Temporary Agency Workers in Italy: Alternative Techniques of Classification

© 2004 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd, 9600 Garsington Rd., Oxford OX4 2DQ, UK and 350 Main St., Malden, MA 02148, USA.

Temporary Agency Workers in Italy:Alternative Techniques of Classification

Giuseppe Porro — Andrea Vezzulli — Stefano Maria Iacus

Abstract. Cluster analysis and the classification tree technique are applied toinvestigate the relationship between the individual characteristics of Italian tem-porary help agency (THA) workers and their probability of achieving a temporaryjob. The application aims to show some advantages of these techniques withrespect to traditional econometric tools. Sketches of the most common profilesamong Italian THA workers are obtained as a result. Besides the typical THAworker pointed out by previous studies (young male workers, with a medium–highlevel of education, living in the Northern regions), two new profiles have been iden-tified: the first comprising male manual workers with previous job experience,whose average age is over 30 and whose educational level is low; the second com-prising young female workers with a medium–high level of education, working inthe service sector or in the public sector. The results are compared with the moreusual logit analysis and show their robustness.

1. Introduction

In this paper we apply two different techniques of classification(hierarchical cluster analysis and classification trees) to a micro-dataset on temporary help agencies (THAs) employment in Italy.We analyse the temporary labour supply, pointing out the impactof the individual characteristics of the workers on the probabilityof achieving a temporary job. The purpose of the paper is twofold:

Giuseppe Porro (author for correspondence), Department of Economics andStatistics, University of Trieste, P.le Europa 1, I-34127 Trieste, Italy. E-mail:[email protected].

Andrea Vezzulli — Stefano Maria Iacus, Department of Economics, Universityof Milan, Via Mercalli 32, I-20122 Milan, Italy.

LABOUR 18 (4) 699–725 (2004) JEL C25, C81, J21, J40

• we wish to show some advantages of these methodologies withrespect to the more usual logit analysis. In particular, while clusteranalysis is familiar to labour economists, the classification treetechnique is not commonly used in labour economics studies, andits potential usefulness deserves to be examined more closely;

• through the application of these techniques, we can describe the‘typical’ THA worker in Italy, i.e. we can identify the workers withthe highest probability of being temporarily employed andexamine the characteristics they jointly exhibit.

THAs have been active in the Italian labour market since 1998. Theemployment share of THAs seems to be rather negligible, in Italy(THA workers represent — in terms of full-time employees —around 0.4 per cent of all employees in 2001) as in the other coun-tries where THAs have been operating for several years.1 Neverthe-less, THAs are going to assume a significant role in a labour marketrequiring increasing degrees of flexibility, especially following theincreasing liberalization of the Italian labour intermediationmarket in 2002 (Istat, 2002).

While the theoretical literature on THAs is still somewhatlimited,2 the main features of temporary employment in Italy havebeen examined by an increasing number of studies (Iacus, Porro,2001, 2004; Isfol, 2001; Ministero del lavoro, 2000, 2001a, b, 2003;Montanino and Sestito, 2003; Nannicini, 2002, 2004) through thedata provided by administrative sources (Centri per l’impiego) ordirectly by THA companies. In particular, Iacus and Porro (2001,2004) provide an econometric analysis — via logit models — of thetemporary labour supply through studying the data of one of themain THA companies in Italy for the period 1998–2000.

We apply the classification techniques mentioned above to thesame dataset, with the aim of verifying to what extent the ItalianTHA worker can be exhaustively identified — as is usually the case— with a young, male, blue-collar worker in the industrial sector.In fact we find that, besides the most common profile, other groupsof THA workers can be singled out in the examined period. So far,they represent minority profiles, characterizing the take-off stage of THAs in Italy: nevertheless, they constitute a hint about the possible evolutions of temporary employment that may requirefurther inspection.

Comparable results might be obtained by inspecting the inter-actions among the regressors of a logit model. Adoption of cluster

700 Giuseppe Porro — Andrea Vezzulli — Stefano Maria Iacus

© CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd 2004.

analysis and classification trees produces several methodologicalimprovements, which verify these techniques as useful tools that arecomplementary to the econometric analysis. In particular:

(a) a partition of the observed units into clusters (or leaves, whenusing the classification trees) is a faster, more intuitive pro-cedure — compared to the examination of a list of regressionparameters, representing the significance of all the possibleinteractions among the variables — but it is not less thorough;

(b) the classification trees provide estimates of the probabilities ofgetting a temporary job and represent an alternative to esti-mates obtained via the standard logit model. This allows for atest of the robustness of the standard econometric results.

Classification trees and cluster analysis belong to differentapproaches of analysis: the former is usually intended as a super-vised method while the latter is referred to as unsupervised. In thesupervised methods there is the outcome variable to guide the learn-ing process. In the unsupervised methods, only the features of theunits are observed but there is no outcome to be measured. In ourdataset we do have the outcome (employed or not) which is de-liberately ruled out from the cluster analysis. Once the clusters are formed the outcome variable is used to identify the more‘employable’ workers’ profiles. In this view, cluster analysis is usedto assess the reliability of other supervised statistical machinery.

The paper is structured as follows: the first part is devoted to asynthetic explanation of the techniques; after a description of thedataset, the second part includes the result of the application ofcluster analysis and classification tree analysis to temporaryemployment data and a comment on the findings, with particularregard to the features of the temporary worker, as sketched out bythese techniques, and to a comparison with the econometric exam-ination of the dataset. Concluding remarks are contained in the lastsection.

