explaining upper secondary school dropout: new evidence on the role of local labor markets

Empir EconDOI 10.1007/s00181-014-0829-3

Explaining upper secondary school dropout:new evidence on the role of local labor markets

Kristine von Simson

Received: 13 February 2013 / Accepted: 11 April 2014© Springer-Verlag Berlin Heidelberg 2014

Abstract According to standard human capital theory, local labor market conditionsaffect individual schooling decisions mainly through two channels: (1) the opportunitycost of schooling, and (2) the returns to education. This paper assesses the impact ofboth these channels on upper secondary school dropout among Norwegian youth inthe period 1994–2006. The effect of local labor market conditions is measured usingvariation in youth outflow rates from unemployment to employment across regionsover time. The results show that local labor market conditions play a substantial rolein individual dropout decisions in Norway, with elasticities ranging from 0.1 to 0.3.The opportunity cost of schooling seems to weigh more in the dropout decision forNorwegian youth than the expected returns. However, the results are highly sensitiveto the choice of local labor market indicator. When including the unemployment rate,which is the standard indicator used in empirical applications of schooling decisions,instead of the outflow rate, the estimates become smaller and in most cases insignifi-cant. This indicates that previous studies of school dropout may have understated theimportance of local labor market conditions.

Keywords Local labor markets · Upper secondary school dropout

JEL Classification I21 · R23

1 Introduction

Dropout from upper secondary school is a serious concern in many industrialized coun-tries. Skills acquired in upper secondary school are considered as the minimum level

K. von Simson (B)Institute for Social Research, Postboks 3233, Elisenberg, 0208 Oslo, Norwaye-mail: [email protected]

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necessary to succeed in modern knowledge-based economies, and the consequences ofearly school leaving are severe for individuals, as well as societies. Failure to completeupper secondary school is associated with higher unemployment risk and lower earn-ings (Belfield and Levin 2007; Campolieti et al. 2010; Falch et al. 2010), which in turnhave direct impacts on public spending and income through increased welfare expen-ditures and lower tax revenues. Upper secondary school is a prerequisite for highereducation, and individuals without upper secondary school qualifications have muchlower propensity to participate in further education and training than higher skilledpersons (OECD 2012). Moreover, increasing the proportion of youth who success-fully complete upper secondary school may decrease social inequality and contributeto economic growth.1

This paper aims at identifying factors that influence the decision to leave uppersecondary school prior to completion. The main focus is on the impact of local labormarket conditions on individual dropout behavior. The desire to work may be an impor-tant motivation for some youth to leave upper secondary education before graduation.According to standard human capital theory (Becker 1964), local labor market condi-tions affect the schooling decision mainly through two channels: (1) the opportunitycost of schooling, measured by expected foregone earnings and (2) the returns to educa-tion, measured by the expected increase in lifetime income. A depressed labor marketimplies fewer job openings and lower wages, which in turn decrease the opportunitycost of schooling and discourage youth to drop out from school. On the other hand, ifwages for higher educated workers are expected to decrease relative to those of lowereducated workers, some students may choose to leave school early even though currentopportunity costs are low. If youth are myopic and tend to undervalue the future, assuggested by Oreopolous (2007), current employment opportunities may weigh heav-ily in their schooling decision. Empirically, this paper distinguishes between the twomotives, opportunity costs and lifetime income, by contrasting the effects of differentlabor market indicators on dropout behavior.

While most papers investigating the relationship between labor market conditionsand schooling use the unemployment rate to characterize the state of the labor market,the outflow rate from unemployment to employment may be more appropriate whenmodeling individual dropout decisions. When students contemplate whether or not toquit school, they will be influenced by the probability of finding work if they searchfor it. The proportion of individuals leaving unemployment for work seems like theobvious choice to represent this probability. The outflow rate may also reflect thetime horizon of myopic youth. The unemployment rate incorporates transitions fromunemployment to employment and vice versa, thus reflecting long-run employmentprospects. The outflow rate, on the other hand, focuses on the first transition only. Therelevance of the outflow rate might thus be consistent with a particularly short-runfocus of the individuals.

Theoretical considerations also point in the direction of outflow rates. As predictedby the human capital theory, the level of schooling depends on current and lifetime

1 Hanushek and Woessmann (2012) investigate the association between cognitive skills and economicgrowth and find support for a causal interpretation of the impact of skills produced in schools on economicgrowth.

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earnings. Modern theories of wage formation, in turn, predict that wages depend on thedevelopment of employment prospects; that is, the risk and consequences of becomingunemployed. It is the underlying flows between employment and unemployment whichare important—not the stock of unemployed persons per se. Indeed, Gaure and Røed(2007) show that the unemployment rate is a poor proxy for the development ofemployment prospects. Comparing unemployment and outflow rates with a GDP-based business cycle indicator, they find that the outflow rate performs much better inexplaining cyclical fluctuations in GDP than the unemployment rate. Moreover, theoutflow rate is shown to outperform the unemployment rate in studies of both regionalwage formation and interregional migration (Carlsen et al. 2006).

With the use of extensive register data comprising the full population of upper sec-ondary school entrants in Norway between 1994 and 2003, I analyze how regionaloutflow rates from unemployment to employment affects the probability that a studentleaves school before completion. Although upper secondary education is not manda-tory in Norway, enrollment is practically universal. Over 95 % of Norwegian youthhave a direct transition to upper secondary school immediately after finishing compul-sory education (Statistics Norway 2010). Despite the promising enrollment rates, only70 % of each student cohort successfully completes their program within a period of 5years. The analysis is performed within a duration framework, which makes it possibleto estimate the effect of time-varying variables—such as current outflow rates. Theeffect of local labor markets is identified using variation in outflow rates across 89regions over 13 years. The combination of a large number of local labor markets anda long observation period strengthens identification.

This paper adds to the literature on schooling decisions by distinguishing betweenmeasures affecting the immediate opportunity costs, and measures affecting the per-ceived long-term income gains. The opportunity cost of schooling is approximatedby the outflow rate from unemployment to work for youth aged 16–24, while returnsto schooling are measured by the relative outflow rate for those who have completedupper secondary education. Adding the relative outflow rate to indicate varying returnsto education is an improvement compared to other similar studies.

Furthermore, this paper provides evidence on the merits of alternative labor marketindicators. Previous studies of upper secondary school dropout using the unemploy-ment rate to characterize the labor market have given mixed results. While most studiesfind a negative relationship between unemployment and school dropout, this effect isnot always significant and some even find a positive association. As argued earlier, theunemployment rate may be an inaccurate indicator of employment prospects, whichmay be one explanation for the lack of consensus in the literature. I investigate thisdirectly, by comparing the performance of the youth unemployment rate to the youthoutflow rate.

The rest of the paper proceeds as follows: The next section reviews relevant the-ory and literature regarding the relationship between labor market conditions andschooling decisions with a focus on upper secondary school, while Sect. 3 provides anoverview of the Norwegian educational system. Section 4 presents the data, followedby a presentation of the econometric model in Sect. 5. The results are shown in Sect.6, while Sect. 7 concludes.

