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Wage Mobility in theItalian Labour Market

Comparing Search, Matchingand Traing Models

Giovanni Sullis

ARACNE

Discussion Paper n. 2, 2004

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This Discussion Paper series collects the contributions coming out from the research partnership betweenISFOL and the Dipartimento di Scienze Economiche of the Università di Roma “La Sapienza”. Boththe research partnership and the discussion paper series are coordinated by Marinella Giovine, SergioBruno and Paolo Piacentini.

Questa collana raccoglie i contributi elaborati nell’ambito della convenzione di ricerca tra Isfol edil Dipartimento di Scienze Economiche dell’Università di Roma “La Sapienza”. Sia la convenzio-ne di ricerca che la collana di discussion papers sono coordinati da Marinella Giovine, SergioBruno e Paolo Piacentini.

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The three papers that are being edited in succession in this discussion paper series —respectively by Sulis, Patriarca and Naticchioni-Panigo- are the results of the development of one of the main topics investigated within the program of the research partnership DSE-ISFOL in the years 2003-2004, i.e. the interpretation and the quantitative analysis of labour mobility and earnings dynamics in Italy. Each paper is an autonomous development of the research interest of the authors, who remain responsible for results and comments. Nevertheless, these papers have found a common source of inspiration from interaction, seminar activity and discussion among the members of the research group. I tre lavori che vengono pubblicati in successione in questa collana —rispettivamente di Sulis, Patriarca e Naticchioni-Panigo- sviluppano una delle tematiche centrali della convenzione di ricerca DSE-ISFOL per gli anni 2003-2004, i.e. modelli interpretativi e analisi quantitativa della mobilità occupazionale e salariale. Questi lavori rappresentano sviluppi autonomi degli interessi di ricerca dei singoli autori, che rimangono pertanto responsabili di risultati e commenti, pur traendo tuttavia inspirazione dall’attività seminariale, dall’interazione e dal confronto all’interno del gruppo di ricerca.

Wage Mobility in the Italian LabourMarket: Comparing Search, Matching

and Training Models

Giovanni Sulis¤

Abstract

This paper studies the sources of wage growth and the process ofturnover for a sample of Italian workers employed in the private sec-tor. Di®erent theories based on the job-speci¯c skill investment aretested and discussed. Results indicate a very large proportion of wagecuts upon moving to a new job and lack of duration dependence inreservation wages. As a consequence, mobility patterns and wagedynamics can hardly be explained by a single model; each model is in-stead only able alone to capture some stylised facts. On the contrary,heterogeneity in mobility rates seems to play an important role.Keywords: Wage Mobility, On-the-Job Search, Matching, Training,Duration Dependence, Heterogeneity.JEL Classi¯cation: J31, J41, J64.

¤University of Cagliari, University of Essex and CRENoS. Address for correspondence:Department of Economics, University of Essex, Wivenhoe Park, CO4 3SQ Colchester,United Kingdom. Email: gsulis@essex.ac.uk.

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1 Introduction

This paper studies the sources of wage growth and the process of turnover fora sample of Italian workers employed in the private sector. Di®erent theoriesbased on the idea of job-speci¯c capital, that are able to account for the mainstylised facts about labour mobility and wage dynamics, are explicitly testedone against the other. I also focus my attention on the importance of unob-served heterogeneity as an alternative explanation of the above phenomena.Providing a quantitative evaluation of the relative importance of di®erenttheories is interesting for a couple of reasons. First, it allows to study theprocess of wage growth over the life-cycle and to determine the bene¯ts andcosts of mobility. Second, it provides evidence of the importance of speci¯chuman capital in employment relationships. Finally, when comparing resultsamong di®erent countries with di®erent institutional structures, it allows toshed some light on the importance of labour market institutions and speci¯ccapital in shaping career pro¯les and labour market outcomes in general.The main facts regarding worker mobility are as follows. First, long-term

employment relationships are common; second, most new jobs end early;third, the probability of job change declines with tenure after increasing dur-ing the very ¯rst periods on the job (Farber, 1999). Di®erent models areable to explain the above facts and patterns that emerge when analysing rawdata regarding job and wage mobility. The on-the-job search, the matchingand the on-the-job training model share the common idea that workers and¯rms invest in some form of speci¯c capital when they establish an employ-ment relationship. This agreement involves costs and returns that have tobe shared by the two parts. Both voluntary and involuntary separations de-termine some losses in terms of accumulated job-speci¯c human capital thatin general are not economically e±cient.1 Although these theoretical modelsprovide an explanation for the stylised facts; heterogeneity in mobility rates

0This paper is based on the ¯rst part of my PhD dissertation at the University ofCagliari written while I was at the University of Essex. I would like to thank my supervisorsMelvyn Coles and Amanda Gosling for help and guidance. A previous version of this work(January 2003) circulated under the title "Salari e mobilitµa del lavoro in Italia," as part ofthe joint project between Isfol - Area Mercato del Lavoro and the Department of EconomicSciences at the University of Rome - La Sapienza. The ¯rst English version was written inMay 2003. I wish to thank all the participants in the project DSE-ISFOL for commentsand suggestions, and for giving me access to the data. A special thank goes to EmilianoRustichelli and Paolo Piacentini for their help and encouragement. Seminar participantsin Essex, Cagliari, Rome, Leuven (VIIIth SMYE), Munich (VIth IZA Summer School)and Taormina (XVIIIth AIEL Meeting) provided valuable comments and suggestions. Allthe remaining errors and mis-interpretations are mine own.

1See Oi (1962), Hashimoto (1981), Hall and Lazear (1984).

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in the population of workers and jobs can account for the same facts.2

Many studies analyse in detail the mobility process at work in the Ital-ian labour market, most of them use the same panel data set used in thiswork. Recently, in a complete and exhaustive study, Contini (2002) pro-vides evidence regarding °ows and earnings' mobility for the period 1986-96.The dynamics of employment, job creation, job destruction and the di®er-ences between worker and job °ows are presented and discussed in detail. Aparticular attention is then devoted to the dynamics of wage distributionsand inequality looking at transition probabilities across di®erent deciles andtheir evolution over time.3 Contini and Villosio (2000) concentrate on jobchanges and wage dynamics looking in more detail at wage growth and itsdeterminants for a sample of workers from the same data set. In particular,they explore the importance of ¯rm's characteristics on the determination ofwages and decompose the total wage growth in two parts: one due to meanwage growth across the ¯rm of origin and destination, and another wagepremium that movers get when quitting. The importance of worker and¯rm heterogeneity in the determination of wages is also acknowledged byCasavola et al. (1999). Their paper provides important information aboutseparate contributions of both observable and unobservable worker's and¯rm's characteristics on wages of movers and stayers. The paper leaves theinterpretation of the main results open to di®erent theoretical explanations.A somewhat di®erent aspect of labour mobility is then studied by Brugiaviniand Brunello (1998). They mostly concentrate on labour market historiesof young workers. Particular emphasis is put on the average number of jobsheld in the labour market controlling for years of experience.4 Naticchioniand Rustichelli (2003) provide also detailed evidence of medium and long-runwage dynamics also controlling for di®erent de¯nitions for quits and layo®s.All the above contributions do not interpret their results in any speci¯c

theoretical framework.5 However, some recent papers try to look at mobilityrates and wage dynamics in a more directed perspective. Rosolia (2002)provides detailed evidence of earning losses for a sample of displaced workers.

2See Farber (1999) for a detailed discussion of the relative importance of speci¯c capitaltheories and heterogeneity.

