Sources of Wage Growth andReturns to Tenure in Italy
Michele Battisti
Department of Economics, Simon Fraser University
battisti@sfu.ca
September 24, 2009PRELIMINARY AND INCOMPLETE, PLEASE DO NOT QUOTE
Abstract
This paper estimates the returns to experience and job tenure using a simultaneous equation
model that accounts for the potential endogeneity of seniority in the wage determination
process. It uses panel data for Italian workers’ histories based on social security records, forthe years 1985-1999. This dataset has not been used for this research purpose to this date.
The hypothesis of exogeneity of seniority can be tested and is rejected due to endogeneityof the match effect. The first two years on the job are associated with a two percent yearlywage increase. Effects of seniority are very small afterwards. The paper also discusses some
implications of these results.

1

1

Introduction

This paper uses a long panel dataset of Italian workers to estimate the return to labour
market experience and seniority for Italian men and women for years 1985-1999. By doing
so, the analysis also concerns the role of mobility at different levels of a person’s career andthe impact of mobility on wage profiles of Italian employees.
Good theoretical analysis and precise estimation of the accumulation of human capitaland in particular of the returns to tenure (in the various forms in which tenure can beinterpreted) is crucial for a better understanding of the prevalence of long-term matches,
but also for better evaluating the welfare consequences of turnovers in the labour market,
especially in a context with increasing labour market mobility. Estimating the returns toexperience and seniority provides guidelines for the design of labour market programs. Assessing the costs and benefits of mobility sheds light on the mechanism underlying humancapital accumulation, and helps evaluating the welfare effect of displacement for workers.
The estimation of the impact of experience and seniority on wages is carried out usinga very flexible model that allows for the inclusion of random person and match effects andfor endogeneity of seniority in the wage equation through the simultaneous estimation of ahazard model for employment duration. The main results show that returns to seniority are
sizeable only in the first few years, and are very small afterwards.

2

Background

Understanding human capital accumulation and the sources of wage growth has arguably
been one of the most debated topics in labour economics. Within the human capital literature, which flourished over the last few decades after Becker’s (1964) seminal contribution,
there has been much discussion concerning the distinction between general and specific human capital and their respective returns. More recently, empirical research has shown the2existence of different forms of specific human capital. Researchers found evidence of the importance of industry-specific human capital over firm-specific human capital (Parent 2000).
Those results are integrated with more recent evidence showing that displacements seem tobe associated with a large deterioration of workers’ skill portfolio and that industry-specifichuman capital seems to be of secondary importance (Poletaev and Robinson 2008).
As stressed in Dustmann and Meghir (2005), studying individual wage growth and its
determinants is of crucial importance for most labour market programs. Furthermore, thefocus on wage growth, returns to experience and returns to seniority offer very useful insights
for understanding job mobility and its consequences in terms of benefits and costs, which isitself very useful for policy-makers.
All the main predictions of the theory of human capital rely heavily on the definition ofgeneral and specific human capital. Therefore, investigating the returns to experience and totenure (job tenure, firm tenure, occupation tenure, industry tenure, sector tenure) provides
crucial insights about the empirical importance and role of different types of human capital.
The interest in the characteristics and evolution of specific human capital has resulted
in a very large body of empirical research attempting to estimate returns to experience,
returns to seniority and the impacts of mobility. The debate focusing on empirical issues
arising with the estimation of returns to experience and seniority has been a long and fruitfulone. Seminal earlier contributions from the late 1980s and early 1990s include: Altonji andShakotko (1987), Brown (1989), Topel (1991) and Neal (1995). These contributions all
recognize that an Ordinary Least Squares (OLS) regression of wages on tenure suffers an
endogeneity problem whenever any unobservable characteristic of the firm, job and/or worker
affects both wage and job duration. A range of solutions has been offered, each with some
limitations.
After these initial contributions, and largely thanks to advancement in the econometrictheory and techniques involved in the estimations, as well as to the availability of better data3

for addressing these issues, more reliable estimates have been produced. Furthermore, the setof questions addressed is now larger, especially due to the increased availability of integrated
employer-employee data. The estimation of the return to tenure can now be carried out
in models where there are worker, firm and match fixed effects, in which returns to humancapital and experience may be heterogeneous, and where exogenous demand effects can betaken into account.
The complex relationship between tenure, productivity and wages is investigated by
Dohmen (2004) using Dutch data, finding no evidence for strong impact of performance
controls on seniority-wage profiles. More attention has also been paid to the assumptions
made about observability of workers’, firms’ and match’s characteristics by agents vis-avis the econometricians (see Gibbons, Katz, Lemieux, and Parent 2005). Also, matchedemployer-employee data made it possible for researchers to allow more detailed analysisof the implications of the human capital theory and of labour search and match theories(Jovanovic 1979). A very interesting example in this sense is Nagypal (2007), which tries todistinguish between two ways in which one can interpret the endogeneity between tenure andthe hazard rate of separation, i.e. specific human capital accumulation on the one hand, andincreased information about match quality on the other. This is also a good example of thetension between a pure interpretation of human capital theories and of matching theories.
This paper will be in some sense agnostic about the mechanisms that underlie the relationship between tenure and wage growth, and focuses on empirics. The primary goal is toestimate the importance of returns to tenure1 in the Italian labour market, and further goals
include isolating the relative importance of mobility across occupations (within the same
employer), firms (changing firm, not occupation), sector and geographic area. Movements
to and from unemployment can also be taken into account.
Given this focus, the most closely related literature contributions are Dostie (2005) and1

Tenure and seniority will be used interchangeably here4

Dustmann and Meghir (2005). Dostie (2005) uses French data and estimates returns toseniority in a model where he can allow for correlated individual and job unobserved heterogeneity. He finds no significant returns to seniority after controlling for matching considerations. Dustmann and Meghir (2005) focus on similar issues but frame their analysis much
more as a study of wage impacts of different sources of human capital2 . They use German
data and distinguish between skilled and unskilled workers, and find that returns to firm
tenure are positive for all workers, and larger for unskilled workers. Returns to sector tenure
are positive for skilled workers, but are not significantly different from zero for unskilledworkers.
This work will attempt at addressing these issues using Italian data. The focus here will
be on firm and sector tenure, on the wage impacts of mobility (not only between jobs andsector, but also geographic) and on the impacts of mobility on the life-cycle earnings of workers. The next section briefly outlines the empirical methodology that will be implemented
in the paper.

