Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.84.6.332-338

Full Terms & Conditions of access and use can be found at

http://www.tandfonline.com/action/journalInformation?journalCode=vjeb20

Journal of Education for Business

ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20

Mathematical Content of Curricula and Beginning

Salaries of Graduating Students

B. Brian Lee & Jungsun Lee

To cite this article: B. Brian Lee & Jungsun Lee (2009) Mathematical Content of Curricula and Beginning Salaries of Graduating Students, Journal of Education for Business, 84:6, 332-338, DOI: 10.3200/JOEB.84.6.332-338

To link to this article: http://dx.doi.org/10.3200/JOEB.84.6.332-338

Published online: 07 Aug 2010.

Submit your article to this journal

Article views: 22

View related articles

ABSTRACT.

R

he฀ purpose฀ of฀ the฀ present฀ study฀ was฀to฀evaluate฀the฀effect฀of฀math-ematical฀ content฀ in฀ college-level฀ cur-ricula฀on฀graduates’฀beginning฀salaries,฀ which฀ vary฀ depending฀ on฀ their฀ major.฀ Many฀studies฀identify฀factors฀that฀influ-ence฀ students’฀ choice฀ of฀ major,฀ which฀ in฀ turn,฀ is฀ a฀ primary฀ determinant฀ of฀ their฀subsequent฀career฀choices฀(Eccles,฀ 1994;฀Gianakos฀&฀Subich,฀1988;฀Hack-ett,฀ 1985;฀ Lent,฀ Brown,฀ &฀ Hack1994;฀Gianakos฀&฀Subich,฀1988;฀Hack-ett,฀ 1994;฀Wallace฀&฀Walker,฀1990).฀In฀the฀ present฀ technology-oriented฀ economy,฀ more฀jobs฀are฀available฀in฀highly฀skilled฀ fields฀with฀high฀salaries,฀including฀engi-neering,฀ science,฀ and฀ business.฀ Thus,฀ it฀ is฀ desirable฀ for฀ students฀ to฀ choose฀ majors฀ in฀ which฀ they฀ can฀ develop฀ the฀ skills฀and฀knowledge฀that฀are฀in฀demand฀ in฀the฀industry฀to฀have฀a฀better฀employ-ment฀opportunity.฀

College฀ students’฀ choice฀ of฀ major฀ is฀ a฀ function฀ of฀ many฀ factors,฀ such฀ as฀ gender,฀socioeconomic฀status,฀and฀early฀ academic฀performance฀(Maple฀&฀Stage,฀ 1991;฀ Trusty,฀ Robinson,฀ Plata,฀ &฀ Ng,฀ 2000).฀ In฀ particular,฀ early฀ academic฀ preparation฀ has฀ drawn฀ attention฀ from฀ researchers฀ as฀ a฀ determinant฀ choice฀ of฀ college฀majors;฀researchers฀have฀focused฀ on฀preparation฀in฀high฀school฀for฀several฀ key฀ subjects฀ including฀ mathematics,฀ reading,฀science,฀vocabulary,฀history,฀and฀ geography฀(Maple฀&฀Stage;฀Trusty฀et฀al.).฀ Trusty฀ et฀ al.฀ indicated฀ that฀ mathematics฀ and฀ reading฀ are฀ leading฀ indicators฀ but฀

that฀ their฀ significance฀ varies฀ by฀ gender.฀ Further,฀ Hackett฀ (1985)฀ indicated฀ that฀ mathematics฀self-efficacy฀is฀an฀important฀ predictor฀ of฀ a฀ mathematics-related฀ major฀ choice.฀ In฀ accordance,฀ students฀ who฀ do฀ not฀ develop฀ an฀ appropriate฀ level฀ of฀ mathematics฀ skills฀ during฀ their฀ elementary฀ and฀ secondary฀ school฀ years฀ may฀ not฀ feel฀ confident฀ enough฀ to฀ take฀ mathematics-related฀ courses฀ in฀ college฀ because฀ of฀ their฀ previous฀ unsuccessful฀ attempts฀ with฀ them.฀ Moreover,฀ these฀ students฀ may฀ prematurely฀ limit฀ their฀ options฀ in฀ quantitatively฀ oriented฀ majors฀ in฀ which฀ they฀ could฀ find฀ good฀ employment฀ opportunities฀ with฀ high฀ salaries.฀Instead,฀these฀students฀may฀direct฀ their฀ interests฀ toward฀ nonquantitative฀ and฀ soft฀ subjects,฀ which฀ in฀ general,฀ provide฀ few฀ employment฀ opportunities฀ with฀ low฀ salaries฀ (Murnane,฀ Willett,฀ Duhaldeborde,฀&฀Tyler,฀2000;฀Murnane,฀ Willett,฀&฀Levy,฀1995).฀

Murnane฀et฀al.฀(2000)฀identified฀math-ematics฀ as฀ the฀ primary฀ cognitive฀ skill฀ of฀ high฀ school฀ seniors฀ that฀ determines฀ their฀subsequent฀earnings.฀They฀reported฀ a฀ high฀ correlation฀ between฀ mathemat-ics฀ and฀ reading฀ in฀ measuring฀ cognitive฀ skills,฀ but฀ they฀ showed฀ mathematics฀ to฀ have฀ a฀ stronger฀ association฀ than฀ read-ing฀ with฀ subsequent฀ earnings฀ (Murnane฀ et฀al.).฀However,฀Murnane฀et฀al.฀did฀not฀ examine฀ the฀ influence฀ of฀ mathematical฀ skills฀on฀young฀students฀in฀choosing฀their฀ college฀ major฀ and฀ occupation,฀ which฀

Mathematical฀Content฀of฀Curricula฀and฀

Beginning฀Salaries฀of฀Graduating฀Students

B.฀BRIAN฀LEE JUNGSUN฀LEE

PRAIRIE฀VIEW฀A&M฀UNIVERSITY PRAIRIE฀VIEW,฀TEXAS

T

ABSTRACT.฀The฀authors฀examined฀an฀ association฀between฀mathematical฀content฀ in฀college-level฀curricula฀and฀beginning฀ salaries฀of฀graduating฀students฀on฀the฀basis฀ of฀data฀collected฀from฀a฀public฀university฀ in฀the฀southern฀region฀of฀the฀United฀States.฀ The฀authors฀classified฀the฀mathematical฀ content฀requirements฀of฀the฀curricula฀into฀ the฀following฀5฀groups฀according฀to฀the฀ number฀of฀mathematics฀courses฀required฀ and฀their฀content:฀(a)฀Group฀1฀(1฀elective฀ in฀basic฀mathematics),฀(b)฀Group฀2฀(2฀elec-tives฀in฀basic฀mathematics),฀(c)฀Group฀3฀ (Business฀Mathematics฀I฀and฀II),฀(d)฀Group฀ 4฀(Engineering฀Mathematics฀I฀and฀II),฀ and฀(e)฀Group฀5฀(more฀than฀2฀courses฀in฀ advanced฀mathematics).฀Graduates฀from฀ Group฀5฀earned฀$10,383฀more฀than฀did฀ those฀from฀Group฀1.

Keywords:฀beginning฀salaries,฀college฀cur-ricula,฀mathematical฀skills

Copyright฀©฀2009฀Heldref฀Publications

may฀be฀one฀of฀ the฀ primary฀ factors฀ that฀ are฀ attributed฀ to฀ a฀ difference฀ in฀ their฀ future฀earnings.฀

The฀present฀article฀evaluates฀quantita-tively฀oriented฀versus฀nonquantitatively฀ oriented฀majors฀on฀the฀basis฀of฀the฀level฀ of฀mathematical฀content฀required฀in฀cur-ricula.฀ Beginning฀ salaries฀ of฀ students฀ who฀major฀in฀less฀quantitative฀fields฀are฀ lower฀ than฀ those฀ in฀ more฀ quantitative฀ fields฀ including฀ engineering,฀ science,฀ and฀ business.฀ This฀ observation฀ could฀ augment฀Hackett’s฀(1985)฀and฀Murnane฀ et฀ al.’s฀ (2000)฀ findings฀ by฀ establishing฀ causal฀effects฀of฀three฀variables:฀cogni-tive฀ skills,฀ choice฀ of฀ major,฀ and฀ begin-ning฀salaries฀of฀graduates.

We฀reviewed฀the฀undergraduate฀catalog฀ at฀ a฀ southern฀ state’s฀ flagship฀ university฀ and฀ its฀ graduates’฀ beginning฀ salaries฀ by฀ major฀ over฀ 3฀ academic฀ years฀ (2005– 2007).฀The฀university฀provides฀beginning฀ salaries฀statistics฀every฀academic฀semester฀ through฀a฀survey฀of฀graduating฀students.฀ Majors฀ at฀ the฀ university฀ are฀ classified฀ into฀ five฀ groups฀ on฀ the฀ basis฀ of฀ the฀ number฀of฀mathematics฀courses฀required฀ in฀their฀curricula,฀and฀their฀contents.฀For฀ example,฀ Group฀ 1’s฀ curricula฀ required฀ one฀ elective฀ in฀ basic฀ mathematics,฀ and฀ Group฀ 5’s฀ curricula฀ required฀ more฀ than฀ two฀advanced฀mathematics฀courses.฀

We฀tested฀the฀hypothesis฀by฀compar-ing฀average฀beginning฀salaries฀between฀ groups฀ and฀ performing฀ a฀ regression฀ analysis฀ while฀ controlling฀ moderating฀ variables,฀ semester฀ and฀ college.฀ Our฀ results฀ indicated฀ that฀ graduating฀ stu-dents฀who฀completed฀curricula฀requiring฀ the฀most฀rigorous฀mathematical฀content฀ (i.e.,฀ Group฀ 5)฀ earned฀ $10,383฀ more฀ than฀their฀counterparts฀in฀Group฀1.฀

The฀ remainder฀ of฀ the฀ present฀ article฀ includes฀ a฀ review฀ of฀ the฀ related฀ litera-ture฀ and฀ a฀ proposed฀ research฀ hypothe-sis,฀a฀description฀of฀the฀sample฀selection฀ procedure฀and฀testing฀models,฀empirical฀ results,฀and฀concluding฀remarks.฀

Literature฀Review฀and฀ Hypothesis฀Development

Trusty฀ et฀ al.฀ (2000)฀ examined฀ the฀ effects฀ of฀ gender,฀ socioeconomic฀ sta-tus,฀and฀early฀academic฀performance฀on฀ choice฀of฀college฀major.฀Socioeconom-ic฀status฀was฀measured฀as฀a฀composite฀ variable฀ using฀ the฀ following฀

informa-tion฀ for฀ parents฀ of฀ students:฀ income,฀ educational฀ levels,฀ and฀ occupational฀ type.฀Trusty฀et฀al.฀measured฀early฀aca-demic฀ performance฀ by฀ scores฀ on฀ four฀ eigth-grade฀ cognitive฀ tests฀ including฀ mathematics,฀reading,฀science,฀and฀his-tory฀ or฀ geography.฀ In฀ general,฀ math-ematics฀ is฀ the฀ strongest฀ predictor฀ of฀ men’s฀ choice฀ of฀ major,฀ and฀ reading฀ is฀ the฀ strongest฀ predictor฀ of฀ women’s฀ choice฀ of฀ major฀ (Trusty฀ et฀ al.).฀ None-theless,฀Trusty฀ et฀ al.฀ cautioned฀ against฀ interpreting฀ their฀ findings฀ as฀ showing฀ a฀ serious฀ correlation฀ between฀ math-ematics฀ and฀ reading.฀ In฀ other฀ words,฀ students฀who฀do฀well฀in฀one฀field฀often฀ excel฀ in฀ the฀ other฀ as฀ well.฀ Maple฀ and฀ Stage฀ (1991)฀ also฀ indicated฀ choice฀ of฀ college฀ major฀ as฀ the฀ interactive฀ out-come฀ of฀ gender,฀ socioeconomic฀ sta-tus,฀ and฀ academic฀ performance.฀ High฀ mathematics฀ and฀ science฀ test฀ scores฀ influence฀students฀to฀take฀more฀courses฀ in฀ mathematics฀ and฀ science฀ in฀ high฀ school;฀thus,฀those฀students฀are฀inclined฀ to฀ study฀ mathematics฀ and฀ science฀ at฀ college.฀ Further,฀ postsecondary฀ educa-tion฀ choice฀ is฀ closely฀ associated฀ with฀ subsequent฀ vocational฀ choice฀ (Eccles,฀ 1994;฀ Gianakos฀ &฀ Subich,฀ 1988;฀ Lent฀ et฀al.฀1994;฀Wallace฀&฀Walker,฀1990).฀

In฀ a฀ similar฀ line฀ of฀ research,฀ Hackett฀ (1985)฀ examined฀ choice฀ of฀ major฀ as฀ a฀ function฀ of฀ mathematics฀ self-efficacy,฀ which฀ emphasizes฀ “the฀ role฀ of฀ cognitive-mediational฀ factors,฀ specifically฀ expectations฀ of฀ personal฀ effectiveness”฀ in฀ mathematics฀ or฀ related฀ subjects฀ (p.฀ 47).฀ Mathematics฀ is฀ a฀ subject฀ that฀ requires฀ students฀ to฀ follow฀ a฀ series฀ of฀ sequential฀ courses฀ because฀ they฀ build฀ concepts฀ step฀ by฀ step.฀ In฀ accordance,฀ students฀ who฀ lack฀ mathematics฀preparation฀face฀difficulty฀ with฀ grasping฀ concepts฀ in฀ college฀ mathematics฀ courses.฀ The฀ frustration฀ they฀experience฀leads฀to฀their฀premature฀ decision฀ not฀ to฀ study฀ mathematics-related฀ fields.฀ Hackett’s฀ findings฀ support฀ the฀ role฀ of฀ mathematics฀ self-efficacy,฀ which฀ predicts฀ mathematics฀ anxiety฀ and฀ mathematics-related฀ major฀ choices.฀ Also,฀ self-efficacy฀ predicts฀ mathematics-related฀major฀choices฀even฀ better฀than฀does฀measured฀ability.฀