3He also provides some relevant information regarding institutional aspects that char-acterise the Italian labour market.

4They also use duration models to estimate hazard functions for the probability ofpermanently leaving private employment.

5Dell'Aringa and Piccirilli (2002) propose basically the same exercise using a di®erentsubsample of workers from the INPS dataset. Although their paper and mine di®er insome respects, they basically share the same basic idea. I became aware of their workwhen this paper was already written. Future work could better di®erentiate between thetwo papers.

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He interprets his results in terms of a model of general and speci¯c humancapital ¯nding a negative relationship between tenure in the previous joband earning losses following displacement. He also ¯nds that unemploymenthas a negative e®ect on the capability of those workers to recover their lossafter being re-employed. Although the two papers are strictly related; in mywork, I look at wage dynamics for both stayers and movers, explicitly lookingat mobility decisions of workers under di®erent hypotheses and comparingwages of both groups, instead of relying on the identi¯cation strategy adoptedin his paper. In this respect, the two contributions can be considered ascomplementary. My work also extends his ¯ndings in another direction: itlooks at regional di®erences in the patterns of wage mobility and workers'behaviour. The latter is the main point that my paper shares with thework by Naticchioni et al. (2002). They test for the e®ects of EmploymentProtection Legislation (EPL) on worker °ows using di®erent regional areas ascontrols for their speci¯cation. However, in their work no attention is devotedto wage mobility. Finally, Sestito and Viviano (2003) use the standard jobsearch model to interpret their estimate of a reservation wage equation usingLabour Force Survey data. They also look at regional di®erences betweenthe North and South of Italy.On the other hand, a large amount of evidence has been provided for

the process of mobility and turnover in the US.6 Using Mortensen's (1988)theoretical framework, Mortensen and Neumann (1989) test the theoreticalmodels that I discuss in this paper using U.S. data.7 The two main im-plications that are tested in my and their work are the relative importanceof job-to-job transitions in shaping wage pro¯les (i.e., the standard searchmodel) and the quantitative relevance of true positive duration dependencein reservation wage o®ers (i.e., matching and training models). Given thehigher °exibility of the matching model in explaining wage mobility pat-terns, with both upward and downward paths, one should expect to ¯ndsome evidence of a positive relationship between tenure and the probabilityof quitting after controlling for the wage earned, as predicted by the abovemodels.My results can be summarised as follows. During the three years, workers

moving between jobs represent about 10% of the overall population. Veryyoung workers have higher mobility rates than older workers; in particular,

6Reviewing the literature in the ¯eld is outside the scope of this paper. Devine andKiefer (1991) and Kiefer and Neumann (1989) explicitly look at those dynamics in a searchpersepctive.

7Surprisingly, their results are not signi¯cantly di®erent from mine. Given the com-pletely di®erent performance and structure of the two labour markets, this result is per sea puzzling one and future research can shed some light on this point.

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14% of men with less than 25 years of age change job at least once in theperiod considered. This process of mobility is not always accompanied by up-ward wage mobility. A very large proportion of moves with a monthly unem-ployment spell (voluntary transitions) terminate in a wage cut. The negativee®ect of unemployment periods on re-employment wages is also found whenlooking at conditional wage functions on the new job. On average moverswith some unemployment earn 4% less than those that quit directly to a newjob. When controlling for the wage previously earned, the e®ect of tenure inthe old job on the new wages is not signi¯cantly di®erent from zero. In otherwords, no signi¯cant duration dependence in the reservation o®er of workersis found. The same results are con¯rmed when estimating the probability ofhaving a wage cut upon moving to a new job. Interesting di®erences emergewhen comparing di®erent e®ects of unemployment spells in di®erent parts ofItaly. Previously unemployed workers in the South earn 1% less against 4%for those in other parts of the Country. This is particularly true for youngworkers. Simple OLS estimates of the mover-stayers comparison indicatesthat previous wages are able to explain almost 80% of observed variationin earnings. However, when controlling for individual heterogeneity, resultsindicate that the e®ect of previous wage turns out to be negative and thatupward wage mobility seems to play a role, even if the magnitude of wagegains is very small.The rest of the paper is organised as follows. In the next section I illus-

trate the theoretical models that are able to explain the facts object of study.I do not make use of almost any technicality to ease exposition, highlightinginstead the main di®erences among them. When necessary, I refer to thespecialised literature for further references. Section 3 is divided in two parts:¯rst, I describe the sample used and discuss patterns and regularities; then Ipresent the empirical models proposed to test the implications derived fromthe theory and discuss the results obtained. I use simple linear regressions,probit and ¯xed e®ects models. The last section concludes and illustratessome avenues for future research.

2 Theoretical Framework

One of the most prominent and regular evidences that emerges from cross-section studies is the positive and concave relationship between wage earnedand labour market experience. The same type of evidence is also foundwhen looking at the correlation between wages and job tenure. On the otherhand, a negative and convex relationship is found when looking at separation

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probabilities and experience (or alternatively tenure).8 The probably mostappealing theoretical interpretation of these facts is that experience shouldre°ect the contribution of general human capital to the wage pro¯le, and thattenure can be interpreted as a measure of job speci¯c capital. However, thesestatistical correlations don't necessarily re°ect causal relationships betweenwages and tenure and between wages and experience. The observed patterncan be due to heterogeneity and sample selection in the population of workersthat induce this kind of spurious correlation without any direct interpretationof estimated coe±cients.9

An explanation for this positive empirical relationship is found in the spe-ci¯c human capital theories. These theories di®er according to the assump-tions and hypotheses that are imposed on workers' and ¯rms' behaviour,however they all share the idea that workers and ¯rms invest in job-speci¯cskills.10 Other contributions focus their attention on the role of the relation-ship between wages and tenure as a device to reduce shirking or discourageworkers from applying if they are too mobile or not likely to be productive.11

This paper concentrates on the job-speci¯c skill models to analyse theprocesses of mobility and between ¯rms wage dynamics.12 In particular, Icompare implications of di®erent models for job turnover and wage growth.The main idea that is behind the on-the-job search, the matching and on-the-job training (or human capital) models is that the worker acquires somespeci¯c skills while employed at a particular ¯rm. This can be the resultof pure training, of learning by doing or similar forms of investments. Theprocess takes place over time and as the employment relationship lengthens,the accumulation of these skills is subject to decreasing returns. However,the models show di®erent \causality" directions for the relationship betweenwage growth on the job and tenure. The human capital hypothesis is thatworkers with more tenure have higher wages because they accumulated morejob-speci¯c skills. The direction in this case is from tenure to wages. On theother hand, job search theory o®ers an interpretation for reverse causality. Inan environment characterised by lack of information and di®erent job o®ers,if workers are allowed to search while on the job, the probability they quit

8One of the ¯rst papers that illustrates these ¯ndings is proposed by Mincer and Jo-vanovic (1981).

9See Farber (1999) for a clear formal exposition of this problem.10See, among others, Becker (1964), Mincer (1974), Jovanovic (1979a, b), Burdett (1978)

and Hashimoto (1981).11Monitoring or agency theories and adverse selection models are examples in this di-

rection.12It is also important to mention that at the moment, I do not concentrate on the

estimation of average returns to tenure. This is also a complicated task that is outside thescope of this work and is left for future research.

