Does the structure of the workforce predict firms’ exit?
A test on Russian footwear companies
Gustavo Rinaldi
Tanaka Business School, Imperial College1
April 2007
Revised version of the paper presented at the 7th Missouri Economics Conference, organized by
the Federal Reserve Bank of St. Louis and University of Missouri, March 30 - 31, 2007, in
Columbia, MO. Published on the web site http://gustavorinaldi.wordpress.com/
1
Postal address: Tanaka Business School, South Kensington Campus, SW7 2AZ, London, UK
Tel. 0044 1952 612131 / 0044 788 065461; e-mail: [email protected]
1
D o e s t h e s t r u c t ur e of t h e w or kf or c e pr e di c t f i r m s ’ e xi t ?
A t e st o n R u s si a n f o o t w e a r c om p a n i e s
Abstract
The hypothesis that the structure of a firm’s workforce has significance as a predictor of its
‘exit’ is tested on a data set of Russian footwear firms in the years 1994-1999.
‘Structure of workforce’ is given the specific meaning: ratio of direct to total labour, relative
to the ratio in other firms in the same industry and with similar technology. ‘Exit’ is taken to
cover: departure from this industry, merger into other firm, shut down followed by spin off
and simple shut down.
Earlier studies have shown that in periods of upheaval, certain categories of employee are
more likely than others to be protected from dismissal (e.g. Shleifer and Vishny, 1988,
Lichtenberg and Siegel, 1990). This can be attributed to forms of collusion between
supervisor and agent (Tirole, 1986), to the fact that different staff may incorporate different
amounts and types of human capital (Becker, 1962 and 1965; Blakemore and Hoffman, 1989;
Idson and Valletta, 1996; Devereux, 2000) or fixed cost (Oi, 1962), or that certain employees
may have agreed to contracts with delayed payments (Lazear 1979; Idson and Valletta, 1996)
or rank order tournaments (Lazear and Rosen, 1981). It is common to many studies that in
periods of upheaval, staff reductions tend to fall on production rather than on administrative
employees.
This paper takes the further step of conjecturing that the extent of ‘top-heaviness’ in a firm’s
workforce, relative to that of its peers, reflects the scale of upheaval it is undergoing and
therefore serves as one predictor of exit. The hypothesis that workforce structure has
significance as a predictor of exit is subjected to a range of tests, none of which reject it.
Keywords: workforce structure, exit, direct labour.
JEL Codes: M5, L2, J21
2
S E C T I O N I I nt r o d uc t i o n
The hypothesis that the structure of a firm’s workforce has significance as a predictor of its
‘exit’ is tested on a data set of Russian footwear firms in the years 1994-1999. ‘Structure of
workforce’ is given the specific meaning: ratio of direct to total labour, relative to the ratio in
other firms in the same industry and with similar technology. ‘Exit’ is taken to cover:
departure from this industry, merger into other firm, shut down followed by spin off and
simple shut down. Earlier studies have shown that in periods of upheaval, certain categories
of employee are more likely than others to be protected from dismissal. Shleifer and Vishny
(1988) affirm that hostile take over often concern firms, where managers try to maintain full
scale operations and try to protect employees from dismissal.2 These same managers usually
are more respectful of implicit contracts than challengers.3 In a firm, which is target of a
merger bid “the group of employees that top executives may try hardest to protect are their
immediate subordinates: managers and administrators employed at corporate or divisional
headquarters.” (Lichtenberg and Siegel, 1990:384). It is common to many studies that in
periods of upheaval, staff reductions tend to fall on production rather than on administrative
employees. This has been attributed to forms of collusion between supervisor and agent
(Tirole, 1986), to the fact that different staff may incorporate different amounts and types of
human capital (Becker, 1962 and 1965; Blakemore and Hoffman, 1989; Idson and Valletta,
1996; Devereux, 2000) or fixed cost (Oi, 1962), or that certain employees may have
negotiated implicit contracts with delayed payments (Lazear 1979; Idson and Valetta, 1996)
or rank order tournaments4 (Lazear and Rosen, 1981).