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2 Theoretical background and literature review

The standard way to regard schooling decisions in an economic environment is thehuman capital-investment framework, originating with the seminal works of Becker(1964) and Ben-Porath (1967).2 According to this framework, individuals will attendschool as long as the marginal benefit of an additional year of schooling exceedsor equals the corresponding marginal cost. Marginal costs include direct costs in theform of tuition and other school-related costs, as well as opportunity costs measured asforegone earnings due to postponed labor market entry. Marginal benefits are related tothe future labor market gains of more schooling in terms of income and employment.Current labor market conditions affect the schooling decision mainly through theopportunity costs. A temporarily depressed labor market reduces the expected gainsfrom job-search and leads to lower wages, which in turn decrease the opportunity costsand increase the demand for schooling. However, a slack labor market may also inducestudents to revise downwards their expectations about the wage premium associatedwith more schooling. The relevant measure in this context is relative employmentprospects. If students expect labor market conditions for higher skilled persons todeteriorate more than those for lower skilled persons, they may want to leave schoolearly even though current opportunity costs are low.

As pointed out by Micklewright et al. (1990), poor economic conditions may alsoaffect school attendance through increased parental unemployment. Youth with unem-ployed parents may be forced to leave school because they no longer can affordtuition or because they have to contribute to household income (“added worker”effect). It may be argued that the effect through parental unemployment is of lessimportance in the Norwegian setting, as upper secondary education is free of chargeand unemployment benefits are relatively generous. However, the theoretical ambi-guity of the schooling decision to economic conditions indicates that care should betaken when interpreting the impact of local labor market conditions on educationalattainment.

This paper is concerned with the dropout decision of youth already enrolled inupper secondary education. As the enrollment decision is made, one would expectthat the students have made the calculation that the benefits outweigh the costs. Whenthey nevertheless choose to drop out, this may be due to the arrival of new information,leading students to adjust their calculations about costs and benefits (see Bradley andLenton 2007 for a discussion of this). As nearly every youth enroll in upper secondaryeducation in Norway, the initial investment decision may be of less importance; instead,the students may continuously weigh the updated costs against the benefits.

Recent empirical literature concerning the association between labor market con-ditions and schooling confirms the predictions from economic theory. The rest ofthis section reviews some of the recent literature investigating upper secondary schoolchoices.3 A large part of this literature focuses on the enrollment decision, and the main

2 See Card and Lemieux (2001) for an excellent presentation and discussion of this framework.3 Some recent examples of studies investigating the relationship between higher education choices andlabor market conditions are Dellas and Sakellaris (2003) looking at college enrollment in the US; Pietro(2006) investigating university dropout in Italy and Messer and Wolter (2010), investigating time-to-degree

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conclusion from these studies is that enrollment is countercyclical. Card and Lemieux(2001) investigate trends in educational attainment among young people in the UnitedStates in the period 1968–1996 using panel data methods, and find a modest positiveeffect of higher unemployment on enrollment and completed education. However, thisstudy uses prime-age unemployment as a proxy for labor market conditions. As higherprime-age unemployment is associated with higher parental unemployment, this couldpotentially dampen the impact of increased opportunity costs on the demand for school-ing. Using similar panel data models as Card and Lemieux (2001), Clark (2011) exam-ines the relationship between the youth unemployment rate and enrollment in post-compulsory education in the UK from 1975 to 2005. He concludes that youth unem-ployment has large impacts on enrollment, suggesting elasticities on the order of 0.2.

Petrongolo and San Segundo (2002) look at the enrollment decision among Spanishyouth. The youth unemployment rate is included as a proxy for the opportunity costof schooling, while the adult unemployment rate signals poor future employmentprospects. Modeling the individual enrollment decision as a multinomial logit, theyfind that enrollment is positively affected by youth unemployment and negativelyaffected by adult unemployment. However, the association is weak, and the authorsconclude that the main determinant of enrollment is the education level of the parents.This conclusion is also shared by Mocetti (2012), who find that local labor marketshave no effects on the propensity to continue with post-compulsory schooling forItalian youth. Black et al. (2005) investigate the impact of long-term changes in locallabor market conditions on enrollment rates in Kentucky and Pennsylvania in the 1970sand 1980s. Focusing on relative wages of those with and without high school diploma,they conclude that a long-term increase in the earnings of low-skilled workers has asubstantial negative impact on enrollment rates.

Studies examining the dropout decision of already enrolled youth tend to concludethat worse labor market conditions decrease the probability that young people dropout of school. Rees and Mocan (1997) investigate how local labor market conditionsaffect the high school dropout rate for American youth in New York in the period1978–1987. Using a panel data model with district and year fixed effects, they findthat higher unemployment rates discourage youth from dropping out of high schoolin New York. Bradley and Lenton (2007) estimate a duration model of individualdropout behavior for youth enrolled in post-compulsory education in the UK. Theyfind that high local unemployment reduces the risk of dropout. Evans and Kim (2008)use an economic shock produced by the opening of casinos on an Indian reservationto explore the impact of local labor market conditions on the demand for education.They find that the opening of casinos improved employment and wages of low-skilledworkers, thus increasing dropout from high school and reducing college enrollmentrates. Aparicio (2010) investigates the effect of a transitory change in labor marketconditions for low versus high educated workers in Spain on upper secondary schooldropout. Using the Spanish housing boom, which improved employment and wagesfor low-skilled male workers, she concludes that increases in relative employment

Footnote 3 continuedin Switzerland. All mentioned studies find a significant impact of labor market conditions on the educationchoices studied.

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prospects lead to a higher propensity to drop out of upper secondary school for men,relative to women.

Falch et al. (2010) include local labor market conditions in their analysis of dropoutbehavior among Norwegian youth, and find no statistical relationship between localunemployment and dropout. Another Norwegian study (Gjefsen 2010) conclude thatincreased youth unemployment leads to decreased dropout for students enrolled inacademic study programs, while it increases dropout for students enrolled in vocationalprograms. However, both these studies may suffer from measurement error, as theymeasure unemployment the year of enrollment and not the year the students actuallydrop out of school. For the same reason, their results may be affected by effects on theselection of students who enter upper secondary school.

3 Upper secondary education in Norway

Education in Norway is compulsory from the age of 6 until the age of 16. Aftercompleted compulsory education, every student has a statutory right to attend 3 yearsof upper secondary education free of charge. This right must be fully utilized overa period of five continuous years (six for some vocational programs), and must bestarted within 5 years after completed compulsory school.4 The take-up rate is veryhigh: 95 % of those who complete compulsory school have a direct transition to uppersecondary education.5 When applying for upper secondary education, students maychoose between 12 different programs: three academic study programs which qualifyfor higher education, and nine vocational education programs which certify for workin a number of different occupations (around 200 recognized trades).6 Around half ofthe students opt for vocational programs, with an overweight of male students.

A standard course through upper secondary education lasts 3 years. The exceptionis vocational programs with apprenticeships which follow a 2 + 2 model: 2 years ofschool-based training followed by 2 years of apprenticeship in a company (recognizedtrade). Around 70 % of the vocational courses are leading to an apprenticeship. Theauthorities at county level are responsible for providing apprenticeships. However,there are no guarantees of an apprenticeship contract, and the supply of apprentice-training places is shown to vary pro-cyclically with economic conditions (Askildenand Nilsen 2005; Høst 2008). Students who are not able to find an apprentice-trainingplace after finishing the two first years in school, are entitled to a third year of practicaltraining in school which leads to the same vocational qualification as the apprentice-ship. Another option is to request a transfer to the academic study program, completinga year of supplementary studies qualifying for higher education. While 56 % of thestudents complete their program within the standardized time span (3 years for aca-demic programs, 4 years for vocational programs), around 30 % fail to complete it

4 This means that the right must be utilized within the year the individual turns 24.5 Figures from Statistics Norway show that the take-up rate has been relatively stable since the introductionof Reform94 in 1994.6 Before 2006, the total number of programs was 16: three academic study programs and 12 vocationalprograms.

