Does Employee Downsizing Work?
Evidence from Product Innovation at
Manufacturing Plants∗
Hiroko Okudaira†

Miho Takizawa ‡Kenta Yamanouchi§

February, 2022
Abstract
Workforce downsizing is an important management decision for firms to recover frombusiness downturns. Using administrative data on the population of manufacturing
plants and their products in Japan, we examined how layoff plants reallocated theirinternal resources and whether they eventually increased their productivity or secured
innovative gains. Accounting for staggered timings of layoff events, we found that
layoff plants successfully reduced their production levels by reducing both capital andlabor inputs. However, even years after the layoff announcement, layoff plants did notoutperform control plants in terms of introducing new products or increasing productivity. These results were accompanied by a disproportionate decline in the proportionof young workers and lower compensation among senior workers.
Keywords: layoff, employee downsizing, product innovation, productivity, firm-specificskill investment, Great Recession, staggered difference-in-differences.
This study was conducted as part of the project, “Determinants of Firm Dynamics: Causal Inference
Approach Microeconometric analysis of firm and industry growth”, undertaken at the Research Institute of
Economy, Trade and Industry (RIETI). The authors are grateful to Kyoji Fukao, Kaoru Hsono, Morikawa
Masayuki, and Youichi Sekiguchi as well as the seminar participants at RIETI for their helpful comments.
Emi Shibata provided excellent research assistance. We acknowledge research grants from the Omron Foundation and the Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research #19K01706#20K01635). An analysis using Census of Manufacture was conducted following an agreement approved by
the Japanese Ministry of Economy, Trade, and Industry (METI). The analysis using Basic Survey on Wage
Structure was conducted following an agreement approved by the Japanese Ministry of Health, Labour andWelfare. The analysis using Economic Census for Business Frame and Establishment and Enterprise Census
of Japan was conducted following an agreement approved by the Japanese Ministry of Internal Affairs andCommunications (MIAC). The analysis using Economic Census for Business Activity was conducted following an agreement approved by MIAC and METI.
†
Business School, Doshisha University, Karasuma-Imadegawa, Kamigyo-ku, Kyoto city, Kyoto 602-8580,
Japan (hokudair@mail.doshisha.ac.jp).
‡
Department of Economics, Gakushuin University, 1-5-1, Mejiro, Toshima-ku, Tokyo 171-8588, Japan(miho.takizawa@gakushuin.ac.jp).
§
Department of Economics, Kagawa University, 2-1 Saiwaicho, Takamatsu, Kagawa 760-8523, Japan(yamanouchi.kenta@kagawa-u.ac.jp).
∗

2

1

Introduction

Workforce downsizing is an important management decision for firms to recover from business downturns. By laying off workers, firms intend to allocate their internal resources efficiently in response to external changes to prepare themselves for the next stage of growth. 1
However, whether downsizing leads firms to a successful recovery and eventually promotes innovation remains controversial. Previous studies have reported non-positive effects of firm downsizing. For instance, in the context of Spanish firms, Muoz-Bulln andSnchez-Bueno (2011) found no evidence to support an improvement firms’ financial performance, such as in their ROA, after a reduction in their workforce. Similarly, Kawai (2014)
established a negative correlation between downsizing and the subsequent sales growthamong Japanese multinational firms. In their extensive review of downsizing literature
conducted until 2008, Datta et al. (2010) reported that studies on stock market returns predominantly found negative returns after a firm’s layoff announcement. Previous studies
typically assessed the impact of downsizing on firms’ aggregate performance. However,there exists limited information regarding how downsizing firms allocate their internal resources and whether they can increase their productivity or secure innovative gains.
This study used the administrative data of manufacturing plants in Japan to provide
the first comprehensive evidence of downsizing effect on firms’ production decisions, productivity, and product innovation along with supplementary evidence on employees’ age
distribution at each plant. We first extracted layoff announcement information for firms
listed on Japanese stock exchanges. We then compared the performance trajectories ofplants whose headquarters announced layoffs to those of their similar counterparts without any layoff announcements. Plants with layoff announcements may be systematicallydifferent from other plants in terms of managerial traits or production technologies, raising concerns regarding causal interpretations. To address this issue, we carefully con1

In fact, this is an important reason why policy makers are concerned about making employment protection legislation too stringent. Comparing establishments with or without a high firing cost, previous studies
reported that strict firing restrictions significantly reduced firm productivity in China (Guo et al., 2021), India(Schwab, 2020), Italy (Cingano et al., 2016), Japan (Okudaira et al., 2013), Sweden (Bjuggren, 2018), and theUS.(Autor et al., 2007).

3

structed appropriate comparison groups by following recent advancements in the staggered difference-in-difference (DID) literature (Baker et al., 2021; Borusyak et al., 2021;
Callaway and Sant’Anna, 2021; de Chaisemartin and DHaultfuille, 2020; Goodman-Bacon,2021; Imai and Kim, 2021; Sun and Abraham, 2021; Athey and Imbens, 2022). We also
merged information on employee age distribution from another administrative survey tointerpret the mechanism in light of firm-specific human capital investments.
Based on large-scale layoffs announced between 2008 and 2012, we found that layoff
plants had significantly lower employment levels than control plants after the announcement. According to our estimates, layoff plants reduced their employment level by approximately 10%, and they could not recover at least in the succeeding four years. Theirproduction level also fell by 20%, followed by an even stronger reduction in capital inputs
by 30%. These results suggest that the plants successfully adjusted their scale of production
by announcing layoffs. However, our analyses also revealed that layoff plants did not outperform control plants in terms of labor productivity, even years after the layoff event. One
reason for this non-positive effect is the substantial reduction in capital intensity. Despite alarge reduction in the labor force, their capital intensity per worker was reduced owing to astronger cutback in their capital. In fact, we found that plants did not introduce new products or abolish existing ones in comparison to the control plants, implying a sluggish levelof investment. Thus, employee downsizing helped plants scale down their productionlevel; however, it did not initiate subsequent product innovations or productivity gains, atleast during our sample period.
We further explored the implications of firm-specific skill investments in interpretingthe mechanisms behind the absence of innovation and productivity gains. An importantconcern for layoff firms is the loss of skilled workers. In particular, when both firms andworkers invest in workers’ firm-specific skills, the decision regarding who should be laid
off is a sensitive divestment decision for firms. Lazear and Gibbs (2014); Lazear and Freeman (1997) rationalizes this decision in light of human capital investment theory. According to their argument, employers would benefit by laying off the youngest and oldest work4

