, compared to around 12% for the OECD on average and 4% for the US in 2017 (OECD, 2021).

> 4 Again, conducting our match as of four years prior to the takeover allows us to test for pre-trends leading up to the takeover.

> 5We note that we do not match on total labor income from all employers or total income (sum of total labor income and income from various government benefits).

> 6Municipalities (355 in 2019) are the third tier of public administration in the Netherlands after the central government and the provinces (12 in total).

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# **2.3 Summary Statistics**

Table 1 shows summary statistics of takeover events for our sample of matched target firms. In our sample, 30.8% of events are horizontal takeovers defined as takeovers between firms in the same 3-digit industry. We characterize 8.1% of our events as a vertical takeover: a takeover between a service-industry firm and a manufacturing firm. This classification is not exhaustive and over half of the events in our sample meet neither takeover definition. Incorporating firm location, 67% of takeovers take place between firms in the same province and 20.3% take place between firms that are both in the same 3-digit industry and the same province. Further, 11.3% of events are in tradeable and 40.4% of events are in non-tradeable industries.<sup>7</sup> To assess how takeovers affect labor market concentration, we create a projected takeover-induced labor market concentration change measure using historical worker flows (see Arnold, 2021).<sup>8</sup> We find that only two of our takeovers have a non-negligible impact on labor market concentration.

Table 1: Takeover Characteristics

|Event Type|Share (%)|
|---|---|
|Horizontal (Same 3 digit industry)|30.8|
|Same province|67.0|
|Same province and 3-digit industry|20.3|
|Same province and 2-digit industry|23.8|
|Vertical|8.1|
|Between tradeable frms|11.3|
|Between non-tradeable frms|40.4|
|High Labor Market HHI Change|0.0|
|Number of Takeovers|1,013|

Note: This table shows the characteristics of the takeovers in our sample. All industry definitions rely on Standard Industry Classifications. A horizontal takeover is defined as a takeover between firms in the same 3-digit industry. A vertical takeover is defined as a takeover between a service-industry firm and a manufacturing firm. There are 12 provinces in the Netherlands. High HHI changes in the labor market correspond to takeovers that generate a change in HHI greater than or equal to the change of labor market concentration of 0.21 (see Arnold, 2021).

A few important conclusions can be drawn from Table 1. First, takeovers in this period are

> 7Tradability of a given product is identified using the industry code of the firm. A tradeable product is one that is sufficiently portable to be sold long distances or across borders. For example, massages are non-tradeable while computers are tradeable. We use the definitions put forward by OECD (2018).

> 8We follow an analogous procedure to Arnold (2021) to create a measure of expected labor market concentration for a given takeover. The primary difference in our approach is the difference in geography used. While Arnold (2021) uses United States commuting zones, we use labor market regions. The size of this geographic definition is between municipalities and provinces and is analogous to the commuting zone definition constructed by Statistics Netherlands. There were 36 different labor market regions in the Netherlands during our time of study.

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heterogeneous. However, only a small subset are large enough such that we would expect large changes in labor market concentration.<sup>9</sup> Finally, the tradability of products of the consolidating firms as well as the product and geographic overlap between them vary. This suggests that to some extent product market concentration might change as a result of some of these takeovers.

