Testing the higher education-led growth hypothesis in a small island: an empirical investigation from a new version of the Solow growth model

Qual Quant (2014) 48:729–744DOI 10.1007/s11135-012-9798-9

Testing the higher education-led growth hypothesisin a small island: an empirical investigationfrom a new version of the Solow growth model

Salih Katircioglu · Sami Fethi · Hamit Caner

Published online: 8 November 2012© Springer Science+Business Media Dordrecht 2012

Abstract This study employs a new version of the Solow Growth Model in order to investi-gate the higher education-led growth (HELG) hypothesis in the case of North Cyprus. Resultsreveal that a long-run equilibrium relationship exists between real income growth of NorthCyprus and its determinants, namely capital, labor, and the higher education sector. Resultsshow that real income growth converges to its long-term equilibrium level by 10.9 %. Grangercausality tests suggest undirectional causality from higher education growth to real incomegrowth in North Cyprus. Therefore, the HELG hypothesis can be inferred for the TurkishCypriot economy.

Keywords Higher education · Economic growth · Bounds test ·Conditional Granger Causality

1 Introduction

Investigating the relationship between international trade expansion and economic growth hasbeen a popular topic in development economics. There are various ways through which inter-national trade (including services) expansion can contribute to economic growth (Omotor

S. Katircioglu (B)Department of Banking and Finance, Eastern Mediterranean University, 95 Famagusta,Via Mersin 10, North Cyprus, Turkeye-mail: [email protected]

S. FethiDepartment of Business Administration, Eastern Mediterranean University, 95 Famagusta,Via Mersin 10, North Cyprus, Turkeye-mail: [email protected]

H. CanerDepartment of Educational Sciences, Eastern Mediterranean University, 95 Famagusta,Via Mersin 10, North Cyprus, Turkeye-mail: [email protected]

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730 S. Katircioglu et al.

2008). There is a significant volume of research that investigates the empirical relationshipbetween international trade and economic growth (particularly with respect to trade-led,export-led, and import-led growth hypotheses). However, the same cannot be said aboutthe empirical relationship between international tourism and economic growth (Gunduzand Hatemi-Jb 2005). Furthermore, the results of the studies investigating the relation-ship between international tourism and economic growth are still inconclusive (Katircioglu2009a,b,c; Gunduz and Hatemi-Jb 2005).

There is an unverified question of whether tourism growth actually causes economicgrowth or vice versa. Empirical studies of the relationship between tourism and economicgrowth have been less rigorous in the tourism literature (Oh 2005). International tourismreceipts are a major source of foreign exchange together with export revenues. They alsohelp to compensate current account deficits; as tourism spending serves as an alternativeform of exports and contributes to the ameliorated balance of payments in many countries(Oh 2005). On the other hand, since international tourism contributes to every sector of theeconomy, budget deficits also benefit from these activities via tax revenues. As McKinnon(1964) argues international tourism brings foreign exchange that can be used to import inter-mediate and capital goods which are used to produce goods and services, which in turn leads toeconomic growth. Balaguer and Cantavella-Jordá (2002) prove the validity of the tourism-ledhypothesis for the Spanish economy, which is one of the largest recipients of internationaltourist earnings (5.9 % of its GDP) in the world. However, the question of whether thishypothesis can be proved for other countries remains. Therefore, the tourism-led hypothesisdeserves further attention in the other economies.

Gunduz and Hatemi-Jb (2005) empirically confirmed the TLG hypothesis for Turkeyby making use of leveraged bootstrap causality tests. They found unidirectional causalityrunning from international tourist arrivals to the economic growth of Turkey. Ongan andDemiroz (2005) also investigated the impact of international tourism receipts on the long-term economic growth of Turkey by using the Johansen technique and vector error correctionmodeling. They found bidirectional causality between international tourism and economicgrowth, which means an expansion in international tourism stimulates growth in the Turkisheconomy and that growth in the Turkish economy stimulates an expansion in internationaltourism. However, unlike the findings of Gunduz and Hatemi-Jb (2005), Ongan and Demiroz(2005), and Katircioglu (2009a) rejects the TLG hypothesis for the Turkish economy usingthe Johansen approach and the bounds test for level relationships. Both tests carried out byKatircioglu (2009a) did not reveal any long-run relationship between international tourismand economic growth in Turkey.

Katircioglu (2009b) confirmed a long-run equilibrium relationship between internationaltourism and economic growth in South Cyprus. However, the TLG hypothesis was not con-firmed for South Cyprus as tourism growth for this country is output-driven. On the otherhand, Katircioglu (2009c) confirmed the long-run equilibrium relationship between interna-tional tourism and economic growth in the case of Malta. Furthermore, the Granger causalitytest results of Katircioglu (2009c) suggest that both the TLG and output-driven tourismhypotheses can be inferred for Malta, since there is bidirectional causation between inter-national tourism and economic growth. Dristakis (2004) examined the impact of tourism onthe long-term economic growth of Greece by using causality analysis and found evidence ofbidirectional causality between international tourism and economic growth. Cortés-Jiménezand Pulina (2010) also support the TLG hypothesis for Spain and Italy by using multivariatecointegration techniques and Granger causality tests.