2
Also for this reason, at least in Europe, mergers work as restructuring devices (Gugler and Yurtoglu,
2004).
3
Implicit contracts underpin long term employment contracts; they can often imply that middle and
upper managers receive a reward at the end of their career for performances that they have offered
years earlier. These contracts imply that these staff are not fired in the last years of their career.
4
Rank order tournament is a procedure which puts the retribution of each employee in relation with the
rank that the employee achieves and not with his/her productivity. The system is fair if all employees
3
This paper takes the further step of conjecturing that the extent of ‘top-heaviness’ in a firm’s
workforce, relative to that of its peers, reflects the scale of upheaval it is undergoing and
therefore serves as one predictor of exit.5 The hypothesis that workforce structure has
significance as a predictor of exit is subjected to a range of tests, none of which reject it.
Section II presents the hypothesis. Section III describes the econometric tests, which have
been used for a first empirical verification of these implications. Section IV describes the data
and presents the results. Section V presents some conclusions.
S E C T I O N I I I m pl i ca t i o ns f or e xi t
Starting from the evidence, reviewed above, that when facing a crisis firms tend to fire
proportionally more direct than indirect labour, we suggest that in an industry that uses a
homogeneous technology6, the firms with a relatively high ratio of direct
7
to total labour8
will be those less at risk of crisis than other firms. This leads to the testable hypothesis that
can compete for certain ranks and if “winners” can enjoy their retribution till their scheduled
retirement.
5
Other authors have suggested that managers endowed with much managerial talent manage large
firms or major parts of firms (Lucas, 1978) and in some way have a larger span of control. Wellmanaged firms will be relatively light on top. Similarly a cheaper transmission of knowledge, which
tends to be a feature of relatively innovative and competitive firms, increases the span of control of
managers (Garicano, 2000). Such firms , because they are well managed, will stand a good chance of
surviving, i.e. their probability of exit will be low. Therefore, again, top-heaviness in a sample of firms
will be associated with probability of exit. Colombo and Del Mastro (1999) have described in detail
the hierarchy of a set of firms in terms of number of managerial levels, span of control and allocation
of decisions and Del Mastro (2002) has looked at their determinants.
6
Osterman (1986), Beede and Montes (1997), Rajan and Wulf (2003), Rajan and Zingales (2001) are
some of the studies, which suggest several explanations about the influence of technological and
institutional factor on the managers’ span of control.
7
Direct labour, blue collars, lower ranking staff , production workers, junior workers and the
subordinates are concepts with wide areas of intersection.
8
Total labour is the sum of direct and indirect labour; indirect labour, white collars, higher ranking
staff, supervisory workers, senior workers and
the hierarchy are concepts with wide areas of
intersection.
4
within such an industry, a firm with a relatively high ratio of direct to total labour will stand a
relatively high chance of survival (i.e. a relatively low likelihood of exit).
S E C T I O N I I I Ec o n om e t r i c s p ec i f i ca t i o n of a n em p i r i c a l t e st
The hypothesis is tested using a concept of ‘exit’ which covers shut down, exit to another
industry, and exit with spin off (including the cases of firms absorbed by other firms), but not
ownership changes that do not involve merger with other firms. The Russian footwear
industry was undergoing a process of privatisation and it would be difficult to distinguish
between those legal changes, which were effects of specific characteristics of firms and those
which were effects of a government policy. The empirical model is estimated using a probit
specification, but similar results have been obtained using logit or linear probability model.
Additional tests in terms of hazard rates or log linear survival models offer results consistent
with those of the probit model. In it, the dependent variable EXIT = 1, if a firm that is
observed in the year t cannot be observed as such in the same industry in the year t+1.