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is inversely related to the wage they are actually earning. Since wages arecompared to alternatives, relatively higher wages \cause" longer expectedduration for a job paying that wage. In what follows, I brie°y describe thethree models separately and then discuss their empirical implications.13

The pure on-the-job search model is proposed by Burdett (1978). Workersare assumed to be identical and risk neutral, they discount the future at rater and maximise their expected wealth in in¯nite time. When employed theyget a wage w and face a common (exogenous) distribution of wages F (w).Workers are allowed to search on the job. At every moment in time, they canreceive alternative job o®ers at rate ¸1. The latter depends on their searchintensity s. Since workers can get better o®ers, there are some gains that canbe obtained from search, so s it is assumed to depend on the wage earned w.The quit rate, conditional on the wage, is equal to

q(w) = ¸1(s(w)) [1¡ F (w)] ;where the probability of quitting depends on the search intensity and theprobability that the new job o®ers an higher wage. Workers are assumedto change job only for higher wages and no downward wage mobility canbe explained into this model. Since, workers get paid their marginal pro-ductivity and the latter doesn't change over time, no wage growth on thejob is possible. The wage pro¯le predicted by this model is °at at the ¯rmbut increasing (and discontinuous) over the individual career. However, themodel still implies a positive empirical relationship between wages and jobtenure. The reason is the sorting process at work in the labour market.Workers in low paid jobs search more intensively and have higher acceptanceprobabilities for new o®ers, this corresponds to higher quit rates and shortertenure durations. At the same time, workers that spent more time in thelabour market had more time to get better wages, from this fact emerges thepositive relationship between wages and experience. The cross-sectional pos-itive relationship between wages and tenure (or experience) still holds. Forthe same reasons, the negative relationship between quit rates and wages isdetected, although the quit rate, given the wage, is constant with respect totenure and experience.14

13Mortensen (1988) develops a general stochastic model of optimal job separation be-haviour. More formally, he demonstrates that the job-training and job-matching hypothe-ses can be jointly studied and their resulting expected optimal separation strategies have(almost) identical empirical implications. The interested reader is referred to this paperfor a formal treatment of the issues discussed in this section.

14By unconditional quit rate it is intended the average over all the individuals, on theother hand, the conditional probability of observing a quit is conditioned on the wageearned, i.e. is the average over all individuals earning that particular wage (Mortensenand Neumann, 1989).

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In two separate contributions, Jovanovic (1979a, 1979b) proposes hismodel of job matching.15 Workers and ¯rms are assumed to be ex-antehomogeneous but when they meet in pairs and form a match, heterogeneitiesarise. Some matches are more productive than others, however the onlypossibility workers and ¯rms have to discover the true productivity of thematch is to commence the employment relationship. The true productivityof a worker in a match can be only learned over time. This learning processtakes place by repeated observations of the productivity. At every momentin time, a noisy signal is revealed where workers know the distribution ofthis signal across all ¯rms. The wage paid is interpreted as the expectedvalue of the true productivity on the current job, conditional on previousproductivity observations. Matches can be destroyed if the wage falls be-low some reservation value and workers can restart the process again at anytime. As time spent at the ¯rm increases, the precision of the estimate oftrue productivity increases. Given a constant value for alternative matchesand the increasingly better estimate of true productivity, the model impliesthat, conditional on the wage, the reservation wage for the current job willdecrease with tenure. Formally, the quit rate at tenure t, conditional on thewage w is equal to

q(w; t) = ®Ft(wr(w; t));

where Ft represents the estimate at tenure t of the productivity distributionon the current job and ® is the arrival rate of productivity observations.Here productivity is constant but wages change over time. Again there is apositive relation between wages and tenure, because good matches last. Thenegative correlation between tenure and quit rates emerges when comparingworkers facing the same distribution of wages. Given the wage earned, animportant implication of the model is that the quit rate increases with tenure.Downward wage mobility is also allowed because of relatively higher expectedwages in alternative jobs. The reason for this is an higher probability of¯nding a better match.A di®erent perspective is adopted in the model of human capital or on-

the-job training.16 Here, no uncertainty is assumed about initial productiv-ity. However, productivity and wages change over time because of training.Wage growth is assumed to follow a stochastic process and wage incrementsare positive and decreasing in tenure. The reservation wage on the job for

15For the purposes of this study, it is important to stress that the search and matchingmodels are distinct and di®erent. Next advances in the literature and textbooks tend touse the two terms together causing some confusions.

16Becker (1964) and Mincer (1974) are standard references.

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Tenure, Time

T0 T1 T2 T3

Wage Worker-A

Worker-B

w*

Figure 1: Tenure and Quitting Behaviour

alternative o®ers is an increasing function of the wage earned, given tenure;and decreasing function of tenure, given the wage. The latter follows fromthe fact that, as time at the ¯rm increases, alternative wages o®er higherexpected wage growth. Thus, conditioning on the wage, quit rates shouldincrease with tenure and quit rates should decrease with wage, conditionalon tenure. Again, unconditional job-to-job transitions show the standarddecreasing pattern when related to tenure, but this is simply the result ofthe self-selection mechanism at work.Previous results are summarised in Figure ?? where the quitting be-

haviour of two workers in di®erent jobs is analysed. Both workers face thesame (concave) wage-tenure pro¯le, they only di®er in terms of tenure accu-mulated on the job. At w¤, worker A has tenure T2 ¡ T1 = TA while workerB has tenure T3 ¡ T0 = TB, where TB > TA: Although they both earn thesame wage, worker A faces better prospects staying at the current job andwill have lower probability of quitting.The three models presented above share the common idea that some

job-speci¯c capital is accumulated when workers and ¯rms establish a jointrelationship. The models are all able to explain the positive correlation be-tween wages and tenure, and the negative one between quit rates and tenurethat appear in empirical cross-sectional studies. Most importantly, it is pos-sible to derive straight clear implications that can be explicitly tested in thedata.17 As I show later, the matching and the training model have (almost)

17However, as previously discussed, heterogeneity in mobility rates can account for the

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identical implications and distinguishing among them is not a simple task.In particular, they take into account the possibility of quitting to unemploy-ment for those workers that see their wages falling below some reservationvalue. This is not possible in the on-the-job search model where the wageis constant and equal to the productivity of the worker. The matching andtraining models have identical implications concerning the job-to-job transi-tion rate: conditional on the wage, the probability of quitting should increasewith tenure (i.e., positive duration dependence in reservation wages).

3 Empirical Analysis

3.1 Data

The source of my data is the Italian Social Security Institute (INPS). Datafrom administrative archives is representative of the population of employedworkers in the private non-farm sector. Given its characteristics, this data setis fully classi¯ed as a matched employer-employee one.18 The constructionof the data set proceeds as follows. Each ¯rm ¯lls a form for each workeremployed containing information on total earnings paid to the worker duringthe year and the number of weeks paid. On the worker side, information onsex, age, tenure at the ¯rm, and wages earned is available.19 On the ¯rmside, it is possible to have information on the size of the workforce (averagenumber of employees during the year), the (mean) earnings of those workers,the geographical location, and the sector of activity. The unit of analysis isneither the worker nor the ¯rm, it is instead the match that constitutes thesingle unit of observation.20

This data is a powerful instrument to analyse job and wage mobility ofworkers. Data based on administrative records reduce the bias generated byattrition and common errors of respondents in survey data. On the other

same facts without any role for job speci¯c capital.18See Casavola et al. (1999) and Contini (2000) for a detailed description of the data.

See also Haltiwanger et al. (1999) for an excellent treatment of the main issues related tothe creation and analysis of these type of information and Abowd and Kramarz (1999) fora survey of the main contributions in the ¯eld.

