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Transportation infrastructure investment, growth andinternational trade in TurkeyYeşim Kuştepeli a , Yaprak Gülcan a & Sedef Akgüngör a

a Faculty of Business , Dokuz Eylül University , Kaynaklar yerleşkesi, Buca, 35160 İzmir,TurkeyPublished online: 01 Jun 2011.

To cite this article: Yeşim Kuştepeli , Yaprak Gülcan & Sedef Akgüngör (2012) Transportation infrastructure investment,growth and international trade in Turkey, Applied Economics, 44:20, 2619-2629, DOI: 10.1080/00036846.2011.566189

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Applied Economics, 2012, 44, 2619–2629

Transportation infrastructure

investment, growth and

international trade in Turkey

Yes� im Kus� tepeli*, Yaprak Gulcan and Sedef Akgungor

Faculty of Business, Dokuz Eylul University, Kaynaklar yerles� kesi, Buca,35160 _Izmir, Turkey

Investment in transportation infrastructure facilitate the movement of the

goods; leading to higher standards of living for the people of the whole

globe. Although infrastructure is indispensable to achieve the main

development targets in developing countries, such as urbanization,

industrialization and sustainable economic development (Kim, 2006), the

relationship between infrastructure expenditures, economic growth and

international trade is inconclusive. The aim of this study is to investigate

the effect of investment on highway infrastructure on international trade

and economic growth in Turkey for the period of 1970 to 2005. The

empirical results from causality and cointegration analysis suggest only a

very weak short run effect of share of exports in Gross National Product

(GNP) on highway transportation expenditures but no long run relation-

ships between highway infrastructure expenditures, economic growth and

international trade in Turkey.

Keywords: transportation infrastructure; highways; economic growth;

trade; cointegration analysis

JEL Classification: H54; O40; C40; F10

I. Introduction

Infrastructure is indispensable in achieving the main

development targets of countries such as urbaniza-

tion, industrialization, export growth and overall

sustainable economic development (Rostow, 1960;

Kim, 2006). Through improvements in telecommuni-

cations systems and flow of information and tech-

nology, the movement of money became easier and

less costly (Chase-Dunn, 1999). At the same token,

improvements in transportation infrastructure

(highways, railways, ports, etc.) have done the same

effect for the movement of the goods and have led to

higher standards of living for the people of the whole

globe.Improvements in transportation have tangible and

intangible benefits, such as reducing operation times

and costs, increasing labour productivity and effi-

ciency in organizations. Services provided by infra-

structure are fundamental to economic activity.

Increased accessibility to transport improvements

facilitates mobility of goods and services increase.

Lower costs and ease of access to markets cause a

range of sectoral, spatial and regional effects

(Aschauer, 1989; Gramlich, 1994; Bougheas et al.,

2000; Lem, 2002).

*Corresponding author. E-mail: yesim.kustepeli@deu.edu.tr

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online � 2012 Taylor & Francis 2619http://www.informaworld.com

DOI: 10.1080/00036846.2011.566189

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There is a vast amount of literature on the

economic effects of transportation infrastructure,

where different approaches of analysis are used in

estimating production and cost functions is inconclu-

sive (Cullison, 1993; Groote et al., 1999; Njoh, 2000;

Banister and Berechman, 2001; Button, 2002). Ayogu

(2007) summarizes the theoretical literature on infra-

structure and growth and tries to assess how much

infrastructure matters in different contexts. Boarnet

(1995) suggests that public investment is productive if

it gives some regions a local competitive advantage

over other regions. The effects of infrastructure

investment may be apparent at small geographic

scales but not on larger units of observation (Lall,

2007; Moreno, 2007).The infamous study by Aschauer (1989) finds that

output elasticity of public transportation investment

is positive. Several studies such as Munnell (1992),

Sanchez-Robles (1998), Bougheas et al. (2000),

Bhatta and Drennan (2003), Pereira and Andraz

(2005) and Lakshmanan (2007) assert Aschauer’s

result.Bougheas et al. (2000) introduce infrastructure as a

cost-reducing technology similar to Romer’s (1987)