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within 5 years. The proportion of a cohort leaving upper secondary education withouta diploma is almost twice as large for vocational programs as for academic programs,and has been relatively stable during the last decade.7

The labor market may play a larger role in individuals’ schooling decision in Nor-way than in other countries. As pointed out by Card and Lemieux (2001), the impor-tance of current labor market conditions depends on the possibility of reenrollment.If reenrollment is feasible, students may take advantage of favorable current labormarket conditions, and then return to school when opportunity costs increase again.Reenrollment is not uncommon in Norway; students can relatively easily leave theeducational system and re-enter it later on. Adults who have not completed upper sec-ondary education as youth are entitled to such education upon application, and figuresfrom OECD (2011) show that Norway is among the OECD countries with the largestshare of upper secondary graduates over the age of 25.8 The dropout decision maythus be more sensitive to current variations in labor market conditions in Norway thanin countries where reenrollment is difficult or not feasible.

4 Data and descriptive statistics

The analysis is based on register data comprising the entire population entering uppersecondary education in Norway from 1994 to 2006. The data consist of yearly recordsof school attendance, as well as detailed background information such as gender, age,country of origin, parents’ education and income, and place of residence. Informationabout labor market conditions is gathered from an administrative database provided byStatistics Norway (FD-Trygd). This database includes several welfare and employmentregisters at the individual level for the observation period, together with demographicinformation such as age, gender and region of residence. From this database, it ispossible to construct unemployment rates, as well as outflow rates from unemploymentto employment for different demographic profiles.

4.1 Sample selection

I select all youth who enroll in upper secondary education for the first time between1994 and 2003, and who have completed compulsory school in Norway. Youth whopostpone enrollment in upper secondary school are more prone to drop out than thosewho enroll immediately after completed compulsory school (Falch and Nyhus 2009).These youth are thus excluded from the sample. The starting year is chosen due to theimplementation of Reform 94, which among others reorganized the upper secondaryeducational system and made it a legal right. The standard amount of time requiredto successfully complete upper secondary school is 3 years (four for some vocationalprograms). As I only have information about labor market conditions until the school-year 2006/2007, this means that 2003 is the last year the students may enroll in upper

7 Figures provided by Statistics Norway, http://www.ssb.no.8 One reason for the large reenrollment rates among Norwegian adults may be the combination of free ofcharge upper secondary education and great loan facilities for students.

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secondary education and still have the possibility to successfully complete within theobservation period.

I define a cohort as all students starting upper secondary education in a given year.Table 9 in the Appendix shows how many students are in each cohort in the sample,and how they are divided between academic and vocational study tracks. There are492,078 first-time enrollers in upper secondary education in the sample. The size ofeach cohort has been relatively stable over the period; around 50,000 individuals enrollin upper secondary education each year. The share of students entering each of thetwo study tracks has, however, been shifting: while 57 % of the students started anacademic study track in 1994, only 46 % did so in 2003.

October each year it is recorded in the education registers whether the student is stillin upper secondary education or not. A student is defined as having left school if he/sheis not found in the education register the following school-year. Dropout is then definedas leaving school without graduating. As shown in Table 9, 145,600 students (29.6 %)are defined as dropouts in my sample. The cohort dropout frequency has not changedmuch: around 30 % of each cohort leaves upper secondary education without complet-ing. Vocational students are, however, much more prone to drop out than the studentsfollowing an academic study track, with dropout frequencies almost twice as large.

4.2 Descriptive statistics

Table 1 shows descriptive statistics of all enrolled students, as well as those who dropout, and reveals interesting differences between the groups. Women are overrepre-

Table 1 Descriptive statistics for the whole sample and for dropouts only, separated by study track

Whole sample Dropouts only

All Academic Vocational All Academic Vocational

Female 48.7 54.4 41.3 43.1 48.5 39.6

Immigrant background 4.9 5.3 4.5 6.3 7.4 5.6

Householdincome (NOK)a

324,104 361,247 274,803 285,276 328,193 257,622

Parents’ educationlevelb

Compulsory school 12.0 7.4 18.2 18.8 12.2 23.1

Upper secondaryschool

50.7 43.1 60.8 54.1 46.6 58.9

Low universitylevel (1–3 years)

26.6 33.9 17.1 19.9 28.6 14.3

High universitylevel (4+ years)

9.8 14.9 3.2 6.0 11.4 2.5

Unknown 0.8 0.7 0.8 1.1 1.1 1.1

Years spent inupper secondaryschool

3.0 3.0 3.2 2.6 2.7 2.5

All statistics in percentage, unless otherwise stateda Weighted yearly average of household income from the individual was 7–16 years, measured in 2000-NOKb The highest education level of both parents, measured when the individual was 16 years

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sented among students enrolled in academic programs, while the opposite is true forvocational programs. Immigrants constitute around 5 % of the sample, and they areslightly more prone to enter an academic study track. There seems to be a negativeselection into vocational study programs in terms of family background; vocationalstudents are more likely to have low educated parents and to come from lower incomehouseholds. As the theoretical duration of many vocational programs is 4 years, theaverage time spent in upper secondary education is slightly longer for vocationalstudents than for academic students.

Male students are more likely to drop out of upper secondary education than females,especially among vocational students. Compared to the whole sample, dropouts seemto have a more disadvantaged family background; they are more likely to have grownup in families with a lower household income and to have lower educated parents.Interestingly, students who drop out spend almost the same amount of time in uppersecondary education compared to the whole sample. This may indicate that an impor-tant reason for why students drop out is failure to pass final exams. Another reasonmay be that those who drop out are more prone to re-take a grade, and thus stay longerin school.

4.3 Local labor markets

The main purpose of this paper is to investigate whether local labor market conditionshave an impact on the probability to drop out of upper secondary school. This is doneby including the outflow rate from unemployment to work constructed from the beforementioned administrative database based on economic regions. Economic regions aregeographic travel-to-work areas on the level between municipality and county, andare constructed so as to correspond to genuine local labor markets.9 There are 89economic regions in Norway. Gaure and Røed (2007) argue that the outflow rate fromunemployment is a better measure of labor market tightness than the unemploymentrate. They show that the unemployment rate reacts with a lag to business cycle fluc-tuations and tends to behave pro-cyclically around the business cycle turning points.Although they advocate the use of a human capital adjusted outflow rate, they find thatthe crude outflow rate also performs well in explaining business cycle fluctuations inNorway.