3

Endogeneity of Job Seniority

Let i = {1, ..., N } be a worker, and t = {1, ..., T } be a time period. J(i, t) is then theemployer of worker i at time t3 .
Using a linear framework, many studies estimated an equation of the form:ln(wiJ(i,t)t ) = Xit β0 + β1 (seniorityiJ(i,t)t ) + β2 (experienceit ) + iJ(i,t)t

(1)

where
2

The difficulties in measuring labour productivity makes it hard to distinguish between human capital
accumulation and informational aspects of matches empirically3
In the empirical section, J(i, t) ≡ j for simplicity, given the structure of the data used here, which does
not allow the identification of firms

5• wiJ(i,t)t is the real wage of worker i working for firm J(i, t) in period t;• seniorityiJ(i,t)t is equal to the duration of the match J(i, t) up to period t;• experienceit is equal to the total labour market experience of worker i.
The error term iJ(i,t)t can be decomposed into a person fixed effect component, a firm
fixed effect component, a match component and a component that will be match-, time- andperson-specific:

iJ(i,t)t = θi + ψJ(i,t)t + δiJ(i,t) + νiJ(i,t)t(2)

OLS yields unbiased estimates of β0 and β1 in (1) only if both experience and seniority
are uncorrelated with unobserved heterogeneity at the individual, firm or match level (seeDostie 2005), which have an impact on the dependent variable.
Many studies have presented theoretical and empirical reasons why person, firm andmatch effects might be important in reality. The job search literature provided a theoreticalframework in which to critically think of the relationship between wages, tenure and mobility
decisions. Continuing matches are a self-selected subset of previously existing matches. For
example, more productive firms might attract more productive or more motivated workers;
matches might survive only if workers are productive; employers might use wages as a level
for retaining employees (Dostie 2005). In all these cases, job seniority is correlated withindividual, firm and match characteristics. The endogeneity of seniority cannot easily besolved by instrumenting for it (as commonly pursued in earlier work), given that the sourceof endogeneity is likely to come from unobservable characteristics of either worker, firm ofmatch.

6

3.1

Wage equation and employment duration

The recent literature, starting with Lillard (1999), has attempted to exploit the advantagesof longitudinal datasets to estimate this endogeneity problem and control for endogeneity
directly. A person effect and a match effect can be estimated by modelling wages andemployment duration simultaneously, as in Dostie (2005). Using panel data on work histories
as in this paper does not allow the econometrician to include firm fixed effects, but a similar
empirical strategy as in Lillard (1999) can be adopted nevertheless.
The match effect will include heterogeneity coming from the firm size, and thus notincluding a firm component impacts the interpretation of the match effect but does notaffect the remaining estimates. Dostie (2005) applies a very similar methodology: even
if the data used include linked employer-employee information, the author only includes amatch effect.
The simultaneous model estimated in the following sections includes a wage equation
and a model for employment duration. Wages are only observed for accepted job offers, andtherefore represent the outcome of a sample selection process by which employments spells
terminate if the worker does not accept the wage offer. Quits and layoffs are regarded as
equivalent in this context, although it is necessary to acknowledge that there may be very
different motivations for the termination of a job. The theoretical starting point for thisanalysis is a simple job search model in which on-the-job search is possible, as in Pissarides(1994). The model for employment decision is presented in the following, simplifying theanalysis in Lillard (1999).

3.2

Modeling employment duration

A worker i works at firm j at time t − 1. At the beginning of period t, she is offered a wage
wij0 t , which she will accept and continue working for firm j0 if the offered is higher than some7

R
reservation wage wijt
. This reservation wage may be a function of personal characteristics,

experience, and tenure in firm j0 . The crucial point here is that we observe wij0 t only if it issuch that both parties found it optimal to continue the relationship.
Suppose for example that a worker gets an offer from a different firm j1 . This offer willR
have an impact on wijt
. Let this offer be wij1 t , and assume it depends on the individual

specific component (which depends on unobservables), on experience and on the matchcomponent in the following way:ln(wij1 t ) = f (experienceit ) + θi + δij1

(3)

Also, assume the reservation wage for worker i at firm j0 isRln(wijt) = ln(wij0 t ) + g(experienceit , seniorityijt )(4)

Person i will decide to move ifRln(wij1 t ) > ln(wijt)(5)

Assume she will stay in case of indifference. Substituting, i will move if:

f (experienceit ) + θi + δij1 > ln(wijt ) + g(experienceit , seniorityijt )=⇒ δij1 > ∆

(6)
(7)

where
∆ ≡ ln(wijt ) + g(experienceit , seniorityijt ) − f (experienceit ) − θiLet pt be the probability of person i receiving on offer at all at the beginning of period t.
This probability is taken as exogenous here for simplicity. Then the conditional probabilityof observing a job separation can be written as:8

h(ln(wijt ), experienceit , seniorityijt ) = pt [1 − F (∆)]

(8)

where F is the cumulative distribution function of δij1 . This assumes that pt and δij1 areuncorrelated. Intuitively, equation (8) means that the probability of a match destructionin this framework is given by the probability of having an offer at all times the probabilityof that offer being large enough to be accepted. This model can easily accommodate theinclusion of unemployment as an outside option that leads to workers rejecting very low
offers.