Lack฀of฀mathematical฀preparation฀not฀ only฀limits฀students’฀educational฀choices,฀ but฀ also฀ reduces฀ their฀ subsequent฀

earn-ings.฀ Murnane฀ et฀ al.฀ (2000)฀ examined฀ how฀high฀school฀seniors’฀cognitive฀skills฀ are฀associated฀with฀their฀future฀earnings.฀ Cognitive฀ skills฀ are฀ measured฀ as฀ high฀ school฀seniors’฀mathematics฀and฀reading฀ scores฀that฀are฀included฀in฀the฀National฀ Longitudinal฀Survey฀of฀the฀High฀School฀ Class฀of฀1972฀(NLSHC)฀and฀High฀School฀ and฀Beyond฀(HSB).฀Mathematics฀scores฀ highly฀correlate฀with฀reading฀scores,฀and฀ the฀former฀explains฀subsequent฀earnings฀ better฀ than฀ the฀ latter.฀ Thus,฀ Murnane฀ et฀ al.฀ adopted฀ mathematics฀ score฀ alone฀ as฀ a฀ proxy฀ for฀ cognitive฀ skills.฀ Their฀ results฀show฀the฀positive฀effect฀of฀math-ematics฀ scores฀ on฀ subsequent฀ earnings.฀ For฀example,฀an฀additional฀point฀on฀the฀ mathematics฀ score฀ of฀ male฀ high-school฀ seniors฀ in฀ 1982฀ led฀ to฀ a฀ 1.5%฀ gain฀ in฀ annual฀earnings฀by฀the฀age฀of฀27.฀Also,฀ mathematics฀ score฀ is฀ a฀ strong฀ predictor฀ of฀educational฀attainments฀in฀college.฀In฀ accordance,฀ it฀ is฀ important฀ for฀ students฀ to฀ develop฀ essential฀ cognitive฀ skills,฀ in฀ particular฀mathematics,฀at฀an฀early฀stage฀ of฀their฀education.฀

We฀ could฀ interpret฀ Murnane฀ et฀ al.’s฀ (2000)฀ findings฀ in฀ several฀ ways.฀ First,฀ cognitive฀ skills฀ could฀ be฀ a฀ proxy฀ for฀ students’฀ ability฀ to฀ identify฀ and฀ grasp฀ opportunities฀ in฀ their฀ lives.฀ In฀ other฀ words,฀ a฀ good฀ student฀ in฀ school฀ may฀ outperform฀ others฀ in฀ society.฀ Second,฀ high฀mathematics฀scores฀could฀enhance฀ senior฀ high-school฀ students’฀ chances฀ of฀ earning฀a฀college฀degree.฀In฀turn,฀a฀col-lege฀degree฀would฀help฀students฀to฀earn฀ more฀ money฀ than฀ would฀ a฀ high฀ school฀ diploma฀only.฀Uchitelle฀(2005)฀discussed฀ the฀ substantial฀ gap฀ in฀ salary฀ between฀ those฀people฀with฀a฀bachelor’s฀degree฀or฀ higher฀ and฀ their฀ counterparts฀ with฀ only฀ a฀ high-school฀ education.฀ For฀ example,฀ by฀ the฀ end฀ of฀ 2004฀ the฀ median฀ wage฀ of฀ full-time฀ workers฀ with฀ a฀ bachelor’s฀ degree฀ or฀ higher฀ was฀ $986฀ per฀ week,฀ but฀only฀$574฀for฀the฀latter.฀Murnane฀et฀ al.’s฀findings฀support฀this฀proposition฀by฀ indicating฀that฀high-school฀students฀with฀ high฀mathematics฀scores฀produce฀higher฀ educational฀attainment฀in฀college.฀

Last,฀ as฀ discussed฀ previously,฀ suc- cess฀in฀high-school฀mathematics฀cours-es฀ providcess฀in฀high-school฀mathematics฀cours-es฀ high-school฀ seniors฀ with฀ self-confidence฀ in฀ quantitative฀ sub-jects.฀ Thus,฀ they฀ may฀ be฀ motivated฀ to฀ choose฀ quantitative฀ subjects฀ as฀ their฀ college฀major.฀In฀our฀knowledge-based฀

economy,฀ firms฀ demand฀ high-skilled฀ workers฀ who฀ are฀ trained฀ in฀ special-ized฀ areas฀ during฀ their฀ postsecondary฀ education.฀For฀example,฀in฀1960฀there฀ were฀ only฀ 5,000฀ computer฀ program-mers฀ in฀ the฀ United฀ States,฀ but฀ that฀ increased฀ to฀ approximately฀ 1.3฀ mil-lion฀ computer฀ programmers฀ in฀ 1999.฀ The฀ share฀ of฀ managerial฀ and฀ profes-sional฀ jobs฀ in฀ total฀ employment฀ has฀ increased฀from฀22%฀in฀1979฀to฀28.4%฀ in฀1995฀( KF26฀J8525:฀America’s฀Work-force฀ Needs,฀ 1999).฀ Education฀ should฀ be฀ the฀ solution฀ to฀ equip฀ students฀ with฀ the฀ skills฀ and฀ knowledge฀ that฀ are฀ in฀ demand฀ in฀ the฀ growing฀ sectors฀ of฀ the฀ economy.฀In฀particular,฀there฀are฀three฀ fields฀in฀which฀more฀college฀graduates฀ are฀needed:฀mathematics,฀science,฀and฀ engineering฀ (KF26฀ J8525:฀ America’s฀ Workforce฀ Needs).฀ Furthermore,฀ the฀ academic฀ success฀ of฀ students฀ in฀ these฀ three฀ fields฀ is฀ dependent฀ upon฀ their฀ mastery฀of฀basic฀mathematical฀skills.

In฀addition,฀the฀salaries฀of฀college฀grad-uates฀ vary฀ depending฀ on฀ their฀ major฀ at฀ college.฀In฀The฀Wall฀Street฀Journal ,฀Pay-Scale฀Inc.฀shared฀survey฀results฀regarding฀ the฀median฀starting฀salaries฀in฀50฀majors฀ from฀ 1.2฀ million฀ people฀ with฀ bachelor’s฀ degrees฀ (“Salary฀ Increase฀ by฀ Major,”฀ 2008).฀When฀comparing฀the฀top฀and฀bot-tom฀10฀majors฀on฀the฀pay฀scale,฀the฀mean฀ of฀the฀top฀10฀majors฀is฀$59,710,฀whereas฀ the฀ mean฀ of฀ the฀ bottom฀ 10฀ majors฀ is฀ $35,280,฀ with฀ a฀ difference฀ of฀ $24,430.฀ Furthermore,฀the฀former฀comprises฀seven฀ majors฀in฀engineering,฀computer฀science,฀ and฀ two฀ health-care-related฀ subjects,฀ but฀ the฀ latter฀ comprises฀ 10฀ majors฀ in฀ liberal฀ arts.฀These฀results฀demonstrate฀that฀high-er฀ paying฀ jobs฀ are฀ concentrated฀ in฀ fields฀ in฀ which฀ basic฀ mathematical฀ skills฀ and฀ knowledge฀ are฀ mandatory฀ for฀ academic฀ success.฀Also,฀this฀result฀is฀consistent฀with฀ Murnane฀et฀al.’s฀(2000)฀findings฀that฀stu-dents’฀ cognitive฀ skills฀ are฀ a฀ predictor฀ of฀ their฀future฀earnings.฀

In฀ the฀ presence฀ of฀ substantial฀ differ- ences฀in฀beginning฀salaries฀among฀col-lege฀ graduates฀ and฀ varying฀ degrees฀ of฀ mathematical฀ content฀ that฀ are฀ required฀ depending฀on฀the฀major฀fields,฀we฀mea-sured฀differences฀in฀graduating฀students’฀ salaries฀ that฀ can฀ be฀ attributed฀ to฀ their฀ curriculum’s฀ mathematical฀ content฀ by฀ developing฀ a฀ research฀ hypothesis฀ in฀ an฀ alternative฀form฀as฀follows:

Hypothesis฀1฀(H₁):฀The฀mathematical฀ content฀in฀curricula฀predicts฀graduating฀ students’฀beginning฀salaries.

Sample฀Selection฀and฀Empirical฀ Model฀Construction

We฀ selected฀ one฀ state฀ university฀ in฀ the฀southern฀region฀of฀the฀United฀States.฀ On฀ its฀ Web฀ site,฀ this฀ university฀ pro-vides฀ its฀ undergraduate฀ catalog฀ and฀ its฀ graduating฀ students’฀ beginning฀ salary฀ statistics.฀ The฀ university’s฀ main฀ cam-pus฀ comprises฀ nine฀ colleges:฀ College฀ of฀Agriculture฀and฀Life฀Science฀(CAL),฀ College฀of฀Architecture฀(COA),฀College฀ of฀ Business฀ (COB),฀ College฀ of฀ Educa- tion฀and฀Human฀Resources฀(CEH),฀Col-lege฀of฀Engineering฀(COE),฀College฀of฀ Geosciences฀(COG),฀College฀of฀Liberal฀ Arts฀(CLA),฀College฀of฀Science฀(COS),฀ and฀ College฀ of฀ Veterinary฀ Medicine฀ (CVM).฀We฀eliminated฀CEH฀and฀CVM฀ from฀ our฀ analysis.฀ CEH฀ offers฀ seven฀ undergraduate฀degree฀programs,฀and฀the฀ required฀mathematics฀courses฀vary฀from฀ program฀ to฀ program.฀ However,฀ most฀ educational฀ graduates฀ are฀ hired฀ as฀ ele-mentary฀school฀teachers.฀In฀accordance,฀ we฀ could฀ not฀ evaluate฀ an฀ association฀ between฀ the฀ number฀ of฀ mathematics฀ courses฀required฀in฀different฀educational฀ majors฀and฀education฀graduates’฀begin-ning฀ salaries.฀ Most฀ CVM฀ graduates฀ pursued฀ advanced฀ professional฀ degrees฀ in฀ medicine,฀ so฀ only฀ a฀ small฀ number฀ of฀ CVM฀ undergraduates฀ obtained฀ jobs฀ immediately฀after฀graduating฀(e.g.,฀only฀ two฀CVM฀undergraduates฀were฀reported฀ in฀ the฀ Fall฀ 2006฀ semester’s฀ beginning฀ salary฀statistics฀report).฀

Each฀ semester,฀ the฀ university฀ col-lects฀its฀graduates’฀beginning฀salaries฀in฀ three฀different฀ways:฀a฀survey฀at฀gradu-ation,฀a฀survey฀questionnaire฀mailed฀to฀ graduates,฀ and฀ an฀ online฀ salary฀ survey.฀ The฀ university฀ displays฀ beginning฀ sal-ary฀ statistics฀ by฀ major฀ in฀ each฀ college฀ including฀ the฀ mean,฀ maximum,฀ mini-mum,฀ and฀ standard฀ deviation฀ values.฀ The฀salary฀survey฀includes฀undergradu-ate฀and฀graduate฀degrees฀in฀each฀major.฀ Our฀study฀focused฀on฀salaries฀of฀under-graduates฀ who฀ graduated฀ in฀ academic฀ years฀2005–2007.฀

We฀ reviewed฀ the฀ university’s฀ 2007– 2008฀undergraduate฀catalog฀to฀identify฀ mathematics฀ courses฀ that฀ each฀ major฀

curriculum฀ required.฀ The฀ university฀ adopted฀ a฀ University฀ Core฀ Curriculum฀ that฀ required฀ students฀ to฀ complete฀ 6฀ semester฀ hours฀ in฀ mathematics.฀ How-ever,฀ students฀ had฀ an฀ option฀ to฀ substi-tute฀3฀semester฀hours฀in฀philosophy.฀In฀ accordance,฀ all฀ undergraduate฀ degree฀ programs฀ at฀ the฀ university฀ included฀ at฀ least฀one฀course฀in฀mathematics฀in฀their฀ curricula.฀The฀mathematics฀department฀ offers฀two฀sets฀of฀mathematics฀courses฀ that฀are฀tailored฀for฀nonengineering฀and฀ engineering฀ majors฀ to฀ allow฀ students฀ from฀ other฀ colleges฀ to฀ fulfill฀ the฀ Uni-versity฀ Core฀ Curriculum’s฀ mathemat-ics฀ requirement.฀ The฀ first฀ set฀ includes฀ Business฀Mathematics฀I฀(M141)฀and฀II฀ (M142),฀both฀of฀which฀require฀students฀ to฀complete฀high฀school฀Algebra฀I฀and฀ II,฀ and฀ Geometry.฀ M141฀ covers฀ topics฀ such฀ as฀ linear฀ equations฀ and฀ applica-tions,฀ matrix฀ algebra฀ and฀ applicaapplica-tions,฀ linear฀programming,฀and฀basic฀statistics฀ including฀probability.฀M142฀covers฀top-ics฀ such฀ as฀ derivatives,฀ optimization,฀ logarithms฀ and฀ exponential฀ functions,฀ integrals,฀ and฀ multivariate฀ calculus.฀ The฀ second฀ set฀ includes฀ Engineering฀ Mathematics฀ I฀ (M151)฀ and฀ II฀ (M152).฀ M151฀covers฀topics฀such฀as฀rectangular฀ coordinates,฀ vectors,฀ analytical฀ geom-etry,฀limits,฀derivatives,฀integration,฀and฀ computer฀ algebra.฀ M152฀ covers฀ topics฀ such฀ as฀ differentiation฀ and฀ integration,฀ improper฀ integrals,฀ analytic฀ geometry,฀ vectors,฀ infinite฀ series,฀ power฀ series,฀ Taylor฀ series,฀ and฀ computer฀ algebra.฀ Thus,฀M151฀and฀M152฀deal฀with฀more฀ advanced฀ mathematical฀ concepts฀ than฀ do฀M141฀and฀M142;฀M151฀and฀M152฀ are฀structured฀to฀provide฀students฀with฀ the฀ foundation฀ required฀ for฀ advanced฀ mathematics฀ courses฀ in฀ engineering฀ and฀science.

