The determinants of teachers’ mobility.

Evidence from a panel of Italian teachers.

Gianna Barbieri∗ Claudio Rossetti† Paolo Sestito‡

November 26, 2008

Abstract

In the Italian system teachers are allocated according to a seniority based centralised systemwith no role for the employer’s side, represented by the individual schools, in attracting, selectingand retaining teachers. Also because of the rather limited wages progression, mobility and theseniority based rights to select the most preferred school and geographical location representone of the main opportunities Italian teachers have. The paper examines the main drivers ofthe (voluntary) mobility of Italian teachers. We find a sizable impact of both the teachers’birth-place – whenever allowed to (i.e. after getting a tenured position) teachers try to close thegap between work-place and birth-place – and several school features related to the students’mix. Teachers systematically try to move away from schools where teaching is likely to bemore difficult – because of lower socio-economic background and educational ability of thestudents in the schools – but potentially more relevant in order to boost students’ human capitalaccumulation. On practical grounds the centralized allocation system seems to be so unable toequalize opportunities among different school environments.

Keywords: Teachers labour market, teachers mobility, geographical mobility, schoolcharacteristics

JEL codes: I20, I21, I28, J45, J61

∗Ministry of Education†University of Rome “Tor Vergata”‡Bank of Italy. Department for Structural Economic Analysis

1 Motivation and outline of the paper

Italian teachers are allocated to schools according to a centralised system based upon seniority andadministrative rules. Although the value of teachers to student achievement is well documented(see for example Hanushek 1992, Ehrenberg and Brewer, 1994) individual schools have no role toplay in order to attract (through offer of better wage and working conditions), select and retain bestteachers.1 Such a system should pursue equality among schools - as richer (environment) schoolsmay not compete in order to have the best teachers – and among teachers - fully protected againstthe risks of arbitrariness by school leaders. Furthermore, in a work career with a very tenuous wageprogression, seniority rights as such might also provide a useful work incentive. Seniority providesan opportunity for the tenured teachers wishing to change their location to do that according totheir preferences. Seniority (on the lists for the access to the profession) also identifies the non-tenured teachers who get an yearly contract (the ones in a better position in those lists have alsosome choice among alternatives).

On the other hand such a system has its own costs. Seniority rules imply that teachers’ conducthas no impact whatsoever upon their career. Even the access to a tenured position is governed byseniority, not by the teachers’ actual behaviour or formal qualifications. Matching among teachersand schools according to idiosyncratic factors (i.e. being located close to the sea or in high mountainand so on) is impeded as only over time, and through subsequent costly moves obtained withincreasing seniority, teachers may choose the school most suited to them. This and the lack of anactive role by schools may imply that in the currently enrolled pool of teachers operating in a givenschool only a few are truly motivated. Many of them may be just waiting to acquire the senioritylevel giving them a better chance elsewhere. More broadly, the lack of a role by the individualschools in their staffing process means that they are disinterested to the cost cutting and qualityenhancing aspects of their personnel policy (actually they have no personnel policy at all).

Barbieri, Cipollone, Sestito (2008) produced some first quantitative evidence about the staffingprocedures at the individual school level. More specifically they constructed three sets of measures.The first is a measure of total turnover for each school, including both the turnover due to theannual renewal of the positions filled by non-tenured teachers hired with yearly contracts 2 and therelocations of tenured teachers moving around according to a vacancy chain triggered by retirementand their own mobility requests. The second is a mismatch indicator, counting the percentage ofthe tenured teachers of each individual school who filled in a mobility request being dissatisfiedwith their current work environment. The third is a revealed preference indicator, netting out therequests of moving into a given school, as expressed by the whole population of tenured teachers,

1 Possibly there is some room of manouvre for retaining better people, at least tenured teachers, who may beinformally guaranteed some perks (in terms of hours timing for instance) in order to avoid them to fill a mobilityrequest.

2 In the subsequent section we will more precisely describe the teachers’ career, emphasizing as it starts with along phase of short term contracts (“supplenze brevi”), then yearly contracts and finally with the tenured positions.

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by the requests of moving out from that school expressed by the tenured teachers there currentlyenrolled. Barbieri, Cipollone, Sestito (2008) show that these measures are correlated with theactual school performance, hinting at the possible impact of turnover, as proxy of organizationalturmoil, and teachers’ motivation upon school effectiveness.

In this paper we intend to put forward that analysis by better analysing the drivers of teachersmobility in the centralised system depicted above. To a large extent such an analysis is preliminaryto any identification of the causal links between teachers mobility and school effectiveness. Indeed,in order to identify those links one would need to understand the determinants of teachers’ mobilityso as to exploit those features of the mobility processes which are exogenous with respect to themobility-school effectiveness relationship. Most of the previous research on teacher labour marketshas focused on the factors determining the decision to leave the public school system (Murnaneand Olsen 1990, Dolton 1990, Dolton and van der Klaauw 1995, Stinebrickner 2002, Bonesronninget al. 2003, Hanushek et al. 2004). Nonetheless, there is little research on the important issue ofmobility within the pubic school system (see for example Falch and Strom 2005, and Bradley et al.2006). Furthermore, unlike previous research focusing on actual transfer of teachers, we focus ondesired mobility, and specifically on mobility requests filled by teachers. This evidently reflects thepreference of teachers towards moving to another school, regardless of the matching between sucha preference and vacancies in preferred schools, which contribute to determine a succesful transfer.

Most specifically, we will focus upon a couple of drivers of teachers mobility, namely geographicalfeatures and school features. The former relate to the fact that teachers may start their career farfrom their desired geographical location and then, using the fact that they face a unique employerwith multiple locations, try to reach their most preferred geographical location, usually their birth-place. This is a very important, although little-explored, aspect of teachers mobility (Boyd et al.2005a, and Boyd et al. 2005b). School features relate to a vast array of structural characteristics ofa school which may signal that it is an easier working place. Unlike some previous research focusingon working conditions (see for example Hanushek et al. 2004), we focus here on characteristics ofschools rather than on the characteristics of the school districts. In the education context a schoolwith better working conditions means a school where teaching is less demanding but possibly lessrelevant, as students by themselves are in a better starting condition. This is a problem for theeducation system as teachers will be driven away from the most difficult schools. These will endup being filled by less experienced and less motivated teachers, who are known to be less effectiveon average (Rockoff 2004, Rivkin, Hanushek and Kain 2005). Even those already tenured may bejust waiting for a different location, putting low effort in their current work duties.