2. The techniques

2.1 Cluster analysis

We briefly recall what is meant by cluster analysis. Cluster anal-ysis is concerned with discovering group structure among n indivi-

Temporary Agency Workers in Italy 701


duals, i.e. the observations. Such methods are based on the notionof dissimilarity and measures of this quantity (classical referencesare Gordon, 1981; Hartigan, 1975). A dissimilarity measure d is asymmetric (d(A, B) = d(B, A)) and non-negative index such that d(A, A) = 0. Dissimilarities can be a metric, i.e. d(A, C) £ d(A, B) +d(B, C) or an ultra-metric d(A, C) £ max(d(A, B), d(C, B)), but notnecessarily either. A clustering method is an algorithm that workson an n ¥ n dissimilarity matrix by aggregating (or, vice versa, split-ting) individuals into groups. Agglomerative algorithms start fromn individuals and, at each step, aggregate two of them or move anindividual in a group obtained at some previous step. Such an algo-rithm stops when only one group with all the n individuals isformed. Divisive methods start from a unique group of all individ-uals by splitting it, at each step, in subgroups until the case when nsingletons are formed. These methods are intended mainly forexploratory data analysis and each method is influenced by the dis-similarity measure d used.

As the number of groups is always 1 or n, one has to decide tostop the algorithm at some point. There is no general rule, eventhough some have been proposed in the literature (Tibishirani et al.,2001), so that in any single case a decision has to be made to iden-tify the number of groups. Moreover, as the dimensionality problemarises in this kind of analysis (with a group of n individuals, a tri-angular matrix of at least n ¥ (n - 1) has to be computed andanalysed), we propose the following approach:3 (i) we sample a reasonable number of units from the population; (ii) we apply dif-ferent clustering methods to the sampled units (see below); and (iii)we look for a posteriori regularity with respect to the problem understudy (the percentage employed). Information on the status of theunit (employed yes/no) never enters the clustering procedure. Oncewe have profiles (clusters), we check for the percentage employed.When this percentage is sufficiently high we claim that this profileis probably well accepted by the employers. Then, we stratify thewhole population according to the profiles and test again for thevariable of interest (percentage employed). If the high percentageis confirmed, we have good hints as to the relevant determinants ofthe phenomenon.

To identify the number of groups we have conducted a robustanalysis by selecting both agglomerative and divisive methods withone fixed dissimilarity index — actually the only suitable one forour data — which takes into account continuous, categorical andordinal variables at the same time.4



2.2 Classification trees

CART (Classification and Regression Trees; see Breiman et al.,1984) is a technique recently made available to statisticians and decision makers as a result of the popularity given to it by the data-mining community. Even if sold as a black-box toy, thismethodology can be well described and its statistical properties arewell known, as is the case for other statistical models. Contrary tothe generalized linear model (GLM) approach, CART does notassume any parametric assumption for the mean E(Y |X) — or oneof its transformations — of the response variable Y conditionallyon the values of some variates X and thus no coefficient is esti-mated. The final estimation of the CART methodology is the wholetree, which we are going to explain. Contrary to cluster analysis,which is unsupervised data analysis, the main assumption in CARTis that the set of observations X admits a partition (a classificationinto groups) and the tree is simply a representation of this parti-tion. The CART technique is also seen as a variable selectionmethod and is rather useful compared to linear modelling becauseinteractions between variables are handled automatically and thereis no need to specify a linear model between response variable Yand variates X. Moreover, observation with missing values for someof the covariates can be treated in a large number of cases, asexplained below.

2.2.1 How to build a classification tree. To present the CARTmethodology we will limit ourselves to very simple cases of classi-fication trees in which the response variable is binary (YES/NO),but trees with generic categorical variables can be built in the sameway. If the response variable Y is continuous, simple modificationsof the algorithm and assumptions on the distribution of the resid-uals lead to so-called regression trees. The case of binary responseleads to an easy interpretation of the tree and allows us to assign aprobit score to individuals, as in probit analysis.

As in Clark and Pregibon (1992), we consider one-step look-ahead tree construction with binary splits. Given the set X, we saidthat its partition can be represented as a tree or, better, by its ter-minal nodes called leaves. In the first step, all the observations in Xform a unique group. The group is then split into two subgroups bypartitioning with respect to one covariate Xj, e.g. into the subset ofobservations such that Xj > t and the subset of observations suchthat Xj < t (in the case where Xj is a continuous variable, but similar



methods are conceived for discrete, qualitative and ordered vari-ables). The variable Xj is chosen among the k covariates X1,X2 . . . Xk in such a way that the reduction in deviance inside each node is the maximum achievable. More precisely, this meansthat, at each step, each variable is considered for a split and all pos-sible splits are considered for such a variable. For each of thesebinary splits the deviance of the response variable Y is considered.The split that leads to two subgroups with the minimum devianceis chosen.5 Tree construction is stopped when a minimum numberof observations per leaf is reached or when the additional reduc-tion in deviance is too small. At the end of the process the tree con-tains nodes and leaves. Each leaf is a subset of X that is ashomogeneous as possible with respect to Y. The set of all leaves isthe partition of X.