19It is possible to have information on wages earned on a yearly and weekly base. Usingother information regarding days worked it is straightforward to have also monthly anddaily wages.

20In this data base, as in other matched employer-employee data sets, each worker andeach ¯rm are identi¯ed by a speci¯c code during their permanence in the administrative¯les. For every match, a new code, composed of the union of the ¯rm and worker codes,is created. As the match is destroyed, the worker and the ¯rm still continue maintainingtheir previous code.

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hand, they su®er from some shortcomings that are important to mention.One is the lack of data on the level of human capital: no variable measuringyears of schooling or level of education can be found. Of course, this can be aserious problem when analysing the determinants of wages. Nevertheless, itturns out to be a less important restriction whereas my interest is speci¯callybased on mobility aspects of the labour market, as transitions between thestates of employment and unemployment and turnover behaviour of workers.Data still have information on occupations, that, at some extent, can be usedas a proxy for the level of human capital.21 The second (more serious) prob-lem with this data set is related to the de¯nition of the status of workers andtheir classi¯cation. While the position while employed is illuminating abouttheir activity in the labour market; on the other hand, if the worker exits thepanel, it is impossible to know if the subsequent period of absence from therecords is due to unemployment, work in the public sector, self-employmentor retirement. This relevant problem is also related to the impossibility ofknowing exactly if the worker has been laid o® or the end of the job has beendetermined by a voluntary separation.22

Data from Isfol-Inps data base contains a very large amount of informa-tion that can be used to look at job and wage dynamics.23 The sub-sampleused in this study is obtained drawing all full-time employed workers in Jan-uary 1994; each workers is followed for three years, observing transitions andexits out of the sample and the subsequent wage earned. In Table 1, I reportdescriptive statistics for the sub-sample of workers.24 The sample is mainlycomposed of men, they constitute about 70% of the population of workers.On average workers are 37 years old, with a range that varies between 16 and70 years of age. Blue and White collars constitute the two main categories interms of occupation, with 55% and 40% respectively. Managers and Appren-tices have less then 1% each. The inspection of the tenure variable indicatesclearly that the distribution of employment spells is left skewed with an aver-age duration of spells around 6 years. Not surprisingly, the average numberof months worked is more that 10, given the high proportion of workers thatare constantly employed at the same ¯rm. Earnings data indicate an average

21Most importantly, as is found in most studies, the use of education in wage regressionis not able to capture all the remaining individual heterogeneity that is not explained byindividual's and ¯rm's observable characteristics.

22Both issues are discussed in detail in Brugiavini and Brunello (1998).23Other important studies have used a source of information almost identical to this data

set. In particular, the French Administrative DADS panel is widely used and discussedby Abowd and Kramarz (1999).

24Detailed discussion about the composition of the sample and the distribution of wageswith relative frequencies are available upon request.

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Table 1: Descriptive Statistics

Variable Mean St. Dev. Min Max

sex 0:321 0:466 0 1age 37:94 10:387 16 70

less than 5 employees 0:13 0:241 0 16 to 20 employees 0:20 0:402 0 1

21 to 250 employees 0:31 0:353 0 1more than 250 employees 0:36 0:283 0 1

blue collars 0:55 0:496 0 1white collars 0:40 0:490 0 1managers 0:01 0:121 0 1

apprentices 0:02 0:157 0 1north-west 0:36 0:481 0 1north-east 0:25 0:435 0 1central 0:19 0:394 0 1south 0:18 0:392 0 1

tenure (years) 6:26 3:77 0 11:84daily wage (thous. of lira) 122:61 77:714 10 6438

weekly wage 716:618 469:08 11:4 37; 197yearly wage 32; 953 24; 137 308 898; 877days paid 263:78 82:020 30 365weeks paid 45:147 13:107 5 52

months worked 10:55 2:861 1 12

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weekly wage equal to 716 thousands of Italian lira and a daily wage equal to122 thousands.After this brief presentation of descriptive statistics, in the next subsec-

tion I propose the empirical implications and show the results obtained.

3.2 Empirical Implications

The main aim of this paper is to distinguish among di®erent models explic-itly testing for some of their implications. Here the question of interest isessentially that of ¯nding one model that is able to explain most (if not all)the patterns in the data. This is not a simple task, given the models areoften observationally equivalent and rarely one of them is able to explainalone all the stylised facts. Moreover, to accomplish this task, I face someadditional di±culties that should be reminded. Some problems derive fromthe data itself and, as discussed in the previous part, at this stage it is di±-cult to overcome those limitations. On the other hand, some di±culties arerelated to the econometric tools I use in the paper and the interpretation ofthe results.The study of labour mobility can be carried out following two somewhat

separate strategies. On the one hand, it is possible to estimate structuralmodels of job and wage mobility imposing some very strong restrictions onthe distributions of the observables. In this case, there is a perfect matchbetween the theoretical model and the empirical application. This allowsto get a straight and direct interpretation of the parameters of interest butsu®ers from some other limitations. In particular, structural parametersare estimated using prescriptions from the theoretical model and su®er fromstrong hypotheses that are necessary to modelling and to get closed formsolutions.25 The other possibility is that of using reduced form regressionsand conditional regression functions. Although they can su®er from somewell known problems as endogeneity, measurement error and heterogeneity,and the interpretation of the results should always be cautious, they canprovide interesting insights and shed some light on average e®ects of jobspeci¯c capital on wages. This is the route I follow in this study.Mortensen (1988) demonstrates that expected wealth-maximising separa-

tion strategies for a matching and on-the-job training model are qualitativelyidentical. The characterisation of optimal separation strategies is based onthe existence of a reservation wage that, given tenure, makes the employedworker indi®erent between staying at the ¯rm and quitting to unemployment.

25For a review of this literature see Van den Berg (1999). Sulis (2004) provides anexample of this type of analysis estimating the structural parameters for an equilibriumsearch model using Italian data.

13

On the other hand, given a wage-tenure pair, a reservation o®er exists suchthat the worker is indi®erent between the current wage and the alternativeo®er. Of course, if the reservation o®er is below the new o®er, the workerquits. Under both the matching and human capital model, the reservationo®er increases with the wage earned and decreases with tenure. The keypoint is that the optimal separation strategy is the same under both hy-potheses: both imply that the expected value of employment at the current¯rm increases with the wage and decreases with tenure. Consequently thevalue of (continued) employment decreases with tenure relative to employ-ment on alternative jobs and relative to the value of unemployment. Thisis because those latter values are not dependent on the wage and tenure onthe current job. However, reasons for this mechanism are di®erent in the twomodels. In the human capital model, the value of continued employment,given the wage, falls with tenure because expected future wage growth onthe job decreases with tenure.26 In the matching model, given workers' pref-erences towards the risk in future wage o®ers and the fact that dispersionin future wages on the current job decreases with tenure, the value of futureemployment decreases with tenure.The empirical counterparts of the theoretical models discussed in the

previous section are found in the statistical survival analysis. Two possiblereason for the end of an employment spell (job-to-job transition and job-to-unemployment) call for a competing risk speci¯cation. The instantaneousjob-to-job transition rate is equal to the arrival of alternative o®ers multi-plied by the probability that the randomly drawn o®er is greater than thereservation o®er. Given that the initial wage o®er distribution is indepen-dent of tenure and wage on the current job, both matching and trainingmodels imply that the job-to-job transition rate is decreasing in the wage,given tenure; and increasing in tenure conditional on the wage earned. Thematching model predicts positive duration dependence conditional on thewage earned for employment spells that end with a voluntary transition. Al-ternative o®ers give higher expected future wage growth in the job-trainingmodel, and higher probability of a better match in the job matching case.Di®erent implications emerge instead regarding the job-to-unemployment

transition rate. The latter is equal to the product of frequency of wage changeon the job and the probability that the new wage is below the reservationwage (that basically corresponds to the value of search when unemployed).This transition rate decreases with the current wage, given tenure. In thiscase the models di®er with respect to their predictions regarding the con-ditional duration dependence. In the human capital model, this transition

26By assumption, the wage on the job is characterised by decreasing increments.