Given the lack of any active role by schools, most of the mobility taking place in the teachersmarket is driven by employees’ choices. As such, our work has some broader scope as the cen-tral allocation mechanism operating into the school system has a wider relevance into the publicsector, where workers are allocated according to seniority and administrative mechanisms. Even

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more broadly, teachers provide for an interesting test case as, differently from other labour marketsegments where only the complex interplay of employers’ and employees’ decisions are observed, wecan focus upon workers’ mobility choices. In order to do that, we have to restrict the attention tothe mobility of tenured teachers, who move only insofar as they are filling a request to move else-where. Non-tenured teachers, enrolled with yearly contracts, on the contrary move from one schoolto another depending upon the presence of unfilled positions and have a much more limited choiceset. So most of our analysis will be concerned with the mobility aspirations of tenured teachersonly. As an introduction to that we however start by looking at the overall mobility patterns (ofboth tenured and non-tenured teachers) and its relationship with the geographical driver of mo-bility, an aspect upon which even non-tenured teachers have some room of manoeuvre. Then, wewill analyze teachers’ mobility request from two different perspectives. We first focus on teacherstheirselves and on probability of submitting a mobility application. Then, we focus on schools andon the number of preferences they receive from teachers.

The paper is organised as follows. Section 2 provides a detailed description of teachers career andthe importance of mobility in Italy. Section 3 presents the data and the broad mobility and turnoverpatterns. As we will focus upon geographical drivers of mobility, in section 4 we start presentingthe picture for the evolution, over the working career, of the geographical distance between thecurrent work-place and the birth-place (as a proxy of the desired geographical location) for bothtenured and non-tenured teachers. Then, section 5 present an analysis of tenured teachers’ mobilityrequest. Finally, section 6 offers some conclusions.

2 The Italian teacher employment system

2.1 A brief historical introduction

It is quite an arduous path to become a teacher in Italy as can be seen in the confused recruitmentprocedures and the irregular rate of employment which have characterized the last few decades.After a long period of uncertainty, in 1999 the recruitment of Italian teachers was defined by law.The law provisions establish that applicants aspiring to a permanent position must submit therequired certificates and pass a competitive examination consisting of a written and an oral test.The winners of the competition cover 50% of the available positions, while the other aspirants whohave passed the competition are declared as “qualified” and are put on a provincial permanent liststo cover the rest of the positions (this is the so-called double channel of recruitment). Those whoare not on a permanent list can only act as a substitute to absent teachers by submitting theirapplication directly to the schools.

Since the law was introduced in 1999, only one competition has been advertised in 2000. In2007 the provincial permanent lists were then changed into closed lists3 to give all the included

3 The close lists are lists where no new aspirants are accepted starting from 2007.

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aspirants a chance of obtaining a permanent position.In other words, employment of teachers currently occurs through two channels. The first, is

based on lists that are compiled through competitions and remain valid until the next competition.The second, is based on closed lists compiled at provincial level. Each channel constitutes 50% oftotal employment.

2.2 The teachers’ career. Salary and mobility

The differences between a new teacher and one at the end of his career are both a higher salary anda better chance to choose the school to work in. It is important to observe that, unfortunately, thestatus of a permanent teacher is a point of arrival as opposed to a starting point in the teacher’scareer. In fact, besides the selection and initial professional training, the most diffused form ofcontact with the school is the so-called temporary employment which for the most part lasts manyyears (indeed up to ten years and more). On average new teachers have a minimum experience offive years which can even go up to 15 years. Since temporary positions are not provided by law,aspirant teachers have to wait for a new competition or any available position. In the meanwhilethey have no option but to accept this type of temporary contract.

As said before, aspirants could be recruited for a yearly temporary position through provinciallists according to their teaching qualification. Aspirants may enrol in more than one list. Due tothe rigid organization of the lists and the heterogeneous locations of the schools across Italy, thedemand for teachers largely varies from province to province. This means that in certain provincesthere could be a shortage of aspirants in specific teaching areas while at the same time there couldbe a long list of aspirants waiting to be employed. Candidates could then wait up to 10 years beforefinding a permanent position.

Apart from the provincial lists, aspirants can also apply for a teaching position directly to thehead of the school who manages an internal list which covers short term vacancies (mainly dueto illness and maternity leave). Finally, after a rather long period of temporary employment, aqualified teacher with a favourable ranking on the provincial lists, can be eligible for permanentemployment and choose the position in the province of her list. One must however point out thatthe phase of temporary employment necessarily involves a series of disadvantages such as lack ofremuneration continuity, frequent changes of working locations and uncertain professional future.It is then worth noting that this system is not so different from more decentralized systems, exceptthat schools cannot decide whom they want to hire, but are forced to hire the most experiencedand – to a lesser extent – most qualified candidates. Nonetheless, this would probably be often thecase if school principals had the opportunity to choose the teachers to recruit since teachers’ abilityis very difficult to observe. In fact, Greenberg and McCall (1974) notice that in the US assignmentsare usually done by education and experience, with the more experienced and educated teachersbeing awarded the best assignments.

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When analysing career development, the first important things to consider are starting salaries,opportunities, length of time needed for career advancement and ending salaries. In Italy, the salarydepends on the length of service and not on working performance. Teachers are not evaluated atall. The profession has a salary structure that takes 35 years in which to move from the bottomto the top and flattens over time. Apart from the first two years of permanent employment, thereis a seniority increase every seven years distributed as follows: 3-8 years, 9-14 years, 15-20 years,21-27 and 28-34 years. The top salary can be reached after 35 years of service. Comparing salarylevels at the beginning and the end, the increase for primary, lower and upper secondary schoolare respectively 47, 50 and 57%. This is a quite small increase given that when looking at OECDfigures, teachers at the end of their career receive on average a 71, 71 and 72% higher salary thanthe starting one.

The second aspect we need to take into account is mobility, which is considered the only alter-native to a career based on a merit system. The hard path that the aspirant teachers have to gothrough to enter into the roll and the prospect of a flat career, forces teachers to exploit this possi-bility offered by the system to improve their working conditions. Because of the lack of meritocraticcriterions in determining the salary, the most important career model is represented by the longmarch towards the desired geographical location (most of the time the native province). The mainadvantage of this mobility is the reduction of transfer costs between the school and home. Thereare also other factors that induce mobility such as family-related factors and the aspiration to teachin prestigious schools or to leave schools located in difficult social contests. Hence, the mobilityresulting from this system lead to similar features as in many other countries, where the more ex-perienced teachers are located at high socioeconomic status schools while schools in disadvantagedareas often face difficulties in retaining skilled teachers.

