PAY, TECHNOLOGY AND THE COST OF WORKER ABSENCE

Melvyn Coles (University of Essex)Joseph Lanfranchi (ERMES, Université Panthéon-Assas, Paris II)

Ali Skalli (ERMES, Université Panthéon-Assas, Paris II)and

John Treble (University of Wales, Swansea and ILR, University of Essex)

Revised March 2003

ABSTRACT

Conventional studies of absenteeism concentrate on labour supply. In this paper we analyse records of worker behaviour which enable us to investigate whether or not demand side effects exist. Using a compensating differentials model, we study how the shadow cost of absenteeism varies across firms which operate different technologies, (The shadow cost is the amount a firm would be prepared to pay to achieve a given small reduction in its absence rate.)

It is to be expected that pay for a less reliable workforce would be less than for a more reliable one, and we confirm this expectation. More subtle is the rate at which remuneration should fall with increased unreliability. We claim that just-in-time technology implies that absence will be more expensive for firms adopting it. The loss of productivity when absence occurs will be greater for such firms than for others, and the wage premium for reliability should thus be higher for such firms.

Using a matched employee/employer dataset from France, we are able to establish the existence of statistically significant differences of the kind predicted by the theory, and estimate the shadow cost of worker absence for firms operating different kinds of technology.

ACKNOWLEDGEMENTS: We are grateful to the British Council and the French Ministry of Foreign Affairs for financial support under the Alliance programme, and to Thomas Coutrot (DARES) and INSEE for permitting us access to the data. Coles and Treble are also grateful to ERMES at Université Panthéon-Assas, Paris II for their hospitality and assistance. Earlier drafts of the paper have been presented at seminars at Oxford, Manchester, Newcastle, Arizona, CREST (Paris), the 2001 EEEG Conference (Leicester), the 2001 CAED Conference (Aarhus) and SOLE 2002 (Baltimore). Comments from participants are gratefully acknowledged, especially those from Martyn Andrews, Tim Barmby, Peter Dolton, Francis Kramarz, Naçi Mocan, Ron Oaxaca and Jean-Marc Robin.

PAY, TECHNOLOGY AND THE COST OF WORKER

ABSENCE

Melvyn Coles (University of Essex)

Joseph Lanfranchi (Université Panthéon-Assas, Paris II)

Ali Skalli (Université Panthéon-Assas, Paris II)

and

John Treble (University of Wales, Bangor and ILR, University of Essex)

June 2002

I. INTRODUCTIONThe complexity of patterns of worker absence is well known, but is yielding

reluctantly to studies that use highly detailed information about individual workers

and their work environments. To date, these studies have focussed on how specific

contractual details affect worker absenteeism rates.1 In this paper we instead take an

equilibrium perspective, where we assume heterogeneous firms and heterogeneous

workers participate in a competitive labour market. By understanding why firms with

different technologies may penalise absenteeism differently, we can begin to explain

observed differences in absenteeism rates across workers and firms.

The empirical framework is based on Coles and Treble (1996) who assume

there are two types of firms, those that use an assembly line technology - where

output requires that a sufficient number of workers show up for work - and those that

use a linear production technology in which there are constant marginal returns to

labor. The key insight is that firms who operate assembly line technologies have a

different shadow cost of absenteeism to those which operate a linear technology [e.g.

Weiss (1985), Coles and Treble (1993)]. In the latter technology, an absence by one

particular worker does not affect the marginal productivity of other workers at the

1 See Barmby, Orme and Treble(1991, 1995), Gilleskie(1998), Delgado and Kniesner (1997), Barmby and Sibly(1999), Barmby, Brown and Treble(1998). An earlier literature used more aggregated data. See the survey by Brown and Sessions(1996).

1

firm. This is not true in an assembly line firm, nor in production functions where

labour inputs are strict complements (e.g. Kremer (1993)).

Coles and Treble (1996) consider a market equilibrium in which workers are

also heterogeneous - say, workers have different family responsibilities. In that case, a

competitive labour market implies equilibrium sorting by firms and workers.

Assembly line firms require a low level of absenteeism and pay a relatively high wage

to compensate workers for attending work reliably - we refer to this as the shadow

price of absenteeism. Workers who have a comparative advantage in attending work

reliably - say those who do not have to look after children when those children are

sick - take employment in assembly line firms and enjoy the wage premium attached

to employment there. Conversely those who appreciate greater flexibility in work

attendance, take employment in the linear technology firms and in equilibrium earn a

lower average wage rate (or perhaps a piece rate contract).

Although absenteeism may be costly to firms, observed absenteeism rates are

not necessarily inefficient - the worker who misses a day at work to look after sick

children (or enjoy a sports event) implies a trade off between workplace production

and home production, where home productivity is perhaps subject to idiosyncratic

shocks. Efficient contracting implies firms and workers agree to an acceptable

absenteeism rate and a compensating wage differential. Efficient sorting implies that

those workers who can commit to attending work reliably accept employment in

assembly-line type occupations, and so earn a wage premium reflecting the shadow

price of absenteeism.

The aim of this paper is to identify this shadow price using a unique

data set from France. The key features of the data set are: (i) it contains detailed

information about the operational structure of establishments, including the

organisation of work, and (ii) each establishment’s work force is sampled, so that

information about individual workers is available, including their absence record. We

use firms’ adoption of just-in-time technology, to distinguish high and low

absenteeism cost firms. We find the shadow cost of absenteeism in firms that operate

just-in-time technology to be about twice that in firms that do not. The shadow price

estimates confirm the qualitative nature of the shadow cost estimates, but we have

less faith in them since the data do not allow us to control for individual fixed effects.

The idea of shadow cost provides an alternative concept of absence cost to

those that are commonly used. Standard methods of measuring the costs to firms of

2

absence typically ignore the costs of control. They compare the firm’s existing

absence rate to an unachievable zero rate, whereas the shadow cost idea takes into

account the fact that firms can lose by trying to reduce the absence rate below its

efficient level.

The paper is structured as follows. Section II quickly sketches the arguments

of Coles and Treble (1996) and explains what equilibrium predicts about variations in

wages and absenteeism rates across firms and across workers. Section III then

explains how we can use firm heterogeneity and worker heterogeneity to identify the

shadow cost of absenteeism. In Section IV we describe the INSEE data and explain

how the model is implemented on that data. Section V reports the results and Section

VI draws out the implications of this research for the analysis and management of

worker absence.

II. THEORY2

The empirical work reported here is motivated by a model in which two types

of firm – distinguished by their technology – coexist in an equilibrium with labour

market sorting. For simplicity, we assume that firms can monitor absenteeism at zero

cost. In that case an optimal contract is a pair where the worker earns income

per period, regardless of attendance (the worker has full insurance, and the worker

is permitted an absence rate .3 The contract is assumed to be enforceable, say by

threatening to sack workers who go absent too frequently.

As the insights are based on equilibrium sorting in the labour market, we begin

by describing worker preferences. A useful specification is to assume that a worker's

value of home production, vh, is a random i.i.d. draw from some distribution

where denotes the personal characteristics of worker (e.g., marital

status, number of children, sports fan etc). Given a contractual absence rate ,

assume worker goes absent whenever the value of home production vh exceeds

his/her reservation level where satisfies (so that work

attendance is consistent with absence rate ). Assuming utility is separable in

consumption and home production, then implies worker ’s expected utility is

2 We do not give full technical details here. Interested readers can find these in Coles and Treble(1996).3 Coles and Treble (1993) show that with imperfect monitoring of absenteeism, the firm might offer incomplete sick pay insurance to reduce absenteeism rates

3

where is an increasing concave function describing the value of consuming income

per period. It is straightforward to show that is concave and increasing in both

arguments. The worker's marginal rate of substitution is

where is worker ’s home productivity in the marginal absence state. Further,

note that if two workers, and , are such that first order stochastically

dominates , then worker ’s indifference curve is steeper than worker worker ’s

indifference curve at all ; i.e. their indifference curves have the single crossing

property.

Now consider the firm side of the market, assuming all workers to be equally

productive. In general, given any firm technology and contract , we can solve

for firm ’s optimal employment decision and so obtain its expected profit

. Its equilibrium isoprofit contour is then given by

where denotes equilibrium profit. Below we demonstrate this approach for two

different technologies. However in general we can do this for any technology and for

any number of firms in the market.