4

Empirical model

4.1

Derivation

4.1.1

Hazard model

Employment duration is estimated following the discussion above, and using a hazard model
based on Kiefer (1988). The hazard duration dependence follows a generalised Gompertz
distribution, i.e. a piecewise linear spline. Pollard and Valkovics (1992) and Lillard and Panis(2003b) provide additional information about the characteristics and common notational
conventions for this distribution.
For person i, I use a proportional hazard model of the following form:ln(hij (τ )) =β0 + β1 (malei ) + β20 jobcovjt + β30 seniorityijt + β40 experienceijt (9)+β6 timet + θ1i + φδijt

where

9

• malei is the only personal characteristics considered here4 ;• jobcovjt is a vector of job characteristics, such as occupation, sector, region etc.;• timet is a linear time trend;• θ1i is a random person effect;• δijt is a random match effect, with load parameter φ• all random effects have zero mean;• seniorityijt and experienceijt are piecewise linear splines, so that a spline Υ(τ ) with
ηN nodes is based on the following transformation of the spell duration:min[τ, η1 ]








max[0, min(τ − η1 , η2 − η1 )]Υ(τ ) = ...







max[0, min(τ − ηN −1 , ηN − ηN −1 )]







max[0, τ − ηN ]The survivorship function for equation (9) without time-varying covariates5 is given by
the following equation (also see Lillard and Panis (2003b)):
 Z τ

 Z τ
eβ0 male0
Si (τ ) = exp −h(ι)dι = exp −eα T (ι) dι
0

(10)

0

where T includes the piecewise linear splines of equation (9).
4Education is not available, and age seem to be inappropriate in this model given the sample selectionthat will be made5
As it is the case for the equation estimated in this version of the paper

104.1.2

Wage equation

The wage equation is specified as in the following:ln(wijt ) =γ0 + γ1 (malei ) + γ20 jobcovjt + γ30 seniorityijt(11)+(1 + γ4 malei + θ3i )γ50 experienceijt + γ6 timet+θ2i + δijt + νijt

where, as before, the subscript ijt is used for simplicity instead of iJ(i, t)t, which would
have been redundant notation given that I never specify firm effects and match effects separately. In equation (11), θ2i and θ3i are random person effects and νijt is the person-matchtime specific error term. All other covariates are defined as in equation (9). All random
effects have zero mean. Equation (11) includes random person and match effects, and also
allows personal characteristics and unobserved heterogeneity at the person level to impact
the returns to experience.

4.1.3

Assumptions on parameters

I assume a first-order autoregressive form for the error term in equation (11):νit = ω · νi,t−1 + uit(12)

where uit ∼ iid N (0, σδ2 ). This allows for person-specific correlation among all wage values
within a worker’s career, which holds across jobs.
The model also allows for job-specific heterogeneity in the initial wage level, which is
independent from job to job. For every match j11δijt ∼ iid N (0, σδ2 )4.1.4

Simultaneity

Two sets of elements introduce simultaneity in the two-equation model described above.
Firstly, the individual random effects components of equation (9) and of equation (11) are
jointly normally distributed, i.e.

(θ1i , θ2i , θ3i ) ∼ N (0, Σθ,θ )Secondly, a load factor φ on the match random effect component of equation(11) isintroduced in equation (9).
This model allows us to test exogeneity of seniority in the wage equation, and to test
some of the predictions of a matching model of the labour market. Two tests for exogeneity
are available here, as either coming from the person unobserved heterogeneity or from thematch unobserved heterogeneity. The null hypothesis of zero correlation between θ1i and θ2ior θ3i will be rejected in case of endogeneity. Exogeneity of seniority in the wage equation
would also imply φ = 0, which is the second way of testing exogeneity.

4.2

Estimation

It is possible to derive the full joint marginal likelihood function for the joint model of wage
determination and job duration. For this it is crucial that person and match effects arenested, which allows me to adapt the analysis in Lillard (1999) to the hazard and the wage
equations estimated in this paper. It is necessary to assume independence of individual
likelihoods conditional on person and job heterogeneity (Dostie 2005).
Under the assumption stated above, the full joint marginal likelihood for person i can be

12expressed as:

Li =

h

−Ti

i
− 1 (W −Ξ )0 Σ−1 (W −Ξ )(2π) 2 |Σζi ,ζi |e 2 i i ζi ,ζi i i«2„Z ∞θ1 −µθ |W
1
1
i
σθ |W
2
− 21 − 21i(2πσθ1 |Wi ) e
×
−∞
(Z
«2
„
δ −µ
1

×
δiJ

×

JY
i −1 ZJ=1−1(2πσδ2iJ )− 2 e 2δiJ(2πσδ2iJ )

− 21

e

iJ
δiJ |Wi
σδ |W
i
iJ

− 12

„

δiJ −µδiJ |Wi
σδ |W
i
iJ

(13)

[(hθ1i ,δiJ (ιiJ ))DJ Sθ1i ,δiJ (ιiJ )]dδiJ«2)[(hθ1i ,δiJ (ιiJ ))Sθ1i ,δiJ (ιiJ )]dδiJ

dθ1where Dj is equal to one if the last job of person i has ended, and is equal to zero if it
has not.
In the following, the components if equation (13) are defined and explained. Let Wi be
the vector of wage values observed for person i. Wi can be divided into mean sub-vectors
and residual sub-vectors:

Wi = Ξi + ζi
Mean sub-vectors for job J are given by the following regression equation:

ΞiJ(i,t) = γ0 γ1  1 

[1TiJ(i,t) , γ50 experienceit , γ6 timet ]  1 γ4   malei
1 0



0
1
 0 γ2  +[1TiJ(i,t) , γ30 seniorityiJ(i,t)t ] jobcovJ(i,t)t
1 0

Residual sub-vectors (related to the person and match random effects and to the error
13term) are given by: θ2i δiJ(i,t) ζij = [1TiJ(i,t) , γ50 experienceit ]  + νiJ(i,t)θ3i0Consequently, the covariance matrix of the residuals for all Ti wage observations of person
i is given by:Σζi ,ζi = [1Ti , γ50 experienceit ]Σθ,θ [1Ti , γ50 experienceit ]0 + σδ2 ·IJi ×Ti + diagJi Σνi ,νiwith n u following equation (12).
Conditional on Wi , the mean of the person-specific component entering the job duration
equation, is:µθ1i |Wi = Σθ1i ,ζi Σ−1
ζi ,ζi (Wi − Ξi )Its variance is given by:σθ21i |Wi = σθ21i − Σθ1i ,ζi Σ−1
ζi ,ζi Σζi ,θ1i
where