ers first because firms either invest less or recoup more rents from these workers (Lazear
and Gibbs, 2014; Lazear and Freeman, 1997). Based on a subset of plants with merged age
information, we estimated the impact of layoff announcements on employees’ age distribution within each plant. Consistent with the theoretical argument, we found that employers’
age distribution was significantly skewed toward middle-aged workers after the layoff announcement. This is reasonable because firms retain a relatively larger share of workers
with the highest outstanding rents in terms of firm-specific skill investment. However,
our analyses revealed that such a change also incurred costs: an increase in the proportion of middle-aged workers was compensated by a significant decline in the proportionof employees in their 20s rather than those in their 50s or 60s. Moreover, our supplementary analyses show that workers in their 50s had to work for the same number of hours,
but with significantly lesser bonuses, after the layoff announcement. Taken together, theresults of this study highlight the important detriments of layoffs as a means of internal
resource allocation: a disproportionate decline in the proportion of young employees anddeteriorating working condition among the remaining senior employees.
Our study contributes to the downsizing literature in three ways. First, it provides thefirst direct evidence of product innovation by relying on large-scale administrative data.
Although this study does not provide an inclusive assessment of organizational innovation, it goes beyond earlier studies by analyzing the actual innovative outcomes at the
product level along with the internal resource allocations within the plant. Second, we
contribute to the downsizing literature by carefully accounting for the staggered timingsof layoff events. Some recent studies have raised serious concerns in applying the event
study approach when the treatment has multiple timings and heterogeneous effects overtime (Baker et al., 2021; Borusyak et al., 2021; Callaway and Sant’Anna, 2021; de Chaisemartin and DHaultfuille, 2020; Goodman-Bacon, 2021; Imai and Kim, 2021; Sun and Abraham, 2021; Athey and Imbens, 2022). Intuitively, the bias arises owing to the inclusion ofearly-treated groups in the control group in estimating the treatment effect for later-treatedgroups. After accounting for this bias, some prominent findings in finance and accounting
5

publications failed to survive (Baker et al., 2021). As layoffs occur at different points in timefor each firm, it is essential to follow the proposed methods in the context of our study. To
the best of our knowledge, our study is one of the first to apply a staggered DID framework
in downsizing literature to provide a valid causal inference.
Finally, we quantified one possible mechanism by which previous studies often foundnon-positive effects of employee downsizing on firm performance, namely, a breach of
long-run commitment to employers, particularly among young employees. According to
the theory of firm-specific skill investment, employees are better off by leaving the current
firm and investing in new relation-specific skills at a different firm once they view the layoff announcement as a signal of lower future returns. As young employees have a longer
period to recoup their return on investment and, thus, have a higher option value to switch
their employers, they are more likely to leave the firm. Consistent with this idea, we founda significant disproportionate decline in the share of young workers. Although relationspecific skill investment is relevant to any organizations seeking long-term commitment
with their employees, no empirical studies have tested its implications for the downsizing
strategy. We take advantage of a Japanese setting in which the majority of firms still engage
in relation-specific investments with their employees. An important takeaway to management is that for firms pursuing long-term relationships with their employees, opting for alarge-scale layoff is a tricky option, as it triggers voluntary quits disproportionately among
young employees. Management in such a firm may better plan reskilling or upskilling slack
labor beforehand as an alternative reallocation option before the emergence of a businesscrisis.
The rest of this paper is organized as follows. Section 2 introduces the datasets used in
the analyses. Section 3 describes the identification strategies used in this study. Section 4
presents the results and robustness tests. Section 5 concludes the study.

6

2

Data

2.1

Main Data

Our main analysis draws on plant-level panel data from the Census of Manufacture, whichis conducted annually by the Japanese Ministry of Economy, Trade, and Industry. The
Census of Manufacture covers nearly the entire population of plants in the manufacturingsector in Japan. It contains detailed information on factor inputs and outputs produced ateach plant. Using this information, we calculated the plants’ value- added, capital intensity,
Total Factor Productivity (TFP), and labor productivity. 2 We start our analysis with theannual files from 2005 to 2016 that cover all plants with 30 or more employees (Kou Hyoupanel).3
One advantage of using the Census of Manufacture is that we can proxy the extent
of product innovation by relying on the 6-digit level records of products manufactured
at each plant. The 6-digit level of product information is sufficiently detailed to reflect
major product innovation.4 We aggregated the product information for each plant at eachtime period by counting the number of products they produced. More specifically, a new
product is defined as a product produced in the survey year but not in the previous year.
Similarly, an abolished product is the product produced in the survey year but not in thenext year. The net increase in the number of products was calculated as the number of new
products minus the number of abolished products. We then merged this information withlayoff announcement records to examine the impact of large-scale layoffs on firms’ productdynamics. Table 1 presents the summary statistics of the main data. On average, plants inour sample produced 2.3 products, abolished 0.14 products, and added 0.15 products per2
To calculate TFP, the average of the labor shares by each industry s (αs ) was taken. We then calculated
the TFP of plant i at time t as lnT F Pit = lnV Ait − αs lnLit − (1 − αs )lnKit , where V A, L, and K denotevalue-added, number of regular workers, and fixed assets, respectively.
3
The survey also has other types of annual files that contain information on all plants with 29 or fewer
employees (Otsu Hyou panel). As these files lack information on fixed assets, we decided not to use them inthis study.
4
For example, batteries are broken down to “lead battery (309111),” “alkaline battery (309112),” and“lithium-ion battery (309113),” among others. Dekle et al. (2021) used the same 6-digit product data to examine product dynamics over business cycles.