We฀ classified฀ the฀ curricula฀ into฀ the฀ following฀ five฀ groups฀ depending฀ on฀ their฀mathematical฀content:฀(a)฀Group฀1฀ (G1)฀requires฀one฀elective฀from฀the฀list฀ of฀basic฀mathematics฀courses,฀(b)฀Group฀ 2฀ (G2)฀ requires฀ two฀ electives฀ from฀ the฀ list,฀ (c)฀ Group฀ 3฀ (G3)฀ requires฀ M141฀ and฀ M142,฀ (d)฀ Group฀ 4฀ (G4)฀ requires฀ M151฀and฀M152,฀and฀(e)฀Group฀5฀(G5)฀ requires฀advanced฀mathematics฀courses฀ beyond฀M152.฀

We฀performed฀empirical฀analyses฀on฀ the฀basis฀of฀a฀comparison฀of฀these฀five฀ groups’฀mean฀salaries฀and฀a฀multivariate฀

regression฀ model.฀We฀ regressed฀ gradu-ating฀students’฀beginning฀salaries฀on฀the฀ five฀ groups฀ (G1–5).฀ Because฀ we฀ could฀ not฀assume฀the฀intervals฀among฀the฀five฀ groups฀ to฀ be฀ constant,฀ we฀ included฀ all฀ dummy฀variables฀to฀measure฀the฀incre-mental฀average฀amount฀of฀salary฀in฀each฀ group฀ beyond฀ the฀ base฀ group฀ (G1)฀ as฀ follows:฀

SA_i,t฀=฀β0฀+฀β1DG2฀+฀β2DG3฀+฀β3DG4฀

+฀β4DG5฀+฀εi,t,฀฀ (1)

where฀ SA_i,t฀ =฀ average฀ salary฀ for฀ major฀ i฀ in฀ semester฀ t;฀ DG1฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G1,฀ otherwise฀ 0;฀ DG2฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G2,฀ otherwise฀ 0;฀ DG3฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G3,฀ otherwise฀ 0;฀ DG4฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G4,฀ otherwise฀ 0;฀ DG5฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G5,฀ otherwise฀ 0;฀β0–4฀

=฀ parameter฀ estimates;฀ and฀εi,t฀ =฀

distur-bance฀term.

Equation฀1฀is฀extended฀by฀the฀inclu-sion฀of฀moderating฀variables,฀semesters,฀ and฀ colleges฀ because฀ salaries฀ are฀ sup-posed฀ to฀ increase฀ over฀ time,฀ and฀ they฀ also฀vary฀from฀college฀to฀college.฀Equa-tion฀2฀is฀as฀follows:

SA_i,t฀=฀λ0฀+฀λ1DG2฀+฀λ2DG3฀+฀λ3DG4฀

+฀λ4DG5฀ +฀λ5DCAL฀ +฀λ6DCOA฀ +฀ λ7DCOB฀ +฀λ8DCOE฀ +฀λ9DCOG฀ +฀ λ10DCOS฀ +฀λ11DS05฀ +฀λ12DF06฀ +฀ λ13DS06฀+฀λ14DF07฀+฀λ15DS07฀+฀νi,t

(2) where฀DCAL฀=฀assigned฀a฀unit฀variable฀ for฀CAL,฀otherwise฀0;฀DCOA฀=฀assigned฀ a฀ unit฀ variable฀ for฀ COA,฀ otherwise฀ 0;฀ DCOB฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COB,฀ otherwise฀ 0;฀ DCOE฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COE,฀ otherwise฀ 0;฀ DCOG฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COG,฀ otherwise฀ 0;฀ DCOS฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COS,฀ otherwise฀ 0;฀ DS05฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ spring฀ of฀ 2005฀ semester,฀ otherwise฀ 0;฀ DF06฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ fall฀ of฀ 2006฀ semester,฀ otherwise฀ 0;฀ DS06฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ spring฀ of฀ 2006฀ semester,฀ otherwise฀ 0;฀ DF07฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ fall฀ of฀ 2007฀ semester,฀ otherwise฀ 0;฀ DS07฀=฀assigned฀a฀unit฀variable฀for฀the฀ spring฀ of฀ 2007฀ semester,฀ otherwise฀ 0;฀

λ0–15฀ =฀ parameter฀ estimates;฀ and฀νi,t฀ =฀

disturbance฀term.฀Other฀variables฀are฀as฀ defined฀previously.฀

In฀Equation฀2,฀we฀chose฀CLA฀and฀the฀ Fall฀ 2005฀ semester฀ as฀ base฀ variables.฀ Thus,฀parameters฀of฀college฀dummy฀vari-ables฀ represent฀ an฀ incremental฀ increase฀ in฀ the฀ mean฀ beginning฀ salaries฀ above฀ that฀in฀CLA฀for฀students฀in฀each฀college;฀ parameters฀of฀semester฀dummy฀variables฀ indicate฀ an฀ incremental฀ increase฀ in฀ the฀ mean฀ of฀ beginning฀ salaries฀ above฀ that฀ in฀the฀Fall฀2005฀semester฀for฀students฀in฀ each฀academic฀semester.฀

Empirical฀Results Descriptive฀Statistics

Table฀1฀presents฀descriptive฀statistics฀ of฀selected฀variables฀per฀academic฀year฀ from฀ 2005฀ to฀ 2007.฀ No_Major_C฀

rep-resents฀ the฀ number฀ of฀ majors฀ in฀ a฀ col-lege฀whose฀graduates’฀beginning฀salary฀ data฀are฀available.฀G1–G5฀indicate฀each฀ major฀ curriculum’s฀ mathematical฀ con-tent.฀Ave_Group_C฀represents฀the฀mean฀ of฀a฀college’s฀groups.฀For฀example,฀3.00฀ of฀Ave_Group_C฀for฀COB฀in฀2005฀indi-cates฀ that฀ in฀ the฀ 2005฀ academic฀ year,฀ students฀in฀COB฀were฀required฀to฀com-plete฀ M141฀ and฀ M142.฀ Ave_Stud_C฀ represents฀the฀mean฀number฀of฀students฀ in฀each฀major฀who฀reported฀their฀begin-ning฀ salaries฀ in฀ a฀ college.฀ Ave_Sala-ry_C฀indicates฀the฀mean฀of฀the฀average฀ beginning฀ salaries฀ of฀ each฀ major฀ in฀ a฀ college.฀CLA฀indicates฀the฀lowest฀aver-age฀ beginning฀ salary฀ of฀ its฀ graduates฀ ($33,195)฀in฀2005,฀and฀COE฀shows฀the฀ highest฀ average฀ beginning฀ salary฀ of฀ its฀ graduates฀($58,363)฀in฀2007.฀

TABLE฀1.฀Descriptive฀Statistics฀of฀Variables฀per฀Academic฀Year,฀for฀฀ 2005–2007

฀ ฀ No_Major฀ Ave_Group฀ Ave_Stud฀ Ave_Salary

College฀ Year฀ _C฀ _C฀ _C฀ _C฀($)

CAL฀ 2005฀ 24฀ 2.75฀ 6.33฀ 39,345

CAL฀ 2006฀ 24฀ 2.75฀ 9.17฀ 40,289

CAL฀ 2007฀ 24฀ 2.75฀ 8.88฀ 36,850

COA฀ 2005฀ 3฀ 2.67฀ 26.67฀ 38,927

COA฀ 2006฀ 3฀ 2.67฀ 31.00฀ 41,885

COA฀ 2007฀ 3฀ 2.67฀ 36.00฀ 44,929

COB฀ 2005฀ 5฀ 3.00฀ 54.80฀ 41,080

COB฀ 2006฀ 5฀ 3.00฀ 58.00฀ 44,311

COB฀ 2007฀ 5฀ 3.00฀ 63.60฀ 46,533

COE฀ 2005฀ 18฀ 4.89฀ 30.78฀ 50,491

COE฀ 2006฀ 18฀ 4.89฀ 31.11฀ 55,058

COE฀ 2007฀ 18฀ 4.89฀ 37.06฀ 58,363

COG฀ 2005฀ 6฀ 3.67฀ 2.33฀ 40,107

COG฀ 2006฀ 6฀ 3.67฀ 3.67฀ 34,538

COG฀ 2007฀ 6฀ 3.67฀ 4.00฀ 40,478

CLA฀ 2005฀ 14฀ 1.21฀ 13.64฀ 33,195

CLA฀ 2006฀ 14฀ 1.21฀ 13.00฀ 37,171

CLA฀ 2007฀ 14฀ 1.21฀ 14.78฀ 39,705

COS฀ 2005฀ 6฀ 4.67฀ 3.33฀ 42,505

COS฀ 2006฀ 6฀ 4.67฀ 4.50฀ 40,057

COS฀ 2007฀ 6฀ 4.67฀ 6.50฀ 41,973

Note. ฀CAL฀=฀College฀of฀Agriculture฀and฀Life฀Science;฀COA฀=฀College฀of฀Agriculture;฀COB฀=฀Col-lege฀of฀Business;฀COE฀=฀College฀of฀Engineering;฀COG฀=฀College฀of฀Geosciences;฀CLA฀=฀College฀ of฀Liberal฀Arts;฀COS฀=฀College฀of฀Science;฀No_Major_C฀=฀number฀of฀majors฀in฀which฀graduating฀ students฀reported฀their฀salaries฀by฀college;฀Ave_Stud_C฀=฀average฀number฀of฀students฀by฀major฀ who฀ reported฀ their฀ beginning฀ salaries฀ in฀ a฀ college;฀Ave_Salary_C฀ =฀ mean฀ beginning฀ salaries฀ of฀ graduates฀by฀major฀in฀a฀college.฀Ave_Group_C฀refers฀to฀an฀indicator฀of฀mathematical฀content฀of฀ the฀curricula฀in฀the฀college.฀Curricula฀are฀classified฀into฀five฀groups฀depending฀on฀the฀number฀of฀ mathematics฀courses฀and฀mathematical฀contents.฀Curricula฀in฀Group฀1฀require฀one฀elective฀from฀ the฀list฀of฀basic฀mathematics฀courses฀provided;฀those฀in฀Group฀2฀require฀two฀electives฀from฀the฀ list฀of฀basic฀mathematics฀courses฀provided;฀those฀in฀Group฀3฀require฀Business฀Mathematics฀I฀and฀ Business฀Mathematics฀II;฀those฀in฀Group฀4฀require฀Engineering฀Mathematics฀I฀and฀Engineering฀ Mathematics฀ II;฀ and฀ those฀ in฀ Group฀ 5฀ require฀ M151,฀ M152,฀ and฀ more฀ advanced฀ mathematics฀ courses฀(e.g.,฀Ave_Group_C,฀2.67,฀indicates฀that฀most฀curricula฀in฀a฀college฀fall฀between฀Groups฀ 2฀and฀3).฀

Table฀2฀shows฀summary฀statistics฀per฀ college฀across฀these฀3฀years.฀No_Major_ C2฀indicates฀the฀number฀of฀majors฀with฀ beginning฀ salary฀ information฀ in฀ a฀ col-lege,฀accumulated฀over฀the฀3฀years.฀CAL฀ shows฀the฀greatest฀number฀(115฀majors),฀ and฀ COA฀ shows฀ the฀ least฀ (16฀ majors).฀ Most฀ curricula฀ in฀ CLA฀ require฀ roughly฀ one฀ mathematics฀ course฀ during฀ the฀ 3-year฀ period฀ (1.26฀ as฀ Ave_Group_C2),฀ and฀ most฀ students฀ in฀ COE฀ are฀ required฀ to฀ complete฀ mathematics฀ courses฀ beyond฀M152฀(4.87฀as฀Ave_Group_C2).฀ COE฀ graduates฀ have฀ the฀ highest฀ aver-age฀ salary฀ in฀ the฀ 3-year฀ period,฀ Ave_ Salary_C2฀ ($54,648),฀ whereas฀ CLA฀ graduates฀ experience฀ the฀ lowest฀ aver-age฀ salary฀ ($36,925).฀ The฀ mathematics฀ requirements฀ vary฀ from฀ college฀ to฀

col-lege฀and฀the฀level฀of฀average฀salaries฀in฀ a฀ college฀ appears฀ to฀ be฀ related฀ to฀ that฀ college's฀mathematical฀requirements.฀

Table฀3฀shows฀descriptive฀statistics฀by฀ group.฀As฀ the฀ curricula's฀ mathematical฀ content฀ increases,฀ graduating฀ students’฀ beginning฀salaries฀increase฀accordingly฀ (e.g.,฀ $35,856฀ for฀ G1฀ vs.฀ $50,595฀ for฀ G4).฀ To฀ estimate฀ the฀ statistical฀ signifi-cance฀ of฀ differences฀ in฀ beginning฀ sala- ries฀that฀result฀from฀the฀curricula's฀vary-ing฀mathematical฀content,฀we฀computed฀ the฀incremental฀change฀in฀average฀begin-ning฀ salaries฀ between฀ groups฀ (Inc_Sal-ary_G),฀ and฀ estimated฀ the฀ incremental฀ changes฀in฀t฀as฀well.฀Beginning฀salaries฀ in฀ G3฀ are฀ greater฀ than฀ those฀ in฀ G2฀ by฀ $4,833,฀which฀is฀statistically฀significant฀ at฀the฀.01฀level;฀beginning฀salaries฀in฀G4฀

are฀also฀$9,582฀greater฀than฀those฀in฀G3,฀ which฀ is฀ statistically฀ significant฀ at฀ the฀ .05฀level.฀Nonetheless,฀incremental฀dif-ferences฀ between฀ other฀ groups฀ are฀ not฀ statistically฀significant฀(e.g.,฀the฀average฀ beginning฀ salary฀ in฀ G4฀ did฀ not฀ differ฀ significantly฀ from฀ that฀ in฀ G5).฀ These฀ findings฀ imply฀ that฀ more฀ mathematics฀ courses฀ in฀ curricula฀ do฀ not฀ necessar-ily฀ lead฀ to฀ higher฀ beginning฀ salaries.฀ Thus,฀two฀groups฀are฀collapsed฀into฀one.฀ When฀ G1฀ and฀ G2฀ are฀ combined,฀ the฀ combination’s฀average฀beginning฀salary฀ (Ave_Salary_G2)฀is฀$35,985.฀In฀a฀simi-lar฀way,฀the฀combination฀of฀G4฀and฀G5฀ yields฀$50,457฀as฀an฀average฀beginning฀ salary.฀This฀difference฀of฀$14,472฀is฀sta-tistically฀significant฀at฀the฀.01฀level.