The outflow rate is defined as the yearly average of unemployed individuals leav-ing unemployment for work in a given month relative to the number of unemployedbeing at risk for making such a transition. The yearly average is calculated using theschool-year (August to July) rather than the calendar year (January to December). Iconstruct outflow rates for youth (defined as being between 16–24 years) measuringthe opportunity cost of education. As far as current labor market conditions influencestudents’ expectations about the returns to completing upper secondary school, I con-struct the relative outflow rate for skilled individuals. This is defined as the outflowrate to work for individuals having at least upper secondary education relative to theoutflow rate for individuals having at most compulsory school. Means and standard

9 The economic regions are defined by Statistics Norway.

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.06

.08

.1.1

2.1

4

1995 2000 2005

Youth outflow rate from unemployment to workDropout−rate from upper secondary school

Fig. 1 Dropout rates and youth outflow rates, national averages. The dropout rate is calculated as thenumber of students aged 16–24 years leaving upper secondary education without graduating in a givenschool-year relative to the total number of 16–24 year olds being at risk of leaving school during that sameschool-year. The youth outflow rate is calculated as the number of unemployed individuals aged 16–24years leaving unemployment for work relative to the total number of unemployed young people being atrisk of doing such a transition. All rates are yearly averages over the school-year (August to July), and arecalculated using administrative register data

deviations of the outflow rates during the observation period are found in Table 10 inthe Appendix, as well as a measure of the correlation between them in Table 11.

As a first investigation of the association between youth labor market conditionsand dropout behavior, Fig. 1 shows the development of the youth outflow rate and thedropout rate during the period under consideration at the national level. The dropoutrate is calculated as the number of students aged 16–24 leaving upper secondary edu-cation without completing within a given year relative to the number of students beingat risk of leaving during that same year. From 1994 to 1998 Norway was recoveringfrom a deep recession, and the period was characterized by strong economic expansion.The high economic activity persisted until 2001, after which a new recession began.In 2004, there was an economic turnaround and the economy started growing again.The youth outflow rate mimics the general business cycle situation, with increasingoutflow rates from 1994 to 2001, decreasing outflow rates from 2001 to 2004, and thenagain increasing outflow rates after 2004. The dropout rate, however, does not seemto show any business cycle pattern: it was increasing from 1995 to 1999, decreasinguntil 2001, after which it has been steadily increasing.

Although no clear association emerges from Fig. 1, this may potentially be due tonational trends confounding the pattern. Figure 2 plots the dropout rate and youthoutflow rate for 16 randomly selected regions after having controlled for region andyear fixed effects; that is, the residuals after regressing the dropout rate and youthoutflow rate on region and year dummies. The figure shows large differences in theoutflow rate across the different regions, which is the identifying variation I use in theestimation. The dropout rate also seems to be correlated with the youth outflow ratein some of the regions, although the lines are not parallel.

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−.1

−.0

50

.05

.1−

.1−

.05

0.0

5.1

−.1

−.0

50

.05

.1−

.1−

.05

0.0

5.1

1995 2000 2005 1995 2000 2005 1995 2000 2005 1995 2000 2005

Follo Kongsvinger Gjøvik Drammen

Holmestrand Kragerø Lillesand Flekkefjord

Bergen Høyanger Kristiansund Surnadal

Orkanger Levanger Vesterålen Nord−Troms

Dropout rate Youth outflow rate

Fig. 2 Regional outflow rates and dropout rates, de-meaned. Figure 2 graphs the residuals from a regressionof the dropout and outflow rate on region and year dummies for selected economic regions. For definitionsand data sources, see Fig. 1.

5 Econometric modeling

I follow the youth on a yearly basis from they enter upper secondary school, andmodel the conditional probability that a student will drop out of school during a yearconditional on not having left school up to that point. This is equivalent to a hazard rateor duration model. One of the advantages of using a duration model is the ability ofsuch models to incorporate time-varying covariates. Although some individuals havea higher probability to drop out than others based on background characteristics andpast experiences, current factors are also likely to influence the process of droppingout. For instance, a currently depressed labor market may induce students who are onthe margin of dropping out to stay longer in school, because they have fewer outsideoptions. Or the opposite—when outside options are good, students may choose to dropout earlier than they would have in absence of the boom. Other studies using a hazardrate approach in modeling the dropout decision is among others Arulampalam et al.(2004), Bradley and Lenton (2007), and Rosholm and Jakobsen (2003).

Let T be a non-negative random variable denoting time spent in upper secondaryschool until dropout, and let t be its realization. I use years attended rather than gradecompleted as the measure of schooling. As recordings on school attendance are updatedon a yearly basis, duration is measured in years. The data provide no information aboutthe exact moment of school leaving; the only thing we observe is that the studentleaves school within one-year intervals. Even if the data did provide informationabout the dropout date, this information would probably suffer from measurement

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error. Dropout is often the result of a long-lasting process, and not an instantaneousdecision. Since time is interval-censored, I use a discrete time version of the underlyingcontinuous-time hazard rate. Assuming that the hazard rate takes the proportionalhazard specification, the probability that individual i drops out of upper secondaryschool during year t in region r , given that he/she has not already left school, can beexpressed as follows:

hi (t |xit , srt ) = 1 − exp[− exp

{δ (t) + x ′

i tβ + s′r tσ

}], (1)

δ(t) is the baseline hazard at time t , and captures the time dependence of the hazardrate. x is a vector of observed individual-specific covariates, while s captures the effectof local labor markets. The specification also includes dummy variables for calendaryear and economic region in an attempt to rule out the possibility that labor marketconditions are picking up the effect of other variables that may vary across geographicarea and over time. If the individual leaves school because of completion, the spell iscensored. The same applies if the individual has not yet left school at the end of theobservation period.

Unobserved components, like ability or motivation, may also influence the hazardrate. For instance, Falch and Strøm (2013) find that student achievement at the end ofcompulsory school is one of the main predictors of dropout and delayed completion inNorwegian upper secondary schools. Information about grades is only available from2002 in my data, which indicates the need to control for this by including an unobservedindividual-specific component to Eq. (1). Neglecting unobserved heterogeneity maylead to biased estimates of the duration dependence parameters, as well as the estimatedcovariates. More specifically, ignoring unobserved heterogeneity leads to an estimatedbaseline hazard that is falling faster or rising more slowly than the actual baselinehazard, as well as underestimating the true proportionate response of the hazard to achange in a covariate.

Following standard practice, I control for unobserved heterogeneity by includingthe individual-specific random variable v in Eq. (1):

hi (t |xit , srt , vi ) = 1 − exp[− exp

{δ (t) + x ′

i tβ + s′r tσ + vi

}]. (2)

Unobserved heterogeneity is assumed to be time invariant and uncorrelated withobserved covariates.

Let cit be an outcome indicator which is equal to one if individual i drops out ofupper secondary school during year t , and zero otherwise, and let Yi be the number ofyears observed for individual i . Individual i’s contribution to the likelihood, conditionalon unobserved variables, can then be formulated as:

Li (vi ) =∏

i∈Yi

[hi (t |xit , srt , vi )

cit]

[1 − hi (t |xit , srt , vi )]1−cit . (3)

Since v is unobserved, it has to be integrated out of the likelihood function. Theunobserved heterogeneity is assumed to be normally distributed. Heckman and Singer(1984) argue that parametric distributions of unobserved heterogeneity may seriously

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bias the estimates in duration models, and recommend using a discrete specificationwith an a priori unknown number of mass points. While this semi-parametric approachin principle is the best way to minimize the potential bias caused by misspecification ofthe unobserved heterogeneity, a recent Monte Carlo study by Nicoletti and Rondinelli(2010) gives evidence in favor of normally distributed unobserved heterogeneity indiscrete time duration models. More specifically they show, using Monte Carlo simu-lations, that an incorrect normality assumption biases neither the duration dependencenor the covariate coefficients estimation. The normality assumption makes it possibleto use standard software in the estimation of the model.10

Duration dependence is assumed to follow a piecewise constant specification: Fiveone-year intervals and one open-ended interval for durations longer than 5 years.Variables included in x are time invariant and are measured at the time of enrollment:Sex (dummy equal to one if female); immigrant background (dummy equal to one ifnon-native); field of study (set of dummy variables indicating the field of study theindividual enrolled in);11 mother’s and father’s highest education level (four dummies:compulsory school (ref.), upper secondary school, low university level (1–3 years),high university level (>3 years); mother’s and father’s income (a weighted yearlyaverage of each parent’s income from the student was 7 years until the age of 16). Forvocational students I include a time-varying variable indicating whether the studentis an apprentice or not (dummy equal to one if the student is apprentice). The locallabor market variables are all measured at the regional level: the youth outflow rate;the relative outflow rate of skilled individuals.

