Mukibi-cobams-masters.pdf

ROAD INFRASTRUCTURE AND ECONOMIC GROWTH IN UGANDA 1980-2010

BY

MUKIIBI PAUL 2009/HD06/15161U

A THESIS SUBMITTED TO THE DIRECTORATE OF RESEARCH AND GRADUATE TRAINING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF ARTS DEGREE IN ECONOMICS OF MAKERERE UNIVERSITY

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DECLARATION I Mukiibi Paul hereby declare that this piece of work I have presented here is solely the result of my efforts apart from the work cited from other authors and that it has never been submitted to any university or institution of higher learning for the award of a degree. Signed ……………………………………

Date ……………………………………..

Mukiibi Paul

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CERTIFICATION This certifies that the under-signed supervisors have read this thesis in the process of guiding the author and thereby recommend it for submission to the Directorate of Research And Graduate Training Makerere University in partial fulfillment of the requirements for the award of Masters of Arts in Economics, Degree of Makerere University.

Signed by…………………………….…………. Date …………………………………….. DR. Fred Matovu Signed by………………………………………… Date ………………………………….. DR. James Muwanga

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DEDICATION This work is dedicated to my beloved parents who gave me parental guidance to pursue this course. May the Lord reward you with a meaningful life. I also dedicate this work to my late sister Nasaka Betty I know that you always wanted me to succeed in life. May your soul rest in eternal peace

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ACKNOWLEDGEMENT I thank the Almighty God for the power and energy he has given me including the knowledge and ability to produce this piece of work. I also extend my sincere appreciation to the African Economic Research Consortium (AERC) for their partial scholarship that relieved me from much of the financial stress. I thank them so much otherwise without them, I may not have reached this level. I am also grateful to my supervisors for their tireless guidance throughout the period I was working on this study. Despite their numerous responsibilities, they were always ready to offer their parental and professional advice towards the successful completion of this study. I thank you for the sacrifice you rendered to me throughout the time I needed you. May the creator bless you abundantly.

Special thanks go to my wife who encouraged me to do this work plus all her financial and moral support that helped me to survive, may God reward you in plural. Special regards to my children Paulson and Bettina especially the elder one for his continued inquisitiveness about what I was studying and what for; My siblings Mary, Helen, Christopher and Francis and all my nieces and nephews thanks a lot for your support

I also want to acknowledge the efforts of all my colleagues who contributed to the successfulness of this study in one way and the other.

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Table of contents DECLARATION ......................................................................................................................... i CERTIFICATION ...................................................................................................................... ii DEDICATION .......................................................................................................................... iii ACKNOWLEDGEMENT ......................................................................................................... iv LIST OF TABLES ................................................................................................................... vii Acronyms ................................................................................................................................ viii Abstract...................................................................................................................................... ix CHAPTER ONE ......................................................................................................................... 1 1.0 Background to the study ................................................................................................... 1 1.1 Statement of the problem.................................................................................................. 5 1.2 Objectives of the study ..................................................................................................... 6 1.2.1 Main objective ........................................................................................................... 6 1.2.2 Specific objective ...................................................................................................... 6 1.3 Hypotheses ....................................................................................................................... 6 1.4 Scope of the study ............................................................................................................ 6 1.6 Organization of the study. ................................................................................................ 7 CHAPTER TWO ........................................................................................................................ 9 THE ROAD SUB-SECTOR IN UGANDA ............................................................................... 9 2.1 The Road Network, Vehicle Fleet and Traffic ................................................................. 9 2.2 The Road Transport Industry............................................................................................ 9 2.3 Road Administration and Training ................................................................................. 10 2.4 Road Financing:.............................................................................................................. 11 2.5 Road Engineering and Construction. .............................................................................. 13 2.6 Road Maintenance .......................................................................................................... 13 2.6.1. Organization of Maintenance: ................................................................................ 13 2.6.2 Maintenance Financing: .......................................................................................... 14 CHAPTER THREE .................................................................................................................. 17 LITERATURE REVIEW ..................................................................................................... 17 3.1 Definition of infrastructure ............................................................................................. 17 3.2 Empirical studies on infrastructure development and economic growth ....................... 18 CHAPTER FOUR .................................................................................................................. 36 METHODOLOGY .................................................................................................................... 36 4.0 Introduction .................................................................................................................... 36 4.1 Data type and source ...................................................................................................... 36 4.2 Model specification ........................................................................................................ 36 4.4 Data analysis ................................................................................................................... 41 4.3.1 Unit root test ............................................................................................................ 41 4.3.2 Co-integration and the ECM ................................................................................... 41 4.3.3 Granger causality test .............................................................................................. 43 CHAPTER FIVE ...................................................................................................................... 46 PRESENTATION AND DISCUSSION OF RESULTS .......................................................... 46 5.1 Descriptive Statistics ...................................................................................................... 46 5.3 Cointegration test............................................................................................................ 49 5.4 Error Correction Mechanism .......................................................................................... 51 v

5.5 Impact of road coverage on economic growth rate. ....................................................... 52 5.6 Estimation of the direction of causality between road coverage and GDP. ................... 55 CHAPTER SIX .......................................................................................................................... 57 CONCLUSIONS, RECOMMENDATIONS AND AREAS FOR FURTHER RESEARCH .. 57 6.1. Introduction ................................................................................................................... 57 6.2. Conclusion ..................................................................................................................... 57 6.3 Recommendations .......................................................................................................... 57 6.4. Areas for further research .............................................................................................. 59 REFERENCES ..................................................................................................................... 60 APPENDIX 1 ....................................................................................................................... 66

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LIST OF TABLES Table 2.1:

Financial allocation to the road sector…………………….………

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Table 5.1:

Descriptive statistics ………………………………………….…..

43

Table 5.2:

Test results of unit root test………………………………….……

44

Table 5.3:

Results for residual test from OLS estimation…………………….. 45

Table 5.4:

Johansen co integration test results………………………………….. 46

Table 5.5:

Results for the error correction mechanism…………………..…….. 48

Table 5.6:

Results for the long run relationship…………………………….…. 49

Table 5.7:

Shows results for the long run relationship with shocks ….…….…. 50

Table 5.8

Shows results for granger causality test……………………………. 52

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Acronyms ADF

Augmented Dickey-Fuller

ECM

Error Correction Mechanism

EU

European Union

GDP

Gross Domestic Product

MFPED

Ministry of Finance Planning and Economic Development

MWOT

Ministry of Works and Transport

ODA

Official Development Agency

OECD

Organization of Economic Cooperation and Development

PPP

Public-Private-Partnership

MLG

Ministry of Local Government

MTEF

Medium Term Budget Expenditure Framework

PEAP

Poverty Eradication Action Plan

RAFU

Road Agency Formation Unit

NRM

National Resistance Movement

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Abstract Uganda is a land-locked country with road infrastructure constituting the main mode of transport. However, by 2010 road coverage both paved and unpaved remains low (3112 km and 16888 km respectively), with all-weather paved roads accounting for just 16 percent of entire national road coverage in the country. The low road coverage constrains economic growth in terms of transportation of goods and services but the effect of road infrastructure on GDP has not been empirically investigated. This study fills this gap in knowledge by examining the effect of government spending on road infrastructure development on the economic growth of Uganda for the period 1980-2010. The study employed a Cobb-Douglas production model specifying the functional relationship between government spending on infrastructure development and economic growth. Road coverage in kilometers (sub-divided into paved and unpaved roads) was used as a proxy for government expenditure on roads.

Ordinary Least Square (OLS) method was used for the analysis. Error correction mechanism was used to establish short term equilibrium between road infrastructure and economic growth. Granger causality was also conducted and established a unidirectional causality from GDP to unpaved road infrastructure (using secondary data obtained from various issues of statistical abstracts, Background to the budget from UNRA, UBOS, MFPED as well as World Bank data base).

The results indicated that in the long-run a 1 percent increase in the number of kilometers of paved roads lead to 2.8 percent increase in GDP. Similarly, a 1 percent increase in the unpaved roads leads to 0.4 percent increase in GDP. Basing on the findings of the study government should commit more resources to increase provision of roads especially paved since they are relatively durable as well as making efforts for the unpaved roads to reach all parts including the rural areas to further stimulate economic growth through the multiplier effect. Also, both public and private sectors should increase on the directly productive capital in order to enhance sustainable economic growth in the country.

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CHAPTER ONE INTRODUCTION 1.0 Background to the study Uganda, the “Pearl of Africa” is a country of contrasts. Its economy has expanded at an average rate of 7 percent per year in the last 20 years. But its poverty rates remain significantly high and unimpressive in reflecting this growth rates (Douglas and Richard,2010). It had experienced 15 years of devastating political instability, social and economic collapse that reversed the optimistic situation inherited at the time of independence in 1962. At this time Uganda, had a stable and growing economy and a promising physical and human capital development, superior to that of its neighbouring countries. This optimism gave way to economic destruction, international isolation and emergence of uncompetitive subsistence production system with limited participation in both domestic and international markets coupled with civil struggles in most parts of the country which created insecurity. In 1986, the National Resistance Movement (NRM) after a five year protracted civil war, captured power and initiated a reign of relative political stability, economic reforms that laid a foundation for sustained economic growth and expansion seen to date. Over the period the current government has strived to improve on the infrastructure in the country with emphasis on the energy and transport sectors as a way of stimulating economic growth.

The transport sub-sector contributes immensely to the economic growth and poverty eradication in the country through various ways. An efficient transport infrastructure is vital in supporting economic growth and improvement of the quality of life of the populace (Owen, 1987; Queiros and Gautam, 1992). This can be looked at in line with improvement in mobility especially the 1

case of access to the rural areas where the majority of the population derives its livelihood from and also stimulating production through linking of markets to production centers. It is worth noting that provision of a road facility is on the basis of any of these two reasons; that is construction following the rate of return a particular road is likely to have or following government promise of awarding a particular community for being loyal to the regime. The former reason justifies an efficient transport system that will provide the multiplier effect of road infrastructure consequently leading to increased effect of the road variable to the economic growth of the nation.

Government has a cardinal responsibility of providing public services where private sector is not able to invest in order to sustain economic growth linkages especially those related to National Development Plan (NDP). This is because the undertakings in this sector require a huge capital outlay with long term benefits and are not normally attractive to the private sector. It is therefore, imperative that the Uganda Government provides these public facilities to stimulate development of other sectors, which addresses needs of the poor and the vulnerable groups’ consequently economic growth (Matovu, 2000).

Identifying the determinants of long-run economic growth remains central to the Uganda’s economic policy debate. A number of studies have investigated the changing structure of economic growth in Uganda since the early 1980s (Sennoga and Matovu, 2010; Fan et al, 2007; Musisi, 2007). However, despite these studies, little work has been done to evaluate the importance of road infrastructure towards economic growth.

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According to Nyende et al. (2010), since 2007/08, the National Resistance Movement (NRM) Government has accorded significant importance to the provision of a sound and well‐coordinated transportation system. Thus road infrastructure has been considered as a pre‐requisite for ensuring sustainable socio‐economic development and consolidation of both national unity and security of the country. The linkages of transport to poverty reduction and to long term economic growth are significant and need not to be re-emphasized. Their absence or inadequate provision of them stands out as a stark barrier to economic growth. Road Infrastructure has been a priority in the government’s comprehensive development strategy and its benefits were anticipated to be reflected in better incomes and higher economic growth.

The road sector has been identified as one of the six critical sectors that require substantial budget expenditure if the objective of the GOU’s National Development Plan is to be achieved. The sector contributes to increasing rural incomes and supports the private sector and continues to be one of the fastest growing programs with budget allocation accounting for about 6.5 percent of total Government expenditure. GOU’s spending increased from a level of USD 89.53 million in 2001/02 to USD 93.96 million in 2006/07 while Development Partners contribution increased from USD83.5 million to USD111.12 million during the same period. On average the Government contribution accounted for about 54.6 percent between 2001/02 and 2006/07. The donor agencies/ financial institutions hence contributed the 45.4 percent of the road investment.