Regression฀Results

Table฀4฀includes฀the฀results฀of฀Equa-tion฀1฀by฀regressing฀average฀salaries฀by฀ major฀on฀the฀five฀levels฀of฀the฀curricu-la’s฀ mathematical฀ content฀ (G1–5).฀ We฀ used฀G1฀as฀the฀base฀group฀in฀Equation฀ 1฀ because฀ other฀ groups฀ were฀ expected฀ to฀ show฀ higher฀ beginning฀ salaries;฀ the฀ adjusted฀R2

฀of฀Equation฀1฀is฀.34.฀Param- eters฀on฀DG3–5฀are฀statistically฀signifi-cant,฀consistent฀with฀the฀results฀in฀Table฀ 3.฀ For฀ example,฀ the฀ average฀ beginning฀ salary฀ in฀ G5฀ is฀ greater฀ than฀ that฀ in฀ G1฀ ($35,857)฀by฀$14,583.฀

Beginning฀salaries฀increase฀over฀time฀ and฀are฀also฀affected฀by฀subjects฀that฀stu- dents฀take.฀Thus,฀Table฀5฀shows฀empiri- cal฀results฀from฀Equation฀2,฀which฀con-trols฀over฀both฀semesters฀and฀colleges.฀

Equation฀2฀includes฀CLA฀and฀Fall฀2005฀ semester฀ as฀ base฀ variables,฀ in฀ addition฀ to฀ G1.฀ The฀ adjusted฀R2_{฀ of฀ Equation฀ 2฀ is฀}

.50.฀The฀average฀beginning฀salary฀in฀CLA฀ students฀ who฀ completed฀ one฀ mathemat-ics฀elective฀and฀then฀graduated฀in฀the฀Fall฀ 2005฀semester฀is฀$35,325.฀Students฀in฀G3฀ who฀completed฀more฀mathematics฀courses฀ (e.g.,฀M141฀and฀M142)฀earned฀$5,040.89฀ more฀ than฀ did฀ their฀ counterparts฀ in฀ G1.฀ Furthermore,฀ students฀ who฀ completed฀ more฀advanced฀mathematics฀courses฀such฀ as฀M151฀and฀M152฀and฀other฀mathematics฀ courses฀in฀G5฀earned฀$10,383฀more฀than฀ did฀their฀counterparts฀in฀G1.฀

Last,฀we฀evaluated฀the฀validity฀of฀prin-cipal฀assumptions฀that฀underlie฀Equation฀ 2:฀ the฀ White฀ test฀ for฀ homoscedastic-ity฀ of฀ the฀ errors฀(White,฀ 1980)฀and฀ the฀฀ Shapiro–Wilk฀ (Shapiro฀ &฀ Wilk,฀ 1965)฀

TABLE฀2.฀Descriptive฀Statistics฀of฀Variables฀per฀College,฀for฀Academic฀ Years฀2005–2007฀(N฀=฀373)

College฀ No_Major_C2฀ Ave_Group_C2฀ Ave_Salary_C2฀($)

CAL฀ 115฀ 2.57฀ 38,046

COA฀ 16฀ 2.63฀ 42,278

COB฀ 30฀ 3.00฀ 44,245

COE฀ 94฀ 4.87฀ 54,648

COG฀ 23฀ 4.04฀ 37,810

CLA฀ 68฀ 1.26฀ 36,925

COS฀ 27฀ 4.56฀ 41,415

Note.฀ CAL฀ =฀ College฀ of฀Agriculture฀ and฀ Life฀ Science;฀ COA฀ =฀ College฀ of฀Agriculture;฀ COB฀ =฀ College฀ of฀ Business;฀ COE฀ =฀ College฀ of฀ Engineering;฀ COG฀ =฀ College฀ of฀ Geosciences;฀ CLA฀ =฀ College฀ of฀ Liberal฀Arts;฀ COS฀ =฀ College฀ of฀ Science;฀ No_Major_C2฀ =฀ accumulated฀ number฀ of฀ majors฀in฀which฀graduating฀students฀reported฀their฀salaries;฀Ave_Group_C2฀=฀average฀indicator฀ of฀mathematical฀content฀of฀the฀curricula฀in฀a฀college;฀Ave_Salary_C2฀=฀average฀beginning฀salary฀ of฀graduates฀in฀a฀college.฀

TABLE฀3.฀Descriptive฀Statistics฀of฀Variables฀per฀Group,฀for฀Academic฀ Years฀2005–2007฀(N฀=฀373)

฀ No_Major฀ Ave_Salary฀ Inc_Salary฀ ฀ Ave_Salary Group฀ _G฀ _G฀($)฀ _G฀($)฀ t฀(df฀=฀1)฀ _G2฀($)

1฀ 76฀ 35,856฀ —฀ —฀ 35,985

2฀ 50฀ 36,180฀ 324฀ 0.28฀ —

3฀ 104฀ 41,013฀ 4,833฀ 4.68**_{฀ —}

4฀ 16฀ 50,595฀ 9,582฀ 2.55*_{฀ 50,457}

5฀ 127฀ 50,439฀ –156฀ 0.04฀ —

Note.฀ No_Major_G฀ =฀ accumulated฀ number฀ of฀ majors฀ in฀ which฀ graduating฀ students฀ reported฀ their฀salaries฀over฀the฀six฀regular฀semesters฀from฀2005฀to฀2007;฀Ave_Salary_G฀=฀average฀salary฀ in฀a฀group฀over฀the฀six฀regular฀semesters;฀Inc_Salary_G฀=฀incremental฀change฀in฀Ave_Salary_G฀ between฀Gt฀and฀Gt+1;฀Ave_Salary_G2฀=฀average฀salary฀in฀Groups฀1฀and฀2฀and฀Groups฀4฀and฀5.฀

*_{p฀<฀.05.฀}**_p฀<฀.01.

test฀ for฀ normality฀ of฀ the฀ error฀ distribu-tion.฀Both฀tests฀revealed฀no฀violation฀of฀ these฀assumptions.฀

Conclusion

The฀ purpose฀ of฀ the฀ present฀ article฀ was฀to฀document฀incremental฀monetary฀

rewards฀for฀college฀students฀majoring฀in฀ subjects฀ that฀ require฀ advanced฀ knowl-edge฀of฀and฀skills฀in฀mathematics.฀One฀ stream฀ of฀ education฀ research฀ reports฀ the฀ positive฀ effect฀ of฀ cognitive฀ skills฀ in฀ high฀ school฀ on฀ subsequent฀ earnings฀ but฀ does฀ not฀ explain฀ how฀ high฀

cog-nitive฀ skills฀ enable฀ some฀ students฀ to฀ earn฀more฀than฀others.฀The฀other฀stream฀ evaluates฀ factors฀ that฀ affect฀ students’฀ choices฀of฀educational฀options.฀Students฀ tend฀to฀major฀in฀subjects฀in฀which฀they฀ expect฀a฀high฀probability฀of฀completing฀ all฀ degree฀ requirements฀ successfully.฀ In฀ particular,฀ mathematics฀ and฀ related฀ courses฀at฀the฀college฀level฀require฀stu-dents฀to฀be฀equipped฀with฀a฀background฀ of฀ mathematical฀ concepts฀ and฀ skills.฀ Thus,฀ students’฀ educational฀ choices฀ in฀ quantitatively฀oriented฀fields฀in฀college฀ are฀ influenced฀ highly฀ by฀ the฀ students’฀ levels฀ of฀ mathematics฀ preparation฀ dur-ing฀ their฀ precollege฀ education.฀ On฀ the฀ basis฀ of฀ the฀ two฀ streams฀ of฀ previous฀ studies฀ in฀ education,฀ the฀ present฀ study฀ attempted฀to฀provide฀empirical฀evidence฀ of฀ how฀ students฀ with฀ advanced฀ math-ematical฀ skills฀ could฀ earn฀ more฀ than฀ their฀ counterparts฀ with฀ less฀ developed฀ mathematical฀skills฀by฀focusing฀on฀col-lege฀curricula’s฀mathematical฀content.฀

Our฀ results฀ indicate฀ that฀ monetary฀ rewards฀for฀students฀who฀major฀in฀quan- titatively฀oriented฀subjects฀are฀substan-tial.฀ For฀ example,฀ when฀ undergraduate฀ majors฀ are฀ classified฀ into฀ five฀ groups฀ depending฀ on฀ the฀ mathematical฀ con-tent฀of฀their฀major฀curricula,฀the฀gap฀in฀ beginning฀salaries฀between฀quantitative฀ oriented฀ majors฀ (i.e.,฀ G4฀ and฀ G5)฀ and฀ qualitative฀oriented฀majors฀(i.e.,฀G1฀and฀ G2)฀was฀$14,472.฀In฀general,฀the฀more฀ the฀mathematical฀content฀in฀the฀curricu-la,฀the฀higher฀the฀salaries฀for฀graduates฀ after฀controlling฀for฀the฀effect฀of฀semes-ter฀and฀college.฀Thus,฀the฀present฀study฀ can฀ contribute฀ to฀ educational฀ literature฀ by฀ documenting฀ the฀ additional฀ money฀ students฀ could฀ earn฀ if฀ they฀ build฀ the฀ necessary฀level฀of฀skills฀and฀knowledge฀ in฀mathematics฀during฀their฀elementary฀ and฀secondary฀school฀years.

The฀results฀of฀this฀study฀are฀based฀on฀ an฀analysis฀of฀undergraduate฀major฀cur-ricula฀and฀beginning฀salary฀statistics฀for฀ graduating฀students฀at฀one฀public฀univer-sity;฀thus,฀further฀studies฀are฀required฀to฀ generalize฀the฀aforementioned฀findings.฀ However,฀ because฀ most฀ universities฀ in฀ the฀United฀States฀maintain฀similar฀aca-demic฀ curricula,฀ and฀ job฀ markets฀ are฀ formed฀ competitively,฀ we฀ can฀ reason-ably฀assume฀that฀our฀findings฀at฀this฀one฀ university฀provide฀good฀insight฀into฀how฀ young฀ students’฀ cognitive฀ skills฀ affect฀

TABLE฀4.฀Results฀From฀Regressing฀Beginning฀Salaries฀on฀Mathematical฀ Content฀of฀the฀Curricula,฀for฀Academic฀Years฀2005–2007

Variable฀ Parameter฀estimate฀($)฀ t฀(df฀=฀1)**

Intercept฀ 35,857.00฀ 35.54

DG2฀ 323.52฀ 0.20

DG3฀ 5,157.13฀ 3.89

DG4฀ 14,738.00฀ 6.09

DG5฀฀ 14,583.00฀ 11.43

Note.฀SA_i,t฀=฀β0฀+฀β1DG2฀+฀β2DG3฀+฀β3DG4฀+฀β4DG5฀+฀εi,t.฀SAi,t฀=฀average฀salary฀for฀Major฀i฀

in฀Academic฀Semester฀t;฀DG2฀=฀assigned฀with฀a฀unit฀variable฀for฀Group฀2,฀otherwise฀0;฀DG3฀=฀ assigned฀with฀a฀unit฀variable฀for฀Group฀3,฀otherwise,฀0;฀DG4฀=฀assigned฀with฀a฀unit฀variable฀for฀ Group฀ 4,฀ otherwise,฀ 0;฀ DG5฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ Group฀ 5,฀ otherwise,฀ 0;฀β_0–4฀ =฀ parameter฀estimates;฀εi,t฀=฀disturbance฀term.

**_p฀>฀.01.

TABLE฀5.฀Results฀From฀Regressing฀Beginning฀Salaries฀on฀Mathematical฀ Content฀of฀the฀Curricula฀and฀Moderating฀Variables:฀Academic฀Semesters฀ and฀Colleges฀for฀Academic฀Years฀2005–2007

Variable฀ Parameter฀estimate฀($)฀ t฀(df฀=฀1)

Intercept฀ 35,325.00฀ 26.84**

DG2฀ 734.96฀ 0.43

DG3฀ 5,040.89฀ 3.06**

DG4฀ 8,409.92฀ 3.09**

DG5฀ 10,383.00฀ 4.91**

DCAL฀ –2,018.37฀ –1.31

DCOA฀ 2,366.87฀ 0.95

DCOB฀ 2,649.36฀ 1.20

DCOE฀ 8,110.76฀ 3.53**

DCOG฀ –6,587.49฀ –2.76**

DCOS฀ –4,348.30฀ –1.77†

DS05฀ –1,850.73฀ –1.34

DF06฀ 1,645.59฀ 1.18

DS06฀ 1,437.96฀฀ 1.06

DF07฀ 3,581.70฀ 2.51*

DS07฀ 2,567.89฀ 1.86†

Note.฀ SA_i,t฀ =฀λ₀฀ +฀λ₁DG2฀ +฀λ₂DG3฀ +฀λ₃DG4฀ +฀λ₄DG5฀ +฀λ₅DCAL฀ +฀λ₆DCOA฀ +฀λ₇DCOB฀ +฀

λ₈DCOE฀ +฀λ₉DCOG฀ +฀λ₁₀DCOS฀ +฀λ₁₁DS05฀ +฀λ₁₂DF06฀ +฀λ₁₃DS06฀ +฀λ₁₄DF07฀ +฀λ₁₅DS07฀ +฀ ฀

λ_i,t.฀ DCAL฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ College฀ of฀Agriculture฀ and฀ Life฀ Science฀ (CAL),฀ otherwise฀0;฀DCOA฀=฀assigned฀with฀a฀unit฀variable฀for฀DCOA,฀otherwise฀0;฀DCOB฀=฀assigned฀ with฀a฀unit฀variable฀for฀College฀of฀Business฀(COB),฀otherwise,฀0;฀DCOE฀=฀assigned฀with฀a฀unit฀ variable฀for฀College฀of฀Engineering฀(COE),฀otherwise฀0;฀DCOG฀=฀assigned฀with฀a฀unit฀variable฀for฀ College฀of฀Geosciences฀(COG),฀otherwise฀0;฀DCOS฀=฀assigned฀with฀a฀unit฀variable฀for฀College฀of฀ Science฀(COS),฀otherwise,฀0;฀DS05฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀Spring฀2005฀semester,฀ otherwise฀0;฀DF06฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀Fall฀2006฀semester,฀otherwise฀0;฀DS06฀ =฀assigned฀with฀a฀unit฀variable฀for฀the฀Spring฀2006฀semester,฀otherwise฀0;฀DF07฀=฀assigned฀with฀a฀ unit฀variable฀for฀the฀Fall฀2007฀semester,฀otherwise฀0;฀DS07฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀ Spring฀2007฀semester,฀otherwise฀0;฀λ_0–15฀=฀parameter฀estimates;฀ν_i,t฀=฀disturbance฀term.฀Number฀of฀ observations฀=฀373.฀Adj.฀R2_฀=฀.50.