14

rate increases with tenure, given the wage.27 In the matching model thepredictions are not clear. Although the reservation wage also increases withtenure, duration dependence can become negative.

3.3 Wage Regressions

The interpretation and identi¯cation of true returns to job mobility is not asimple task. Standard reduced forms su®er from biases due to endogeneityand self selection. Since wages and tenure are jointly determined by pastchoices regarding search and quits; the resulting relation is a®ected by si-multaneity and the problem should be taken into account when interpretingthe results. In this paper the objective is to look at the quantitative implica-tions of search, matching and training models; given the above limitations,to accomplish this task, tenure can be assumed and treated as exogenous.28

For descriptive purposes, in Table 2, I report results for standard wageregressions. As discussed before, the empirical relationship between wagesearned and tenure that emerges from this type of exercises doesn't have anycausal interpretation and is attributable to a sorting mechanism at work inthe labour market. The ¯rst column in the Table reports this evidence fora cross section of workers employed in 1994. The weekly wage is regressedon standard demographic characteristics, controls for di®erent occupations,and ¯rm characteristics as the average number of employees, the sector ofactivity and the geographical location of the establishment. The predicted(spurious) increasing and concave correlation emerges with a positive e®ectfor both age (in this case used as a proxy for experience) and tenure, andthe relative negative coe±cient for age and tenure squared. Average returnsto tenure estimated by OLS are equal to 1% per year, with the standard de-creasing e®ect as the spell of employment lengthens.29 Results do not changeby much even when controlling for unobserved heterogeneity in the secondcolumn of the Table. The magnitude of coe±cient changes, with an increasefor age and a decrease for tenure; however, they are still strongly statisti-cally signi¯cant, even after controlling for standard observable worker's and¯rm's characteristics. Given the speci¯cation in ¯rst di®erences used, the

27Reservation wages increase with tenure and expected wage growth decreases withtenure.

28Kiefer and Neumann (1989) and Devine and Kiefer (1991) contain detailed analysisof the empirical analysis of search models with some discussion related to this problem.Rosolia (2002) proposes a method to overcome the problem of endogeneity.

29The bias and existence of these returns are widely discussed in the literature. Esti-mates of real returns to tenure constitutes the topic of a separate paper left for futureresearch.

15

Table 2: Standard Regressions: OLS, Fixed E®ects, Probit

Cross Section Fixed E®ects Probit

sex ¡0:228(¡97:29)

¡ ¡0:073(¡7:72)

age 0:020(28:97)

0:079(59:95)

¡0:004(¡11:43)

age2 ¡0:000(¡19:94)

¡0:000(¡17:01)

¡tenure 0:011

(9:26)0:009(9:51)

¡0:125(¡98:55)

tenure2 ¡0:000(¡2:60)

¡0:000(6:61)

¡occupation yes yes yes¯rm size yes yes yessector yes yes yesarea yes yes yes

observations 97; 282 262; 153 171; 418R2 0:53 0:15 ¡

Dep Var in Cross Section: log of weekly wage.

Dep Var in Fixed E®ects: delta log of weekly wage.

Dep Var in Probit: probability of changing job.

t-statistics in parentheses. Controls included.

16

positive coe±cient for tenure is now indicating that stayers earn more thanmovers. Finally, the reverse pattern between the unconditional probabilityof separation and tenure (and age) is presented in the last column.30 In thiscase tenure has a very strong negative e®ect on the probability of leaving the¯rm, con¯rming the standard pattern observed.The above results indicate that tenure and experience have a statisti-

cal positive e®ect on wages and are negatively related to unconditional quitprobabilities. Even controlling for individual unobserved heterogeneity, onaverage, stayers still increase their wages more than movers. However, asabundantly discussed in previous sections, this result doesn't lead to a the-oretical causal interpretation, and is related to the sorting process in thepopulation of workers. In what follows, I try to look at job and wage mo-bility and test for di®erent implications of search, matching and trainingmodels.31

The ¯rst step to analyse the process of job and wage mobility is to lookat raw data on job changes and wage pro¯les. In Table 3, I report ¯gures forstandard mobility frequencies. In the top panel, results for all the sample areconsidered. The last two columns indicate that about 10% of the workerschange job during the period of time.32 As expected, transitions mainlyregard young workers (see Topel and Ward, 1992). The percentage of jobchanges almost halves when comparing the very young workers (less than 25years of age) and the very old (more than 45). Columns from 1 to 4 showthe composition of this process in terms of outcomes and transition types. Inparticular, the total number of changes is divided in two groups: those thatget higher wages when changing job and those that have a wage cut. (I referto gross weekly wages.) Those two groups are then divided according to thetype of transition.33 The total proportion of successful transitions is slightlyhigher, even if the part of wage reductions stands out quite consistently.Almost 44% of movers take a wage cut while moving. Given that almost

30Some further warnings are in order here. First of all, I do not consider any self-selection issue. I am well aware of the problems caused by this fact. However, in thiscontext, my aim is simply that of showing the spurious relationship at work. The secondrefers to the usual warning relative to the interpretation of the value of the coe±cientsthat do not represent the partial derivatives as in the standard OLS estimations.

31Mortensen and Neumann (1989) propose the same exercise using di®erent samples ofUS workers.

32It should also be reminded that those tabulations refer to single observations and notto workers.

33Following Contini (2002), transitions with an intervening spell of unemployment lowerthan one month are considered as job-to-job. Given the limitations in the data, and therelative di±culty of knowing the real reason for separation, we can hardly be de¯ned asvoluntary transitions.

17

Table 3: Percentages of Job Changes. All Sample and by Sex

change jobwage increase wage decrease tot same

job

age job to job unemp job to job unemp(1) (2) (3) (4)

All Sample15-2526-3536-4546-5556-6262+

323640464551

23181713149

262928282826

191715131314

100100100100100100

131110776

878990939394

total 39 17 28 16 100 10 90Males

15-2526-3536-4546-5556-6262+

333640444348

24181613149

253029282833

181614131310

100100100100100100

14119776

868991939394

total 38 17 29 15 100 10 90Females

15-2526-3536-4546-5556-6262+

293641525050

211820111117

282724262425

21171511128

100100100100100100

111111797

898989939193

total 39 18 26 16 100 10 90

18

Table 4: Percentages of Job Changes by Area of Work

change jobwage increase wage decrease tot

age job to job unemp spell job to job unemp spell(1) (2) (3) (4)

