Across OECD Countries: How Students Incorporate

Economic Conditions into thier Choice of Major ∗

Jennifer Graves†

Zoe Kuehn‡

This version: August 2015PRELIMINARY DRAFT. PLEASE DO NOT CITE.

Abstract

Combining information on college major and year of graduation from PIAACdata with national accounts data on sector-specific growth, we test whether studentsin different OECD countries incorporate information on relative sector growth intotheir choice of college major. First, we find that there are notable differences inhow students adapt to sector-specific economic conditions both by country and byfield of study. Our findings show that compared to many other OECD countries,individuals in the United States seem to be more responsive. Second, we exam-ine which individual and country factors influence whether students’ specializationchoices align with sector-specific economic conditions. We find that across countriesmales are generally more likely to major in shrinking fields than females. However,there are differences across countries in whether students with vocational degreesare more likely to choose shrinking or growing fields, as well as differences in theimpact of labor market conditions on alignment of sector growth and field of study.***Third, we estimate the labor market returns to choosing a major that aligns”well” with sector changes across our sample of OECD countries.***

JEL classification: I23, L16, J24, J62, H52Keywords: PIAAC, educational choices, specialization, sectorial economic condi-tions

∗This paper is part of a research project funded by the Fundacion Ramon Areces within their 13thSocial Science National Competition 2014.†jennifer.graves@uam.es · Universidad Autonoma de Madrid· Departamento de Hacienda Publica ·

Campus de Cantoblanco · 28049 Madrid · Spain.‡zoe.kuehn@uam.es · Universidad Autonoma de Madrid· Departamento de Analisis Economico: Teorıa

Economica e Historia Economica · Campus de Cantoblanco · 28049 Madrid · Spain.

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1 Introduction

If the health care sector is growing in a country relative to other sectors, do students ma-jor at a greater rate in fields within health care? If construction and manufacturing areshrinking within a country, do we see a reduction in specialization in the correspondingfields? In this paper, we present evidence on differences across various OECD countriesand by field on how sector specific economic conditions within a country impact fieldof specialization in higher education. We then estimate which individual characteristics,such as immigrant status, gender and socio-economic background, as well as country-level variables such as concentration of public employment and education expenditures,influence whether students in a country choose fields that align well with sector-specificeconomic conditions.***Lastly, we provide evidence on the returns from choosing a majorthat aligns well with sector-specific economic conditions across countries.***

Students may choose a field of study based on a combination of factors, including theirindividual preferences, available information, as well as their ability and training for thefield. Despite the existence of some students with strong preferences for a particular field,one would nonetheless expect to see a cohort of higher education students adapting onthe margin to changes in relative returns across fields of study, conditional on havingsuch information. If students are not accurately incorporating economic opportunitiesinto their specialization decisions, this could result in lower lifetime earnings and/or timespent in unemployment. At a more aggregate level, this can potentially result in a lackof qualified workers for certain high-growth sectors within countries, potentially higherunemployment, low economic growth of a country, and, to the extent that higher edu-cation is subsidized by the government, could also imply a mis-allocation of governmentresources.

Similar analyses to ours have been carried out for the United States, but much less re-search exists for other countries regarding students’ higher education decisions. Due tocross-country differences in educational systems, cultural differences and labor marketconditions, findings for the United States do not necessarily generalize to other countries.To the best of our knowledge, this is the first study to document differences in stu-dent responses to sector-specific economic conditions in higher education decisions acrosscountries. Studying multiple OECD countries allows us to test how country-level variablesaffect students’ choices, and to examine whether the impact of individual characteristicsare similar in different country contexts.

In this paper, we provide evidence and estimation results for eight different OECD coun-tries: the United States, United Kingdom, Germany, Spain, France, Sweden, Japan, and

Finland.1 Our data consists of individual-level data from PIAAC (Programme for theInternational Assessment of Adult Competencies) 2012, as well as macroeconomic vari-ables from the Worldbank, OECD, and national statistical offices for each country. Wecombine these multiple data sources to create a rich dataset that includes detailed indi-vidual controls, sector-specific economic conditions, and a variety of country-level controlvariables. For our analysis, we line up economic conditions of sectors with students’ choiceof major at a certain point in time some years before graduation, which we refer to as thedecision year, or when students must define their field of specialization.2 We assume thatindividuals are ultimately interested in wage compensation and that students would useavailable information in the years prior to making their specialization decision to assessthe earnings potential of a field of study. In constructing our sector-specific economicmeasures we use value added by sector to approximate wage compensation by sector.3

Under these assumptions, our approach thus allows us to address the following question:Do individuals take into account a sector’s earnings potential when making their highereducation specialization decisions?

The remainder of the paper is organized as follows: the next section provides a briefdiscussion of previous literature, Section 3 describes our data and provides some descrip-tive evidence, in Section 4 we present evidence on the field-specific connections betweensector-specific growth and students’ chosen field of study, in Section 5 we present evidenceregarding which individual and country-level characteristics influence whether studentschoose majors that align well with sector-specific conditions or not, in Section 6 we pro-vide evidence of the robustness of our results, in Section 7 we present evidence on thereturns to alignment of field of study and sector-specific economic conditions, and Sec-tion 8 discusses these findings and concludes.

1This paper is still a work in progress. Future versions will also provide results for Korea and theCzech Republic.

2A recent paper by Bordon and Fuz [forthcoming] analyses the advantages and disadvantages ofdefining choice of major upon college entry, as in most countries, or half-way thorough college as inthe US or Canada. The authors find that the latter increases student welfare, especially for female,low-income and/or low-ability students.

3To the extent that labor shares might change within a sector, an increase in the value-added share of acertain sector together with a decreasing labor share in the same sector would imply an increasing capitalshare. Hence, while the particular sector might become relatively less attractive for workers, entrepreneurswould perceive it as more attractive. As long as students also consider becoming entrepreneurs, theparticular sector will still be a “growing sector” from the students’ perspective.