†_{p฀<฀.10.฀}*_{p฀<฀.05.฀}**_p฀<฀.01.

may฀be฀one฀of฀ the฀ primary฀ factors฀ that฀ are฀ attributed฀ to฀ a฀ difference฀ in฀ their฀ future฀earnings.฀

The฀present฀article฀evaluates฀quantita-tively฀oriented฀versus฀nonquantitatively฀ oriented฀majors฀on฀the฀basis฀of฀the฀level฀ of฀mathematical฀content฀required฀in฀cur-ricula.฀ Beginning฀ salaries฀ of฀ students฀ who฀major฀in฀less฀quantitative฀fields฀are฀ lower฀ than฀ those฀ in฀ more฀ quantitative฀ fields฀ including฀ engineering,฀ science,฀ and฀ business.฀ This฀ observation฀ could฀ augment฀Hackett’s฀(1985)฀and฀Murnane฀ et฀ al.’s฀ (2000)฀ findings฀ by฀ establishing฀ causal฀effects฀of฀three฀variables:฀cogni-tive฀ skills,฀ choice฀ of฀ major,฀ and฀ begin-ning฀salaries฀of฀graduates.

We฀reviewed฀the฀undergraduate฀catalog฀ at฀ a฀ southern฀ state’s฀ flagship฀ university฀ and฀ its฀ graduates’฀ beginning฀ salaries฀ by฀ major฀ over฀ 3฀ academic฀ years฀ (2005– 2007).฀The฀university฀provides฀beginning฀ salaries฀statistics฀every฀academic฀semester฀ through฀a฀survey฀of฀graduating฀students.฀ Majors฀ at฀ the฀ university฀ are฀ classified฀ into฀ five฀ groups฀ on฀ the฀ basis฀ of฀ the฀ number฀of฀mathematics฀courses฀required฀ in฀their฀curricula,฀and฀their฀contents.฀For฀ example,฀ Group฀ 1’s฀ curricula฀ required฀ one฀ elective฀ in฀ basic฀ mathematics,฀ and฀ Group฀ 5’s฀ curricula฀ required฀ more฀ than฀ two฀advanced฀mathematics฀courses.฀

We฀tested฀the฀hypothesis฀by฀compar-ing฀average฀beginning฀salaries฀between฀ groups฀ and฀ performing฀ a฀ regression฀ analysis฀ while฀ controlling฀ moderating฀ variables,฀ semester฀ and฀ college.฀ Our฀ results฀ indicated฀ that฀ graduating฀ stu-dents฀who฀completed฀curricula฀requiring฀ the฀most฀rigorous฀mathematical฀content฀ (i.e.,฀ Group฀ 5)฀ earned฀ $10,383฀ more฀ than฀their฀counterparts฀in฀Group฀1.฀

The฀ remainder฀ of฀ the฀ present฀ article฀ includes฀ a฀ review฀ of฀ the฀ related฀ litera-ture฀ and฀ a฀ proposed฀ research฀ hypothe-sis,฀a฀description฀of฀the฀sample฀selection฀ procedure฀and฀testing฀models,฀empirical฀ results,฀and฀concluding฀remarks.฀ Literature฀Review฀and฀ Hypothesis฀Development

Trusty฀ et฀ al.฀ (2000)฀ examined฀ the฀ effects฀ of฀ gender,฀ socioeconomic฀ sta-tus,฀and฀early฀academic฀performance฀on฀ choice฀of฀college฀major.฀Socioeconom-ic฀status฀was฀measured฀as฀a฀composite฀ variable฀ using฀ the฀ following฀

informa-tion฀ for฀ parents฀ of฀ students:฀ income,฀ educational฀ levels,฀ and฀ occupational฀ type.฀Trusty฀et฀al.฀measured฀early฀aca-demic฀ performance฀ by฀ scores฀ on฀ four฀ eigth-grade฀ cognitive฀ tests฀ including฀ mathematics,฀reading,฀science,฀and฀his-tory฀ or฀ geography.฀ In฀ general,฀ math-ematics฀ is฀ the฀ strongest฀ predictor฀ of฀ men’s฀ choice฀ of฀ major,฀ and฀ reading฀ is฀ the฀ strongest฀ predictor฀ of฀ women’s฀ choice฀ of฀ major฀ (Trusty฀ et฀ al.).฀ None-theless,฀Trusty฀ et฀ al.฀ cautioned฀ against฀ interpreting฀ their฀ findings฀ as฀ showing฀ a฀ serious฀ correlation฀ between฀ math-ematics฀ and฀ reading.฀ In฀ other฀ words,฀ students฀who฀do฀well฀in฀one฀field฀often฀ excel฀ in฀ the฀ other฀ as฀ well.฀ Maple฀ and฀ Stage฀ (1991)฀ also฀ indicated฀ choice฀ of฀ college฀ major฀ as฀ the฀ interactive฀ out-come฀ of฀ gender,฀ socioeconomic฀ sta-tus,฀ and฀ academic฀ performance.฀ High฀ mathematics฀ and฀ science฀ test฀ scores฀ influence฀students฀to฀take฀more฀courses฀ in฀ mathematics฀ and฀ science฀ in฀ high฀ school;฀thus,฀those฀students฀are฀inclined฀ to฀ study฀ mathematics฀ and฀ science฀ at฀ college.฀ Further,฀ postsecondary฀ educa-tion฀ choice฀ is฀ closely฀ associated฀ with฀ subsequent฀ vocational฀ choice฀ (Eccles,฀ 1994;฀ Gianakos฀ &฀ Subich,฀ 1988;฀ Lent฀ et฀al.฀1994;฀Wallace฀&฀Walker,฀1990).฀

In฀ a฀ similar฀ line฀ of฀ research,฀ Hackett฀ (1985)฀ examined฀ choice฀ of฀ major฀ as฀ a฀ function฀ of฀ mathematics฀ self-efficacy,฀ which฀ emphasizes฀ “the฀ role฀ of฀ cognitive-mediational฀ factors,฀ specifically฀ expectations฀ of฀ personal฀ effectiveness”฀ in฀ mathematics฀ or฀ related฀ subjects฀ (p.฀ 47).฀ Mathematics฀ is฀ a฀ subject฀ that฀ requires฀ students฀ to฀ follow฀ a฀ series฀ of฀ sequential฀ courses฀ because฀ they฀ build฀ concepts฀ step฀ by฀ step.฀ In฀ accordance,฀ students฀ who฀ lack฀ mathematics฀preparation฀face฀difficulty฀ with฀ grasping฀ concepts฀ in฀ college฀ mathematics฀ courses.฀ The฀ frustration฀ they฀experience฀leads฀to฀their฀premature฀ decision฀ not฀ to฀ study฀ mathematics-related฀ fields.฀ Hackett’s฀ findings฀ support฀ the฀ role฀ of฀ mathematics฀ self-efficacy,฀ which฀ predicts฀ mathematics฀ anxiety฀ and฀ mathematics-related฀ major฀ choices.฀ Also,฀ self-efficacy฀ predicts฀ mathematics-related฀major฀choices฀even฀ better฀than฀does฀measured฀ability.฀

Lack฀of฀mathematical฀preparation฀not฀ only฀limits฀students’฀educational฀choices,฀ but฀ also฀ reduces฀ their฀ subsequent฀

earn-ings.฀ Murnane฀ et฀ al.฀ (2000)฀ examined฀ how฀high฀school฀seniors’฀cognitive฀skills฀ are฀associated฀with฀their฀future฀earnings.฀ Cognitive฀ skills฀ are฀ measured฀ as฀ high฀ school฀seniors’฀mathematics฀and฀reading฀ scores฀that฀are฀included฀in฀the฀National฀ Longitudinal฀Survey฀of฀the฀High฀School฀ Class฀of฀1972฀(NLSHC)฀and฀High฀School฀ and฀Beyond฀(HSB).฀Mathematics฀scores฀ highly฀correlate฀with฀reading฀scores,฀and฀ the฀former฀explains฀subsequent฀earnings฀ better฀ than฀ the฀ latter.฀ Thus,฀ Murnane฀ et฀ al.฀ adopted฀ mathematics฀ score฀ alone฀ as฀ a฀ proxy฀ for฀ cognitive฀ skills.฀ Their฀ results฀show฀the฀positive฀effect฀of฀math-ematics฀ scores฀ on฀ subsequent฀ earnings.฀ For฀example,฀an฀additional฀point฀on฀the฀ mathematics฀ score฀ of฀ male฀ high-school฀ seniors฀ in฀ 1982฀ led฀ to฀ a฀ 1.5%฀ gain฀ in฀ annual฀earnings฀by฀the฀age฀of฀27.฀Also,฀ mathematics฀ score฀ is฀ a฀ strong฀ predictor฀ of฀educational฀attainments฀in฀college.฀In฀ accordance,฀ it฀ is฀ important฀ for฀ students฀ to฀ develop฀ essential฀ cognitive฀ skills,฀ in฀ particular฀mathematics,฀at฀an฀early฀stage฀ of฀their฀education.฀

We฀ could฀ interpret฀ Murnane฀ et฀ al.’s฀ (2000)฀ findings฀ in฀ several฀ ways.฀ First,฀ cognitive฀ skills฀ could฀ be฀ a฀ proxy฀ for฀ students’฀ ability฀ to฀ identify฀ and฀ grasp฀ opportunities฀ in฀ their฀ lives.฀ In฀ other฀ words,฀ a฀ good฀ student฀ in฀ school฀ may฀ outperform฀ others฀ in฀ society.฀ Second,฀ high฀mathematics฀scores฀could฀enhance฀ senior฀ high-school฀ students’฀ chances฀ of฀ earning฀a฀college฀degree.฀In฀turn,฀a฀col-lege฀degree฀would฀help฀students฀to฀earn฀ more฀ money฀ than฀ would฀ a฀ high฀ school฀ diploma฀only.฀Uchitelle฀(2005)฀discussed฀ the฀ substantial฀ gap฀ in฀ salary฀ between฀ those฀people฀with฀a฀bachelor’s฀degree฀or฀ higher฀ and฀ their฀ counterparts฀ with฀ only฀ a฀ high-school฀ education.฀ For฀ example,฀ by฀ the฀ end฀ of฀ 2004฀ the฀ median฀ wage฀ of฀ full-time฀ workers฀ with฀ a฀ bachelor’s฀ degree฀ or฀ higher฀ was฀ $986฀ per฀ week,฀ but฀only฀$574฀for฀the฀latter.฀Murnane฀et฀ al.’s฀findings฀support฀this฀proposition฀by฀ indicating฀that฀high-school฀students฀with฀ high฀mathematics฀scores฀produce฀higher฀ educational฀attainment฀in฀college.฀

Last,฀ as฀ discussed฀ previously,฀ suc- cess฀in฀high-school฀mathematics฀cours-es฀ providcess฀in฀high-school฀mathematics฀cours-es฀ high-school฀ seniors฀ with฀ self-confidence฀ in฀ quantitative฀ sub-jects.฀ Thus,฀ they฀ may฀ be฀ motivated฀ to฀ choose฀ quantitative฀ subjects฀ as฀ their฀ college฀major.฀In฀our฀knowledge-based฀

334

economy,฀ firms฀ demand฀ high-skilled฀ workers฀ who฀ are฀ trained฀ in฀ special-ized฀ areas฀ during฀ their฀ postsecondary฀ education.฀For฀example,฀in฀1960฀there฀ were฀ only฀ 5,000฀ computer฀ program-mers฀ in฀ the฀ United฀ States,฀ but฀ that฀ increased฀ to฀ approximately฀ 1.3฀ mil-lion฀ computer฀ programmers฀ in฀ 1999.฀ The฀ share฀ of฀ managerial฀ and฀ profes-sional฀ jobs฀ in฀ total฀ employment฀ has฀ increased฀from฀22%฀in฀1979฀to฀28.4%฀ in฀1995฀( KF26฀J8525:฀America’s฀Work-force฀ Needs,฀ 1999).฀ Education฀ should฀ be฀ the฀ solution฀ to฀ equip฀ students฀ with฀ the฀ skills฀ and฀ knowledge฀ that฀ are฀ in฀ demand฀ in฀ the฀ growing฀ sectors฀ of฀ the฀ economy.฀In฀particular,฀there฀are฀three฀ fields฀in฀which฀more฀college฀graduates฀ are฀needed:฀mathematics,฀science,฀and฀ engineering฀ (KF26฀ J8525:฀ America’s฀ Workforce฀ Needs).฀ Furthermore,฀ the฀ academic฀ success฀ of฀ students฀ in฀ these฀ three฀ fields฀ is฀ dependent฀ upon฀ their฀ mastery฀of฀basic฀mathematical฀skills.

In฀addition,฀the฀salaries฀of฀college฀grad-uates฀ vary฀ depending฀ on฀ their฀ major฀ at฀ college.฀In฀The฀Wall฀Street฀Journal ,฀Pay-Scale฀Inc.฀shared฀survey฀results฀regarding฀ the฀median฀starting฀salaries฀in฀50฀majors฀ from฀ 1.2฀ million฀ people฀ with฀ bachelor’s฀ degrees฀ (“Salary฀ Increase฀ by฀ Major,”฀ 2008).฀When฀comparing฀the฀top฀and฀bot-tom฀10฀majors฀on฀the฀pay฀scale,฀the฀mean฀ of฀the฀top฀10฀majors฀is฀$59,710,฀whereas฀ the฀ mean฀ of฀ the฀ bottom฀ 10฀ majors฀ is฀ $35,280,฀ with฀ a฀ difference฀ of฀ $24,430.฀ Furthermore,฀the฀former฀comprises฀seven฀ majors฀in฀engineering,฀computer฀science,฀ and฀ two฀ health-care-related฀ subjects,฀ but฀ the฀ latter฀ comprises฀ 10฀ majors฀ in฀ liberal฀ arts.฀These฀results฀demonstrate฀that฀high-er฀ paying฀ jobs฀ are฀ concentrated฀ in฀ fields฀ in฀ which฀ basic฀ mathematical฀ skills฀ and฀ knowledge฀ are฀ mandatory฀ for฀ academic฀ success.฀Also,฀this฀result฀is฀consistent฀with฀ Murnane฀et฀al.’s฀(2000)฀findings฀that฀stu-dents’฀ cognitive฀ skills฀ are฀ a฀ predictor฀ of฀ their฀future฀earnings.฀

In฀ the฀ presence฀ of฀ substantial฀ differ- ences฀in฀beginning฀salaries฀among฀col-lege฀ graduates฀ and฀ varying฀ degrees฀ of฀ mathematical฀ content฀ that฀ are฀ required฀ depending฀on฀the฀major฀fields,฀we฀mea-sured฀differences฀in฀graduating฀students’฀ salaries฀ that฀ can฀ be฀ attributed฀ to฀ their฀ curriculum’s฀ mathematical฀ content฀ by฀ developing฀ a฀ research฀ hypothesis฀ in฀ an฀ alternative฀form฀as฀follows:

Hypothesis฀1฀(H₁):฀The฀mathematical฀ content฀in฀curricula฀predicts฀graduating฀ students’฀beginning฀salaries.