 σθ1i ,θ2i Σζi ,θ1i = [1Ti , γ50 experienceit ] σθ1i ,θ3i
Estimation of this model cannot be carried out using any of the most common commercial
statistical software packages. However, aML (Applied Maximum Likelihood), a softwaredeveloped by late Lee A. Lillard and Constantijn W.A. Panis (Lillard and Panis 2003a), can14

estimate multilevel nonlinear models of this kind.
The multilevel correlated random effect model is estimated under the assumption thatthe heterogeneity components and the innovation component in the error’s AR(1) process arejointly normally distributed. The estimation is based on the maximisation of the marginal
likelihood.

5

The data

5.1

The WHIP Dataset

The empirical work carried out for this paper is based on the Work Histories Italian Panel(WHIP in the following)6 . WHIP is a database of individual work histories, based on administrative archives from the Istituto Nazionale della Previdenza Sociale 7 (INPS in thefollowing), which is the main institution for social security in Italy. By the Italian labour
market law, it is compulsory to obtain insurance from INPS for all employees in the private
sector, for some categories of employees of the public sector and for most self employed8 .
Detailed descriptions of the WHIP dataset are available from Contini (2002) and Contini
and Trivellato (2005).
The reference population of WHIP is made up by all the people Italian-born and foreignborn who have worked in Italy in any of the years of the panel. From this population, asample has been extracted at random, using four dates of birth for workers for each year.
This results in a dynamic population of about 370,000 people in the freely accessible versionof the dataset and around twice as many in the full version9 .
For all individuals, the main episodes of their working careers are observed. The complete
6

WHIP Work Histories Italian Panel, is a database of work histories developed by Laboratorio Revelli
Centre for Employment Studies. See http://www.laboratoriorevelli.it/whip
7
National Institute for Social Security
8
The only notable exceptions being professionals, such as doctors, lawyers and notaries
9This version of the paper uses the free release of the dataset

15

list of observations includes: private employee working contracts, atypical contracts, selfemployment activities as artisan, trader and some activities as freelancer, retirement spells, aswell as non-working spells in which the individual received social benefits, e.g. unemployment
subsidies or mobility benefits. The workers for whom activity is not observed in WHIP arethose who do not receive insurance from INPS (and those for which it was not possible
to construct a representative sample from INPS data): most public sector employees andfreelancers such as lawyers, notaries and journalists, who have alternative social security
funds. The black market, which represents a significant share of the Italian labour market,
is also absent.
Individual data include gender, year and region of birth, received unemployment compensations or enrolment in other programs10 . WHIP does not include educational attainments.
This paper will use the section of WHIP that concerns employees, for which the databasealso provides some information about employers, such as firm size, location (five regions),
industrial sector (18 sectors in the standard version of WHIP, 34 in the full version)11 .
Information about firms are rare in datasets of this nature, and represent a very promisingsource for addressing a number of questions of interest to researchers and policy-makers.
WHIP includes a series of information about jobs 12 , such as match duration (dates ofmatch creation and match destruction), wage received13 , other benefits received by the employee, special arrangements, occupation, location (which may be different from that of theemployer). The observed period goes from 1985 to 1999, and will be regularly upgraded by
the researchers of Laboratorio Revelli.
10

In this version, only gender is included
See Contini (2002) for further details on the criteria used for identifying firms from an economist perspective in WHIP. The researchers of Laboratorio Revelli that created WHIP did a very extensive work with
the identification of firms in an economic sense, and not in a legal sense that is identified by INPS data.
This makes sure that M&As, changes of names or legal headquarter do not impact the analysis
12
Job (used interchangeably with match) is intended here as a specific employer-employee relationship,
and thus to be clearly distinguished from occupation
13
Net of all employer’s contributions
11

16

5.2

Sample selection

The full sample of the WHIP dataset is not appropriate for being used for the estimationof the returns of experience and seniority. The most important reason is that there is no
information regarding human capital accumulation of individuals (specifically, schooling andtraining) nor on labour market experience before 1985. Therefore, using the full dataset
would only be possible by constructing of a variable measuring potential experience as function of age. This paper attempts instead to select the appropriate sample to avoid theconstruction of this variable and its arbitrariness.
Only workers for whom it is sensible to assume that we can observe them from the
beginning of their careers are included. For this purpose, I eliminate all workers that arematched in 1985, the first year of the WHIP panel. I also eliminate people that are over 25
years of age in 1986. My sample is therefore represented by people that are born in 1961 andlater, and that were not working in 1985. I will assume that the first time these workers areobserved in the sample (in 1986 or later), they are starting their careers. The interpretation
should take account of the fact that all results are based on a population of workers that areon average much younger than the overall Italian labour force. The oldest worker I have in
my sample is 25 years of age is 1986, and thus 38 in 1999.
Other modifications to the data have been carried out before constructing summary
statistics and estimating the model described above. Only nominal wages are included in theWHIP dataset, and so I convert all wage measures into year-1999 Euros, by using aggregate
CPI data from ISTAT14 . Given that WHIP includes number of Full Time Equivalent (FTE)
days for each worker in each year, I constructed annual FTE wages, which have much higher
comparability across workers than total yearly wages directly available from WHIP.
A second issue for the application of the WHIP dataset to this research question comesfrom the different length of jobs. WHIP includes information about start date and end date
14

Istituto Nazionale di Statistica, the Italian National Statistical Agency

17

of each job. These may or may not go across years. However, in order to avoid overweighting
observations for short employment spells (there can be more than one of those for each worker
in each year) and in order to avoid imputations based on yearly observations of wages (onlytotal wages for each year in each job are included), in this version of the paper I use yearlydata. For every worker and every year, a dominant job is identified. First, I eliminate alljobs with less than five full-time equivalent working days. Then I rank jobs by number ofeffective full-time-equivalent days, and then by duration and wages. Only the first job of
this ranking is kept for every worker in every year.
The dataset used for the regressions, which does not include firm size, region, occupationand sector, and thus does not take into account missing values in those variables, consists of
48,475 workers, 101,917 job spells, 252,586 yearly wage observations.