endogenous growth model. Infrastructure as a tech-

nology that reduces costs in the production of

intermediate inputs has more impact on output

rather than infrastructure as an input in the produc-

tion of final goods. The findings are stronger for less

developed countries compared to the developed

countries.Investment on transport capital is shown to be

more productive than investment on average for

developing countries and island states in Africa by

Boopen (2006). Demurger (2001) provides evidence

that geographical location and infrastructure endow-

ment have crucial effect on differences in growth

performance across provinces for 24 Chinese prov-

inces in the period 1985 to 1998. Zhou et al. (2007)

find that the correlation between Gross Domestic

Product (GDP) and the length of highways is 0.96

while the correlation between export and length of

highways with 0.94 for 31 regions of China.This study aims to investigate the effect of trans-

portation infrastructure investment, mainly high-

ways, on international trade and economic growth

in Turkey for the period 1970 to 2005. Contemporary

correlations and cointegration and causality analysis

are used to estimate these effects. The hypothesis

based on literature is that public investment in

highway transportation infrastructure leads to eco-

nomic growth and increases external trade.

Public investment on highway infrastructure inTurkey has been on average 2.36% of the governmentbudget during the period 1970 to 2005. This sharecontinuously fell after 1970s from 5% to 2% in 2005.Real highway infrastructure investment has shown awavy trend, reaching its peak in 2005 (609 thousandTL) followed by 1977 (633 thousand TL). Highwaylength in kilometers has consequently increasedminor from 59 000 km in 1970 to 61 000 km in2005.1 In light of these numbers, it can be concludedthat, although average public investment on highwayinfrastructure is almost 2.5% of the governmentbudget between the years 1970 and 2005, thisinvestment has not been used for new highways; butfor maintenance, repair, etc. However, it is stillinteresting to explore the relationships between high-way transportation infrastructure investment andgrowth and international trade in Turkey since thiswould give an opportunity to give policy recommen-dations for the government.

The rest of this article is organized as follows:Section II explains data and methodology, Section IIIpresents the results and Section IV concludes.

II. Data and Methodology

The focus of the analysis is to investigate the causalrelationship between public expenditures on highwayinfrastructure, growth and trade. The variables usedare public investment in highway transportationinfrastructure, highway length (in km), growth inGross National Product (GNP), total exports andimports. The data set, obtained from the TurkishStatistical Institute, consists of annual observationsextending from 1970 to 2005.

Two models are used to test the hypothesis of thisstudy. In these models, public investments in highwayinfrastructure and highway length are used inter-changeably. Model 1 uses the nominal values of thevariables and uses shares in GNP. In Model 1a,public investment in highway infrastructure is mea-sured by the share of highway expenditures in totalpublic budget (highway expenditure/public budget;HIEP); economic growth is measured by annualgrowth in GNP (GRW); exports are measured by theshare of exports in GNP (exports/GNP; EXPGNP);and imports are measured by the share of imports inGNP (imports/GNP; IMPGNP). Model 1 b includeshighway length (HL) instead of the share of highwayexpenditures in total public budget, holding all othervariables the same.

1 See the Appendix for data.

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Model 2 uses the deflated values of the variables:

Real highway infrastructure expenditure (RHIE),real GNP (RGNP), real exports (REXS) and real

imports (RIMS). All variables in the second model

are deflated by the consumer price index. As in Model1b, highway length (HL) is used in Model 2b

replacing real highway expenditures.Empirical methodology includes variables, contem-

porary correlations, cointegration and causality anal-

ysis. This choice of empirical analysis has several

reasons.

(1) The correlation coefficients provide the con-

temporaneous interactions of the variables.(2) Our data is limited with 36 annual observations.

The data availability for the other relevantvariables that could be added to the analysis is a

problem. Many variables such as labour forceor employment are available only after 1980s.