Table 2: Firm Characteristics

||Acquirer<br>(1)|Target<br>(2)|Target (Control)<br>(3)|Treat vs. Control<br>Diference<br>(4)|Normalized<br>Diference<br>(5)|
|---|---|---|---|---|---|
|Employment|85.37|24.42|28.10|-3.68|-0.02|
||(337.00)|(89.99)|(131.80)|(2.27)||
|Establishments|2.13|1.47|1.25|0.23|0.09|
||(6.22)|(2.06)|(1.35)|(0.08)||
|Firm Age (yrs)|20.54|16.96|17.81|-0.89|-0.05|
||(14.21)|(12.85)|(13.26)|(0.42)||
|Service|0.67|0.73|0.73|-0.00|-0.00|
||(0.47)|(0.44)|(0.01)|(0.00)||
|Manufacturing|0.12|0.09|0.08|0.01|0.03|
||(0.33)|(0.29)|(0.27)|(0.00)||
|Construction|0.08|0.06|0.06|0.00|0.00|
||(0.26)|(0.24)|(0.25)|(0.00)||
|Healthcare|0.06|0.05|0.05|0.00|0.00|
||(0.23)|(0.22)|(0.22)|(0.00)||
|South-Holland|0.24|0.22|0.21|0.01|0.02|
||(0.43)|(0.41)|(0.41)|(0.01)||
|North-Holland|0.16|0.19|0.18|0.00|0.01|
||(0.37)|(0.39)|(0.38)|(0.01)||
|Groningen|0.13|0.13|0.12|0.00|0.02|
||(0.34)|(0.33)|(0.33)|(0.01)||
|Gelderland|0.10|0.11|0.09|0.01|0.05|
||(0.30)|(0.31)|(0.29)|(0.01)||
|Number of Firms|1,013|1,013|1,013|||

Note: This table shows the characteristics of the firms in our sample four years prior to the takeover. The first column corresponds to average characteristics of acquirer firms, the second column corresponds to average characteristics of target firms, and the third column corresponds to firms selected as controls for the target firm. The fourth column shows the mean difference of characteristics between treated and control target firms. Standard deviations are in parentheses. The fifth column shows the normalized difference of characteristics between treated and control target firms _within_ takeover events (see Imbens and Wooldridge, 2009). The table only reports the dominant industries (service, manufacturing, construction) and provinces (South-Holland, North-Holland, Groningen, Gelderland) in our sample.

> 9In Arnold (2021), only consolidations that had a projected change in labor market concentration over 0.21 induced general equilibrium wage effects (in the United States context, 5% of the mergers between firms where each firm employed over 50 people). Only two cases in our matched sample produce changes in the Herfindahl-Hirschman Index (HHI) over that threshold, suggesting there are unlikely to be general equilibrium effects.

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Table 2 summarizes characteristics of the firms involved in takeovers and compares them to the matched set of control firms as of four years prior to the takeover. Column (1) reports statistics on acquirer firms and columns (2)–(3) report statistics on target firms (and their matched controls). Comparing acquirer and target firms, the most striking difference is in the average size of the two types of firms. Acquirers are much larger: they have three times as many employees on average (85.37 versus 24.42) and 40% more establishments on average (2.13 versus 1.47). By construction, given our matching procedure, treatment and control firms are very similar on observable characteristics including size, industry, and region.

Table 3: Worker Characteristics

||Acquirer<br>(1)|Target<br>(2)|Target (Control)<br>(3)|Treat vs. Control<br>Diference<br>(4)|Normalized<br>Diference<br>(5)|
|---|---|---|---|---|---|
|Female|0.54|0.46|0.41|0.00|0.07|
||(0.50)|(0.50)|(0.49)|(0.01)||
|Age (years)|40.91|39.66|39.65|0.51|0.00|
||(11.79)|(11.77)|(11.56)|(1.14)||
|Born in the Netherlands|0.90|0.86|0.81|0.02|0.10|
||(0.30)|(0.35)|(0.39)|(0.00)||
|Tenure (0 years)|0.15|0.24|0.23|0.00|0.02|
||(0.35)|(0.42)|(0.42)|(0.00)||
|Tenure (1-2 years)|0.40|0.52|0.49|0.03|0.04|
||(0.49)|(0.50)|(0.50)|(0.00)||
|Tenure (3+ years)|0.46|0.25|0.28|-0.03|-0.05|
||(0.50)|(0.43)|(0.45)|(0.00)||
|Low Skill|0.22|0.27|0.22|0.00|0.08|
||(0.42)|(0.44)|(0.41)|(0.00)||
|Medium Skill|0.37|0.37|0.40|-0.02|0.04|
||(0.48)|(0.48)|(0.49)|(0.01)||
|High Skill|0.41|0.36|0.38|-0.01|-0.03|
||(0.49)|(0.48)|(0.49)|(0.01)||
|Labor Income|44,637|41,200|43,232|-1,171|-0.04|
||(34,132)|(33,710)|(33,727)|(363)||
|Permanent Contract|0.79|0.75|0.73|0.01|0.03|
||(0.41)|(0.43)|(0.44)|(0.01)||
|Fixed Contract|0.21|0.25|0.27|-0.01|-0.03|
||(0.41)|(0.43)|(0.44)|(0.01)||
|Number of Incumbent workers|111,398|18,251|18,164|||

