does raising the school leaving age reduce teacher effort? evidence from a policy experiment

DOES RAISING THE SCHOOL LEAVING AGE REDUCE TEACHEREFFORT? EVIDENCE FROM A POLICY EXPERIMENT

COLIN GREEN and MARIA NAVARRO PANIAGUA∗

This paper examines the effect of an increase in the compulsory school leavingage on a measure of high school teachers’ effort. Differences-in-Differences estimatesusing count data methods demonstrate that the policy led to teachers increasingtheir hours of sickness absence by roughly 15%. This result implies that raising thecompulsory school leaving age reduces teaching inputs, and hence schooling quality.A policy implication is that these laws should be coupled with measures to compensateteachers for the additional disutility. This also suggests that instrumental variableestimates of returns to education that utilize these changes for identification may bedownwardly biased. (JEL J22, J38)

I. INTRODUCTION

Raising the compulsory school leaving age(henceforth RoSLA) is a key policy instrumentused to increase minimum educational attain-ment levels. Moreover, there is an ongoingdebate in a number of countries, such as theUnited Kingdom and Spain, regarding furtherincreases in the minimum high school leav-ing age. In addition, RoSLA has been widelyused in the literature on returns to educationas a source of exogenous variation in years ofschooling/educational levels (see for instance,Harmon and Walker 1995 for the United King-dom; Pischke and von Wachter 2008 for Ger-many; Pons and Gonzalo 2002 and Arrazolaet al. 2003 who use the 1970 RoSLA in Spain).

However, teachers who take classes in the“affected” years of schooling are unlikely to beindifferent to this policy change.1 Increasing thecompulsory schooling age increases the number

*The authors would like to thank Ian Walker, AlfredoPaloyo, Fernando Lozano, and seminar participants at Lan-caster University, the University of Aberdeen, and the Uni-versity of Cyprus for their helpful comments. The authorsare also grateful to the editor Peter Arcidiacono and twoanonymous referees. M.N.P. gratefully acknowledges finan-cial support from the Spanish Ministry of Science andInnovation Postdoctoral Grant 2008-0583, FEDEA, and theCYCIT project ECO2008-06395-C0503/ECON.Green: Economics Department, Lancaster University, Lan-

caster LA1 4YX, UK. Phone 0044 1524 92950, Fax 00441524 594244, E-mail [email protected]

Navarro: Economics Department, Lancaster University,Lancaster LA1 4YX, UK; FEDEA, Madrid, Spain.Phone 0044 1524 594667, Fax 0044 1524 594244,E-mail [email protected]

1. That is, teaching previously noncompulsory years ofschooling that became mandatory.

of students in those years, but also changesthe distribution of ability and motivation ofstudents that teachers have to instruct. Forinstance, teachers at the latter part of compulsorysecondary school will now have lower abilitystudents and/or those with less interest in for-mal schooling in their class, along with thosestudents who would have voluntarily chosenpost-compulsory schooling in the absence ofthe legislative change. Teaching (and manag-ing) these students is likely to be more difficult.In the absence of compensating differentials, itis difficult to imagine that this will not affectteacher motivation and effort.2

This paper is the first to our knowledge thatinvestigates this motivational effect of compul-sory schooling laws on teachers. Specifically, weexamine the impact of the increase in the schoolleaving age that occurred in Spain in the academicyear 1998–1999 on one element of high schoolteacher behavior, absenteeism. Employing Span-ish Labor Force Survey (SLFS) data that coversthe relevant policy reform period, we estimate

2. To our knowledge generally, and in the particularcase we examine, compulsory schooling changes were notintroduced with an increase in either the supply of highschool teachers or improvements in teacher salaries and/orconditions (Boyd-Barret and O’Malley 1995).

ABBREVIATIONSDID: Differences-in-DifferencesIRR: Incident Rate RatioRoSLA: Raising of the School Leaving AgeSLFS: Spanish Labor Force SurveyZINB: Zero-Inflated Negative BinomialZIP: Zero-Inflated Poisson

1

Economic Inquiry(ISSN 0095-2583)

doi:10.1111/j.1465-7295.2011.00386.x© 2011 Western Economic Association International

2 ECONOMIC INQUIRY

Difference-in-Difference models of absenteeismusing count data approaches. We demonstratethat raising the compulsory schooling age ledto an increase in teacher absenteeism. This isa matter of concern as previous research hasdemonstrated a negative causal effect of teacherabsence on student achievement (Duflo, Hanna,and Ryan 2005; Miller, Murnane, and Willett2007, 2008), and one that is more severe forstudents from lower socioeconomic backgrounds(Clotfelter, Ladd, and Vigdor 2009). This may bethe result of absent teachers being replaced by lessqualified substitutes and/or the disruption inher-ent in the use of replacement teachers.