These factors have many drawbacks in terms of

the empirical analysis to be used:

(a) Regression analysis entails the risk

of omitted variables problem

(heteroscedasticity).(b) Regression analysis with time series data

could lead to spurious regression prob-

lem, when the variables are nonstation-ary. Differencing solves the problem but

decreases the degrees of freedom and

makes the results questionable.(c) Vector AutoRegression analysis is a

widely used analysis where the variables

are differenced until they are stationary.Due to the data problem, this approach

is not applicable for this study.

Taken all together, time series analysis of coin-tegration and causality tests are seen to be the best

approaches for this study.The first step of the cointegration analysis is to test

for the unit roots of the series. Augmented Dickey–

Fuller test (ADF), Phillipps–Perron Test (PP) and

Kwatkowski–Phillips–Schmidt–Shin (KPSS) are usedto check the nonstationarity assumption (Dickey and

Fuller, 1979; Phillips, 1987; Phillips and Perron, 1988;

Kwatkowski et al., 1992). The null hypothesis for theADF and PP tests is that the series in question has a

unit root, whereas the KPSS test has the nullhypothesis of level or trend stationarity.

For cointegration, Johansen (1988) cointegrationtest is applied, where ‘trace’ and ‘maximum eigen-value’ test statistics are used for testing null hypoth-esis that there is no cointegration between series.Johansen cointegration method can be used only if all

variables are I(1). If some variables are I(0), Boundstest based on Unrestricted Error Correction Model(Pesaran et al., 2001) can be used.

III. Empirical Analysis

The first step in cointegration analysis is determining

the integration level of series by ADF, PP, and KPSStests. Although the results of the KPSS test2 seem tobe inconclusive in some extent, it can be concludedthat all series, except growth in GNP, are integratedof order 1 (I(1)). Growth in GNP is integrated oforder zero or I(0).

Model 1

The correlation coefficients in Table 1 indicate thatthe share of highway expenditures in public budget(HIEP) is positively correlated with growth (0.069)and negatively correlated with exports/GNP (�0.541)and imports/GNP (�0.501). Only the correlationcoefficient between HIEP and growth is statisticallyinsignificant. Highway length (HL) is negativelyrelated with growth (�0.096), exports/GNP(�0.029) and positively related with imports/GNP(0.001), but none of these are statistically significant.

Interestingly, highway expenditures are signifi-cantly and negatively correlated with highwaylength (�0.489). This may be interpreted as theexpenditures on highway infrastructure do notincrease the length of the highways in the country,but used for maintenance, repair, etc.

Export/GNP and import/GNP ratios are found tobe negatively correlated with growth. While thenegative correlation between imports/GNP and eco-nomic growth is understandable and insignificant, thenegative relationship between exports/GNP andeconomic growth is questionable.3

Before employing causality and cointegration tests,the lag lengths for Models 1a and b are determined byusing different criteria decreasing from 4 one by one.The optimum lag length is found to be 1.4

2 The results are shown in Table A3 in the Appendix.3 This result is possibly due to the economic crisis in 1994 and 2001, where big devaluations took place. Economic growth wasnegative but exports/GNP reached high levels in these years. See Table A1 in Appendix for the data.4 The Results are available on request.

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Granger causality test results in Table 2 indicatethat there is one way relationship from growth toexports/GNP; and a bilateral relationship betweenexports/GNP and imports/GNP (Granger, 1986). Forthe transportation variables, the only causality isfrom the share of exports in GNP to the share ofhighway expenditures in the budget.

The next step is to conduct cointegration analysis.Since the economic growth variable in Model 1 isintegrated in order zero, it is not possible to applyJohansen cointegration analysis. In such cases, thebounds test can be used. The lower bound critical

values of the Wald test accept that the explanatoryvariables are integrated of order zero, or I(0), whilethe upper bound critical values accept that explana-tory variables are I(1). If the test statistic is I(1), thenull hypothesis of no long-run relationship can berejected. In contrast, the null hypothesis of no lung-run relationship is accepted, when the test statistic isI(0). The result is said to be inconclusive when the teststatistic falls within the critical bounds.