Note: This table shows the characteristics of incumbent workers in our sample who were employed at various firms four years prior to the takeover. The first column corresponds to average characteristics of workers at acquirer firms, the second column corresponds to average characteristics of workers at target firms, and the third column corresponds to workers at firms selected as controls for the target firm. The fourth column shows the mean difference between worker characteristics at the treated and control target firms. Standard deviations are in parentheses. The fifth column shows the normalized difference between worker characteristics at the treated and control target firms _within_ takeover events (see Imbens and Wooldridge, 2009).

Finally, Table 3 summarizes characteristics of the workers of acquirer and acquired firms (four 9
years prior to the takeovers) and matched controls. Again, column (1) reports statistics on acquirer firms and columns (2)-(3) report statistics on target firms (and their matched controls). Comparing the acquirer and target firms, workers at acquirer firms tend to have longer tenure, be slightly higher skilled, have higher labor income, and are less likely to be fixed-term contract workers. Given their presence in the matching algorithm, treatment and control target workers are similar in terms of their average tenure and earnings. Reassuringly, even worker characteristics that are omitted from the matching algorithm, including gender, age, country of birth, and contract type are fairly well balanced. However, treated targets have slightly fewer skilled workers.<sup>10</sup>

# **2.4 Event Study Estimation**

We are primarily interested in the impact of the takeover on incumbent target workers. For workers to enter our estimation sample, they must be employed at the target firm four years prior to the takeover. Importantly, we do not require these workers to remain employed for the interim four years prior to the takeover. This allows us to test for pre-trends in retention and compensation of these workers for the years leading up to the takeover. An advantage of focusing on incumbent workers is that it prevents endogenous selection of workers into our sample (e.g., anticipation effects).

We use a standard event-study framework to estimate how worker outcomes change around the takeover event.

where _i_ indexes workers, _t_ indexes event time, and _Yit_ is an outcome at the level of an individual worker. Takeover _i_ is an indicator for being an incumbent worker employed at a firm involved in a takeover four years prior to the event, the _βk_ are event-time indicators, the _ψi_ are worker fixed effects, the _νt_ are event-time fixed effects, the _γl_ are calendar-year fixed effects. We cluster standard errors at the event level. Because of the staggered timing of these events, we use the weighting approach proposed by Sun and Abraham (2021).<sup>11</sup>

> 10We use actual skill levels as reported in the Education Database as well as imputed skill values using an inverse Mincer equation (see Appendix B). Low-skilled refers to workers who have up to and including a low (junior) secondary education. Medium-skilled refers to workers with upper-secondary or post-secondary education excluding tertiary education. Workers designated as high-skilled have tertiary education.

> 11As demonstrated in Goodman-Bacon (2021) two-way fixed effects models (TWFE) can produce biased estimates in

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Our estimates of the event-time coefficients _βk_ represent the causal effect of being in a target firm under standard difference-in-differences assumptions. In particular, we require that conditional on covariates, the outcomes of the matched comparison group of firms (and their workers for worker-level outcomes) represent a valid counterfactual for the target firms.

To verify the plausibility of our difference-in-differences assumptions, we look at how similar firms and their workers involved in takeovers are to the matched firms and their workers. Examining the cross-sectional characteristics of the firms and workers (Tables 2 and 3, respectively), we find that four years prior to the takeover, the target firms and their matched control firms and workers appear quite similar. Such cross-sectional comparisons, however, do not guarantee that the outcomes of workers at the target and its matched control firm trend similarly. One particular concern is that even if Table 3 suggests that workers do not differentially select into target firms four years prior to the takeover, they may still be differentially retained or compensated in the interim. We validate the comparability of incumbent workers at target and matched control firms by comparing pre-trends in outcome variables in Section 3.