Now consider a competitive labour market. Suppose in a market equilibrium,

worker ’s optimal contract is a pair at some firm j. Obviously given the

choice of , the worker will take employment at that firm which offers the highest

income consistent with equilibrium profit. Define the market frontier,

;

i.e. for each , the market frontier picks out the equilibrium isoprofit contours of

firms with the highest in the market. Figure II below demonstrates this frontier for

4

the two types case described in detail below. On that diagram are two isoprofit

contours - the non-linear one describes the isoprofit contour of an assembly line firm

[see Claim 1 below for details], the other describes the isoprofit contour of a linear

technology firm in a competitive equilibrium. The market frontier is then the

greater of these two functions for each . Claim 1 below implies these isoprofit

contours also have a single crossing property. It follows that for small , the market

frontier corresponds to the assembly line firm's isoprofit contour, while for large it

is the the linear technology firm’s isoprofit contour.

Given the market frontier and individual specific preferences , each

worker then chooses a firm and a corresponding to maximise own utility.

This outcome then describes a Market equilibrium as all contracts are efficient and all

firms earn equilibrium profit. As depicted in Figure II, we obtain a sorting equilibrium

where those workers who prefer high and low take assembly line work, those

who prefer high take employment at the linear technology firm.

Such sorting implies that in equilibrium, the shadow price of absenteeism -

defined as the slope of the isoprofit contour at an equilibrium agreement - is greatest

at firms that use assembly line technologies. To test this empirically, the data section

constructs the average wage rate paid per period attended

Note that ceteris paribus, a higher implies a higher imputed . Of course one

should expect that total income decreases with ; the less frequently a worker

goes to work, the less income he/she receives. However a testable implication of the

model, reflecting the high shadow price of absenteeism in assembly line firms, is that

the average wage rate is also strictly decreasing in in assembly line firms [see

Claim 1 below].

The next section discusses how to identify this wage effect on the data. The

rest of this section illustrates assuming just two types of firms and so formally

justifies the insights given above. As the formal proofs are available in Coles and

Treble (1996) here we simply outline the essential argument.

5

A worked example with two types of firms

In an assembly line production technology, if at least employees show up

for work the resulting output has value . If less than show up output is zero.

Suppose the firm employs workers on contract . Assuming home

productivity shocks are not correlated across employees, the firm's expected profit is

where describes the number of workers who attend, which has the Binomial

distribution.

Define which is the increase in expected

revenue by hiring an extra worker. Weiss(1985) establishes that

where the extra employee generates revenue when

exactly of the other employees show up, and this worker also attends (with

probability ). The firm optimally increases employment while and stops

once . Given the optimal number of hires, denoted , the firm's expected

profit is

If this firm makes equilibrium profit , its isoprofit contour is given by

and we define the shadow cost of absenteeism as the slope of this

contour.

Claim 1 [Coles and Treble (1996)]

Given an assembly line technology,

(a)

and

(b) is strictly decreasing in along an isoprofit contour.

6

Showing that equals is a straightforward algebraic exercise.

Note that if the marginal employee increases his own absenteeism rate , then the

firm’s increased profit by hiring him, , falls at the margin by .

This then describes the marginal pay cut necessary for constant profit. Given this

solution for the slope of the isoprofit line, the optimal hiring rule then implies part (a)

- the slope of the isoprofit line is strictly greater [in absolute value] than .

Part (a) then implies (b), the average wage paid per unit time attended is strictly

decreasing with absenteeism. Figure I depicts this result graphically.

The `bumps' in Figure 1 arise whenever the firm hires an extra worker.

implies zero absenteeism and the firm hires workers at that point. As

increases from zero, the isoprofit line falls sufficiently quickly that , which

is the slope of the line joining to the point , also falls. At ,

the firm hires an extra worker so that . Although the isoprofit frontier is

continuous, its slope is not continuous at . For , the average wage paid

continues to fall. As , the isoprofit contour converges to .

7

0 pk+1 pk+2 pk+3 1

y

etc.

Figure I: An isoprofit line for an assembly line firm

Fortunately the isoprofit contour of a firm with a linear technology is trivial to

derive. Assuming a competitive market with constant returns implies the firm makes

zero profit. Hence given and value of output per employee, the isoprofit

condition implies . That is, workers are paid a constant wage rate

.

As depicted in Figure II, Claim 1(b) implies a single crossing property where

the assembly line firm's isoprofit contour satisfies for small and

for large. The efficiency frontier then corresponds to the

assembly line isoprofit contour for small . In this region, the shadow price of

absenteeism is strictly greater than the average wage paid , and so the

average wage paid is strictly decreasing with . Conversely for large ,

corresponds to the constant returns to labour isoprofit line. In that case, the average

wage paid does not decrease with - workers are essentially paid piece rate for

8

0 1 p

y

etc.

Figure II: The Sorting Equilibrium

U0 – Low absence worker

U0 – High absence worker

each period attended. The sorting equilibrium now goes through as workers with

different preferences choose different contracts on the market frontier .

Suppose that workers are ex-ante heterogeneous - some have a greater

propensity to go absent - for instance, child-raising adults. Not only will an adult go

absent if too sick, but might also miss work if the child is too sick. Of course this is

not an inefficient outcome if the child's welfare exceeds the worker's expected

marginal product. The point is that ceteris paribus, a single mother might be more

likely to go absent than an adult who has no child rearing responsibilities.

Should different workers have different propensities to go absent - whether

they be child carers or sports fans - equilibrium will result in those who have a greater

propensity for reliability being employed in assembly-line type work and earning a

wage premium. Those who need greater flexibility will be employed in firms with

linear technology (who essentially offer piece-rate contracts) which pay less well but

allow greater absenteeism.

The model predicts equilibrium wage and absenteeism dispersion. Not

surprisingly, workers who go absent more earn less. However, assembly line firms

pay a wage premium above that paid by linear firms, and require lower absenteeism.

In particular, interpreting the slope of each tangency point as the shadow price of

absenteeism, assembly line firms pay a higher shadow price. This model forms the

basis of our empirical work.

III. IDENTIFICATION AND OTHER ISSUESTechnologies

An important, but subtle, issue is how those jobs which might be considered as

having a high cost of absence should be characterised. If, for example, in a one-period

world, the sales director goes absent and, as a consequence, the workers’ output is not

sold, that job might be considered as part of an “assembly line” process. The

underlying issue is one of worker complementarity in the production process, where

the absence of one worker adversely affects the marginal productivity of the others.

Unfortunately there is no simple way of identifying all such complementarities in our

data. However, just-in-time is a production technique where the firm´s production

process might be considered as one long assembly line – each component of the

production process relies immediately on the production of an intermediate input. If

9

one part of the process fails because of worker absenteeism, then the whole

production process fails4. This is not the case for non-just-in-time technologies,

where inventories of intermediate product can be held against the eventuality of an

interruption in the flow of output from prior processes in the production sequence. We

therefore use adoption by an establishment of just-in-time methods as an indicator of

assembly line work.

To see how inventories impact on absence costs consider the following

example. Suppose that there are two sub-processes in the production process, each

operated by a single worker. Worker 1 takes input from outside the firm. This is in

perfectly elastic supply. Worker 2 takes input either directly from worker 1, or from

an inventory of Worker 1’s output. As long as there is sufficient flow of input from

these sources to enable him to work, his output is 1. Otherwise, it is . If the

workers are each absent with independent probabilities, p, their joint output is

Clearly the greater the elasticity of supply of semi-finished goods, the greater output

will be. In particular, if the elasticity of supply is perfect (which might be achieved

with an infinite stock of semi-processed product),

, with .

If stocks are always zero (perfect just-in-time), worker 2 is wholly dependent on

worker 1 attending and output is 0 if worker 2 doesn’t turn up.

with . This is formally

identical to the assembly line technology analysed by Coles and Treble(1996).

4 Kremer(1993) describes a similar technology. Aoki.(1988) explicitly relates just-in-time to absenteeism: “The ‘zero inventory’ requirement to dispense with buffer inventory necessitates the effective control of local shocks, such as the malfunction of machines, absenteeism of workers, and quality defects, in order to minimize their effect on the smooth operation of horizontal coordination.

10

The dependent variable

Since we are interpreting the relationship between wage payments and absence

rates as an equilibrium relationship, it does not matter which we use as the dependent

variable in the analysis. It is convenient from a statistical point of view to treat the

wage rate as the dependent variable, since its distribution is less skewed than that of

the absence rate. In fact, the logarithm of the wage paid per unit attendance (which is

the variable we use5) appears to be close to normally distributed. For this reason we

report below the results of regressions of the logarithm of the wage rate on the

absence rate and a variety of other covariates. The specifications are designed to

measure how the slope of the relationship between the wage rate and absence changes

with adoption of just-in-time technology by the firm.

In practice, none of the firms we observe are likely to be pure linear-

technology firms with zero shadow cost. We therefore expect that both just-in-time

and non-just-in-time firms will have higher wages associated with lower absence

rates, but that the relationship will be steeper and have a higher intercept for just-in-

time firms than for non-just-in-time firms.