According to Musisi (2007) the Ugandan government should invest 24 percent of the gross domestic product in physical infrastructure. Then in the mid to long term the investment will have a highly positive effect on various sectors. It is argued that there is insufficient road

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infrastructure in Uganda probably the hindrance to economic growth (Mwakali 2008). Infrastructure investment has the effects of contributing to increased productivity and it is expected to contribute to future economic growth in developing countries where transport system is still insufficient. At the micro level, field studies of immobility among women and men in rural settlements in Africa with poor road access illustrate the frustrations and the high costs of living due to poor road infrastructure (Porter, 2002) Figure 1; The trend of national roads in the country

Source; Various issues of Background to the Budget MFPED

It’s quite evident that there has been minimal increase in the number of paved roads in the country in the period as compared to the unpaved, given the durability of these roads the government needs to increase the construction in order to achieve sustainable growth. For a clear insight to the subject matter, this research examined Uganda’s experience in developing its road infrastructure for economic growth. The patterns of infrastructure supply i.e. expenditure on the transportation sector which is a key infrastructure in national development was analyzed annually from 1980 to 2010 that is looking at the expenditure on roads in the country during this period. 4

Uganda has gone through a series of reforms which can be categorized into two that is the framework of Uganda’s SAP, which focused on the stabilization of the exchange rate and elimination of trade barriers such as tariffs, export taxes and other indirect distortions to trade. The second generation of reforms follows immediately after Uganda had achieved a considerable integration in the global economy and are related to government interventions in order to implement an efficient (no necessarily minimal) regulation system for trade -avoiding excessive non-tariff barriers(NTBs)-, to stimulate the incorporation of non-traditional export sectors and to pursuit agreements promoting regional integration. These reforms are thought to have had considerable effect on the economy of the country starting from 1987 and 1988 respectively. However, Evaluation of these reforms is beyond the scope of this study but due to their effect on GDP the study incorporated dummy variables to assess their contribution.

1.1 Statement of the problem Road infrastructure remains the main mode of transport within Uganda and as a main gateway to neighboring countries. Therefore the quantity and quality of roads impacts greatly on economic activities in the country. Poor road infrastructure has a negative effect on the production of goods and services leading to increased prices which limits the competitiveness of the country’s exports and consequently the returns from international trade. Despite the key role of road transportation, the growth in kilometers of roads for both paved and unpaved has been low over the years. This slow growth affects economic growth of the country but there is limited empirical evidence on the linkage between road infrastructure and economic growth in Uganda. The study hence sought to establish the link between national income and road infrastructure.

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1.2 Objectives of the study 1.2.1 Main objective To examine the effect of road infrastructure on the economic growth of Uganda for the period: 1980-2010.

1.2.2 Specific objective 

To establish the causality between economic growth and expenditure on road infrastructure.



To examine the effect of expenditure on paved and unpaved roads on economic growth.

1.3 Hypotheses  Investment in roads infrastructure does not cause economic growth 

An increase in the coverage of paved and unpaved road infrastructure doesn’t lead to an increase in economic growth.

1.4 Scope of the study The study sought to establish the effect of road infrastructure on economic growth of Uganda and considered all national roads, that is, paved and unpaved roads. However, district roads are excluded due to lack of data for the entire period of the study. The emphasis on national roads is both due to data availability and the multiplier effects associated with these roads towards economic growth. As such, the study sought to find out the effect of road infrastructure provision in terms of coverage of paved and unpaved roads onto the country’s economic growth from 1980 to 2010 and also whether the road infrastructure coverage is at its ideal level to bring about the desired growth.

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1.5 Significance of the study The purpose of this research was to highlight what is understood (and what is not understood) about the linkage between road infrastructure and economic growth and the implications about this for the management of road infrastructure in Uganda. The importance of road infrastructure towards economic growth is observed through directly availing employment opportunities to people carrying out the activity of construction and maintenance. Also, improved road infrastructure greatly improves accessibility as well as linking up of markets. This is motivated in a way that factors of production are easily moved from one location to another and a cross borders consequently improving the country’s competitiveness. Considering Uganda being an agricultural economy with most of the cultivation being carried out in the rural areas, improvement of the road sector will greatly enhance production by way of cheap linkage of markets to the farm lands brought about by reduced transaction costs. The research findings will hence help policy makers determine how much government needs to spend on the road sector to bring about the desired sustainable economic growth rates and also help to decide on which road category to lay much emphasis. 1.6 Organization of the study. The study is organized in six chapters that is Chapter one which presents the background of the country in relation to its economic performance as well as the boundary within which the variables are analyzed. Chapter two presents the transport sector in regard to funding maintenance and coverage in the country. Chapter three shows the various write ups and publications in line with the contribution of road infrastructure to economic growth. The fourth chapter presents the various methods that were used in the analysis of the data and testing of the

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hypothesis. Chapter five presents the findings that were obtained in the analysis and consequently used for recommendation in chapter six.

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CHAPTER TWO THE ROAD SUB-SECTOR IN UGANDA 2.1 The Road Network, Vehicle Fleet and Traffic The total road network in 2007 in Uganda was estimated to be 68,800km and was classified into: (i) The national road network of 10,800km of which 3,000km is paved and 7,800km gravel surfaced; (ii) The district road network of 25,000km (mainly unpaved); (iii) Urban road network of approximately 3,000km (mostly paved); and (iv) The community access roads of 30,000km (unpaved). Uganda has a road density of 285 km per 1000 km sq. This is higher than that of Tanzania of 90 km/1000 km.sq but lower than that of Kenya with a density of 334km/1000 sq.km (UBOS, 2006). The number of vehicles on the road in Uganda was 189,105 in 2000 and rose to 363,658 in 2007. Out of the total vehicle fleet, 45.2 percent (164,506) are motorcycles. The average annual growth rate of the fleet is about 9.85 percent, which indicates a good recovery and rapid growth of the economy. The total vehicle-km travelled on the entire road network increased from 4.69 billion in 2000 to 12.06 billion in 2006. The weighted annual average daily traffic volume (AADT) was 3,624 vehicles/day on the paved roads in June 2007 according to the 2005/2006 UNHS (UBOS, 2006). 2.2 The Road Transport Industry The Transport Licensing Board of the Ministry of Works and Transport (MOWT) licenses vehicles and operators regulates passenger routes, minibuses and sets tariffs. The passenger transport services are provided by 90 local and 16 interstate licensed bus operators. There are 15,000 licensed minibuses that are privately operated and form the Uganda Taxi Operators and Drivers Association, providing about 89 percent of road passenger services. Freight traffic is less 9

organized in the private sector and operates independently on a negotiated basis with suppliers or producers. 2.3 Road Administration and Training MOWT statutorily has the overall responsibility for the development, management and maintenance of the national road network. In line with the Government policy of rationalizing the management of sector agencies and restructuring the civil services, Road Agency Formation Unit (RAFU), a unit accountable to MOWT (but outside the ministry's structure), was established in September 1998 as a transitional semi-autonomous institutional arrangement to manage the implementation of RSDP. RAFU’s establishment was financed under an IDA Credit (Road Sector Institutional Support Technical Assistance Project- RSISTAP) that was closed in December 2007. The GOU was supporting RAFU’s establishment until 30th June 2008, after which RAFU was replaced by Uganda National Roads Authority (UNRA), as per UNRA Act of 2006. UNRA is an autonomous body responsible for overall planning, construction, maintenance and management of the country's national roads. UNRA is headed by an Executive Director who is also a member of the Board. Other members consist of Permanent Secretaries of MOWT and MFPED, National Planning Director, Representative of Engineers Association and two representatives of the private sector.

Facilities for training of staff in the field of civil engineering are well established in Uganda. The college of engineering, designing art and technology of Makerere University and Kyambogo University offer training in areas of civil, mechanical and electrical engineering. In addition, four other technical colleges, thirty-one technical institutes, and twenty-seven public and thirty-six

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private technical schools also train staff in the transport sector. The MOWT has a Public Works Training Centre that offers training for the whole construction industry. 2.4 Road Financing: Uganda receives large amount of concessional loans and grants from the US, Japan, Western Europe and international organizations for building economic infrastructures mainly roads and power stations. This is in an attempt to give the country an advantage of overcoming the high prices that reflect an increase in cost of production resulting from poor infrastructure especially roads. Good infrastructure helps to raise productivity and lowers costs in the directly productive activities of the economy, but it has to be expanded fast enough to meet the demand for infrastructure in the early stage of development. Construction expense for infrastructure such as energy and transportation sector is enormous and construction period is also long. Prediction of demand pattern and investment allocation, which are the key factors of infrastructure development planning, must be based on a long term economic Development trend and land use planning, which predicts the country’s temporal and spatial demographics and economic structure. Dormant as they may seem, road infrastructure propels the economy to the growth path through its multiplier effect for example reduced cost of production and easy factor mobility.

The road sector has been identified as one of the six critical sectors that require substantial budget expenditure if the objective of the GOU’s PEAP is to be achieved according to the Republic of Uganda road sector appraisal report 2007. The sector contributes to increasing rural incomes and supports the private sector and continues to be one of the fastest growing programs with budget allocation accounting for about 6.5 percent of total Government expenditure. Table 2.1 shows the details of the sector funding by components of the program. GOU’s spending

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increased from a level of USD 89.53 million in 2001/02 to USD 93.96 million in 2006/07 while Development Partners contribution increased from USD83.5 million to USD111.12 million during the same period. On average the Government contribution accounted for about 54.6 percent between 2001/02 and 2006/07. Table 2.1 Financial allocation to the road sector in Uganda. Implementation of RSDP2– Maintenance

(US$ MILLION)

ACTUAL 2001/02

2002/03

2003/04

2004/05

2005/06

2006/07

2008/09

2009/10

2010/11

2011/12

37.66

29.85

30.93

37.16

37.65

37.98

74.93

74.31

72.64

72.05

ii) District and Urban Roads Mtc. – GOU

13.04

9.77

11.44

11.54

11.21

11.52

30.89

30.68

30.19

29.94

iii) National & District – Dev. Partners Total Maintenance

13.61

3.54

3.25

2

0.07

0

5.41

6.80

6.79

6.73

64.31

43.16

45.62

50.70

48.92

49.51

111.23

111.79

109.61

108.73

vi) Percentage of GOU's Mtc contribution

79%

92%

93%

96%

100%

100%

95%

94%

94%

94%

vii) Percentage of Dev. Partners contribution

21%

8%

79%

4%

0%

0%

5%

6%

6%

6%

24.82

14.93

16.24

20.12

9.10

11.73

19.42

66.06

24.58

N/A

4.83

4.57

12.40

10.02

8.65

6.34

10.68

12.21

8.96

N/A

62.38

21.77

109.56

102.82

31.1

90.57

174.39

199.88

150.42

N/A

Sub - total Dev.

92.03

41.27

138.20

132.96

48.85

108.64

204.49

278.15

183.96

N/A

Grand Total Total – GOU Total-Donors

173.18

115.75

214.50

216.59

115.42

205.08

341.53

413.69

320.14

N/A

89.68 83.50

32.80 32.95

89.08 125.42

98.21 118.38

81.44 33.98

93.96 111.12

148.61 192.92

193.37 220.32

152.59 167.55

N/A N/A

Year A) Road Maintenance (US$ million ) i) National Road Maint. – GOU

B) Road Development v) National Road. – GOU vi) District and Urban Roads – GOU National & Districts - Dev. Partners

Source: Adapted from MFPED 2012

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2.5 Road Engineering and Construction. The Engineering Division of RAFU usually undertakes minor works and designs, while the major ones are contracted to consultants and contractors. International consulting firms normally carry out major road feasibility and detailed engineering design. The contracting capacity of the country has improved over the years with the training of small and medium-scale contractors. To date, the domestic contractors that have registered with MOWT are 49 large-scale contractors, 62 medium-scale contractors and 111 small-scale contractors. The overall number of local contractors has increased by 25 percent when compared with the number in 2003. Even though the domestic contracting capacity for civil works has improved considerably, the industry still lacks equipment leasing facilities to foster its development, capacity building to bid for large works in excess of Ushs 10 million either directly or through developing strategic partnership with foreign firms. 2.6 Road Maintenance 2.6.1. Organization of Maintenance: The Road Maintenance Section of MOWT is directly responsible for maintaining the classified road network and has divided the country into four regions. The regions operate under the regional engineers and are sub-divided into 22 field stations, which are managed by district engineers. The Maintenance function is still under the Ministry and is expected to be transferred to UNRA not later than 1st July 2009.