Sample฀Selection฀and฀Empirical฀ Model฀Construction

We฀ selected฀ one฀ state฀ university฀ in฀ the฀southern฀region฀of฀the฀United฀States.฀ On฀ its฀ Web฀ site,฀ this฀ university฀ pro-vides฀ its฀ undergraduate฀ catalog฀ and฀ its฀ graduating฀ students’฀ beginning฀ salary฀ statistics.฀ The฀ university’s฀ main฀ cam-pus฀ comprises฀ nine฀ colleges:฀ College฀ of฀Agriculture฀and฀Life฀Science฀(CAL),฀ College฀of฀Architecture฀(COA),฀College฀ of฀ Business฀ (COB),฀ College฀ of฀ Educa- tion฀and฀Human฀Resources฀(CEH),฀Col-lege฀of฀Engineering฀(COE),฀College฀of฀ Geosciences฀(COG),฀College฀of฀Liberal฀ Arts฀(CLA),฀College฀of฀Science฀(COS),฀ and฀ College฀ of฀ Veterinary฀ Medicine฀ (CVM).฀We฀eliminated฀CEH฀and฀CVM฀ from฀ our฀ analysis.฀ CEH฀ offers฀ seven฀ undergraduate฀degree฀programs,฀and฀the฀ required฀mathematics฀courses฀vary฀from฀ program฀ to฀ program.฀ However,฀ most฀ educational฀ graduates฀ are฀ hired฀ as฀ ele-mentary฀school฀teachers.฀In฀accordance,฀ we฀ could฀ not฀ evaluate฀ an฀ association฀ between฀ the฀ number฀ of฀ mathematics฀ courses฀required฀in฀different฀educational฀ majors฀and฀education฀graduates’฀begin-ning฀ salaries.฀ Most฀ CVM฀ graduates฀ pursued฀ advanced฀ professional฀ degrees฀ in฀ medicine,฀ so฀ only฀ a฀ small฀ number฀ of฀ CVM฀ undergraduates฀ obtained฀ jobs฀ immediately฀after฀graduating฀(e.g.,฀only฀ two฀CVM฀undergraduates฀were฀reported฀ in฀ the฀ Fall฀ 2006฀ semester’s฀ beginning฀ salary฀statistics฀report).฀

Each฀ semester,฀ the฀ university฀ col-lects฀its฀graduates’฀beginning฀salaries฀in฀ three฀different฀ways:฀a฀survey฀at฀gradu-ation,฀a฀survey฀questionnaire฀mailed฀to฀ graduates,฀ and฀ an฀ online฀ salary฀ survey.฀ The฀ university฀ displays฀ beginning฀ sal-ary฀ statistics฀ by฀ major฀ in฀ each฀ college฀ including฀ the฀ mean,฀ maximum,฀ mini-mum,฀ and฀ standard฀ deviation฀ values.฀ The฀salary฀survey฀includes฀undergradu-ate฀and฀graduate฀degrees฀in฀each฀major.฀ Our฀study฀focused฀on฀salaries฀of฀under-graduates฀ who฀ graduated฀ in฀ academic฀ years฀2005–2007.฀

We฀ reviewed฀ the฀ university’s฀ 2007– 2008฀undergraduate฀catalog฀to฀identify฀ mathematics฀ courses฀ that฀ each฀ major฀

curriculum฀ required.฀ The฀ university฀ adopted฀ a฀ University฀ Core฀ Curriculum฀ that฀ required฀ students฀ to฀ complete฀ 6฀ semester฀ hours฀ in฀ mathematics.฀ How-ever,฀ students฀ had฀ an฀ option฀ to฀ substi-tute฀3฀semester฀hours฀in฀philosophy.฀In฀ accordance,฀ all฀ undergraduate฀ degree฀ programs฀ at฀ the฀ university฀ included฀ at฀ least฀one฀course฀in฀mathematics฀in฀their฀ curricula.฀The฀mathematics฀department฀ offers฀two฀sets฀of฀mathematics฀courses฀ that฀are฀tailored฀for฀nonengineering฀and฀ engineering฀ majors฀ to฀ allow฀ students฀ from฀ other฀ colleges฀ to฀ fulfill฀ the฀ Uni-versity฀ Core฀ Curriculum’s฀ mathemat-ics฀ requirement.฀ The฀ first฀ set฀ includes฀ Business฀Mathematics฀I฀(M141)฀and฀II฀ (M142),฀both฀of฀which฀require฀students฀ to฀complete฀high฀school฀Algebra฀I฀and฀ II,฀ and฀ Geometry.฀ M141฀ covers฀ topics฀ such฀ as฀ linear฀ equations฀ and฀ applica-tions,฀ matrix฀ algebra฀ and฀ applicaapplica-tions,฀ linear฀programming,฀and฀basic฀statistics฀ including฀probability.฀M142฀covers฀top-ics฀ such฀ as฀ derivatives,฀ optimization,฀ logarithms฀ and฀ exponential฀ functions,฀ integrals,฀ and฀ multivariate฀ calculus.฀ The฀ second฀ set฀ includes฀ Engineering฀ Mathematics฀ I฀ (M151)฀ and฀ II฀ (M152).฀ M151฀covers฀topics฀such฀as฀rectangular฀ coordinates,฀ vectors,฀ analytical฀ geom-etry,฀limits,฀derivatives,฀integration,฀and฀ computer฀ algebra.฀ M152฀ covers฀ topics฀ such฀ as฀ differentiation฀ and฀ integration,฀ improper฀ integrals,฀ analytic฀ geometry,฀ vectors,฀ infinite฀ series,฀ power฀ series,฀ Taylor฀ series,฀ and฀ computer฀ algebra.฀ Thus,฀M151฀and฀M152฀deal฀with฀more฀ advanced฀ mathematical฀ concepts฀ than฀ do฀M141฀and฀M142;฀M151฀and฀M152฀ are฀structured฀to฀provide฀students฀with฀ the฀ foundation฀ required฀ for฀ advanced฀ mathematics฀ courses฀ in฀ engineering฀ and฀science.

We฀ classified฀ the฀ curricula฀ into฀ the฀ following฀ five฀ groups฀ depending฀ on฀ their฀mathematical฀content:฀(a)฀Group฀1฀ (G1)฀requires฀one฀elective฀from฀the฀list฀ of฀basic฀mathematics฀courses,฀(b)฀Group฀ 2฀ (G2)฀ requires฀ two฀ electives฀ from฀ the฀ list,฀ (c)฀ Group฀ 3฀ (G3)฀ requires฀ M141฀ and฀ M142,฀ (d)฀ Group฀ 4฀ (G4)฀ requires฀ M151฀and฀M152,฀and฀(e)฀Group฀5฀(G5)฀ requires฀advanced฀mathematics฀courses฀ beyond฀M152.฀

We฀performed฀empirical฀analyses฀on฀ the฀basis฀of฀a฀comparison฀of฀these฀five฀ groups’฀mean฀salaries฀and฀a฀multivariate฀

regression฀ model.฀We฀ regressed฀ gradu-ating฀students’฀beginning฀salaries฀on฀the฀ five฀ groups฀ (G1–5).฀ Because฀ we฀ could฀ not฀assume฀the฀intervals฀among฀the฀five฀ groups฀ to฀ be฀ constant,฀ we฀ included฀ all฀ dummy฀variables฀to฀measure฀the฀incre-mental฀average฀amount฀of฀salary฀in฀each฀ group฀ beyond฀ the฀ base฀ group฀ (G1)฀ as฀ follows:฀

SA_i,t฀=฀β0฀+฀β1DG2฀+฀β2DG3฀+฀β3DG4฀ +฀β4DG5฀+฀εi,t,฀฀ (1) where฀ SA_i,t฀ =฀ average฀ salary฀ for฀ major฀ i฀ in฀ semester฀ t;฀ DG1฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G1,฀ otherwise฀ 0;฀ DG2฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G2,฀ otherwise฀ 0;฀ DG3฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G3,฀ otherwise฀ 0;฀ DG4฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G4,฀ otherwise฀ 0;฀ DG5฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ G5,฀ otherwise฀ 0;฀β0–4฀ =฀ parameter฀ estimates;฀ and฀εi,t฀ =฀ distur-bance฀term.

Equation฀1฀is฀extended฀by฀the฀inclu-sion฀of฀moderating฀variables,฀semesters,฀ and฀ colleges฀ because฀ salaries฀ are฀ sup-posed฀ to฀ increase฀ over฀ time,฀ and฀ they฀ also฀vary฀from฀college฀to฀college.฀Equa-tion฀2฀is฀as฀follows:

SA_i,t฀=฀λ0฀+฀λ1DG2฀+฀λ2DG3฀+฀λ3DG4฀ +฀λ4DG5฀ +฀λ5DCAL฀ +฀λ6DCOA฀ +฀ λ7DCOB฀ +฀λ8DCOE฀ +฀λ9DCOG฀ +฀ λ10DCOS฀ +฀λ11DS05฀ +฀λ12DF06฀ +฀ λ13DS06฀+฀λ14DF07฀+฀λ15DS07฀+฀νi,t

(2) where฀DCAL฀=฀assigned฀a฀unit฀variable฀ for฀CAL,฀otherwise฀0;฀DCOA฀=฀assigned฀ a฀ unit฀ variable฀ for฀ COA,฀ otherwise฀ 0;฀ DCOB฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COB,฀ otherwise฀ 0;฀ DCOE฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COE,฀ otherwise฀ 0;฀ DCOG฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COG,฀ otherwise฀ 0;฀ DCOS฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ COS,฀ otherwise฀ 0;฀ DS05฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ spring฀ of฀ 2005฀ semester,฀ otherwise฀ 0;฀ DF06฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ fall฀ of฀ 2006฀ semester,฀ otherwise฀ 0;฀ DS06฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ spring฀ of฀ 2006฀ semester,฀ otherwise฀ 0;฀ DF07฀ =฀ assigned฀ a฀ unit฀ variable฀ for฀ the฀ fall฀ of฀ 2007฀ semester,฀ otherwise฀ 0;฀ DS07฀=฀assigned฀a฀unit฀variable฀for฀the฀ spring฀ of฀ 2007฀ semester,฀ otherwise฀ 0;฀ λ0–15฀ =฀ parameter฀ estimates;฀ and฀νi,t฀ =฀

disturbance฀term.฀Other฀variables฀are฀as฀ defined฀previously.฀

In฀Equation฀2,฀we฀chose฀CLA฀and฀the฀ Fall฀ 2005฀ semester฀ as฀ base฀ variables.฀ Thus,฀parameters฀of฀college฀dummy฀vari-ables฀ represent฀ an฀ incremental฀ increase฀ in฀ the฀ mean฀ beginning฀ salaries฀ above฀ that฀in฀CLA฀for฀students฀in฀each฀college;฀ parameters฀of฀semester฀dummy฀variables฀ indicate฀ an฀ incremental฀ increase฀ in฀ the฀ mean฀ of฀ beginning฀ salaries฀ above฀ that฀ in฀the฀Fall฀2005฀semester฀for฀students฀in฀ each฀academic฀semester.฀

Empirical฀Results

Descriptive฀Statistics

Table฀1฀presents฀descriptive฀statistics฀ of฀selected฀variables฀per฀academic฀year฀ from฀ 2005฀ to฀ 2007.฀ No_Major_C฀

rep-resents฀ the฀ number฀ of฀ majors฀ in฀ a฀ col-lege฀whose฀graduates’฀beginning฀salary฀ data฀are฀available.฀G1–G5฀indicate฀each฀ major฀ curriculum’s฀ mathematical฀ con-tent.฀Ave_Group_C฀represents฀the฀mean฀ of฀a฀college’s฀groups.฀For฀example,฀3.00฀ of฀Ave_Group_C฀for฀COB฀in฀2005฀indi-cates฀ that฀ in฀ the฀ 2005฀ academic฀ year,฀ students฀in฀COB฀were฀required฀to฀com-plete฀ M141฀ and฀ M142.฀ Ave_Stud_C฀ represents฀the฀mean฀number฀of฀students฀ in฀each฀major฀who฀reported฀their฀begin-ning฀ salaries฀ in฀ a฀ college.฀ Ave_Sala-ry_C฀indicates฀the฀mean฀of฀the฀average฀ beginning฀ salaries฀ of฀ each฀ major฀ in฀ a฀ college.฀CLA฀indicates฀the฀lowest฀aver-age฀ beginning฀ salary฀ of฀ its฀ graduates฀ ($33,195)฀in฀2005,฀and฀COE฀shows฀the฀ highest฀ average฀ beginning฀ salary฀ of฀ its฀ graduates฀($58,363)฀in฀2007.฀

TABLE฀1.฀Descriptive฀Statistics฀of฀Variables฀per฀Academic฀Year,฀for฀฀ 2005–2007

฀ ฀ No_Major฀ Ave_Group฀ Ave_Stud฀ Ave_Salary

College฀ Year฀ _C฀ _C฀ _C฀ _C฀($)