This leads to a concern that increasing thecompulsory school leaving age may decrease thequality of educational provision in the affectedyears. Furthermore, this has implications forestimation of the returns to education that relyupon RoSLA reforms as an instrumental vari-able. Namely, that if the education treatmentbecause of RoSLA is of lower quality, then thelocal average treatment effect of education onwages will be biased downward.

II. DATA

The policy reform examined consisted ofan extension of free, compulsory, and com-prehensive education from 14 to 16 years. Thereform was the General Regulation of the Edu-cation System passed in 1990.3 Because ofthe economic crisis of the 1990s in Spain, thechange in compulsory schooling was delayeduntil the last quarter of 1998. Primary and highschool curriculum standardization across Spainwas another part of this reform. Primary andearly secondary school standardization occurredbefore the changing of the compulsory schoolleaving age. While curriculum standardizationfor 15- to 16-year-old students occurred at thesame time, the compulsory leaving age wasraised. This could influence high school teacherbehavior if, for instance, this adjustment wasonerous and/or disruptive. Hence, any RoSLApolicy effect could include an adjustment effectas a result of curriculum change. We examinethe potential for the policy effect to be con-founded by this implementation period in laterrobustness checks. From the last quarter of 1998on students that otherwise would have droppedout (in the previous academic year were in the

3. In Spanish, Ley de Ordenacion General del SistemaEducativo , 1990 (LOGSE).

last year of compulsory schooling) were obligedto stay two more years at school. This leadsto compulsory education comprising a total of10 years, divided into two educational levels:primary education (6–12 years old) and lowersecondary education that it is ordinarily com-pleted from the ages of 12–16 years old.4

Increasing the school leaving age may, be-cause of the need to increase the teaching laborforce, lead to a distributional change in teachercharacteristics. This is similar to the point raisedby Jepsen and Rivkin (2009) regarding the con-sequence of decreasing class sizes. Increasingthe teaching workforce to cope with an increasedstudent body may lead to, for instance, less qual-ified or less experienced teachers being hiredwho may be more frequently absent. Alterna-tively, increased compulsory schooling couldlead to larger class sizes which could increaseteacher disutility and/or exposure to contagiousillness and thus increase absence. It does notappear, however, that high school class sizesincreased at the time of RoSLA in Spain. Infact there was a small decrease, from 26.4 stu-dents per class in 1997–1998 to 26.0 and 25.5 in1998–1999 and 1999–2000, respectively (MEC2010b). At the same time, there is no evidenceof increases in high school teacher numbers,either in official data MEC (2010a) or in therepresentative sample of high school teacherswe use. While we cannot be definitive aboutthe source of this apparent discrepancy, thismay have occurred because of the reductionin the age cohort from which high school stu-dents were drawn over the 1990s. For instance,there were 3.3 million people in Spain aged12–16 in 1990 (8.8% of the population) and thisfell to 2.6 million (6.3% of the population) by1998, representing a 31% decrease in this agegroup. This, coupled with the well-documentedstrong employment protection in Spain (mosthigh school teachers are civil servants), meantthat teachers were not fired when student num-bers fell, and this we believe led to an increasein teaching capacity up to the time of the reform.

The data we use is drawn from the SLFS, aquarterly repeated cross-sectional survey that isrepresentative at both the national and regionallevel in Spain. We select a sample of full-timeemployees in the period spanning 1st quarterof 1996 to 4th quarter of 2004. Self-employedworkers are excluded. The full sample consists

4. Although students can stay in school until they are 18(or 21 in the case of pupils with special education needs).

GREEN & NAVARRO: RAISING THE SCHOOL LEAVING AGE AND TEACHER EFFORT 3

of about 988,329 workers, 2.57% of them arehigh school teachers. We are able to identifyhigh school teachers (and distinguish them fromprimary school teachers) in our data as theSLFS reports three digit disaggregated occupa-tion codes (ISCO) for all workers. In Spain,secondary education teachers must have a uni-versity degree, only teach subjects of their fieldof specialization, and are primarily civil ser-vants who attained their post through state orregionally competitive exams. In all estimates,we control for both public sector and temporarycontracts, 85% of the teachers work in the publicsector and 64% are on permanent contracts.

To test the robustness of our results, we use anumber of subsamples. This includes droppingthe vacation period (third quarter of the yearsurveys) as teachers have more summer holi-days than many other workers. Furthermore, toensure that the timing of other holidays is notgenerating our results, we estimate our modelson two successively more restrictive samples.The first is workers in the education industryonly and in the second we include only primaryand secondary school teachers. These latter twogroups have essentially identical holiday sched-ules and provisions. The second sample contains63,811 workers in the education sector, and thethird sample is comprised of 49,711 primaryand secondary school teachers (Table 1). Impor-tantly our key results are robust to the choice ofthese samples.