Essentially, there are three types of mobility, namely due to geographical transfer, change ofsubject taught, or change of school level taught. Geographical mobility is mainly requested inorder to move closer to home, while the remaining two types relates to professional mobility, andin particular to teaching a subject more consistent with the completed degree. The vast majorityof teacher requests are indeed geographical transfer requests. Regarding mobility requests, weneed to consider that not all applications are spontaneous. In fact, some applications are made bynewly tenured teachers who have to apply for requesting the school of their first year of tenure.Furthermore, a small number of teachers have to apply for new destinations because they are laidoff a particular school (so called “soprannumerario”) in order to make room for teachers with higherseniority. Finally, teachers at their second year of tenure may choose whether to apply or not, but ifthey do, they can only apply for schools within the same province where they actually work. Hence,their applications, although not mandatory, are evidently restricted. Most of our analysis will beconcerned with voluntary mobility requests only, restricting our attention to tenured teachers fromat least two years and not laid off from the school.

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3 Data and variables

3.1 The data

The data used in this paper comes from the combination of different administrative registries ofItalian schools and teachers maintained by the Italian Ministry of Education. The first registryis the teachers database, which contains records of gender and date of birth of the teachers, themunicipality where the teacher is born, type of contract (temporary or full tenure), the seniority oftenured teachers, date of tenure for tenured teachers, and a unique anonymized teacher identifier,and a unique anonimized identifier of the school where the teacher works. Unfortunately, there isno information about the family situation of the teachers or their educational background.

Through the anonimized teacher identifier we were able to link the teachers database to theteachers’ mobility applications registry. This registry includes, for each tenured teacher who fillsa mobility application, records of preferences given to new destinations. Specifically, mobilityapplications can be submitted during a school year (on February) and allow teachers from primaryand secondary schools to indicate up to respectively 15 and 20 new preferred destinations. Dataon teacher registries (demographic and mobility) are available for the school years 2005/2006,2006/2007, and 2007/2008.

Through the anonimized school identifier we were able to link the teachers registries with schoolregistries. These school registries contain records of the school type, the municipality where theschool is located, the number of enrolled students, the number of foreign students, the numberof disabled students,4 the number of students who drop out of the school system, the number oftested students, and the number of passed students. Data on school registries are exhaustive forthe school years 2005/2006 and 2006/2007. Hence, complete data on all registries are only availablefor the school years 2005/2006 and 2006/2007.

These administrative data allows us to have extremely rich information about Italian teachersand schools. Furthermore, administrative data does not present problems which are typical ofsurvey data, such as unit and item non-response, measurement errors and bias effects due to inter-action with interviewers. Relative to survey data, the main disadvantage is that they contain verylittle information on socio-economic characteristics of the teachers and on their family situation.

We focus our analysis on all registered Italian teachers5 born between 1940 and 1980 (aged 25 to65 in 2005), teaching on primary, lower-secondary (“Secondaria di I grado”) and upper-secondary(“Secondaria di II grado”) schools during school years 2005/2006 and 2006/2007. For simplicity,from now on we will refer to school year 2005/2006 as year 2005, to school year 2006/2007 as year

4 For primary schools, it was not possible to recover the number of enrolled, foreign, and disabled students forthe single school. In this case, such numbers refer instead to the main institute to which the school belong.

5 Foreign teachers are not included because it does not make sense considering proximity to their birth place. Inany case, we do not observe systematic differences in the distribution of Italian and Foreign teachers by geographicalarea of work.

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2006, and so on. Our final sample consist of 721,856 different teachers working in 18,224 differentschools.

Figure 1 shows the distribution of teachers by year of birth, gender and the type of schoolthey teach in. Two main aspects are relevant. Almost all primary schools teachers are femaleteachers, and lower-secondary schools show a large prevalence of female teachers. Furthermore,female teachers are younger than male ones in all types of school.

3.2 Teacher-level variables

Our measure of mobility request is an indicator for a teacher not submitting a mobility application.As pointed out in Section 2, only tenured teachers can submit mobility applications. This measurewill be our response variable in Section 5.1. Distance from the birth place is our main measurerelated to geographical mobility. Note that considering residential location at birth avoids problemsof endogeneity we would instead face if we used current residential location of teachers. Distanceis computed as the geodetic distance6 between the municipality where the teachers were born andthe municipality where they actually work.7 In addition to distance, we include an indicator forteachers being born in the same province where they work.

We control for a set of teacher characteristics. This set includes age, an indicator for femaleteachers, the seniority of tenured teachers (number of years of tenure), indicators for the geographicarea of birth, and an indicator for teachers being born in a large municipality. Appendix A providesa description of these teacher-level variables.

3.3 School-level variables

For each school we constructed the number of preferences received as the sum of the first preferencesexpressed by teachers in their mobility application. This measure will be our response variable inSection 5.2. The number of preferences received by schools also include applications submittedby just-tenured teachers (defined as tenured teachers from no more than two years) and laid-offteachers. In fact, their applications, although not voluntarily expressed, are indeed preferencesgiven to a particular school.

We also control for a set of school characteristics and features. This set includes both char-acteristics obtained directly from the school registries and other characteristics computed (usingthe school identifier) through summary of characteristics of teachers working in each school. Theformer characteristics are indicators for type of school, indicators for the geographic area where the

6 Geodetic distance is the length of the shortest curve between two points along the surface of the earth. Geodeticdistance behaves well for wide areas of coverage, and takes the earth’s curvature into account.

7 We could identify the municipality where teachers were born and schools are located, but not the exact locationwithin the municipality. Assigning exactly the same geographic localization (latitude and longitude) to different placeswithin the same municipality is a source of approximation error. Nevertheless, we are confident that consequences ofsuch an approximation on our results are negligible.