A final potential concern, given the general equilibrium findings of Arnold (2021), is that there could be spillovers from firms involved in takeovers onto control firms in which case the Stable Unit Treatment Value Assumption would be violated. However, as discussed in Section 2.3, this is unlikely to be a large concern in this context since very few of our events generate a meaningfully large change in labor market concentration. Similarly, we will show that our results are not driven by takeovers that are likely to lead to large changes in product market power, suggesting that our results are not affected by spillovers in product market concentration or price-cost markups (see Section 4.1).

# **2.5 Estimation of Firm Wage Premia**

We are interested in the effect of takeovers on the quality of firms where workers find employment. We estimate a standard Abowd, Kramarz and Margolis (1999) (AKM) model using the universe of Dutch firms for the time period 2006 to 2019. We proxy firm quality by the estimated firm fixed settings with staggered timing of treatment and heterogeneity in treatment effects over time. Further, as demonstrated in Sun and Abraham (2021), these same conditions are present not only when estimating a static parameter but can also affect TWFE estimates of coefficients on lead and lag indicators, which can be contaminated with information from other leads and lags. The authors propose an alternative approach to TWFE which weights cohort-specific treatment effects using predictions of treatment timing to produce an overall average treatment effect.

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effect, which is commonly referred to as the _firm wage premium_ .

We estimate the following regression: where _i_ indexes individuals, _j_ indexes firms, _t_ indexes time, _yit_ is logged normalized earnings for worker _i_ at firm _j_ in year _t_ (normalized earnings are total payments from firm _j_ per year divided by the number of full time days worked at firm _j_ ), the _αi_ are worker fixed effects, the _ψj_ ( _i,t_ ) are firm fixed effects, the _θit_ include a set of year indicators and a flexible polynomial of worker _i_ ’s age, and _εit_ is an idiosyncratic log wage component, capturing purely transitory earnings fluctuations. In this estimation, worker effects _αi_ and firm effects _ψj_ ( _i,t_ ) are separately identified by workers who change employers.<sup>12</sup> In the AKM framework, _αi_ reflects the worker’s time-invariant human capital, such as education and ability, that is rewarded equally across different employers, while _ψj_ ( _i,t_ ) gives the percentage wage premium enjoyed by every worker employed at firm _j_ . Such a premium could represent rent-sharing (Card, Cardoso and Kline, 2016), strategic wage posting behavior (Burdett and Mortensen, 1998; Moscarini and Postel-Vinay, 2013), other components of the wage structure such as an efficiency wage premium, compensating differentials (Sorkin, 2018), or variation in general payment practices (e.g., the presence of unions, corporate culture, or negotiating power). Appendix Figure C1 shows the distribution of and worker For the estimation of the AKM model, we use a number of sample restrictions. A possible concern is that consolidations can impact firms’ wage structure, and hence the estimated firm fixed effects. To avoid such endogeneity, we drop observations during and after the year of the first consolidation we observe in the time period for any firms that eventually consolidate. For workers with multiple jobs within a given year, we keep the job with the highest associated earnings.

> 12The crucial assumption for this interpretation of the AKM decomposition to hold is that the idiosyncratic log earnings component _εit_ is unrelated to the sequence of worker _i_ ’s employers _j_ ( _i, t_ ). For a discussion and test of the validity of this conditional exogenous mobility assumption, see Abowd, Kramarz and Margolis (1999) and Card, Heining and Kline (2013). Schneck (2021) presents an application using similar Dutch micro data as ours.

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# **3 The of Takeovers on Incumbent Worker Outcomes**

We examine worker retention, various measures of earnings and income, and benefit uptake for workers who were present at target firms four years prior to the takeover.<sup>13</sup> We find that workers of target firms experience reductions in employment at the consolidated firm compared to workers at control firms. This reduction translates into lower total labor market income, driven by changes in employment, wages and hours.

Figure 1 shows results for our main employment outcomes, including retention (i.e., employment at the consolidated firm), hours at the consolidated firm, employment at any firm, and hours at any firm. For each outcome, the left panel shows unadjusted results, while the right panel shows regression estimates based on equation (1). These figures allow us to investigate the plausibility of our difference-in-differences assumptions by looking at the estimates prior to the takeover. Both of these figures suggest that incumbent workers who were present four years prior to the takeover and their matched controls followed similar trends in terms of retention, hours worked, and employment.