Identification

Given heterogeneous firms and workers, let denote the personal

characteristics of worker and the technological characteristics of firm .

There are two distinct ways of identifying the slope of the isoprofit line.

5 Unavoidable measurement error in the acceptable absence rate, p, implies a spurious positive correlation between and which biases our estimates upwards. To see why, consider the case where sick pay provides 100% replacement. If the worker is paid per period (regardless of

attendance) then the wage paid per unit attendance is . The pure theory predicts that this

corrected wage is not correlated with absenteeism in linear technology firms, while assembly-line firms will pay higher wages which are negatively correlated with absenteeism. The shadow cost is computed

as .

11

1. The Shadow Cost of Absenteeism

Define the shadow cost of absenteeism as the slope of a firm's isoprofit line.

To identify this cost note that with optimal contracting, the indifference curve of any

employee is tangential to the isoprofit line. Hence, in any competitive equilibrium and

for any given technology , variations in productivity-irrelevant variables which

change the preferences of employees with respect to absenteeism trace out the

corresponding isoprofit line and so identifies the shadow cost of absenteeism.

More formally, define as the matching set of a firm with

technology ; i.e. a worker of type is employed in a firm of type if and only if

. Given such a match, the optimal labour contract jointly determines

where is the income of the worker, and is the allowed absenteeism

rate, and and . Hence conditional on there is a set of

observed labour market outcomes, denoted where

and worker heterogeneity implies a distribution of worker absenteeism.

To identify the data, assume worker characteristics can be separated as into

two groups: productivity-relevant characteristics, , (e.g. education, experience etc)

and productivity-irrelevant characteristics, , (marital status, number of children,

age, etc). The identifying assumption is that conditional on having shown up for work,

the productivity-irrelevant variables do not affect the worker's productivity, but do

affect the worker's ability to attend work reliably. Thus variation in the productivity-

irrelevant variables, , generate pure equilibrium absenteeism dispersion and the

ensuing variation in identifies the isoprofit cost of those firms with

productivity (otherwise the worker is not hired, contradicting ). Hence

Identification Condition I: Conditional on the productivity variables but

excluding the , a regression of income paid, , on allowed absenteeism, ,

identifies the shadow cost of absenteeism at any firm with characteristics .

The condition is illustrated in Figure III below. By controlling for firm

characteristics, and productivity-relevant variables, we fix a single isoprofit line. We

12

can estimate its shape because differences in worker tastes imply that workers choose

different points on it.

2. The Shadow Price of Worker Reliability

In a market equilibrium, suppose worker obtains equilibrium utility and

consider the worker's indifference curve We define the shadow price of

worker reliability as the slope of this indifference curve at the equilibrium contract.

As workers are heterogeneous, we can define the market frontier as

i.e. for each , the market frontier looks at the equilibrium indifference curves

of all workers in the market, and picks the lowest in that set. Given the market

frontier and firm technology , firm chooses a worker [or set of workers] and

a corresponding to maximise firm profit.

Following the argument above, define as the matching set of

workers of type ; i.e. a worker of type is employed in a firm with technology

13

0 1 p

y

Figure III: Identification Condition I

Indifference curves for workers with different productivity irrelevant characteristics.

Estimated relationship

if and only if . If worker is employed in firm , optimal contracting

implies a contract . However conditional on , variations in

trace out the shadow price of worker reliability. Hence:

Hence

Identification Condition II. Conditional on all worker characteristics but

excluding the , a regression of income paid on allowed absenteeism

identifies the shadow price of worker reliability - the labour supply of reliable

attendance.

The condition is illustrated in Figure IV. Given a contract is observed, we

know it satisfies . Further, at the margin the slope of this frontier

corresponds to the slope of worker ’s indifference curve at that point. By controlling

for individual characteristics , variations in then trace out the shadow

price of worker reliability.

14

0 1 p

y

Figure IV: Identification Condition II

Isoprofit lines shown as linear, but need not be.

Estimated relationship

IV. DATA AND EMPIRICAL IMPLEMENTATION

The empirical analysis in this paper is based on two main sources of

information : The Enquête sur le Coût de la Main-d’Oeuvre et la Structure des

Salaires en 1992 (ECMOSS) and the Enquête Relations Professionnelles et

Négociations d’Entreprise (Réponse) conducted for the same year.

ECMOSS is a large scale survey which was carried out by INSEE, the French

National Statistics Institute, from January to May 1993. A postal questionnaire was

sent to workplaces belonging to firms with more than ten employees from the non-

agricultural private sector. This process resulted in a representative sample, stratified

according to establishment size, region and industry and covering 15,859 all-sized

establishments. The surveyed units were also asked to draw a representative random

sample of their employees and instructed to provide information about the sampled

individuals6. The final result was a further employer-reported data set relating to

148,976 employees. In the taxonomy chosen by Abowd and Kramarz (1999), the final

sample is a representative cross-section of firms with representative data on workers.

Réponse was the first survey conducted by the French Ministry of Labour

designed to study industrial relations within workplaces and was inspired by similar

official surveys in UK (Wirs), Australia (Awirs) and Ireland. It complements

ECMOSS with information about unions and bargaining activity, industrial relations

and changes in technology and work organisation. The sample used in Réponse was

drawn from a subset of the ECMOSS population, consisting of 12,293 establishments

from firms with at least 50 employees. In 3,091 of these establishments, face-to-face

interviews were conducted with workplace top managers or human resources

directors, leading to 2,998 usable questionnaires. Two thirds of the workplaces

surveyed in Réponse also answered ECMOSS. Merging the two datasets results in a

usable sample of 1,983 establishments. This subsample amounts to about 15% of

workplaces already included in ECMOSS.

The basic empirical model consists of two linear equations:

6 The random draw technique is described in detail on the postal form send by INSEE.

15

where is the wage per hour attended of individual i in firm j;

is the absence rate of individual i in firm j;

indicates whether or not the firm has adopted just-in-time technology;

is a vector of individual characteristics;

is a vector of firm characteristics;

is an individual specific error;

is a firm-specific error

and is a match-specific error;

are parameters (the last four are vectors).

The main aim of the paper is to obtain credible estimates of the parameters

. The theory predicts that , the intercept of the wage equation for

just-in-time establishments, and , the intercept for non-just-in-time establishments,

should both be positive with , while , the slope of the relationship between

wage and absence rates in just-in-time establishments, should be negative, and , the

slope for non-just-in-time establishments, should be non-positive, with .

Three important econometric issues arise in estimating the system :

1. Some information about firms and individuals is missing, which

might create omitted variable biases.

2. The choice by establishments to implement the just-in-time

production method as well as the choice by individuals to be employees of

such establishments are likely not to be random. If this is the case, just-in-time

is endogenous and its inclusion on the right-hand side of our estimated

equations will induce a bias.

3. A number of measurement issues in the data set need to be handled

carefully.

These issues are discussed in turn:

1. Omitted variable bias. The two identification conditions make this point

less serious than it appears at first. Indeed, as shown above, Identification Condition I

requires that only productivity-relevant individual characteristics, Xp, and firm

characteristics, Z, be controlled for. By estimating an employer fixed-effects model,

16

we restrict the set of potentially omitted variables to employee productivity-relevant

indicators only, which is the case in any standard earnings equation. Likewise,

Identification Condition II necessitates exclusion of any firm characteristic from the

set of regressors. Hence, only among the group of individual characteristics, X, either

productivity-relevant or -irrelevant, are some indicators likely to be omitted. Of

course, estimation of a ‘Two-Way’ fixed effects model would be a means to

overcome the omitted variables bias. Our data set is a single cross-section and does

not enable us to estimate an individual fixed-effects model.7

We report estimates of the basic specification using the entire set of

observations pooled across firms. A conventional technique for eliminating firm-

based fixed effects is to compute within estimates, by using the data in mean-

difference form. According to Identification Condition I above, such a regression

identifies the shadow price of absenteeism if the vector excludes productivity-

irrelevant elements.

Note that a between regression, based on variable means, is unlikely to

produce satisfactory estimates of the effects we are attempting to measure.

Identification Condition I requires inclusion of a full description of firm

characteristics, and our data are unable to provide sufficient detail. The second

condition requires personal characteristics of individual workers to be included. The

between specification includes these only as means across firms (they may be viewed

as firm characteristics in this form).

These arguments imply that the following estimates will be informative:

7 Note yet that Iidentification Ccondition I requirimposes that the determinants of at least worker reliability at leastdeterminants be allowed to vary. It follows that attempts to estimate So, for the purpose of estimation of the shadow price of reliability, the shadow price of reliability using the individual worker fixed effect model will ould have suffer from identification problems.