We use information on the hours of absenceper week reported as being due to sickness togenerate our dependent variable.5 We calculate

TABLE 1Descriptive Statistics 1996–2004

AllWorkers

EducationWorkers

High/PrimarySchool

Teachers

Total observations 988,329 63,811 49,711Absence due to

illness18,349 927 789

Excluding summer quarter

Total observations 742,458 48,433 37,771Absence due to

illness14,012 805 694

Source: SLFS, authors’ own calculations.

5. The SLFS has been demonstrated to have an interna-tionally consistent definition of absence (Barmby, Ercolani,and Treble 2002).

this variable as the difference between usualhours and actual hours for those that reportthe reason of any difference between them asdue to sickness.6 Generally, paid sick leave isvery generous in Spain. While coverage mayvary slightly by sector or firm level agree-ment, the norm is 1 month of absence leavefully paid per year worked in Spain up to amaximum of 18 months leave. If the workerreaches their maximum leave entitlement theyhave to attend a special panel which assessestheir sickness.

We appreciate that using sickness absenceonly may be quite restrictive. In unreported esti-mates our main results are robust to using morebroad definitions where we include differencesin usual and actual hours because of other formsof absence including personal/family responsi-bilities, bad weather, summer schedule/flexiblehours, and “other reasons.”

Figure 1 reports minutes of sickness absencebefore and after RoSLA for three groups, highschool teachers, primary school teachers, and allother workers. Prior to the policy introduction,high school teachers had lower absence levelsthan primary school teachers. This is similarto the pattern found in other jurisdictions suchas in Australia (Bradley, Green, and Leeves2008) and in the United States (Clotfelter, Ladd,and Vigdor 2009). More detail is provided inAppendix Figures A1 and A2 which show yearlyabsence rates before and after RoSLA, again forthese three groups. These figures suggest thatRoSLA increased high school teacher absencebut not that of other workers. Moreover, thisincrease does not seem to dissipate over oursample period.

A range of control variables are available inthe SLFS. We use gender, age, age squared, mar-ital status, education, public sector, type of con-tract, industry dummies, occupation dummies,and size of the firm/establishment. We also con-trol for year, quarter, and regional (CCAA) fixedeffects so as to take regional differences intoaccount such as wage differences and unemploy-ment rates because of different industrial struc-tures within regions and patterns of morbidity.7

6. We consider usual hours as synonymous with contrac-tual hours. This is similar in spirit to the approach used inprevious research such as Hamermesh, Myers, and Pocock(2008) and Lozano (2011).

7. CCAA stands for Autonomous Communities in Span-ish. Spain is administratively divided into 17 regions and twoautonomous cities. This division corresponds to the NUTSlevel 2 established by Eurostat for statistical purposes.

4 ECONOMIC INQUIRY

FIGURE 1Minutes of Absence for Full-Time Workers Before and After the Reform

4342

20

30

3435

010

2030

40

Min

utes

Abs

ence

per

wee

k

Worker High School

Teacher

Primary School

Teacher

Pre-R

oSLA

RoSLA

Pre-R

oSLA

RoSLA

Pre-R

oSLA

RoSLA

All workers

2258 2249

1790

1959 1961 1969

050

01,

000

1,50

02,

000

2,50

0

Min

utes

Abs

ence

per

wee

k

Worker High School

Teacher

Primary School

Teacher

Pre-R

oSLA

RoSLA

Pre-R

oSLA

RoSLA

Pre-R

oSLA

RoSLA

Workers with non-zero absence

III. METHODOLOGY

In our baseline model, workers’ minutes ofabsence per week can be specified as follows:

Absmit = φ + δRoSLAit + γβHSTi(1)

+ βRoSLAit × HSTi + αXi + εi

i = 1, . . . , 988,329 and

t = 1996Q1, . . . , 2004Q4

where Absmit corresponds to the minutes ofabsence of worker i in the period t . RoSLAit

is an indicator that takes value of unity if theworker is observed during the reform period.HSTi is a dummy variable that equals 1 if theworker is a high school teacher and 0 other-wise. And the interaction term RoSLAit × HSTi

equals 1 for treated individuals (HSTeachers) inthe post-treatment period (after the RoSLA wasimplemented). The OLS estimate of β is equiv-alent to the Differences-in-Differences (DID)

estimator and thus provides the absence causedby the reform for the treated group (i.e., theabsence caused by the RoSLA for secondaryschool teachers) (Cameron and Trivedi 2005,890–91).