Higher education is an important global phenomenon. Each year millions of people pursuetheir higher education at overseas institutions. Thus, higher education can be considered as

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Testing the higher education-led growth hypothesis 731

a type of student tourism that contributes to national income, employment, and the wealthof local citizens. This fact is of particular importance to developing countries. Stevens andWeale (2003) mention that living standards in most countries, and especially those in Europe,have risen over the last millennium due to developments in education. It is obvious that oneof the most important factors that affect private demand for secondary or higher education isthe household income level and the costs incurred by a family when it takes the decision toinvest in education (Beneito et al. 2001). However, there are generally accepted social andeconomic factors affecting household demand for education such as the parents’ education,the geographical location of the place of residence, the size and composition of the family,the occupation of the primary earner and the family’s own consideration of its social status(Beneito et al. 2001). On the other hand, there are some external factors that might alsoaffect the decision to study abroad such as political and economic conditions of the targetedcountry or region, geographical location of the targeted institution, student fees, scholarshipopportunities, medium of instruction and the accreditation of the diploma that is receivedfrom these institutions.

An important reason for studying education from an economic viewpoint is the impactof education on the reduction of inequalities of income (Ram 1989) and the relationshipbetween education and the labor market (Beneito et al. 2001). Some studies have focusedon the estimation of the rate of return of education (Psacharopoulos 1989; Al-Qudsi 1989;Psacharopoulos and Woodhall 1985).

Empirical studies focusing on the relationship between international tourism and eco-nomic growth are limited and less rigorous in the literature as mentioned before. This factis also valid for the empirical relationship between higher education and economic growthof countries. There have been very few studies that have employed the latest econometrictechniques to provide new impetus to the empirical research on the link between educa-tion growth and economic growth (see Glewwe and Jacoby 2004; Blankenau and Simpson2004; Stevens and Weale 2003; Dahlin 2002). However, to the best of the authors’ knowl-edge, there is no study till date that investigates the empirical relationship between highereducation development and economic growth.

1.1 Small islands

There are many islands, which are small and isolated. They have a relatively small resourcebase, undiversified economic structure, heavy dependence on imports, and a large agricul-tural, fishing and subsistence sector. Many small islands receive large sums of aid andremittances, and depend on preferential trading agreements (Prasad 2008). Also arguedby Prasad (2008) is the fact that many small islands develop a unique set of economicand political strategies to cope with their vulnerabilities. These economic strategies mainlyfocus on the services sector, in particular tourism and financial markets. Internationaltourism is a major source of foreign exchange for small countries as well as the largerones. Small countries and in particular small islands are more dependent on tourism thanlarger countries due to the fact that their economies are based on only a few sectors.Export-oriented services tend to represent the unique characteristics of small islands andtherefore provide a basis for a potential comparative advantage (Mehmet and Tahiroglu2002). The tourism sector has emerged as an engine of growth in many small islandsdue to its ability to create employment, increase foreign exchange earnings, and attractcapital investment. However, the tourism dependency of smaller economies correlatesdirectly with their size and limited human resource capital. This in turn, serves to inhibit

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the development of viable industries outside of tourism (Jayawardena and Ramajeesingh2003).

Although international tourism is seen as an engine of growth in many small island states,some researchers such as Wilkinson (1987) suggest that tourism specialization is not aneffective development strategy for small island states due to the fact that the opportunitycost to their economies are very high. Small islands are highly import-dependent; there-fore, this creates leakages of foreign exchange earnings gained from international tourismand/or merchandise exports. A study by UNEP (United Nations Environment Programme)(2002) lists five major areas of import expenditures for the tourism sector: imports for con-struction, imports of consumer goods, overseas promotional expenditures, repatriation ofprofits, and amortization of external debt incurred by the industry (see also Jayawardena andRamajeesingh 2003). UNEP states that foreign exchange outflows from small islands aremainly because of their resource constraints. It is also argued that larger economies do notface these constraints and are likely to benefit more from better inter-sectoral effects thansmaller islands (Jayawardena and Ramajeesingh 2003).

1.2 Aim and importance of the study

The issues mentioned above deserve further attention. Thus, this study empirically investi-gates the possible long-run and causal link between higher education development and realincome growth in the small island state of North Cyprus. The Turkish Republic of NorthernCyprus (TRNC) was established in 1983 in an already divided island and is not recognizedby any country other than mainland Turkey. North Cyprus has a population of over 300,000, a14,421.77 US$ per capita income (SPO 2010) and is located in a strategic location in the East-ern Mediterranean. The TRNC does not have any foreign trade relationships with countriesother than Turkey due to its political non-recognition. Therefore, international tourism andthe emergence of the higher education sector are two major sources of foreign exchange forthis small island state. However, the tourism sector also faces great difficulties in attractinginternational tourists because of problems such as the lack of direct flights to North Cyprusand high transportation costs.