Consider the example in Figure 1: this is a classification treeexplaining the risk of having a heart attack on the basis of age, sexand level of cholesterol based on fictitious data. The response vari-able is binary: ‘having an attack’ or not. Age is a continuous vari-able and cholesterol has two levels only (‘low’ and ‘high’). In thisexample, the first variable split is the level of cholesterol. Then ageis split into two groups, namely young (age <35) and mature people.Then gender is split and again the age, for the subgroup of agedmales (age >35). The tree gives a partition of X into five regionsrepresented as boxes in Figure 1. The proportion of people in theregion who had a heart attack is reported inside each box. This iswhere the binary response is important: by assigning to each obser-vation in a leaf the proportion of people suffering a heart attackwe then have an estimate of the risk of having a heart attack, giventhe partition of X in terms of the three variables, which is analo-gous to a logit/probit model estimate.

Any new observation is classified by dropping it down throughthe tree. Note that even observations with missing values for somecovariates can be classified this way. For example, for people withhigh cholesterol, the tree model predicts a high risk of attack as25/30 = 0.83, independent of sex and/or age. This risk is similar tothat of old males (age >65) with low cholesterol, i.e. 50/60 = 0.83.Note further that this approach also allows for a description of theinteractions between levels of variables without having to includethis in a model, unlike what is required in any regression applica-tion. In this example, age is a high risk factor only for males.

Normally, CART techniques produce very large and ramifiedtrees which can imply overfitting. In such cases, several ‘pruning’



techniques are available. In general, it is always worth looking atthe tree carefully with a view to pruning it, and this is what we havedone in our specific application.

3. The data and the result of the application

The data have been provided by one of the main THA com-panies operating in Italy since the beginning of 1998 and describethe activity of the company during the take-off period (1998–2000).The dataset comprises 50,030 candidates for temporary jobs.Dropping those records containing missing values, we obtain a



Figure 1. Classification tree for heart attack explained by age, sex andlevel of cholesterol (fictitious data). The sample size is 230.The number of heart attacks over the number of observationsin the subgroup is shown in parentheses. Frame boxes are theleaves of the tree. The age variable has been split twicehierarchically

subset of 18,276 individuals. Their characteristics are described in Table 1. Besides reckoning whether or not they obtain a tempo-rary job (we call them ‘employed’), we can observe the followingvariables:

• gender;• age;• educational level: ‘low’ indicates compulsory education; ‘medium’

indicates high-school level; ‘high’ indicates a university degree;• previous job experience, with any kind of job contract: the vari-

able is binary (‘yes’ or ‘no’);• attendance at vocational training courses: the variable is binary

(‘yes’ or ‘no’);• knowledge of informatics: the variable is binary (‘yes’ or ‘no’); ‘yes’

indicates knowledge of the main informatic tools and softwarepackages;

• knowledge of foreign languages: the variable is binary (‘yes’ or‘no’); ‘yes’ indicates knowledge of at least one foreign language;6



Table 1. Characteristics of the 18,276 candidates for temporary work

Employed (%) 5.71Gender (% M) 47.1Age (years)

Minimum 15.051st quartile 23.84Median 27.54Average 28.833rd quartile 32.20Maximum 64.23

Education (%)Low 11.0Medium 55.6High 33.4

Job experience (Yes) 69.2%Training courses (Yes) 1.9%Informatics (Yes) 61.8%Languages (Yes) 84.7%Means of transport (Yes) 42.3%Residence (%)

North-west 33.5North-east 27.8Centre 4.0South 34.7

• availability of any means of transport: the variable is binary (‘yes’or ‘no’);

• macroarea of residence.

The variables available for all 50,030 candidates (called ‘group B’)concern age, gender and residence: in looking at them a bias can benoticed in the 18,276-sized subset (called ‘group A’), with respect tothe residence area: as Table 2 shows, candidates resident in theSouthern regions are over-represented in group A. In order tocorrect the bias, a sample (called ‘Sample A in B’) has been drawnfrom group B, respecting the composition of the whole populationaccording to residence.7

As is shown in Section 4.3, in Iacus and Porro (2004) a logitmodel has been estimated, alternatively using the units in ‘group A’and in ‘Sample A in B’: the sign and the significance of the esti-mated coefficients do not show considerable changes. Running theCART analysis on the stratified sample just mentioned (‘Sample Ain B’), we notice that the first splitting step isolates the residents inthe South, as in the tree for all the 18,726 observations shown inFigure 5. So the remarked-upon bias affecting the 18,726 withrespect to the 50,030 candidates is smoothed over.


Owing to the dimensionality problems indicated in the previoussection, the technique has been applied to a 1,000-sized randomsample. Both the agglomerative and the divisive algorithms havebeen stopped at the six-groups level. The results are presented inTable 3 (the divisive approach) and Table 4 (the agglomerative



Table 2. Comparison of the groups composing the labour supply,according to age, sex and residence

Group A (18,276) Group B (50,030)

Average age (years) 28.8 29.4Gender (% M) 47.1 49.0Residence (%)

North-west 33.5 39.0North-east 27.8 32.3Centre 4.0 8.8South 34.7 19.9

approach). As will be explained in the next section, the character-istics of the clusters with the highest employment probability (5A,6D and 4A, 4D: see Figure 2) have been used to stratify two samplesfrom the whole population (see Tables 5 and 6) in order to verifythe persistence of a good probability of employment.