5.3

Summary Statistics

This section is divided into summary statistics information pertaining to the worker, job andannual levels.

5.3.1

Workers

This section provides summary statistics concerning the sample of workers used in thispaper. Given that the goal here is to illustrate the characteristics of the sample, rather
than providing an overview of the Italian labour market, all statistics in this section are
based upon the same dataset that is used in the regressions. The number of observations
is slightly lower for some of the tables. This is simply due to some missing values. Data is
this subsection are based on workers’ first job only, when this is relevant. This is done forclarity, and these distributions are not sensitive to this choice.
In the sample used here, 61 percent of the workers are male, 39 percent are female. They
are much younger than the average worker in the Italian labour market. As a result of the18

restrictions imposed here, most of the workers in the sample are observed in a match for the
first time between the age of 20 and 25, as shown in Figure 1.
Table 1 shows the distribution of workers of the dataset used for regressions by regionof birth. It shows that immigrants are over-represented in comparison to the overall Italianlabour market of the same period. The primary cause for this is likely to be the age structureof the sample. Combining Table 1 and 2 illustrates in a summary fashion the migration
pattern of workers born in the South and on the two large Italian islands (Sicily and Sardinia)
to the North of the country15 .
The WHIP dataset that is used here differentiates between five occupational categories ofworkers. The distribution of workers by occupation is shown in Table 3, with data based oneach worker’s first job in our sample. Virtually all of the workers in our sample are in lowerlevel occupations, i.e. apprentices, workers or clerks. This is due partly to the employment
insurance system in Italy, and partly to the sample selection process including only workers
at the start of their careers. Around 90 percent of the workers are in a full-time job and10-percent workers are in a part-time job.
Table 4 shows the distribution of workers by sector of the firm for which they work.
Manufacturing is the largest sector (39 percent of workers), followed by Wholesale and Retail
Trade (17 percent) and by Construction (12 percent). Table 5 breaks down that distribution
along gender. Females are, relative to males, more concentrated in the Wholesale and Retail
Trade sector, in the Hotel and Restaurant sector, and in other services (Computing, rental,
research and other business and Other Social and personal services sectors).
Given that the empirical strategy used here relies on identifying person and job random
effects through mobility, it is crucial to provide some information on how much mobility isobserved. Graph 2 shows that, taking account of the length of the panel, there is a relatively
15

Much more detailed information about mobility could be extracted from this dataset. It is not the focusof this paper

19large degree of mobility in the data, as stressed by Contini and Trivellato (2005) for the
Italian market as a whole. We observe one employment spell for 47 percent of the sample,
two spells for 25 percent of the sample, three spells for 14 percent of the sample. Around 15
percent of the workers in our sample have four jobs or more in the 14-year period consideredhere16 . These high mobility figures are largely due to the fact that workers in this sample
are in the first years of their careers.
5.3.2

Jobs

The unit of observation for the statistics presented in this section is the employment spell17 .
Table 6 shows that in 63 percent of employment spells the worker is a male. Employment
spells last on average just under two years. In the sample used here, 23 percent of the spells
are censored, i.e. the employment relationship is continues until the last period of ourpanel. Workers enter employment spells with 1.11 years of experience on average. Table 7
and Table 8 present separate information for female and for male workers. In this sample,
females stay on the job slightly longer than males and enter an employment spell with slightly
less experience, although these differences are not statistically significant.
5.3.3

AnnualIn the sample used here, we observe wages every year. The summary statistics below are
based upon real wages in 1999 Euros. As shown in Table 9, workers in this sample receive on
average around 16,500 Euros in yearly wages, calculated on a full-time full-year equivalent,so that spells of shorter lengths and part-time jobs are comparable. Real wages are top-codedat around 100,000 Euros18 . At the start of the year, workers have on average 2.68 years ofexperience, and have accumulated tenure on the job of 1.64 years.
16

It is important to stress that very short employment spells and of course jobs that are not recorded by
INPS are excluded
17”Employment spell”, ”Job” and ”Match” are used interchangeably here
18
Total annual wages are also top-coded, and thus so are the FTE wages constructed here20

5.3.4

Earnings Profiles

Figure 3 shows that there is a strong positive unconditional correlation between experienceand wages. Early studies using OLS regressions were picking up this correlation and interpreting it as a causal relationship. It is interesting to note the decline for the highest level ofexperience observed. The most likely interpretation of this, which is visible in the equivalentgraph for seniority earning profile (Figure 4), is that the set of jobs for which experience andseniority are highest (i.e. jobs that existed for the whole period studies here) are special anddifferent from the others, possibly due to composition by region and sector, and on average
pay less than matches with lower levels of experience and tenure.
Finally, an unconditional time-earning profile is presented in Figure 5. This is constructedfrom average real wages for females and males in each year. It clearly includes a positive
time trend as well as business cycle fluctuations. Wages seem to vary over time in a very
similar manner for women and men, suggesting that changes in real wages through changes
in inflation may be sizeable.

6

Results

Regression results are presented in three separate tables. Table 10 presents the results forthe hazard model of employment duration; Table 11 presents the estimates for the wage
equation; Table 12 presents the estimates for the variance-covariance matrix of the heterogeneity components and of the error structure. Column SM in all three tables presents theestimates for hazard model and the wage equation estimated simultaneously.