Routine and periodic maintenance activities are carried out by direct labour (force account) and by contractual service of small scale local contractors that are usually supervised by the

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Ministry's personnel at the district or area offices. The district roads are maintained by district administrations with the supervision of the MOWT in liaison with the MLG. 2.6.2 Maintenance Financing: The amount allocated for maintenance of the road network is contained in the Transport Sector Investment & Recurrent Expenditure Programme (TSIREP) that is revised annually within the Government’s MTEF. Table 2.1 above shows that over the last six years maintenance budget allocation by GOU and Donor’s has declined from USD64.31 million in 2001/02 to USD49.51 million in 2006/07, though the allocation by GOU has remained around USD50.0 million per annum. According to the MOWT, the maintenance requirement for the national, district and urban roads per year is about USD 100.0 million. Comparing this maintenance requirement with actual allocation, the historical funding gap has increased from about USD 35.7 million (2001/02) to USD 50.5 million (2006/07), indicating that only about 50.4 percent of the requirements are met. This could be attributed to the decrease in Development Partners’ support for maintenance and Government failure to increase its maintenance allocation annually by USD2.0 million (as agreed) starting 2003/04 until self-sufficiency in funding is achieved in 2008/09. This under funding has reflected in the national road condition of the country, which has declined from 60 percent in good to fair condition in 2003 to 47.5 percent in 2007. This is very low when compared with the road condition in acceptable condition in Kenya and Tanzania, which are 67percent and 84percent, respectively (UNRA, 2012). The maintenance issue is a concern of Donors including the Bank that is being addressed by the creation of the Road Fund and UNRA.

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GOU has recently agreed to the establishment of a fund to cover the maintenance requirements for all public roads. To ensure that individuals contribute to the provision of road infrastructure in the country the GOU agreed to the establishment of the fund to cover maintenance requirements for all public roads. The Road Fund Bill was approved by the Cabinet on 24 th June 2007 and later sent to Parliament for approval. Road Fund based on road user charges is expected to guarantee a regular and steady flow of funds for maintenance. As of July 2007, the GOU had increased the fuel excise duty by 18 percent, that is 130 UGX (from 720 to 850) and 80 UGX (from 450 to 530) on petrol and diesel respectively. Up to now, the revenue from the excise duty on fuel has been channeled to the consolidated fund and apportioned through the normal budget process. Thus, the GOU maintenance contribution is expected to double with the introduction of the dedicated road fund from USD49.51 million in 2006/07 to USD105.82 million in 2007/08 and sustained at an average of about USD102.0 million per year till 2010/11. During the same period, it was planned that Donors contribute about USD7.0 million per year for clearing maintenance backlog.

As per the Act establishing the Road Fund, statutory allocation will be on the basis of 63.5 percent of its annual revenue to UNRA for maintenance of the national roads, 14.5 percent for districts roads, 7.5 percent for Kampala roads, 5.7 percent for other urban roads, 7.0 percent for UNRA administrative and operational costs and 1.8 percent for the Fund’s administration. The other sources of revenue for the Road Fund include international transit fees/cross border charges, axle load fines; weight - distance charges and others will be determined by the Board of the Fund, when it is fully operational in January 2008 (African Development Fund, 2007). The Board will have nine members (four from public and the remaining 5 from the private sectors).

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The Secretariat would be headed by an Executive Director, with three directorates for financial management, audit and information and support services. In addition to the establishment of the Road Fund, the establishment of UNRA and the transfer of maintenance, axle load control and other activities from MOWT to UNRA by July 2009 would strengthen the institutional aspect of road maintenance.

In conclusion the chapter has presented the various roles of the various parties charged with the road sub sector in Uganda that is what the ministries and the different authourities are charged with in regard with construction and maintenance. It has also highlighted the various avenues through which the funds are obtained and allocated as well as the various coverage of roads in the country.

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CHAPTER THREE LITERATURE REVIEW 3.0 Introduction This chapter presents the definitions of the key terms used in the study and the various empirical studies done on infrastructure and economic growth. 3.1 Definition of infrastructure Infrastructure is referred to as social overhead capital by many development economists. Hirshman, (1958) provided appropriate definition of infrastructure for the discussion1, as social overhead capital encompassing activities that share technical features such as economies of scale and economic features like spillovers from users to non-users. The social capital acts as a principal to expand private sector investment, and in contrast, social capital becomes relatively lacking along with the expansion of private capital and productive activities.

Social overhead capital contributes to enhancing productivity and assists in the realization of the potential ability of human capital, and creates situations in which that potential can fully function. It also contributes directly and indirectly to improving the safety and quality of people’s lives. Within the scope of infrastructure, electric power, ports, roads, and telecommunications are often used as the services and intermediate goods that are essential for the productive processes of private sector Hirshman, (1958).

1. Hirshman proposed the concept of social overhead capital, which supplements direct productive capital, and commented the relationship between direct productive capital of the private sector and social overhead capital, which is mainly built by public bodies. Definition of infrastructure is also discussed in detail in Yoshida (2000).

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Infrastructure has been defined in terms of the physical facilities (roads, airports, utility supply systems, communication systems, water and waste disposal systems etc.), and the services (water, sanitation, transport, energy) flowing from those facilities according to Sida (1996). Fox (1994) defines public infrastructure as ‘those services derived from the set of public works traditionally supported by the public sector to enhance private sector production and to allow for household consumption’. He further states that the importance of infrastructure as an instrument of economic development and potentially, poverty reduction, is reflected in the high level of investment which national governments and international donor agencies put into infrastructure development.

3.2 Empirical studies on infrastructure development and economic growth Uganda is a land locked country with road infrastructure constituting the main mode of transport, however insufficient allocation have been directed to the road sub sector. Poor road conditions and transportation system hinder movement of goods and people in the urban areas. Lack of adequate infrastructure could also be a disincentive to both local and foreign investors in our country. According to (World Bank, 1992), inadequate infrastructure Constrains productivity at all levels, as such infrastructure reduces the productivity of firms and households and this affects the aggregate productivity of the economy. Transportation plays a major role in economic development both urban and national. It also has a broader role in shaping development and the environment. The interface between transportation investment and economic development has broad ramification that goes beyond the basic purpose of moving goods and people. Transportation facilities located in a specific place provide services to businesses (and households) within a specific geographic area, and their use is directly related to moving goods and people between two points. It is also essential in the operation of a market economy 18

According to Matovu, (2000), it is important to note that, when government prioritizes road infrastructure spending, the growth effects have been shown to be substantial due to the increased household productivity which results from the positive externality effects associated with good infrastructure, for example, linking up of markets as well as employment. Williamson and Canagarajah, (2003) and World Bank, (2002) also argue that roads, agriculture, water and sanitation may yield higher returns for employment and income creation in Uganda than primary health care, education and that the poverty action fund, through the promotion of a narrow interpretation of pro-poor programmes has led to the skewing of budget allocations away from programmes that may have resulted in greater poverty reduction.

One of the important aspects of infrastructure investments is that they have different short and long-run effects. Most of the studies on infrastructure productivity and economic growth analyses suggest that short term effects are in the form of increases in employment and long-term effects include both changes in employment and private output. A study by (Shah, 1992) tested these effects using a system of non-linear equations consisting of variable cost functions and derived input demand equations for 26 Mexican three-digit industries between 1970 and 1986. The findings revealed that public infrastructures (electricity, communication, and transportation) have a weak complementarity with private capital only in the long run. However, labour shows complementarity with infrastructure in both short-and long-run with the degree of complementarity being higher in the short run as opposed to long run

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The evidence in the literature shows that infrastructure plays a critical and positive role in economic growth in an attempt to enhance to development. Infrastructure interacts with the economy through multiple and complex processes (Adeola, 2005). Infrastructure represents an intermediate input to production, thus changes in road infrastructure quality and quantity affects the profitability, production and invariably the levels of income, output and employment (Adeola, 2005). Moreover, infrastructure services raise the productivity of other factors of production (Kessides, 1993). The provision of infrastructure in most developing countries is the responsibility of the government. This is because of the characteristics of infrastructure investment. Road Infrastructure supply is characterized by high set-up costs, its lumpiness and indivisibility precludes the private sector from investment in the developing countries.

Studies have demonstrated a positive link between improvements in the road infrastructure and economic growth. Nworji and Oluwaiye (2012), showed that expenditure on roads, power or communication reduces production costs, stimulate private sector investment and profit margin of firms, create increased employment and wealth; thereby improving the growth in the economy. Consequently inadequate transportation limits a nation's ability to utilize its natural resources, distribute food and other finished goods, integrate the manufacturing and agricultural sectors, and supply education and medical services. However, there is little empirical evidence linking transportation improvements to economic growth more especially in developing countries as opposed to developed countries where the link has been established (Aschauer,1989a,b,c), (Gramlich,1994; Sanchez.,1998; Canning et al,1994; Easterly and Rebelo,1993). In developing countries it is not generally known whether an investment in transportation infrastructure is more productive than investments in other sectors of the economy

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nor is it known whether capital expenditures on one mode of transportation is more productive than those spent on another.

Viggo, Jean and Hansen (2012), in their study for a proposed construction of a new road infrastructure in Helgeland, used a cost benefit analysis in which costs like construction expense, resettling of people and so on were weighed against benefits of reduced time on the road, reduced maintenance costs and increased traffic on the road. They found out a positive ratio consequently implying that the project was feasible. A similar analysis was carried out by Asensio and Roca (2001) and Daegoon et.al (2012) all establishing that investment in the road infrastructure was feasible for the respective countries investigated. However, it’s important to note that the CBA by its self doesn’t take into account the whole set of economic effects. Emphasis is on assessing the benefits against the costs of such investments for example in the UK the awareness about the discussion led to the re-discussion of the main project evaluation techniques by the Leitch committee consequently calling for a more broader framework (Glaister,1999).

Ndulu (2006) drew from the existing literature of the various channels or means through which infrastructure affects growth. In his study, he argued for the big push in promoting infrastructure that is necessary not only to break out of under development but, more importantly, to be on the path to sustainable growth. Focus on infrastructure is now seen in the purview of implementing public investment in social services, which are geared towards attainment of the Millennium Development Goals (MDGs) rather than competing for the government’s scarce resources.

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Yoshino and Nakahigashi (2000) estimated the productivity effect of social capital stock by industry, sector and region, and clarified the relationship between social capital stock and economic development. As a result, (1) by industry, the productivity effect of social capital stock is large in the tertiary industry, (2) by sector, the productivity effect of social capital stock is large in information and telecommunication, and environment sectors, and (3) by region, and the effect is large in regions with large urban areas. To see the result of their analysis from the view point of the development of developing countries, relationship between social capital and economic growth is examined from statistical data hence the need for this study.

Byoungki, (2006) argues that road infrastructure development is one of the most integral parts of public policies in developing countries. Supporting infrastructure development in developing countries by advanced countries is extremely an important field towards enhancing economic growth consequently development. This can be inferred from the fact that many international organizations such as World Bank and OECD are actively promoting the improvement of infrastructure by providing various support programs to developing countries. However, the precise relationship between infrastructure and economic growth is still frequently debated upon.

Yoshida (2000) presented a positive analysis from various angles of the correlations between economic growth and the infrastructure in Japan, such as the energy, electricity, and transportation sectors over the last century in order to derive lessons that can be useful to developing countries. He divided Japan’s economic development phase into five with major characteristics, and discussed the patterns of demand and investment in infrastructure over one century. He found out that the growth rate of demand in infrastructure was much higher than that

22

of per capita GNP in the early stage of development, and public investment in infrastructure was big. He also found that infrastructure investment in rural areas had a trend to correct the regional income disparities. He insisted that the lessons learned from Japan’s development experience are a major intellectual asset for developing countries. He emphasized that developing countries expect Japan and Korea, (former developing countries), to take reasonable leadership in international aid.

Canning and Pedroni, (1999) investigated the long run consequences of infrastructure provision on per capita income in a panel of countries over the period 1950-1992. They used simple tests devised for the existence and sign of the long run impact of infrastructure on economic growth allowing for non-stationarity and co integration in the time series, they found out that infrastructure induced long run economic growth. Nevertheless, they also found a great deal of variation in the results across individual countries with a great deal of heterogeneity in the results across countries. It is however, important to note that panel data is usually associated with heterogeneity as a result of aggregating (pooling) data with similar characteristics in an attempt to come up with series data.

Somik (1999) carried out an empirical analysis that employed the modified Cobb- Douglas production function to test the efficacy of public infrastructure investments in the development process of 15 Indian lagging, intermediate and leading states. He found out that the composition of public investments is important in facilitating growth, and that public investment is a necessary but not sufficient condition for regional economic growth.