Our dependent variable, minutes of absence,is a count variable. Moreover, there is an excessof zero outcomes. This could occur during thereference week both if (a) the worker/teachernever gets sick and does not skip work dur-ing the reference week but could have beenabsent in the case of illness (sampling zeros);(b) the worker/teacher always goes to workbecause of commitment and motivation despiteillness (structural zeros). As a result, we estimatezero-inflated models that allow for these exces-sive number of zeroes in addition to overdis-persion in the zero-inflated negative binomial(ZINB) case. Importantly, zero-inflated mod-els permit the zero absence of teachers to beexplained in a different manner than that ofthose workers that are absent for more than


zero hours. It combines a count density witha binary process in such a way that a binarymodel is estimated to predict, with probabilityψi , whether a worker will always have a zerocount (i.e., type b). Then, a count model (Pois-son or negative binomial) chosen with proba-bility 1 − ψi is generated to predict the countsfor those who will not always have a zero count(type a). Then Absmi has a zero-inflated dis-tribution given by Long (1997, 242–50). Theappropriateness of the zero-inflated models overtheir non zero-inflated counterparts (i.e., Poissonvs. zero-inflated Poisson [ZIP], negative bino-mial vs. zero-inflated negative binomial) canbe tested using the approach set out by Vuong(1989).

We estimate both ZIP and ZINB models:

Pr(Absmi = k|xi, zi)(2)

={f1(0) + (1 − f1(0))f2(0) if k = 0

(1 − f1(0))f2(k) if k ≥ 1

where f1 corresponds to a binary process (i.e.,logit) and f2 is a Poisson or negative binomialdensity function. An assumption of Poissonmodels is that the variance equals the samplemean:

E(μi |xi) = exp(xiβ) = var(μi |xi).(3)

If this is not the case and the variance exceedsthe mean then the data are said to be over-dispersed and the Poisson model will be ineffi-cient. This can be overcome by using a negativebinomial model that adds a random term thatis assumed to be uncorrelated with the model’scovariates. The Poisson is nested in the negativebinomial model, as it is the case where α = 0 inEquation (5). The null hypothesis of no overdis-persion, α : Ho(α = 0), can be determined via aLikelihood-ratio test.

Zero-inflated Poisson:

Pr(Absmi = 0|xi, zi) = ψi + (1 − ψi )e−μi(4)

Pr(Absmi = k|xi)=(1−ψi )(e−μi μk

i )/(k!).

Zero-inflated negative binomial:

Pr(Absmi = 0|xi, zi) = ψi(5)

+ (1 − ψi )((α−1)/(α−1 + μi ))

α−1

Pr(Absmi = k|xi)=(1 − ψi )(�(α−1 + k))/

(�(α−1)k!)((α−1)/(α−1 + μi ))α−1

(μi/(μi + α−1))k.

IV. RESULTS

Table 2 presents estimates of the effect ofRoSLA on high school teacher absence behav-ior. Two sets of estimates are reported forthe ZIP and ZINB models, respectively. TheVuong test (reported in each of the tables) sug-gests that our zero-inflated models are a sig-nificant improvement over standard Poisson ornegative binomial models. The likelihood-ratiotest in all cases rejects the null hypothesis ofno overdispersion (p value = .000). It indicatesthat because of overdispersion the ZINB canimprove goodness-of-fit over the ZIP. Nonethe-less, we report both sets of estimates to demon-strate that our results are not being driven bychoice of model. For both models the policyeffect dummy (RoSLA × HSTeacher) demon-strates a statistically significant increase in highschool teacher absenteeism as a result of theRoSLA. Coefficient estimates from count datamodels can be difficult to interpret so we alsopresent the incident rate ratio (IRR) or expo-nentiated coefficient eβ. Thus, an IRR greaterthan one indicates that the expected count in theexposed group is greater than the expected countin the unexposed group. These demonstrate thatthe effect of the policy was to increase thehigh school teacher’s hours lost through sick-ness absence by 15% (IRR = 1.15).

Comparing high school teacher absence be-havior and that of all other workers may be toobroad. For instance, there may be unobservedchanges to the incentives for worker absenceoccurring for other occupations/industries dur-ing the same period. Alternatively, there may besome unobserved shock to teachers’ absence thatcoincides with the policy change. These mayserve to bias our estimates of the policy effectin some unknown way. To mitigate this problemwe examine our two successively more restric-tive subsamples of workers, education workersand teachers (high school and primary school).As previously mentioned, focusing on the edu-cation sector aims to eliminate the possibilityof bias originating from unobserved changes inabsence incentives in other industries. Whilecomparing primary and high school teachersonly has the added advantage that these workersface a very similar holiday structure and leavetiming. The cost of these robustness checks aredecreased sample size and potentially less pre-cise estimates.