6.1

Employment duration

Table 10 presents all the results for the hazard equation. Model H1 does not include individual heterogeneity, which is introduced in H2. Column SM refers to the estimates of the21

simultaneous model. The choice of nodes for the piece-wise linear splines for experience andseniority are based on best fitting the data19 .
For males, the conditional probability of match destruction in any year is around 10
percentage points higher than for females, and this estimate is robust to including individual
heterogeneity and/or estimating the model simultaneously. Ceteris paribus, probability ofjob destruction increases by around two percent a year between 1986 and 1999, as shownby the coefficients on the time trend component. The impacts of seniority and experience
are as expected. The first two years of seniority are associated with a lower probability ofmatch destruction. However, the estimates are much lower once individual heterogeneity
is introduced, falling from 42 percent to 13 percent. After the first two years seniority has
a much smaller impact on the probability of match destruction. The effect of seniority on
employment duration is also highly nonlinear. Very similar considerations arise from looking
at the effects of experience on the probability of match destruction, with a very large effectfor the first years and a lower effect for higher levels. All these results are similar to theresults in Dostie (2005) who uses French data.

6.2

Wage Equation

Table 11 is organised as follows: column W1 presents estimates of equation (11) not including any unobserved heterogeneity component; W2 introduces an intercept and slope
random effect component for individual heterogeneity; W3 also includes a match heterogeneity component; SM, as above, presents estimates for the simultaneous model accounting
for endogeneity of seniority and experience in the wage determination process.
Males earn consistently higher wages than females in this sample: their conditional wages
are almost five percentage points higher. Interestingly, this result is robust to the introduction of person and match fixed effects suggesting that the source of the effect is not personal
19

Lillard and Panis (2003b) discusses the choice of nodes in further details

22unobserved heterogeneity. This is in contrast with Dostie (2005) where males have lower
baseline wages than females. Table 11 shows that males also have significantly higher returns to experience. For example, looking at column SM the yearly return for the first four
years of experience is 4.5 percent for females, and 5.5 percent for males20 . The time trend
shows that real wage growth has been positive but very small in this period, estimated as
0.53 percent a year in the simultaneous model.
Focusing on column SM, it is clear that after the first four years experience does nothave high returns in terms of wages. The average return to one year of experience within the
4-8 band is 0.88 percent for females, and around one percent for males. After the 8th year,
returns are a little higher, at 1.4 percent for females, 1.7 percent for males. As in Dostie(2005) the estimates of the returns to experience are not strongly affected by enriching our
specification.
The returns to seniority estimated in column W1 are around 2.9 percent a year for the
first two years, not significantly different from zero for years 2-4 and 8+, and are positive foryears 4-8. These estimates decrease once we introduce heterogeneity at the person and matchlevel (especially for the returns to experience after the first two years), but by far less thanin Dostie (2005), where they become negative for all years in the simultaneous regression.
My estimates of the return to seniority in the simultaneous model are 2.1 percent for the
first two years, negative for the following two years and very close to zero afterwards. Theseestimates are more similar to those of Abowd, Kramarz, and Margolis (1999).

6.3

Heterogeneity components and error structure

Given the involved structure of the model, the estimates concerning the variance-covariancematrix of the heterogeneity components for person and match effects are presented separately
in Table 12.
20

Calculated as (1 + 0.2124) × 0.0450

23

The second column presents results from models H1 and W2 estimated separately, andthus only includes individual-level heterogeneity components in the wage equation. Bothintercept and slope individual unobserved heterogeneity are found to be significant at the
one-percent level. σθ3 is also very large, showing the importance of allowing for wage growth
to be different across individuals as a function of unobservables.
The third column of Table 12 adds an individual heterogeneity component to the hazard
model and a match heterogeneity component to the wage equation. They are both large
and highly significant, and have an impact on the other coefficients as discussed above. In
particular, the seemingly highly autoregressive nature of the error term of the wage equation
drops dramatically as a consequence, as shown by the fall of ω from 0.66 in column H1+W1
to 0.11 in column H2+W3.
Finally, the comparison between the third column and column SM, which refers to thesimultaneous model, allows me to test endogeneity of seniority in the wage equation. As
discussed above, two tests are available here. Using the correlation between the person effectin the hazard equation and in the wage equation I fail to reject the null of exogeneity. This
result is in contrast with results in Dostie (2005), Abowd, Kramarz, and Margolis (1999) and
Lillard (1999). There is no evidence of endogeneity through the correlation of the person
random effects in the hazard model and in the wage equation.
On the other hand, the estimate for φ in the hazard model, the coefficient of the match
random effect from the wage equation, is negative and statistically significant at any conventional significance level. The estimated coefficient implies that a job that has a match effectone standard deviation higher than zero in the wage equation, has a predicted probability ofdestruction 14.34% lower21 than a job with the average match effect. ”Good” job matches,
i.e. matches with ceteris paribus high starting wages, and/or matches in ”good” firms also
last significantly longer22 . Longer-lasting matches are not a random sample of all matches
21
22(−0.7739) × (0.1954)
Given that firm effects are not included here, this match effect would include both a firm effect and a24

in terms of wages; they tend to have on average much higher wages. On the basis of thisresult, we can reject the null hypothesis of exogeneity of seniority in the wage equation. An
empirical model that ignores the endogeneity of job duration will yield biased estimates of
the return to seniority.