The bounds test results do not indicate anyevidence of a long-run relationship between highwayexpenditures and highway length, growth and trade

Table 2. Granger-causality test results for Model 1

Null hypothesis F-statistic Probability

Model 1aGRW does not Granger cause HIEP 1.64941 0.20826HIEP does not Granger cause GRW 0.21817 0.64360EXGNP does not Granger cause HIEP 3.14859*** 0.08551***HIEP does not Granger cause EXGNP 1.71451 0.19973IMGNP does not Granger cause HIEP 0.87520 0.35653HIEP does not Granger cause IMGNP 0.58878 0.44852

Model 1bGRW does not Granger cause HL 0.26205 0.61223HL does not Granger cause GRW 0.41848 0.52231EXGNP does not Granger cause HL 0.09505 0.75986HL does not Granger cause EXGNP 0.26901 0.60757IMGNP does not Granger cause HL 0.00201 0.96453HL does not Granger cause IMGNP 0.87522 0.35652

EXGNP does not Granger cause GRW 0.45925 0.50285GRW does not Granger cause EXGNP 7.74901** 0.00895**IMGNP does not Granger cause GRW 2.28638 0.14033GRW does not Granger cause IMGNP 2.61058 0.11597IMGNP does not Granger cause EXGNP 10.4749** 0.00281**EXGNP does not Granger cause IMGNP 3.81786* 0.05950*

HIEP does not Granger cause HL 1.45213 0.23702HL does not Granger cause HIEP 0.03938 0.84395

Note: *, ** and *** denote test statistic significance at 5, 1 and 10% levels, respectively.

Table 1. Correlation coefficients for Models 1a and b

HIEP HL GRW EXGNP IMGNP

HIEP 1.000 �0.489** 0.0692 �0.541** �0.502**(0.02) (0.688) (0.001) (0.002)

HL �0.489** 1.000 �0.0961 �0.0292 0.001(0.002) (0.577) (0.866) (0.994)

GRW 0.0693 �0.0961 1.000 �0.387* �0.288(0.688) (0.577) (0.02) (0.089)

EXGNP �0.541** �0.0292 �0.387* 1.000 0.939**(0.001) (0.866) (0.02) (0.000)

IMGNP �0.502** 0.001 �0.288 0.939** 1.000(0.002) (0.994) (0.089) (0.000)

Notes: Numbers in parentheses are p-values.* and ** denote test statistic significance at 5 and 1% levels, respectively.

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as the F-statistic is smaller than the critical value at allsignificance level for both models (Table A4 in theAppendix).

Model 2

The second model includes the deflated values of thevariables and highway length (HL). The correlationcoefficients for Model 2 are in contrast to those forModel 1. Table 3 shows that investment in highwayexpenditures in real terms and highway length (HL)are both positively correlated with imports (RIM),exports (REX) and real gross national product(RGNP). The ones for real highway expendituresare smaller in value and statistically insignificant butthe ones for highway length are around 0.70 and aresignificant at 1%.

Real highway expenditures and highway length arepositively correlated which is also significant (0.355).Real investment on highways has some positive effectof increasing the length of highways in the country.Real exports and real imports are both positively andsignificantly correlated with real GNP (0.953 and0.977, respectively).

With optimum lag length of 4 for both Models 2aand b,5 Granger causality test results indicate thatreal values of the highway expenditures and thehighway length in Model 2 do not have a causalityrelationship with the other variables. There is abilateral causality among real exports, real importsand real GNP (Table 4).

As all variables in Model 2 are integrated orderone, Johansen cointegration tests can be performedon the series. According to these tests,6 there is nocointegrating vectors, because both computed traceand maximum eigenvalue statistics are below of the

critical values, except Model 2b. The evidence of onecointegrating vector for Model 2b by the trace test isweak and the resulting cointegrating vector parame-ters do not support the theory. As the maximumeigenvalue test is believed to be more reliable inliterature (Johansen and Juselius, 1990), it is con-cluded that there is no comovement among relevantvariables in Model 2b.