North-West15-2526-3536-4546-5556-6262+

344043484650

221514111314

283131292821

171412131314

100100100100100100

total 41 15 30 14 100North-East

15-2526-3536-4546-5556-6262+

343842464771

21151311100

263232313021

18161312147

100100100100100100

total 39 15 30 15Central

15-2526-3536-4546-5556-6262+

303440434343

251919121514

242827312519

221915141624

100100100100100100

total 37 18 27 17 100South

15-2526-3536-4546-5556-6262+

202837474550

32292418169

202021222832

28221913119

100100100100100100

total 36 24 21 19 100

19

65% of separations is characterised by a job-to-job transition without anyintervening unemployment spell, it is interesting to notice that 28% of moverstakes a wage cut even if no unemployment spell is detected. This proportion isconstant across all the age groups. The age-separation pro¯le doesn't alwaysshow a clear pattern. What seems to be interesting is that the transition type(i.e. job-to-job against unemployment period) matters di®erently for olderand younger workers. While young workers that change job decreasing theirwage do it mainly because of an unemployment spell, older workers seemto change their job only if a clear possibility of higher wages attracts them(almost 70% of transitions for them is a job to job one). In the middle andbottom panels of Table 3, the same analysis is performed separating men andwomen. Women have a slight higher probability of getting a wage increasewhen moving directly to a new job. While very young women have lowerprobability of getting higher wages when moving to a new job, their oldercounterparts do much better than men (52% against 44%). The proportionof job-to-job quits that is characterised by wage cuts is again surprisinglyconstant across di®erent age groups and slightly lower for women.Table 4 presents the same frequencies dividing the sample according to the

region of activity of workers. Successful transitions are mainly concentratedin the South, where surprisingly periods of unemployment do not preventworkers from getting a higher wage when re-employed. The proportion ofsuccessful transitions is quite di®erent among di®erent parts of the country.In the South, 24% of transitions are completed resulting in higher wages (evenwith an unemployment spell) against 15% and 18% for the rest of Italy.34 Agepro¯les also show some di®erences across di®erent regional contexts. Whileyoung workers in the North have a probability of 0.34 of getting higher wageswhen quitting directly to a new job; in the South, the same ¯gure is equalto 0.20. These di®erences disappear with age for all the four subregions.Finally, it is worth noting that although successful transitions following anunemployment period decrease with age, still remarkable di®erences existbetween the northern and southern part of the Country.35

The above results indicate that the process of mobility in the labourmarket is characterised by a large amount of variability across di®erent agegroups and that the type of transition doesn't have a clear e®ect on the wage

34It should be noted that the total proportion of job-to-job transitions in the North ishigher (70% against 57%).

35However, this data can su®er from problems related to spurious job changes (e.g., some¯rms that just change their legal status) and the problem of temporary layo®s. Contini(2002) discussed these issues in detail. He also discusses the problem of workers that areformally employed for some months during the year but work without being covered byany contract during the rest. This problem is particularly pronounced in the South.

20

outcome. This evidence suggest that a pure on-the-job search model can havesome di±culties to ¯t the data given the relevant proportion of job-to-jobquits resulting in a wage cut. At the same time, a standard training model isnot able to explain wage increases when moving to a new job when the wageis modelled as a deterministic process. The only model that can captureboth is the matching model. Given the higher °exibility it o®ers, we canexpect that this should be able more than others to ¯t the data. Matchingand training models share also one important prediction that is possible totest in the data. I do this in what follows. The following linear equation isestimated using ordinary least squares

wit = ¯Xit + di + wit¡1 + tit¡1 + ²it (1)

where wit is the log of weekly wage earned on the new job, Xit is a vector ofworker's and ¯rm's observable characteristics (including a quadratic in age)observed at time of change, dit is dummy variable equal to one if an unem-ployment spell is detected when moving, wit¡1 is the (log of) weekly wageearned on the previous job, tit¡1 is the elapsed completed duration of theprevious job (tenure) and ²it is white noise. Results are presented in Table5. Analyse ¯rst the whole sample. Not surprisingly, movers with a periodof inactivity earn on average 4% less than movers that quit and go directlyto another job.36 However, as discussed in the theoretical section, the mainimplication of training and matching models is that of a positive durationdependence in reservation wages. Including previous wage and tenure ac-complishes this task. The wage earned on the previous job, a proxy for thereservation wage o®er on the job, is included.37 After conditioning on pre-vious wage, matching and training models predict that higher tenure shoulddecrease the reservation wage o®er on the job. Longer tenure should increasethe probability of moving to another job because of better prospects on newjobs. Given that the optimal separation strategy has the reservation wageproperty, the e®ect of previous tenure should be negative (indicating positiveduration dependence). In this case, the positive and not signi¯cant e®ect ofthis variable doesn't allow for an interpretation in that direction.

36In°ation can play a role here. However, the same exercise has been carried out usinga monthly wages de°ated with the national CPI index giving similar results.

37Two warnings is in order here. The ¯rst is that tenure is treated as predetermined. Thesecond is that the wage previously earned as a proxy for reservation wage can determine aform of misspecitication in the model. While for job-to-job transitions, this doesn't causeany problem because the reservation wage is the wage actually earned, for unemployedpeople, the reservation wage is for sure less than the previous wage. Kiefer and Neumann(1989) propose to allow for di®erent e®ects of past wages for those who change job tojob and those who do experience a spell of unemployment to overcome this problem andconclude that no important di®erences emerge.

21

Table 5: Wage Regressions: OLS Estimates, Job Changers Only

All Sample Men Women

sex ¡0:085(¡20:25)

¡ ¡age 0:009

(7:31)0:007(5:18)

0:010(4:80)

age2 ¡0:000(¡5:00)

¡0:000(¡3:34)

¡0:000(¡3:54)

unemp. spell ¡0:042(¡10:30)

¡0:040(¡8:14)

¡0:043(¡6:19)

wageOld Job 0:547(113:2)

0:554(93:37)

0:524(63:95)

tenureOld Job 0:001(1:88)

0:000(0:27)

0:002(3:04)

occupation yes yes yes¯rm size yes yes yessector yes yes yesarea yes yes yes

observations 26; 054 16; 589 8; 288R2 0:62 0:65 0:55F 1625:99 1245:26 411:28

Dependent variable: log of weekly wage (new job).

t-statistics in parentheses. Controls included.

22

Table 6: Wage Regressions: OLS Estimates. Job Changers Only

North-West North-East Central South

sex ¡0:098(¡13:20)

¡0:099(¡14:17)

¡0:066(¡6:56)

¡0:056(¡5:51)

age 0:006(2:84)

0:005(2:68)

0:007(2:48)

0:011(3:88)

age2 ¡0:000(¡1:28)

¡0:000(¡1:50)

¡0:000(¡1:63)

¡0:000(¡3:43)

unemp. spell ¡0:047(¡6:32)

¡0:043(¡6:24)

¡0:028(¡2:98)

¡0:010(¡1:17)

wageOld Job 0:519(60:12)

0:529(61:04)

0:548(47:80)

0:537(49:30)

tenureOld Job ¡0:000(¡0:38)

0:000(0:75)

0:000(1:96)

0:000(0:69)

occupation yes yes yes yes¯rm size yes yes yes yessector yes yes yes yes

observations 8209 7498 4383 4787R2 0:64 0:64 0:65 0:60F 615:88 581:59 350:72 311:26

Dependent variable: log of weekly wage (new job).

t-statistics in parentheses. Controls included.