As shown in Table 1, students seem to be negatively selected into vocational pro-grams in terms of family background, which in turn may influence the dropout prob-ability. Moreover, and particularly important in this paper, the relationship betweenlocal labor market conditions and dropout is likely to differ between the two studytracks due to the apprenticeship system. Economic theory predicts that the demand foreducation is countercyclical. Poor labor market conditions reduce the opportunity costof schooling, leading more students to participate in education. However, as the sup-ply of apprentice-training is pro-cyclical, this may potentially counteract the expectednegative relationship between labor market conditions and schooling. By separatelyanalyzing the impact of local labor market conditions for students enrolled in voca-tional and academic tracks, the empirical relevance of the supply-side constraints forvocational students is explored. However, I do not control for selection into the differ-ent study tracks. All results must thus be interpreted conditional on entry into eithervocational or academic study tracks.

One potential concern with the analysis is reverse causality. There are at least twosources of reverse causality in my setting. Firstly, if students who drop out of schoolregister as unemployed, the number of unemployed youth, that is the denominator

10 More specifically, the model is estimated using the command xtcloglog in STATA.11 Academic programs offer three fields of study: General, economics, and management studies; Music,dance, and drama; Sport and physical education. Vocational programs consist of 12 different study fields:Health and social studies; Agriculture, fishing, and forestry; Arts, crafts, and design studies; Hotel and food-processing trades; Building and construction trades; Technical building trades; Electrical trades; Engineeringand mechanical trades; Chemical and processing trades; Woodworking trades; Media and communication;and Sales and service.

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in the youth outflow rate, would increase. This leads to lower youth outflow rates,and downward biased estimates of the expected positive effect of the youth outflowrate. Secondly, even though young dropouts do not register as unemployed, this doesnot mean that they are not looking for work. Potential dropouts presumably makeup a substantial fraction of the youth labor supply, and are likely to compete for thesame jobs as young unemployed. Shocks to dropout behavior may thus translate intoreduced outflow rates directly, again leading to downward biased estimates of theyouth outflow rate. The importance and size of the bias are difficult to assess, makingreverse causality a serious concern.

There are reasons to believe that reverse causality is of less concern in the Nor-wegian setting. The incentives to register as unemployed are very low for Norwegianschool-leavers, as previous wage income is a requirement for receiving unemploymentbenefits. Moreover, a study of labor market outcomes of Norwegian dropouts fromupper secondary school shows that only 7 % of the dropouts are registered as unem-ployed immediately after leaving school (von Simson 2012). However, the concernthat dropouts and young unemployed compete for the same jobs still exists. To explorethis source of bias further, I turn to Labor Force Survey (LFS) data. As mentioned inthe previous section, youth are in general not entitled to unemployment benefits andthus have low incentives to register. This also applies to other kinds of benefits, likesickness payment. The LFS may provide better information about the actual activityof these youth. For instance, an unemployed individual in the LFS is someone whostates that he/she has been looking for work during the past four weeks and is avail-able for such work within the next two weeks. LFS unemployment is thus generallyhigher than registered unemployment, particularly for youth. In addition to their mainactivity, individuals in the LFS are asked what they consider as their main task. Thepossible categories are Studying, Staying at home, Being without work (unemployed),and Disabled or Other.

Table 12 in the Appendix shows how youth 16–24 years and dropouts are distributedover the activities and tasks in the 2001 LFS. A dropout is defined as someone who iscurrently not in education and who has not completed upper secondary school. Around12 % of the youth 16–24 years are classified as dropouts, according to the 2001 LFS.The large majority (over 80 %) of the dropouts are classified as being part of the laborforce (either employed or unemployed); however, dropouts constitute only 16–18 %of work-seeking youth, depending on whether work-seeking students are consideredas unemployed or students. This fraction is relatively constant over the period 1994–2006. The above discussion indicates that although shocks to dropout behavior wouldimpact the outflow rate, the magnitude of this impact is most likely not very large.12

I thus estimate the model without instrumenting for the youth outflow rate, keepingin mind that my estimates may understate the importance of the youth labor market ifreverse causation is important.

12 In order to check for endogeneity, I have done a Durbin–Wu–Hausman test where the youth outflow rateis instrumented using the outflow rate for skilled (having at least completed upper secondary education)adults (25–55 years old). The instrument is highly significant in the first stage, with f tests ranging from11893.67 to 16648.47. In the second stage, the residual from the first stage is added to the model togetherwith the full set of coefficients. The residuals do not enter any of the specifications significantly, whichsupports the conclusion that endogeneity is not a great concern in this setting.

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6 Results

This section presents the empirical results. I first report estimates from a series ofmodels of the impact of local labor markets on the dropout probability. I then brieflydiscuss the estimated effect of the other variables in my model. Estimations are per-formed separately for academic and vocational study tracks, and for boys and girls. Ido not control for selection into the different study tracks, and the results must thusbe interpreted conditional on entry.

6.1 Local labor market effects

Results from the estimations are reported in Tables 2 and 3 below. I have estimatedthree different specifications. Column (1) in the tables reports estimates from a modelincluding the youth outflow rate only. In column (2) expectations about future returnsto education and observed covariates are added to the model. The expectations aboutfuture returns to completed upper secondary education are measured by the outflow

Table 2 The effect of local labor markets on the individual probability to drop out from academic programs

(1) (2) (3)

Boys (n = 121,811)

Current labor market conditions, 16–24 years 0.264*** 0.245*** 0.249***

(0.049/0.092) (0.050/0.093) (0.050)

Relative outflow rate No −0.139 −0.142**

(0.062/0.097) (0.063)

Observed covariates No Yes Yes

Unobserved heterogeneity No No Yes

Girls (n = 138, 693)

Current labor market conditions, 16–24 years 0.288*** 0.281*** 0.281***

(0.047/0.085) (0.049/0.117) (0.051)

Relative outflow rate No −0.090 −0.091

(0.062/0.114) (0.062)



Column (1) reports the estimates from a model including the youth outflow rate only, in addition to durationdependence, year and region dummiesColumn (2) reports the estimates from a model including the youth outflow rate, the relative outflow rate(defined as the outflow rate from unemployment to work for skilled (having at least completed uppersecondary school) relative to unskilled individuals), observed covariates (immigrant status, study track,parents’ income and education level), in addition to duration dependence, region and year dummies. SeeTable 7 for the estimated coefficients for the observed covariates from this specificationColumn (3) reports the estimates from a similar model as column (2), but including unobserved heterogeneityas well. Italic standard errors indicate unclustered errors*** Significance on the 1 % level** On the 5 % level* On the 10 % level with clustered errors, except for column (3) where standard errors are not clustered