Summary of the results

Although highway length is found to be positivelycorrelated with economic growth, exports/GNP andimports/GNP ratios, the only statistically significantcausality for the transportation variables is fromexport/GNP ratio to nominal highway transportationexpenditures.

For the first model, economic growth is found tocause exp/GNP; while exp/GNP and imp/GNP havea bilateral causality relationship. For the secondmodel, all variables except the real highway infra-structure investment and highway length have bilat-eral causality relationships in pairs.

The results of the cointegration analysis in Table 5show that there is not a long-run relationship amongthe relevant variables used in the models.

IV. Concluding Remarks

One of the major research issues in transportation isthe relationship between transport infrastructure andeconomic growth at the national as well as regionaland local levels. This article contributes to theexisting research by exploring short- and long-run

Table 3. Correlation coefficients for Models 2a and b

RHIE HL RGNP REX RIM

RHIE 1.000 0.355* 0.268 0.279 0.285(0.33) (0.114) (0.99) (0.92)

HL 0.355* 1.000 0.677** 0.688** 0.728**(0.33) (0.000) (0.000) (0.000)

RGNP 0.268 0.677** 1.000 0.953** 0.977**(0.114) (0.000) (0.000) (0.000)

REX 0.279 0.688** 0.953** 1.000 0.977**(0.099) (0.000) (0.000) (0.000)

RIM 0.285 0.728** 0.977** 0.977** 1.000(0.92) (0.000) (0.000) (0.000)

Notes: Numbers in parentheses are p-values.* and ** denote that null hypothesis reject at 5 and 1% significance levels, respectively.

5 The results are available on request.6 The results are available on request.

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relationships across transportation expenditures,

growth and trade in Turkey.Although the correlation coefficients signal some

relationship between the highway infrastructure,

growth and trade; the causality and cointegrationanalysis’ results fail to show evidence of a long-run

relationship between these variables. There is only

weak short-run causality from the share of exports inGNP on highway expenditures.

These results may be due to two important reasons:

First, either insufficient funds are devoted to highwayinfrastructure by the government and/or the funds

are used inefficiently. The share of public investment

on highway infrastructure investment has been atmost 5% of the government budget, continuouslyfalling from 5% to 2% and averaging 2.36% betweenthe years 1970 and 2005 in Turkey. Highway length inkilometers has increased only from 59.000 in 1970 to61.000 in 2005. During the period 1970 to 2005,highway kilometers in Turkey grew 4.1% indicatingthat there was very little new highway building. Thiscan be observed when the distribution of the type ofthe highways transportation network is analysed.7

There has been major improvement in terms ofreducing the length of stabilized and crush stoneroads and earth roads. However, the same

Table 4. Granger causality test for Model 2

Null hypothesis F-statistic Probability

Model 2aRIM does not Granger cause RHIE 1.37499 0.27348RHIE does not Granger cause RIM 1.42940 0.25604REX does not Granger cause RHIE 0.88253 0.48981RHIE does not Granger cause REX 1.01196 0.42177RGNP does not Granger cause RHIE 1.30057 0.29919RHIE does not Granger cause RGNP 0.08606 0.98590

Model 2bRIM does not Granger cause HL 0.71281 0.59163HL does not Granger cause RIM 0.96104 0.44753REX does not Granger cause HL 1.17440 0.34807HL does not Granger cause REX 1.48822 0.23841RGNP does not Granger cause HL 1.02060 0.41753HL does not Granger cause RGNP 0.17178 0.95061

REX does not Granger cause RIM 5.57129** 0.00275**RIM does not Granger cause REX 2.77196* 0.05138*RGNP does not Granger cause RIM 6.14683** 0.00162**RIM does not Granger cause RGNP 2.77196* 0.05138*RGNP does not Granger cause REX 6.61641** 0.00107**REX does not Granger cause RGNP 4.05880** 0.01239**

RHIE does not Granger cause HL 1.44255 0.25199HL does not Granger cause RHIE 0.92467 0.46673

Note: * and ** denote test statistic significance at 5 and 1% levels, respectively.