Table 3 reports the estimates for these twosubsamples, where for brevity we only report

6 ECONOMIC INQUIRY

TABLE 2Changes in Compulsory Schooling Laws and Teacher Absenteeism, All Workers, SLFS 1996–2004

ZIPa ZINBb

RoSLA × HSTeacher 0.139∗∗ (0.069) 0.140∗∗ (0.071)[1.150]∗∗ [1.150]∗∗

HSTeacher −0.202∗∗∗ (0.063) −0.203∗∗∗ (0.065)[0.817]∗∗∗ [0.817]∗∗∗

RoSLA −0.028∗ (0.014) −0.029∗∗ (0.015)[0.972]∗ [0.972]∗∗

Age 0.001 (0.001) 0.001 (0.001)[1.001] [1.001]

Age2 0.000 (0.001) 0.000 (0.001)[1.000] [1.000]

Female 0.003 (0.003) 0.003 (0.003)[1.003] [1.003]

Married 0.004 (0.003) 0.004 (0.003)[1.004] [1.004]

Secondary education −0.009 (0.007) −0.009 (0.007)[0.991] [0.991]

Higher education −0.004 (0.008) −0.004 (0.008)[0.996] [0.996]

Public sector −0.027∗∗∗ (0.006) −0.027∗∗∗ (0.006)[0.974]∗∗∗ [0.974]∗∗∗

Temporary contract −0.006 (0.005) −0.006 (0.006)[0.994] [0.994]

Establishment sizeWorkers 0–5 −0.022∗∗∗ (0.005) −0.022∗∗∗ (0.005)

[0.978]∗∗∗ [0.978]∗∗∗

Workers 6–10 −0.002 (0.005) −0.002 (0.005)[0.998] [0.998]

Workers 11–19 0.004 (0.004) 0.004 (0.004)[1.004] [1.004]

Workers 20–49 −0.003 (0.005) −0.004 (0.005)[0.997] [0.996]

Observations 988,329 988,329Vuong test 1,222.14 −2.60p value .0000 .9953Likelihood-ratio test 1.6 × 106

p value .0000

Notes: Controls for industry, workers’ occupation, region, year, and quarter are included but not reported. Robust standarderrors clustered at the regional level are in parentheses. IRR are in brackets.

aZero inflated Poisson.bZero inflated negative binomial.∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, the 5%, and the 1% levels, respectively.

the key policy variable estimates. Again theseestimates reveal that the change in compulsoryschooling laws lead to an increase in teachers’hours of sickness absence. Moreover, restrictingour sample leads to an increase in the magnitudeof this effect to 18%–20%, suggesting that ourearlier estimates were biased downward.

An additional issue is that we do not pos-sess a nonpolicy control group (i.e., schoolsor regions where there is no RoSLA). Hence,there is the potential that our policy effect is

in actuality because of some contemporaneousexogenous shock. To examine this, we conductrobustness checks exploiting the regional vari-ation in pre-RoSLA high school participationrates. Specifically, we split our sample accordingto the post-compulsory high school participa-tion rates of the region prior to the reform.The intuition is that schools in regions whereparticipation rates beyond compulsory educa-tion were previously lower should be moreaffected by RoSLA, that is, a larger proportion


TABLE 3Changes in Compulsory Schooling Laws and Teacher Absenteeism, Alternative Subsamples

ZIPa ZINBb

Education WorkersHigh/Primary

School Teachers Education WorkersHigh/Primary

School Teachers

RoSLA × HSTeacher 0.162∗∗∗ (0.053) 0.180∗∗ (0.077) 0.172∗∗∗ (0.058) 0.189∗∗ (0.081)[1.176]∗∗∗ [1.198]∗∗ [1.187]∗∗∗ [1.208]∗∗

HSTeacher −0.163∗∗∗ (0.055) −0.162∗∗ (0.072) −0.170∗∗∗ (0.059) −0.168∗∗ (0.074)[0.849]∗∗∗ [0.850] [0.843]∗∗∗ [0.846]∗∗

RoSLA −0.029 (0.036) −0.145 (0.134) −0.032 (0.039) −0.155 (0.136)[0.971] [0.865] [0.968] [0.857]

Observations 63,811 49,711 63,811 49,711Vuong test 331.68 288.96 3.04 2.89p value .0000 .0000 .0012 .0019Likelihood-ratio test 8.0 × 104 6.5 × 104

p value .0000 .0000

Notes: All other controls are as in Table 2. Robust standard errors clustered at the regional level are in parentheses. IRRare in brackets.