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2 Related Literature

Literature, in most cases looking at US data, has discussed and studied this questionextensively. Predicted future earnings are found to play a large role in choice of collegemajor, but that this varies by subject studied, the possibility of going on to gradu-ate school, gender, race, socio-economic status, and student ability (Berger [1988], Eideand Waehre [1998], Montmarquette, Cannings, and Mahseredjian [2002], Davies andGuppy [1997]). A recent study by Bradley [2012] incorporates many of these factorsat once. Using data for the United States from 1982-2010, the author analyzes how busi-ness cycle fluctuations affect college enrollment, major switching, and time-to-degree andshe finds effects to vary by area of study. The fact that economic conditions matter forstudent decisions regarding higher education does not seem surprising; even less so consid-ering important business cycle effects on starting wages that persist over time, implyinglarge effects on lifetime earnings (see e.g. Raaum and Røed [2006] or Kwon, Milgrom andHwang [2010]). Additionally, Robst [2007] looks at subject-match between college majorand work place activities in the US and finds that workers who are mismatched earn lessthan adequately matched workers with the same amount of schooling. If specializing inshrinking fields makes it more likely that individuals will have to switch sectors in thefuture, this might entail additional costs. For instance, Neal [1995] finds displaced workersforced to switch industries suffer far greater wage losses than those who take up a newjob within their sector. The costs of switching sectors are also higher for those with moreyears of education. Decisions made in higher education regarding area of specializationhence appear to have important long-term effects on earnings.

Long-term effects of specialization decisions made in higher education are not, however,uniform across sub-groups of the population. For instance, literature finds that muchof the gender wage gap in starting wages is due to different fields of specialization bymales and females (see e.g. Gerhart [1990] or Zafar [2013]). Ma [2011] finds that, inthe United States, the extent of occupational segregation by gender, race, and nativityat the national level influences patterns in individual college major choice. This couldpotentially lead to persistence in earnings differences across gender, race, and immigrantstatus. Some of these results, however, could also be due to student perception (or misper-ception) of returns to college majors. While some studies show that students can predictstarting salaries quite well (Webbink and Hartog [2004]), others show that the ability topredict future salaries varies across the student population. For example, some studiesfind gender differences in students’ perceived rates of return to higher education (Anchoret al [2011]) while others find that poorer students make worse predictions about startingwages (Betts [1996]). Only few findings exist for countries other than the US. Aina, Baiciand Casalone [2011], studying Italian graduates, and Messer and Wolter [2010] , study-

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ing Swiss university graduates, both find that labor market conditions have a significantimpact on individual time-to-degree. For younger students, Aparicio Fenoll [2010] findsthat Spanish male students respond to lower returns to education during a housing boomby dropping out of high-school.

Considering implications beyond those at the individual level, in a country where studentsadjust their specialization decisions according to growing or shirking sectors, structuralchange might happen more smoothly. 4 Diprete el al [1997] find that direct impedimentsto labor mobility across sectors differ by country and the degree of welfare state provision.A large literature using relatively long time periods finds that structural change is essen-tial for economic growth (e.g. Caselli and Coleman [2001]).5 Additionally, the empiricalliterature finds industry-specific human capital to be a costly barrier to inter-industrylabor mobility (Parent [2000], Weinberg [2004]).6 It is possible that adequate choice offield of study might be important for easing employment shifts across sectors. How wellstudents choose their fields of study in accordance with sectors of high growth potentialcould thus have important macroeconomic impacts.

3 Data

3.1 Sources and Sample Creation

Our main data source comes from PIAAC 2012. According to the OECD PIAAC website(http://www.oecd.org/site/piaac), data collection occurred between August 1, 2011 andMarch 31, 2012 in 24 different OECD countries, and data was collected on approximately“166,000 adults, representing 724 million adults aged 16 to 65”. The PIAAC Survey ofAdult Skills was designed to assess adult proficiency in a variety of skills relevant to theworkplace. While it focuses on adults and questions naturally differ, its general surveystructure is quite similar to that of previous PISA (“Programme of International StudentAssessment”) data collection efforts.

4Messina [2006] finds that cross-country differences in structural change are related to differences intechnological change, in savings rates and market regulations. However, direct impediments to labormobility across sectors have been studied less.

5some studies using detailed sectorial divisions but short time periods find industry shifts to have littleimpact on aggregate growth (e.g. Foster, Haltiwanger, and Krizan [2001])

6It is still the case that most macroeconomic models of structural change assume perfectly mobilelabor across sectors (see e.g. Echevarria [1997] and Ngai and Pissarides [2007]).

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For the purposes of our study, there are a few key variables included in the PIAAC datathat are particularly useful. First, the questionnaire asks “What was the area of study,emphasis or major for your highest level of qualification?” This is recorded with possiblecoded responses broken into the following 9 categories: 1) general programmes; 2) teachertraining and education science; 3) humanities, languages and arts; 4) social sciences,business and law; 5) science, mathematics and computing; 6) engineering, manufacturingand construction; 7) agriculture and veterinary; 8) health and welfare; 9) services. Thelevel of this degree attained is also coded using 7 categories (ISCED: 0 to 6).7 Whilethe PIAAC data is a single cross-section, fortunately, the data also asks about timing ofwhen the degree was obtained. The age at which each individual finished the mentioneddegree is recorded in the data, along with the current age of each individual. We canthus back out the date at which each respondent completed their degree. Combining theareas of study and levels of higher education attained with individual timing of highereducation attainment allows us to make use of variation across time for our estimations.Specifically, we align sector-specific economic conditions with each individual at the timewhen they would have been making decisions regarding specialization in higher education(undergraduate or graduate level studies).Our final sample thus consists of individualswho have finished post-secondary education (ISCED 4B or above) and specialized in oneof the seven fields which we are able to assign to economic sectors.