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Devarajan, Swaroop and Zou (1996) drew analytical conclusions about developing countries based on the endogenous growth theory in order to verify which type of government expenditures promotes economic growth. They estimated the relationship between the composition of public expenditure and economic growth using data from 43 developing countries over 20 years. This estimation showed that an increase in the share of current expenditure has positive and statistically significant effects on economic growth and that infrastructure has a negative effect on the economic growth rate because infrastructure in developing countries is oversupplied compared to the economic scale. Its however, important to note that most developing countries’ are financed by external sources as a matter of fact an endogenous growth theory doesn’t bring out the concrete relation.

Calderon and Chong, (2004) investigated the impact of infrastructure development on economic growth and income distribution using a large panel data set encompassing over 100 countries and spanning the years 1960‐2000. The authors used a variety of generalized method of moments (GMM). It was found out that growth is positively affected by the stock of infrastructure assets. Furthermore, income inequality declines with higher infrastructure quantity and quality.

Bougheas, et al. (1999) in their analysis of a symmetric two-country model, that examines the effects of road infrastructure on specialization and the volume of trade. In their analysis, they convey the message that upgrading of transport and communications networks, which reduces transport costs and facilitates trade of goods both within and across national borders. Any investment in infrastructure by the domestic economy is likely to benefit not only domestic but also foreign producers and consumers. The symmetric nature of their model used, does not allow

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the authors to address coordination issues, such as the question of how countries might share the costs and benefits of infrastructure provision, which give rise to the possibility of either overinvestment or underinvestment.

Kocherlakota and Yi (1996) presented evidence supporting endogenous growth models using time series data for the United States and the United Kingdom for over a period of 100 years. They investigated the relationship between shocks to public capital and subsequent changes in GDP. This was mainly based on empirical evidence from UK and US. They incorporated in various policy variables that raised the economic variables including the infrastructure. They found out that policy variables do not improve economic growth rate permanently.

World Bank, (1994) reported that there was a close relationship between road infrastructure and economic growth in Asia and in many case studies, such as those on the direct and indirect economic impact of infrastructure in farming sector in India. In the case of China, the coverage of intercity transport networks is one of the thinnest in the world. China’s transportation investments amounted to only 1.3 percent of GNP annually during 1981-90, a period of rapid growth in transportation demand. Since the onset of China’s open door policy in 1979, economic growth averaging 9 percent a year has resulted in an unprecedented expansion in intercity traffic with growth averaging 8 percent a year for freight and 12 percent a year for passengers.

Easterly and Rebelo, (1993) verified whether or not changes in the level of various policy variables permanently increased the economic growth rate, and clarified whether or not investments related to information and telecommunications raised the economic growth rate.

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They found that public infrastructure investment is a large fraction of both total and public investment, and infrastructure in transportation and communication is consistently correlated with economic growth. The rate of return in these sectors is 63 percent and elasticity of change in output with respect to a 1 percent change in the level of infrastructure is 0.16.

Considering Aschauer, (1989a, 1989b, 1989c), there has been a reappearance in the debate about the productivity effects of infrastructure towards economic performance. This debate is reviewed in the World Bank’s World Development Report (1994) which finds a large range of empirical results on the importance of infrastructure for economic growth, with estimates ranging from no effect, to rates of return in excess of 100 percent per annum. The results are far tempting especially for a growing economy like Uganda that has consistently expanded its expenditure on provision of road infrastructure but still gets disappointing contributions.

According to Haynes (1991), the role of infrastructure in regional economic development has often been examined since the Second World War. Definitions of infrastructure vary widely from economic and social overhead capital to the general provision of public goods. The concept of social infrastructure has been traditionally linked to education, health, social and recreational support and partly to environmental concerns. This human capital perspective is augmented by a direct orientation to the welfare of human re-sources and its consequences, which is assumed to increase labour productivity. Economic infrastructure is also viewed as a complement to productivity. The availability and quality of reliable economic infrastructure appear to influence economic productivity and social welfare in at least two basic ways. These are: Direct

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contribution to output (augmenting the productivity of private in-puts) and enhancing a region's amenities and influence location decision of private industries.

Seitz and Licht (1992) examined the relationship between infrastructure and public capital for 11 federal states in (West) Germany for the period 1970-1988 using a trans-log cost function. Their study concluded that public capital (infrastructure) formation encourages private investment. The study also empirically demonstrated that a distinction between investment infrastructures versus equipment is of critical importance in the context of private capital because the effects on the former are of far greater importance than the effects on the latter.

Greene and Villanueva (1991) analyzed private investment data for 23 developing countries over the period 1975-87 and showed that the ratio of public sector investment to GDP had a significant positive effect on the ratio of private sector investment to GDP. Panel data for nine countries Barbados, Costa Rica, the Dominican Republic, Guatemala, Haiti, and Honduras. Mexico, Panama, and Trinidad and Tobago - for the period 1971-79 showed that the level of public sector investment has a positive effect on private investment (Blejer and Khan, 1985). Specifically, the study concluded that infrastructure investments exert a positive influence on real private investment.

Many researchers have attempted to examine the effect of government expenditure on economic growth. For instance, Laudau, (1983) examined the effect of government (consumption) expenditure on economic growth for a sample of 96 countries, and discovered a negative effect of government expenditure on growth of real output. Komain and Brahmasrene, (2007)

27

examined the association between government expenditures and economic growth in Thailand, by employing the Granger causality test. The results revealed that government expenditures and economic growth are not co-integrated. More so, the results indicated a unidirectional relationship, as causality runs from government expenditures to growth. Contrary to Laudau, (1983), the results also illustrated a significant positive effect of government spending on economic growth.

Olugbenga and Owoye, (2007) investigated the relationships between government expenditure and economic growth for a group of 30 OECD countries during the period 1970-2005. The regression results showed the existence of a long-run relationship between government expenditure and economic growth. In addition, the authors observed a unidirectional causality from government expenditure to economic growth for 16 out of the countries, thus supporting the Keynesian hypothesis. They also found out that, causality run from economic growth to government expenditure in 10 out of the countries, confirming the Wagner’s law. Finally, the authors found the existence of feedback relationship between government expenditure and economic growth for a group of four countries.

Liu Chih-HL, Hsu, Younis, (2008), examined the causal relationship between GDP and public

expenditure for the US data during the period 1947-2002. The causality results revealed that total government expenditure causes growth of GDP. On the other hand, growth of GDP does not cause expansion of government expenditure. Moreover, the estimation results indicated that public expenditure raises the US economic growth. The authors concluded that, judging from the causality test Keynesian hypothesis exerts more influence than the Wagner’s law in US.

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Loizides and Vamvoukas, (2005), employed the trivariate causality test to examine the relationship

between government expenditure and economic growth, using data set on Greece, United Kingdom and Ireland. The authors found that government expenditure granger causes economic growth in all the countries they studied. The finding was true for Ireland and the United Kingdom both in the long run and short run. The results also indicated that economic growth granger causes public expenditure for Greece and United Kingdom, when inflation is included

Donald and Shuanglin (1993), used a semi-log model and a linear trend model to examine the growth rate of selected macroeconomic variables in the Indian economy. Granger Causality Test has been employed to test the direction of causality between national income, public expenditure and its various selected components. The results showed that the public expenditure had registered a higher growth rate than the national income. Among the various components of public expenditure, debt obligations in the form of interest payments registered a higher growth as compared to others. The Granger Causality Tests confirm a bi-directional relationship between national income and public expenditure and economic services. However, the causality between national income and India’s expenditure on social and defense services were found to be independent and finally, a unidirectional relationship between GDP and interest payments was also established. Kalam and Aziz (2008), investigated, the empirical validity of ‘Wagner's law,' the relationship between ‘social progress' and ‘growth of state activity' in an economy, using Bangladesh data from 1976 to 2007 in a bivariate as well as a trivariate framework incorporating ‘population size' as a third variable. The estimated results provided evidence in favour of Wagner's law for Bangladesh in both the short-run and long run. There was a long-run cointergration relation 29

among real government expenditure, real GDP and the size of population. The government expenditure was positively tied with the real GDP (1.14), per capita GDP (1.51) and population size (0.21). Both the real GDP and GDP per capita Granger caused total government expenditure to change. Population size also came up as a significant stimulus for public spending to grow in both the long-run and short-run.

Ziramba (2009) tested Wagner's law by analyzing the causal relationships between real government expenditure and real income for South Africa for the period 1960-2006. The paper tested the long-run relationship between the two variables using the autoregressive distributive lag approach to procedure developed by Toda and Yamamoto, which uses a vector auto regression model to test for the causal link between the two. Evidence of cointergration was sufficient to establish a long-run relationship between government expenditure and income. However, support for Wagner's law would require unidirectional causality from income to government expenditure. Therefore, co-integration was seen as a necessary condition for Wagner's law, but not sufficient. The research did find a long-run relationship between real per capita government expenditure and real per capita income. Results for the short-run causality found bidirectional causality. On the basis of empirical results in the paper, one may tentatively conclude that Wagner's law finds no support in South Africa.

Guisan and Aguayo (2005), analyzed the causality between real values of expenditure on Research and Development,, and Gross Domestic Product, (GDP), in 15 countries of European Union and the United States for 1993-2003, they employed the Granger causality test and an interdependent dynamic model. They found out the that RD expenditure had lower averages per inhabitant of many European countries, in comparison with the US, to them this played an

30

important role to explain lower levels of real GDP per inhabitant and lower rates of Employment in the European counties studied. They concluded that the counties should foster support to research in several European countries in all fields in order to bring about significant growth of their economies.

Tatum (1993) addressed the question of whether public investment effects economic growth or whether economic growth affects public investment by analyzing lagged data from both perspectives.

He concluded that there is essentially no impact of infrastructure capital on

productivity. If anything, reductions in economic growth lead to reductions in infrastructure capital. In contrast, Otto and Voss (1996, 1998) analyzed quarterly data from 1959 through 1992 for the Australian economy and concluded that public capital does contribute significantly to productivity. Moreover, they found no evidence of causality from private production to public capital stocks.

Holtz-Eakin (1994), also studied this question.

He analyzed data for the

contiguous 48 states, which covered 1969 to 1986 and found no relationship between productivity and public sector capital from either the state level or regional level. However, his study, just as those mentioned above (Aschauer, 1989; Munnell, 1990; Lynde and Richmond, 1993; Tatum, 1993; Otto and Voss 1996), also used aggregate measures of government capital.

Prabia De (2006) used recent literature to examine the importance of transaction costs in explaining trade, access to markets, and regional cooperation under globalization. In his study, he argues that in most Asian countries, transaction costs are a greater barrier to trade integration than import tariffs. By estimating a structural model of economic geography using cross‐country data on income, infrastructure, transaction costs and trade of selected Asian economies, the results showed that the transaction costs are statistically significant and important determinants 31

in explaining variation in trade in Asia. Calderon and Serven (2004) showed that in Peru, for instance, if infrastructure was improved in Costa Rica, it would increase the income share of the poorest quartile of the population from 5.6 percent to 7.5 percent in the country.

Cooray, (2009), used an econometric model that takes government expenditure and quality of governance into consideration, in a cross-sectional study that includes 71 countries. The results revealed that both the size and quality of the government expenditure are associated with economic growth.

Abu-Bader, Abu-Qarn AS (2003), employed multivariate co-integration and variance decomposition approach to examine the causal relationship between government expenditures and economic growth for Egypt, Israel, and Syria. In the bivariate framework, the authors observed a bi-directional (feedback) and long run negative relationships between government expenditure and economic growth. Moreover, the causality test within the trivariate framework (that included share of government civilian expenditures in GDP, military burden, and economic growth) illustrated that military burden has a negative impact on economic growth in all the countries. Furthermore, civilian government expenditures have positive effect on economic growth for both Israel and Egypt.

Folster and Henrekson, (2001) studied the relationship between government expenditure and economic growth for a sample of wealthy countries for 1970-95 period, using various econometric approaches. The authors submitted that more meaningful (robust) results are generated, as econometric problems are addressed. In India, Ranjan and Sharma, (2008)

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examined the effect of government development expenditure on economic growth during the period 1950-2007. The authors discovered a significant positive impact of government expenditure on economic growth. They also reported the existence of cointergration among the variables. Al-Yousif, (2000) indicated that government expenditure has a positive relationship with economic growth in Saudi Arabia. On his part, Ram, (1986), studied the linkage between government expenditure and economic growth for a group of 115 countries during the period 1950-1980. The author used both cross sectional and time series data in his analysis; he confirmed a positive influence of government expenditure on economic growth. Such studies motivated the researcher to establish the link between economic growth and government expenditure on roads for Uganda’s case for effective policy formulation and implementation.