17

Specification Type Exclusions

A Benchmark all regressors in vectors X and Z

B Within vector Xd of productivity irrelevant variables

C Pooled vector Z of firm characteristics

Estimates using specifications B and C satisfy Identification Conditions I and II

respectively. Since neither of these regressions use explicit information about the

firm, we need not worry about missing firm information. It is worth noting that

Specifications B and C are not symmetric in the sense that B controls exactly for firm-

specific effects through the mean-differencing, while C does not control exactly for

individual-specific effects, since some relevant individual characteristics may be

omitted from . There is reason to believe therefore that Specification B is cleaner

than Specification C.

2. Endogeneity bias. Since JIT is a qualitative variable, any potential

endogeneity bias can be accounted for by estimating a switching regression model

with endogenous switching as long as a valid instrument is available for identification

purposes. The switching regression model is appropriate for our application, because

we are primarily interested in investigating the differences between the parameters

estimated for the two types of firm. This method is quite expensive computationally

and also raises additional identification issues.

The switching régime model requires the estimation of a probit selection

equation, modelling either workers’ choices of employment (Specification B) or the

firms’ choices of technology (Specification C), and two separate régime equations

including the appropriate Mill’s ratio.8 The identification arguments imply that the

specification and interpretation of these equations should differ depending on which

identification condition one is using.

Begin with Identification Condition II. Here the probit equation models the

firms’ choices of technology: just-in-time or not just-in-time. Firms will choose to

adopt just-in-time if they make greater profits by doing so. For just-in-time to enable

8 See Maddala (1983), pp. 223-228, and Lee (1978).

18

the firm to make greater profits, the available labour force must have a sufficient

supply of reliable workers. Therefore, in specifying the selection equation for the

within estimations, we use local labour market characteristics as the determinants of

the probability that a firm uses just-in-time.

Although the latent variable underlying the just-in-time choice by firms is

profit, the condition for just-in-time to confer a profit advantage is that there be a

reliable local workforce. Let denote a reservation level of labour force reliability,

so that firm j chooses just-in-time iff . Suppose there is a set of firm ’s

characteristics that determines so that where is

a normal disturbance with mean zero and finite variance. Hence, the choice of just-in-

time is observed for each firm for which . Wages are then determined

as follows:

Assume is correlated with and . One gets :

where is the covariance of uhj and uj. Denoting

, and using Identification Condition II, equation

implies:

19

where and are residuals with zero conditional means. Estimates of and

therefore indicate the nature of self-selection of firms into the just-in-time and

non-just-in-time production methods. Positive selection into each method implies

and .

There are two sets of variables that explain this choice. First, any local labour

market characteristics determining the local labour force’s propensity to be absent

should be included. Second, various characteristics of the firms may drive the just-in-

time decision. For instance, some firms may be too small, some might operate in an

industry where the technology is not available, some might face union pressure,

output market conditions may be such as to make just-in-time less profitable etc.

Hence, the probit equation should also include any firm characteristic that might

constrain or facilitate the choice of just-in-time by a firm. Finally, there is no reason

why wages should enter the structural form probit equation and therefore, the

individual characteristics in the wage equation should not enter the reduced form

probit equation.

Turning now to the supply side of the model, the decision as to whether an

individual chooses to work for a just-in-time firm or not is based on the criterion that

his expected utility differential is positive. The model is similar to that in Kostiuk

(1990), where the probit equation is of the form: and the individual

will choose a just-in-time job if and only if where is the corresponding latent

variable.

For the within regression (Identification Condition I), the probit should

include non-productivity related characteristics, representing pure taste differences

between individuals. In the within specification, these variables are omitted from the

régime equations, so that the model is identified by this omission.

Wages in this case are determined as follows :

and one gets, in contrast to :

20

where is the covariance of uhi and ui. Hence, denoting

, and using Identification Condition I, equation

becomes

and there will be positive selection into each method if and . Thus the

supply side and the demand side models differ both in the sign of the covariances, and

in the computation of the selection terms.

Note that both Identification Conditions lead to specifications in which

appropriate exclusion restrictions for identification of all three equations exist. In the

case of Identification Condition I, the variables in Xd should be excluded from the

regime equations, and therefore constitute valid instruments in the probit equation.

Similarly, the variables in are excluded from the regime equations under

Identification Condition 2, and constitute valid instruments in the relevant probit.

3. Measurement Error. We discuss four different potential sources of

measurement error, and indicate how we have tried to handle them, where necessary.

a) Perhaps the most serious source of measurement error relates to the just-in-

time indicator. Although we know whether each establishment uses the Just-in-

time production method or not, we are not able to distinguish those employees

within any establishment who are directly involved in a Just-in-time production

process and those who are not. It is easy to show that imputation of a value of 1

for the Just-in-time dummy to individuals who are not concerned by it, makes the

isoprofit frontier less steep than it would have been had we had the full

information. Hence, if our testing strategy confirms the predictions of the theory,

21

then our tests are likely to be even more conclusive if based on the right statistical

information.

b) It is not obvious how to assign the X variables to the subvectors Xp and Xd

of productivity-relevant and productivity-irrelevant variables. In particular, we are

unsure of the status of the age and part-time variables. To resolve this issue, we

report results using a variety of different specifications of the Xd vector.

c) The theory is probably best interpreted as a theory of manual work, and the

first set of results reported below uses data on blue-collar workers (ouvriers and

employés) alone. However, for completeness, we also report results for all

workers, which include managers (cadres), technicians and foremen (techniciens

and agents de maîtrise).

d) The measure of the absence rate that we have adopted relies on the idea

that , the observed absence rate, is a good proxy for the allowable rate of

absence in firm . This in turn relies on the idea that with a sufficiently large

sample and in a static environment, converges to . Our samples are finite,

and there are two classes of workers for whom they are particularly small: i)

workers who are not present for the entire year; and ii) part-time workers. For

these workers we observe fewer contracted days than for full-time workers who

are employed for the entire year and so we would expect the variance of the

measurement error, , to be large for them. In the main results, we have

excluded workers who are not present the entire year and included part-time

workers. We report results for all workers present in the firm for more than a

quarter year, as well as results for full-time workers only as robustness checks.

V. RESULTSResults for blue–collar workers

Because the arguments above imply many analytic options, we are selective in

reporting our results. We report in detail the main sets of results: uncorrected

regressions within each régime (Specification A, B and C) and those generated using

the endogenous switching régime model (Specification B and C) – these are in Tables

1, 3 and 4 at the end of the paper. The tables show these only for blue-collar workers,

22

who are employed full-time during the entire year. The remaining results are reported

in less detail, the aim being to concentrate on the robustness of the estimated

relationships in the face of varying specifications of the data. We do not report the

complete set of estimated coefficients. Instead, we simply give the estimated slopes

and intercepts with related diagnostic statistics.9

Uncorrected estimates

In Table 1, the first four columns give the results of the within regression, for

Specification A and B. This is estimated using mean-differenced data, and so does not

yield a direct estimate of the intercept. The remaining four columns report the results

of the pooled regression for Specifications A and C. All coefficients are estimated by

dividing the sample into two parts corresponding to firms who say they use just-in-

time methods and those who say they don’t, and running a separate regression for

each subgroup. They are therefore similar in nature to the switching regression

estimates, but exclude the Mill’s ratio terms. Table 2 provides a summary of the slope

and intercept estimates, together with the significance levels of F-tests for the

hypothesis that the coefficients are equal to zero. The intercepts for the within

estimates in Table 2 are calculated by evaluating the estimated relationship for an

absence rate of zero.

In the within regressions, there is a significant difference between the two

slopes, and both frontiers have a significantly negative slope. In the pooled

regressions, the slope of the wage/absence locus for non-just-in-time firms is not

significantly different (5%) from zero, while for just-in-time firms it is significantly

negative. The difference between the estimated slopes is not significant. The

estimated intercepts are also consistent with the theoretical predictions. They are all

positive, with higher estimated intercepts in the just-in-time régime than in the non-

just-in-time régime.

Switching régime estimates

Recall that the switching régime technique is necessary because of the

endogeneity of the firms’ choice whether or not to install just-in-time technology. The

discussion of the results begins with a discussion of our choice of instruments in the

probit equations, then moves to a discussion of the within results (Identification

Condition I) and the pooled results (Identification Condition II) in turn.

9 Complete sets of results are available from the authors on request.

23

The results of the switching régime estimation are presented in Tables 3

(Within) and 4 (Pooled). The within equations are specified according to

Identification Condition I, and the probit equation includes three variables measuring

productivity-irrelevant personal characteristics that are excluded from the two régime

equations: dummy variables for gender and marital status, and the number of children.

It appears that female workers are less likely to work in a just-in-time firm than male

workers, while married workers are more likely to work in a just-in-time firm than

unmarried ones. The number of children has no significant impact on this decision.