7

year.
To explore the impact of layoff announcements on firm-specific investment, we complemented our analysis with age composition information available in the Basic Survey
on Wage Structure (BSWS) conducted by the Ministry of Labour, Health, and Welfare. As
Japan is still known for its lifetime employment practice (Kambayashi and Kato, 2016), age
is an important proxy for the extent of firm-specific skill investment. By drawing on theemployee questionnaires of the BSWS, we calculated age distributions along with meansof bonus and work hours within each plant and merged the information with plant-level
observations in Census of Manufacture. 5 The bottom half of Table 1 shows the summary
statistics for the proportion of employees in a specific age group, along with other outcome
variables. As the BSWS randomly samples a subset of employees in the surveyed plants,
distributional information (e.g., means and proportions) is subject to measurement error. 6

2.2

Layoff Announcement

Our data on layoff announcements were taken from a survey compiled by Tokyo Shoko
Research Ltd. (TSR).7 The survey collects information on large-scale layoffs announced bymajor listed companies and OTC-registered companies. 8 It contains the layoff announcements of well-known global corporations. We merged the firm-level layoff announcements
provided by the TSR with plant-level observations of the Census of Manufacture by the
telephone number of the plant’s headquarters. Figure 1 shows the number of plants whose
headquarters announced layoffs in the merged dataset. Unsurprisingly, the number of lay5

Plant-level observations in the BSWS and Census of Manufacture were merged by key identifiers available in Economic Census for Business Activity (METI and MIAC), Economic Census for Business Frame andEstablishment and Enterprise Census of Japan (MIAC).
6
The employee sampling rate is determined by the number of regular workers and industrial classification.
It is set between 1 (total number) and 1/5 when the number of regular workers is 499 or less and between
1/10 and 1/90 when the number of regular workers is 500 or more. The larger the establishment size, thelower is the employee sampling rate.
7
TSR is a major credit research company in Japan, mainly engaged in the credit research of domestic andoverseas companies as well as the construction of database based on corporate information. We thank TSR
for providing us with layoff announcement data.
8
The survey is based on “the Disclosure of Corporate Information” section of the Timely Disclosure of
Corporate Information.

8

offs surged during the Great Recession of 2009.
To exploit all announcement events in our estimations, we used the unbalanced panel
data of plants. Thus, our estimate measures the aggregate treatment effect, including theeffects of compositional changes arising from a plant’s entry or exit. To understand how
the compositional effect affects the aggregate treatment effect, we also examined the impactof layoff announcements on the probability of long-term selective attrition (see Section 4.2
and Appendix Table 1).
It is important to note that the layoffs in our dataset were announced as buyout offers to
employees. Some firms strategically target buyout offers to specific age groups by setting
a minimum age requirement. Although some of our layoff announcement data containage requirement information, we did not use this information in our estimations owing toa small sample size or the lack of statistical power. Instead, we examined whether layoff
announcements induced any changes in the age distributions of employees at the targeted
plants by relying on the BSWS data. We used the results of this analysis to interpret themechanism behind our main results.

3

Identification Strategy

To evaluate the consequences of layoff announcements on firm performance, we adopted
an event study approach. In this approach, we compared the performance trajectories ofplants with layoff announcements to those of their similar counterparts without any layoff
announcements. We start our specification with a standard DID model with two-way fixed
effects.

Yit =

w
X

γ s I(eventsit ) + xit β + δt + θi + it ,

(1)

s=−w

where w is an arbitrary event window. Yit is an outcome variable for plant i in year t, suchas the number of new products, productivity, or proportion of young workers in manage-

9

rial positions. The treatment indicator I(eventsit ) takes a value of one if plant i announces
layoffs at s years from period t. The model also controls for a vector of plant characteristics,
xit . To identify this model, treated plants should have followed the same outcome path as
the control plants, had the treated plants not been affected by the layoff announcement.
Researchers typically test the validity of this common trend assumption by examining themagnitude of the estimates of γ s for s ≤ −1 and ensuring that the treated and control plants
follow the same outcome trends prior to the treatment event. Such a DID specification has
been adopted across a wide range of studies, including prominent works in finance andaccounting (Beck et al., 2010; Fauver et al., 2017; Wang et al., 2021).
However, recent advancements in the econometrics literature have raised serious concerns about estimating DID models when the timings of treatments are staggered (Baker
et al., 2021; Borusyak et al., 2021; Callaway and Sant’Anna, 2021; de Chaisemartin and
DHaultfuille, 2020; Goodman-Bacon, 2021; Imai and Kim, 2021; Sun and Abraham, 2021;
Athey and Imbens, 2022). Particularly, the standard DID model, such as equation (1), treats
early adopters as control units when estimating the treatment effect of late adopters. Whenthe treatment has multiple timings and heterogeneous effects across time, the dynamic evolution of the treatment effect has a bias on the DID estimate owing to the inclusion of early
adopters in the comparison group. In fact, the DID estimate can provide the opposite signof the true average treatment effect on the treated. In his replications of the earlier studies with a standard DID approach in finance and accounting journals, Baker et al. (2021)
showed that some important findings in these studies failed to survive after correcting forthe bias arising from the inclusion of early adopters in the control group. 9 To identify thetreatment effect of staggered events, we need to construct appropriate control groups foreach of the treatment events and ensure the validity of the common trend assumption for9