Addus (1989) examined road transportation in 15 African countries for 1982-83. He presented an overview of facilities which when compared to the U.S. highlighted the severity of an inadequate road network and a severe shortage of vehicles.

However, no linkage of

improvements to economic growth was provided. He explored the reasons for the inadequate road conditions indicating the problems presented by climate, terrain, difficult roadway engineering, high construction costs, and circuitous routes.

Additionally, the problems of

political instability were noted. Not only do regional conflicts and civil wars divert important resources (financial and human) away from road construction, they have also resulted in the destruction of existing bridges and roads. This study is an improvement of Addus’ study since the researcher in incorporating in a long time scope and concentrates on one country consequently avoiding variations brought about by aggregating countries.

This is because

different countries have different political, climatic and administration situations.

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Feltenstein and Ha (1995) studied the relationship between the public infrastructure and private output in sixty-three sectors in Mexico, aggregated into sixteen groups over the years 1970-1990. The dependent measure was sectoral gross domestic product. Independent measures included wages, the cost of capital, and the nominal values of the stocks of three types of infrastructure: electricity, transport, and communications. Public expenditures on infrastructure in electricity and communications tend to reduce sectoral production costs whereas expenditures on transportation infrastructure increase those costs. They note that they can offer, “No good explanation for the counterintuitive results for transport.

Devarajan, Swaroop and Zou (1996) drew analytical conclusions that road infrastructure negatively affected economic growth and attributed this to the tendency of developing countries over supplying roads. This study is an improvement on the other studies on economic growthgovernment expenditure relationship in Uganda for a couple of reasons which include; consideration of government expenditure on road infrastructure reflected by coverage in kilometers to be a very important variable that affects economic growth. Secondly, this study employs a methodology that captures causality as well as cointegration that many studies have not incorporated. Consequently, the findings reveal the true effect of the road infrastructure on the economy as opposed to the regularly used CBA which mainly weighs costs against benefits and in much situations neglect to incorporate the environment aspect in its analysis due to the hardships of correctly attaching a right estimate (Glaister, 1999).

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In conclusion, it’s important to note that the various studies reviewed, used different methodologies and consequently came up with relatively divergent results, for example, Laudau, (1983) and Komain and Brahmasrene, (2007). Similarly, Donald and Shuanglin (1993) used a semi-log model on government expenditure and economic growth and established a bidirectional causality as opposed to Komain and Brahmasrene, (2007) who established unidirectional causality.

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CHAPTER FOUR METHODOLOGY

4.0 Introduction This chapter presents the methodology the researcher used in the study of road infrastructure and economic growth. It includes the data type, source and sample selection procedure that were used to show the methods of data collection, data processing and analysis 4.1 Data type and source The study used secondary data. Secondary data entailed the already collected data on the relevant variables specified in the model that were acquired from World development indicators (World Bank data base) that is data for labour, gross domestic product and other direct physical capital stock (acquired by subtracting the expenditure on roads from the gross capital stock). The data on road coverage both paved and unpaved were obtained from various key economic indicators from MWHC as well as from statistical abstracts compiled by the UNRA. World Bank provides data on gross capital stock which includes the road sector expenditure, in order to obtain the required data for other direct physical stock; the study reduced the gross capital stock by the road expenditure data as shown in the appendix 2. 4.2 Model specification There is growing demand for transport infrastructures and the scarcity of the public resources to match it, the government has been under growing pressure to establish priorities among different investments. In order to do this the most commonly used method is the Cost- Benefit Analysis which has an advantage of quantifying in monetary terms different advantages and disadvantages of various projects (i.e. costs and benefits), however the method has a weakness of failing to 36

incorporate in the environmental effect of the projects undertaken (Glaister, 1999). Secondly, the CBA does not adequately tell whether the road infrastructure is at its optimal level to bring about simultaneous economic growth. As such there was need for a broader framework of analysis to cover the general road network of the country rather than the CBA which is mainly specific for a particular project, consequently the application of a Cobb-Douglas production function that optimizes government spending and also satisfies the natural condition for production efficiency according to (Barro, 1990). The production function employed is as below;



Yt  At K t Lt



…………………………………………..………..………….(4.1)

Where A is the total factor productivity K is expenditure on capital stock L is the labour and Y is the national income It’s worth noting that population has been included as a proxy for labour in its aggregate form due to the fact that it’s difficult to precisely determine the productive labour and secondly, whether directly involved in production or not still that population has an effect on overall production whether positive or not. It’s also worth noting that the study aimed at assessing the contribution of road infrastructure (that is to say it’s not directly involved in the production process) on the GDP of the country as such the study disaggregated the capital stock variable in order to be specific, that is having two forms of capital stock one being directly involved in the production process and the other playing an indirect role in production Consequently the new production function becomes a simple model exhibiting constant returns to scale which was adapted by Barro (1990) as specified below. 37





Yt  At H t Gt Lt

1  

………………..…………………..….…… (4.2)

Where; Y is the national income A is the total factor productivity H is expenditure on other types of physical capital G is expenditure on road infrastructure L is the population

Supporting Barro’s model, Morrison and Schwartz (1996) provided evidence that infrastructure provision improves the productivity of both the private sector and the public sector and that it also increase output. Introducing in natural logarithm yields expression (4.3); it is worth noting that after the introduction of natural log the respective coefficients represent elasticities. That is the degree of responsiveness of a unit change in the independent variable onto the dependent variable.

ln Yt  A0   ln H t   ln Gt  1     ln Lt  et

……………………….. (4.3)

Where; α is elasticity of other direct physical stock variable β is the elasticity of the road expenditure variable (1- α - β) is the elasticity of labour variable on output e is the error term Currently the road infrastructure is broadly categorized into two that is paved and unpaved as such the study further disaggregated the road expenditure variable into two sub components that is expenditure on paved and un paved. Incorporating the sub categories into equation (4.3) yields equation (4.4) below. 38

ln Yt   0  1 ln H t   2 ln M t   3 ln N t   4 ln Lt  et ……………….……… (4.4) Where; M represents expenditure on unpaved roads N expenditure on paved roads The preceding expression is a double logarithmic model which states that national income depends on direct capital expenditure, road expenditure and labour. It is important to note that the coefficients in this model represent elasticities as stated earlier. However, due to the problem of acquiring data on road expenditure and the inflationary nature of this data, the study used road coverage in terms of kilometers as a proxy for road expenditure. This because what is spent on a particular road is to increase on the length in way of kilometers implying that the longer the length of a particular road implies increased spending from government on that particular road. Secondly, there is a high variability in terms of expenditure in different regions arising from different costs as such the study used coverage to minimize on this variability. In addition to that a kilometer of a road in an area could serve the same purpose as the same kilometer in another region as opposed to say same expenditure allocation in two different areas.

Similarly important to note is that expenditure on the road today has no impact on the current GDP but rather on next period’s GDP; consequently to measure the contribution of road infrastructure on the economy, the study incorporated in lagged values of the respective log variables as shown in equation (4.5) below representing road network in terms of coverage. Lags were incorporated in the study to cater for the period spent from the initial expenditure on the road and the time when it’s finally available for use. This differs between the paved and unpaved with the former taking a longer period of construction than the latter.

39

yt  o  1ht  2mt  3mt i   4nt  5nt i  6lt  et .. (4.5) Due to the non stationarity nature of time series data, the study transformed all the variables into growth rates and this does not only make variables stationary but also makes the regression results non spurious (Gujarati, 2003 pp822). Thus variables below indicate growth rate of each of the variable.

yt *  o  1ht *  2mt * 3mt i *  4nt * 5nt i * 6l * et

……. (4.6)

Where * are included to mean growth rate of variables. i represents the respective periods being lagged Due to the reforms that took place, the study investigated any effect caused by such reforms. For example trade liberalization and foreign exchange reform in 1987 and 1988 respectively. The reforms are assumed to have an impact on the variables of concern through their effect on the flow of goods in and out of the country. The study mainly investigated the change in trend of growth rate due to such reforms and as a result the above equation was modified to cater for such reforms by including a dummy variable to examine the change in the trend of growth. This yields equation (4.7).

yt *   0  1ht *  2 mt * 3mt i *  4 nt *  4 nt i * 5lt *  6 dl   7 dj  et Where dl is dummy variables trade liberalization dj is a dummy for foreign exchange reform

40

…. (4.7)

4.4 Data analysis This was done using the statistical package of Eviews that is, testing for the unit roots of the time series data that were used. The unit root test was intended to determine the stationarity status of the series data. Due to the common characteristic of time series data being non stationary at level (Cheung and Lai, (1999) and Pedroni, (1998a)), the test helped evaluate the stationarity status. As such equation (4.7) was differenced and this led to obtaining of stationary data series. It is also important to note that after differencing the logarithmic model the sets obtained represented growth rates of individual variables. The study also employed a granger causality test to determine what variable leads to the other, co integration as well as an OLS regression analysis in an attempt to test the hypotheses. 4.3.1 Unit root test According to Cheung and Lai ,(1999)and Pedroni,(1998a) there are considerable evidence for presence of unit roots in GDP time series data as such there was need to make the data stationary. There are various statistical ways of testing for unit roots of time series data; specifically the researcher employed the Dickey-Fuller (DF) and Augmented Dickey-Fuller (ADF) test. Since it is a common characteristic of time series data to be integrated of order one (Gujarati, 2003 pp822). The study simply differenced the series to make them stationary consequently easy to work with. The differencing was done independently for the respective data series 4.3.2 Co-integration and the ECM It’s important to note that expenditure on road infrastructure comes as a result of diversion of funds from directly productive capital investment, as such there was need to determine the

41

growth maximizing level of road coverage to bring about sustainable growth in the economy. (Granger,1986) states it that’ a test for cointergration can be thought of as a pre-test to avoid ‘spurious’ situations, from the economic point of view we aim at exploring whether variables have an equilibrium or a long run relationship (Gujarati, 2003). In an attempt to determine whether there exists equilibrium between road infrastructure and national income the researcher found out that the respective infrastructure data series were integrated of the same order with the income data series. Secondly apart from being non stationary the researcher also evaluated the residuals obtained after regressing (infrastructure and income) the variables and ascertained that they were stationary. This was accompanied with likelihood ratio test for confirmation.

yt   0  1mt   2 nt  ut …………………………..……...……………….(4.8.1) The expression assessed is written as;

ut  yt  1mt   2 nt  0

………..………………………………………... (4.8.2)

Where; the parameters are defined as before. After determining that the variables were co-integrated (had a long run relationship) it was crucial to establish the short run behavior of the variables as well. To do this the researcher used the ECM first developed by Sargan, (1984) and later popularized by Engel and Granger, ( 1987) in an important theorem known as Granger representation theorem. According to the theorem ut in equation (viii) becomes the equilibrium error. The ECM is represented as in the proceeding

equation.

yt  0  1mt  2nt  3h  4l  5ut 1   t …………… (4.9) Where;

42

 Denotes the first difference operator of the respective variable  t is the random error term ut 1 is the one period lag value of the equilibrium error

The ECM above states that differenced natural logarithm income depends on the specific differenced log road infrastructure coverage and also the lagged value of the equilibrium error term. Ho; 5  0 , that is GDP growth instantaneously adjusts with changes in road coverage infrastructure in the short run that is the level road infrastructural coverage is in line with the growth rates of the country. 4.3.3 Granger causality test According to Gary, (2000), time does not run backward implying that a precedent event has an effect on the successive event and this is a common characteristic of most time series data. As a result of this fact it was imperative to carry out a causality test. Policy wise we needed to know which variable caused what such that the emphasis is targeted towards the causing variable to influence the other variable. In this regard the researcher carried out a granger causality test between income and the respective road infrastructure variable. The fact that the research incorporated in lags to determine causality, the Schwarz and Akaike information criterion were used to determine the lag length for the respective data series. The following Schwarz formula was used to guide in lag determination P max= 12(n/100) ^0.25 where p is the number of lags to be incorporated and the number of observations. The expression below represents the causality equation. n

n

i 1

i 1

yt  mt 1  yt 1  t  dl  dj …………………..………… (4.10.1) 43

H 0 :   0 This null hypothesis was examined using the F- statistic test to determine whether lagged values of the unpaved road infrastructure had links to (caused) national income. A similar causality test was examined for the case of the paved road as in the expression below. n

n

i 1

i 1

yt   nt 1   yt 1  t  dl  dj ………………………..……. (4.10.2) H 0 :   0 This null hypothesis was examined using the F- statistic test to determine whether lagged values of the paved road infrastructure had links to (caused) national income. Similarly a counter regression was analyzed to determine the type of causation that is evaluating national income on the unpaved road infrastructure as represented in the equation (xiii) below.

n

n

i 1

i 1

mt  mt 1  yt 1  t  dl  dj

……..…………………...…. (4.11.1)

dl Is dummy for financial liberalization

H 0 :   0 n

n

i 1

i 1

nt   nt 1   yt 1   t  dl  dj ...................................... (4.11.2)

H0 :   0 Similarly for the above expressions xiii and xiv for national income to cause the respective infrastructure variables the null hypotheses are rejected implying that the coefficients are 44

statistically not equal to zero. Due to the reforms that took place, the study also incorporated in dummy variables to cater for such reforms. In this case, the study examined if the trend of growth was also influenced by these reforms.