When the regressors from Specification B (human capital variables, age, tenure, part-

time status, temporary status, and foreign) are also included in the equation, the effect

of marital status disappears, but gender remains a significant determinant of just-in-

time choice.

The within estimates for each régime (Table 3, columns 5 and 6) confirm the

main message of the uncorrected results. Firstly, the estimated covariances, which

appear as the coefficients of Mill’s ratio in each case confirm positive selection into

both régimes.10 Second, the estimated relationship between the absence rate and the

wage rate is negatively sloped in both just-in-time and non-just-in-time régimes.

Third, it is almost twice as steep in the just-in-time régime (-0.3440), as in the non-

just-in-time régime (-0.1867). Fourth, the coefficients of the human capital, age, tenure

and foreign variables all seem to indicate reasonable patterns of wages in both

régimes.

The pooled estimates tell a similar qualitative story. The pooled equations are

identified by Identification Condition II, and the probit equation includes firm

characteristics: size, output market indicators, and unionisation; and a set of indicators

of local labour market characteristics: mean age, marriage rate and fecundity.11 All

these are excluded from the two régime equations. Once again, the results appear

sensible. Firms choosing just-in-time are in labour markets with relatively young

workers, with high marriage and fecundity rates. The results relating to output market

conditions all seem reasonable. Seasonal demand inhibits just-in-time adoption, as

does increasing demand, while erratic demand patterns encourage it. Small firms and

10 That is, the coefficient of and the coefficient of .

11 These variables have been added into our dataset from an extraneous source, the Enquête Emploi, 1991. (See Data Appendix)

24

unionised firms are less likely to adopt just-in-time, as are firms which work for

specific customers.

Turning to the substantive régime equations, once again the coefficients of

Mill’s ratio indicate positive selection.12 The slopes of the relationship between wage

and absence rates are negative, and there is a significant difference between the two

régimes. The main difference between the two sets of results is that the pooled

estimates (Specification C) are about half the size of the within.

We are thus left with a clear qualitative outcome, but a less clear quantitative

one: We find that the estimated slopes of the iso-profit lines are as our theory would

predict, as are the estimated slopes of the indifference curves. But the two sets of

estimates, which should in theory be the same, are not. As we pointed out above, the

within estimates are almost certainly cleaner that the pooled estimates, because we

can deal with firm fixed effects and not with individual fixed effects.

Another possibility reason for this outcome is that when using Identification

Condition I, firms hire many workers and so many different types of workers are

hired with the same technology ; i.e. is non-degenerate and so we get

variation across for each . Identification I therefore works. However suppose

workers only have one job each and that no two workers are the same. In that case

is degenerate and as there is no variation the model is not

identified.

Robustness

The results displayed in Tables 1, 3 and 4 and summarised in Tables 2 and 5

treat the data in a specific way. There are, however, some choices we made when

preparing the data that may be unjustified. By undertaking a number of parallel

estimations in which alternative choices are made, we attempt to investigate the extent

to which our results are robust to changes in the decisions we made in specifying the

form of the equations to be estimated. We do not report all the regression results,

instead we present summary tables with the same format as Tables 2 or 5. Interested

readers can obtain the full set of results from the authors.

12 That is, the coefficient of and the coefficient of .

25

Choice of X d

Table 3 uses a particular definition of Xd, the vector of productivity irrelevant

personal characteristics of workers

.

Table 6 shows the consequences of three alterations to this, motivated by uncertainty

as to whether age and part-time status affect productivity or not. The first row

reproduces row 4 of Table 5. The last shows the consequence of dropping the

quadratic age term and the part-time indicator from the list of omitted variables in that

specification. The third shows the consequence of adding back part-time status to the

list of omitted regressors, while the second drops the quadratic age term. The results

are not at all sensitive to these changes.

Extension to white-collar workers

We have run the analyses above using only the blue-collar workers in the

sample. This is because we believe these to be the workers most likely to be affected

by the nature of technology. However, for the sake of completeness, we have also

analysed the complete dataset including other occupations than the ouvriers and

employés that make up the blue collar workers. Table 7 shows the results.

The relationships estimated using the whole dataset, differ qualitatively from

the blue-collar results in one respect only. The estimated intercept differences have

the wrong (significant) signs. The qualitative pattern of slope differences is

unchanged, although the size of these effects is generally magnified. We conclude that

these rather less than satisfactory results endorse our original decision to concentrate

on blue-collar workers alone.

One possible explanation of the result arises if white collar workers are similar

in both types of firm, they receive generally higher pay than blue collars and have

lower absence. Under these circumstances, the estimated isoprofit lines will be

downward sloping, and the estimated intercepts can undergo a reversal of the kind we

observe.

Inclusion of sampled workers employed for only part of the year

The dataset includes some workers who, for a variety of reasons, were not

employed by the firm for the entire year. This implies that the absence record will

exist for these workers for only part of the year, increasing the variance of the

26

measurement error in the absence rate. Table 8 shows comparable estimates to those

in Table 5, including all workers who were employed by the firm for at least 90 days

of the sample year, 1992. The results are little affected by the inclusion of these extra

workers, and we conclude that this is not a major issue.

Exclusion of sampled workers employed part-time

The information relating to part-time contracts in the dataset is incomplete

since a part-time contract specifying 2.5 full days per 5-day week cannot be

distinguished from one specifying 5 half-days per 5-day week. Because of this, the

measured absence rate will have higher variance for these workers than for others. We

have therefore rerun the analysis using a dataset that excludes these workers.

The results are somewhat worse than when the part-timers are included, but

remain robust for the within Specification B. The point estimate of the non-just-in-

time régime increases quite markedly, resulting in the estimated difference between

the régime slopes falling by about a half.

VI. CONCLUSIONWe have investigated the relationship between rates of pay and rates of

absence, and how this relationship differs between firms with different technologies.

It is to be expected that firms generally would pay no higher wages for a less reliable

workforce than for a more reliable one, and we confirm this expectation. More subtle

is the rate at which remuneration should fall with increased unreliability. We claim

that just-in-time technology implies that absence will be more expensive for firms

adopting it. The loss of productivity when absence occurs will be greater for such

firms than for others, and the wage premium for reliability should thus be higher for

such firms. This is what we find.

What are the implications of these estimates? The slope of the equilibrium

relationship between the wage rate and the absence rate can be interpreted as a

measure of the loss due to increased absence. The within estimates imply that non-

just-in-time production is more robust to absenteeism than just-in-time production.

Our estimate (within) of the wage cut that non-just-in-time firms impose (-0.1759)

implies that a one percentage point increase in the absence rate can be condoned if

27

wages are reduced by 0.53 centimes per hour- or about Ff8.94 per worker per

annum.13 The equivalent calculation for just-in-time workers gives an estimate of

about Ff15.45 per worker per annum. Applied across the entire French labour force,

this amounts to about Ff154m or €23.5m.

This compares with British estimates by the CBI14 of the entire cost of absence

in the region of £10bn per annum (c.€6.8bn). Their estimate is, of course, the answer

to a different question than the one posed in our work. The CBI try to answer the

question: ‘What would happen to costs and revenues, if firms could reduce

absenteeism rates to zero without incurring any additional costs of absenteeism

reduction?’ We have tried to answer the question: ‘How much would firms be

prepared to pay to reduce absence at the margin?’

From a practical point of view, we believe our question to be of more interest.

Any manager knows that absence cannot be reduced costlessly. We have tried to

model and measure some of the factors that determine the cost of absence. The

outcome is encouraging, since the effect shows up no matter which estimation

strategy we use, although we cannot recommend great reliance on the numerical

values of our estimates. We hope that the issues that we have raised here will

encourage the development and use of more informative data in order that they may

be studied more effectively.

13 Assuming a mean absence rate of 3%, the elasticity of the wage rate with respect to the absence rate in JIT firms is -0.3053*0.03=-0.0092. A 1% increase in the absence rate to 3.03% would thus involve reducing the wage by 0.0092% if a JIT firm were to stay on the same isoprofit frontier. The equivalent calculation for non-JIT firms leads to a wage cut of 0.0053%. Since about 1/3 of firms adopt JIT, the mean cut is 0.0066% The mean wage rate in our date is approximately 56Ff per hour, and 56Ff*0.000066=0.0037Ff. The French workforce is about 25m in number, and there are about 35*48 hours in a working year. The annual wage cost of a 1% increase in absence is thus roughly 0.0037*25*106 *35*48Ff =154mFf.

14 For example, Confederation of British Industry (1994)

28

REFERENCESAbowd J.M. and F. Kramarz (1999): ‘The analysis of labor markets using matched employer-employee data.’, in O.C. Ashenfelter and D.Card (eds.), Handbook of Labor Economics, Vol 3B, chapter 40, pp 2630-2710.