Panels (a) and (b) of Figure 1 show results for two measures of employment at the consolidated firm: panel (a) shows a binary measure of retention at the consolidated firm, where retention is defined as being employed by either the target or its acquirer in a given year and panel (b) shows total hours worked at either the target or its acquirer. We find that in the four years following the takeover, workers are 6 percentage points (8.5%) less likely to be retained and work 77.61 (7.3%) fewer hours at the consolidated firm compared to control firm workers.

Although workers at target firms are less likely to remain employed at their original firms, it may be the case that these workers find employment at other firms. Panels (c) and (d) show employment results when we examine employment at any firm. Panel (c) suggests that while the likelihood that workers separate from their original firms increases substantially, there is only a small reduction in their overall employment probability. We find that workers are 1 percentage point (1%) less likely to be employed anywhere in the four years after the takeover, significant at the 10% level. We observe a small, statistically insignificant short-term reduction in total hours worked, which recovers three years after the takeover.

> 13Appendix E presents the corresponding estimates for incumbent workers employed at the acquirer firms. We find that these workers do not experience large changes in retention or earnings.

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<!-- Start of picture text --> 1<br>8<br>6<br>4<br>2<br>-4 -2 ; _0 2 4<br><!-- End of picture text --> <!-- Start of picture text --> .05<br>0<br>¢<br>-.05 t<br>-1<br>4 -15<br>Time Relative to Takeover<br><!-- End of picture text --> <!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400 “4 2 0 2 4<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>0<br>-50<br>-100<br>-150<br><!-- End of picture text --> <!-- Start of picture text --> 01<br>-.010 °<br>-.02<br>4 -03<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>95<br>9<br>85<br>v 2. 0 2 4<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>20<br>0<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> 1700<br>1600<br>1500<br>1400<br>1300<br>1200<br>-4 -2 0 2 4<br><!-- End of picture text -->
<!-- Start of picture text --> 48000<br>46000<br>44000<br>42000<br>40000<br>-4 -2 Time Relative0 to Takeover 2 4<br><!-- End of picture text --> <!-- Start of picture text --> 1000<br>0 $ © °<br>-1000<br>-2000<br>~3000<br>4 3 5 1 0 1 2 3 4<br>Time Relative to Takeover<br><!-- End of picture text --> <!-- Start of picture text --> 48000<br>46000<br>44000<br>42000<br>40000 3 2 Py 2 a<br>Time Relative to Takeover<br><!-- End of picture text --> <!-- Start of picture text --> 1000<br>0<br>-1000<br>-2000<br>3 3 3 1 0 1 > 3 4<br>Time Relative to Takeover<br><!-- End of picture text -->
<!-- Start of picture text --> 1000<br>0 ¢ e +<br>-1000<br>-2000<br>-3000<br>-4000<br>-4 -3 -2 -1 0 1 2 3 4<br>Time Relative to Takeover<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>20<br>0<br>-20<br>-40<br>-60<br>-4 -3 -2 -1 0 1 2 3 4<br>Time Relative to Takeover<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0 e<br>6<br>-10<br>-4 -3 -2 -1 0 1 2 3 4<br>Time Relative to Takeover<br><!-- End of picture text -->
<!-- Start of picture text --> 01<br>.005<br>0<br>005<br>-4 2 0 2 4<br><!-- End of picture text --> <!-- Start of picture text -->
-01<br>005<br>0<br>-.005<br>4 4<br>Time Relative to Takeover<br><!-- End of picture text -->
<!-- Start of picture text --> Baseline —_e—<br>Female —>——_<br>Education-Low —_— >.<br>Education-Medium —e——<br>Education-High —_.+——_<br>Age < 30 ————_—e——<br>Age 30-45 —_.—<br>Normalized AgePay-Low > 45 —>—<br>Normalized Pay-Medium —__>+————_<br>Normalized Pay-High —_e——<br>Tenure 0 ———_-e——_.<br>Tenure 1-2 Years —_—>—<br>Tenure > 2 Years — >.<br>Fixed-Term ©<br>Permanent e<br>-.2 -.15 -1 -.05 0<br><!-- End of picture text --> <!-- Start of picture text --> Baseline —e—<br>Female<br>Education-Low<br>Education-Medium<br>Education-High —_——<br>Age < 30<br>Age 30-45 —e—<br>Normalized AgePay-Low > 45<br>Normalized Pay-Medium+ ———-®——<br>Normalized Pay-High —e—<br>Tenure 0<br>Tenure 1-2 Years —_o—<br>Tenure > 2 Years —o—_<br>Fixed-Term<br>Permanent ©<br>-2 0-1 0 A 2 3<br><!-- End of picture text -->
duplicative workers are less likely to be retained at the consolidated firm.