By applying a standard DID specification to bank branching deregulations that occurred at different
points in time across the US, Beck et al. (2010) found that the deregulation reduced state-level income inequality; however, this result no longer holds in specifications correcting for the inclusion of early adoptersin a comparison group (Baker et al., 2021). Baker et al. (2021) also presented similar replication failures for
Fauver et al. (2017), which showed the positive impact of corporate board reforms on firm values, and for
Wang et al. (2021) which found a negative impact of the staggered legalization of stock repurchases on firm
investments.

10each of these events.
To overcome the issues arising from staggered DID, this study adopted a strategy suggested in recent literature — stacked event-by-event approach proposed in Cengiz et al.
(2019). In particular, we first define “event” by the timing of firms’ announcement of layoffs. We then created an event-specific dataset by stacking the treated and “clean” control
plants. For the 2009 event, for example, we stacked all the plants that announced layoffs
in 2009 and control plants that never experienced layoffs during our sample period into
one dataset.10 By stacking event-specific data in this manner, we avoided early adopters in
the control group. For each event-specific stacked dataset, h, we estimated the following
equation:

Yith =w
X

s=−w

γ sh I(eventsith ) + t ∙ τph + t ∙ τjh + δth + θih + ith ,(2)where Yith is an outcome variable for plant i in year t for dataset h. Similar to equation(1), we purged out the plant fixed effects specific to dataset h. We also controlled for the
event-specific region (p) and 3-digit industry (j)-specific linear trends, t ∙ τph , and t ∙ τjh toseparate unobserved shocks specific to region or industry. The estimated treatment effects,γ̂ sh , compared the performance trajectories between the treated and control plants for s > 0.
We tested the validity of the common trend assumption in dataset h by examining themagnitude of the estimates of γ sh for s ≤ −1.
To estimate a single set of average treatment effects γ s across all event years and provide
a comprehensive assessment, we stacked all the event-specific datasets:

Yith =w
X

s=−w

γ s I(eventsith ) + t ∙ τph + t ∙ τjh + δth + θih + ith .

(3)

Inclusion of the term δth essentially allows us to separate the dynamic treatment effects
from year effects specific to the event group.
10

Control plants in our sample includes those plants which received employment adjustment subsidy.
Thus, the estimated treatment effects reflect the difference between layoff plants and plants receiving thesubsidy.

11

4

Results

4.1

Are Layoff Announcements Exogenous?

Firms announcing layoffs may be systematically different from other firms a priori in termsof unobserved managerial traits or technology to substitute labor with capital. As such differences can mask the true causal effect of layoff announcements, it is important to compare
layoff plants with those of otherwise identical plants. To ensure the common trend assumption, we estimated equation (2) separately for each year and examined whether there were
any systematic differences in the outcomes between layoff plants and their control plants
prior to the layoff announcement. We took the logarithm of the number of employees as
the dependent variable. As our main data cover the years between 2005 and 2016, we fixed
our event window at three years before and after layoff announcements (w = 3) to enhancecomparisons across event years.
Figure 2 presents the estimates of γ sh for each event year. Most of the event years show
significant drops in the number of employees following the announcement. This is consistent with the original purpose of layoff announcements. Except for 2010, plants experienced approximately 15 to 30 % reduction in their workforce at some point in the following years. Plants showed immediate declines in 2009, 2011, and 2012. However, this was
not evident for the remaining event years, although most of their estimates still showed anegative impact. One possible reason is that firm-level layoff information does not necessarily identify the plants or branches targeted for layoffs. 11 Moreover, the estimates for the2008 and 2010 events had relatively large confidence intervals, implying a lack of statisticalpower due to the smaller number of treated plants (see Figure 1).
Importantly, the estimates for the pre-announcement periods are insignificant and close
to zero, except for the layoff announcements in 2013. Thus, except for the 2013 event,11
We matched the layoff information by the telephone numbers of the headquarters; however, the phone
numbers of some headquarters are not available in the Census of Manufacture. It is also possible thatbranches targeted at the firms’ layoff announcements were not registered as manufacturing establishments;
the firms did not target the manufacturing branches for the layoff.

12the treatment and control plants were comparable in that they followed the same outcome trends before the event year. We consider this to be an evidence of the exogeneityof the layoff shock between 2008 and 2012, conditional on industry- or region-specific linear trends: firms announced layoffs presumably because of unexpected or idiosyncratic
demand shocks during this period. On the contrary, the 2013 layoff announcements predict the pre-treatment employment level of treated plants. It is likely that the 2013 layoff
announcements were targeted at those plants that had redundant workers even three years
prior to the announcement. In the following analyses estimating equation (3), we stacked
event-by-event datasets between 2008 and 2012 but excluded the 2013 dataset to ensure thevalidity of the common trend assumption.