For creating measures of sector-specific economic conditions, we turn to data beyond thePIAAC dataset. For instance, for the United States, we consider data on value addedby sector, available from the Bureau of Economic Analysis from 1947 onwards. Just asan illustrative and not comprehensive example, Table 3.1 displays the correspondence offields of specialization and economic sector used for the US. In the first part of the series(1947-1976), data on value added by activity is available for 31 of the 89 subsectors. For1977-2012, we have data on more detailed (89 subsectors) sectors and in particular onvalue added by “Computer and electronic products”, “Information and data processingservices” and “Computer systems design and related services”, which we match to the fieldof study “Science, mathematics and computing.” We conduct similar matching of outsidesector data to fields of study for all other countries. For detailed information about datasources and correspondence of field to sector for each country we refer the reader to ourCompanion appendix.8 Our final constructed dataset is representative of each countrystudied for the working-age population, 16-65 years old, with a post-secondary degree

7ISCED stands for International Standard Classification of Education. The classifica-tion system is designed by the United Nations to be comparable across countries. De-tails on ISCED coding can be found at: http://www.uis.unesco.org/Education/Pages/

international-standard-classification-of-education.aspx.8 Our Companion Appendix can be found at:

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(professional/vocational degree or higher), in 2012, with our variation in sector-specificconditions coming from variation in the years in which individuals in the data completedtheir highest level of education. We also use other economic variables in our analysis. Inparticular, we consider data on unemployment rates, employment protection legislation,union density, mean job tenure, public employment, and education expenditures. We alsoconstruct a recession dummy indicating years with two consecutive quarters of negativegrowth in GDP per capita.

Table 3.1: Correspondence PIAAC Field and Economic Sectors: US

Field Sector

1946-1976

Teacher training and education science Educational services, Government**Humanities, languages and arts InformationSocial sciences, business and law Finance, insurance, real estate, rental, and leasing,

Professional and business servicesScience, mathematics and computingEngineering, manufacturing and construction Mining, Construction, Utilities, ManufacturingAgriculture and veterinary Agriculture, forestry, fishing, and huntingHealth and welfare Health care and social assistance

1977-2012

Teacher training and education science Educational services, Government**Humanities, languages and arts Publishing industries (includes software),

Motion picture and sound recording industriesBroadcasting and telecommunicationsPerforming arts, spectator sports, museums, and related activities

Social sciences, business and law Finance, insurance, real estate, rental, and leasing, Legal servicesMiscellaneous professional, scientific, and technical services,Management of companies and enterprises, Administrative and support services

Science, mathematics and computing Manufacturing of Computer and electronic products,Information and data processing servicesComputer systems design and related services

Engineering, manufacturing and construction Mining, Construction, UtilitiesManufacturing less Computer and electronic productsPipeline transportation, Waste management and remediation services

Agriculture and veterinary Agriculture, forestry, fishing, and huntingHealth and welfare Health care and social assistance

*a fraction of government’s VAT corresponds to VAT share of public education, see Companion Appendix for details.

We construct two different measures that aim to capture the sector-specific economic con-ditions prevailing (j) years before graduation in year (t). We refer to this year, (t − j),as the individual’s decision year. Our first measure is “sector share” (share), the shareof value added in year (t − j) produced by sectors corresponding to the field. Second,we consider the “change in sector share” (change) that describes the evolution over the 5years prior to the decision year of the share of value added by sectors. This measure isdefined as the percentage point difference in share of value added by sectors correspondingto the field in the 5 year span between years (t− j − 5) and (t− j).

Our measures of sector-specific economic conditions thus crucially depend on the individ-ual’s decision year, i.e. the lag (j) with which we measure them. We choose these differentlags such that they align with logical timing for when one might expect a student to make

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his or her specialization decision. This will naturally differ by country and degree levelachieved. For example, an individual obtaining a professional/vocational degree may onlydecide on their field of specialization 2 years before their recorded completion date, whilefor a bachelor’s degree a 4 or 5 year lag before graduation is likely appropriate. To deter-mine the most appropriate decision years by country and education level, we consider avariety of sources to determine the average time span between declaring one’s major andgraduation in each country, referencing information such as required years for declaringa major at top universities in the country and official guidelines for duration of studies.Table 3.2 displays our choice for these decision years. Again, detailed sources and expla-nations for these choices can be found in our Companion Appendix.

Table 3.2: Decision years for graduation year (t) by country and degree level

Vocational Bachelor’s

United States t− 2 t− 3United Kingdom t− 2 t− 3Finland t− 4 t− 4France t− 2 t− 3Germany t− 3 t− 4Japan t− 2 t− 4Spain t− 2 t− 4Sweden t− 2 t− 3

3.2 Descriptive Statistics

Table A.5 in the Appendix displays descriptive statistics for individual level data from thepooled sample that includes all eight countries.9 These variables all come from PIAACindividual-level data. Around 46% of all individuals in our sample are men, ranging from54% in the German sample to 41% in Finish sample. Almost half of all individuals werebetween 20 and 25 years old when they finished their highest level of education whilearound one fourth was between 25 and 30. The fraction of foreign-born individuals inour sample is around 11%, ranging from 0.2% in Japan to 22% in the UK. Around 6%of individuals in our pooled sample were raised in bilingual homes, in Spain this numberincreases to 17% while it is only 1% in Japan. Parents of around 81% of individuals wereborn in the respective country, which is the case for 65% of individuals in the UK sampleand 98% for the Japanese sample. On average around 36% and 40% of individuals have

9For descriptive statistics for each country we refer to our Companion appendix.