CAL฀ 2005฀ 24฀ 2.75฀ 6.33฀ 39,345

CAL฀ 2006฀ 24฀ 2.75฀ 9.17฀ 40,289

CAL฀ 2007฀ 24฀ 2.75฀ 8.88฀ 36,850

COA฀ 2005฀ 3฀ 2.67฀ 26.67฀ 38,927

COA฀ 2006฀ 3฀ 2.67฀ 31.00฀ 41,885

COA฀ 2007฀ 3฀ 2.67฀ 36.00฀ 44,929

COB฀ 2005฀ 5฀ 3.00฀ 54.80฀ 41,080

COB฀ 2006฀ 5฀ 3.00฀ 58.00฀ 44,311

COB฀ 2007฀ 5฀ 3.00฀ 63.60฀ 46,533

COE฀ 2005฀ 18฀ 4.89฀ 30.78฀ 50,491

COE฀ 2006฀ 18฀ 4.89฀ 31.11฀ 55,058

COE฀ 2007฀ 18฀ 4.89฀ 37.06฀ 58,363

COG฀ 2005฀ 6฀ 3.67฀ 2.33฀ 40,107

COG฀ 2006฀ 6฀ 3.67฀ 3.67฀ 34,538

COG฀ 2007฀ 6฀ 3.67฀ 4.00฀ 40,478

CLA฀ 2005฀ 14฀ 1.21฀ 13.64฀ 33,195

CLA฀ 2006฀ 14฀ 1.21฀ 13.00฀ 37,171

CLA฀ 2007฀ 14฀ 1.21฀ 14.78฀ 39,705

COS฀ 2005฀ 6฀ 4.67฀ 3.33฀ 42,505

COS฀ 2006฀ 6฀ 4.67฀ 4.50฀ 40,057

COS฀ 2007฀ 6฀ 4.67฀ 6.50฀ 41,973

Note. ฀CAL฀=฀College฀of฀Agriculture฀and฀Life฀Science;฀COA฀=฀College฀of฀Agriculture;฀COB฀=฀Col-lege฀of฀Business;฀COE฀=฀College฀of฀Engineering;฀COG฀=฀College฀of฀Geosciences;฀CLA฀=฀College฀ of฀Liberal฀Arts;฀COS฀=฀College฀of฀Science;฀No_Major_C฀=฀number฀of฀majors฀in฀which฀graduating฀ students฀reported฀their฀salaries฀by฀college;฀Ave_Stud_C฀=฀average฀number฀of฀students฀by฀major฀ who฀ reported฀ their฀ beginning฀ salaries฀ in฀ a฀ college;฀Ave_Salary_C฀ =฀ mean฀ beginning฀ salaries฀ of฀ graduates฀by฀major฀in฀a฀college.฀Ave_Group_C฀refers฀to฀an฀indicator฀of฀mathematical฀content฀of฀ the฀curricula฀in฀the฀college.฀Curricula฀are฀classified฀into฀five฀groups฀depending฀on฀the฀number฀of฀ mathematics฀courses฀and฀mathematical฀contents.฀Curricula฀in฀Group฀1฀require฀one฀elective฀from฀ the฀list฀of฀basic฀mathematics฀courses฀provided;฀those฀in฀Group฀2฀require฀two฀electives฀from฀the฀ list฀of฀basic฀mathematics฀courses฀provided;฀those฀in฀Group฀3฀require฀Business฀Mathematics฀I฀and฀ Business฀Mathematics฀II;฀those฀in฀Group฀4฀require฀Engineering฀Mathematics฀I฀and฀Engineering฀ Mathematics฀ II;฀ and฀ those฀ in฀ Group฀ 5฀ require฀ M151,฀ M152,฀ and฀ more฀ advanced฀ mathematics฀ courses฀(e.g.,฀Ave_Group_C,฀2.67,฀indicates฀that฀most฀curricula฀in฀a฀college฀fall฀between฀Groups฀ 2฀and฀3).฀

336

Table฀2฀shows฀summary฀statistics฀per฀ college฀across฀these฀3฀years.฀No_Major_ C2฀indicates฀the฀number฀of฀majors฀with฀ beginning฀ salary฀ information฀ in฀ a฀ col-lege,฀accumulated฀over฀the฀3฀years.฀CAL฀ shows฀the฀greatest฀number฀(115฀majors),฀ and฀ COA฀ shows฀ the฀ least฀ (16฀ majors).฀ Most฀ curricula฀ in฀ CLA฀ require฀ roughly฀ one฀ mathematics฀ course฀ during฀ the฀ 3-year฀ period฀ (1.26฀ as฀ Ave_Group_C2),฀ and฀ most฀ students฀ in฀ COE฀ are฀ required฀ to฀ complete฀ mathematics฀ courses฀ beyond฀M152฀(4.87฀as฀Ave_Group_C2).฀ COE฀ graduates฀ have฀ the฀ highest฀ aver-age฀ salary฀ in฀ the฀ 3-year฀ period,฀ Ave_ Salary_C2฀ ($54,648),฀ whereas฀ CLA฀ graduates฀ experience฀ the฀ lowest฀ aver-age฀ salary฀ ($36,925).฀ The฀ mathematics฀ requirements฀ vary฀ from฀ college฀ to฀

col-lege฀and฀the฀level฀of฀average฀salaries฀in฀ a฀ college฀ appears฀ to฀ be฀ related฀ to฀ that฀ college's฀mathematical฀requirements.฀

Table฀3฀shows฀descriptive฀statistics฀by฀ group.฀As฀ the฀ curricula's฀ mathematical฀ content฀ increases,฀ graduating฀ students’฀ beginning฀salaries฀increase฀accordingly฀ (e.g.,฀ $35,856฀ for฀ G1฀ vs.฀ $50,595฀ for฀ G4).฀ To฀ estimate฀ the฀ statistical฀ signifi-cance฀ of฀ differences฀ in฀ beginning฀ sala- ries฀that฀result฀from฀the฀curricula's฀vary-ing฀mathematical฀content,฀we฀computed฀ the฀incremental฀change฀in฀average฀begin-ning฀ salaries฀ between฀ groups฀ (Inc_Sal-ary_G),฀ and฀ estimated฀ the฀ incremental฀ changes฀in฀t฀as฀well.฀Beginning฀salaries฀ in฀ G3฀ are฀ greater฀ than฀ those฀ in฀ G2฀ by฀ $4,833,฀which฀is฀statistically฀significant฀ at฀the฀.01฀level;฀beginning฀salaries฀in฀G4฀

are฀also฀$9,582฀greater฀than฀those฀in฀G3,฀ which฀ is฀ statistically฀ significant฀ at฀ the฀ .05฀level.฀Nonetheless,฀incremental฀dif-ferences฀ between฀ other฀ groups฀ are฀ not฀ statistically฀significant฀(e.g.,฀the฀average฀ beginning฀ salary฀ in฀ G4฀ did฀ not฀ differ฀ significantly฀ from฀ that฀ in฀ G5).฀ These฀ findings฀ imply฀ that฀ more฀ mathematics฀ courses฀ in฀ curricula฀ do฀ not฀ necessar-ily฀ lead฀ to฀ higher฀ beginning฀ salaries.฀ Thus,฀two฀groups฀are฀collapsed฀into฀one.฀ When฀ G1฀ and฀ G2฀ are฀ combined,฀ the฀ combination’s฀average฀beginning฀salary฀ (Ave_Salary_G2)฀is฀$35,985.฀In฀a฀simi-lar฀way,฀the฀combination฀of฀G4฀and฀G5฀ yields฀$50,457฀as฀an฀average฀beginning฀ salary.฀This฀difference฀of฀$14,472฀is฀sta-tistically฀significant฀at฀the฀.01฀level. Regression฀Results

Table฀4฀includes฀the฀results฀of฀Equa-tion฀1฀by฀regressing฀average฀salaries฀by฀ major฀on฀the฀five฀levels฀of฀the฀curricu-la’s฀ mathematical฀ content฀ (G1–5).฀ We฀ used฀G1฀as฀the฀base฀group฀in฀Equation฀ 1฀ because฀ other฀ groups฀ were฀ expected฀ to฀ show฀ higher฀ beginning฀ salaries;฀ the฀ adjusted฀R2 ฀of฀Equation฀1฀is฀.34.฀Param- eters฀on฀DG3–5฀are฀statistically฀signifi-cant,฀consistent฀with฀the฀results฀in฀Table฀ 3.฀ For฀ example,฀ the฀ average฀ beginning฀ salary฀ in฀ G5฀ is฀ greater฀ than฀ that฀ in฀ G1฀ ($35,857)฀by฀$14,583.฀

Beginning฀salaries฀increase฀over฀time฀ and฀are฀also฀affected฀by฀subjects฀that฀stu- dents฀take.฀Thus,฀Table฀5฀shows฀empiri- cal฀results฀from฀Equation฀2,฀which฀con-trols฀over฀both฀semesters฀and฀colleges.฀

Equation฀2฀includes฀CLA฀and฀Fall฀2005฀ semester฀ as฀ base฀ variables,฀ in฀ addition฀ to฀ G1.฀ The฀ adjusted฀R2_{฀ of฀ Equation฀ 2฀ is฀} .50.฀The฀average฀beginning฀salary฀in฀CLA฀ students฀ who฀ completed฀ one฀ mathemat-ics฀elective฀and฀then฀graduated฀in฀the฀Fall฀ 2005฀semester฀is฀$35,325.฀Students฀in฀G3฀ who฀completed฀more฀mathematics฀courses฀ (e.g.,฀M141฀and฀M142)฀earned฀$5,040.89฀ more฀ than฀ did฀ their฀ counterparts฀ in฀ G1.฀ Furthermore,฀ students฀ who฀ completed฀ more฀advanced฀mathematics฀courses฀such฀ as฀M151฀and฀M152฀and฀other฀mathematics฀ courses฀in฀G5฀earned฀$10,383฀more฀than฀ did฀their฀counterparts฀in฀G1.฀

Last,฀we฀evaluated฀the฀validity฀of฀prin-cipal฀assumptions฀that฀underlie฀Equation฀ 2:฀ the฀ White฀ test฀ for฀ homoscedastic-ity฀ of฀ the฀ errors฀(White,฀ 1980)฀and฀ the฀฀ Shapiro–Wilk฀ (Shapiro฀ &฀ Wilk,฀ 1965)฀ TABLE฀2.฀Descriptive฀Statistics฀of฀Variables฀per฀College,฀for฀Academic฀

Years฀2005–2007฀(N฀=฀373)

College฀ No_Major_C2฀ Ave_Group_C2฀ Ave_Salary_C2฀($)

CAL฀ 115฀ 2.57฀ 38,046

COA฀ 16฀ 2.63฀ 42,278

COB฀ 30฀ 3.00฀ 44,245

COE฀ 94฀ 4.87฀ 54,648

COG฀ 23฀ 4.04฀ 37,810

CLA฀ 68฀ 1.26฀ 36,925

COS฀ 27฀ 4.56฀ 41,415

Note.฀ CAL฀ =฀ College฀ of฀Agriculture฀ and฀ Life฀ Science;฀ COA฀ =฀ College฀ of฀Agriculture;฀ COB฀ =฀ College฀ of฀ Business;฀ COE฀ =฀ College฀ of฀ Engineering;฀ COG฀ =฀ College฀ of฀ Geosciences;฀ CLA฀ =฀ College฀ of฀ Liberal฀Arts;฀ COS฀ =฀ College฀ of฀ Science;฀ No_Major_C2฀ =฀ accumulated฀ number฀ of฀ majors฀in฀which฀graduating฀students฀reported฀their฀salaries;฀Ave_Group_C2฀=฀average฀indicator฀ of฀mathematical฀content฀of฀the฀curricula฀in฀a฀college;฀Ave_Salary_C2฀=฀average฀beginning฀salary฀ of฀graduates฀in฀a฀college.฀

TABLE฀3.฀Descriptive฀Statistics฀of฀Variables฀per฀Group,฀for฀Academic฀ Years฀2005–2007฀(N฀=฀373)

฀ No_Major฀ Ave_Salary฀ Inc_Salary฀ ฀ Ave_Salary Group฀ _G฀ _G฀($)฀ _G฀($)฀ t฀(df฀=฀1)฀ _G2฀($)

1฀ 76฀ 35,856฀ —฀ —฀ 35,985

2฀ 50฀ 36,180฀ 324฀ 0.28฀ —

3฀ 104฀ 41,013฀ 4,833฀ 4.68**_{฀ —}

4฀ 16฀ 50,595฀ 9,582฀ 2.55*_{฀ 50,457}

5฀ 127฀ 50,439฀ –156฀ 0.04฀ —

Note.฀ No_Major_G฀ =฀ accumulated฀ number฀ of฀ majors฀ in฀ which฀ graduating฀ students฀ reported฀ their฀salaries฀over฀the฀six฀regular฀semesters฀from฀2005฀to฀2007;฀Ave_Salary_G฀=฀average฀salary฀ in฀a฀group฀over฀the฀six฀regular฀semesters;฀Inc_Salary_G฀=฀incremental฀change฀in฀Ave_Salary_G฀ between฀Gt฀and฀Gt+1;฀Ave_Salary_G2฀=฀average฀salary฀in฀Groups฀1฀and฀2฀and฀Groups฀4฀and฀5.฀

*_{p฀<฀.05.฀}**_p฀<฀.01.

test฀ for฀ normality฀ of฀ the฀ error฀ distribu-tion.฀Both฀tests฀revealed฀no฀violation฀of฀ these฀assumptions.฀

Conclusion

The฀ purpose฀ of฀ the฀ present฀ article฀ was฀to฀document฀incremental฀monetary฀

rewards฀for฀college฀students฀majoring฀in฀ subjects฀ that฀ require฀ advanced฀ knowl-edge฀of฀and฀skills฀in฀mathematics.฀One฀ stream฀ of฀ education฀ research฀ reports฀ the฀ positive฀ effect฀ of฀ cognitive฀ skills฀ in฀ high฀ school฀ on฀ subsequent฀ earnings฀ but฀ does฀ not฀ explain฀ how฀ high฀

cog-nitive฀ skills฀ enable฀ some฀ students฀ to฀ earn฀more฀than฀others.฀The฀other฀stream฀ evaluates฀ factors฀ that฀ affect฀ students’฀ choices฀of฀educational฀options.฀Students฀ tend฀to฀major฀in฀subjects฀in฀which฀they฀ expect฀a฀high฀probability฀of฀completing฀ all฀ degree฀ requirements฀ successfully.฀ In฀ particular,฀ mathematics฀ and฀ related฀ courses฀at฀the฀college฀level฀require฀stu-dents฀to฀be฀equipped฀with฀a฀background฀ of฀ mathematical฀ concepts฀ and฀ skills.฀ Thus,฀ students’฀ educational฀ choices฀ in฀ quantitatively฀oriented฀fields฀in฀college฀ are฀ influenced฀ highly฀ by฀ the฀ students’฀ levels฀ of฀ mathematics฀ preparation฀ dur-ing฀ their฀ precollege฀ education.฀ On฀ the฀ basis฀ of฀ the฀ two฀ streams฀ of฀ previous฀ studies฀ in฀ education,฀ the฀ present฀ study฀ attempted฀to฀provide฀empirical฀evidence฀ of฀ how฀ students฀ with฀ advanced฀ math-ematical฀ skills฀ could฀ earn฀ more฀ than฀ their฀ counterparts฀ with฀ less฀ developed฀ mathematical฀skills฀by฀focusing฀on฀col-lege฀curricula’s฀mathematical฀content.฀

Our฀ results฀ indicate฀ that฀ monetary฀ rewards฀for฀students฀who฀major฀in฀quan- titatively฀oriented฀subjects฀are฀substan-tial.฀ For฀ example,฀ when฀ undergraduate฀ majors฀ are฀ classified฀ into฀ five฀ groups฀ depending฀ on฀ the฀ mathematical฀ con-tent฀of฀their฀major฀curricula,฀the฀gap฀in฀ beginning฀salaries฀between฀quantitative฀ oriented฀ majors฀ (i.e.,฀ G4฀ and฀ G5)฀ and฀ qualitative฀oriented฀majors฀(i.e.,฀G1฀and฀ G2)฀was฀$14,472.฀In฀general,฀the฀more฀ the฀mathematical฀content฀in฀the฀curricu-la,฀the฀higher฀the฀salaries฀for฀graduates฀ after฀controlling฀for฀the฀effect฀of฀semes-ter฀and฀college.฀Thus,฀the฀present฀study฀ can฀ contribute฀ to฀ educational฀ literature฀ by฀ documenting฀ the฀ additional฀ money฀ students฀ could฀ earn฀ if฀ they฀ build฀ the฀ necessary฀level฀of฀skills฀and฀knowledge฀ in฀mathematics฀during฀their฀elementary฀ and฀secondary฀school฀years.