Barmby, T.A., S. Brown and J.G. Treble (1997): ‘Absenteeism and overtime: a dynamic model with endogenous constraints’ University of Newcastle-upon-Tyne, mimeo.

Barmby, T.A., C.D. Orme and J.G. Treble (1991); ‘Worker Absenteeism: An Analysis using Microdata’ Economic Journal, 101, pp 214-229.

Barmby, T.A., C.D. Orme and J.G. Treble (1995): ‘Worker Absence Histories: A Panel Data Study’, Labour Economics 2, 53-65

Barmby, T.A. and S.Sibly (1999) ‘Analysing absence behaviour using event historymodels’ Newcastle, mimeo.

Brown, S. and J. Sessions(1996), ‘The economics of absence: theory and evidence.’, Journal of Economic Surveys, 10, pp 23-53.

Coles, M.G. and J.G. Treble (1993) ‘The price of worker reliability’ Economics Letters v41(1993), pp149-155.

Coles, M.G. and J.G. Treble (1996) ‘Calculating the cost of absenteeism’ Labour Economics 3, (1996), pp169-188.

Confederation of British Industry (1994) Managing Absence – In Sickness and In Health, Centre-File, Quality Business Solutions.

Delgado, M. and Kniesner, T. (1997): ‘Count data models with variance of unknown form: an application to a hedonic model of worker absenteeism.’, Review of Economics and Statistics, 79, (1997), pp 41-49.

Gilleskie, D. (1998): ‘A dynamic stochastic model of medical care use and work absence’ Econometrica, 66(1) January 1998, pp1-45.

Kostiuk, P. F. ‘Compensating differentials for shift work’, Journal of Political Economy, Vol. 98, No. 5, Part 1. (Oct., 1990), pp. 1054-1075

Kremer, M (1993): ‘The O-ring theory of economic development’, Quarterly Journal of Economics, August 1993.

Lee, L.-F.(1978): ‘Unionism and wage rates: A simultaneous equation model with qualitative and limited dependent variables’, International Economic Review, vol. 19, pp.415-33.

Maddala, G.S. (1983): Limited-dependent and qualitative variables in econometrics, Cambridge University Press.

29

Rosen, S. (1986) ‘The Theory of Equalizing Differences’ in O.C. Ashenfelter and R. Layard (eds) The Handbook of Labor Economics, Volume 1, North-Holland, pp. 641-692.

Weiss, A. (1985): ‘Absenteeism and Wages’ Economics Letters, 19, 277-279.

30

Within Estimates Pooled EstimatesSpecification A B A C

JIT non-JIT JIT non-JIT JIT non-JIT JIT non-JITConstant 3.6525

(64.06)3.7475 (124.4)

2.9629 (36.11)

3.1716(63.65)

4.0089 (798.1)

3.9775(1001)

3.2038 (36.9)

3.252 (54.3)

Absence Rate -0.2939 (7.28)

-0.1615(5.18)

-0.3053 (8.13)

-0.1759 (5.96)

-0.1368 (2.13)

-0.0413 (0.83)

-0.1481(2.67)

-0.0712(1.62)

Part-time(dummy)

- - 0.0291 (0.78)

0.0662 (4.27)

- - -0.0077 (0.16)

0.1950 (10.17)

PermanentJob (dummy)

- - 0.1123 (3.89)

0.0468 (2.35)

- - 0.0718 (1.83)

0.0298 (1.03)

Age - - 0.0197 (7.57)

0.0149 (8.25)

- - 0.0296(7.69)

0.0179(6.52)

Age2/1000 - - -0.2353 (7.28)

-0.1695(7.67)

- - -0.3185 (6.68)

-0.2126 (6.32)

Tenure - - 0.0101 (7.73)

0.0126 (13.19)

- - 0.0109 (6.57)

0.0180 (13.67)

Tenure2/1000 - - -0.1024 (2.77)

-0.1776 (5.87)

- - -0.1337 (2.70)

-0.2274(5.24)

General Lower secondary

- - 0.0333(2.77)

0.0449 (3.88)

- - 0.0736(3.76)

0.0807(5.04)

General Upper Secondary

- - 0.0638 (3.12)

0.0572 (4.02)

- - 0.1403 (4.88)

0.1438(7.42)

Vocational Lower Second’y

- - 0.0550 (7.37)

0.0670(9.61)

- - 0.1039 (10.47)

0.0844 (9.37)

Vocational Upper Second’y

- - 0.0686 (3.44)

0.0831 (6.00)

- - 0.1814 (6.35)

0.1920(10.03)

Under-graduates - - 0.1037 (1.40)

0.1252(4.25)

- - 0.1877 (1.76)

0.3292 (8.28)

Graduates - - 0.2342(1.98)

0.0303 (0.30)

- - 0.4016 (2.65)

0.3013 (1.96)

Vocational Tertiary Ed.

- - 0.1157 (3.86)

0.1281 (6.65)

- - 0.2860 (6.80)

0.2589(9.82)

Educational Level Unknown

- - 0.0100 (0.90)

0.0104 (1.18)

- - 0.0203 (1.82)

0.0381(4.56)

Foreign - - -0.0036 (0.33)

-0.0359(4.12)

- - -0.0365(2.49)

-0.0205(1.75)

Number of Children

- - - - - - -0.0012(0.36)

-0.0018(0.61)

Female - - - - - - -0.1711(19.49)

-0.1137 (16.66)

Married - - - - - - 0.0093 (0.91)

0.0180 (2.32)

Adjusted R2 0.6406 0.6651 0.7014 0.7250 0.0012 -0.0000 0.2964 0.3087

No. of Obs 2962 4960 2919 4853 2962 4961 2886 4749

Table 1: Uncorrected pooled and within estimates(t-statistics in parentheses)

31

Slope InterceptJIT Non-JIT Difference JIT Non-JIT Difference

PooledA: Bench-

mark-0.1368(0.0165)

-0.0413 (0.2027)

-0.0895(0.1194)

4.0089(<.0001)

3.9775(<.0001)

0.0315(<.0001)

C: With regressors

-0.1481(0.0038)

-0.0712(0.0525)

-0.0768(0.1386)

4.0093(<.0001)

3.9792(<.0001)

0.0301(<.0001)

WithinA: Bench-

mark-0.2939 (<.0001)

-0.1615(<.0001)

-0.1323(0.0047)

4.0137(<.0001)

3.9810(<.0001)

0.0327(<.0001)

B: With regressors

-0.3053 (<.0001)

-0.1759 (<.0001)

-0.1295(0.0034)

4.0131(<.0001)

3.9827(<.0001)

0.0305(<.0001)

Table 2: Implied values and F-test probability values for parameters of estimated isoprofit frontier and indifference curve

(uncorrected - blue-collar workers)

Note to Table 2: The intercept estimates in rows 3 and 4 are computed by imputing régime specific mean values to each of the variables in the regression, except the intercept and the absence rate, which is set to zero.

32

Probit SelectionEquation

Régime Equations (Spec. A)

ProbitSelectionEquation

Régime Equations(Spec. B)

JIT non-JIT JIT non-JITAbsenceRate (abs1)

0.1937 (0.97)

-0.3300(8.25)

-0.1982(6.34)

-0.6568(4.59)

-0.3440 (9.21)

-0.1868(6.27)

Part-timedummy

- - - 1.2256(84.34)

-0.5366 (7.82)

-0.1476(5.18)

Permanentdummy

- - - -0.0639(0.20)

0.1358 (4.75)

0.0551(2.72)

Age - - - -0.0218 (2.45)

0.0277 (10.24)

0.0177(9.66)

Age2/1000 - - - 0.0000(0.000)

-0.2270 (7.09)

0.1521(6.87)

Tenure - - - 0.0133(4.51)

0.0033 (2.25)

0.0105(10.69)

Tenure2

/1000- - - 0.6053

(9.53)-0.2981 (6.96)

-0.3396(9.59)

General Lower - - - -0.2105(8.149)

0.1215 (7.49)

0.1023(7.72)

General Upper - - - -0.3897(15.70)

0.2388 (8.84)

0.1539(8.61)

Vocational Lower

- - - -0.1391(12.27)

0.1029 (11.60)

0.0983(12.58)

Vocational Upper

- - - -0.4588(21.99)

0.2664 (9.41)

0.1912(10.12)

Under-graduates

- - - -0.9928(12.52)

0.5456(6.36)

0.3145(8.62)

Graduates - - - 0.1012(0.02)

0.1658 (1.42)

0.0163(0.16)

Vocational Tertiary

- - - -0.4612(10.86)

0.3213 (8.85)

0.2313 (10.05)

Ed. Level Unknown

- - - -0.5332(172.8)