45

CHAPTER FIVE PRESENTATION AND DISCUSSION OF RESULTS This chapter presents and discusses results on the impact of road expenditure on economic growth plus the direction of causality between the two. It also includes results from the unit root test and error correction model for establishing the long run relationship between economic growth rate and growth rate in road infrastructure. All the variables that were used in this chapter were transformed into logarithms and therefore, their first difference represents growth rates. 5.1 Descriptive Statistics Table 5.1 Shows the descriptive statistics of the percentage growth rates of the variables used in the study. % Growth rate of

% Growth rate

% Growth rate

% Growth

% Growth

direct capital stock

of unpaved roads

of Paved roads

rate of

rate of

population

GDP

Mean

25.453

3.709

2.148

3.3107

9.146

Median

12.165

0.085

2.090

3.359

5.0347

Maximum

245.639

125.849

4.808

3.645

62.828

Minimum

-71.544

-14.842

0.048

2.990

-41.814

Std .Dev.

52.571

22.973

1.509

0.181

23.684

Skewness

2.4155

5.067

-0.009

-0.099

0.634

Kurtosis

11.259

27.541

1.623

2.3097

3.665

Observation

31

31

31

31

31

Source; Own computations based on data from World development indicators (World Bank) and

Background to the Budget MFPED (various issues)

Unpaved road coverage has a mean growth rate of 3.7 percent with some periods registering a decrease of -15 percent up to a maximum of 126 percent. It can be noted that the deviation from 46

the mean growth rate is 23 percent implying variability in unpaved road coverage. The negative value may be attributed to the rain seasons which on many occasions wash away some roads and

Secondly, as a result of the gradual increase in the length of paved roads bringing about reduction in coverage of the Unpaved road. Paved road coverage has a mean growth rate of 2.1 percent with minimum road coverage of 0 percent up to a maximum of 4.8 percent. It can be noted that the deviation from the mean growth rate is 1.5 percent implying variability in paved road coverage. The positive skewness values, in the three variables indicate that there are higher chances of the variables increasing than falling. The negative skewness in the paved road growth and population are almost insignificant.

It can be noted that population growth rate has a mean value of 3.3percent with a maximum value of 3.6 percent and lowest value of 2.9 percent (this may be as a result of continued government effort to encourage the population through continued medical facilities and birth controls). Labour growth rate also has a standard deviation of 0.2 and this figure is relatively small compared to that of other forms of capital growth rate with a standard deviation of 52 percent and the rest of the variable. Growth in other direct physical capital has the largest standard deviation implying that it has more variability compared to the rest of the variables and also registered the highest growth rate.

5.2 Unit root test According to Cheung and Lai (1999) and Pedroni (1998a) there is considerable evidence for presence of unit roots in GDP time series data as such there was need to make the data

47

stationary. The study hence tested for unit root test in the process of cleaning data and this was mainly carried out to get the appropriate characteristic form of the data that was used. In most cases, most of the time series data is non-stationary and this was confirmed by the data that was used. ADF and DF tests were carried out to test for the presence of unit root in the data. None of the variables was stationary at level apart from population. All the variables that were used apart from population were stationary after first difference meaning that they were integrated of the same order one and also the numbers of lags were different for different variables. The computed ADF values or the tau statistic were more than the critical values in absolute sense meaning that they are stationary. This implies that working with the new differenced data set didn’t yield spurious results.

The appropriate number of lags were chosen basing on Akaike and Schwarz info criterion by the Schwarz formula of P

max=

12(n/100) ^0.25 and also considering the lowest AIC information

criteria. That is including the number of lags where Akaike info criterion is lowest. The major implication is that capital expenditure, gross domestic product and road coverage are not stationary however, their growth rates are stationary, since the study transformed the variables into logarithms and therefore, the first difference represents growth rate in these variables. Lags are mainly considered to reduce auto-correlation that may exist among variables over time. Table 5.2 unit root test results of the of the variables before any transformation Variable

Critical value at 1%

ADF computed

I(d)

Constant

Trend

GDP

-2.645

-2.756

I(1)

No

No

CAPITAL

-3.675

-4.611

I(1)

Yes

No

POPULATION

-4.295

-4.341

I(0)

Yes

Yes

48

UNPAVED ROAD

-2.645

-5.052

I(1)

No

No

PAVED ROADS

-3.675

-3.722

I(1)

Yes

No

Note constant and trend were considered at 5% level of significance 5.3 Cointegration test Following the unit root test of the variables presented in Table5.2 it was established that the respective logs of the variables were also all integrated of order one (I (1)), except for the labour variable which was I (0) that is though they were non stationary, their first differences are stationary. Although there was a high possibility of obtaining spurious regressions when working with non-stationary time series data sets first discovered by (Yule, 1926), it was quite tempting that regressing the log of national income against log of road coverage that is equation (4.8.1) could lead to residuals which were stationary. According to Granger and Newbold, (1974) the rule of thumb to suspect whether the estimated regression is spurious is when the coefficient of determination is more than the Durbin Watson (R2>d) from results.

The study tested for the stationarity of residuals and found out that they were stationary implying that though individual variables were not stationary the combination of the variables yielded stationary residuals that were stationary thus there exist a long run relationship between the road coverage and GDP variable. The researcher hence concluded that the variables are co integrated. This also implies shocks that happen in any of the variables can be traced in the long run. Testing for stationarity of the residuals is one of the simplest tests that can be carried out to test for the cointergration of variables and once the residuals are stationary then variables are co integrated. The results are presented in table 5.3.

49

Table 5.3 Residuals test for unit root from OLS estimation equation (4.8.1)

ADF Test Statistic

-2.456

1% Critical Value*

-2.642

5% Critical Value

-1.953

10% Critical Value

-1.622

From the Table 5.3, it is shown that the unit root test of the residual obtained after running equation (4.8.1) are stationary at 5 percent and to be exact at 2 percent, this is because the absolute value of the computed test result is higher than that of the critical value consequently we reject the null for presence of unit root as shown in the appendix attached. The results indicate that there is existence of a long run relationship from the cointegration test. This result is in line with findings of (Ziramba, 2009) who established a long run relationship between government expenditure and national income in South Africa. The study also conducted a Johansen cointegration test to compliment the residual test above. Whenever the likelihood ratios exceed the critical values at either 5 percent or 1 percent then the variables are cointegrated. In this case at 5 percent at most one cointegrating equation hypothesis is rejected consequently the alternative of at least one cointegrating equation is failed to be rejected and the results are presented in the Table 5.4. Table 5.4 Johansen Cointegration Test Series: Y M N H Warning: Critical values were derived assuming no exogenous series

Eigen value 0.867 0.574 0.249 0.0276

Likelihood Ratio

5 Percent Critical Value

1 Percent Critical Value

89.294 32.731 8.8266 0.7858

47.21 29.68 15.41 3.76

54.46 35.65 20.04 6.65

50

Hypothesized No. of CE(s) None ** At most 1 * At most 2 At most 3

*(**) denotes rejection of the hypothesis at 5% (1%) significance level L.R. test indicates 2 cointegrating equation(s) at 5% significance level.

From the results above the study establishes that there exists a long run relationship between economic growth and road infrastructure. This has been proven by both the residual test and the Johansen test. Due to this output there was need to investigate the behavior of the variables in the short run and this called for the ECM as in the proceeding section. 5.4 Error Correction Mechanism Table 5.5 Short run relationship Variable ∆ unpaved Road growth rate ∆ paved Road growth rate ∆ Capital growth rate DJ ECM(-1)

Coefficient 2.39 0.085228 0.236 -0.29 0.237

t-Statistic 2.056 0.495 3.029 -1.983 1.732

Prob. 0.050 0.626 0.006 0.058 0.000

Dependent variable change in growth rate of GDP

Following the cointegration test results, the study also conducted a short run relationship between growth rate in road coverage and growth rate in GDP in which the ECM was employed. After employing the ECM, changes in growth rates in unpaved road infrastructure was found to be significant in the short run. That is, a percentage change in the coverage of unpaved roads leads to a 2% increase in the GDP, this can be attributed to the fact that the rural areas where the biggest proportion of the population resides is engaged in agriculture and these areas are linked by unpaved roads consequently easing transportation of produce.

Paved road was not significant and this could be due to the fact that since we are talking about the short run, the period could be short to realize any contribution of the paved road since they normally need relatively longer period for completion. Among other short run determinants of growth rate in GDP included: direct physical capital with a positive effect. The labour variable 51

was found to be insignificant consequently dropped from the model, this may be as a result of the nature of the population with the biggest proportion of about 85 percent in 2010 (UBOS, 2010) residing in rural areas and engaged in subsistence agriculture which mainly has no great impact on GDP.

Secondly, since Uganda’s population growth is not accompanied with the improvement in labour it leads to retardation in GDP as a result of the majority being dependants. The lagged error term is also significant at 1 percent level of significance implying that the variation between the observed and the expected results don’t take up time to be equal. The gap between the estimated and the expected rate will not take time to be covered up. This instantaneous adjustment can be mainly attributed to employment opportunities that arise during the construction of roads especially in the ground breaking process for both the paved and unpaved. The structural breaks represented by the dummy variables are seen to have no impact on the growth rate in the short run. The short run constant is significant implying that there are other factors that are significant though not included in the model such factors may include the political environment. 5.5 Impact of road coverage on economic growth rate. Table 5.6 OLS analysis without the dummy variables Variable Coefficient Std. Error C 13.797** 4.475 unpaved Road 0.266 0.142 paved road 2.189 1.264 Capital stock 0.488** 0.075 labour -1.221 0.781 R-squared 0.927274 The values with asterisks are statistically significant at 1% level of significance.

52

t-Statistic

3.085 1.874 1.733 6.504 -1.564

The study investigated the effect of road infrastructure provision on economic growth. Table 5.6 presents the findings obtained before the dummy variables for liberalization and currency reform of 1987/88 were incorporated. It can be noted that R2 is 0.93 indicating that the variables explain about 93 percent of the model as shown in the appendix 2. However, this high coefficient of determination may raise suspicions of multicollinearity but since there was transformations to natural logarithms as well as differencing to make the variables stationary the high value of the R2 does not affect the interpretation of the results. Expenditure on other types of directly productive capital, for example machinery, has a significant positive effect on current GDP. Similarly, coverage unpaved road infrastructure has a positive and statistically significant effect on the GDP. It is important to note that the lags to the variables of interest were all statistically insignificant and as such were excluded from the model. This implies that the most contributing factor to growth rate in GDP is the current expenditure on directly productive capital stock and road infrastructure. The major reason for the significancy of capital stock is that capital stock involves a number of components which cut across different sectors for example the direct investment in energy; other means of transport by government as well as direct investment by the private individuals greatly have an effect on GDP. Unpaved roads do have a positive effect on GDP due to fact that they create a link between the production centres (especially for agricultural production) and the market outlets. The effect of paved roads on GDP, though positive is not statistically significant. This is perhaps because paved roads only account for 16 percent of total national road network in the country. The effect of investment in roads on GDP is more significant for unpaved roads.

53

These results are partly in agreement with results of other scholars for example Olugbenga and Owoye (2007) who established a long term relation between government expenditure on infrastructure on economic growth. Also in line with (Fan et al, 2007) who covered road expenditure amongst different regions in Uganda and found out that expenditure on marrum and tarmac were not significant at all however expenditure on feeder roads played a vital role on growth and poverty reduction using the cost benefit analysis approach. The results are however contrary to (Ziramba, 2009), (Laudau, 1983) and (Devarajan, Swaroop and Zou, 1996) also found a negative effect of government expenditure on roads towards economic growth.