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parents with at least completed secondary and tertiary education. The former statistic ishighest in Finland (44%) and lowest in Spain (21%) while the later is highest in Sweden(50%) and lowest in Finland (25%). The variable “number of books in the home as achild” is often used to capture home environment, and is interpreted as a proxy for house-hold literacy, as well as parental socio-economic status. Around one third of individualsrecall having had 26 to 100 books (3rd category out of 6) at home when they were chil-dren, while around 1/5 recall between 100 and 200 books and between 201 and 300 booksat home as a child (4th and 5th categories out of 6). The percentage of working-agedpeople in our sample with a professional/vocational degree is 39%. These numbers rangefrom 30% in Spain to 49% in Germany. The remaining fraction holds a bachelor’s degreeor higher. Regarding fields of study, most individuals major in “Social Science, Businessand Law”(26%), followed by “Engineering, Manufacturing and Construction” (18%), and“Health and Welfare”(15%). “Agriculture and Veterinary” is the smallest field in allcountries, with only around 0.7-3% of individuals with majors in this field.

In Table A.6 in the Appendix we display country level variables together with our twomeasures of sector-specific economic conditions. These variables all come from outsidedata sources are and combined with the PIAAC individual data using timing of studenteducational decisions. We observe that most individuals in our sample graduated duringthe second half of the 1990’s. All other country variables are reported with a the corre-sponding lag respective to individuals’ graduation years (see Table 3.2). On average un-employment rates were around 7-8% when individuals made their specialization decisions,ranging from a low 2.8% in Japan to a high 15% in Spain. Data on public employment asa percentage of the civilian labor force show an average percentage of around 18%. Japanhas the lowest public employment of 6% compared to a high 29% in Sweden. Expenditureon education, measured as a fraction of GDP (Educ/GDP), ranges from 3.5% (Germany)to 6% (US). We also consider two different employment protection measures from theOECD on protection from individual and collective dismissals and restrictions regardingthe use of temporary contracts. Higher values indicate stronger worker protection. Val-ues for the former measure range from 0.25 to 2.8 in the US and Spain respectively. Theindex for the protection of temporary workers ranges from 9.26 in the US to 3.5 in France.

Turning to our two measures of sector-specific economic conditions: Not surprisingly, sec-tors with the largest value-added shares are also the ones with most graduates. Valueadded of sectors associated with “Engineering, Construction and Manufacturing” and“Social Science, Business and Law” account for between 24-35% and 13-26% of GDP re-spectively. “Health and Welfare” accounts for about 4-8% of value added. The field withfewest graduates “Agriculture and Veterinary” is associated with sectors with very littlevalue added share. Regarding our second measure “change in sector share”, most sec-

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tors experience both periods of reductions and growth, but changes over five year periodstend to be rather small. The exceptions are value added shares of sectors associated with“Engineering, Construction and Manufacturing”, which only experience reductions ( 1.5percentage points on average), and value added shares of sectors associated with “SocialScience, Business and Law”, which grow (1.2 percentage points on average).

4 How lagged sector variables affect student special-

ization in specific fields

4.1 Descriptive Evidence

In Figure 4.1 we provide a visual first glance at our empirical approach. For a selectionof countries and fields of studies we display how the share of individuals majoring in afield (graduating in year t) changes over time and we combine this graph (top panel) witha graph that displays the evolution of the share of the value added in the correspondingsector (bottom panel) and a graph that displays the 5-year change (middle panel). Thearea between each plotted value and 0 is shaded in this middle panel, so that positive andnegative changes over the past 5 years can be more easily observed. These last two sector-specific variables (middle and bottom panels) are measured with the country-specific lagfor obtaining a bachelor’s degree (see Table 3.2) to account for the fact that studentsmust decide on an area of specialization before they complete their degree. It should benoted that the top panel in each graph is measured with error. This is because propor-tions majoring in each field category in a given year are calculated using individuals inthe PIAAC data who complete their degree in that year, while the PIAAC data is notrepresentative when divided into bins in this manner. To account for this, years withvery few observations are dropped, and we use a three-year moving average. Despite thislimitation, the graphs provide a convenient visual representation of how sector growthvariables align with student specialization.

For instance, consider the upper left hand graph (country: Germany; field: Social ScienceBusiness and Law). Overall, between 1973 and 1991, the share of individuals graduatingwith majors in “Social Science Business and Law” in Germany increased, but during1991 -1996 it decreased and increased again afterwards. Looking at the bottom twopanels one can observe a possible reason for this temporary drop. These graphs arelabeled with the same graduation dates, but plot sector variables with the correspondingdecision lags for a typical bachelor degree. This alignment this illustrates sector-economic

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Figure 4.1: Major Choice and Sector-Specific Economic Conditions

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conditions faced by students at the time they have to make their specialization decisions.Those graduating in 1991 made their decision in 1986, a time of a falling VAT share ofsectors associated to Social Science Business and Law. In addition, - as can be observedfrom the middle panel- fields associated with this sector were producing relatively lessvalue added compared to the 5 year span before their decision years. Given that weobserve relatively fewer individuals graduating after 1991, this panel of figures providean example where students’ major choices appear to align well with what was happeningwithin the corresponding sector. On the other hand, inspection of the upper right-handgraph for “Engineering, Manufacturing and Construction” for the UK shows higher sharesof individuals majoring in these fields in periods of general downward trends.

Looking at the lower left (country: United States, field: Science, Math and Computing)and right hand graph (country: United States, field: Health and Welfare), we observesome graduating years, where VAT share growth at the decision year moves opposite tosector changes in the 5 years prior, while at other times the two measures move moreclosely together. Hence, in both cases, we observe some variation in how well the share ofgraduates in a field aligns with the corresponding value-added sector share and change insector share. This suggests that for some fields within a country students may be betteror worse at adapting their decisions to changes in sector-specific economic conditions.