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school is located, an indicator for the school being located in a large municipality, and indicatorsabout the students in the school. Specifically, we include indicators for the school’s fraction of dis-abled students, of foreign students, of students who drop out of the school system, and of studentsfailing to pass to the next year8 being in the top decile of schools of the same type. Hence, theseindicators identify schools with the highest fractions relative to all other schools of the same type.These indicators are used as proxies for socio-economic background and educational ability of thestudents in the school, which are not directly observable from the data. Hence, such indicatorsshould allow us to identify most difficult schools for teaching. Because most of foreign studentsare immigrants from poor countries, a high fraction of foreign students can be used as an indicatorfor low economic background. We are aware that a high fraction of students failing to pass to thenext year cannot be unambiguosly related to a low average ability in a particular school. Nonethe-less, we expect a positive relation between the two. Analogous considerations should also applyto the fraction of students who drop out of the school system, that is also possibly related to thesocio-economic background of the students.

The school characteristics computed through summary of characteristics of their teachers arethe total number of teachers of the school, the fraction of non-tenured teachers, the fraction oftenured teachers aged less than 40, from 41 to 50, and more than 60, and an indicator for schoolsexpanding the number of enrolled students (the number of students in the current year is greater thenumber of teachers in the previous year). Finally, to control for the geographical driver of teachers’mobility when considering preferences expressed in favour of a particular school, we computed, foreach school, the average distance between the place of birth of all teachers filling a mobility request(applicants) and the school. Furthermore, we include both the fraction of applicants born andthe fraction of applicants working in the same province where the school is located. Appendix Bprovides a description of these school-level variables.

3.4 Descriptive statistics

About 16% of teachers included in our final sample are non-tenured. Tenured teachers since nomore than two years are about 4%, while laid-off teachers are only 1%. Table 1 shows descriptivestatistics on teachers. The first two numerical columns show descriptive statistics for all teachersincluded in our dataset.9 Average distance from the place of birth is 120 kilometers, and only about33% of teachers do not work in their province of birth. Average age is about 47 and slightly lessthan 80% of teachers are female. The majority of teachers are born in the South of Italy.

The last two numerical columns show descriptive statistics for the subsample of teachers involvedwith voluntary mobility, namely tenured teachers from at least two years not laid off from the school.

8 This is obtained as the number of failing students over tested students. The latter do not coincide with totalenrolled students because of drop-out students and net transfers from/to other schools.

9 The fraction of teachers not submitting a mobility application and average tenure are not reported for the fullsample, which includes also non-tenured teachers.

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About 87% of these teachers do not submit a mobility application. Average tenure is about 18years. On average, tenured teachers from at least two years not laid off from the school workcloser to their birth place and are older than the other teachers (non-tenured teachers, just-tenuredteachers and laid-off teachers).

Table 2 shows descriptive statistics on schools. Schools receive on average 4 preferences. Primaryschools are about 33%. The average number of teachers working in the schools is 40. The fractionof non-tenured teachers is 20%. The average fraction of tenured teachers aged 40 or less is 15%.Table 3 shows the sample proportions of difficult schools by geographical area. Most of the schoolswith a high fraction of foreign students are located in the North or in the Centre of Italy. On theother hand, the schools with the fraction of disabled students, of drop-out students, and of studentsfailing to pass to the next year being in the school-type specific top decile are mainly located inthe South.

4 The geographical driver

The distance between the place where teachers are born and the place where they work is one of themain driving forces for teachers mobility. Boyd et al. (2005a) and Boyd et al. (2005b) show thatteachers express preferences to teach close to where they grew up and, controlling for proximity,they prefer areas with characteristics similar to their hometown. In this section we look at theoverall mobility patterns (of both tenured and non-tenured teachers) and its relationship with thegeographical driver of mobility, an aspect upon which even non-tenured teachers have some roomof manoeuvre.

Figure 2 shows the average distance of all teachers in our dataset by age. 25-year-old teacherswork on average 300 kilometers away from their place of birth. Average distance decreases until theage of 40. Then, for teachers aged 40 to 65 distance is around 100 km. This picture is consistentwith the hypothesis that distance from the birth-place is an important driver for teachers’ mobility.In fact, although possibly starting their career far away from the municipality where they are born,teachers seem to gradually get closer to their place of birth as age increases.

Specifically, in Italy such pattern is almost entirely due to teachers born in the South, which arethe majority of all Italian teachers. Figure 3 shows the average distance by age and teachers’ areaof birth. Teachers born in the North or in the Centre of Italy work on average about 50 kilometersaway from their home town. Differences by age are negligible. The profile of teachers born in theSouth is completely different. In fact, for the latter the distance is on average higher than 400kilometers until the age of 30. The average distance sharply decreases between the age of 30 and40. Then, after the age of 40 the distance is around 150 kilometers. Hence, teachers born in theNorth or in the Centre of Italy work on average close to their birth-places. On the other hand,many teachers born in the South of Italy start their career in a school located in the Centre or in

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the North. Among the reasons for this there is a higher probability of finding a job. Then, throughthe years as their seniority increases they get closer to their home towns. Nevertheless, some ofthem remain away from home until the end of their career.

This is confirmed by Figure 4, which shows the percentage composition of teachers by age andarea of birth distinguished by schools’ geographical location. In the schools of the country as awhole, we observe a high prevalence of teachers born in the South. In the Southern schools we findonly a few teachers from the other two areas at any age. In the schools located in the North ofItaly about 50% of the teachers aged 25 come from the South. Such a percentage gradually reducesdown to 20% for 40-year-old teachers. A similar, although less strong, pattern is observed in theschools located in the Centre of Italy. This evidently confirms the fact that many teacher born inthe South of Italy start their career in a school located in the Centre or in the North. Then, many– but not all – of them come back closer to their places of birth.

One of the reasons why teachers born in the South starts their careers in the North is also thepossibility of shortening the time needed to become tenured teachers. Table 4 shows average ageof teachers who got their tenure in 2006 by schools area and teachers’ area of birth. On averageteachers working in a school located in the North become tenured teachers when they are about39 years old. For teachers working in a school in the South the average age is slighly more than40. Nonetheless, teachers born in the South who work in a school located in the North become fulltenured teachers when they are less than 39. Hence, for teachers born in the South there seems tobe a trade-off between distance and time to get full tenure.

Important differences in average distance are also observed by gender and type of contract.Figure 5 shows the average distance by age and gender. From the age of 30, male teachers workon average more distant from their home towns than female teachers. Figure 6 shows the averagedistance by age and type of contract. We distinguish teachers with a temporary contract, just-tenured teachers and full tenured teachers from at least two years. Not surprisingly, tenured teacherswork closer to their birth-place at any age. Furthermore, teachers with temporary contracts workon average much more distant than just-tenured teachers until the age of 45.10

After such age what is shown in the figure is mostly noise. In fact, both non-tenured andjust-tenured teachers aged 45 or older are very few.