7

Concluding remarks

There are solid theoretical reasons to believe that seniority and wages are simultaneously
determined. The searching and matching literature offers a theoretical framework for understanding wage determinations and employment duration that takes this simultaneity into
account. This paper uses data for a sample of Italian workers in years 1986-1999 to estimate
the effect of seniority on wages taking account of the endogeneity of seniority in the wage
equation. To the best of my knowledge, this is the first estimation of the returns to seniority
using Italian panel data.
The simultaneous estimation of the wage equation and of a hazard model for employment duration produces estimates that are not biased by endogeneity problems. Throughthis estimation, some of the predictions of the matching literature can be evaluated. The
methodology employed here also allows the inclusion of experience and seniority as piecewise linear splines, so that their effect can very over time and provide us information on
nonlinearity in the response.
Results show that in the Italian labour market, which is considered among the mostrigid in the OECD countries (Contini and Trivellato 2005), job search and job match considerations are important determinants of wages. Wage determination does not seem to be
entirely explained by collective bargaining, as stressed also by Contini, Leombruni, Pacelli,and Villosio (2007). Individual unobservables have a large effect both on the probability ofpure match effect25

job destruction, on the wage levels and on its growth. The match heterogeneity component
is significant in the wage equation. The negative correlation of the match random effectcomponent between the wage equation and the hazard equation is clear evidence of the endogeneity of seniority in the wage equation coming through unobserved match heterogeneity.
”Good” jobs pay more and last longer.
The simultaneous model shows that for this sample there are short-term returns to seniority, at around two percent a year for the first two years, but after the first two years
returns are very small or negative. The effects of experience on wages are also concentrated
in the first years in the labour market, and are very small afterwards. The implied losses
for young workers from match destruction appear to be moderate. Also, programs trying tocreate stable matches by subsidising firms hiring young workers seem to have low returns in
the terms of future wages of those workers.
The WHIP dataset offers possibilities for expanding this research in a number of directions. A future release of the data will include more recent years, more detailed informationon the characteristics of the firm for which each worker works, and a firm identifier will also
be added. Furthermore, information about sector and size of the firm has the potential of
deepening our understanding of the roots of returns to tenure, in particular distinguishing
between job-specific productivity and reasons relating to informational problems. Finally, it
would be of great interest to look beyond the average returns to seniority, investigating the
distribution of the returns to seniority across sectors, occupations, and regions.

26

APPENDICESA

Summary statistics

A.1

Workers
Age of people entering the labour force

0

1000

Number of workers
2000 3000 4000

5000

For sample utilised in regressions

15

20

25
30
Age in years at start of first job

35

40

Source: Elaborations from WHIP data.

Figure 1: Distribution of workers by age at which they start their first job

27

Table 1: Region of birth of workers
Item
North-WestNorth-East
CentreSouth
Islands
AbroadTotal

Frequencies Percentages
12,033
24.9
8,872
18.3
7,775
16.1
10,222
21.1
4,924
10.2
4,531
9.4
48,357100.0

Source: Elaborations from Whip Data

Table 2: Region of Italy where jobs are located
Item
North-WestNorth-East
CentreSouth
Islands
AbroadTotal

Number
14,727
11,672
9,425
8,355
4,286
5
48,470Per cent
30.4
24.1
19.4
17.2
8.8
0.0
100.0

Source: Elaborations from WHIP Data

Table 3: Distribution of occupations for each workers’ first job
Item
NumberApprentice
10,152Worker25,599Clerk
12,634
Manager52
Director
30
Total
48,467

Per cent
20.9
52.8
26.1
0.1
0.1
100.0

Source: Elaborations from Whip Data

28

Table 4: Economic sector of the firm
Item
Number Per centNatural resource extraction
97
0.20
Manufacturing
18,714
38.73
Electricity and natural gas
130
0.27
Construction
5,722
11.84
Wholesale and retail trade, auto reparations
8,454
17.50
Hotels and restaurants
4,948
10.24
Transport, warehouses and communications
1,960
4.06
Banking and financial intermediaries
990
2.05
Computing, rental, research and other business sectors
5,476
11.33
Other Social and personal services
1,825
3.78
Total
48,316100.00Source: Elaborations from WHIP Data

Table 5: Economic sector of the firm by Sex

Economic sector of the firm - Ateco91
Natural resource extraction
Manufacturing
Electricity and natural gas
Construction
Wholesale and retail trade, auto reparations
Hotels and restaurants
Transport, warehouses and communications
Banking and financial intermediaries
Computing, rental, research and other business sectors
Other Social and personal services
Total
Source: Elaborations from WHIP Dataset

29

Sex
FemaleMale Total
0.04
0.31
0.20
36.08 40.50 38.73
0.18
0.33
0.27
1.76 18.57 11.84
21.34 14.93 17.50
12.95
8.44 10.24
2.54
5.07
4.06
2.55
1.71
2.05
16.31
8.01 11.33
6.25
2.13
3.78
100.00 100.00 100.00

Distribution of workers by number of jobs
Years 1986−1999
3.7%3.6%
7.2%

13.6%
47.1%

24.7%

One job
Three jobs
Five jobs

Two jobs
Four jobs
More than five jobs

Source: WHIP dataset

Figure 2: Distribution of workers by number of jobs

30

A.2

Jobs

Table 6: Summary statistics for Job Covariates
Variable
Mean (Std. Dev.) Min.
Dummy for Males
0.63(0.48)
0Spell duration
1.87(2.47)
0.08
Dummy for censored spells
0.23(0.42)
0Experience at beginning of spell (years)
1.11(1.94)
0Max.
N
1101921
13.91 101921
1
10192112.93 101921

Table 7: Summary statistics for Job Covariates - Females only
Variable
Mean (Std. Dev.) Min. Max.
N
Spell duration
1.98(2.56)
0.08 13.91 37881
Dummy for censored spells
0.24(0.43)
01
37881
Experience at beginning of spell (years)
1.06(1.95)
012.93 37881

Table 8: Summary statistics for Job Covariates - Males only
Variable
Mean (Std. Dev.) Min. Max.
N
Spell duration
1.81(2.42)
0.08 13.91 64040
Dummy for censored spells
0.23(0.42)
0
1
64040
Experience at beginning of spell (years)
1.14(1.93)
012.88 64040

31

A.3

AnnualTable 9: Summary statistics for Year-level Covariates
Variable
Mean (Std. Dev.) Min.
Max.
Real yearly wages (FTE, in 1999 Euros) 16578.48(7675.47)
099921.64
Experience starting the year (in years)
2.68(2.86)
0
12.93
Tenure starting the year (in years)
1.64(2.37)
012.93