3.2 Classification tree

The results of the CART technique being applied to the 18,276individuals are shown in Figure 5. Because the scope of our analy-sis is a description of the typical temporary workers’ profiles, the



Table 3. Summary statistics for the six clusters of the divisive method

Cluster

1D 2D 3D 4D 5D 6D

Employed (%) 5.31 42.86 4.89 7.69 50 12.86Individuals 320 7 532 65 6 70Average age (years) 28.6 29.5 28 29.19 28.67 32.1Gender (% M) 47.5 57.1 47.9 50.8 16.7 62.9Age (years)

[14, 25) 31.87 28.6 35.90 26.15 16.7 22.9[25, 30) 30.62 28.6 29.32 36.92 50.0 24.3[30, 35) 19.06 28.6 19.55 16.92 16.7 17.1[35, 40) 10.31 0 6.63 10.77 16.7 15.7[40, 55) 7.81 14.3 7.52 9.23 0 20.0[55, 66) 0.31 0 0.75 0.00 0 0.00

Education (%)Low 9.38 14.3 8.27 3.1 0 45.7Medium 54.37 71.4 62.03 60.0 16.7 50.0High 36.25 14.3 29.70 36.9 83.3 4.3

Job experience (Yes) 81.6 100 63.3 29.2 83.3 85.7Training courses (Yes) 0 100 0 0 100 1.43Informatics (Yes) 70.6 85.7 57.1 100 50 21.4Languages (Yes) 100 100 100 0 50 0Means of transport (Yes) 100 85.7 0 4.62 0 100Residence (%)

North-west 35.63 42.9 28.95 23.08 50.0 38.6North-east 37.81 28.6 21.43 24.62 33.3 38.6Centre 4.69 0 3.57 6.15 16.7 2.9South 21.88 28.6 46.05 46.15 0 20.0



Table 4. Summary statistics for the six clusters of the agglomerativemethod

Cluster

1A 2A 3A 4A 5A 6A

Employed (%) 3.55 2.76 6.19 11.11 16.98 7.84Individuals 169 181 339 54 53 204Average age (years) 27.6 28.96 28.39 29.65 33.35 28.06Gender (% M) 50.3 52.5 47.2 37.0 64.2 46.6Age (years)

[14, 25) 32.54 35.36 35.99 24.07 18.9 31.86[25, 30) 36.09 23.20 28.61 37.04 24.5 32.84[30, 35) 20.12 23.20 17.70 14.81 15.1 19.12[35, 40) 8.88 6.63 7.67 9.26 17.0 10.78[40, 55) 2.37 10.50 9.14 10.50 24.5 5.39[55, 66) 0 1.10 0.88 0.00 0 0.00

Education (%)Low 0 18.2 10.9 3.7 54.72 3.92Medium 46.7 59.1 67.8 57.4 41.51 56.37High 53.3 22.7 21.2 38.9 3.77 39.71

Job experience (Yes) 0 42.5 100 88.9 90.57 100Training courses (Yes) 0.59 0.55 0.59 7.4 0 2.94Informatics (Yes) 100 0 57.5 94.44 0 100Languages (Yes) 90.53 98.8 100 1.85 0 93.14Means of transport (Yes) 26.6 53.6 0 0 100 100Residence (%)

North-west 25.44 24.86 35.10 24.1 35.85 37.75North-east 9.47 18.78 30.97 31.5 41.51 43.14Centre 5.92 5.52 2.36 7.4 1.89 3.92South 59.17 50.83 31.56 37.0 20.75 15.20

Figure 2. Relevant features of cluster analysis profiles

‘pruning’ procedure has been carried out, and therefore a signifi-cant number of candidates have been left in each terminal leaf.8 Four leaves have been identified where the incidence ofemployed people seems to be above the average for the population(see Figure 3): their characteristics are discussed in the nextsection.

A new employment probability has also been estimated for eachindividual in the population, using the percentage of employedpeople over the total number of candidates included in each ter-minal leaf: the estimates are compared with the results of logitanalysis (see Figure 4).



Table 5. Characteristics of individuals in the stratified sample accordingto profile No. 1 (see Figure 2). ‘Group consistency’: the samplecomprises 1,507 individuals from a population of 17,276.‘Work ratio’: in the sample there are 161 employed in relationto 980 employed in the whole population

Employed (%) 10.68(161/1507)

Gender (% M) 100Age (years)

[14, 25) 31.19[25, 30) 26.94[30, 35) 16.79[35, 40) 10.55[40, 55) 13.14[55, 66) 1.39

Education (%)Low 28.5Medium 71.5High 0

Job experience (Yes) 100%Training courses (Yes) 1.73%Informatics (Yes) 42.7%Languages (Yes) 66%Means of transport (Yes) 100%Residence (%)

North-west 45.9North-east 54.1Centre 0South 0

Notes: Group consistency = 0.0872 (1,507/17,276); Work ratio = 0.1643 (161/980).