TABLE 4Changes in Compulsory Schooling Laws and Teacher Absenteeism, by Previous RoSLA

Participation Rates in Post-Compulsory Secondary Education

ZIPa ZINBb

<20% ≥20% <20% ≥20%

RoSLA × HSTeacher 0.359∗∗∗ (0.078) 0.073 (0.070) 0.397∗∗∗ (0.087) 0.080 (0.073)[1.432]∗∗∗ [1.076] [1.487]∗∗∗ [1.084]

HSTeacher −0.342∗∗∗ (0.086) −0.084 (0.074) −0.374∗∗∗ (0.095) −0.089 (0.078)[0.711]∗∗∗ [0.919] [0.688]∗∗∗ [0.915]

RoSLA −0.094 (0.109) −0.015 (0.048) −0.125 (0.111) −0.018 (0.054)[0.910] [0.985] [0.883] [0.982]


p value .0000 .0000

Notes: All other controls are as in Table 2. Robust standard errors clustered at the regional level are in parentheses. IRRare in brackets. <20% and ≥20% columns comprise 7 and 11 regions, respectively.


of students in these schools are treated by thepolicy reform. Hence, if it is this policy that isactually causing the change in teacher absence,teachers in areas that previously had lower post-compulsory participation rates in secondary edu-cation should respond more. We re-estimate ourabsence models split according to whether theschool was in a region with greater than or lessthan 20% post-compulsory participation rates

prior to RoSLA,8 and report these estimatesin Table 4.9 The results demonstrate that the

8. We estimate this for the education sector sample asthere are insufficient observations if we use the teacher’sonly sample. These results are robust to alternative splitsof the regions such as greater or less than 25% of studentsattending post-compulsory secondary education prior to thereform.

9. Specifically they are biased downwards causing over-estimation of significance levels.

8 ECONOMIC INQUIRY

policy caused teacher absence to increase byalmost 50% in those regions with lower post-compulsory high school education participationbefore the reform was implemented. This resultincreases our confidence that the rise in absencewe observe is caused by the policy reform.

We conduct four final sets of robustnesschecks. First, we investigate whether our resultsare generated by the year of policy implemen-tation. For instance, this may have caused dis-ruption to classrooms and teachers leading to anincrease in absence. We do this by excluding theyear of the reform from our sample. These esti-mates are reported in the first row of Table 5 andreveal that omitting the year of the reform doesnot alter the key results; the magnitude remainsat approximately 20%. We use this approach asthe basis to investigate the longevity of the pol-icy effect by further omitting the year after thepolicy reform. The results in the last row ofTable 5 demonstrates again the robustness of themain result and suggests that the policy contin-ued to have an effect on high school teacherabsence until, at least, 2 years after the reformyear. This effect remained of the same magni-tude. In addition, no policy effect was evidentwhen we investigated a placebo reform for theyear prior to the actual RoSLA.

Second, we re-estimate our models excludingthe summer quarter of the SLFS. This is per-formed for two reasons, first the bulk of schoolholidays occur in the summer quarter hencethe opportunity (or need) for teachers to take

sickness absence in this quarter are diminished.Second, it has been suggested that increasesin temperature are generally associated withincreases in absence (Connolly 2008). Estimatesof these further restricted models are reportedin Table 6. The pattern of these estimateslargely follows those reported in Tables 2 and 3.Together the results in Tables 3, 4, and 6 suggestthat our estimated impact of RoSLA on teacherabsence is not being driven by unobserved vari-ations in holiday availability/timing or unob-served shocks to the absenteeism of teachers.

As a further robustness check that our resultsare not being driven by an unobserved exoge-nous shock to absence at the time of the reform,we examine RoSLA’s impact on a group ofworkers who should be unaffected. Specifically,we exclude all the education sector workersfrom the sample and treat the remaining pub-lic sector workers as the treatment group. InTable 7, we show that the coefficient for this“incorrect” treatment group is not significantlydifferent from 0. This further suggests that thechange in teacher absence is because of thechange in the school leaving age and not somecontemporaneous shock. One might also beconcerned that the March 11, 2004 attacks inMadrid may also have influenced Spanish workbehavior regarding absenteeism. For instance,Hotchkiss and Pavlova (2009) have demon-strated that the September 11 attacks changedthe working hours of New York residents. Weinvestigated this by deleting 2004 from our

TABLE 5Changes in Compulsory Schooling Laws and Teacher Absenteeism, Implementation Effects and

Policy Longevity

ZIPa ZINBb



School Teachers

Excluding the reform period (1 year)

RoSLA × HSTeacher 0.182∗∗∗ (0.050) 0.182∗∗ (0.075) 0.191∗∗∗ (0.055) 0.192∗∗ (0.079)[1.199]∗∗∗ [1.200]∗∗ [1.210]∗∗∗ [1.211]∗∗

Observations 61,268 47,783 61,268 47,783

Excluding the reform period (2 years)

RoSLA × HSTeacher 0.184∗∗∗ (0.049) 0.181∗∗ (0.076) 0.194∗∗∗ (0.055) 0.193∗∗ (0.080)[1.202]∗∗∗ [1.199]∗∗ [1.214]∗∗∗ [1.212]∗∗

Observations 52,748 41,064 52,748 41,064

Notes: All other controls as in Table 2. Robust standard errors clustered at the regional level are in parentheses. IRR arein brackets.