4.2 Estimation Strategy and Regression Results

Next, we examine these connections more formally using a regression framework. To thisend, we estimate the following equation separately for each country:

Mi,f,t = α + βSf,t−j + λZi + φCt−j + γV Di + δV DiSf,t−j + ε, (4.1)

where M is a dummy variable for whether individual i majors in a specific field f . Re-gressions are run separately for each country and for each of the 7 majors (f). Thesubscript (t) refers to the year of graduation and is used to determine which year’s vari-ables to consider for sector- and country level variables. Sf,t−j is the variable for thesector-specific economic conditions measured in decision year (t− j), or (j) years beforegraduation year (t). Zi is comprised of non-time-varying individual characteristics andCt−j are country-level variables, also measured for the decision year. Given observed dif-ferences in fields of specializations for professional/vocational versus university degrees,we also include information on type of highest degree obtained, i.e. we include a dummyvariable for vocational degree (V Di) as highest degree, and we interact this dummy vari-able with our measure of sector-specific economic conditions. Estimating Equation 4.1relates the information displayed in the different graphs of Figure 4.1, controlling for thecountry’s general economic situation at the time of decision making, using variables such

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as the unemployment rate, whether the economy is in a recession and the duration of thisrecession. Inclusion of such controls ensures that estimated coefficients of interest reflectsector-specific conditions and not just business cycle effects. 10 Additionally, exploring therelationship between sector growth and student majors in a regression framework avoidsmeasurement error issues that arise when individual majors are aggregated by year as inthe previously discussed Figure 4.1

Our main coefficient of interest in Equation 4.1 is β (as well as δ on the interactionwith vocational) indicating the degree to which students consider sector-specific economicconditions for their choice of major. Running these specification for each of the eightcountries and each of the seven fields using our two different measures of sector-specificeconomic conditions (share of value added and change in share by sector) results in over100 specifications. We therefore do not report estimation results for each individual spec-ification. Instead, we provide a summary table in our Companion Appendix (providingdetailed results upon request) and discuss findings from these specifications here.

5 How country and individual characteristics relate

to a student adapting or not adapting to sector-

specific information in their specialization decisions

5.1 Estimation Strategy

In our second set of estimations we want to further explore the link between students’choice of college major and specific declining or growing industries. We check if and howincorporating information on sector specific conditions depends on students’ individualcharacteristics. For example, are girls more likely than boys or foreigners more likely thannatives to choose majors in declining/rising fields? To this end, we group the growing ordeclining sectors within each country together regardless of fields of study and we run thefollowing regression:

OwnMjrCondi,t−j = α + λZi + φCt−j + ε, (5.2)

where OwnMjrCondi,t is a measure of the relative decline or rise of the sectors corre-sponding to the individual’s chosen field of study, Zi denotes individual characteristics.

10We have also tried running Equation 4.1 without the dummy for vocational degree and its interactionwith sector-specific economic conditions. Estimate for β do not vary much when doing so.

12

We define OwnMjrCondi,t, in four different ways, combining both the field of major (f)the individual (i) completed in time (t) as well as how the corresponding sectors werechanging between (t− j− 5) and the decision year (t− j). Our first measure is a dummyvariable that takes on value one if the share in value added of the sectors correspondingto the individual’s field of study experienced a positive change between years (t− j − 5)and years (t − j). We also consider a measure for above average sector share growth.This dummy variables takes on value one if the change in sector share between years(t − j − 5) and (t − j) was above the average of all changes experienced by fields whichincreased their value added shares. Our last two measures - min and max - are indicatorsvariables that take on value one if the individual majored in a field that was at the timethe fastest/slowest growing/declining sectors in their country. In other words, these twomeasures capture the worst faring sectors and best faring sectors in terms of economicgrowth in the years prior to the student’s decision year.

5.2 Regression Results

Apart from the type of degree, other individual characteristics like gender and socio-economic status as well as country-time-specific factors like unemployment or educationexpenditures might condition how well individuals align their specialization decisions withsector-specific economic conditions. Coefficients λ and φ in Equation 5.2 signal the degreeto which these characteristics and factors affect this alignment. The full set of individual-level control variables included in each of the four specifications (repeated by country) are:gender, whether the individual is foreign born, whether their native language is differentfrom that of the country, the interaction of foreign born and not native language, bilingualhome, whether the parent is a native of the country, a parent having completed secondaryeducation or tertiary education, and a set of incidator variables for the number of booksat home as a child (to proxy for literacy/SES). The full set of country controls included ineach specification are: the unemployment rate, union density, percentage of employmentin the public sector, education spending as a proportion of GDP, a measure of collectivebargaining, a dummy for whether the country was in a recession and the duration ofthis recession in years. Table 5.3 presents a summary of significant results for estimatedcoefficient λ (detailed results are available upon request).

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Table 5.3: Summary:Individual Characteristics and Major-Sector Alignment

Sectors associated with student’s field of major experienced growth of type:

positive above maximum minimumaverage

Finland Vocational 0.0245 0.1095*** 0.0686** * -0.0234Male -0.1743*** -0.084** -0.0281 0.1831***Not natv lang .0046 -0.0612 -0.0556 -0.056Foreign born 0.0856 0.0953 0.1316 -0.066parents tert edu 0.0226 0.0162 0.0363 -0.0134Books 500+ -0.1783* 0.0319 0.0749 -0.0735

France Vocational -0.0319 -0.0247 -0.0407 0.0476*Male -0.1902*** -0.125*** -0.0986*** 0.2337***Not natv lang 0.0125 -0.0933 -0.0583 0.0959Foreign born -0.0803 -0.1147** -0.1373*** 0.089parents tert edu 0.0391 -0.0162 -0.0389 -0.0116Books 500+ 0.0237 -0.0935 -0.0574 -0.0097

Germany Vocational 0.0023 -0.014 -0.0934** 0.1032**Male -0.1953*** -0.0971*** -0.0206 0.2411***Not natv lang -0.149 -0.2498 -0.2277 0.132Foreign born -0.0627 -0.1339 -0.1571 0.0557parents tert edu 0.0315 -0.0041 0.0397 0.0111Books 500+ 0.0653 -0.0983 -0.0469 0.0766