DIRECT
is the explanatory variable that I use to test the hypothesis. It is a normalisation
(for year of observation) of the ratio  between the number of direct labour d and that of the
total workforce of the firm, direct and indirect labour (d+i),

d
d i
The other explanatory variables are a set of firm characteristics, which are widely accepted as
affecting the exit of firms (see table 1). They include the entry rate in the year when the firm
entered. Several studies (Boeri and Bellmann, 1995; Geroski 1995; Mata et al., 1995; Love,
1996; Caves 1998, Ilmakunnas and Topi, 1999, OECD, 2002) emphasise that industries,
which experience high entry rates, then experience high exit rates or that those entrants which
enter contemporarily to many other entrants are more at risk than other firms. In this sense
some authors have considered the problem of “congestion at entry” (Boeri and Bellmann,
1995:492). PRESENT_IN_92 is a binary variable, which states that a firm already existed in
5
the data base in 1992 (the first year of available data). It may indicate firms created many
years before and is an indicator of age. This is a variable, which many studies (Boeri and
Bellmann, 1995; Geroski, 1995; Caves, 1998; Gibson and Harris, 1996; Carroll and Hannan,
2000; Anderson and Tushman, 2001; Chen 2002) consider as an important determinant of
exit. Many of these authors argue that exit is a phenomenon mostly affecting new firms and
more rarely affecting established firms.
Such theoretical models as Jovanovic (1982) and Erikson and Pakes (1995) suggest that a
larger size is an indicator of the success of a firm and many empirical studies about exit (e.g.
Baldwin and Gorecki, 1991; Boeri and Bellmann, 1995; Geroski, 1995; Caves 1998; Gibson
and Harris, 1996; Carroll and Hannan, 2000; Anderson and Tushman, 2001; Chen 2002) have
found a negative relation between the size of firms and the likelihood of their exit. I find
extremely plausible that large firms have the option of shrinking before exiting, while small
firms can just exit, therefore I control for size. Other authors (Baden-Fuller, 1989; Deily,
1991; Boeri and Bellmann, 1995; Hunter and Isachenkova, 2001) included profitability
among those variables that they used to explain exit. It is plausible that firms, which do not
TABLE 1  VARIABLE DEFINITIONS
PRESENT_IN_92
=
PRODUCTIVITY
=
SIZE
=
DIRECT
=
PROFIT
=
ENTRY_RATE
=
9
Dummy variable indicating that the firm has been created at least in the
year 1992, when data records start to be available, or before; it may
indicate much older firms.
Firm output in constant value terms divided by total number of employees.
Ratio between the number of employees of the firm in the year of
observation and the number of employees of the median firm in the year
of observation.
It is the residual of the regression having:
on the left hand side the value of  divided by the average value of  for
the firms observed in the same year9;
on the right hand side the time trend and SIZE.
Dummy variable indicating that the firm is making profits.
It is the residual of the regression having:
on the left hand side the entry rate during the year when the firm entered
the industry;
on the right hand side the time trend and SIZE.
The definition of  is given in Appendix.
6
make losses, consider more the option of expanding than that of exiting. Some studies
(Gibson and Harris, 1996; Pavcnik, 2002) have explicitly introduced productivity as an
explanatory variable for exit. In these works more productive companies are less likely to exit
than other companies. I include controls for the absence of losses and for the level of
productivity.
S E C T I O N I V D a t a an d r e s ul t s
The tests were carried out on firms in the Russian footwear industry in the years 1994-1999.
Data were obtained on all Russian footwear firms/establishments employing at least 100
workers or less than 75% owned by individuals or industrial divisions of non industrial
enterprises.10 Additional data about prices have been taken from the Russian Statistical
Yearbook (Goskomstat, 2001). For the purposes of these tests “exit” is taken to cover the
cases of firm shut down, exit to other industry, and exit with spin off which includes the cases
of firms absorbed by other firms. Of a total sample of 1373 observations, 228 observations
could not be used, because of missing records, and tests have been carried out on 1145
observations. All variables were derived from the firm/establishment level data. The probit
estimator in LIMDEP (version 8.0) was used to estimate coefficients, standard errors and
marginal effects in the probability of exit with respect to changes in the explanatory variables
(Xi). Marginal effects are calculated as:
E[y|x]/x = f (x’ ) 
where f (x’ ) is the predicted probability of exit at the mean value of x’. Table 2 presents the
probit estimation results and marginal effects. The model as a whole is highly significant and
the hypothesis that the ratio of direct labour is a co-predictor of the exit of firms is not
rejected by this test.