10 Given the longitudinal nature of our data, we also analysed the change in distance faced by teachers betweenany two consecutive years. Specifically, we observe important differences related to the change in teachers’ state.Just-tenured teachers show on average an increase in distance, denoting that in order to get full tenure they are likelyto pay the price of working far away from home. On the other hand, tenured teachers from at least two years showon average a strong negative change in distance.

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5 School features and teacher mobility requests

We showed in the previous section that proximity to the birth-place is an important factor drivingteachers mobility. Nevertheless, we expect also factors other than distance from home, such as thecharacteristics of the schools, and in particular socio-economic background and educational abilityof the students in the school, to be important determinants of teachers’ mobility. Specifically, aftercontrolling for the driving force represented by distance from home, we expect teachers trying toleave difficult schools.

Although salaries play a role in location decisions (Murnane and Olsen 1990, Dolton and van derKlaauw 1999, Figlio 2002, and Hanushek, Kain and Rivkin 2004), several empirical studies haveshown that teachers’ preferences are highly influenced by non-pecuniary factors such as studentcharacteristics. For example, using data about Queensland state school teachers Bradley et al.(2006) find that after controlling for pecuniary factors, such as wage, non-pecuniary factors, suchas class size and location, affect movement decisions. Investigating the factors that affect theprobabilities that teachers switch or exit the public schools Hanushek, Kain and Rivkin (2004) showthat teacher mobility is much more strongly related to characteristics of the students, particularlyrace and achievement, than to salary. Using data for the San Diego school system, Greenberg andMcCall (1974) show that internal mobility of teachers is governed by nonpecuniary differences asstudent socioeconomic status. Prost (2006) shows that French teachers tend to switch when theywork in schools with a high share of less able students, of students from minorities and of studentsfrom economically disadvantaged background.

In this section we analyze teachers mobility requests, taking into account both distance fromthe birth-place and a set of school characteristics and features. In what follows teachers’ mobility isanalyzed from two different perspectives. We first focus on teachers theirselves and on probabilityof submitting a mobility application. Then, we focus on schools and on the number of preferencesthey receive.

5.1 The teachers: analysis of the probability of submitting a mobility applica-

tion

In this section we analyse teachers’ mobility applications, which evidently offer the opportunity foranalysing teachers’ mobility in greater details. Because we are interested here in voluntary mobilityrequests, we focus only on teachers for whom submitting an application is a choice. For this reason,we confine ourselves to tenured teachers from at least two years and not laid off from the school.

Figure 7 shows the fraction of teachers not submitting a mobility application by age and areaof birth. Such a fraction increases from 30 to 55 years, being close to one from 55 years on.Furthermore, we observe a clear ordering of such a fraction by teachers’ area of birth. At any age,teachers born in the South are more likely to apply than teachers born in the Centre, and the latter

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are more likely to apply than teachers born in the North.We model here the teachers preferences about staying in the current school versus applying for a

transfer to another school. Given that schools have no active role in hiring teachers, we do not havethe problem of separating teachers preferences from school principals preferences. Specifically, weemploy a logit model for the probability that the i-th teacher does not submit a mobility application(Ai = 1) as a function of a set of individual characteristics Zi and a set of characteristics of theschool j where teacher i works Qji

Pr(Ai = 1|Zi) = Λ(γ + δZi + ψQji),

where γ, δ and ψ are unknown parameters and Λ(·) = exp(·)/(1 + exp(·)) is the logistic function.Individual characteristics Zi include a third-degree polynomial in the distance between teachers’

birth-place and work-place, an indicator for teachers being born in the same province where theywork, a second-degree polynomial in years of tenure, a second-degree polynomial in age,11 anindicator for female teachers, indicators for the geographic area of birth, and an indicator forteachers being born in a large municipality. The set of characteristics of the school where teacherswork Qji includes indicators for type of school, indicators for the geographic area where the schoolis located, an indicator for the school being located in a large municipality, the total number ofteachers of the school, the fraction of non-tenured teachers, the fraction of tenured teachers agedless than 40, from 41 to 50, from 51 to 60 and more than 60, and an indicator for schools expandingtheir teaching staff. Furthermore, Qji also includes indicators for the school’s fraction of disabledstudents, of foreign students, of drop-out students, and of students failing to pass to the next yearbeing in the top decile of schools of the same type. Finally, we include year fixed-effects.

Table 5 shows maximum likelihood estimation of the logit model for the probability of notsubmitting a mobility application. Standard errors are corrected for within-school clustering. Mostof the coefficients have the expected sign. Both age and years of tenure are positively related tothe probability of not applying. Confirming the description provided in the previous section, thecoefficients of the polynomial in distance are highly statistically significant and implies a U-shapedrelation between distance and the probability of submitting a mobility request. Not working in theprovince of birth reduces the probability of not applying. Female teachers are less likely to applythan male ones. Figure 8 shows predicted probability of not submitting a mobility application forthe baseline teacher as a function of distance (Panel 1), tenure (Panel 2) and age (Panel 3). Thefirst panel of the figure indicates a U-shaped relation between distance from the birth-place andthe probability of not submitting a mobility request, with a minimum at slightly less than 300kilometers. This means that after a certain distance, teachers working very far from their birth-place - as previously shown these are mainly teachers born in the South who work in the North -

11 Coefficients on higher degree polynomials are not statistically significant.

13

are not very likely to come back. The second panel indicates an increasing (at a decreasing rate)relation between years of tenure and the probability of not applying. Finally, the third panel ofFigure 8 shows that, after controlling for distance and tenure, there is a flat relation between ageand the probability of not submitting a mobility request from age 30 to 40. Nonetheless, there arefew tenured teachers aged less than 40. From the age of 40 such a probability starts to decreaseuntil the age of 65.

Altough proximity to the birth-place is part of the story explaining teachers mobility, schoolscharacteristics and features are also strongly related to teachers mobility request. First of all, thereis a clear difference by type of schools. Ceteris paribus, relative to lower-secondary schools (thereference category of this model) teachers working in primary schools or upper-secondary schoolsare less likely to apply. In particular, among upper-secondary schools, teachers working in classicor scientific lyceums are less mobile. Teachers working in large schools have lower probability ofapplying. In general, teachers tend to remain in schools where there is a higher fraction of youngtenured teachers and leave schools with higher fraction of tenured teachers aged 60 or over.