TABLE 6Changes in Compulsory Schooling Laws and Teacher Absenteeism, Excluding Summer Quarter

ZIPa ZINBb



School Teachers

RoSLA × HSTeacher 0.145∗∗∗ (0.044) 0.182∗∗∗ (0.070) 0.151∗∗∗ (0.048) 0.188∗∗ (0.076)[1.156]∗∗∗ [1.200]∗∗∗ [1.163]∗∗∗ [1.207]∗∗

HSTeacher −0.140∗∗∗ (0.047) −0.163∗∗ (0.065) −0.143∗∗∗ (0.050) −0.166∗∗ (0.070)[0.870]∗∗∗ [0.850]∗∗ [0.867]∗∗∗ [0.847]∗∗

RoSLA −0.019 (0.046) −0.144 (0.132) −0.020 (0.047) −0.153 (0.138)[0.982] [0.866] [0.980] [0.858]


p value .0000 .0000



TABLE 7Changes in Compulsory Schooling Laws and Public Sector (Non-Education Worker) Absenteeism

All Periods Excluding Summer Quarter

ZIPa ZINBb ZIPa ZINBb

RoSLA × Treatment 0.056 (0.046) 0.053 (0.048) 0.045 (0.061) 0.043 (0.064)[1.057] [1.055] [1.046] [1.044]

Treatment −0.082∗ (0.046) −0.080∗ (0.048) −0.069 (0.061) −0.066 (0.064)[0.921]∗ [0.923]∗ [0.934] [0.936]

RoSLA −0.052∗∗ (0.026) −0.051∗ (0.027) −0.037 (0.031) −0.035 (0.033)[0.949]∗∗ [0.951]∗ [0.964] [0.966]

Observations 923,586 923,586 693,317 693,317Vuong test 1,186.29 −2.66 1,022.04 −2.03p value .0000 .9961 .0000 .9787Likelihood-ratio test 1.5 × 106 1.3 × 106

p value .0000 .0000



sample and re-estimating our main models; ourkey policy estimates were unaffected by this.

Bertrand, Duflo, and Mullainathan (2004)demonstrate that standard errors in DID estima-tors are inconsistent when panels with longertime periods are used and the dependent vari-able is serially correlated. We examine this bycollapsing the time dimension of our data intotwo periods, pre- and post-RoSLA. We thenre-estimated (1) across these two periods andthese are reported in Table 8. Our resulting

estimate of the RoSLA effect is statisticallysignificant at the 1% level, and suggests a largerpolicy effect of at least 50%. This suggests thatthe statistical significance of our RoSLA esti-mates is not the result of serial correlation inabsenteeism.

Having established a robust policy effect, wenow seek to provide some approximate quantifi-cation of the magnitude of the policy effect onteacher absence. In addition, while the estimateshave been concerned with intensive margins of

10 ECONOMIC INQUIRY

TABLE 8Changes in Compulsory Schooling Laws andTeacher Absenteeism Collapsed, Excluding

Summer Quarter

ZIPa ZINBb

EducationWorkers

EducationWorkers

RoSLA× 0.496∗∗∗ (0.173) 0.653∗∗∗ (0.171)HSTeacher [1.643]∗∗∗ [1.921]∗∗∗

HSTeacher −0.183 (0.197) −0.314 (0.224)[0.833] [0.730]

RoSLA −0.399∗∗ (0.200) −0.332 (0.213)[0.671]∗∗ [0.718]

Observations 12,347 12,347Vuong test 117.24 6.09p value .0000 .0000Likelihood-ratio test 2.0 × 105

p value .0000

Notes: All other controls as in Table 2. Robust standarderrors clustered at the regional level are in parentheses. IRRare in brackets.

aZero inflated Poisson.bZero inflated negative binomial.∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%,

the 5%, and the 1% levels, respectively.

absence, we also provide some information onthe incidence of sickness absence which can beconsidered a measure of the extensive margin.Panel A of Table 9 reports the RoSLA marginaleffects from a probit of the probability of aworker taking sickness absence in the reference

week. For our education worker sample thisreveals that the increase of high school teach-ers’ absence because of the reform was 1.6 and2.4 percentage points excluding summer holi-days, respectively. Slightly lower magnitudesare reported for the high school versus primaryschool sector although there is a loss of preci-sion in the estimates and these miss statisticalsignificance at standard levels.