There are important implications and motivations for doing this study: First, internationaltrade plays an extremely important role amidst economic concerns. However, there is littlemention of international tourism, in spite of its importance amongst foreign expenditureitems (Luzzi and Flückiger 2003). A majority of the empirical studies on tourism forecastinghave been built on tourism demand functions. Shan and Wilson (2001) mention that severalareas remain incomplete in these types of studies and hence deserve further attention. Forexample, the role of international trade as one of the determinants of tourism demand isnot well recognized in the literature. Thus, this study will not only search for the empiricalrelationship of international tourism growth with economic growth but also the relationshipbetween international students’ flow and economic growth in this small island state. Second,the econometric techniques used in previous studies of international tourism are generallypoor as they do not incorporate new developments in econometrics such as co-integration andGranger causality concepts (Shan and Wilson 2001; Lim 1997; Song et al. 1997; Witt and Witt1995). Third, there are very few studies in the literature analyzing the impact of educationon economic performance or growth. To the best of author’s knowledge till date, there is noempirical study investigating the relationship between the development of higher educationsector and economic growth. Thus, this study is the first of its kind that it investigates thelong run equilibrium relationship and direction of causality between higher education growth

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and economic growth in the case of North Cyprus. Finally, the Cyprus problem has been onthe world agenda for more than 40 years. The south of Cyprus has become a member of theEuropean Union (EU) whereas the north of the island does not benefit from EU membership.Thus, this political stalemate will continue to attract attention in the international arena andthe results of this study are also expected to give important messages to policy makers.

1.3 The emergence of the higher education sector in North Cyprus

The services sector in North Cyprus was given priority basically as a result of the politicalisolation and embargoes faced by the country in every field. The 1980s became a transi-tion period from the manufacturing industry to services with a focus on tourism and highereducation. The tourism sector was also under embargoes, so the island could not attract thenecessary amount of tourists needed to stimulate significant growth in the economy. Touristsfrom abroad were targeted by allowing the opening of casinos on the island. Now, many casi-nos have opened in North Cyprus, and attract tourists from Turkey and the south of Cyprus.Legalized gambling is prohibited in both countries. There were 808,682 tourists visitingNorthern Cyprus in 2008 of which 80 % Turkey. Net tourism revenues constituted 9.69 % ofgross domestic product (GDP) in 2008 (SPO 2010).

On the other hand, the demand for higher education in North Cyprus showed a consider-able increase by the 1990s, mainly because of students from Turkey and overseas advertisingespecially in Africa and the Middle East. There are six universities in North Cyprus: EasternMediterranean University (EMU, the oldest and the largest which was established in 1979),Near East University (NEU), Lefke European University LEU), Girne American Univer-sity (GAU), Cyprus International University (CIA) and the North Cyprus campus of MiddleEast Technical University (which is a university from Turkey) (METU). At the beginning of2008–2009 academic year, there were 45,634 students studying at these six universities ofwhich 20.40 % were Turkish Cypriots, 72.95 % were from the mainland Turkey, and 6.65 %were from various overseas countries (SPO 2010). Overseas students have been coming toNorth Cyprus for higher education since 1982. Since then there has been a steady increasein the number of overseas students who come from more than 68 countries across the world.Having internationally recognized and accredited universities in North Cyprus contributesto the image of the country in the international arena. The expansion of infrastructure andfacilities at the universities of North Cyprus continues at an unprecedented rate and maynow be compared favorably to their international counterparts. Therefore, the higher educa-tion sector in North Cyprus is now the most important sector earning considerable foreignexchange and contributing to the growth of this small and non-recognized island state.

The paper proceeds as follows. Section 2 presents theoretical setting of empirical meth-odology. Section 3 defines the data and the methodology of the study. Section 4 provides theresults and discussions, and the paper concludes with Sect.5.

2 Theoretical setting

2.1 New version of the Solow growth model: adding exchange rate and education policiesto the model

Knowledge is the quality of society’s textbooks; human capital is the amount of timethat has been spent reading them Mankiw (1995)

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In this section, we analyze the impact of exchange rate and education policies along withphysical investment in the education sector, and the number of labors on economic growth.Human capital and education have crucial roles in explaining growth, and neither of them arenew issues.1 Denison (1967) points out that education increases the productivity of labourallowing workers to be used more efficiently through new technologies. Alderman et al.(1996) also stress that developing countries spend over $100 billion a year on education,health and other human capital investments.

Higher education is a global phenomenon as mentioned before. Living standards in manydeveloped and developing countries, especially those in Europe, have risen over the lastmillennium due to developments in education. These economies tend to grow faster thaneconomies without having proper education policies. As pointed out by Romer (1990), aneconomy with a larger total knowledge leads to faster growth. This implies that policiesinfluencing capital accumulation have growth effects but policies using human capital in theresearch and development sector will cause higher growth.

Temple and Voth (1998) attempt to examine the link between human capital, equipmentinvestment and industrialization. They argue that equipment investment and industrializationshould be accompanied by human capital to stimulate productivity growth.