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Table 3 The effect of local labor markets on the individual probability to drop out from vocational programs

(1) (2) (3)

Boys (n = 132,426)

Current labor market conditions, 16–24 years 0.078 0.090** 0.133***

(0.031/0.041) (0.032/0.044) (0.037)

Relative outflow rate No −0.030 −0.022

(0.040/0.052) (0.046)



Girls (n = 102,961)

Current labor market conditions, 16–24 years 0.078 0.068 0.069

(0.039/0.055) (0.039/0.052) (0.043)

Relative outflow rate No −0.091 −0.109**

(0.050/0.066) (0.054)



Column (1) reports the estimates from a model including the youth outflow rate only, in addition to durationdependence, year and region dummies. Italic standard errors indicate unclustered errorsColumn (2) reports the estimates from a model including the youth outflow rate, the relative outflow rate(defined as the outflow rate from unemployment to work for skilled (having at least completed uppersecondary school) relative to unskilled individuals), observed covariates (immigrant status, study track,parents’ income and education level), in addition to duration dependence, region and year dummies. SeeTable 7 for the estimated coefficients for the observed covariates from this specification.Column (3) reports the estimates from a similar model as column (2), but including unobserved heterogeneityas well.*** Significance on the 1 % level** On the 5 % level* On the 10 % level with clustered errors, except for column (3) where standard errors are not clustered

rate for skilled individuals (having at least upper secondary qualifications) relative tothat of unskilled individuals (having at most compulsory education). An increase in therelative outflow rate for skilled individuals is expected to have a negative impact on thedropout probability, as the returns to completing upper secondary school increases andthus may induce students to stay longer in school. Column (3) presents results whenindividual-specific unobserved heterogeneity is included in the model. As the labormarket variables are included as log transformations, their estimates can be directlyinterpreted as elasticities (Jenkins 2005).

As local labor market conditions are measured at the regional level, while thedependent variable is measured at the individual level, standard errors are clusteredat the economic region (Moulton 1990). The exception is the specification includingunobserved heterogeneity, column (3), as this procedure is not yet incorporated in thecommand used in the estimation.13 In order to be able to compare the standard errors

13 One solution would be to bootstrap the standard errors by region. This is however problematic, as themodel includes region dummies. When the bootstrap samples are drawn, not all samples include all 89regions. This leads to missing coefficients, and the bootstrap fails.

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across columns, the tables report both the unclustered and clustered standard errors incolumn (1) and (2). Unclustered standard errors are reported in italic.

The estimated impact of youth outflow rates suggests a substantial positive effectof youth outflow rates on dropout behavior from academic study programs. A 1 %increase in the youth outflow rate leads to an increase in the individual propensity todrop out with 0.26 % for boys and 0.29 % for girls. When including the expectationsvariable and observed covariates, the estimate decreases slightly to 0.25 for boys and0.28 for girls. Controlling for individual-specific unobserved heterogeneity does notaffect the estimates in any substantial way.

As shown in column (2), expectations about future returns do not seem to have animpact on the propensity to drop out from academic study programs. For both boysand girls, the estimate has the expected negative sign, but is far from significant. Theweak correlation between expected labor market conditions and dropout may be aresult of youth being myopic (Oreopolous 2007), not considering future labor marketreturns when making their dropout decision. Another reason, as pointed out by Clark(2011), may be that youth use national rather than regional labor market indicatorswhen forming expectations about future returns. A third reason may be the compressedwage distribution and low returns to education in Norway (see e.g., Trostel et al. 2002),making relative movements less important.

Table 3 shows that the youth outflow rate has a smaller impact on the dropout prob-ability for vocational students than for academic students. The size of the estimatesindicates elasticities of 0.1 for both boys and girls, but the effect is only significant forboys when including observed covariates. Controlling for unobserved heterogeneityincreases the estimated effects somewhat. Expectations about future returns to educa-tion as measured by the relative outflow rate for skilled individuals have the expectedsign. The variable is, however, only significant for girls when including unobservedheterogeneity. As standard errors are not clustered in this specification, the significanceof the results may be overestimated.

As mentioned earlier, a plausible explanation for why the estimated impact of theyouth outflow rate is smaller for students enrolled in vocational programs than inacademic programs is the apprenticeship system. The recruitment of apprentices inNorway is mainly determined by the current labor market situation (Askilden andNilsen 2005). Although youth who fail to obtain an apprenticeship have the right to athird year in school, the motivation to complete such a year may be very low and thuslead some students to drop out of school altogether. The negative impact on dropoutrates from higher outflow rates through the opportunity costs may thus be counteractedby an increase in apprenticeships offered when economic conditions are good.

To explore this further, I have done separate estimations for students in the voca-tional track enrolled in programs leading to apprenticeships and programs where allcourses are classroom based. Around 10 % of the vocational students in my sampleare enrolled in the latter type of programs, with an overweight of girls. The results arepresented in Table 4.

The outflow rate has a much larger effect on the dropout probability among stu-dents enrolled in classroom-based courses than among students enrolled in coursesleading to an apprenticeship. This supports the hypothesis that the pro-cyclicality ofapprenticeships dampens the positive association between outflow rates and dropout.

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Table 4 The effect of local labor markets on the individual probability to drop out from vocational programs

Type of program

Classroom-based Apprenticeship-based

Boys

Current labor market conditions, 16–24 years 0.315** 0.078*

(0.148) (0.046)

Relative outflow rate −0.147 −0.034

(0.174) (0.053)

Number of individuals 9,881 417,941

Girls

Current labor market conditions, 16–24 years 0.044 0.017

(0.098) (0.061)

Relative outflow rate −0.182* −0.076

(0.095) (0.082)


The specification includes observed covariates (immigrant status, study track, parents’ income and educationlevel), duration dependence, year and region dummies, but not unobserved heterogeneityThe relative outflow rate is defined as the outflow rate from unemployment to work for skilled (having atleast completed upper secondary education) relative to unskilled individuals*** Significance on the 1 % level** On the 5 % level* On the 10 % level. All standard errors are clustered

A 1 % increase in the outflow rate to work increases the probability that a male studentwill drop out from a classroom-based vocational course with 0.3 %. The correspond-ing increase in the dropout probability for a boy enrolled in a program leading to anapprenticeship is 0.08 %, but this estimate is barely significant. For girls, the effect isalso larger for classroom-based programs, but none of the estimates are significant.

Tables 2, 3 and 4 reveal large gender differences. For boys, the effects are signifi-cant for all tracks, but strongest for classroom-based vocational programs, followed byacademic courses and least strong for apprenticeship-based vocational programs. Incontrast, for girls, the effects are only significant for academic programs, and insignif-icant for both classroom-based and apprenticeship-based vocational programs. Thesedifferences are interesting and deserve attention. Self-selection of individuals into thedifferent study tracks based on unobservable characteristics may be one explanationfor the observed differences. To explore this further, I look at the destination statesafter dropout for boys and girls, and see whether there are any systematic differencesin labor market outcomes between the genders.