Table 5. Summary of all models’ long run test results

Model Variables Trace testMaximumeigenvalue test Bounds test

1a Highway infrastructure expenditure/public budget,growth in GNP, exports/GNP, imports/GNP

– – No

1b Highway length, growth in GNP,exports/GNP, imports/GNP

– – No

2a Real highway infrastructure expenditure, growthin GNP, real exports, real imports

No No –

2b Highway length, growth in GNP,real exports, real imports

Weak No –

7 See Table A5 in the Appendix for distribution of the type of the highway transportation network.

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improvement cannot be observed for stone block andprimitive roads. Stone block roads decreased from207 km to only 133 km in 36 years while there was still888 km of primitive roads in Turkey by 2005.Furthermore, asphaltic concrete has increased from1.450 km in 1970 to 8.820 km in 2007 (which is sixfolds); whereas 15.882 km of surface treatmentreached 50.302 in 2005. It is quite clear that thepublic spending on highway transportation have beengoing primarily to replace (asphalting) or to upgradeexisting roads. The results therefore are no surprise.

Second, the lack of any evidence of an effect ofpublic highway infrastructure on growth and trademay stem from the lack of use of variables other thanGNP and external trade such as employment and percapita income. Future research aims to enhance theanalysis by adding the relevant variables by enlargingthe data historically such as starting from 1850s whensignificant infrastructure investment took place. Thiswould also enable us to use different model forms(such as multivariate regression, with and without astructural break), which might give more accurateresults.

Acknowledgements

This study is part of a European Science FoundationEurocores Inventing Europe ProgrammeCollaborative Research Project ‘The Developmentof European Waterways, Road and RailInfrastructures: A Geographical Information Systemfor the History of European Integration (1825–2010)’. The authors thank The Scientific andTechnological Research Council of Turkey(TUBITAK) Social Science and HumanitiesResearch Grant Committee (SOBAG) for financiallyproviding support for the Turkish individual projectfor the period 2007 to 2010.

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Phillips, P. C. B. and Perron, P. (1988) Testing for a unitroot in time series regression, Biometrika, 75, 335–46.

Romer, P. M. (1987) Growth based on increasing returnsdue to specialization, American Economic Review, 77,56–62.

Rostow, W. W. (1960) The Stages of Economic Growth: ANon-Communist Manifesto, Cambridge UniversityPress, Cambridge.

Sanchez-Robles, B. (1998) Infrastructure investment andgrowth: some empirical evidence, ContemporaryEconomic Policy, 16, 98–108.

Zhou, J., Yang, L., Xu, Y. and Liu, C. (2007) The economicperformance of transportation infrastructure: anempirical study on the recent development of China,World Transactions on Engineering and TechnologyEducation UICEE, 6, 193–7.

Appendix

Table A1. The data for Model 1

HL HIEP GRW EXGNP IMGNP

1970 59 453 0.05 4.44 3.21 5.171971 59 469 0.05 7.05 3.88 6.721972 59 448 0.03 9.17 3.94 6.961973 59 279 0.03 4.91 4.62 7.321974 59 499 0.03 3.26 3.92 9.651975 59 069 0.04 6.06 2.90 9.811976 59 615 0.04 9.00 3.58 9.371977 59 407 0.05 2.99 2.82 9.331978 59 718 0.04 1.23 3.35 6.731979 60 059 0.03 �0.49 2.95 6.621980 60 761 0.02 �2.78 4.17 11.341981 60 712 0.03 4.81 6.46 12.281982 60 954 0.03 3.09 8.71 13.411983 59 297 0.03 4.21 9.21 14.851984 59 112 0.03 7.11 11.74 17.701985 59 302 0.02 4.30 11.67 16.631986 59 139 0.02 6.76 9.75 14.521987 58 915 0.02 9.81 11.62 16.151988 58 851 0.02 1.45 12.83 15.771989 58 552 0.02 1.63 10.70 14.541990 59 128 0.02 9.37 8.51 14.641991 59 221 0.02 0.35 8.93 13.831992 59 842 0.02 6.40 9.18 14.271993 59 770 0.02 8.14 8.44 16.191994a 59 832 0.02 �6.08 13.83 17.781995 59 999 0.01 7.95 12.58 20.761996 60 225 0.01 7.12 12.57 23.621997 60 841 0.01 8.29 13.53 25.021998 60 885 0.01 3.86 13.11 22.31