Here, we adopt the frameworks introduced by Mankiw et al. (1992), Knight et al. (1992;1993), Mankiw (1995), Edwards (1989), Agénor (1991), and Kamin and Rogers (2000) toinvestigate the role of education and exchange rate policies in economic growth.

Let us consider the following Cobb–Douglas production function:

Yt = K αt Hβ

t (At Lt )1−α−β, (1)

where Y is real output, K is the stock of physical capital, H is the stock of human capital,L is the raw labour, A is a labour-augmenting factor reflecting the level of technology andefficiency in the economy and the subscript t indicates time.

We assume that α + β < 1, so there are constant returns to factor inputs jointly anddecreasing returns separately. Raw labour and labour-augmenting technology are assumedto grow according to the following functions:

Lt = L0en t (2)

At = A0egt+P E Xθ , (3)

where n is the exogenous rate of growth of the labour force, g is the exogenous rate of tech-nological progress, PEX is a vector of policy and the other factors that can affect the levelof technology and efficiency in the economy, and θ is a vector of coefficients related to thispolicy and other variables.

In this model, variable A depends on exogenous technological improvements, the degreeof trade in the economy and the level of other variables such as education infrastructure. Itis obvious that A in this study differs from A used by Mankiw et al. (1992). This modifi-cation is more likely to be particularly relevant to the empirical cases of economic growthin developing and developed countries. In these countries, technological improvements areencouraged by exporting education which indicates the total amount of real exchange rate asthe equivalent of the nominal exchange rate, world price of foreign tradeables and domesticprice for nontraded goods as well as the level of infrastructure, which tend to increase theproductive sector’s efficiency (Knight et al. 1993; Edwards 1989; Agénor 1991; Kamin andRogers 2000; Gunduz and Hatemi-Jb 2005; Balaguer and Cantavella-Jordá 2002).

1 See Becker (1964), Schultz (1961, 1968) and Psacharopoulos (1984, 1988) for more details.

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Furthermore, in the steady state, output per worker grows at the constant rate g (theexogenous component of the growth rate of the efficiency variable A). This outcome can beobtained directly from the definition of output per effective worker as follows:

Yt

At Lt= (kt )

α(ht )β

Yt

Lt= At (kt )

α(ht )β . (4)

Let y∗t =

(YtLt

)∗. Taking logs both sides of Eq. (4), we get Eq. (5): ln

( YL

)∗ = ln A+α ln k∗+β ln h∗ (t is omitted), where At = A0e(g t+P E Xθ)

ln

(Y

L

)∗= ln A0 + g t + θ ln P E X + α

1 − α − βln sK + β

1 − α − βln s H

− α + β

1 − α − βln(n + g + δ). (5)

Equation (5) indicates steady state output per worker or labor productivity where a vector ofpolicy and the other variables exist. To determine the new version of Solow growth model,we need to find the transitional dynamics by using a log-linearization method. This gives thefollowing growth Equation:

ln y − ln y(0) = g + (1 − e−λ t )

×[

ln A0 + gt + θ ln P E X + α

1 − α − βln sK

+ β

1 − α − βln s H − α + β

1 − α − βln (n + g + δ ) − ln y(0)

], (6)

where PEX is a vector of policy and the other factors that can affect the level of technologyand efficiency in the economy.

Note that, the real exchange rate is defined as, PEX = E PTPN

,where E is the nominal exchange rate, measured as the number of units of local currency

per unit of foreign currency, P∗T is the world price of foreign tradeables in terms of foreign

currency and PN is the domestic price for non-traded goods.Now, if we rearrange Eq. (6), we have the following equation, which indicates steady-state

output per worker, or labour productivity evolving around the steady-state path.

ln yt+1 − ln yt = g + (1 − e−λt )

[ln A0 + gt + θ ln P E X + α

1 − α − βln sK

t

+ β

1 − α − βln s H

t − α + β

1 − α − βln(nt + g + δ) − ln yt

], (7)

where λt = (nt + g + δ) (1 − α − β).As we mentioned in the previous chapter, Eq. (7) can be expressed as follows, by omitting

the log notation:

� yt = c + μ[

y − A0 − A1 P E X − A2T − A3sK − A4s H − A5(n + g + δ)]

t−1

� yt = c + μ[

y − y∗]t−1 (8)

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Equation (8) leads an error correction mechanism, so we construct our modified ECM asfollows:

�ln yt = c0 + μet−1 +m∑

i=0

φi� ln sKt−i +

p∑j=0

ηi� ln s Ht− j

+r∑

k=0

πi� ln(nt−k + g + δ) +s∑

z=0

δi � ln PE Xt−z+εt , (9)

where PEX stands for a vector of policy and other factors that can influence the level oftechnology and efficiency in the economy. Other variables are defined as before.