4. Main findings

To some extent, the identification of the temporary workers’ pro-files is arbitrary: it actually depends on the level at which we stopthe aggregation/disaggregation of the data, in the case of clusteranalysis, and on the constraints we impose on the structure of theclassification tree. On the other hand, no external interventionleads, of course, to trivial results. Alternative degrees of aggrega-tion and alternative optimizing constraints can be considered forboth the techniques:9 by inspecting the dendrogram10 and the clas-sification trees we have chosen the aggregation level that allows forthe description of profiles that are significant from the labour



Table 6. Characteristics of individuals in the stratified sample accordingto profile No. 2 (see Figure 2). ‘Group consistency’: the samplecomprises 2,980 individuals from a population of 17,276.‘Work ratio’: in the sample there are 260 employed in relationto 980 employed in the whole population

Employed (%) 8.72(260/2,980)

Gender (% M) 0Age (years)

[14, 25) 47.7[25, 30) 52.3[30, 35) 0[35, 40) 0[40, 55) 0[55, 66) 0

Education (%)Low 0Medium 61.2High 38.8

Job experience (Yes) 100%Training courses (Yes) 2.99%Informatics (Yes) 100%Languages (Yes) 90.8%Means of transport (Yes) 47.1%Residence (%)

North-west 46.31North-east 36.44Centre 3.96South 13.29

Notes: Group consistency = 0.1724 (2,980/17,276); Work ratio = 0.2653 (260/980).



Figure 3. Relevant profiles from classification tree analysis

Figure 4. Density estimation of the distribution of the employmentprobability based on the standard logit model (continuouslines) versus the tree-based model. Both distributions showbimodality even though on different scales



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market viewpoint and for comparison with the econometric analy-sis (see the comparison of the probit curves at the end of thesection). As general criteria for profile selection we considered thepercentage of employed people in the groups (with respect to the population mean) and secondarily the numerical consistency ofthe groups themselves.


Among the six clusters identified by the divisive algorithm, groups4D and 6D exhibit the highest percentage of workers who achieveda temporary job11 (see Table 3).

In group 6D, about 13 per cent of the candidates have beenemployed.12 Almost two-thirds of the workers in the group aremales. The average age is around 32, significantly higher withrespect to the sample (28.57); moreover, 35 per cent of the enrolledworkers are over 35 years of age. More than three-quarters of theworkers live in the Northern regions and 45 per cent have the lowesteducation level (compulsory education).

Group 4D contains 7.7 per cent of enrolled workers who havefound a temporary job. The variable ‘sex’ is equally distributed andthe average age is around 29 years, slightly higher with respect tothe whole sample, with a high concentration between 25 and 30years. Almost half of the workers are resident in the Southernregions and the education level is noticeably high (almost 40 percent have a university degree).

The agglomerative algorithm stopped at six clusters produces twogroups with a high incidence of employment (see Table 4): group5A, where 17 per cent of the enrolled workers have found a job, andgroup 4A, where the percentage of employed individuals is around11 per cent. Both the clusters are small and they are absorbed bytwo major groups at the following step of the algorithm; despitethis, it is worth noting that in these groups we find the same pro-files described by means of the divisive algorithm.13

In group 5A, in fact, male workers represent 64 per cent of thecluster, and the average age is higher than 33 years, showing a strongconcentration in the 35–40 and 40–55 year classes (over 40 per centof the candidates). The education level is low: around 55 per centof the workers completed only compulsory education, while lessthan 4 per cent have a university degree. As in group 6D, of the divi-sive algorithm, more than three-quarters of the enrolled workersreside in the Northern regions and similarities can also be found in



the other features (previous work experience, availability of meansof transport).

On the other hand, group 4A contains a larger proportion offemale workers (63 per cent). The average age is around 29, and 37per cent of the workers are between 25 and 30 years old. The edu-cation level is high: only 3.7 per cent of the workers achieved onlycompulsory education and about 39 per cent have a universitydegree. With respect to group 4D obtained with the divisive algo-rithm, the incidence of workers resident in the Southern regions islower, while the percentage of residents in the North-east rises to31.5 per cent.

It is worth noting that units in cluster 5A are almost all includedin cluster 6D. The overlap is less evident for clusters 4A and 4D:about 25 units belong to both clusters.

As Figure 2 shows, the application of the cluster analysis, there-fore, produces two main sketches of workers’ profiles whose indi-vidual characteristics allow for a higher probability of achieving atemporary job (Nos. 1 and 2), together with a profile (No. 3) of theworkers with the lowest THA job opportunities:14 profile No. 1describes a male worker whose age is higher with respect to theaverage of the enrolled workers. He lives in the North, has a lowlevel of education, previous work experience and means of trans-port available. Profile No. 2, on the other hand, describes a femaleworker, between 15 and 30 years old, with a high level of education(high school diploma or university degree), with previous workexperience and probably resident in the South or in the North-east.It should be noted that while profile No. 1 identifies a typical THAmanual worker according to the empirical evidence from the wholeTHA labour market, profile No. 2 may partly reflect the specifici-ties of the recruitment and employment activity of the THAcompany examined in this paper.15 With regard to profile No. 3,concerning workers with the lowest probability of achieving a tem-porary job, the main evidence is the imbalance between the recruit-ment activity of the THA company in the Southern regions (wherethe enrolled workers often exhibit medium–high levels of formaleducation) and the employment capacity in the same area.