4.2

Impact of Layoff Announcement on Firm Performance and ProductInnovation

This section examines the performance dynamics that plants followed after announcing
large-scale layoffs. We stacked event-by-event datasets between 2008 and 2012 and estimated equation (3) using several performance measurements as dependent variables. Figure 3 shows the estimation results. Consistent with Figure 2, plants reduced their employment levels by approximately 10% after the firms’ layoff announcement. This effect persists
for at least four years following the announcement. Firms also significantly reduced theirvalue-added, ln(V alueAdded), and fixed assets, ln(K). A gradual reduction in capital occurred, implying high adjustment costs for fixed assets. Changes in input factors suggestthat firms’ primary reason for the layoff was to reduce the scale of their production, which
was most likely driven by a negative demand shock.
While plants successfully adjusted the production level by layoffs, the adjustment did
not lead to a substantial improvement in their productivity levels. Figure 3 also showsthat the plants’ total factor productivity, ln(T F P ), plummeted and took four years to recover to a level comparable to that of the control plants. As expected, capital intensity,13

ln(K/L), increased in the year of the layoff announcement; however, it continued to decline, consistent with a substantial reduction in fixed assets, ln(K). Thus, plants decreased
their level of capital more than they did their employment level. Even after the layoff announcements, the plants had redundant labor. In fact, the layoff plants had lower laborproductivity, ln(V alueAdded/L), compared to the control plants, although the differenceswere not significantly different from zero. To summarize, we obtained no evidence thatthe layoff plants improved their performance in comparison to their control counterparts,
although they adjusted their scale of production. 12
Figure 4 shows that layoff announcements did not help plants promote product innovations in subsequent years. Here, equation (3) was estimated by replacing the dependent
variables with the product information available in the Census of Manufacture. The layoff
plants did not add new products or abolish the existing products when compared against
their comparison group. The changes in the total number of products were also unaffected.
Thus, the layoff plants did not recover in the sense that they did not engage in any productinnovation, even years after the layoff announcements.
Appendix Figures and Table present the results of the sensitivity analyses in light of
longer-term impacts as well as selective attrition. In summary, none of these analyses alters
our main implications in Figures 3 and 4. The long-run impact of extending the estimation
window are depicted in Appendix Figures 1 and 2. Here, we focus on a stacked dataset
specific to the 2009 event and estimate equation (2) over an extended sample period from2005 to 2016. Thus, we estimated the treatment effect for up to seven years after the layoff
announcement. Our results in this extended window are similar to the previous findingspresented in Figures 2 and 3. However, we observed slightly different impacts on productivity. Appendix Figure 1 shows no significant negative impacts of layoff announcements
on total factor productivity and labor productivity. We also observe a recovery in capitalintensity six years after the announcement. The increase in capital intensity was driven by
12

It is important to note that our estimations are based on plant-level dataset, rather than firm-level. Our
sample includes both plants affected by the headquarter’s layoff announcement and the plants of the same
firm but unaffected by layoff. In this sense, our treatment estimates measure the intention to treat (ITT) effect.

14

an increase in fixed assets. However, the value-added was still significantly lower among
the layoff plants, even seven years after the event. We also found no clear evidence ofsignificant improvements in plants’ product creations in Appendix Figure 2, although theestimates were admittedly imprecise. Again, the layoff helped to scale down their production level; however, it did not trigger any substantial product innovations, even in thelonger span.
Appendix Table 1 demonstrates whether the selective attrition of plants explains our
main findings. Since we estimated our models on unbalanced panel data of plants, theresults obtained measure the aggregate treatment effects, including the effect arising fromcompositional changes owing to plants’ entry or exit. If layoff announcements affected
plants’ decision to exit the market, in particular, the negative estimates of plants’ performance would be underestimated owing to survivorship bias. This point was examinedby first considering each event dataset h and constructing cross-sectional data containing
a plant’s attrition dummy for the plants observed in the event year, as demonstrated inAppendix Table 1. We then estimated the logistic model of a plant’s attrition probability
separately for each event year (h):P rob(Attritionih = 1) = xi β h + γ h I(eventih ) + ih ,(4)where the dependent variable is an attrition indicator that takes one if the plant i is neverobserved after three years from the event year. We included industry and region dummies
as plant control variables xi . Appendix Table 1 shows the odds ratios for the impact oflayoff announcements for each event year.

4.3

Mechanism: Firm-specific Skill Investment

One important concern for layoff firms is the possibility of losing highly skilled workers.
In particular, when both firms and workers commit to a long-term relationship, such as inJapan, firms are concerned about dismissing the accumulated firm-specific human capital.
15

This section tests whether the layoff announcement skewed workers’ distribution to a specific age group and examines whether the loss in firm-specific human capital explains why
plants did not excel in terms of product innovations even years after adjusting their scaleof production.
To serve this purpose, we estimated equation (3) by replacing the dependent variables
with the proportion of employees in a specific age group at each plant. We constructed the
proportions from the employee questionnaires of the BSWS. We estimated the model forplants that appeared in both the Census of Manufacture and BSWS. Due to the availability of key identifiers to merge the BSWS and the Census of Manufacture, we used stacked
event-by-event datasets only for the event years between 2008 and 2010. Therefore, a sample in this estimation is a subset of the main sample used to estimate Figures 3 and 4 andshould be considered as providing complementary evidence.
Figure 5 presents the estimation results for each age group. The bottom-right graphshows the impact on the mean years of employees’ tenure at each plant. We found that
layoff announcements affected employees’ distribution unevenly across age groups. In
particular, layoff announcements did not significantly reduce the proportion of employees in their 50s or 60s. Since layoff announcements reduced the number of employees by
10%, according to Figure 4, the number of employees decreased even if the proportion remained the same. We originally expected that the layoff announcement would shift theage distribution downward because some firms targeted buyout offers to older employees. However, we found that the layoff announcement skewed the age distribution toward
the middle age: the layoff announcements increased the proportion of employees in their
40s one year after the announcement, while they decreased the proportion of employees intheir 20s.
Lazear and Freeman (1997) rationalized employer’s strategy regarding who should belaid off when they have invested in the employees’ firm-specific skill. 13 One important implication is that employers are better off by targeting the layoff to the employees at both
13

Lazear and Gibbs (2014, 82-85) provided a clear illustration of this idea.