# **4.2.1 Over-placed Workers**

As Panel (b) of Figure 5 shows, hours reductions appear to be increasing in the wage rate. A potential channel through which labor restructuring might materialize is disrupting implicit contractual relationships with employees who earn substantial firm-specific rents. To investigate this channel, we leverage estimates of firm and worker effects (Abowd, Kramarz and Margolis, 1999; Card, Heining and Kline, 2013) using a similar method to Schmieder, von Wachter and Heining (2022) and Lagaras (2023).

We identify over-placed workers, workers at a target firm with a higher employer wage premium than is expected for workers’ skills that are valued equally across employers. We estimate the median firm wage premium within each quintile of the worker fixed effects distribution. We then characterize over-placed workers as those at firms with a higher-than-median firm wage premium. These over-placed workers are receiving more generous pay from the target firm than the average worker with the same level of human capital receives. Thus, for a consolidated firm, these workers are likely more expensive than the human capital that they provide to the firm.

Figure 6 shows retention and hours results for over-placed and not over-placed workers. A starkly larger retention effect is found for over-placed workers: over-placed workers experience 13.05% and 11.9% reductions in retention and hours, respectively, at the consolidated firm compared to statistically insignificant 3.9% and 2.8% reductions for not over-placed workers. Differences between over-placed and not over-placed workers are statistically significant at the 95% level. Further, when we examine other outcomes in Appendix Figure E4, we see that these workers experience much larger overall reductions in work at any firm (including both the extensive margin of employment and the intensive margin of hours worked) as well as larger reductions in labor income and total income. Together, these results suggest that over-placed workers experience sustained losses after losing their high firm-specific rents post-takeover. These findings are consistent with consolidating firms restructuring their workforce in order to increase efficiency post-takeover.

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<!-- Start of picture text --> Not Over-Placed —_e——_<br>Over-Placed —_ ><br>-.2 -.15 -1 -.05 0<br><!-- End of picture text --> <!-- Start of picture text --> Not Over-Placed<br>Over-Placed ———_ ><br>-.2 -.15 -1 -.05 0<br><!-- End of picture text -->
<!-- Start of picture text --> 0-33 % ——<br>33-66 % ————7—_——<br>66-100 % —_ e——-<br>-.3 -.2 -1 0<br><!-- End of picture text --> <!-- Start of picture text --> 0-33 %<br>33-66 %<br>66-100 % ——e—<br>-.3 -.2 -1 0 1<br><!-- End of picture text -->
degree of overlap in skills and duties of the workforce of consolidating firms (Lee, Mauer and Xu, 2018). Higher HCR implies greater potential economies of scale and therefore potential job loss for incumbent workers. We use individual-level skill data to generate a measure of human capital relatedness following an analogous approach to the occupation-based measure used in Lee, Mauer and Xu (2018).

To construct our HCR index, we flag all workers who are present at firm _j_ four years prior to the takeover and earlier (that is, we do not incorporate observations from _t −_ 3 onward). We restrict the sample to firms where we have at least ten observations of employee skills in this period. We then create firm _j_ ’s human capital profile vector _Hj_ , which indicates the number of firm _j_ ’s workers that fall into each of three given skill categories divided by the total number of workers at that firm _j_ : _Hj_ = ( _H_ low _,j, H_ medium _,j, H_ high _,j_ ). For a given takeover with acquirer _A_ and target _T_ , we then construct a time-invariant HCR index defined as The HCR index is increasing in the overlap of skill categories of the two firms; it is equal to zero if the firms employ workers of completely different skill levels and is equal to one if the distribution of workers across skill levels is identical in the two firms. As seen in Figure 8, the distribution of the HCR index is skewed with 83%, 66%, and 28% of events having an index over 0.8, 0.9 and 0.95, respectively.