0.2175 (9.01)

0.1243 (7.79)

Female -0.2771 (85.37)

- - -0.153(21.92)

- -

Married 0.0485 (2.02)

- - 0.0275(0.54)

- -

Children 0.0125 (0.037)

- - 0.0182(1.77)

- -

Foreign - - - 0.1150(4.21)

-0.0674 (5.34)

-0.0693 (7.35)

Intercept -0.2610 (77.50)

4.0687 (59.00)

-3.4476(90.18)

-0.6448(4.407)

3.8171(31.96)

2.9784(54.31)

Mill’s ratio - -0.3896(10.37)

0.4680 (12.36)

- -0.5878 (9.73)

0.4190 (8.69)

Log-likelihood -5018 - - -4726 - -

Adjusted R2 - 0.6530 0.6785 - 0.7106 0.7312No. of Obs 7787 2929 4858 7638 2888 4754

Table 3: Switching Régime estimates (within)(In parentheses: 2 statistics (Probit); t-statistics (Régime))

33

Probit Equation15Régime Equations

Specification A Specification CJIT Non-JIT JIT Non-JIT

Mean Age -0.0667 (12.47)

AbsenceRate (abs1)

-0.1882(3.08)

-0.0487 (0.99)

-0.1644 (3.01)

-0.0738 (53.89)

Marriage Rate 1.6615(9.73)

Part-timeDummy

- - -0.0499 (1.08)

0.1980 (10.32)

Dummy for Children

1.6976 (25.32)

PermanentDummy

- - 0.0753 (1.95)

0.0302 (1.04)

Log Plant Size 0.2597(338.3)

Age - - 0.0295 (7.78)

0.0178 (6.48)

Demand Seasonal

-0.1940 (38.14)

Age2/1000 - - -0.3139 (6.68)

-0.2106 (6.26)

Activity Regular -0.1594(18.71)

Tenure - - 0.0091 (5.58)

0.0178 (13.53)

Demand Increasing

-0.0809(6.54)

Tenure2

/1000- - -0.1287

(2.64)-0.2276 (5.24)

Price Competition

0.2758 (38.02)

General Lower

- - 0.0813 (4.22)

0.0815 (5.10)

Quality Competition

0.7594(93.87)

General Upper

- - 0.1458 (5.14)

0.1474 (7.59)

Particular Customers

-0.1872(19.65)

Vocational Lower

- - 0.1020 (10.43)

0.0851 (9.44)

Union -0.0627(2.60)

Vocational Upper

- - 0.1839 (6.54)

0.1934 (10.11)

- Under-graduates

- - 0.1740 (1.66)

0.3317(8.34)

- Graduates - - 0.4401 (2.95)

0.3024 (1.97)

- Vocational Tertiary

- - 0.2848 (6.87)

0.2599 (9.86)

- Ed. Level Unknown

- - 0.0346 (3.12)

0.0395 (4.72)

- Foreign - - -0.0366 (2.53)

-0.0213 (1.82)

- Number of Children

- - -0.0020 (0.58)

-0.0019 (0.64)

- Female - - -0.1603 (18.38)

-0.1114 (16.21)

- Married - - 0.0096 (0.97)

0.0175 (2.25)

Intercept -2.0786 (8.75)

Intercept 3.7850(281.8)

4.0713(298.7)

3.1343 (36.47)

3.2825 (53.89)

- Mill’s ratio 0.3129( 17.85)

-0.0825 (7.18)

0.1660 (9.62)

-0.0281 (2.70)

Log-likelihood -3280Adjusted R2 0.0981 0.0100 0.3177 0.3095No. of Obs 7916 2958 4958 2884 4745

Table 4: Switching Régime estimates (pooled)(In parentheses: 2 statistics (Probit); t-statistics (Régime))

15

34

16 Since some observations are lost due to missing values when new variables are introduced, the probit relating to Specification C was estimated using 7629 observations rather than 7916. The estimates differ only in very minor ways and we have chosen to report only the latter set of estimates.

Slope InterceptJIT Non-JIT Difference JIT Non-JIT Difference

PooledA: Bench-

mark-0.1882 (0.0010)

-0.0487 (0.1620)

-0.1395(0.0378)

4.0107(<.0001)

3.9778(<.0001)

0.0329(<.0001)

C: With regressors

-0.1644(0.0013)

-0.0738(0.0464)

-0.0905(0.0981)

4.0099(<.0001)

3.9793(<.0001)

0.0305(0.0282)

WithinA: Bench-

mark-0.3064(0.000)

-0.1668(0.000)

-0.1396(0.003)

4.0132(0.000)

3.9817(0.000)

0.0315(0.000)

B: With regressors

-0.3440(0.000)

-0.1868(0.000)

-0.1572(0.001)

4.5635(0.000)

3.7449(0.000)

0.8186(0.000)

Table 5: Implied values and F-test probability values for parameters of estimated isoprofit frontier and indifference curve (Switching régime estimates - blue-collar workers)

Note to Table 5: See Note to Table 2

Xd Slope Intercept

JIT Non-JIT Difference JIT Non-JIT Difference-0.3440(0.000)

-0.1868(0.000)

-0.1572(0.001)

4.5635(0.000)

3.7449(0.000)

0.8186(0.000)

Add Age and Age2 -0.3124(0.000)

-0.1706(0.000)

-0.1419(0.002)

4.0136(0.000)

3.9820(0.000)

0.0315 (0.000)

Add Part-time -0.3189(0.000)

-0.1761(0.000)

-0.1428(0.001)

4.0137(0.000)

3.9822(0.000)

0.0315(0.000)

Add Age, Age2 and Part-time

-0.2957(0.000)

-0.1331(0.000)

-0.1627(0.000)

4.0134(0.000)

3.9814(0.000)

0.0319(0.000)

Table 6: Implied values and F-test probability values for parameters of estimated indifference curve

Slope Intercept

35

JIT Non-JIT Difference JIT Non-JIT Difference

PooledA: Bench-

mark-0.7773(<.0001)

-0.6772(<.0001)

-0.1000(<.0001)

4.2130(<.0001)

4.2311(<.0001)

-0.0180(<.0001)

C: With regressors

-0.5285(<.0001)

-0.4322(<.0001)

-0.0963(0.1360)

4.2074(<.0001)

4.2276(<.0001)

-0.0201(0.4789)

WithinA: Bench-

mark-0.8244(0.000)

-0.6966(0.000)

-0.1278(0.0652)

4.2139(0.000)

4.2322(0.000)

-0.0183(0.001)

B: With regressors

-0.5863(0.000)

-0.4463(0.000)

-0.1400(0.024)

5.1484(0.000)

3.5849(0.000)

-0.0190(0.000)

Table 7: Implied values and F-test probability values for parameters of estimated isoprofit frontier and indifference curve

(blue collar and white-collar)

Slope InterceptJIT Non-JIT Difference JIT Non-JIT Difference

Pooled

A: Bench-mark

-0.1483(0.00540

-0.0084(0.4301)

-0.1399(0.0317)

3.9948(<.0001)

3.9611(<.0001)

0.0337(<.0001)

C: With regressors

-0.1352(0.0044)

-0.0739(0.0410)

-0.0613(0.1797)

3.9939(<.0001)

3.9636(<.0001)

0.0303 (<.0001)

WithinA: Bench-

mark-0.2819(0.000)

-0.1593(0.000)

-0.1225(0.010)

3.9983(0.000)

3.9647(0.000)

0.0336(0.000)

B: With regressors

-0.3028(0.000)

-0.1801(0.000)

-0.1227(0.123)

4.4818(0.000)

3.7316(0.000)

0.7501(0.000)

Table 8: Implied values and F-test probability values for parameters of estimated isoprofit frontier and indifference curve

(blue collar workers employed for 90 days or more)

Slope InterceptJIT Non-JIT Difference JIT Non-JIT Difference

PooledA: Bench-

mark-0.1863(0.0013)

-0.0656(0.0983)

-0.1207(0.0659)

4.0200(<.0001)

4.0119(<.0001)

0.0081(0.1021)

C: With regressors

-0.1645(0.0015)

-0.0832(0.0424)

-0.0814 (0.1343)

4.0196(<.0001)

4.0143(<.0001)

0.0053(0.1788 )

WithinA: Bench-

mark-0.3757(0.000)

-0.2846(0.000)

-0.0911(0.0437)

4.0256(0.000)

4.0180(0.000)

0.0280(0.0068)

B: With regressors

-0.3834(0.000)

-0.2991(0.000)

-0.0842(0.048)

4.6428(0.000)

3.5813(0.000)

1.0615(0.000)

Table 9: Implied values and F-test probability values for parameters of estimated isoprofit frontier and indifference curve

(full-time blue collar workers)

36

DATA APPENDIX

The results in this paper use three data sources: the 1992 Labour Cost and

Wage Structure Survey (Enquête sur le Coût de la Main-d’Oeuvre et la Structure des

Salaires, ECMOSS) which has been conducted by INSEE, the French National

Statisics Institute, the Industrial Relations and Firm-level Negotiations Survey

(Enquête sur les Relations Professionnelles et les Négociations d’Entreprise) called

REPONSE and collected by the French Ministry of Labour for the same year and the

1991 wave of the French Labour Force Survey (Enquête Emploi) again carried out by

INSEE.