Furthermore, we observe a strong negative relation between the probability of not submitting amobility application and the fraction of disabled students and foreign students. On the other hand,after controlling for other school characteristics, a high fraction of students who drop out of theschool system or failing to pass to the next year are not significantly related to the probability ofa mobility request. These results confirm that after controlling from proximity to home and otherteacher and school characteristics, teachers are trying to leave most difficult schools.

These results are confirmed when the model is estimated separately for primary, lower-secondaryand upper-secondary schools. Table 6 shows maximum likelihood estimation of the logit model forthe probability of not submitting a mobility application by school type. Only coefficients on schoolswith high fraction of disabled students, of foreign students, of drop-out students, and of studentsfailing to pass to the next year are reported. A high fraction of foreign students in the school isa strong deterrent to remaining in all type of schools. Teachers in primary and lower-secondaryschools with a high fraction of disabled students have a higher probability of submitting a mobilityapplications. This seems to be not true for teachers working in upper-secondary schools, wherepresence of disabled students is not significant. In lower-secondary schools even a high fractionof drop-out students and students failing is significantly related to a higher probability of filling amobility request.

Finally, we estimated the model allowing for school-specific unobserved heterogeneity. Specifi-cally, we included school random effects to capture general time-invariant retention in each school,which is not due to observable characteristics. Table 7 shows maximum likelihood estimation of therandom effect logit model for the probability of not submitting a mobility application. The model isestimated for teachers working in all schools, and separately for teachers working in different typesof schools. The intra-class correlation coefficient ρ is highly significant in all cases. The percentage

14

of the latent error variance attributable to school unobserved heterogeneity is about 10%. Evenincluding school random effects does not alter our previous findings.

5.2 The schools: analysis of the applications received

In this section, teachers’ mobility is analyzed from another perspective. In the previous sectionwe focused on teachers and on whether or not they want to leave a certain school. In this sectionwe focus on schools. Given that some teachers submit a mobility application, we investigate herewhich are the schools they want to move to. Hence, this allows us to investigate which are thefeatures that are more likely to make a school particularly attractive for teachers. Because we areconsidering here the preferences received by schools, in this section we also include applicationssubmitted by just-tenured teachers and laid-off teachers. In fact, their applications, although notvoluntarily expressed, are indeed preferences given to a particular school.12

We employ a poisson model for the number of preferences received by schools, where the ex-pected value of preferences received by the j-th school (Pj) is modeled as a function of a set ofcharacteristics of the school Vj

E(Pj |Vj) = exp(φ+ τVj)

where φ and τ are unknown parameters. Pj is obtained as the sum of the preferences expressed byteachers in favour of school j in their mobility application. 13 The set of characteristics of the schoolVj includes the average distance between school j and the places of birth of all teachers filling amobility request (applicants), the fraction of applicants born and the fraction of applicants workingin the same province as school j, indicators for type of school, indicators for the geographic areawhere the school is located, an indicator for the school being located in a large municipality, thetotal number of teachers of the school, the fraction of non-tenured teachers, the fraction of tenuredteachers aged less than 40, from 41 to 50, from 51 to 60 and more than 60, an indicator for schoolsexpanding their teaching staff, and indicators about the students in the school. Specifically, Vj alsoincludes indicators for the school’s fraction of disabled students, of foreign students, of drop-outstudents, and of students failing to pass to the next year being in the top decile of schools of thesame type. Finally, we include year fixed-effects.

Table 8 shows maximum likelihood estimation of the poisson model for the number of preferencesreceived by schools. Most of the coefficients show a picture which is coherent with our findings in theprevious section. First of all, coherently with the story about the importance of the distance betweenteachers’ place of birth and their place of work, the polynomial in average distance of all applicants

12 Considering only the preferences of tenured teachers from at least two years not laid off from the school (forwhom submitting an application is not mandatory) provided very similar results.

13 Here Pj is obtained by summing up only the first preferences contained each mobility application filed byteachers. We also estimated the model giving a strictly positive weight to all preferences expressed by teachers.Different systems of weights are used for the preferences – uniform, linearly decreasing, exponentially decreasing.Results are extremely robust to these different specifications.

15

from the school implies a negative relation between distance and the number of preferences receivedby the school. Specifically, average distance of all applicants - who are potential movers - from theschool is added to control for preferences received by schools due to geographical mobility.

Nonetheless, after controlling for geographical mobility, school characteristics matter. Upper-secondary schools (especially classic lyceums and social science schools) receive more preferencesthan lower-secondary schools (the reference type of school in this model). After controlling foraverage distance, some schools’ features are important determinant of their capacity of attraction.Specifically, the total number of teachers in a school is positively related to the number of preferencesreceived. This is possibly reflecting a scale effect, with larger schools having more visibility toteachers. The fraction of tenured teachers aged 60 or more has a positive effect on the number ofpreferences received. This positive effect can be explained by the fact that the presence of teachersclose to retirement attracts younger applicants due to a higher possibility of vacancies in suchschools. This may reflect a strategic behaviour ot teachers applying to schools where they mayhave a higher probability for their request to be satisfied.

Indicators used as proxies for difficult schools are all negatively related to the number of pref-erences received by schools. Specifically, a high fraction of disabled students, foreign students,students who drop out of the school system, and students failing to pass to the next year are allnegatively related to the number of preferences received.

We also estimated the model separately by school type, and including school random effects.Table 9 shows maximum likelihood estimation of the poisson model for the preferences receivedby school type. Only coefficients on schools with high fraction of disabled students, of foreignstudents, of drop-out students, and of students failing to pass to the next year are reported. Thesecoefficients, although not significant for primary schools, are negative and significant for lower-secondary and upper-secondary schools. Finally, Table 10 shows maximum likelihood estimationof a random effect poisson model for the preferences received by school type. A model with schoolrandom effects cannot be rejected in favour of a model without school unobserved heterogeneity.Even after controlling for school unobserved heterogeneity our main findings are confirmed.