10
The source of the data is Goskomstat, the Russian Statistical Agency which prepares the Registry of
the Russian Industry.
7
Firms with a relatively higher ratio of direct labour are also those less likely to exit; results
are controlled for entry rates, size, age (PRESENT_IN_92), for profits and for productivity;
TABLE 2 PROBIT REGRESSION RESULT FOR THE MODEL OF FIRM EXIT
(N = 1145)
Mean
St.
Deviation
Constant
i
p/Xia
-1.45
(0.40)
-0.12
(0.04)
**
DIRECT
0.00
0.13
-0.92
( 0.40)
-0.08
(0.04)
*
ENTRY_RATE
0.00
0.03
20.67
( 8.90)
1.72
(0.73)
*
SIZE
1.79
2.17
-0.35
( 0.08)
-0.03
(0.00)
**
PROFITS
0.52
0.50
-0.37
( 0.13)
-0.03
(0.01)
**
PRODUCTIVITY
0.45
0.57
-0.32
( 0.15)
-0.03
(0.01)
PRESENT_IN_92
0.73
0.45
-0.02
(0.13)
-0.00
(0.01)
Log-Likelihood
- 277
Restricted (Slopes=0) Log-L
- 332
Chi-Squared (17)
111
Pseudo R2 (Veal Zimmerman)
0.24
Note: (standard errors)
a
p/Xi is the change in the
percentage probability of exit
for a unit increase in Xi
calculated at the mean of Xi
* significant at 4% level
** significant at 1% level
control dummy variables for year and type of owner have been added too. All the results of
the control variables are consistent with results of most of previous studies. Larger, more
productive and more profitable firms were less likely to exit than other firms. Those entrants
which had entered at the same time of many other entrants were more at risks than other
firms. Results do not change in a relevant way when using winsorized data.
8
S E C T I O N V C o n c l u si o n
This paper examines the hypothesis that the ratio of direct labour is a predictor of exit
intended as departure from the industry, merger into other firm, shut down followed by spin
off and shut down. This hypothesis is consistent with results reported by researchers on the
topics of takeovers, specific human capital, labour as a quasi-fixed factor, contracts with
delayed payments, rank order tournaments, managerial talent and functional flexibility. The
empirical tests were carried out on the Russian footwear industry in the years 1994-1999. The
results do not reject the hypothesis that the structure of the workforce is a predictor for exit
(the cases of firm shut down), exit to another industry, and exit with spin off which includes
the cases of firms absorbed by other firms. These results are consistent with the findings of
Lichtenberg and Siegel (1990) who noticed that US plants, which were going to undergo
ownership change had a higher ratio of non production to production employees, but they go
further linking these results with other theoretical implications and confirming the possibility
of using labour-force structure in a model predicting exit.
Specific tests concerning ownership changes and further tests in other industries or other
countries could further test this link between the structure of the workforce and a firm’s
probability of survival. Readily available information about its workforce could therefore
contribute to a better understanding of its likely future.
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Appendix
If P(exit) is the probability of the exit of a firm, if C indicates crisis in the firm and z is a
vector of variables widely used and generally accepted in industrial organisation studies to
explain exit (they include size, entry rates, productivity, profitability of firms, year dummy
variables and types of ownership).11 Let’s formulate this rather obvious assumption:
1)  P(exit)/  C
> 0
1)
Where:
P(exit) = f ( C + z)
2)
Additionally we use the notation d to refer to direct labour and i to refer to indirect labour.
Therefore, the ratio  of direct labour in a firm will be given by:

d
d i
3)
The hypothesis that we formulate is the following:
d [ P(Exit)] /d  < 0.
4)
The literature supports a statement indicating that there is a negative relation between the
ratio of direct labour to total labour and the probability that a firm is in crisis.
d / dC < 0 and
dC/ d  <0
5)
but for 1)
P(exit)/  C
> 0
Then P(exit)/   < 0.
11
6)
7)
The introduction of such other variables as density of firms of the same industry in the same region,
regional per capita income and growth rates would be possible and it would not change this model; it
has been avoided to keep it simple.
12