3 Data and methodology

The data used in this paper are quarterly figures covering the period 1980Q1–2010Q4 andthe variables of the study are real gross domestic product (GDP) (y) and gross capital forma-tion (fixed capital investments) (GCF) both at constant 2005 USD prices, total employment(EMP), total number of students studying at higher education institutions of North Cyprus(HE) and real exchange rates (RER). Data was gathered from the State Planning Organizationof North Cyprus (SPO 2010) and from the World Development Indicators (2012) (in order toget the consumer price index of United States). There are several alternatives to measure tour-ism variables in the literature (Katircioglu 2009a,b,c; Gunduz and Hatemi-Jb 2005): Tourismreceipts, the number of nights spent by visitors from abroad and the number of internationaltourist arrivals from abroad. Since a great majority of higher education students in NorthCyprus come from other countries, the higher education variable was proxied by the totalnumber of students studying at these institutions on the island. This is justified by the fact thatstudent tourism is a part of international tourism. On the other hand, empirical analyses in thisstudy also include the real exchange rates as well. Katircioglu (2010), Oh (2005), Gunduzand Hatemi-Jb (2005) and Balaguer and Cantavella-Jordá (2002) suggest that real exchangerates should be included in the existence of international tourism in order to deal with thepotential omitted variable problem. Thus, real exchange rates were added to bounds tests,the conditional error correction model, and Granger causality tests as deterministic variables,which were calculated by multiplying Turkish Lira per US dollar and consumer price index(2005 = 100) in North Cyprus, and then dividing it by consumer price index (2005 = 100) inthe United States.

The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP)2 unit root tests areemployed to test the integration level and the possible long run relationship among thevariables (Dickey and Fuller 1981; Phillips and Perron 1988). The PP procedures, whichcompute a residual variance that is robust to auto-correlation, are applied to test for unit rootsas an alternative to ADF unit root test (Katircioglu 2010).

To investigate the long-run relationship between each pair of variables under consid-eration, the bounds test for level relationship within the ARDL (the autoregressive dis-tributed lag) modeling approach was adapted in this study. This model was developed byPesaran et al. (2001) and can be applied irrespective of the order of integrationof the variables

2 PP approach allows for the presence of unknown forms of autocorrelation with a structural break in thetime series and conditional heteroscedasticity in the error term.

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(irrespective of whether regressors are purely I (0), purely I (1) or mutually co-integrated).The ARDL modeling approach involves estimating the following error correction models:

� ln Yt = a0Y +n∑

i=1

biY � ln Yt−i +n∑

i=0

ciY � ln Xt−i

+n∑

i=0

diY � ln Zt−i + σ1Y ln Yt−1 + σ2Y ln Xt−i + σ3Y ln Zt−1 + ε1t . (10)

In Eq. (10), � is the difference operator, lnYt is the natural log of the dependent variable,lnXt and lnZt are the natural logs of the independent variables and ε1t is serially independentrandom errors with mean zero and finite covariance matrix.

Again, in Eq. (10), the F test is used for investigating a (single) long-term relationship.In the case of a long-term relationship, the F test indicates which variable should be nor-malized. In Eq. (10), when lnY is the dependent variable, the null hypothesis of no levelrelationship is H0 : σ1Y = σ2Y σ3Y = 0 and the alternative hypothesis of a level relationshipis H1:σ1Y �= σ2Y �= σ3Y �= 0.

In the case of a level relationship, a conditional ECM using the ARDL approach isemployed in the present study in order to estimate Eq. (5). Also as suggested byPesaran et al. (2001), the time series properties of the key variables in the conditional ECMsof the present study can be approximated by double-log EC (p) (error correction at p lag lev-els that might be different for each explanatory variable) models under the ARDL approach,augmented with appropriate deterministics such as intercepts and time trends (Katircioglu2010). Then, the conditional ECM of interest using the ARDL approach can be written as:

� ln Yt = �β0 +p−1∑j=1

ϕ j� ln Yt−i+k∑

i=1

βi0� ln Xit

+k∑

i=1

q-1∑

j=1βij�Xi,t− j+φ�Zt + γ (1, p)ECTt−1 + ut , (11)

where φ j , βi j , and ϕ are the coefficients for the short-run dynamics of the model‘s con-vergence to equilibrium. The coefficient of γ (1, p) denotes the speed of adjustment and isexpected to be negative.

In the case of level relationships based on the bounds test, conditional Granger causalitytests should be carried out under the conditional ECM that uses the ARDL approach. Bydoing so, the short-run deviations of series from their long-run equilibrium path are alsocaptured by including an error correction term (see also Narayan and Smyth 2004). There-fore, conditional error correction models for Granger causality in the present study can bespecified as follows:

� ln Yt = α0 + φp11(L)� ln Yt + φ

q12(L)� ln Xt + φr

13(L)� ln Zt + δECTt−1 + u1t

(12)

� ln Xt = α0 + φp21(L)� ln Xt + φ

q22(L)� ln Yt + φr

23(L)� ln Zt + δECTt−1 + u2t ,

(13)

where

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φp11(L) =

P11∑i=1

φp11,i Li φ

p12(L) =

P12∑i=0

φp12,i Liφ

p13(L) =

P13∑i=0

φp13,i Li

φp21(L) =

P21∑i=1

φp21,i Li φ

p22(L) =

P22∑i=0

φp22,i Li φ

p23(L) =

P23∑i=0

φp23,i Li .