The study also catered for structural breaks by incorporating dummies for trade liberalization and foreign exchange reform in1987 and1988, respectively. This was in order to see how such reforms influenced both road expenditure and growth; the results indicate that such reforms have not impacted much on growth. These results are presented as in the table below. Table 5.7 OLS analysis with structural shocks Variable

Coefficient

Std. Error

t-Statistic

17.313**

5.512

3.141

paved road

2.828*

1.392

2.031

Unpaved road

0.377*

0.185

2.042

Capital stock

0.486**

0.076

6.395

Population

-1.786

0.920

-1.941

DJ

0.014

0.015

0.974

DL

0.111

0.149

0.741

C

The values with asterisks are statistically significant at 1% and 5% level of significance.

54

These results when the dummy for foreign exchange liberalization and trade reform were incorporated were compared with those without the dummies there were no significant changes in the coefficients. Still it was expenditure on other direct capital stock as well as road infrastructure that could explain growth rate in GDP as well as road infrastructure. 5.6 Estimation of the direction of causality between road coverage and GDP. Table 5.8 presents the results for Granger causality test. Pairwise Granger Causality Tests Sample: 1980 2010 Lags: 1 Null Hypothesis: Y does not Granger Cause N

F-Statistic 1.79990

Probability 0.19090

N does not Granger Cause Y

1.38455

0.24960

N(-1) does not Granger Cause Y

1.38455

0.24960

Y does not Granger Cause N(-1)

1.79990

0.19090

Y does not Granger Cause M

3.05835

0.09169

M does not Granger Cause Y

2.26657

0.14380

M(-1) does not Granger Cause Y

2.26657

0.14380

Y does not Granger Cause M(-1)

3.05835

0.09169

The study investigated the direction of causality between the growth rates of the variables. When we look at GDP not granger causing the paved road variable both in current and previous period we reject the null consequently establish a unidirectional causality between GDP and road coverage on current unpaved road as well as on lagged unpaved road infrastructure. That is causality moves from GDP to the unpaved road variable but not the reverse. From Table 5.8 we can see that we reject the null that expenditure on GDP does not granger cause unpaved road at 9 percent and fail to reject the alternative. Similarly we reject the null for GDP granger causing lagged unpaved road coverage at 9 percent and fail to reject the alternative this result is in line 55

with that of Kalam and Aziz, (2008). These results are also partly in agreement with Olugbenga and Owoye, (2007) who established a unidirectional causality from GDP to government expenditure thus confirming the Keynesian hypothesis. This is due to the fact that as national income increases government automatically realizes the need to increase on accessibility of those areas which could have led to the increase in income. Secondly, the construction of unpaved roads is relatively cheap, may explain why the country has more coverage than the paved road. They are however, in disagreement with Liu Chih-HL, Hsu, Younis, (2008), and Donald, Shuanglin (1993) who established a unidirectional causality from total government expenditure to GDP.This may be a case especially looking infrastructure in terms of the amounts rather than the coverage on ground in terms of kilometers.

56

CHAPTER SIX CONCLUSIONS, RECOMMENDATIONS AND AREAS FOR FURTHER RESEARCH 6.1. Introduction This chapter presents a summary of the findings and draws conclusions on effect of road infrastructure on economic growth based on the findings. It also includes policy recommendations and areas for further research. 6.2. Conclusion This study focused on the effect of government expenditure on road infrastructure (both paved and unpaved) would have on economic growth of the country. It was established that in the long run a percentage increase in both paved and unpaved road had effects on the economic growth. Short run analysis too established a positive effect from road infrastructure to economic growth. This helped to test the hypothesis which confirmed that government expenditure on road infrastructure would accelerate the growth of the economy and this is strengthened by the positive causal relationship. It is however, important to note that the study only concentrated on national roads and left out district roads due to the difficulty in obtaining data. Road transport remains the major mode facilitating movement of goods and people across the country to accelerate economic and business activities given the fact that Uganda is land locked. Similarly important to note is that the other capital stock variable has a very high significant value to GDP. 6.3 Recommendations Arising from the conclusion are the following recommendations:

57

From the findings, the study recommends for increased construction and regular maintenance of the unpaved roads. This has an advantage in way that the paved roads have a long life span compared to the unpaved roads. It will also help in providing increased access of rural areas to the various markets consequently stimulating the agricultural sector and also will increase the country’s competitiveness especially in the East African region through the reduced transaction costs brought about by increased access across borders. Putting much emphasis on the paved road has a great advantage in that the road can be used throughout the year as opposed to the unpaved that become dusty during the dry season which impact health hazards to the populace and impassable during heavy rains seasons. Following the granger causality test its clear that unpaved road which connects most rural areas should be increased in terms of length due to the multiplier effect of the unpaved roads, brought about by boosting up tourism, increasing access to medical facilities, as well as linking up markets to farmland. Similarly due to the long duration periods of paved roads the government needs to increase its coverage in order to sustain its economic growth.

Directly productive capital has been found out to have a positive effect on GDP as such there is need for both government and the private sector to increase their investment. On the side of government, it needs to concentrate on investments which require lump sum capital with low rates of returns for example in the energy sector ,railway reconstruction to supplement road transport as well as provide a conducive investment atmosphere to attract foreign investors. This may be in form of ensuring security in all parts of the country, tax incentives as well as availing relatively affordable investment loans to investors

58

6.4. Areas for further research This study examined the effect of the infrastructure provision to the economic growth of Uganda and established a positive relation between paved road and economic growth. It was also established that, the country has low supply of road infrastructure to bring about sustainable growth of the economy. It is also important to note that the study mainly concentrated on national roads leaving out district and local council roads, future studies are therefore encouraged to find out the effect of all roads on the economic performance of the country.

59

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Gramlich E. M. (1994), “Infrastructure Investment: A Review Essay,” Journal of Economic Literature, Vol. XXXII, pp 1176-1196. Granger.C.W (1986), “Development in the Study of Co-Integrated Economic Variables,” Oxford Bulletin of Economics and Statistics,vol.48,p.226 Granger .C.W and Newbold. P (1974) “Spurious Regression in Econometrics,” Journal of Econometrics,vol.2 pp111-120 Greene, J and D Villanueva (1991): 'Private Investment in Developing Countries', IMF Staff Papers”, vol 38, no 1, pp 33-58. Government of India (various years): Annual Survey of Industries, Central Statistical Organization, New Delhi Gujarati. D.N (2003), “Basic Econometrics” 4th ed., McGraw Hill companies,Inc pp.822 Guisan and Aguayo (2005), ‘Employment, Development and Research Expenditure in the Europen Union; Analysis of Causality and comparison with The United State,19932003’. International Journal of Applied Econometrics and Quantitative Studies Vol.2-2 (2005) Hanan J.G, (1998), “Access to Markets and the Benefits of Rural Roads,”Policy Research Working paper series 2028, The World Bank. Haynes, K E (1991): “The Role of Infrastructure in Regional System Dynamics”, Residential Address Western Regional Science Association, Monterrey, California, February 26. Hirshman Albert (1958), “The Strategy of Economic Development,” Westview Press.Il Holtz-Eakin, Douglas (1994), “Public Sector Capital and the Productivity Puzzle,” The Review of Economics and Statistics, Vol. LXXVI, no. 1 (February), pp. 12-21. J.D.Sargan (1984).”Wages and Prices in the united Kingdom: A study in Econometric Methodology.” in K.F and D.F Hendry,eds Basil Blackwell Oxford, U.K Kazushi Ohkawa and Henry Rosovsky (1973),”Japanese Economic Growth,” Stanford University Press. Kessides, C. (1993). “The Contributions of Infrastructure to Economic Development”: A Review of Experience and Policy Implications. World Bank Discussion Papers No. 213. The World Bank. Komain J, Brahmasrene T, (2007). “The Relationship Between Government Expenditures

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Sanchez-Robles B. (1998), “Infrastructure Investment and Growth: Some Empirical Evidence,”Contemporary Economic Policy, Vol. XVI, pp 98-108. Seitz. Helmut and G Licht (1992). “The Impact of the Provision of Public Infrastructures on

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Yoshino N and Nakahigashi M.(2000), ”Economic Effects of Infrastructure;” Japan’s Experience after World War Ⅱ, JBIC Review, No.3. pp.3-19. Yoshida T (2000), “Japan’s Experience in Infrastructure Development and Development Cooperation”, JIBC Review, No.3 Dec, pp.62-92. Yule G.U. (1926),”Why Do We Sometimes Get Nonsense Correlations Between Time Series? A study in Sampling and the Nature and of Time Series,” journal of the Royal Statisticsal Society, vol,89,1926 pp.1-64 http://mplatas.blogspot.com/2008/04/q-on-roads-in-uganda-with-dr-mwakali.html

65

APPENDIX 1 Table 5.3 Unit root test on residuals

ADF Test Statistic

-2.455671

1% Critical Value*

-2.6423

5% Critical Value

-1.9526

10% Critical Value

-1.6216

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(E) Method: Least Squares Date: 12/03/14 Time: 20:44 Sample(adjusted): 1981 2010 Included observations: 30 after adjusting endpoints Variable

Coefficient Std. Error

t-Statistic

Prob.

E(-1)

-0.274957

-2.455671

0.0203

R-squared

0.165117

Mean dependent var

0.016685

Adjusted R-squared

0.165117

S.D. dependent var

0.184172

S.E. of regression

0.168281

Akaike info criterion

-0.693594

Sum squared resid

0.821239

Schwarz criterion

-0.646888

Log likelihood

11.40391

Durbin-Watson stat

1.451445

0.111968

66

Table 5.4 Cointegration test Johansen Test Date: 5/02/15 Time: 22:06 Sample: 1980 2010 Included observations: 29 Series: Y M N H P Lags interval: 1 to 1 Data Trend:

None

None

Linear

Linear

Quadratic

Rank or No. of CEs

No Intercept No Trend

Intercept No Trend

Intercept No Trend

Intercept Trend

Intercept Trend

242.903910662 296.795481395 313.602307887 327.273070378 333.904171061 336.296013765

293.343884347 310.211363562 323.904313642 333.393326359 335.964179914 336.296013762

293.343884347 310.347252684 324.167968032 337.13849066 344.058186418 346.263005803

302.477283044 319.389392722 332.374826538 339.307290523 344.058187428 346.263005803

-15.0278559077 -17.9858952686 -18.3863660612 -18.5705565778 -18.2692531766 -17.6755871562

-18.1616471964 -18.6352664525 -18.889952665 -18.8547121627 -18.3423572354 -17.675587156

-18.1616471964 -18.5756725989 -18.7702046919 -18.9061028042 -18.6247025116 -18.0181383313

-18.4467091754 -18.9234063946 -19.129298382 -18.917744174 -18.555737064 -18.0181383313

Log Likelihood by Model and Rank 0 1 2 3 4 5

242.903910662 296.787243885 310.988635466 321.98353107 327.15718835 327.519119561 Akaike Information Criteria by Model and Rank

0 1 2 3 4 5

-15.0278559077 -18.0542926817 -18.3440438253 -18.4126573152 -18.0798060931 -17.4151116938

Schwarz Criteria by Model and Rank 0 1 2 3 4 5

-13.849152606 -16.4041080593 -16.2223778822 -15.8195100514 -15.0151775087 -13.8790017887

-13.849152606 -16.2885625142 -16.1704038539 -15.8359649178 -15.0160320639 -13.9037365907

-16.7472032343 -16.7493411698 -16.5325460616 -16.0258242386 -15.0419879906 -13.9037365905

-16.7472032343 -16.6425991841 -16.3185018243 -15.9357704839 -15.1357407385 -14.0105471054

-16.796524553 -16.8017404515 -16.5361511182 -15.8531155896 -15.0196271589 -14.0105471054

L.R. Test:

Rank = 3

Rank = 3

Rank = 2

Rank = 3

Rank = 1

Error correction mechanism Dependent Variable: DY Method: Least Squares Date: 05/18/15 Time: 08:36 Sample(adjusted): 1981 2010 Included observations: 30 after adjusting endpoints Variable

Coefficient

Std. Error

t-Statistic

Prob.

DM DN DH

2.399020 0.085228 0.235931

1.167119 0.172657 0.077890

2.055506 0.493626 3.029010

0.0504 0.6259 0.0056

67

DJ E(-1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

-0.290033 -0.118512 0.490057 0.408466 0.145205 0.527110 18.05501 1.991756

0.146231 0.109777

-1.983388 -1.079573

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

0.0584 0.2906 0.085190 0.188795 -0.870334 -0.636801 6.006271 0.001573

Table 5.6 Estimation of the effect of road expenditure on GDP Dependent Variable: Y Method: Least Squares Date: 12/05/14 Time: 18:55 Sample(adjusted): 1981 2010 Included observations: 30 after adjusting endpoints Variable

Coefficient

Std. Error

t-Statistic

Prob.