Table 5 shows the labor market states the dropouts enter after dropping out. As theexact date of dropout is not observed in the data, the destination state is measured inOctober the following school-year after dropout.

Table 5 reveals no large differences in the destination states for boys and girls. Asmilitary service is mandatory for boys in Norway, boys are more prone to enter militaryservice after dropping out. Girls, on the other hand, start to work or enter another edu-cation than upper secondary education. Boys in classroom-based vocational programs

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Table 5 Labor market state after dropout, percentage share

Labor market state General track Vocational track,classroom-based

Vocational track,apprenticeship-based

Girls Boys Girls Boys Girls Boys

No registration 36.7 33.9 52.9 48.9 38.1 35.6

Health-related benefits 4.9 5.4 6.2 12.3 3.9 3.8

Unemployed 6.5 6.4 16.7 15.3 12.2 12.7

Employed 41.7 27.9 22.1 19.4 38.5 35.7

Military service/birth 1.1 20.4 0.5 3 1.1 7.4

Education 9.3 5.9 1.6 1.1 6.2 4.8

Total 100 100 100 100 100 100

Labor market state is measured in October the school-year after dropout

are also more likely to receive health related benefits than girls. Except for these dif-ferences, the shares entering the different labor market states are exceptionally equalbetween the genders.14 The differences are larger and more systematic between studytracks, with youth in classroom-based vocational courses being more likely to enterunemployment, receive benefits or be absent from all official registers than youth inacademic programs or apprenticeship-based vocational programs.

6.2 Unemployment versus outflow rates

The large part of the literature includes the unemployment rate instead of the outflowrate when measuring the impact of labor market conditions on educational attainment.In order to assess the importance of the labor market tightness measure chosen, I re-estimate the model without observed covariates [column (1) in Tables 2 and 3] usingthe youth unemployment rate. The youth unemployment rate is constructed fromthe same administrative registers as the outflow rate, and is defined as the numberof unemployed youth aged 16–24 years relative to the youth labor force. The resultsfrom the estimations are shown in Table 6. Column (1) presents estimates of the modelincluding only the youth unemployment rate, while column (2) shows the results whenthe youth outflow rate is added to the specification. None of the specifications includeunobserved heterogeneity in order to allow for clustered standard errors.

The results show that the youth unemployment rate has the expected negative signin all specifications, but that it usually enters the model without statistical significance.Compared to the elasticities from Tables 2 and 3, the effect of local labor markets isin most cases smaller when including the youth unemployment rate than the youthoutflow rate. The only exception is for girls enrolled in vocational programs, where the

14 A problem with register data and youth is that many youth have few rights to benefits based on previouslabor market income. As shown in Table 5, a large part of the youth is not found in any of the officialregisters. Some of these youth may be at home with children, but have no rights to birth benefits (and isthus not found in the birth-benefit-register). Some may be abroad traveling, and others again may just beinactive. In other words, youth not found in the registers are a very heterogeneous group.

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Table 6 Results when including the youth unemployment rate

Academic track Vocational track

(1) (2) (1) (2)

BoysYouth unemployment rate −0.070 0.065 −0.021 0.027

(0.089) (0.100) (0.047) (0.050)Youth outflow rate No 0.310*** No 0.108**

(0.111) (0.044)Girls

Youth unemployment rate −0.055 0.102 −0.111** −0.093(0.076) (0.096) (0.056) (0.064)

Youth outflow rate No 0.353*** No 0.042(0.106) (0.059)

Column (1) reports estimates from a model including the youth unemployment rate only, in addition toduration dependence, year, and region dummiesColumn (2) reports estimates from a model including the youth outflow rate, as well as the youth unem-ployment rate, in addition to duration dependence, year, and region dummiesNone of the specifications include the relative outflow rate, observed covariates or unobserved heterogeneity*** Indicates significance on the 1 % level** On the 5 % level* On the 10 % level with clustered standard errors

youth unemployment rate has a larger and significant effect on the dropout probability.When adding the youth outflow rate to the model in column (2), the estimate of theyouth unemployment rate gets smaller and in some cases changes sign. The estimateof the youth outflow rate, however, seems to be robust to the inclusion of the youthunemployment rate. The conclusion is that the youth outflow rate performs better thanthe youth unemployment rate both in terms of statistical and economic significancein most cases, and that the youth outflow rate seems to be robust to the inclusion ofthe youth unemployment rate. One reason for this may be the time horizon of theyouth, as the outflow rate is consistent with a short-run focus of the youth, while theunemployment rate reflects long-term changes. Similar results are found by Carlsenet al. (2006) in their study of labor market impacts on regional wage formation andinterregional migration. This suggests that care should be taken when choosing labormarket indicator.

6.3 The impact of observed covariates

Table 7 shows the effect of some of the observed covariates on the dropout proba-bility from the specification with unobserved heterogeneity.15 Standard errors are notclustered in this specification; however, only the regional level standard errors are sig-nificantly affected when clustering standard errors in the other specifications, whilethe individual level standard errors change very little.

15 Table 7 corresponds to column (2) in Tables 2 and 3. Complete estimation results including durationdependence, year, and region dummies are available upon request.

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Table 7 The effect of observed covariates (standard errors in parentheses)

Academic track Vocational track

Girls Boys Girls Boys

Immigrant 0.262*** 0.307*** 0.088** 0.210***

(0.037) (0.044) (0.041) (0.036)

Mother’s income −0.065*** −0.035*** −0.078*** −0.067***

(0.011) (0.012) (0.012) (0.010)

Father’s income −0.053*** −0.074*** −0.065*** −0.051***

(0.006) (0.007) (0.007) (0.006)

Mother’s education (compulsory ref.)

Upper secondary −0.398*** −0.390*** −0.378*** −0.475***

(0.066) (0.078) (0.068) (0.056)

Lower university 1–3 years −0.620*** −0.647*** −0.651*** −0.718***

(0.067) (0.079) (0.073) (0.059)

Higher university >3 years −0.604*** −0.879*** −0.472*** −0.713***

(0.082) (0.096) (0.125) (0.104)

Father’s education (compulsory ref.)

Upper secondary −0.286*** −0.570*** −0.599*** −0.564***

(0.052) (0.064) (0.054) (0.044)

Lower university 1–3 years −0.506*** −0.831*** −0.817*** −0.670***

(0.055) (0.067) (0.063) (0.048)

Higher university >3 years −0.617*** −0.951*** −0.890*** −0.763***

(0.060) (0.072) (0.080) (0.063)

Number of individuals 138,693 121,811 102,961 132,426

The estimates correspond to column (2) in Tables 2 and 3. All specifications include duration dependence,region and year dummies, in addition to the local labor market variables.*** Indicates significance on the 1 % level, ** on the 5 % level, * on the 10 % level

The estimated coefficients all show the expected signs, and confirm findings fromother studies of dropout behavior. Immigrants are more likely to drop out from uppersecondary school compared to natives, particularly immigrant boys. The higher edu-cated parents, the less likely it is that the student drops out of school before completion.Household income also has a significant effect on individual dropout behavior. Theeffect of the background variables is strong and highly significant.16

6.4 Alternative measures of the returns to schooling

Although relative employment prospects measured by the relative outflow rate forskilled versus unskilled individuals may be one way to measure the returns to school-ing, the theory originally emphasizes the role of earnings differences. The returns to

16 Note, however, that Falch and Strøm (2013) find that after controlling for achievement at the end ofcompulsory school, much of the importance of the family background variables disappears.