In Eqs. (12) and (13), � denotes the difference operator and L denotes the lag operator where(L)�lnYt = �lnYt−1. ECTt−1 is the lagged error correction term derived from the long-runequilibrium model. Finally, μ1t and μ2t are serially independent random errors with meanzero and finite covariance matrix. Finally, according to the conditional ECM for causalitytests, having statistically significant t ratios for ECTt−1 in Eqs. (12) and (13) would meetconditions to have long-run causations while significant F ratios satisfy conditions for havingshort-run causations (Katircioglu 2010).

4 Results and discussion

Table 1 gives ADF and PP unit root test results for the variables of the study. Real income(y), GCF, and RER seem to be stationary at their level according to the ADF tests but theseare not confirmed according to the PP tests. EMP is stationary at its first difference based onboth ADF and PP tests. On the other hand, the HE variable seems to be stationary at levelaccording to both ADF and PP tests. In the case of y, GCF, and RER, the PP procedure willbe taken into consideration for the present study since the PP approach compute a residualvariance that is robust to auto-correlation and are applied to test for unit roots as an alternativeto ADF unit root test as mentioned before. As a result, y, GCF, EMP, and RER are said to beintegrated of order one, I (1), while HE is integrated of order zero, I (0), in the present study.

Unit root tests have provided mixed results for the variables of this study. Therefore, thebounds test will be employed in this study to investigate a long-run equilibrium relationshipbetween real income and its regressors within the ARDL modeling approach as suggestedby Pesaran et al. (2001). Critical values for F statistics for small samples are presented inTable 2 as taken from Pesaran et al. (2001). Table 3 gives results of the bounds test for thelevel relationship between real income and its regressors under three different scenarios assuggested by Pesaran et al. (2001), pp. 295–296); that are with restricted deterministic trends(FIV), with unrestricted deterministic trends (FV) and without deterministic trends (FIII).Intercepts in these scenarios are all unrestricted.3

Results in Table 3 suggest that the application of the bounds F test using the ARDLmodeling approach suggest the existence of a level relationship (a long-run equilibrium rela-tionship) between real income and its regressors under FIII scenario for the lag level of three,under all scenarios for the lag level of four, and under FIII and FIV scenarios for the laglevel of five. Although optimum lag was two (based on AIC and SBC), none of those threescenarios suggested a level relationship between real income and its regressors since the nullhypothesis of H0 : σ1Y = σ2Y = 0 cannot be rejected; however, it is seen and concluded thatreal income (y) is in a level relationship with its set of regressors in the present study in thelag levels (3, 4, and 5) other than optimum lag. Pindyck and Rubinfeld (1991) point out thatit would be best to run tests for a few different lag structures and make sure that the resultswere not sensitive to the choice of lag length. Thus, final conclusion based on this argument

3 For detailed information, please refer to Pesaran et al. (2001, pp. 295–296).

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Tabl

e1

AD

Fan

dPP

test

sfo

run

itro

ot

Stat

istic

s(l

evel

s)ln

yL

agln

GC

FL

agln

Em

pL

agln

HE

Lag

lnR

ER

Lag

τT

(AD

F)−4

.22*

*(1

2)−3

.52*

*(1

2)0.

40(1

2)−2

.52

(10)

−1.3

8(1

2)τ μ

(AD

F)−1

.59

(12)

−1.3

5(1

2)−2

.03

(12)

−3.3

7**

(6)

−2.1

1(1

2)τ

(AD

F)1.

21(1

2)1.

03(1

1)1.

22(1

2)0.

45(8

)−1

.81*

**(1

2)τ

T(P

P)−2

.55

(6)

−2.1

4(7

)−1

.25

(6)

−0.5

7(6

)0.

76(7

)τ μ

(PP)

0.32

(6)

−0.1

0(7

)−2

.22

(6)

−3.2

3**

(6)

−1.6

4(7

)τ

(PP)

1.83

(6)

1.36

(7)

0.91

(6)

2.55

(8)

0.59

(9)

Stat

istic

s(fi

rstd

iffe

renc

es)

�ln

y/L

Lag

�ln

GC

FL

ag�

lnE

mp

Lag

�ln

HE

Lag

�ln

RE

RL

ag

τT

(AD

F)−2

.42

(12)

−2.4

9(1

2)−4

.34*

(12)

−5.0

8*(5

)−1

.00

(11)

τ μ(A

DF)

−2.7

5***

(12)

−2.7

0***

(12)

−3.0

1**

(12)

−3.5

3*(5

)−0

.40

(11)

τ(A

DF)

−2.2

1**

(12)

−2.4

0**

(12)

−2.4

3**

(12)

−1.2

2(1

0)−0

.73

(11)

τT

(PP)

−6.2

0*(5

)−5

.43*

(5)

−6.7

5*(6

)−6

.11*

(6)

−11.