Panel B of Table 9 reports the coefficientsof an OLS regression for those workers thattook absence due to sickness. Because of thereform, high school teachers take 308 moreminutes of absence than their other educationsector counterparts. They take 337 more minutesthan primary education teachers because ofthe reform, and the effect is that of 280 and352 minutes for the two samples, respectivelywhen we exclude the summer quarter fromthe data. These are broadly in line with the15%–20% magnitude of policy effect reportedin our earlier count data models.

V. CONCLUSION

RoSLA is seen as a key instrument forincreasing basic education levels within society.At the same time, it has been relied upon bymany researchers as a source of exogenousvariation in educational attainment in economet-ric studies of the returns to education. In this

TABLE 9Quantifying the Effect of RoSLA on Extensive and Intensive Margins of Absence

All Periods Excluding Summer Quarter

EducationWorkers

High/PrimarySchool Teachers

EducationWorkers

High/PrimarySchool Teachers

Panel A. Sickness absence (incidence), probit marginal effects

RoSLA × HSTeacher 0.016 0.011 0.024 0.013(0.009)∗ (0.012) (0.011)∗∗ (0.019)

HSTeacher −0.010 −0.009 −0.012 −0.012(0.008) (0.012) (0.011) (0.020)

RoSLA 0.005 0.011 0.010 0.025(0.012) (0.012) (0.016) (0.020)

Observations 21,782 17,420 12,172 9,561

Panel B. Minutes of absence (OLS)

RoSLA × HSTeacher 307.570 336.969 279.665 351.523(93.672)∗∗∗ (140.689)∗∗ (80.259)∗∗∗ (127.227)∗∗

HSTeacher −302.293 −299.363 −259.205 −308.671(94.654)∗∗∗ (129.098)∗∗ (83.303)∗∗∗ (117.256)∗∗

RoSLA −53.577 −258.431 −31.578 −266.458(87.580) (265.185) (105.083) (264.981)

Observations 927 789 805 694

Notes: All other controls as in Table 2. Robust standard errors clustered at the regional level are in parentheses.∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, the 5%, and the 1% levels, respectively.


paper, we asked the question, how do teach-ers react to the changes in teaching environmentimplicitly created by retaining students at schoolwho would have otherwise left? Specifically,we focus on one potential response, changes inteacher absence behavior.

We examined changes in high school teacherabsence behavior because of an increase inthe school leaving age in Spain in the aca-demic year 1998–1999. Using representativelabor force data we demonstrated that teachersaffected by this reform increased their absen-teeism by 15%, rising to almost 50% in regionsthat traditionally had lower post-compulsoryschool participation. Our interpretation of thisresult is that more onerous teacher environ-ments lead to decreases in effort by high schoolteachers. This result is of importance for tworelated reasons. Given previous research thatestablishes a link between teacher absence and

lower student performance, our results demon-strate that increasing the compulsory schoolleaving age has the potential to reduce edu-cational quality. Second, our results suggestthat researchers using RoSLA or other policychanges that may affect teaching conditions asan instrumental variable should consider theirpossible effects on educational “quality.”

This paper has considered the effect ofRoSLA on absence; future research shouldconsider the range of other potential reac-tions of teachers (i.e., turnover, job satisfaction)and the subsequent effect these have on chil-drens’ outcomes.

Finally, a policy recommendation that isderived from our work is that education authori-ties should consider the need for increased com-pensation or improved working conditions forteachers adversely affected by increasing thecompulsory school leaving age.

APPENDIX

FIGURE A1

Weekly Minutes Absence by Occupation Group and Year, SLFS 1996–2004

15.5

528

.91

35.8

2

010

2030

4050

Min

utes

Abs

ence

per

wee

k

Worker High SchoolTeacher

Primary SchoolTeacher

1996

1997

1998

1999

2000

2001

2002

2003

2004

1996

1997

1998

1999

2000

2001

2002

2003

2004

1996

1997

1998

1999

2000

2001

2002

2003

2004

All workers

12 ECONOMIC INQUIRY

FIGURE A2

Weekly Minutes Absence by Occupation Group and Year; Workers Who Report Sickness Absence, SLFS 1996–2004

1608

1981

1982

050

01,

000

1,50

02,

000

2,50

0

Min

utes

Abs

ence

per

wee

k

Worker High SchoolTeacher

Primary SchoolTeacher

1996

1997

1998

1999

2000

2001

2002

2003

2004

1996

1997

1998

1999

2000

2001

2002

2003

2004

1996

1997

1998

1999

2000

2001

2002

2003

2004

Workers with non-zero absence

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does raising the school leaving age reduce teacher effort? evidence from a policy experiment

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