The last two columns of Table 5 look at the same e®ect distinguishingbetween men and women. For both sexes, the negative impact of a period ofunemployment stands out clearly, with an average loss of 4% in terms of wageearned. For both men and women, the e®ect of previous tenure on wages ispositive, indicating that matching and training models have di±culties tocapture these patterns of labour mobility. However, the e®ect is statisticallysigni¯cant only for women, indicating a sound rejection of those models.The evidence from Table 2 also suggests that women have slightly lowerprobabilities of having wage cuts when moving directly to a new job. Thiscould suggest that a standard on-the-job search model can relatively better¯t labour market histories of female workers than those of men. In Table6, the same exercise is performed for di®erent regional areas. Results showsome remarkable insights. First, age and gender e®ects show some variability.Reductions in earnings upon re-employment are less pronounced for womenand in the South. The age e®ect almost doubles for workers in the Souththan those in the North. Periods of unemployment spell have di®erent e®ectsin di®erent regional areas. In the North, workers earn 4% less if previouslyunemployed, while in the Central regions and in the South this e®ect is

23

Table 7: Probit Estimates: Wage Cuts

All Sample Men Women

sex 0:310(14:55)

¡ ¡age ¡0:026

(¡4:13)¡0:027(¡3:52)

¡0:016(¡1:38)

age2 0:000(2:48)

0:000(2:19)

0:000(0:44)

unemp. spell 0:243(11:83)

0:239(9:54)

0:250(¡6:92)

wageOld Job 1:579(55:19)

1:581(46:64)

1:633(31:14)

tenureOld Job ¡0:009(¡3:32)

¡0:006(¡2:00)

¡0:015(¡3:17)

occupation yes yes yes¯rm size yes yes yessector yes yes yesarea yes yes yes

observations 24; 877 16; 589 8; 288

Dependent variable: prob of wage cut (new job).

z-statistics in parentheses. Controls included.

reduced to 2% and 1% respectively.38

An alternative method to look at these e®ects is provided in Table 7,where results from a probit model are reported. The dependent variable isthe probability of taking a wage cut when changing the job, and formally Irun the following estimation

yit = ¯Xit + di + wit¡1 + tit¡1 + ²it; (2)

where yit is equal to one if the worker gets a wage cut when moving andzero otherwise. Other controls are as in equation (1). Here results shouldbe interpreted in exactly the opposite direction as before. As in the OLSregression, the exit from employment seems to reduce the subsequent wageearned, with a slightly higher e®ect for women. On the other hand, thenegative sign for the previous tenure after controlling for previous wagesindicates that longer job durations decrease the probability of a wage cut.This indicates the lack of positive duration dependence in reservation wagespredicted by the matching and training models.Previous regressions show some interesting results that deserve some com-

ments. The OLS estimates indicate that, once previous wages are controlled

38This con¯rms results from Table 3, however, the estimated e®ect for the South is theonly that is not statistically signi¯cant.

24

for, the nil e®ect of tenure on the next salary can be interpreted as againstthe matching and training models, because no evidence of a declining reser-vation wage while on the job is found. This is consistent with the standardon-the-job search model. The probit estimates suggest again that matchingand training models do not seem to explain the pattern in these data. Onthe other hand, mobility frequencies in Tables 3 and 4 indicated the di±cultyfor a standard search model to explain a very large proportion of wage cutswithout any intervening period of unemployment. Comparing results formraw mobility data and wage regressions indicates that the type of transitiondoesn't have any particular impact on the wage outcome, and that includingobservable characteristics doesn't help a lot in explaining the above results.The only relevant di®erences are found looking at these patterns in di®erentregional areas.Previous analyses concentrate on the behaviour of movers trying to dis-

tinguish among di®erent types of transition. However, it is also interestingto compare the outcomes of those that move to those who stay. Address-ing this issue is a complicated task that is again related to the problem ofheterogeneity in the population of workers. In Table 8, the following crosssection is estimated for all the sample of workers

wit = ¯Xit + ci + di + w1t¡1 + ²it: (3)

The weekly wage earned is regressed on standard observables¡Xit, a dummyequal to one if the worker changed the job during the year¡cit, a dummyfor the event of unemployment¡dit, and the wage earned on the previousjob¡w1t¡1. Again, ²it is a white noise error term. Those controls are in-cluded in the regression at various stages. In column 1, I allow only for adummy for the event of changing the job. The negative e®ect is statisticallysigni¯cant with an average reduction in earnings equal to 5%, even aftercontrolling for standard observable characteristics. Allowing for unemploy-ment spells, in column 2, the e®ect of changing job is almost halved, whilethose who pass through unemployment reduce their earning by more than8%. Still, standard patterns regarding age, gender, occupation and indus-try e®ects (not reported) stand out. However, when controlling for previouswages (column 3), the above controls loose all their signi¯cance. The latterspeci¯cation seems able to explain almost all the variability in wages (withan R-squared equal to 0.84). I return to this later. In the last two remainingcolumns of Table 8, the same exercise is performed separately for men andwomen. Interestingly, while for men the inclusion of previous wage has thesame e®ect on other controls that we saw for the whole sample, this is nottrue anymore for women. Changing job as a negative impact on both groups,even this is slightly higher for men. Viceversa, for women, unemployment

25

Table 8: Wage Regressions: Mover-Stayers Comparison 1996

All Sample(1)

All Sample(2)

All Sample(3)

Males Females

sex ¡0:246(¡94:15)

¡0:245(¡94:02)

¡0:050(¡33:06)

¡ ¡age 0:025

(29:28)0:025(29:88)

0:000(1:24)

¡0:000(¡1:46)

0:003(3:21)

age2 ¡0:000(¡21:75)

¡0:000(¡21:69)

0:000(0:29)

0:000(2:56)

¡0:000(¡2:22)

job change ¡0:054(¡15:16)

¡0:025(¡6:07)

¡0:036(¡15:35)

¡0:034(¡13:22)

¡0:040(¡8:25)

unemp. spell ¡ ¡0:085(¡6:07)

¡0:014(¡3:78)

¡0:015(¡3:60)

¡0:011(¡1:49)

wageOld Job ¡ ¡ 0:837(412:97)

0:852(371:67)

0:802(197:60)

occupation yes yes yes yes yes¯rm size yes yes yes yes yessector yes yes yes yes yesarea yes yes yes yes yes

observations 77; 634 77; 634 77; 634 52; 826 24; 713R2 0:51 0:51 0:84 0:87 0:75F 3380:04 3528:19 16590:03 14328:14 3031:86

Dependent variable: log of weekly wage.

t-statistics in parentheses. Controls included.

26

Table 9: Wage Regressions: Mover-Stayers Comparison 1996

North-West North-East Central South

sex ¡0:055(¡22:19)

¡0:059(¡20:54)

¡0:039(¡11:90)

¡0:045(¡8:86)

age 0:000(0:76)

0:000(0:00)

¡0:000(¡0:24)

0:002(¡2:20)

age2 0:000(0:43)

0:000(0:52)

0:000(0:24)

¡0:000(1:43)

job change ¡0:035(¡8:86)

¡0:050(¡12:26)

¡0:030(¡5:61)

¡0:021(¡3:49)

unemp. spell ¡0:027(¡4:05)

¡0:028(¡4:07)

¡0:020(¡2:41)

¡0:009(¡1:15)

wageOld Job 0:824(235:35)

0:828(206:41)

0:854(199:28)

0:834(174:20)

occupation yes yes yes yes¯rm size yes yes yes yessector yes yes yes yes

observations 28; 695 20; 116 14; 575 14; 153R2 0:84 0:83 0:84 0:87F 6853:33 4484:88 4427:74 2983:88

Dependent variable: log of weekly wage.

t-statistics in parentheses. Controls included.