29*

(7)

τ μ(P

P)−6

.21*

(5)

−5.4

4*(5

)−6

.64*

(6)

−5.4

4*(6

)−1

1.24

*(8

)τ

(PP)

−6.0

6*(6

)−5

.32*

(6)

−6.7

7*(6

)−4

.74*

(7)

−9.0

6*(8

)

Not

e:y

repr

esen

tsre

algr

oss

dom

estic

prod

uct;

GC

Fis

gros

sca

pita

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mat

ion,

Em

pem

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men

t,H

Eis

the

tota

lnum

ber

stud

ents

inth

eun

iver

sitie

s,an

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real

exch

ange

rate

s.τ

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pres

ents

the

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eral

mod

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tren

d;τ μ

isth

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odel

with

adr

ifta

ndw

ithou

ttre

nd;τ

isth

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ostr

estr

icte

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odel

with

outa

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tand

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d.N

umbe

rsin

brac

kets

are

lag

leng

ths

used

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test

(as

dete

rmin

edby

AIC

set

tom

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mov

ese

rial

corr

elat

ion

inth

ere

sidu

als.

Whe

nus

ing

PPte

st,n

umbe

rsin

brac

kets

repr

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tNew

ey–W

estB

andw

ith(a

sde

term

ined

byB

artle

tt–K

erne

l)∗ ,

∗∗an

d∗∗

∗ den

ote

reje

ctio

nof

the

null

hypo

thes

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the

1,5

and

10%

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spec

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unit

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0

123


Table 2 Critical values for ARDL modeling approach

k = 3 0.10 0.05 0.01

I (0) I (1) I (0) I (1) I (0) I (1)

FIV 2.97 3.74 3.38 4.23 4.30 5.23FV 3.47 4.45 4.01 5.07 5.17 6.36FIII 2.72 3.77 3.23 4.35 3.74 5.06tV −3.13 −3.84 −3.41 −4.16 −3.96 −4.73tIII −2.57 −3.46 −2.86 −3.78 −3.43 −4.37

Source: Pesaran et al. (2001), pp. 300–301 for F statistics and pp. 303–304 for t ratiosNotes: k is the number of regressors for dependent variable in ARDL models, FIV represents the F statisticof the model with unrestricted intercept and restricted trend, FV represents the F statistic of the model withunrestricted intercept and trend, and FIII represents the F statistic of the model with unrestricted intercept andno trend. (2) tV and tIII are the t ratios for testing σ1Y = 0 in Eq. (5) and ω1Y = 0 in Eq. (6), respectivelywith and without deterministic linear trend

Table 3 Bounds test for level relationship

Variables With deterministic trends Without deterministic trend Conclusion

FIV FV tV FIII tIII

Fy (lny/lnGCF, lnEmp, lnHE) H0p=2∗ 2.83a 3.28a −3.33b 3.26b −3.17b Rejected3 3.61b 4.35b −3.77b 4.39c −3.77b4 4.11c 4.93c −3.91c 5.04c −3.97c5 4.28c 4.03b −2.54a 5.40c −2.80b

Notes: Akaike Information Criterion (AIC) and Schwartz Criteria (SC) were used to select the number of lagsrequired in the bounds test. p shows lag levels and ∗ Optimum lag selection in each model as suggested by bothAIC and SC. FIV represents the F statistic of the model with unrestricted intercept and restricted trend, FVrepresents the F statistic of the model with unrestricted intercept and trend, and FIII represents the F statisticof the model with unrestricted intercept and no trend. tV and tIII are the t ratios for testing σ1Y = 0 in Eq.(10) with and without deterministic linear trend. a Statistic lies below the lower bound, b statistic falls withinthe lower and upper bounds, and c statistic lies above the upper bound

is that real income in North Cyprus is in a level relationship with GCF, EMP, HE, and RERvariables as estimated from the growth model. Finally, the results from the application of thebounds t-test in the ARDL model are less clear-cut and do not generally allow the impositionof the trend restrictions except the FV and FIII scenarios in lag four according to the resultsof Table 3 (Pesaran et al. (2001), p. 312).

Having long run relationship in bounds test, the ARDL approach can be now adapted toestimate the level coefficients as also discussed in Pesaran and Shin (1999) and formulatedin Eq. (5) of the present study. The resulting estimates of level relationships under the ARDLspecification in the Solow growth model (lags: 2, 2, 2, 0) in Eq. (5) can be given by:The Solow growth model in Eq. (5)4

4 We assume that most of the variables in our modified model (e.g., GCF, EMP, HE) vary over time. A con-siderable amount of theoretical and empirical literature has indicated that the real sector as well as educationalsector play a crucial role in promoting economic growth whereas real exchange rate (RER) do not vary overtime. This means that A0, gt and RER can be considered as a constant term A0 in Eq. (5). From the regressionpoint of view, RER can be also thought as deterministic variable so it does not take place in regression equationas modeled in EVIEWS 6.0 software codes for the estimations under the ARDL approach (see also Cellini(1997)).