It is worth noting that the two clusters referring to profile No. 1show an age distribution which is, to some extent, bimodal: in par-ticular, more than 40 per cent in group 5A and 35 per cent in group6D are over 35; this tendency towards a polarization of the workers’age is confirmed by the age distribution limited to the employed inthe clusters. These workers are most likely not in search of their first



job: in these cases, therefore, the THA intervention might be areplacement opportunity. In fact, it should be examined whether theintroduction of THAs in Italy is accompanied by the progressivecreation of a category of non-standard middle-aged workers, thusworsening the average stability and life condition of workers, orwhether it constitutes a new opportunity of replacement in the standard labour market. To this end we would analyse the rate ofconversion of THA labour relationships into standard contracts:unfortunately the dataset does not provide information about theevolution of the labour contract of THA workers. Montanino andSestito (2003) examine the data on conversions from a sample oftemporary workers drawn from the archives of one of the majorTHA companies: they indicate that the temporary workers whosecontract has an intermediate length (approximately between 6 and12 months) have the highest probability of conversion into perma-nent employment: it is noticeable, therefore, that the workers whosecharacteristics correspond to profile No. 1 have temporary labourrelationships with a duration significantly higher than the average,thus supporting the idea of THA employment as a means ofreplacement for the unemployed.

As we mentioned above, the cluster analysis has been applied toa 1,000-sized sample from an 18,276-sized population. As a test ofour results, we have stratified the remaining part of the populationaccording to the main features of profile Nos. l and 2, and checkedthe probability of temporary employment of the stratified samples.Tables 5 and 6 show that both the samples obtained by stratifica-tion exhibit an employment probability significantly higher than theaverage probability of the whole population.

4.2 Classification tree analysis

Four leaves (A, B, C, D) have been identified as containingworkers with the highest probability of employment, using as a criterion the ratio of employed both to the total number ofemployed in the population and to the number of workersincluded into the leaf (see Figure 3). All the emerging profilesconcern workers — mostly living in the Northern regions — underthe age of 40: thus the group of middle-aged workers belongingto profile No. 1 coming from cluster analysis seems to be unableto constitute an autonomous profile in the classification trees andis broken up into different leaves according to territorial or humancapital variables.



Despite the evidence that most of the temporary workers in Italyare male, in the four leaves the female incidence prevails.16 It is worthnoting that the split with respect to the gender variable intervenesnot so far from the terminal leaves in the complete tree, therebyshowing that gender, in this specific dataset, is not a primary selec-tion variable. Once again, this shows the complementarity of thetwo techniques.

Leaf A is the most interesting, incorporating over 2,600 workerswhose age is between 14 and 25 years (the average age is 19.5 years,and therefore noticeably lower with respect to the whole dataset).They are resident mainly in the North and the distinctive feature ofthe group is the availability of previous professional experience,which is a splitting variable and hence characterizes all the workersin the leaf, although the level of formal education is undoubtedlyhigh: more than 70 per cent of the workers have a high schooldiploma and 21 per cent have a university degree.17 Leaf B also hasseveral features which make it similar to leaf A: slightly less thantwo-thirds of the workers are between 25 and 30 years old; they alllive in the North-west, have medium–low levels of education and,although professional background is not a splitting variable for this leaf, more than 88 per cent of the workers declare previous jobexperience.

These workers, whose estimated employment probability is about10 per cent, are similar to the typical temporary worker profile —according to the empirical evidence derived from studies of theItalian THA market — where THA employed are supposed to beyoung male manual workers with medium-low levels of education,living in the Northern urban areas and having their first job ex-periences in industrial firms. The analysis of detailed tasks of theemployed workers in leaf A allows us to know that in fact most ofthe employed are blue-collar workers in manufacturing or theservice sector; contrariwise, only 10 per cent of them can be classi-fied definitively as clerical workers. Similar tasks are performed byworkers belonging to profile No. 1 coming from cluster analysis. Onthe other hand, profile No. 2 most likely describes female workers— who are crosswise distributed among the identified leaves — inthe service sector: they represent the peculiarity in the recruitmentand employment activity of the THA company whose data weexamine in this paper, but at the same time they anticipate —according to several observers18 — a trend in the Italian THAmarket, progressively spreading from manual tasks in the manu-facturing sector to clerical duties in service firms.



The evolution pattern of THA employment in the Italianeconomy, from this viewpoint, seems to follow the opposite direc-tion with respect to what has been observed in the USA (Estevão,Lach, 2000): at the beginning of the 1980s the largest share of THAworkers was employed in the service and public sectors, whileduring the 1980s and the 1990s THA employment in the publicsector declined sharply and the proportion of manufacturing firmsincreased. The shift towards the private service sector and manu-facturing sector has been accompanied by an increase in the inci-dence of male manual workers.19

4.3 Comparing the logit model and the classification tree

The effect of each individual feature on the employment prob-ability is shown by the logit model estimated in Iacus and Porro(2004), using the observations contained in ‘Group A’ and in‘Sample A in B’20 separately (see Table 7). The main result is theevidence that — contrary to expectations — several variables arenot significant: in particular, sex and level of education seem not tohave a clear impact on the employment probability of THA candi-dates.21 This makes the adoption of alternative techniques useful inorder to investigate the interactions among variables which maydescribe the typical profiles of THA workers.