Earning Profiles
Experience profile using average yearly wages
Separate for Females and Males
14000 16000 18000 20000 22000

A.4

0

5

10
Experience in years
Females
Males

Source: Elaborations from WHIP Data

Figure 3: Experience Profile based on annual data

32

15

N
252594252594252594

14000 16000 18000 20000 22000 24000

Tenure profile using average yearly wages
Separate for Females and Males

0

5

10
Tenure in years
Females

Males

Source: Elaborations from WHIP Data

Figure 4: Tenure Profile based on annual data

33

15

Time effect using average yearly wages

12000

14000

16000

18000

20000

Separate for Females and Males

0

5
10Time in years since 1986
Females

Males

Source: Elaborations from WHIP Data

Figure 5: Time effect based on annual data

34

15

B

Regression Tables
Table 10: Estimation results - Hazard Equation
Dependent variable: ln(hiJ(i,t)t (τ ))ModelsVariables
H1
H2
SM
Constant-0.1831*** -0.4155***-0.4257***(0.0100)(0.0135)(0.0137)
0.1078***Male
0.0932*** 0.1063***(0.0064)(0.0103)(0.0103)
0.0187***
Time trend
0.0227*** 0.0182***(0.0010)(0.0013)(0.0013)
Seniority
0-2nd year
-0.4243*** -0.1371***-0.1291***(0.0071)(0.0085)(0.0086)
0.0298***
2nd-4th year
-0.0352*** 0.0274***(0.0099)(0.0104)(0.0104)-0.0480***
4th-8th year
-0.1010*** -0.0543***(0.0097)(0.0103)(0.0103)
8th year +0.0091
0.0413*0.0414*(0.230)(0.0222)(0.0230)
Experience0-4th year
-0.1834*** -0.2435***-0.2426***(0.0046)(0.0039)(0.0046)
4th-8th year
-0.0555*** -0.0321***-0.0362***(0.0064)(0.0065)(0.0066)-0.0705***
8th year +-0.0718*** -0.0707***(0.0145)(0.0145)(0.0145)
log L-328098.81 -325956.61-392220.75
N = 101, 917
H1: Hazard model without Individual Unobserved Heterogeneity
H2: Hazard model with Individual Unobserved Heterogeneity
SM: Simultaneous model using H2 specification for the job duration equation
Asymptotic Standard Errors in Parenthesis
Significance: *=10%; **=5%; ***=1%35

Table 11: Estimation results - Wage Equation
Dependent variable: ln(wiJ(i,t)t )ModelsVariables
W1
W2
W3
SM
Constant
9.4376*** 9.4233*** 9.4265*** 9.4266***(0.0028)(0.0034)(0.0035)(0.0035)
Male
0.0428*** 0.0468*** 0.0469*** 0.0469***(0.0025)(0.0034)(0.0033)(0.0033)
Male × Exp.
0.3182*** 0.2390*** 0.2208*** 0.2124***(0.0327)(0.0281)(0.0273)(0.0271)
Time trend
0.0048*** 0.0059*** 0.0057*** 0.0053***(0.0003)(0.0003)(0.0003)(0.0003)
Seniority
0-2nd year
0.0288*** 0.0262*** 0.0263*** 0.0213***(0.0015)(0.0013)(0.0012)(0.0012)
2nd-4th year
-0.0020-0.0123*** -0.0159*** -0.0167***(0.0021)(0.0020)(0.0016)(0.0016)
4th-8th year
0.0165*** 0.0079*** 0.0043*** 0.0036***(0.0022)(0.0016)(0.0011)(0.0011)
8th year +0.00430.0008-0.0002-0.0009(0.0021)(0.0047)(0.0032)(0.0021)
Experience0-4th year
0.0399*** 0.0467*** 0.0451*** 0.0450***(0.0012)(0.0011)(0.0012)(0.0012)
4th-8th year
0.0072*** 0.0071*** 0.0083*** 0.0088***(0.0013)(0.0005)(0.0004)(0.0004)
8th year +0.0146*** 0.0124*** 0.0133*** 0.0137***(0.0027)(0.0011)(0.0008)(0.0008)
log L-77705.74 -68860.01 -66459.90 -392220.75
N = 252, 586
W1: Wage model without Individual Unobserved Heterogeneity
W2: Wage model with Individual Unobserved Heterogeneity
W3: Wage model with Individual and Match Unobserved Heterogeneity
SM: Simultaneous model using W3 specification for the job duration equation
Asymptotic Standard Errors in Parenthesis
Significance: *=10%; **=5%; ***=1%36

Table 12: Estimation results - Estimates for Variance components and Parameters
Models
H1+W1
H1+W2
H2+W3
SM
Individual Heterogeneity variance components
0.6680***σθ10.6713***(0.0072)(0.0074)
0.2621***σθ20.2819***0.2626***(0.0006)(0.0007)(0.0007)σθ31.0938***1.1696***
1.1711***(0.0289)(0.0306)(0.0308)-0.5538***ρθ2 θ3-0.4834***-0.5513***(0.0057)(0.0058)(0.0058)
0.0126ρθ1 θ2(0.0113)ρθ1 θ3-0.0115(0.0203)
Match Heterogeneity variance componentsσδ0.1946***
0.1954***(0.0005)(0.0004)φ-0.7339***(0.0378)
Error structure
0.1057***
ω
0.6654*** 0.3720***0.1075***(0.0004)(0.0008)(0.0015)(0.0015)
0.2397***σν0.2925*** 0.2691***
0.2399***(0.0001)(0.0001)(0.0002)(0.0002)
H1+W1: no unobserved heterogeneity components
H1+W2: Individual Unobserved Heterogeneity in the Wage equation only
H2+W3: Hazard model with Individual Unobserved Heterogeneity,
Wage equation with Individual and Match
Unobserved Heterogeneity Components
SM: Simultaneous model
Asymptotic Standard Errors in Parenthesis
Significance: *=10%; **=5%; ***=1%37

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39