(continued )

2626 Y. Kus� tepeli et al.

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Table A2. The data for Model 2

HL RHIE RGNP REX RIM

1970 59 453 271 406 381 449 1225 19721971 59 469 280 301 414 024 1607 27811972 59 448 226 801 446 132 1760 31071973 59 279 236 081 490 970 2268 35931974 59 499 283 659 571 139 2236 55131975 59 069 350 663 615 688 1787 60431976 59 615 500 008 659 124 2361 61761977 59 407 633 342 662 184 1868 61751978 59 718 405 295 676 919 2265 45531979 60 059 319 479 745 464 2200 49331980 60 761 214 490 653 889 2728 74151981 60 712 366 552 724 323 4681 88911982 60 954 367 408 732 266 6381 98201983 59 297 383 660 731 664 6739 10 8651984 59 112 340 996 784 546 9211 13 8901985 59 302 257 549 863 062 10 071 14 3551986 59 139 272 311 928 284 9053 13 4811987 58 915 264 157 979 896 11 389 15 8241988 58 851 255 756 971 555 12 462 15 3191989 58 552 265 499 1 061 212 11 357 15 4281990 59 128 347 405 1 141 285 9710 16 7111991 59 221 305 079 1 098 346 9814 15 1951992 59 842 318 042 1 123 466 10 317 16 0361993 59 770 410 208 1 224 142 10 332 19 8151994 59 832 405 227 1 155 257 15 981 20 5391995 59 999 165 751 1 240 785 15 611 25 7631996 60 225 286 739 1 311 909 16 494 30 9841997 60 841 325 551 1 386 135 18 753 34 6761998 60 885 263 271 1 366 885 17 915 30 4991999 60 923 238 027 1 212 723 17 304 26 4702000 61 090 272 615 1 255 961 17 323 33 9942001 61 305 384 352 1 143 031 24 869 32 8572002 61 369 420 016 12 287 830 24 260 34 6842003 61 491 518 711 12 718 435 25 157 36 9162004 61 814 420 195 13 830 861 28 970 44 7352005 61 939 690 001 14 240 198 28 842 45 838

Source: Turkish Statistical Institute (TurkStat), Statistical Indicators (1923–2006), http://www.turkstat.gov.tr/yillik/stat_indicators.pdf.

Table A1. Continued

HL HIEP GRW EXGNP IMGNP

1999 60 923 0.01 �6.08 14.27 21.832000 61 090 0.01 6.34 13.79 27.072001a 61 305 0.01 �9.54 21.76 28.752002 61 369 0.01 7.94 1.97 2.822003 61 491 0.01 5.87 1.98 2.902004 61 814 0.01 9.86 2.09 3.232005 61 939 0.02 7.60 2.03 0.32

Source: Turkish Statistical Institute (TurkStat), Statistical Indicators (1923–2006), http://www.turkstat.gov.tr/yillik/stat_indicators.pdf.Note: aEconomic crisis occurred in these years.