16

ends of the age distribution: firms have not invested so much in those who just started
their career; firms have already recouped their returns from the earlier investment in firmspecific skills from the workers close to their retirement. Thus, employers should not target
workers in the middle of their career for their layoffs since these workers have the highest
outstanding rents to recoup over the coming years. In light of this theoretical implication,
it is reasonable that the plants in our sample retained a relatively large share of workers intheir 40s than control plants a year after the announcement because this cohort is presumably the one with the highest outstanding rents in terms of firm-specific skill investment.
However, our analyses also revealed that such a change comes at some cost. First, layoff plants had a disproportionately lower share of young employees than control plants.
According to Figure 5, the increase in the proportion of middle-aged workers was compensated by a significant decline in the proportion of employees in their 20s compared tothose in their 50s or 60s. Studies argue that creativity declines with age (Acemoglu et al.,2014; Florida, 2002), which may explain the stagnant productivity and innovation. Our
supplementary analysis in Appendix Figure 3 implies that the reduction in the proportionof employees in their 20s occurred owing to increased separation among this cohort. 14 According to firm-specific skill investment theory, employees decide to invest in skills specificto their firms when they foresee sufficient long-term returns. If the layoff announcement
signals a lower return on their firm-specific skill investment in the future, employees may
be better off by leaving the firm and investing in new relation-specific skills at a different
firm. As younger employees have a longer period to recoup the return to the investment
and thus have a higher option value to switch their employers, they are more likely to leave
the firm. Therefore, Figure 5 highlights an important detriment of layoffs as a means of afirm’s internal resource allocation: it triggers a breach of long-run commitment to employers, particularly among young employees, once they foresee it as a signal of lower return
14

Unfortunately, our datasets do not contain the information on voluntary quits or acceptance of buyout
offers for the layoff. Instead, we estimated the same model as before but with a different dependent variable:
the number of new graduates hired, available in the BSWS. The estimation results in Appendix Figure 3 showthat layoff announcements have no significant impact on the number of newly hired graduates. As Japanesefirms hire young full-time employees mainly through new graduates, we conjecture that the reduction in the
proportion of those in their 20s occurred owing to increased separations among the same cohort.

17

to their vested investment.
Another explanation for the absence of productivity or innovative gains is the deteriorated working condition of employees in their 50s. These employers usually retrieve returns on their earlier investment in firm-specific human capital in the form of high wages.
To explore the impact of layoff announcements on older employees, we estimated the impacts on plant-level mean bonus or the number of hours employees worked by age group,
which we constructed again from the employee questionnaires of the BSWS. The estimation results in Appendix Figures 4 and 5 show a significant reduction in the annual bonus
for employees in their 50s after the layoff announcement; however, their work hours were
unaffected.15 Thus, senior employees who remained in the firm worked for the same hours
but received lesser compensation. These results are consistent with studies reporting thedemotivation effect of firm downsizing on the remaining workforce (Kawai, 2014).
The evidence presented in this section should be interpreted with a caveat, since it isbased on plant observations in the BSWS and does not constitute the full sample that weused in the main analysis in the previous section. Moreover, the BSWS was not originallydesigned as a longitudinal survey. Plants with low sampling rates are likely to be replacedin the next survey year.16 Thus, the estimated impact of layoff announcements on the BSWS
sample is subject to compositional changes across survey years. More specifically, significant changes in the proportion of employees in their 20s and 40s were observed only inthe first year, following a layoff announcement. Owing to the limitations of the BSWS, wewere unable to identify whether these temporal changes were truly transitory or driven by
the replacement of plants in the BSWS sample.
15This result is in contrast to Genda et al. (2015), who reported that the working hours of regular male
employees increased at downsizing firms based on the data in the early 2000s. As the BSWS is an administrative survey of employers, our information on work hours may be understated by employers. We admitthat the estimates shown in Appendix Figure 5 are subject to measurement errors and are overestimated if
layoff plants tend to understate working hours. The true impact on work hours may be negative. However,
we consider that this possibility does not change our implications.
16
Some plants with high sampling rates remain in the sample. The larger the establishment size, the higher
the sampling rate.

18

5Discussion and Conclusion

Firms can recover from business downturns in several ways. We explored the mechanismof workforce downsizing by examining how layoff firms reallocated their internal resources
and whether they were able to eventually increase their productivity or secure innovative
gains. To serve this purpose, we exploited administrative data on an entire populationof manufacturing plants in Japan along with 6-digit product replacement records at eachplant. We also complemented our analysis with another administrative survey to estimate
the impact on the age distribution of employees at each plant.
Relying on the large-scale layoffs announced between 2008 and 2012 in Japanese listed
firms, we found that employee downsizing helped plants reallocate their internal resources
and scale down their production level; however, it did not initiate subsequent productivity gains or product innovations even years after the layoff event. More specifically, we
found that layoff plants reduced their employment level by approximately 10%. Their production levels also fell by 20%, followed by an even stronger reduction in capital inputs
by 30%. Despite a large reduction in the labor force, the capital intensity per worker was
reduced owing to an even stronger cutback in their capital. In fact, we found that layoff
plants did not introduce new products or abolish the existing ones in comparison to thecontrol plants, implying a sluggish level of investment. We also found that employees’
age distribution was significantly skewed toward middle-aged workers after the layoff announcement. Thus, firms adopted an efficient strategy in that they retained workers withthe highest outstanding rents in terms of firm-specific human capital investment (Lazear
and Gibbs, 2014; Lazear and Freeman, 1997). However, our analyses revealed that sucha change also incurred costs: an increase in the proportion of middle-aged workers wascompensated by a significant decline in the proportion of employees in their 20s rather
than those in their 50s or 60s. Moreover, our supplementary analyses show that workers intheir 50s had to work for the same number of hours, but with significantly lesser bonuses,
after the layoff announcement. Taken together, the results of this study highlight the im-19