Japan Vocational -0.0136 -0.1242*** -0.0764** * 0.0565 *Male -0.1136*** 0.028 0.0074 0.2402***Not natv lang 0.4888*** 0.8302*** 0.7111*** 0.0057Foreign born -0.2942 -0.0125 -0.1475 0.8225parents tert edu -0.0717 -0.0342 -0.0367 0.0562Books 500+ 0.2132*** 0.1168* 0.1144 -0.1789***

Spain Vocational -0.136*** -0.0771*** -0.0622** 0.0788***Male -0.1073*** -0.0401 -0.0354 0.0781***Not natv lang -0.0176 -0.0297 -0.0518 -0.0413*Foreign born 0.0882 0.0326 0.1125 -0.0285parents tert edu -0.0464 -0.0492 -0.0541 0.0236Books 500+ 0.0724 0.0081 0.0414 -0.0025

Sweden Vocational -0.0788** -0.0987*** -0.0614** 0.0786**Male -0.0103 0.1043*** 0.0635*** 0.05**Not natv lang 0.0353 0.0468 0.0665 -0.0523Foreign born -0.0627 -0.0481 -0.0315 0.1676parents tert edu -0.0568 -0.0011 0.0215 0.0898***Books 500+ -0.209** -0.1452 -0.0426 0.1766***

UK Vocational -0.1111** -0.0837*** -0.0479 0.1275***Male -0.1929*** -0.0345 -0.0572* 0.2295***Not natv lang 0.1739*** 0.1748 0.1346 0.0775Foreign born 0.1084 0.0916 0.0729 -0.0011parents tert edu -0.0288 -0.0022 -0.0198 -0.0148Books 500+ -0.0871 0.0241 0.0544 0.0439

US Vocational -0.1361*** -0.0878* -0.098** 0.0924***Male -0.1426*** 0.0054 0.0836*** 0.2371***Not natv lang 0.0955 0.2387*** 0.1393*** -0.105Foreign born -0.039 0.0935 0.0772 -0.0293parents tert edu -0.0077 0.0518 0.0097 -0.0376Books 500+ -0.0417 -0.003 0.0622 -0.0133

Pooled Sample Vocational -0.0601*** -0.0501*** -0.0514*** 0.0701***Male -0.1408*** -0.0304*** -0.0107* 0.1744***Not natv lang 0.0805** 0.1071*** 0.0945*** -0.0093Foreign born -0.0321 0.0005 -0.00992 0.1262parents tert edu -0.0148 -0.0049 -0.0053 0.0129Books 500+ -0.0177 -0.0199 0.025 0.0024

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Level of education: Individuals obtaining a professional degree as their highest degreeare less likely to choose a major in a field corresponding with field-specific positive growth,above average growth or maximum growth. They are more likely to choose a major inthe minimum growth area. In other words, those obtaining a professional degree arenot incorporating sector-specific information very well into their specialization decisions.For all countries, we estimate significant coefficients for at least one of our measures ofalignment. In countries like the United States, Spain and Sweden this result is particularlyrobust. The one exception to this pattern is Finland, where those with professional degreesare more likely to major in fields of maximum and above average growth.

Gender: Another striking pattern are the estimated coefficients for male, indicatingthat men are less likely to choose majors experiencing positive growth in all countriesexcept for Sweden. A similar pattern is found for above average growth and maximumgrowth fields, as well as minimum growth fields (more likely). In particular, sectors re-lated to “Engineering, Manufacturing and Construction” were traditionally and are stillmale dominated. During the process of deindustrialization, these sectors have experiencedimportant reductions in their VAT shares. While men in Sweden are also found to bemore likely to choose minimum growth fields, they generally appear to choose a majorthat better aligns with sector-specific growth. This finding is especially interesting in thelight of Sweden as a country with important advances in gender equality. In 2013 Swedenwas ranked the 4th country with the lowest gender gap among 142 countries by the GlobalGender Report of the World Economic Forum.11 The only other exception we observe isfor the United States, as we also see a significant positive coefficient for men choosing themaximum growth field.

Foreigners: Regarding immigrant status we obtain fewer significant results that differmuch by country. In France, foreign born individuals are less likely to specialize in fieldsof maximum or above average growth. On the other hand, for some countries we find evi-dence that individuals whose native language differs from the one spoken in their countryis residence are found to better align their choice of major with sector-specific economicconditions. In particular, for the United States and Japan we find that those individualsare more likely to choose above average or maximum growth fields. They also seem tobe more likely to choose positively growing fields in Japan and the UK and less likely tochoose minimum growth fields in Spain.

11While the other relatively gender equal country in our sample - Finland- was ranked higher (2ndplace), in “Economic participation” Sweden was ranked better than Finland.

15

Socio-economic status There are surprisingly very few patterns based on socioeco-nomic status influencing whether students incorporate sector-specific economic conditionsinto their choice of major. For Sweden, higher socio-economic status (measured by thenumber of books at home) seems to be related with a student being less likely to choosea positive growth and more likely to choose the minimum growth field. The last resultalso holds when parental education is used to measure socio-economic status. Estimatesfor Finland point toward a similar direction while we find the opposite result for Japan.Otherwise, there do not appear to be any consistent patterns for other countries.

Turning to our country controls, Table 5.4 presents a summary of significant results forestimated coefficient φ (detailed results are available upon request).