ECMOSS is a representative cross-section of firms with representative data on

workers. It provides information on 15,859 establishments from the non-agricultural

private sector as well as an employer-reported description of individual characteristics

of 148,976 of their employees. The surveyed establishments belong to firms with at

least 10 employees. As can be seen from Table A1, most of the variables used in this

paper are drawn from ECMOSS.

No information on production methods is available in ECMOSS so we use

REPONSE as a complementary source providing information about the adoption of

just-in-time methods. The REPONSE sample was drawn from a subset of the

ECMOSS population consisting of 12,293 establishments from firms with at least 50

employees. Among these, 3,091 production units were surveyed and 2,998 returned a

usable questionnaire. Only two thirds of the REPONSE respondents also responded

ECMOSS, resulting in a merged sample of 1,983 workplaces. The average number of

workers per establishment is 10, but varies between establishments, partly because the

sampling rate falls as establishment size decreases.

As discussed in Section III of the paper, employers’ choice of production

method is based on the availability of a sufficiently reliable local workforce. Local

labour market characteristics were drawn from the 1991 wave of the LFS. For each of

the 99 sub-regions (départements) of France, we computed the mean age, the

proportion of partnered individuals in the labour force as well as the proportion of

labour force members with children. Since we know the geographical location of each

establishment in ECMOSS, merging in information from the LFS resulted in no loss

of information.

37

REPONSE contains no information on individual characteristics, so we know

whether an establishment uses the just-in-time method or not, but we are unable to

identify those employees (sampled in ECMOSS) who are involved in, or concerned

by it. By equally treating all workers in just-in-time establishments, we are

involuntarily introducing a measurement error bias in our estimates. We argue in the

text that this bias will tend to weaken our results. We also try to reduce it by

restricting our sample to blue-collar workers since these are more likely to have just-

in-time related jobs than white collars. As can be seen from Section V, the results

differ significantly depending on whether the sample used includes blue and white

collar workers or the former only.

The data provide us with the number of days of absence reported by the

establishment for each worker, but extracting the absence rate from this information

faces two difficulties. First, there are no zeroes coded in the original ECMOSS and

71% of the individual records contain a missing value. It seems unlikely that all

establishments would fail to report any employees with no absence. We assume that

some reported zeroes have been recorded as missing. We tackled this problem by

supposing that firms reporting absence for some of their workers reported it for all,

and we imputed a zero where employees with a missing value, were employed in

firms where some employees had values recorded. Zeroes were imputed to 10,205

individuals, that is to 52% of the entire sample. Of the rest, 5,729 have positive

recorded absence and 3,632 have missing values and were employed in

establishments which recorded only missing values. This last group were dropped

from the sample so that the final data set has no establishments with a zero absence

rate.

Second, there are two problems concerning the recording of the annual

number of days of absence of each worker. Not all workers were employed in their

establishment for the entire year, and we have no explicit record of the number of

contractual days. The first of these problems is discussed in the text, and we deal with

it by restricting the main analysis to the sub-sample of workers who have been

employed by the establishment for the entire year. We also report a robustness check

which uses a sample of individuals who worked for the same establishment during 3

months at least. The second problem arises because we know only the number of days

of the year for which each worker was employed by the establishment, which includes

38

non-contracted days, paid holiday entitlements, public holidays and weekends. For

full-time workers we chose simply to divide the number of days of absence by 365.

For part-time workers the issue is a bit more complicated. Since the part-time

indicator in ECMOSS tells us only what percentage of the corresponding full-time

number of hours an individual works, we are unable to distinguish between those who

work part-time every day of the week and those who work a few entire days in the

week or any combination of these. Therefore, it is not clear how calculation of the rate

of absence should account for part-time workers since although we know they might

have different working time schedules, we have no precise information on these. To

handle this problem, we constructed two measures of the absence rate: one where the

number of days of absence is simply divided by 365; a second one where it is divided

by 365 times the percentage of part-time work. A robustness check is reported in

Section V, based on the sample of full-time workers only.

The hourly wage rate is calculated as the annual payment to the worker

divided by the annual number of paid hours. The former includes payment for basic

hours worked, payment for overtime hours, all other premiums as well as sickpay.

The latter comprises the number of hours the worker did during the year whether

contractual or overtime hours, but excludes absence hours.

The main results we present in the paper are based on the sample of blue-

collar workers, including full and part-time employees. Table A1 below reports

summary statistics of this sample. For robustness check considerations, we also

consider different samples details of which are available upon request. None of the

samples include establishments where only one worker remains after deletion of

missing observations. This is because the within estimates use deviations from the

establishment mean, which are uniquely zero for firms with only one worker

recorded. To ensure the comparability of the pooled and the within estimates, we use

the same sample for both sets of estimates. The differences in the sample sizes in

Tables 1, 3 and 4 are caused only by the loss of observations due to missing values.

The product market related employer characteristics used in the just-in-time

probit are drawn from ECMOSS. Respondents were asked to describe the trend of

output during the 5 years prior to 1992. This is the variable labelled ‘Increasing

production’ in Table A1. They were also asked to assess whether their output volume

is usually affected by seasonality. Where the respondent’s answer was ‘YES’, the

variable labelled ‘Seasonal output’ has been given value 1. Perhaps more subtle is the

39

interpretation of the variable referred to as ‘Regular activity’ in Table A1.

Establishments were asked whether, besides the usual seasonal movements, the

volume of output is rather regular or rather irregular. We use this variable as an

indicator of the predictability of an establishment’s output.

The final group of product market related variable is that indicating

establishments’ competition criteria. To be more specific, for their product that

yielded the highest cash-flow, respondents were asked to say whether (i) prices, (ii)

product quality and (iii) efforts to adapt to customers with specific needs are

important competition criteria or only secondary ones. For each of these criteria, the

corresponding dummy variable has been given value 1 when respondents said it was

an important factor.

40

Variables JIT Non JIT Total SourceContinuous variables

Hourly wage (French francs) 56.64(14.70)

55.19(14.95)

55.73(14.87)

ECMOSS

Absence rate (uncorrected for part-time work) 0.031(0.07)

0.029(0.07)

0.030(0.07)

“

Absence rate (corrected for part-time work) 0.032(0.07)

0.033(0.09)

0.032(0.08)

“

Age 38.08(9.64)

38.19(9.94)

38.15(9.83)

“

Job tenure 13.28(9.21)

10.51(8.18)

11.55(8.68)

“

Number of children 1.21(1.33)

1.13(1.25)

1.16(1.28)

“

Part-time rate 0.98(0.09)

0.93(0.18)

0.95(0.15)

“

Plant size 593.76(570.53)

341.19(429.80)

435.60(502.25)

“

Regional mean age 38.23(0.91)

38.33(0.85)

38.29(0.88)

LFS

Regional proportion of partnered individuals 0.74(0.04)

0.74(0.04)

0.74(0.04)

“

Regional proportion of individuals with children 0.70(0.05)

0.69(0.07)

0.69(0.06)

“

Qualitative variablesMarried 69.62 66.92 67.93 ECMOSSFemale 33.43 44.17 40.16 “Foreigners 9.05 8.51 8.71 “Permanent contract 98.94 98.64 98.75 “Highest qualification :

-General lower secondary 4.76 4.76 4.76 “-General upper secondary 2.03 3.06 2.68 “-Vocational lower secondary 34.62 27.72 30.30 “-Vocational upper secondary 2.09 3.25 2.82 “-Undergraduates 0.14 0.63 0.44 “-Graduates 0.07 0.04 0.05 “-Vocational tertiary education 0.91 1.61 1.35 “-Unknown 19.69 33.81 28.53 “

Increasing production (last 5 years) 45.53 48.55 47.42 “Regular activity 73.79 78.33 76.63 “Seasonal output 38.84 45.36 42.92 “Main competition criteria :

-Prices 88.08 77.48 81.44 “-Product Quality 96.59 89.76 92.31 “-Customers with specific needs 80.51 76.85 78.22 “

Presence of union delegates 72.85 60.85 65.33 “Just in Time 37.38 62.62 100.00 REPONSE

Table A1   : Descriptive statistics

41