periods do not signi¯cantly a®ect re-employment earnings compared to thosewho stay on the job. In Table 9, I look again at regional di®erences. Themover-stayers comparison is carried out for di®erent regions. Some interest-ing results emerge. Movers earn less than stayers in all the di®erent partsof the country, however, the e®ect is slightly stronger in the North, wheremovers earn, on average, 5% less than stayers. As already veri¯ed, workersin the South seem not to su®er large losses in terms of wages after unemploy-ment spells. The negative e®ect is the smallest one and is not statisticallysigni¯cant. However, for all the regional areas, the strong e®ect of previouswages stands out clearly.Previous results indicate that wage variation is really driven by unob-

served heterogeneity in the population of workers, and that reliable compar-isons of the mover-stayers groups should take this into account. In Table 10,this issue is explicitly addressed controlling for individual ¯xed e®ects. Re-sults indicate interesting di®erences with respect to simple OLS regressions.When controlling for unobservable individual characteristics, all controls turnto be signi¯cant in explaining wage dispersion. The e®ect of age is di®er-entiated across groups with a 9% average e®ect in the whole sample. Sexdi®erences are quite remarkable, with an average e®ect of 10% for males and

27

Table 10: Wage Regressions: Mover-Stayers Comparison, Fixed E®ects

All Males Females N-West N-East Central South

age 0:092(38:77)

0:103(37:93)

0:076(16:38)

0:088(22:65)

0:097(23:00)

0:094(18:13)

0:067(10:31)

age2 ¡0:000(¡12:05)

¡0:000(¡13:61)

¡0:000(¡4:51)

¡0:000(¡5:49)

¡0:000(¡8:34)

¡0:000(¡6:45)

¡0:000(¡1:56)

job change 0:008(6:06)

0:007(4:73)

0:009(3:26)

0:006(2:58)

0:002(0:89)

0:003(1:14)

0:026(7:05)

unemp. spell ¡0:029(¡11:17)

¡0:034(¡11:59)

¡0:016(¡3:36)

¡0:033(¡7:09)

¡0:042(¡9:16)

¡0:017(¡3:05)

¡0:018(¡3:03)

wageOld Job ¡0:255(¡78:43)

¡0:233(¡60:30)

¡0:293(¡49:59)

¡0:263(¡48:85)

¡0:217(¡35:34)

¡0:233(¡32:02)

¡0:303(¡37:81)

occupation yes yes yes yes yes yes yes¯rm size yes yes yes yes yes yes yessector yes yes yes yes yes yes yes

observations 172; 780 117; 538 55; 242 63; 175 44; 800 32; 981 31; 824R2(within) 0:1406 0:1585 0:1226 0:1552 0:1557 0:1460 0:1277

F 586:34 460:42 158:99 272:19 201:33 128:11 102:88

Dependent variable: Delta log of weekly wage.

t-statistics in parentheses. Controls included.

7% for females. Workers in the South show also lower values for the agecoe±cient (6%). However, the most interesting result comes from the e®ectof changing the job. Previous evidence indicated that job mobility was notrewarded in terms of wage gains, and a large proportion of workers had wagecuts when moving directly to a new job. Now, the e®ect of changing jobis positive, even if the magnitude is quite small (0.8% for the all sample).Still experiencing some unemployment determines, as expected, some lossesin terms of re-employment wages: on average those who spend some timein unemployment reduce their wage by 2%. Di®erences across sexes and re-gional areas are evident, whereas this e®ect is lower in the South and forwomen. Finally, it is interesting to note that higher previous wages have anegative e®ect on wage growth indicating concave patterns for wages.

4 Conclusions and Further Research

This paper tries to link empirical evidence on wage mobility and theoreticalconsiderations. First, it provides some descriptive ¯gures on the processes atwork in the Italian labour market during the period 1994-96. Some stylisedfacts emerge from the explorative analysis. Then, using standard econometrictools, I try to interpret those evidences using di®erent models that should in

28

principle explain the observed patterns. The on-the-job search, the matchingand training models are discussed in the ¯rst part of the paper; in particu-lar, the idea of a joint investment in job-speci¯c capital for the worker-¯rmpair is the common element behind those theories. Their di®erent empiricalimplications are then proposed with some warnings in the interpretation ofthe results.The starting point of my analysis is the ¯nding that wages and tenure are

positively correlated in a cross section. I infer that this statistical relationshipdoesn't have any causal interpretation and is generated by a selection mecha-nism in the labour market. Controlling for unobserved heterogeneity doesn'tsolve the problem and the pattern persists. After discussing some raw dataabout job mobility and wage dynamics, I run various types of regressions totest a speci¯c implication of the matching and training model. In particularI look at positive duration dependence in reservation wages. Given the wageearned, those models predict that workers with higher tenure should havehigher transition probabilities towards new jobs. No ¯nding in that directionis found. A very high proportion of wage cuts following job-to-job transitionsstands out in the data. This evidence contrasts with the prediction of a stan-dard on-the-job search model. Comparing men and women, some interestingresults emerge that can be interpreted using theoretical models discussed.Women's behaviour seems closer to a pure search model than the men's one,with lower probabilities of having a wage cut when moving directly to a newjob and a sound rejection of the matching model. Important regional di®er-ences are also detected. Interesting results emerge from the mover-stayerscomparison both using OLS and ¯xed e®ects estimators. Simple OLS regres-sions indicate that, on average, job changers earn less than stayers, and thatunemployment spells have a signi¯cant negative e®ect on wages. Previouswages seem then able alone to explain all the observed variation in earningsindicating some persistence of wage di®erentials in the short run. However,controlling for individual heterogeneity, some evidence of very small wagegains from mobility are detected.The overall evidence suggests that neither of the models seems able alone

to capture all the facts about job and wage mobility and that heterogeneityplays a substantial role in the patterns of mobility rates and wage dynamics.The interpretation of my results is also subject to some further warningsand caveats. Some of them are related to data problems, others to the pureinterpretation of results. In particular, the data base allows to distinguishonly between employment and out-of-sample state. Exit from the samplecan result in very di®erent states: unemployment, public employment, self-employment and retirement. This can be a serious limitation when discussingmobility patterns and wage gains. Moreover, data doesn't allow to distin-

29

guish with certainty between quits and layo®s. Following some suggestionsproposed in the literature for this and similar data sets, the evidence is thendiscussed with caution. The second set of warnings refers to problems re-lated to the econometric speci¯cation used. Here, reduced forms su®er fromwell know endogeneity problems. In particular, analysing the worker's his-tory, some variables (as tenure) are necessarily the result of past choices and(optimal) quit decisions. This problem of simultaneity can be hardly solvedwhen using standard tools and when analysing wages and tenure. The routeI follow in this paper is to treat tenure as exogenous when analysing condi-tional regression functions.The main contribution of the paper is that of linking theory and empirical

evidence, trying to interpret some facts using a speci¯c theoretical framework.More speci¯cally, trying to distinguish among di®erent implications of threeversions of the very same model.39 The main result is that neither of thedi®erent versions proposed is able alone to explain all the patterns in thedata. Each of them contributes only one part to our understanding of theprocess of mobility in the labour market. Surprisingly, my results are alsovery similar to those obtained by Mortensen and Neumann (1989) for USdata. Some important extensions that are in my research agenda can bementioned. First, looking at ¯rm data is possible to have more informationregarding real versus ¯ctitious quit decisions. In particular, plant closuresand advance notice for layo®s can help to distinguish between pure voluntarymobility and spurious one illuminating about the real search behaviour ofworkers. Finally, controlling for sector-speci¯c skills and di®erent bargainingagreements across di®erent jobs can also help to give further insights andbetter interpretations regarding the object of this study.

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30

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