Economy As measures of the overall state of the economy in each country, we includethe unemployment rate, a dummy for whether the country is in a recession and a variableindicating the duration of such a recession. In the pooled sample, a higher unemploymentrate is related to a student being more likely to choose a positive growth field and aboveaverage growth field, and less likely to choose the minimum growth field. This is in linewith what one would expect to observe: that students take economic factors more seri-ously when job opportunities are more scarce. On the other hand, being in a recessionis negatively associated with choosing an above average or maximum growth field, whichmay reflect a preference for stability rather than wage growth. The number of years acountry has been in a recession has the largest impact of the three measures, and is asso-ciated with students becoming more likely to choose above average and maximum growthfields. Beyond the pooled sample, however, we can see that there is quite a bit of variationacross countries in student responses to economic variables. This likely reflects a mixtureof wanting to choose a field that has a higher wage, weighed against the stability and jobsecurity of such jobs within the country. For example, in both France and Spain, as theunemployment rate rises, students are less likely to choose a major corresponding to posi-tive growth or above average growth fields. On the other hand, individuals in Finland, theUS, Sweden and the UK seem to be more likely to choose growing fields in times of higherunemployment. The evidence is less clear when we measure overall economic conditionsby our recession indicator of two consecutive quarters of a fall in GDP per capita.

16

Table 5.4: Summary: Country-Time Factors and Major-Sector Alignment

Sectors associated with student’s field of major experienced growth of type:

positive above maximum minimumaverage

Finland Unemply 0.0867*** 0.0389** 0.0368** -0.0325**Union 0.0259 0.0339** 0.0296** -0.0074Public Emply -1.7596 -8.1574** -4.0254 -8.6702***Indv Dismiss -0.6554** 0.4829** -0.0218 0.0388Educ/GDP 71.1955*** 9.9992 2.546 -16.4096*Recession 0.3245** 0.3086** 0.2676** 0.0742Numb years reces -0.2561*** -0.2034*** 0.1102** -0.0029

France Unemply -0.15** -0.282*** -0.0472 0.027Union 1.9241*** 1.793*** 1.6743*** -0.2087Public Emply -12.5722 -173.0293*** -51.1506 55.7247*Indv Dismiss 2.2066 12.0038*** 2.8408 -2.3364Educ/GDP 200.3915*** 77.8919 -102.3921 -34.7799Recession -0.6128 -1.7004*** -0.658 0.4934*Numb years reces 0.3265 0.9156*** 0.5056* -0.309*

Germany Unemply 0-0.0055 0.073 -0.0313 0.0622Union -0.0097 0.0835 -0.0446 0.1081**Public Emply 6.298 8.8723 -0.2118 -17.8509*Indv Dismiss -2.942 -3.07** 0.3556 1.8945Educ/GDP 77.2014** 42.9623 -14.5642 -45.0609Recession -0.088 -0.4604** 0.0549 -0.1703Numb years reces -0.2247* -0.0419 0.0183 0.2608**

Japan Unemply 0.389*** 0.3073*** 0.0268 -0.1271Union -0.1599** -0.0092 -0.1165 0.0099Public Emply -226.8678*** -147.2215*** -25.796 46.1753Indv Dismiss 0.1791 1.1478*** 0.3061 -0.3646Educ/GDP -14.7142** 16.7743*** 1.0843 -11.4268**Recession -0.4395*** 0.0542 0.0543 -0.0539Numb years reces 0.1945** -0.0172 -0.0369 0.0697

Spain Unemply -0.0659*** -0.0372*** -0.0134 0.0117Union -0.0298 0.05** 0.0339* 0.0022Public Emply 17.4711*** 13.0687*** 10.1902*** 1.1148Indv Dismiss 1.217*** 0.065 0.1836 0.0046Educ/GDP 150.8309*** 38.516 30.7548 4.9963Recession 0.1426 -0.2869* -0.4809*** -0.1811*Numb years reces -0.0031 0.3199*** 0.3571*** 0.1158

Sweden Unemply 0.0681*** -0.007 0.0056 -0.0219 *Union 0.0388** 0.0316** 0.0137 -0.0054Public Emply -7.3871** -2.3672 1.7374 -1.5526Indv Dismiss -3.0006*** 0.9764 -0.3919 -0.5258Educ/GDP -24.4673*** -4.5018 -7.3555 -0.3986Recession -0.7529*** -0.6253*** -0.3439*** 0.3341**Numb years reces 0.8834*** 0.603*** 0.317** -0.3167**

UK Unemply 0.0763 0.1877*** 0.1551*** -0.0357Union -0.0067 0.5192*** -0.3552*** 0.084Public Emply -2.9451 43.2865*** 19.0196* -3.2634Indv Dismiss -1.9042*** 0.9371** 0.9421 0.1075Educ/GDP 89.6961** 20.5246 24.1515 -12.8179Recession 0.0619 -0.1126 -0.1455 -0.21Numb years reces -0.2369 0.1827 0.1858 0.2283

US Unemply 0.1683*** 0.1902*** 0.0475 -0.1478***Union 0.0083 -0.4441*** -0.1468 0.2315***Public Emply 47.9275*** 37.8866*** 46.431*** -21.5731***Indv Dismiss 0 0 0 0Educ/GDP 8.1029 -2.7898 76.6095** 17.1808Recession 0.2332 0.716*** 0.1698 -0.4684Numb years reces -0.2643 -0.5764*** -0.2169 0.4177

Pooled Sample Unemply 0.0709*** 0.0589*** 0.0225 -0.033*Union 0.2239*** 0.1274** 0.1361** 0.0268Public Emply 22.4794** -28.4577*** -0.4757 6.2631Indv Dismiss -0.6124 1.5679*** 0.5268 -0.1477Educ/GDP 20.9317 24.9221* 1.3543 -12.3396Recession -0.1414 -0.2633*** -0.1352 -0.0227Numb years reces 0.0524 0.1478** 0.1275* 0.058

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Labor Market Higher union density in a country (but not necessarily within a stu-dent’s specific field) is associated with students being more likely to choose above averagegrowth and maximum growth fields in Sweden, Finland, Spain, and (especially) France,while this is just the opposite in the US and the UK and to a lesser extent Japan andGermany. The relationship between choosing growing or shrinking fields and measuresof employment protection results in ambiguous findings for Finland and the UK, positivefor some countries (France, Japan, Spain, pooled sample) and negative for others (Ger-many,Sweden).12 Also the relationship with the share of public employees turns out to bevery country-specific. For example, in Japan, higher public employment is very stronglyassociated with students being less likely to choose positive growth fields or above averagegrowth fields. For Sweden, France, and Finland we also estimate significant coefficientsof the same sign. On the contrary, in Spain, the UK and the US students are found tobe more likely to major in positively, above average, and maximum growth fields whenpublic employment is high. In Germany, we also see that students are less likely to choosethe minimum growth field when public employment is relatively high.