The฀results฀of฀this฀study฀are฀based฀on฀ an฀analysis฀of฀undergraduate฀major฀cur-ricula฀and฀beginning฀salary฀statistics฀for฀ graduating฀students฀at฀one฀public฀univer-sity;฀thus,฀further฀studies฀are฀required฀to฀ generalize฀the฀aforementioned฀findings.฀ However,฀ because฀ most฀ universities฀ in฀ the฀United฀States฀maintain฀similar฀aca-demic฀ curricula,฀ and฀ job฀ markets฀ are฀ formed฀ competitively,฀ we฀ can฀ reason-ably฀assume฀that฀our฀findings฀at฀this฀one฀ university฀provide฀good฀insight฀into฀how฀ young฀ students’฀ cognitive฀ skills฀ affect฀ TABLE฀4.฀Results฀From฀Regressing฀Beginning฀Salaries฀on฀Mathematical฀

Content฀of฀the฀Curricula,฀for฀Academic฀Years฀2005–2007

Variable฀ Parameter฀estimate฀($)฀ t฀(df฀=฀1)**

Intercept฀ 35,857.00฀ 35.54

DG2฀ 323.52฀ 0.20

DG3฀ 5,157.13฀ 3.89

DG4฀ 14,738.00฀ 6.09

DG5฀฀ 14,583.00฀ 11.43

Note.฀SA_i,t฀=฀β0฀+฀β1DG2฀+฀β2DG3฀+฀β3DG4฀+฀β4DG5฀+฀εi,t.฀SAi,t฀=฀average฀salary฀for฀Major฀i฀

in฀Academic฀Semester฀t;฀DG2฀=฀assigned฀with฀a฀unit฀variable฀for฀Group฀2,฀otherwise฀0;฀DG3฀=฀ assigned฀with฀a฀unit฀variable฀for฀Group฀3,฀otherwise,฀0;฀DG4฀=฀assigned฀with฀a฀unit฀variable฀for฀ Group฀ 4,฀ otherwise,฀ 0;฀ DG5฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ Group฀ 5,฀ otherwise,฀ 0;฀β_0–4฀ =฀ parameter฀estimates;฀εi,t฀=฀disturbance฀term.

**_p฀>฀.01.

TABLE฀5.฀Results฀From฀Regressing฀Beginning฀Salaries฀on฀Mathematical฀ Content฀of฀the฀Curricula฀and฀Moderating฀Variables:฀Academic฀Semesters฀ and฀Colleges฀for฀Academic฀Years฀2005–2007

Variable฀ Parameter฀estimate฀($)฀ t฀(df฀=฀1)

Intercept฀ 35,325.00฀ 26.84**

DG2฀ 734.96฀ 0.43

DG3฀ 5,040.89฀ 3.06**

DG4฀ 8,409.92฀ 3.09**

DG5฀ 10,383.00฀ 4.91**

DCAL฀ –2,018.37฀ –1.31

DCOA฀ 2,366.87฀ 0.95

DCOB฀ 2,649.36฀ 1.20

DCOE฀ 8,110.76฀ 3.53**

DCOG฀ –6,587.49฀ –2.76**

DCOS฀ –4,348.30฀ –1.77†

DS05฀ –1,850.73฀ –1.34

DF06฀ 1,645.59฀ 1.18

DS06฀ 1,437.96฀฀ 1.06

DF07฀ 3,581.70฀ 2.51*

DS07฀ 2,567.89฀ 1.86†

Note.฀ SA_i,t฀ =฀λ₀฀ +฀λ₁DG2฀ +฀λ₂DG3฀ +฀λ₃DG4฀ +฀λ₄DG5฀ +฀λ₅DCAL฀ +฀λ₆DCOA฀ +฀λ₇DCOB฀ +฀

λ₈DCOE฀ +฀λ₉DCOG฀ +฀λ₁₀DCOS฀ +฀λ₁₁DS05฀ +฀λ₁₂DF06฀ +฀λ₁₃DS06฀ +฀λ₁₄DF07฀ +฀λ₁₅DS07฀ +฀ ฀

λ_i,t.฀ DCAL฀ =฀ assigned฀ with฀ a฀ unit฀ variable฀ for฀ College฀ of฀Agriculture฀ and฀ Life฀ Science฀ (CAL),฀ otherwise฀0;฀DCOA฀=฀assigned฀with฀a฀unit฀variable฀for฀DCOA,฀otherwise฀0;฀DCOB฀=฀assigned฀ with฀a฀unit฀variable฀for฀College฀of฀Business฀(COB),฀otherwise,฀0;฀DCOE฀=฀assigned฀with฀a฀unit฀ variable฀for฀College฀of฀Engineering฀(COE),฀otherwise฀0;฀DCOG฀=฀assigned฀with฀a฀unit฀variable฀for฀ College฀of฀Geosciences฀(COG),฀otherwise฀0;฀DCOS฀=฀assigned฀with฀a฀unit฀variable฀for฀College฀of฀ Science฀(COS),฀otherwise,฀0;฀DS05฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀Spring฀2005฀semester,฀ otherwise฀0;฀DF06฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀Fall฀2006฀semester,฀otherwise฀0;฀DS06฀ =฀assigned฀with฀a฀unit฀variable฀for฀the฀Spring฀2006฀semester,฀otherwise฀0;฀DF07฀=฀assigned฀with฀a฀ unit฀variable฀for฀the฀Fall฀2007฀semester,฀otherwise฀0;฀DS07฀=฀assigned฀with฀a฀unit฀variable฀for฀the฀ Spring฀2007฀semester,฀otherwise฀0;฀λ_0–15฀=฀parameter฀estimates;฀ν_i,t฀=฀disturbance฀term.฀Number฀of฀ observations฀=฀373.฀Adj.฀R2_฀=฀.50.

†_{p฀<฀.10.฀}*_{p฀<฀.05.฀}**_p฀<฀.01.

338

their฀subsequent฀earnings฀by฀evaluating฀ the฀mathematical฀content฀that฀their฀col-lege฀major฀curricula฀require.฀

Salaries฀ of฀ graduating฀ students฀ may฀ be฀ affected฀ by฀ other฀ factors฀ including฀ race,฀gender,฀and฀socioeconomic฀status,฀ in฀ addition฀ to฀ academic฀ performance฀ (specifically,฀ mathematical฀ knowledge฀ and฀ skills).฀ Because฀ we฀ performed฀ our฀ investigation฀on฀the฀basis฀of฀salary฀sur-veys฀ of฀ graduating฀ students฀ that฀ were฀ summarized฀ by฀ major,฀ no฀ additional฀ data฀ about฀ individual฀ graduating฀ stu-dents฀were฀available.฀In฀accordance,฀the฀ interpretation฀of฀the฀findings฀in฀the฀pres-ent฀study฀should฀be฀made฀with฀caution.฀ Future฀ research฀ could฀ extend฀ the฀ find-ings฀ by฀ including฀ more฀ variables฀ from฀ multiple฀universities฀across฀the฀nation.

NOTE

B.฀Brian฀Lee฀is฀an฀accounting฀professor฀whose฀ research฀ interests฀ include฀ earnings฀ management,฀ relevance฀of฀accounting฀information฀in฀the฀capital฀ markets,฀international฀accounting,฀and฀curriculum฀ development.

Jungsun฀ Lee฀ is฀ a฀ graduate฀ student฀ in฀ educa-tion฀ whose฀ research฀ interests฀ include฀ curriculum฀ development,฀ instructional฀ technology,฀ and฀ child฀ education.

Correspondence฀ concerning฀ this฀ article฀ should฀ be฀addressed฀to฀B.฀Brian฀Lee,฀Prairie฀View฀A&M฀ University,฀P.O.฀Box฀519,฀MS฀2310,฀Prairie฀View,฀ TX฀77446,฀USA.฀E-mail:฀brlee@pvamu.edu

REFERENCES

Eccles,฀J.฀S.฀(1994).฀Understanding฀women’s฀edu-cational฀and฀occupational฀choices.฀Psychology฀ of฀Women฀Quarterly,฀18,฀585–609.

Gianakos,฀I.,฀&฀Subich,฀L.฀M.฀(1988).฀Student฀sex฀ and฀sex฀role฀in฀relation฀to฀college฀major฀choice.฀

Career฀Development฀Quarterly,฀36,฀259–268. Hackett,฀ G.฀ (1985).฀ Role฀ of฀ mathematics฀

self-efficacy฀in฀the฀choice฀of฀math-related฀majors฀of฀ college฀women฀and฀men:฀A฀path฀analysis.฀ Jour-nal฀of฀Counseling฀Psychology,฀32,฀47–56.

KF26฀ J8525:฀America’s฀ workforce฀ needs฀ in฀ the฀ 21st฀ century,฀ 106th฀ Cong.,฀ (1999)฀ (testimony฀ of฀Robert฀Atkinson).฀

Lent,฀R.฀W.,฀Brown,฀S.฀D.,฀&฀Hackett,฀G.฀(1994).฀ Toward฀ a฀ unifying฀ social฀ cognitive฀ theory฀ of฀ career฀ and฀ academic฀ interest,฀ choice,฀ and฀ per-formance.฀Journal฀ of฀Vocational฀ Behavior,฀ 45,฀ 79–122.

Maple,฀ S.฀A.,฀ &฀ Stage,฀ F.฀ K.฀ (1991).฀ Influences฀ on฀the฀choice฀of฀math/science฀major฀by฀gender฀ and฀ethnicity.฀American฀Educational฀Research฀ Journal,฀28,฀37–60.

Murnane,฀ R.฀ J.,฀Willett,฀ J.฀ B.,฀ Duhaldeborde,฀Y.,฀ &฀ Tyler,฀ J.฀ H.฀ (2000).฀ How฀ important฀ are฀ the฀ cognitive฀skills฀of฀teenagers฀in฀predicting฀sub-sequent฀ earnings?฀Journal฀ of฀ Policy฀ Analysis฀ and฀Management,฀19,฀547–568.

Murnane,฀R.฀J.,฀Willett,฀J.฀B.,฀&฀Levy,฀F.฀(1995).฀ The฀ growing฀ importance฀ of฀ cognitive฀ skills฀ in฀ wage฀determination.฀Review฀of฀Economics฀and฀ Statistics,฀77,฀251–266.

Salary฀ increase฀ by฀ major.฀ (2008,฀ July฀ 31).฀The฀ Wall฀ Street฀ Journal.฀Retrieved฀April฀29,฀2009,฀ from฀ http://online.wsj.com/public/resources/ documents/info-Degrees_that_Pay_you_Back-sort.html

Shapiro,฀S.฀S.,฀&฀Wilk,฀M.฀B.฀(1965).฀An฀analysis฀ of฀ variance฀ test฀ for฀ normality.฀Biometrika,฀ 52,฀ 591–611.฀

Trusty,฀ J.฀ C.,฀ Robinson,฀ R.,฀ Plata,฀ M.,฀ &฀ Ng,฀ K.฀ M.฀ (2000).฀ Effects฀ of฀ gender,฀ socioeconomic฀ status,฀ and฀ early฀ academic฀ performance฀ on฀ postsecondary฀ educational฀ choice.฀Journal฀ of฀ Counseling฀&฀Development,฀78,฀463–472. Uchitelle,฀ L.฀ (2005,฀ October฀ 7).฀ Salary฀ gap฀

between฀high฀school฀and฀college฀education฀nar-rows.฀New฀York฀Times,฀p.฀14.

Wallace,฀ G.฀ R.,฀ &฀ Walker,฀ S.฀ P.฀ (1990).฀ Self-concept,฀ vocational฀ interests,฀ and฀ choice฀ of฀ academic฀ major฀ in฀ college฀ students.฀College฀ Student฀Journal,฀23,฀361–367.

White,฀ H.฀ (1980).฀ A฀ heteroskedasticity-consis-tent฀ covariance฀ matrix฀ estimator฀ and฀ a฀ direct฀ test฀ for฀ heteroscedasicity.฀Econometrica,฀ 48,฀ 817–838.฀

���฀�������฀��฀��������฀���������฀������฀��������฀��������฀��฀��������฀����������฀�������฀ ��������฀��������฀�����������฀��฀��������฀�����������฀����������฀���฀���������฀�����������฀��฀ ������������฀�������฀��������������฀���������฀����฀����฀���฀�������฀��฀�������������฀��������฀ ���฀�������฀���฀��������฀��฀�฀�������฀��฀������฀���฀�������฀��฀��������฀���������฀��฀���������฀ ���������฀��฀�����������฀����฀���฀��������฀�������฀��฀��������฀���������฀���฀���฀��������฀ ���������฀��฀��������฀���������฀��฀���฀��������฀��������฀������������

���������� ������ ����

������� �������� �������

����������฀����฀��������� �������฀������฀������������฀������ �����������฀฀���฀������฀�����฀���฀�����฀���฀������

��������������฀฀����฀������฀�����฀����฀�����฀�����฀����฀�����฀���฀������ ���฀���฀���฀�������฀�������฀���฀����

�����������������

��������������������

���������฀���฀�����฀�������฀������������฀������� ������������฀ � ������

��฀ ���฀ ������

�����������฀ ��฀ ���������� ��฀ ������������฀�฀��฀ ������������

������������������������������ ���������������