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Table 8 Results when including relative wages

Academic track Vocational trackBOYS: (1) (2) (1) (2)

Current labor market conditions, 16-24 years

0.199**(0.097)

0.231**(0.099)

0.072(0.046)

0.074*(0.044)

Relative outflow rate-0.152(0.103)

0.016(0.054)

Relative wages-0.253(0.587)

0.128(0.313)


GIRLS:Current labor market conditions, 16-24 years

0.241***(0.081)

0.261***(0.078)

0.041(0.057)

0.049(0.055)

Relative outflow rate-0.073(0,116)

-0.048(0.070)

Relative wages-0.706(0.588)

0.077(0.384)

Number of individuals 123,579 93,998Notes: Column (1) reports the estimates from a model including the youth outflow rate, the relative outflow rate, observed covariates (immigrant status, study program, parents’ income and education level), in addition to duration dependence, region and year dummies. The specification corresponds to column (2) in Table 2and Table 3.Column (2) reports the estimates from a similar specification as column (1), except the relative outflow rate is replaced with relative wages.The relative outflow rate is defined as the outflow rate from unemployment to work for skilled (having at least completed upper secondary school) relative to unskilled individuals.Relative wages are defined as the yearly average of monthly wages adjusted for working hours for skilled (having at least completed upper secondary school) relative to unskilled individuals.

*** indicates significance on the 1% level, ** on the 5% level and * on the 10 percent level.

education are not simply the chances of getting a job, but also the expected stream ofincome due to a completed educational program. This may be particularly relevant ina country like Norway, where the level of unemployment is low. It is thus interestingto see whether changes in the wage differentials between skilled and unskilled indi-viduals affect the decision to drop out, and whether this would change the results forthe proxy variable for the opportunity costs.

In order to investigate this, I run regressions using relative wages for skilled versusunskilled individuals to measure the benefits of schooling. As before, skilled individu-als are defined as persons having completed at least upper secondary schooling, whileunskilled individuals have at most completed compulsory school. Wages are definedas monthly wages adjusted for working hours. As the data only provide informationabout wages from 1995 and onwards, the first cohort is excluded in the estimation.Table 8 reports the results from the estimations. Column (1) reports the original results,using the relative outflow rate to measure returns to education, but excluding the 1994cohort in order to get comparable results. Column (2) presents results using relativewages instead of the relative outflow rate.

Including relative wages instead of relative outflow rates does not change the resultsin any substantial manner. Relative wages have no significant effect on the probabilityto drop out from neither academic nor vocational programs, and the estimate for the

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current outflow rate changes very little as well. One reason for why relative wages donot have any large effect on the probability to drop out may be the compressed wagedistribution in Norway, making relative wage movements less important.

7 Conclusion

This paper investigates the impact of local labor market conditions on individualdropout behavior for students enrolled in upper secondary school in Norway from1994 to 2006. Distinguishing between measures affecting the immediate opportunitycosts and measures affecting the perceived long-term income gains, the effect of locallabor markets is identified using variations in outflow rates from unemployment towork across regions over time. The probability that a student drops out of uppersecondary education is modeled as a discrete time duration model, which makes itpossible to estimate the effect of time-varying variables—such as current outflowrates. The results show that current youth outflow rates have the expected impact aspredicted by economic theory on individual dropout behavior. A 1 % increase in youthoutflow rates raises the probability that a student drops out of upper secondary schoolbefore completion with 0.1–0.3 %.

The estimated effects of local labor market conditions found in this paper are ratherlarge compared to other similar studies. One reason may be that it is relatively easyto re-enter upper secondary education in Norway, making schooling decisions moresensitive to local labor market conditions. Another reason may be the choice of labormarket tightness indicator. When including the unemployment rate instead of the out-flow rate, the estimated impact becomes much smaller and in most cases insignificant.One reason for this may be the short-run focus of youth, making the outflow ratemore appropriate than the unemployment rate when modeling dropout decisions. Thisindicates that results are sensitive to the chosen measure of labor market indicator,and that care should be taken when choosing which labor market indicator to include.Lastly, the results suggest that current opportunity costs matter more for the dropoutdecision than future expected income prospects.

Acknowledgments This paper is part of the project “The Educational System in Norway”, funded by theNorwegian Research Council. I am grateful for useful comments and suggestions from Erling Barth, PålSchøne and Steinar Strøm, as well as three anonymous referees.

Appendix

See Tables 9, 10, 11 and 12.

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Table 9 Number of students in each cohort

Cohort Whole sample Academic track Vocational track

Number of stu-dents enrolling

Percentagedropping out





1994 49, 118 28.1 28, 115 21.3 21, 003 37.1

1995 49, 224 28.9 26, 985 19.5 22, 239 40.1

1996 49, 194 30.2 27, 045 22.8 22, 149 39.3

1997 48, 990 29.4 26, 471 21.4 22, 519 38.9

1998 48, 980 28.2 26, 227 18.9 22, 753 38.9

1999 47, 566 28.7 25, 614 18.7 21, 952 40.3

2000 48, 234 31.5 25, 565 22.3 22, 669 41.9

2001 48, 639 30.3 23, 685 19.7 24, 954 40.4

2002 50, 662 30.9 23, 928 20.4 26, 734 40.3

2003 51, 471 29.7 23, 625 20.8 27, 846 37.3

Total 492, 078 29.6 257, 260 20.6 234, 818 39.4

Table 10 Means and standard deviations of outflow unemployment rates

Outflow rates Mean SD

Youth 0.19 0.04

Unskilled 0.14 0.03

Skilled 0.15 0.03

Table 11 Correlation coefficients of outflow unemployment rates

Youth Unskilled Skilled

Youth 1 – –

Unskilled 0.82 1 –

Skilled 0.61 0.69 1

Table 12 Main activity and main task for youth and dropouts 16–24 years in the 2001 LFS

Main activity Main task Dropouts 16–24 years All youth 16–24 years

Freq. % Freq. %

Conscript Conscript 1, 985 2.44 17, 567 2.71

Employed Employed 51, 453 63.33 299, 608 46.21

Self-employed 587 0.72 4, 094 0.63

Family worker 399 0.49 3, 374 0.52

Not stated 402 0.49 1, 714 0.26

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Table 12 continued

Main activity Main task Dropouts 16–24 years All youth 16–24 years

Freq. % Freq. %

Seeking work/ unemployed Student 0 0 28, 006 4, 32

Disabled 78 0.1 78 0.01

Staying at home 222 0.27 385 0.06

Without work 5, 864 7.22 12, 438 1.92

Other 633 0.78 1, 211 0.19

Laid off 187 0.23 582 0.09

Outside the labor force Student 0 0 207, 924 32.07

Disabled 327 4.02 5, 098 0.79

Staying at home 2, 706 3.33 7, 068 1.09

Without work 3, 045 3.75 632 0.97

Other 1, 876 2.31 374 0.58

Total 78, 306 100 639, 362 100

Temporarily absent Employed 8, 413 10.35 48, 338 7.45

Self-employed 21 0.03 429 0.07

Family worker 43 0.05 207 0.03

Not stated 65 0.08 235 0.04

Dropouts are defined as youth who are currently not in education and have not completed upper secondaryschool

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