Do takeovers between firms with higher HCR generate higher rates of job loss? Figure 9 shows the percentage change estimates of retention for events with above and below median HCR. We show results for both overall retention as well as hours worked at the consolidated firm, post-takeover. We see that the highest HCR events have a modestly higher impact on retention than lower HCR events: takeovers between firms with less than 50% HCR induce a statistically insignificant 7.2% and 5.9% reduction in retention and hours worked, respectively, compared to a 9.0% and 8.0% reduction for workers at takeovers that have a higher degree of HCR.

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<!-- Start of picture text --> Q<br>oS<br>co<br>2S<br>ow<br>><br>o<br>vo<br>x=<br>&<br>6s<br>5<br>2<br>=<br>=]<br>2<br>oS<br>S<br>TM<br>°<br>0 20 40 60 80 100<br>Human Capital Relatedness<br><!-- End of picture text --> <!-- Start of picture text --> Low HCR<br>High HCR ——>——————_<br>-.15 -1 -.05 0<br><!-- End of picture text --> <!-- Start of picture text --> Low HCR<br>High HCR ——-————<br>-.15 -1 -.05 0 05<br><!-- End of picture text -->
# **4.3 Alternative Mechanisms**

Our heterogeneity results thus far are consistent with firms taking part in labor restructuring post-consolidation to reduce labor inputs from over-placed workers and duplicative workers. The literature has proposed alternative mechanisms that may lead to takeover-induced job loss, such as increased product or labor market concentration (see e.g., Geurts and Van Biesebroeck, 2019; Arnold, 2021). We now rule out that these mechanisms are driving our results.

We first investigate whether takeovers that are likely to produce larger changes in concentration in the product market are associated with larger reductions in retention and hours for incumbent workers. Figure 10 shows the difference-in-differences estimates from separate regressions for our two measures of employment at the consolidated firm. We investigate whether retention impacts differ for takeovers that are horizontal (i.e., between firms in the same 3-digit industry) or vertical (i.e., between a service and manufacturing firm). Because horizontal takeovers can potentially involve monopoly-induced reductions in production, one may expect that such consolidations would be associated with larger job loss. However, we find that horizontal takeovers induce a 5.7% reduction in retention, a slightly _smaller_ effect compared to the overall average effect. In contrast, vertical takeovers are actually associated with a large 45% reduction in retention. Next, we use industry codes to differentiate takeovers of firms producing tradeable and non-tradeable items. Again, while non-tradeable consolidations would be expected to generate greater market power and therefore reduced production and employment, we find that retention effects are _lower_ among non-tradeable firms. Takeovers are associated with, respectively, 9.3% and 8.9% drops in retention for non-tradeable and tradeable firms. Thus, reductions in employment and hours for incumbent workers do not appear to be driven by changes in product market concentration.

The other alternative mechanism for takeover-induced job loss that has been explored in previous work is labor market concentration. As discussed in Section 2.3, very few of our events (2 out of 1,013) have a non-negligible impact on firm monopsony power. Thus, increasing labor market concentration cannot be driving our results.

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<!-- Start of picture text --> Baseline —<br>Horizontal —o—<br>Vertical |;———————-e—___<br>Tradeable —_e—<br>Non-Tradeable —<br>-.6 -4 -.2 0<br><!-- End of picture text -->

<!-- Start of picture text --> Baseline —e—<br>Horizontal<br>Vertical ———-(7———.<br>Tradeable ——_e——_<br>Non-Tradeable<br>-.6 -4 -.2 0<br><!-- End of picture text -->
market concentration and few takeovers induce large changes in labor market monopsony power. These findings suggest that while takeovers may allow firms to take advantage of economies of scale and become more efficient, they are costly for workers at these firms.

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