Transportation infrastructure, growth and trade in Turkey 2627

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Table

A3.Unitroottest

results

ADF

PP

KPSS

ztDzt

ztDzt

ztDzt

(a)

(b)

(a)

(b)

(a)

(b)

(a)

(b)

(a)

(b)

(a)

(b)

HIE

�2.32

�3.60**

�4.14*

�4.40*

�2.42

�2.81

�5.28*

�6.76*

0.67**

0.14***

0.41***

0.21**

(0)

(1)

(4)

(4)

(6)

(4)

(17)

(19)

(5)

(1)

(4)

(4)

HL

�0.49

�1.42

�5.92*

�6.02*

�0.47

�1.42

�5.92*

�6.02

0.44***

0.15**

0.17

0.07

(0)

(0)

(0)

(0)

(1)

(1)

(2)

(3)

(4)

(4)

(2)

(4)

GRW

�6.37*

�6.29*

�6.81*

�6.83*

�6.37*

�6.28*

�16.32*

�17.44*

0.06

0.05

0.05

0.04

(0)

(0)

(1)

(1)

(1)

(1)

(5)

(6)

(1)

(1)

(3)

(3)

EXGNP

�2.48

�2.32

�7.82*

�7.93*

�2.39

�2.27

�7.94*

�8.41*

0.35***

0.14***

0.20

0.12***

(1)

(0)

(0)

(0)

(2)

(2)

(2)

(4)

(4)

(3)

(4)

(8)

IMGNP

�2.04

�2.32

�5.76*

�5.12*

�2.12

�1.64

�5.78*

�6.13

0.29

0.12***

0.22

0.11

(0)

(4)

(0)

(1)

(1)

(0)

(3)

(7)

(4)

(3)

(3)

(8)

RHIE

�2.13

�2.22

�5.79*

�5.78*

�2.11

�2.31

�5.88*

�6.11*

0.16

0.11

0.26

0.20**

(0)

(0)

(0)

(0)

(3)

(2)

(11)

(13)

(2)

(2)

(13)

(16)

REX

0.65

�2.07

�6.64*

�7.00*

0.90

�1.90

�6.71*

�7.04*

0.69**

0.15**

0.28

0.02

(0)

(0)

(0)

(0)

(1)

(2)

(2)

(1)

(5)

(4)

(2)

(0)

RIM

0.93

�1.62

�5.67*

�5.97*

3.16

�1.50

�5.69*

�7.85*

0.69**

0.18**

0.29

0.18**

(0)

(0)

(0)

(0)

(9)

(3)

(4)

(8)

(5)

(4)

(4)

(9)

RGNP

1.43

�1.39

�3.96*

�4.34*

1.28

�1.59

�6.15*

�6.46*

0.71**

0.18**

0.23

0.05

(4)

(4)

(3)

(3)

(3)

(1)

(2)

(3)

(5)

(3)

(2)

(3)

Notes:

(a)indicatesmodel

withconstantbutnotrend,(b)indicatesmodel

withconstantandtrend;z t

andDz t

alsoreflectthelevel

andfirstdifference

ofthevariables,

respectively.

*,**and***indicate

thatthenullhypothesisisrejected

atthe1,5and10%

levels,respectively.

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Table A5. The distribution of the type of the highways transportation network (1970–2005)

Asphalt roads

Asphalticconcrete

Surfacetreatment

Stoneblock roads

Stabilised andcrush stone roads

Earthroads

Primitiveroads

1970 1450 15 884 207 29 715 9331 30331975 1764 21 899 253 27 260 6267 20561980 2707 31 355 116 18 745 4955 21811985 3176 37 630 129 13 750 2757 16701990 4261 43 200 136 8803 1356 13721995 4835 45 849 121 6552 1330 13122000 6057 49 709 134 3026 1144 10202005 8820 50 302 133 2207 1329 888

Source: T.C. Ministry of Public Works and settlement, Department of Planning.

Table A4. Bounds test for Model 1

F-statistics k 10% 5% 1%

Model 1a1.951 3 I(0) I(1) I(0) I(1) I(0) I(1)

2.57 3.46 2.86 3.78 3.43 4.37

Model 1b0.377 3 I(0) I(1) I(0) I(1) I(0) I(1)

2.72 3.71 3.23 4.35 4.29 5.61

Notes: k denotes the number of independent variables in Equation 5. Optimal lag length is 1, determined by AIC.Critical values are cited from Pesaran et al. (2001), Table CII(iii), Case III: unrestricted intercept and no trend.

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