123


Table 4 The ARDL errorcorrection estimation for Solowgrowth (2, 2, 2, 0)∗ model inNorth Cyprus

Adj. R2 = 0.845, SE ofRegr. = 0.024, AIC =−4.547,SBC =−4.355, F stat. = 89.638,F prob. = 0.000D–W stat. = 2.067Note: ∗ Lag structure in the model

Regressor Coefficient SE p value

ût−1 −0.109 0.023 0.000�lnyt−1 0.430 0.072 0.000�lnGCF 0.628 0.046 0.000�lnGCFt−1 −0.355 0.064 0.000�lnEmp 0.265 0.078 0.001�lnHE 0.022 0.033 0.499�lnRER 0.012 0.011 0.271Intercept −0.002 0.003 0.546

Table 5 Results of Granger causality

Dependent variable F statistics (probability values) t stat (prob) for ECTt−1

�lnyt �lnGCFt �lnEmpt �lnHEt

�lnyt – 1.055 7.875* 0.356 −2.868*[0.390] [0.000] [0.876] [0.005]

�lnGCFt 0.950 – 5.413* 0.312 −0.888[0.452] [0.000] [0.904] [0.376]

�lnEmpt 1.822 0.553 – 1.694 −1.083[0.116] [0.735] [0.144] [0.281]

�lnHEt 0.932 0.161 2.885** − −1.031[0.463] [0.975] [0.018] [0.305]

Note: ∗, ∗∗ Rejection of null hypothesis, respectively, at 0.01 and 0.05 levels

ln yt = 0.547 (ln GCFT)(0.000)

− 0.284 (ln Empt)(0.679)

+ 0.069 (ln HEt)(0.161)

+ 5.034 + ut(0.447)

,

where ût is the error correction term and probability values are given in parentheses. It isonly GCF that is found to be statistically significant and have positive sign.

In the next stage, the conditional ECM regression associated with the above level relationshipshould be estimated. The results are presented in Table 4.

Results in Table 4 shows that the ECT is not so high, but statistically significant and hasan expected sign; equilibrium correction term in the Solow growth model is −0.109. Thisshows that the dependent variable (real income, y) converges (by 10.9 %) to its long-termequilibrium level. On the other hand, the lagged short term coefficients of y, GCF, EMP arestatistically significant while those of HE and RER, and the intercept are not.

Finally, the direction of causality can be now established within the conditional ECMmechanism as a long-run context. F statistics for short-run causations and t statistics ofECTs for long-run causations are presented in Table 5.

Results from Table 5 reveal that there is unidirectional causality that runs from highereducation growth (including GCF and EMP variables as well) to real income (y) growth inthe long-term since the t ratio of ECT is statistically significant and has an expected sign inthe case where real income is the dependent variable. Results in Table 5 did not reveal anycausation in any other way in the long-term; but short-term causations that run from EMP toy, EMP to GCF, and EMP to HE have been observed since F statistics for the related modelsare statistically significant. Short run causation that runs from HE to real income has notbeen obtained in this study as can be seen from Table 5. To summarize, the major finding ofthe present study is that higher-education-led growth hypothesis can be confirmed in the case

123


of North Cyprus in the long-term using the Solow growth modeling framework as a result ofconditional error correction models and Granger causality tests.

5 Conclusion

This study investigated the long-run equilibrium relationship and the direction of causalitybetween higher education growth and real income growth in North Cyprus by employingthe Solow growth modeling approach. Results of the bounds test reveal that a long-run equi-librium relationship is confirmed for the higher-education growth model in the case of theTRNC. The major finding of this study is that higher education sector development precedesa change in real income growth in North Cyprus as a result of the conditional error correctionmodel and Granger causality tests in the long term; real income converges to its long-termequilibrium level by 10.9 % in the Solow growth model and conditional causality tests sug-gest undirectional causation that runs from higher education growth to real income growth inthe long-term period. Therefore, the HELG hypothesis is confirmed for the Turkish Cyprioteconomy.

This finding has a very important implication for the Turkish Cypriot authorities: theTurkish Cypriot administration should give more priority to higher education institutionsbecause of the fact that this sector has started to replace the traditional tourism sector owingto the political problems in the island. Inspite of the political problems in Cyprus over thepast 40 years, higher education institutions have shown tremendous development and thefindings of this study have confirmed this reality. Furthermore, Turkish Cypriot authoritiesshould concentrate on improving educational quality and should concentrate on marketingthese institutions worldwide since this sector is the locomotive of the economy more so thaninternational tourism and the foreign trade sectors. Being aware of the fact that many uni-versities are newly opened in Turkey, the mainland of North Cyprus, as well as in the othercountries of the Mediterranean, Turkish Cypriot universities should also focus on increasingquality in education, increasing student satisfaction, succeeding in international accreditationapplications, and advertising these institutions worldwide with these assets.

This study has shown that there is a great need to evaluate similar relationships betweeneducation, namely higher education, and economic growth for other countries that attractconsiderable numbers of overseas students. Therefore, further studies are recommended forthe purpose of comparison with the results of this study.

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Testing the higher education-led growth hypothesis in a small island: an empirical investigation from a new version of the Solow growth model