portant detriments of layoffs as a means of internal resource allocation. The loss of young
employees and deteriorating working condition among the remaining senior employees
may explain why layoff plants did not increase productivity or secure innovation. An important takeaway to management is that for firms pursuing long-term relationships with
their employees, opting for a large-scale layoff is a tricky option, as it triggers voluntary
quits disproportionately among young employees. Management in such a firm may better
plan reskilling or upskilling slack labor beforehand as an alternative reallocation option
before the emergence of a business crisis.

20

Table 1: Summary Statistics
N

Mean

SD

P25

P50

P75

ln(L)
ln(K)
ln(V alueAdded)ln(T F P )ln(K/L)ln(V alueAdded/L)
N of new products
N of abolished products
N of total products

477023477023477023477023477023477023477023477023477023

4.37
10.12
11.12
3.88
5.75
6.75
0.15
0.14
2.3

.81
1.68
1.27
0.9
1.34
.83
0.51
0.5
1.79

3.76
4.16
9.16 10.09
10.28 10.93
3.39
3.89
5.1
5.87
6.28
6.72
0
0
0
0
1
2

4.78
11.09
11.79
4.39
6.56
7.19
0
0
3

Proportion of employees
20s
30s
40s
50s
60s
Mean tenure
N of hired new graduates
Mean annual bonus (in 104 JPY ≈ 90USD )Mean monthly hours of work

36727
36727
36727
36727
36727
36727
36727
36727
36727

0.18
0.26
0.25
0.23
0.06
12.98
5.29
71.94
175.26

0.11
0.1
0.17
0.25
0.11 0.18
0.25
0.33
0.11 0.17
0.24
0.32
0.12 0.14
0.21
0.3
0.08
0
0.04
0.09
5.09 9.24 12.63 16.44
16.73
0
1
4
54.84 28.95 62.55 105.44
21.8 162.4 175.3 188.67

Source: Plant-level observations in Census of Manufactures, from 2005 to 2016. Age information is available
only for those plants also observed in Basic Surveys on Wage Structure.

21

150
100
50
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 1: Number of Plants Affected by Layoff Announcement
Note: Bars indicate the numbers of plants whose headquarters announced buyout offers tolay-off their employees in our main sample of Census of Manufacture (METI), 2005-2016. Layoff
an-nouncement information is taken from a survey conducted by TSR.

22

Y = ln(Number of Employees)

-.2

0

.2

event year = 2009

-.4

-.4

-.2

0

.2

event year = 2008

t-3

t-2

t-1

t

t+1 t+2 t+3

t-3

t-1

t

t+1 t+2 t+3

-.2

0

.2

event year = 2011

-.4

-.4

-.2

0

.2

event year = 2010

t-2

t-3

t-2

t-1

t

t+1 t+2 t+3

t-3

t-1

t

t+1 t+2 t+3

-.2

0

.2

event year = 2013

-.4

-.4

-.2

0

.2

event year = 2012

t-2

t-3

t-2

t-1

t

t+1 t+2 t+3

t-3

t-2

t-1

t

t+1 t+2 t+3

Figure 2: Exogeneity of Layoff Announcements by Event Year
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γsin equation (2)). All models control for plant fixed effects and industry-specific and region-specificlinear trends. The models are estimated on stacked event-by-event data sets, separately for each
event year. Spikes indicate 95% confidence interval.

23

.2

ln(K)

-.2

-.4

-.1

-.2

0

0

.1

ln(L)

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4ln(TFP)-.3 -.2 -.1 0 .1 .2

-.4

-.2

0

.2

ln(Value Added)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4ln(Value Added/L)-.3 -.2 -.1

0.1-.3 -.2 -.1 0 .1 .2

ln(K/L)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1t

t+1 t+2 t+3 t+4

Figure 3: Impacts on Internal Resource Allocation and Outcomes
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γsin equation (3)). All models control for plant fixed effects, event-specific dummies to indicate time
periods, event-and-industry-specific linear trends, and event-and-region-specific linear trends.The
models are estimated on stacked event-by-event data between 2008 and 2012 (N = 1,529,854). Spikes
indicate 95% confidence interval.

24

N of abolished products

-.2

-.1

0

-.15 -.1 -.05 0.1.05 .1.2

N of new products

t-3

t-2

t-1

t

t+1

t+2

t+3

t+4

t-3

t-2

t-1

t

t+1

t+2

t+3

t+4

-.2

-.1

0

.1

.2

Changes in total N of products

t-3

t-2

t-1

t

t+1

t+2

t+3

t+4

Figure 4: Impacts on Product Creations and Destructions
Note:. Each graph plots the estimated impact of layoff announcement at each time period (e.g., γsin equation (3)). All models control for plant fixed effects, event-specific dummies to indicate time
periods, event-and-industry-specific linear trends, and event-and-region-specific linear trends.The
models are estimated on stacked event-by-event data between 2008 and 2012 (N = 1,529,854). Spikes
indicate 95% confidence interval.