The kernel estimate of the distribution of the employment prob-ability (probit curve) has been used in Iacus and Porro (2004) topredict the employability of a THA candidate, i.e. to find the thresh-old separating employed and non-employed candidates in terms oftheir probit. The goodness of the model is evaluated on the basisof the minimization of a predictability error index (PE), defined asfollows:

where ‘false positives’ are candidates whose probit is over thethreshold but who have not found a temporary job and ‘false nega-tives’ are effective workers whose probit is under the threshold.From a graphical analysis it emerges that the probit curve isbimodal (see Figure 4), hence the local minimum of the curve ( p =3.48 per cent) has been taken as the threshold.22 Table 8 shows theperformance of the logit model in terms of a misclassificationmatrix for in-sample and out-sample subsets. Out-sample data com-prise 1,000 observations drawn from the 18,276-sized population

PE = ( ) + ( )12

12false positives false negatives ,



and not used to estimate the parameters of the logit model. In-sample data are the remaining 17,276 observations used to estimatethe model. The same approach has been applied to validate CARTanalysis (see Table 9) and comparable results have been obtainedfor this classifier. It should be remembered that the percentage ofthe employed over the total number of people included in each ter-minal node of the tree has been used to assign the employmentprobability of each individual in that leaf. As in the logit case, the



Table 7. Coefficients of the logit model (test statistic values are inparentheses). Dependent variable: probability of finding atemporary job. For the definition of ‘Sample A in B’ see thetext. Base levels are ‘low’ for the education variable; ‘Centre’for the residence variable

Group A Sample A in Bn = 18,276 n = 7,998

(Intercept) -2.1108*** -2.2803***(-8.02) (-6.93)

Gender (M) 0.1008 0.0618(1.51) (0.67)

Age -0.0380*** -0.0322***(-7.32) (-4.49)

Job experience (Yes) 0.2532** 0.0462(2.81) (0.37)

Training courses (Yes) 1.6718*** 1.6920***(12.43) (8.81)

Informatics (Yes) 0.1916* 0.2902**(2.49) (2.66)

Languages (Yes) -0.3343*** -0.1869(-3.73) (-1.46)

Means of transport (Yes) 0.2944*** 0.2794**(4.38) (2.95)

Education (medium) -0.1385 -0.0840(-1.24) (-0.54)

Education (high) -0.1755 -0.2323(-1.40) (-1.32)

Residence (North-east) 0.4726** 0.5437**(2.63) (2.81)

Residence (North-west) 0.6230*** 0.5715**(3.51) (3.00)

Residence (South) -1.033*** -0.9356***(-5.14) (-3.56)

Note: Significance levels: *** = 0 per cent; ** = 1 per cent; * = 5 per cent.Source: Iacus and Porro (2004).

probit curve shows a bimodal tendency (with a local minimum at p = 6.67 per cent; see Figure 4). Despite the significant differencebetween the empirical thresholds, which reflects only a difference inscale, we obtain a value of 0.88 for the correlation coefficientbetween the estimates of probit with the two techniques, thus indi-cating the strong consistency between the employment probabilityassigned to the same candidate by the two techniques (logisticregression and classification trees). The two methods are also con-sistent with respect to the PE index, as shown in Tables 8 and 9,with tree classification performing slightly better.

5. Concluding remarks

While the usual econometric analysis can shed some light on theimpact of single variables over the employment probability of thecandidates to a temporary job, the techniques applied in this papercan better take into account the interactions among the levels of



Table 8. In-sampling and out-sampling validation for the logit model

In-sample validation Out-sample validationPE = 36.6% PE = 33.05%

Predicted not Predicted Predicted not Predictedemployed (%) employed (%) employed (%) employed (%)

Not employed 38.41 62.59 41.84 58.16Employed 10.61 89.39 7.94 92.06

Table 9. In-sampling and out-sampling validation for the tree model

In-sample validation Out-sample validationPE = 32.84% PE = 33.51%

Predicted not Predicted Predicted not Predictedemployed (%) employed (%) employed (%) employed (%)

Not employed 67.48 32.52 71.08 28.92Employed 33.16 66.84 38.10 61.90

the variables and help in describing the typical profiles of tempo-rary workers.

In fact, three main results are derived from examination of thedata of one of the most important THA companies operating inItaly since the introduction of intermediated temporary work (the‘Treu law’, 1997):

(a) Classification tree analysis strengthens the evidence pointed outby previous studies on THA employment, clearly describing the most successful candidate as a male worker, whose age isusually under 30 years, living in the Northern regions, with amedium–high level of formal education and with previous workexperience, which seem to have a significant influence on his/heroccupational opportunities. Generally they are manual workers,even if clerical profiles are not absent: therefore, the professionalcontent of their tasks seems considerably lower than the humancapital they have accumulated through their schooling invest-ment. It is confirmed, in other words, that THA workers havea higher average educational level than other non-standardworkers — in particular, fixed-term and part-time workers —(Marelli and Porro, 2000), owing mainly to their age, and thatthey use THA jobs as a means of entering the labour market.

(b) Cluster analysis indicates that similar features, apart from alower schooling level, are exhibited by a small group of workers— with an average age significantly higher with respect to themean of THA workers — which presumably comprises workerswho lost a previous job (unemployed stricto sensu). It would beuseful — in order to evaluate the impact of THA intermedia-tion on the employment and life conditions of workers — toexamine whether the temporary job is, to these workers, anopportunity to achieve (or to achieve again) a standard labourcontract. Unfortunately, a lack of information in our datasetdoes not allow for more than an insight, suggesting that THAsmight actually be an interesting additional means of replace-ment.23 This question therefore deserves further and more accu-rate examination.