C M M(-1) N N(-1) H P DL DJ

16.73465 0.559685 1.748190 0.188570 0.008332 0.559514 -1.497450 -0.289051 -0.172370

5.911820 3.003389 2.894844 0.248825 0.335715 0.101449 0.919064 0.198444 0.194771

2.830711 0.186351 0.603898 0.757841 0.024819 5.515211 -1.629321 -1.456588 -0.884990

0.0100 0.8540 0.5524 0.4570 0.9804 0.0000 0.1182 0.1600 0.3862

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

0.927566 0.899972 0.184416 0.714196 13.49878 1.251922

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

22.42119 0.583092 -0.299919 0.120440 33.61473 0.000000

Table 5.7 OLS without dummy variables Dependent Variable: YP Method: Least Squares Date: 5/10/15 Time: 18:30 Sample: 1980 2010 Included observations: 31 Variable C NP MP HP LP R-squared

Coefficien t 13.79696 2.189841 0.265659 0.487544 -1.221501 0.928617

Std. Error

t-Statistic

Prob.

4.474504 3.083461 0.0048 1.263447 1.733227 0.0949 0.141758 1.874035 0.0722 0.074962 6.503851 0.0000 0.781114 -1.563794 0.1300 Mean dependent var 22.37348 68

Adjusted R-squared S.E. of regression

0.917635 0.181337

Sum squared resid

0.854960

Log likelihood Durbin-Watson stat

11.66857 1.019692

S.D. dependent var 0.631851 Akaike info criterion 0.430230 Schwarz criterion 0.198942 F-statistic 84.55811 Prob(F-statistic) 0.000000

Table 5.7 OLS relationship with structural reforms Dependent Variable: YP Method: Least Squares Date: 12/11/14 Time: 17:46 Sample: 1980 2010 Included observations: 31 Variable

Coefficient

Std. Error

t-Statistic

Prob.

C NP MP HP LP DJ DL

17.31335 2.827594 0.376989 0.485957 -1.786073 0.014437 0.111044

5.512060 1.391917 0.184585 0.075995 0.920177 0.014823 0.149791

3.140995 2.031438 2.042362 6.394601 -1.941010 0.973923 0.741327

0.0044 0.0534 0.0523 0.0000 0.0641 0.3398 0.4657

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

0.932662 0.915828 0.183315 0.806508 12.57286 1.221499

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

22.37348 0.631851 -0.359539 -0.035735 55.40219 0.000000

OLS estimation without the paved road variable Dependent Variable: YP Method: Least Squares Date: 05/17/15 Time: 11:53 Sample: 1980 2010 Included observations: 31 Variable

Coefficient

Std. Error

t-Statistic

Prob.

C MP HP LP DJ DL

9.850654 0.335639 0.514316 -0.055157 0.003156 0.066015

4.358848 0.194594 0.079236 0.368523 0.014578 0.157133

2.259922 1.724818 6.490962 -0.149669 0.216480 0.420124

0.0328 0.0969 0.0000 0.8822 0.8304 0.6780

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

0.921084 0.905301 0.194441 0.945185 10.11352 1.020648

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

OLS estimation without the unpaved road variable Dependent Variable: YP

69

22.37348 0.631851 -0.265388 0.012158 58.35842 0.000000

Method: Least Squares Date: 05/17/15 Time: 12:02 Sample: 1980 2010 Included observations: 31 Variable

Coefficient

Std. Error

t-Statistic

Prob.

C NP HP LP DJ DL

14.97259 2.514103 0.478975 -1.286283 0.008845 -0.075456

5.723344 1.468553 0.080589 0.941622 0.015465 0.126044

2.616056 1.711960 5.943400 -1.366029 0.571959 -0.598652

0.0149 0.0993 0.0000 0.1841 0.5725 0.5548

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

0.920959 0.905151 0.194595 0.946680 10.08901 0.926776

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

22.37348 0.631851 -0.263807 0.013739 58.25833 0.000000

Table 5.8 Causality Results Pairwise Granger Causality Tests Date: 12/06/14 Time: 06:39 Sample: 1980 2010 Lags: 1 Null Hypothesis: N does not Granger Cause M

Obs

F-Statistic

Probability

30

6.66183

0.01560

3.74001

0.06367

1.79990

0.19090

1.38455

0.24960

NA

NA

NA

NA

6.66183

0.01560

3.74001

0.06367

3.05835

0.09169

2.26657

0.14380

3.74001

0.06367

6.66183

0.01560

NA NA

NA NA

M does not Granger Cause N Y does not Granger Cause M

30

M does not Granger Cause Y M(-1) does not Granger Cause M

30

M does not Granger Cause M(-1) N(-1) does not Granger Cause M

30

M does not Granger Cause N(-1) Y does not Granger Cause N

30

N does not Granger Cause Y M(-1) does not Granger Cause N

30

N does not Granger Cause M(-1) N(-1) does not Granger Cause N N does not Granger Cause N(-1)

30

70

M(-1) does not Granger Cause Y

30

Y does not Granger Cause M(-1) N(-1) does not Granger Cause Y

30

Y does not Granger Cause N(-1) N(-1) does not Granger Cause M(-1)

30

M(-1) does not Granger Cause N(-1)

71

1.38455

0.24960

1.79990

0.19090

2.26657

0.14380

3.05835

0.09169

6.66183

0.01560

3.74001

0.06367

Appendix Two Data set used before transformations

Year Population GDP ($) 1980 12549779 1244610000 1981 12930714 1337300000 1982 13324390 2177500000 1983 13738114 2240333333 1984 14181630 3615647464 1985 14661481 3519695203 1986 15180721 3923244143 1987 15736176 6269521859 1988 16320417 6508931651 1989 16922648 5276480933 1990 17534839 4304399516 1991 18156095 3321729208 1992 18788440 2857457786 1993 19430461 3220439178 1994 20081152 3990430537 1995 20740726 5755818793 1996 21407693 6044585327 1997 22084527 6269333313 1998 22780451 6584972907 1999 23507800 5973058906 2000 24275641 6193246632 2001 25088033 5840503703 2002 25943441 6178563467 2003 26838428 6336696289 2004 27766986 7940362663 2005 28724869 9013834490 2006 29711397 9942597753 2007 30728747 12292813801 2008 31778799 14239026768 2009 32864328 14824492062 2010 33987213 16030996179

Direct K paved Gross K Road expenditure Roads expenditure($) expenditure($) ($) km 76600000 49480037 27119963 1632 75000000 25234819 49765181 1685 198000000 25991864 172008136 1733 166000000 17847746 148152254 1768 294307749 23764181 270543568 1828 307340435 11183967 296156467 1829 331393472 5390386 326003086 1902 609285669 3037275 606248394 1979 702466667 1044565 701422101 2050 587600948 388001519 199599430 2079 546834360 216191929 330642431 2080 503948345 131702097 372246248 2096 455448132 80578356 374869775 2097 490981281 70507245 420474035 2099 585868082 86476392 499391689 2105 714413688 110951220 603462468 2149 1219355425 111431229 1107924196 2220 1139674298 116958688 1022715609 2289 1083014215 115605368 967408847 2371 1172666368 103842606 1068823762 2485 1206681132 200882374 1005798758 2565 1127337495 184352788 942984707 2642 1249146299 195490492 1053655807 2700 1329701321 190454851 1139246470 2742 1599640840 206233425 1393407415 2751 2015055848 252714465 1762341382 2781 2100907695 269185117 1831722578 2837 2714628866 345229382 2369399483 2848 3271811368 390596872 2881214496 2968 3254397453 377741692 2876655761 2989 3761451618 379324081 3382127537 3112

72

unpaved km 8605 8541 8428 8367 8273 8261 8014 7937 6759 6413 6149 5958 5960 6253 6727 6797 6807 6895 6971 6973 6987 7002 7009 7023 7029 7031 7043 7052 7532 17011 16888

Data set after transformation to natural logarithms and growth rates

lnp

lng

Y %growth rate

N% growth

M% growth

Pop % growth rate

H% growth rate

Year

lny

lnN

LnM

1980

20.9421

16.3452

17.1158

7.3976

9.0601

-41.8141

1.0000

1.0000

3.0030

1.0000

1981

21.0139

16.3751

17.7228

7.4295

9.0526

7.4473

3.2475

-0.7438

2.9902

83.5002

1982

21.5014

16.4051

18.9631

7.4576

9.0393

62.8281

2.8487

-1.3230

2.9991

245.6395

1983

21.5299

16.4357

18.8138

7.4776

9.0321

2.8856

2.0196

-0.7238

3.0578

-13.8690

1984

22.0085

16.4675

19.4159

7.5110

9.0208

61.3888

3.3937

-1.1235

3.1773

82.6118

1985

21.9816

16.5007

19.5064

7.5115

9.0193

-2.6538

0.0547

-0.1451

3.3276

9.4672

1986

22.0902

16.5355

19.6024

7.5507

8.9889

11.4655

3.9913

-2.9900

3.4803

10.0780

1987

22.5590

16.5715

20.2228

7.5903

8.9793

59.8045

4.0484

-0.9608

3.5936

85.9640

1988

22.5964

16.6079

20.3686

7.6256

8.8186

3.8186

3.5877

-14.8419

3.6455

15.6988

1989

22.3865

16.6442

19.1118

7.6396

8.7661

-18.9348

1.4146

-5.1191

3.6236

-71.5436

1990

22.1829

16.6797

19.6165

7.6401

8.7240

-18.4229

0.0481

-4.1166

3.5537

65.6530

1991

21.9238

16.7145

19.7351

7.6478

8.6925

-22.8294

0.7692

-3.1062

3.4817

12.5827

1992

21.7732

16.7488

19.7421

7.6483

8.6928

-13.9768

0.0477

0.0336

3.4235

0.7048

1993

21.8928

16.7824

19.8569

7.6492

8.7408

12.7029

0.0954

4.9161

3.3600

12.1654

1994

22.1072

16.8153

20.0289

7.6521

8.8139

23.9095

0.2859

7.5804

3.2940

18.7687

1995

22.4735

16.8476

20.2182

7.6728

8.8242

44.2405

2.0903

1.0406

3.2318

20.8395

1996

22.5224

16.8793

20.8258

7.7053

8.8257

5.0169

3.3039

0.1471

3.1651

83.5945

1997

22.5589

16.9104

20.7457

7.7359

8.8386

3.7182

3.1081

1.2928

3.1127

-7.6908

1998

22.6081

16.9414

20.6901

7.7711

8.8495

5.0347

3.5824

1.1022

3.1026

-5.4078

1999

22.5105

16.9728

20.7898

7.8180

8.8498

-9.2926

4.8081

0.0287

3.1430

10.4831

2000

22.5467

17.0050

20.7290

7.8497

8.8518

3.6863

3.2193

0.2008

3.2141

-5.8967

2001

22.4881

17.0379

20.6646

7.8793

8.8540

-5.6956

3.0019

0.2147

3.2918

-6.2452

2002

22.5444

17.0714

20.7755

7.9010

8.8550

5.7882

2.1953

0.1000

3.3528

11.7363

2003

22.5696

17.1053

20.8536

7.9164

8.8569

2.5594

1.5556

0.1997

3.3916

8.1232

2004

22.7952

17.1394

21.0550

7.9197

8.8578

25.3076

0.3282

0.0854

3.4013

22.3096

2005

22.9220

17.1733

21.2899

7.9306

8.8581

13.5192

1.0905

0.0285

3.3916

26.4771

2006

23.0201

17.2070

21.3285

7.9505

8.8598

10.3038

2.0137

0.1707

3.3767

3.9369

2007

23.2323

17.2407

21.5859

7.9544

8.8611

23.6378

0.3877

0.1278

3.3668

29.3536

2008

23.3793

17.2743

21.7815

7.9956

8.9269

15.8321

4.2135

6.8066

3.3601

21.6010

2009

23.4195

17.3079

21.7799

8.0027

9.7416

4.1117

0.7075

125.8497

3.3588

-0.1582

2010

23.4978

17.3415

21.9418

8.0430

9.7344

8.1386

4.1151

-0.7231

3.3597

17.5715

73