6 Conclusions

In the Italian system teachers are allocated according to a seniority based centralised system withno role for the employer’s side, represented by the individual schools, in attracting, selecting andretaining teachers. Also because of the rather limited wages progression, mobility and the senioritybased rights to select the most preferred school and geographical location represent one of the mainopportunities Italian teachers have. The paper examines the main drivers of the voluntary mobilityof Italian teachers.

Specifically, we focused upon two drivers of teachers mobility, namely geographical distance

16

from the birth-place and school features. The former relate to the fact that teachers may starttheir career far from their desired geographical location (actually their birth-place) and then, tryto reach it. School features relate to a vast array of structural characteristics of a school whichmay signal that it is a difficult working place. In the education context this means a school whereteaching is less demanding but possibly less relevant, as students by themselves are in a betterstarting condition. This is a problem for the education system as teachers will be driven away fromthe most difficult schools. These will end up being filled by less experienced and less motivatedteachers. Even those already tenured may be just waiting for a different location, putting low effortin their current work duties.

We find a sizable impact of both the teachers’ birth-place and several school features related tothe students’ mix. Specifically, teachers systematically try to fly away from schools far from theirbirth-place and where teaching is likely to be more difficult in favour of schools closer to home andwhere socio-economic background and ability of students is higher. Specifically, a high fraction ofdisabled students, foreign students, drop-out students, and students failing to pass to the next yearresult to make a school less attractive for teachers. Nonetheless, these schools are potentially morerelevant in order to boost students’ human capital accumulation. Hence, on practical groundsthe centralized allocation system is so unable to equalize opportunities among different schoolenvironments.

17

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Boyd D., Lankford H., Loeb S., Wyckoff J. (2005a). “The Draw of Home: How Teachers’ Preferences forProximity Disadvantage Urban Schools”. Journal of Policy Analysis and Management, Vol. 24, No.1, 113-132.

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Loeb S., Page M.E. (2000). “Examining The Link Between Teacher Wages And Student Outcomes: TheImportance Of Alternative Labor Market Opportunities And Nonpecuniary Variation”. Review ofEconomics and Statistics, 82(3):393–408.

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Table 1: Descriptive statistics on teachers.

All teachers Tenured teachers fromat least two years not

laid off from the schoolMean SD Mean SD

noapplic 0.878 0.327tenure 18.26 8.03dist 119.4 246.4 98.8 221.2diffprov 0.325 0.468 0.297 0.457age 47.29 8.57 49.64 7.24female 0.783 0.412 0.783 0.412t:N 0.301 0.459 0.308 0.462t:C 0.154 0.361 0.156 0.363t:large 0.142 0.349 0.139 0.346Obs. 1,365,086 1,076,101Teachers 721,856 561,097

Table 2: Descriptive statistics on schools.

Mean SDnpref 4.399 5.953avdist 460.2 97.5fracborn:appl 0.018 0.017fracwork:appl 0.018 0.017prim 0.328 0.470classic 0.024 0.154scient 0.044 0.205socscie 0.015 0.120tech 0.111 0.314prof 0.079 0.270art 0.014 0.120s:N 0.370 0.483s:C 0.179 0.383s:large 0.083 0.276totteachers 39.65 27.15fracnonten 0.199 0.161fracage:(.,40) 0.148 0.131fracage:(41,50) 0.372 0.153fracage:(60,.) 0.036 0.055expansion 0.434 0.496highfracdisab 0.099 0.298highfracforeign 0.100 0.300highfracleaving 0.086 0.280highfracfail 0.100 0.300Obs. 36,023Schools 18,224

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Table 3: Sample proportion of difficult schools by geographical area.

Schools with a high fraction ofSchool area disabled students foreign students drop-out students failing studentsNorth 0.079 0.189 0.066 0.081Centre 0.102 0.150 0.079 0.086South 0.114 0.008 0.104 0.121

Table 4: Average age of just-tenured teachers in 2006 by schools area and teachers’ area of birth.For each cell we report, in order, average age and the cell sample size.

Teacher area of birthSchool area North Centre South ItalyNorth 39.1 40.5 38.5 39.0

3,794 174 1,350 5,318Centre 41.4 40.7 40.8 40.7

87 1,499 548 2,134South 39.5 39.9 40.5 40.5

91 41 3,193 3,325Italy 39.1 40.6 40.0 39.8

3,972 1,714 5,091 10,777

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Table 5: Estimated coefficients of the logit model for the probability of not submitting a mobilityapplication (* significant at 5%; ** significant at 1%).

(dist - 100)/100 -0.226 **((dist - 100)/100)2 0.085 **((dist - 100)/100)3 -0.006 **diffprov -0.046 **tenure - 20 0.056 **(tenure - 20)2/10 -0.006 **age - 50 0.078 **(age - 50)2/10 0.024 **female 0.035 **t:N 0.112 **t:C -0.047 *t:large 0.047 **prim 0.900 **classic 0.813 **scient 0.748 **socscie 0.175 **tech 0.194 **prof -0.216 **art 0.222 **s:N 0.569 **s:C 0.440 **s:large 0.014(totteachers - 50)/10 0.060 **fracnonten 0.097fracage:(.,40) 0.185 **fracage:(41,50) -0.051fracage:(60,.) -0.292 *expansion 0.023 *highfracdisab -0.112 **highfracforeign -0.120 **highfracleaving -0.016highfracfail -0.030Constant 1.378 **Obs. 1,076,101Pseudo R2 0.123Log-lik. -350181.3

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Table 6: Estimated coefficients of the logit model for the probability of not submitting a mobilityapplication by school type (* significant at 5%; ** significant at 1%).

Primary Lower- Upper-Secondary Secondary

highfracdisab -0.040 -0.154 ** -0.147 **highfracforeign -0.137 ** -0.112 ** -0.166 **highfracleaving 0.003 0.008 -0.099 **highfracfail -0.033 -0.087 ** -0.013Obs. 413,656 281,150 381,295Pseudo R2 0.148 0.110 0.108Log-lik. -105217.4 -108798.4 -133329.1

Table 7: Estimated coefficients of the random effect logit model for the probability of not submittinga mobility application (* significant at 5%; ** significant at 1%).