Education Spending There does not seem to be a clear-cut relationship between ex-penditure on education and alignment of major choice to sector-specific economic con-ditions. Results differ by country. However, for most countries as well as the pooledsample we observe that higher spending on education is generally associated with choicesof growing fields.

6 Robustness of Results and Additional Tests

7 Returns to Alignment of Field to Sector-Specific

Economic Conditions

8 Concluding Remarks

In this paper, we present evidence on how sector specific economic conditions within acountry impact field of specialization in higher education, finding that students’ adjust-ment to sector conditions is both field specific and differs across countries. We generallysee that the US appears to be more responsive to sector specific economic conditions than

12The zero effect estimated for the US is due to the fact that measures of employment protection inthe US are constant over time.

18

many other OECD countries. The majority of previous research on this topic has beenconducted for the United States, and yet different educational and labor market settingsshould call into question whether results found for the United States can necessarily begeneralized. Our findings show that many students’ responses to sector conditions differacross various OECD countries, as well as between the US and such countries, These arethus important distinctions to document in order to gain understanding of higher educa-tion decisions in different countries’ contexts.

We also estimate which individual characteristics and country-level variables influencewhether students in a country choose fields that align well with sector-specific economicconditions. We see that many aspects, such as individual immigrant status and language,as well as country-level measures of labor market conditions and education spending,have very country-specific relationships with student specialization decisions. On theother hand, variables such as level of higher education achieved and gender have rela-tively universal impacts. Of particular interest is the finding that males adjust poorly tosector conditions relative to females in almost all countries. We intend to explore thisconnection further in extensions to the work presented here. We also find the lack of influ-ence of socioeconomic status on student success in adapting to sector-specific conditionsa rather surprising finding.

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A Appendix

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Table A.5: Summary Statistics (weighted): Individual-level data from PIAAC, pooledsample

Variable Mean Std. Dev. Country Min. Country Max.

male 0.4645 0.4975 0.4134 0.5360age upon completing major•16− 19 0.0515 0.2156 0.0274 0.0853•20− 24 0.4892 0.4595 0.2544 0.8775•25− 29 0.2543 0.4155 0.0549 0.3915•30− 34 0.0892 0.2746 0.0163 0.1345•35+ 0.1157 0.2949 0.0083 0.2021foreign born 0.1127 0.2891 0.0024 0.2176not native language 0.0926 0.2641 0.0010 0.1827foreign born*not native language 0.0750 0.2354 0.0005 0.1585bilingual home 0.0589 0.2197 0.0142 0.1684parents native 0.8063 0.3599 0.6471 0.9793parents secondary edu 0.3556 0.4706 0.2120 0.4400parents tertiary edu 0.3999 0.4786 0.2464 0.5043books at home as child•1− 10 0.0548 0.2215 0.0261 0.0924•11− 25 0.1025 0.2976 0.0459 0.1451•26− 100 0.3224 0.4629 0.1965 0.3971•101− 200 0.1996 0.3992 0.1700 0.2237•201− 500 0.1956 0.3906 0.1344 0.3087•500+ 0.1252 0.3242 0.0709 0.2242Major/post-secondary degree in• Education 0.0951 0.2909 0.0660 0.1438• Humanities 0.1066 0.3052 0.0677 0.1680• Social Science 0.2551 0.4340 0.1803 0.3174• Science 0.0975 0.2894 0.0432 0.1512• Engineering 0.1822 0.3798 0.1013 0.2668• Agriculture 0.0213 0.1412 0.0071 0.0316• Health care 0.1451 0.3509 0.1168 0.1871Highest education• Vocational 0.3922 0.4842 0.3018 0.4940

N=14072

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Table A.6: Summary Statistics (weighted): Country-level data and measure of sector-specific economic conditions, pooled sample

Variable Mean Std. Dev. Country Min. Country Max.

year of graduation 1998 9.12 1993 2003unemployment 7.5678 2.4219 2.8027 15.2179public employment 0.1830 0.0125 0.0637 0.2920union density 35.1002 4.2058 10.8665 78.0224Employment protection measures• Individual dismissals 2.0089 0.1535 0.257 2.8216• Temporary workers 1.8139 0.3917 0.25 3.5319Average tenure*Median tenure*Expenditure on Education/GDP 0.0508 0.0054 0.0352 0.0588Sector share• Education 0.0455 0.0041 0.0281 0.0564• Humanities 0.0266 0.0040 0.0109 0.0443• Social Science 0.1875 0.0242 0.1301 0.2630• Science 0.0533 0.0105 0.0259 0.0757• Engineering 0.2853 0.0297 0.2350 0.3479• Agriculture 0.0277 0.0116 0.0149 0.0466• Health care 0.0596 0.0081 0.0371 0.0784Change in sector share• Education 0.0016 0.0043 -0.0005 0.0043• Humanities 0.0003 0.0037 -0.0017 0.0016• Social Science 0.0120 0.0131 0.0004 0.0200• Science 0.0040 0.0074 -0.0012 0.0180• Engineering -0.0148 0.0143 -0.0230 -0.0021• Agriculture -0.0064 0.0043 -0.0122 -0.0019• Health care 0.0040 0.0057 0.0026 0.0074

N=14072; *does not include US, because only numbers on median tenure are available for US (average:).

24