25

Proportion (30s)-.15 -.1 -.05 0 .05 .1-.15 -.1 -.05 0 .05 .1

Proportion (20s)t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

Proportion (50s)-.15 -.1 -.05 0 .05 .1-.15 -.1 -.05 0 .05 .1

Proportion (40s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

Mean Years of Tenure

-4

-2

0

2

4

-.15 -.1 -.05 0 .05 .1

Proportion (60s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1t

t+1 t+2 t+3 t+4

Figure 5: Impacts on Age Composition of Employees (sub-sample)
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γsin equation (3)). All models control for plant fixed effects, event-specific dummies to indicate time
periods, event-and-industry-specific linear trends, and event-and-region-specific linear trends.The
models are estimated on stacked event-by-event data between 2008 and 2010 for those observations with age information from the Basic Survey on Wage Structure (Japanese Ministry of Labour,
Health, and Welfare). N = 63,839. Spikes indicate 95% confidence interval.

26

ln(K)-.2-.1-.6 -.4 -.2

0

0

.1

.2

ln(L)

2005

2007

2009

2011

2013

2015

2005

2007

2011

2013

2015

2013

2015

ln(TFP)-.4 -.2-.6 -.4 -.2

0

0

.2

.2

.4

ln(Value Added)

2009

2005

2007

2009

2011

2013

2015

2005

2009

2011

ln(Value Added/L)-.4 -.2-.6 -.4 -.2

0

0

.2

.2

.4

ln(K/L)

2007

2005

2007

2009

2011

2013

2015

2005

2007

2009

2011

2013

2015

Appendix Figure 1: Longer-term Impacts on Internal Resource Allocation
and Outcomes (Event year = 2009 Only)
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γs inequation (2)). All models control for plant fixed effects, dummies to indicate time periods, industryspecific linear trends, and region-specific linear trends. The models are estimated on stacked eventby-event data in 2009 (N = 466,526). Spikes indicate 95% confidence interval.

27

N of abolished products

-.2

-.1

-.1

0

0

.1

.1

.2

.2

.3

.3

N of new products

2005

2007

2009

2011

2013

2015

2005

2007

2009

2011

2013

2015

-.4

-.2

0

.2

.4

Changes in total N of products

2005

2007

2009

2011

2013

2015

Appendix Figure 2: Longer-term Impacts on Product Creations and Destructions (Event year = 2009 Only)
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γs inequation (2)). All models control for plant fixed effects, dummies to indicate time periods, industryspecific linear trends, and region-specific linear trends. The models are estimated on stacked eventby-event data in 2009 (N = 466,526). Spikes indicate 95% confidence interval.

28

-10

-5

0

5

10

N of new graduates hiring

t-3

t-2

t-1

t

t+1

t+2

t+3

t+4

Appendix Figure 3: Impact on New Graduate Recruitment
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γs inequation (3)). All models control for plant fixed effects, dummies to indicate time periods, industryspecific linear trends, and region-specific linear trends. The models are estimated on stacked eventby-event data between 2008 and 2010 for those observations with age, bonus, and hours of work
information from the Basic Survey on Wage Structure (Japanese Ministry of Labour, Health, andWelfare).N = 63,833. Spikes indicate 95% confidence interval.

29

-30

0

30

Mean bonus (30s)

-60

-60

-30

0

30

Mean bonus (20s)t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

Mean bonus (50s)

-60

-60

-30

-30

0

0

30

30

Mean bonus (40s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

Mean bonus (all)

-60

-30

0

-60-300 30

30

Mean bonus (60s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1t

t+1 t+2 t+3 t+4Appendix Figure 4: Impact on Mean Annual Bonus (in 10000 JPY ≈ $90 )Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γs inequation (3)). All models control for plant fixed effects, dummies to indicate time periods, industryspecific linear trends, and region-specific linear trends. The models are estimated on stacked eventby-event data between 2008 and 2010 for those observations with age, bonus, and hours of work
information from the Basic Survey on Wage Structure (Japanese Ministry of Labour, Health, andWelfare). Spikes indicate 95% confidence interval. “Mean bonus” indicates the mean annual bonus
in the previous survey year.

30

0

10 20

Mean hours of work (30s)-20 -10-20 -100

10 20

Mean hours of work (20s)t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

0

10 20

Mean hours of work (50s)-20 -10-20 -100

10 20

Mean hours of work (40s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1

t+1 t+2 t+3 t+4

Mean hours of work (all)-20 -100-20-100 1020

10 20

Mean hours of work (60s)t

t-3 t-2 t-1t

t+1 t+2 t+3 t+4t-3 t-2 t-1t

t+1 t+2 t+3 t+4Appendix Figure 5: Impact on Mean Monthly Hours of Work
Note: Each graph plots the estimated impact of layoff announcement at each time period (e.g., γs inequation (3)). All models control for plant fixed effects, dummies to indicate time periods, industryspecific linear trends, and region-specific linear trends. The models are estimated on stacked eventby-event data between 2008 and 2010 for those observations with age, bonus, and hours of work
information from the Basic Survey on Wage Structure (Japanese Ministry of Labour, Health, andWelfare).Spikes indicate 95% confidence interval.

31

Appendix Table 1: Impacts on Long-term Attrition (Odds Ratio)
(1)
2008

(2)
2009

(3)
2010

(4)
2011

(5)
2012

Layoff announcement

1.059(0.622)0.655(0.249)0.277(0.285)1.381(0.787)0.477(0.297)

N

41,831

39,26938,88235,87535,330

Event year (h)Note: Dependent variable is an attrition dummy variable which takes one if the plant is never observed after
three years from the event year. Each cell presents odds ratio estimated from a logistic model. All models
control for industry and region dummies. The models are estimated on the stacked event-by-event data,
separately for each event year. *** p < 0.01, ** p < 0.05, * p < 0.1.

32

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