All Primary Lower- Upper-schools Secondary Secondary

highfracdisab -0.105 ** -0.053 -0.145 ** -0.059highfracforeign -0.104 ** -0.155 ** -0.059 -0.127 **highfracleaving -0.006 0.048 0.003 -0.010highfracfail -0.029 -0.022 -0.032 -0.026Obs. 1,076,101 413,656 281,150 381,295Log-lik. -342361.0 -103328.4 -106105.4 -138587.1ρ 0.102 0.092 0.108 0.084LR test ρ = 0 15,640.6 3,777.9 5,386.0 4,916.0

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Table 8: Estimated coefficients of the poisson model for the number of applications received byschools (* significant at 5%; ** significant at 1%).

(avdist - 500)/100 -0.065 **((avdist - 500)/100)2 -0.092 **((avdist - 500)/100)3 -0.033 **fracborn:appl -0.377fracwork:appl -0.268prim -0.263 **classic 0.855 **scient 0.648 **socscie 0.903 **tech 0.149 **prof 0.086 **art 0.422 **s:N 0.105 **s:C -0.053 *s:large 0.023(totteachers - 50)/10 0.142 **fracnonten -2.887 **fracage:(.,40) -0.920 **fracage:(41,50) -1.142 **fracage:(60,.) 1.311 **expansion 0.090 **highfracdisab -0.048 *highfracforeign -0.071 **highfracleaving -0.036highfracfail -0.144 **Constant 2.378 **Obs. 36,023Log-lik. -92,123.1

Table 9: Estimated coefficients of the poisson model for the number of applications received byschool type (* significant at 5%; ** significant at 1%).

Primary Lower- Upper-Secondary Secondary

highfracdisab -0.025 -0.175 ** -0.043highfracforeign 0.002 -0.098 ** -0.100 **highfracleaving -0.002 -0.060 * -0.209 **highfracfail -0.012 -0.243 ** -0.142 **Obs. 11,818 13,844 10,361Log-lik. -27,616.9 -31,203.2 -29,762.2

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Table 10: Estimated coefficients of the random effect poisson model for the number of applicationsreceived by school type (* significant at 5%; ** significant at 1%).

All Primary Lower- Upper-schools Secondary Secondary

highfracdisab -0.064 ** -0.028 -0.136 ** -0.061 *highfracforeign -0.058 ** -0.031 -0.074 * -0.059 *highfracleaving 0.015 0.009 0.029 -0.106 **highfracfail -0.045 ** 0.012 -0.118 ** -0.057 **Obs. 36,023 11,818 13,844 10,361Log-lik. -76,918.5 -24,646.0 -26,446.2 -25,075.7ln α -1.043 ** -1.146 ** -0.933 ** -1.389 **LR test α = 0 30,409.2 5,941.7 9,514.0 9,372.9

25

Figure 1: Distribution of teachers by year of birth, gender and school type.

0

2

4

6

8

10

1940 1945 1950 1955 1960 1965 1970 1975 1980 1940 1945 1950 1955 1960 1965 1970 1975 1980

Men Women

Prim Low−sec Up−sec

Tea

cher

s (in

thou

sand

s)

Year of birth

Figure 2: Average distance (km) by age.

100

150

200

250

300

25 30 35 40 45 50 55 60 65Age

26

Figure 3: Average distance (km) by age and teachers’ area of birth.

0

100

200

300

400

500

25 30 35 40 45 50 55 60 65Age

N C S

Figure 4: Percentage composition of teachers by age and area of birth distinguished by schools’geographical area.

0

20

40

60

80

100

0

20

40

60

80

100

25 30 35 40 45 50 55 60 65 25 30 35 40 45 50 55 60 65

Schools: North Schools: Centre

Schools: South Schools: Italy

Teachers: North Centre South

Age

27

Figure 5: Average distance (km) by age and gender.

100

150

200

250

300

350

25 30 35 40 45 50 55 60 65Age

Men Women

Figure 6: Average distance (km) by age and type of contract.

100

150

200

250

300

350

25 30 35 40 45 50 55 60 65Age

Non ten Just ten Ten

28

Figure 7: Fraction of teachers not submitting a mobility application by age and area of birth.

0.60

0.70

0.80

0.90

1.00

30 35 40 45 50 55 60 65Age

N C S

Figure 8: Probability of not submitting a mobility application as a function of distance, tenure andage.

0.76

0.78

0.80

0.82

0.84

0.86

0 200 400 600Distance

Panel 1

0.50

0.60

0.70

0.80

0.90

1.00

0 10 20 30 40 50Tenure

Panel 2

0.70

0.75

0.80

0.85

0.90

0.95

30 40 50 60 70Age

Panel 3

29

APPENDICES

A Description of teacher-level variables

This appendix presents the description of the teacher-level variables.

Variable Description

noapplic Indicator for a teacher not submitting a mobility applicationtenure Number of years of tenuredist Distance (km) between the municipality where the teacher was born and the municipality

where she actually worksdiffprov Indicator for not working in the province of birthage Agefemale Indicator for female teacherst:N Indicator for a teacher being born in the North of Italyt:C Indicator for a teacher being born in the Centre of Italy

t:large Indicator for a teacher being born in a large municipalitya)

Notes: a) Large municipalities are Rome, Milan, Naples, Turin, Palermo, Genova, Bologna, and Florence.

30

B Description of school-level variables

This appendix presents the description of the school-level variables.

Variable Description

npref Number of preferences received by a schoolavdist:appl Average distance between a school and the place of birth of applicantsfracborn:appl Fraction of applicants born in the province where a school is locatedfracwork:appl Fraction of applicants working in the province where a school is locatedprim Indicator for primary schoolclassic Indicator for classic lyceumscient Indicator for scientific lyceumsocscie Indicator for social science schooltech Indicator for technical schoolprof Indicator for professional schoolart Indicator for artistic schools:N Indicator for a school being located in the North of Italys:C Indicator for a school being located in the Centre of Italys:large Indicator for a school being located in a large municipalitytotteachers Total number of teachers working in a schoolfracnonten Fraction of non-tenured teachers working in a schoolfracage Fraction of tenured teachers in different age groupsexpansion Indicator for a school increasing the number of enrolled students (the number of students

in the past year not greater than the number of students in the current year)highfracdisab Indicator for a school’s fraction of students with disability being in the

top decile of schools of the same typehighfracforeign Indicator for a school’s fraction of foreign students being in the top decile

of schools of the same typehighfracleaving Indicator for a school’s fraction of students who drop out of the school system

being in the top decile of schools of the same typehighfracfail Indicator for a school’s fraction of students failing to pass to the subsequent

year being in the top decile of schools of the same type

31