(c) A different profile of candidates whose employment opportu-nities are above the average seems to reflect the specificity of theTHA company which provided the dataset. These workers aregenerally female, young and have medium-high levels of edu-cation. They are most likely the outcome of the activity —noticeably higher with respect to the other THA companies —



of this operator in the service sector (large-scale retail trade)and public sector. However, according to studies on the diffu-sion of temporary employment in Italy, the increase in thefemale labour force is a progressively strengthening trend: there-fore, the THA company we have examined — which was origi-nally established as a non-profit firm and has one of the mainItalian trade unions as a promoting partner — is anticipating ageneral tendency and extending the range of firms and workerswhich make up the temporary labour market.

Acknowledgements

The authors wish to thank Daniele Checchi, Michele Santoni and the participants at a seminar held in the Department of Eco-nomics, University of Milan. The authors are also grateful to ananonymous referee for valuable comments and suggestions.

Notes

1 For instance, in the USA in 1999 (where temporary help agencies have oper-ated — mainly on a short-term basis — since the 1920s) THAs provided about 0.9per cent of total employment: see Di Natale (2001). Recent analyses on temporarywork in the UK, France, Sweden and Spain can be found in Booth et al. (2002),Blanchard and Landier (2002), Holmlund and Storrie (2002) and Dolado et al.(2002).

2 See, in particular, Autor (2003), Houseman et al. (2001) and Neugart andStorrie (2002).

3 This approach is feasible for the features of our dataset. Other methods to solvethe dimensionality problem (such as k-means) are not suitable for our data as theyhave only categorical variates.

4 For these data the algorithm for computing the dissimilarity matrix is called‘daisy’ and agglomerative and divisive clustering are named ‘agnes’ and ‘diana’,respectively (see Kaufman and Rousseeuw, 1990, for details).

5 This method of tree building is optimal at each step and hence is hierarchical.Optimization of a tree as a whole is an untreatable problem as it leads to a com-binatorial explosion of the number of potential splits to be analysed.

6 It should be noted that acquaintance both with informatics and languages isself-declared by the candidates and is not tested systematically by the THAcompany.

7 For a comparison with the characteristics of the Italian labour force, see Iacusand Porro (2004).

8 The criterion for measuring impurity inside the node is the Gini index; the stop-ping rule is the following: a leaf is not split when it is smaller than 170 units orwhen the gain in homogeneity is not appreciable. Supervised pruning has been



applied when the proportion of the employed in the node over the employed inthe whole population is not too low (this is because we need the profile in the nodeto be representative).

9 They have, in fact, been considered: an eight-cluster classification has beenexamined and alternative criteria for the classification tree have been considered(e.g. the minimization of a loss function whose arguments are the degree ofdeviance and the number of leaves), without obtaining significant advantages interms of description of the most relevant profiles.

10 The dendrogram is an appealing graphical representation of the process ofclustering. Each singleton is represented at the bottom of the graph and the grouprepresenting the whole population is at the top. The distance between groups isrepresented as segments whose length is proportional to the measured distancebetween them. Looking at it suggests possible numbers of clusters. Dendrogramanalysis applies to both agglomerative and divisive algorithms.

11 Groups 2D and 5D have been ignored because of their small size. Neverthe-less, cluster 2D might be assimilated to profile No. 1 and cluster 5D to profile No.2.

12 In this context we indicate as enrolled those workers who have applied to theTHA and are candidates for a temporary job, and as employed those enrolledworkers who have found at least a temporary job by means of the THA.

13 In order to test the homogeneity of the distributions of the variables insidethe clusters we ran chi-squared tests without rejecting the null hypothesis at stan-dard significance levels; similarities (and dissimilarities) of the binary variables canbe checked by reference to Tables 3 and 4.

14 The evidence is clearer with the agglomerative algorithm (see clusters 1A and2A in Table 3). The main feature of this profile is the residence of workers in theSouthern regions: despite their levels of education, which in several cases are high,this reveals the difficulty of employment of THAs with respect to their recruit-ment ability in the regional area.

15 See Iacus and Porro (2001). Unfortunately, the dataset does not provide directinformation about the activity sector of temporary workers: the detailed descrip-tion of tasks may be used to roughly fill in the lack of knowledge.

16 This is partly due to the specificity of the THA company which provided thedata: the percentage of female workers in this company is higher than the rest ofthe temporary labour market (Iacus and Porro, 2001).

17 The job experience of graduate workers aged less than 25 is most likely occa-sional or part-time job experience gained while they were students.

18 See Marelli and Porro (2000) and the studies quoted in the paper.19 See also Golden (1996) and Segal and Sullivan (1997).20 See Section 3.21 The lack of correlation between formal education and temporary employment

opportunities is also recorded by other studies of the Italian THA market: see, inparticular, Montanino and Sestito (2003).

22 This choice of threshold has to be intended as the ‘natural’ threshold betweenthe two subpopulations as the curve presents bimodality. In different situationswhen the bimodality is not that clear other thresholds, such as the mean or themedian of the probit, can be adopted and this can affect the performance of themodel in terms of the PE index.

23 Note that this topic has become more important since the government’sremoval, in 2002, of the ‘exclusiveness of the company’s scope’ (esclusivita del-



l’oggetto sociale), i.e. the prohibition — introduced by the ‘Treu law’ — of THAsacting as complete labour intermediaries.

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Temporary Agency Workers in Italy: Alternative Techniques of Classification

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