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Roadway infrastructure management and investment behavior studies for developing countries: A multicriteria approach to road improvement decision-making
Garrett, Charles Colin, Ph.D.
The Ohio State University, 1991
UMI 300 N. Zeeb Rd. Ann Aibor, MI 48106
ROADWAY INFRASTRUCTURE MANAGEMENT AND INVESTMENT BEHAVIOR STUDIES FOR DEVELOPING COUNTRIES - A MULTICRITERIA APPROACH TO ROAD IMPROVEMENT DECISION MAKING
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By
Charles Colin Garrett, B.Eng, M.Eng.
* * * *
The Ohio State University 1991
Dissertation Committee: Approved by Earl E. W hitlatch Yeou-Shang Jenq Jason H su VI A dviser Department of Civil Engineering To My Wife and Children
ii ACKNOWLEDGEMENTS
The gratitude which I would like to express to those who made this study possible goes much deeper than prose could connote. My sincerest appreciation is extended to Dr. Zoltan Nemeth for his guidance, patience and insight throughout my academic life at The Ohio State University. To all other members of my advisory committee, Dr. Yeou-Shang Jenq and Dr. Jason Hsu, I would like to give special thanks for their unselfish contributions and persistent encouragement even at times when the focus of the study was still unclear. To Dr. Earl Whitlatch for his critical evaluation and suggestions on refinement. Dr. Frannie Humplick instilled confidence in me during the early stages of the study with the keen interest she showed. Her methodical overview made it easier to construct the framework of the study. To her I extend my gratitude. The study would not have been possible without the cooperation of the technical personnel of the Ministry of Works and members of the budget committee of the State Planning Secretariat, Guyana. The willingness with which they made the data available and the speed with which they responded to requests, deserve special acknowledgement. To the university of Guyana I am heavily indebted for affording me an opportunity to further my studies. Finally I would like to express special appreciation to my wife and children for understandably enduring three and one half years of single parent cohabitation while I pursued my academic goals. VITA
June 25,1950 ...... Born Georgetown Guyana.
1976 ...... B.Eng. University of Guyana, Guyana.
1976-1980 ...... Geotechnical and Materials engineer, The Soils Materials and Research Laboratories, Guyana.
1980...... M.Eng. California Polytechnic State University, San Luis Obispo, Cali fornia.
1980-1982 ...... Construction Engineer, Roads Divis ion Ministry of Works, Guyana.
1982-1985 ...... Chief Roads Officer, Road Adminis tration Division Ministry of Trans port, Guyana.
1985-Present ...... Senior Lecturer, Department of Civil Engineering, University of Guyana.
PUBLICATIONS: Garrett, Charles, 'Pavement Design in Guyana - Flexible Pavements', Proceedings of the FURORIS congress (Future of Roads and Rivers in Suriname and Neighbouring Region), Suriname 1982.
FIELDS OF STUDY Major Field: Civil Engineering TABLE OF CONTENTS
DEDICATION i i
ACKNOWLEDGEMENTS...... iii
VITA ...... v
LIST OF TABLES...... xi
LIST OF F IG U R E S ...... xiii
CHAPTER PAGE
INTRODUCTION ...... 1
ON INVESTMENT BEHAVIOR...... 3
A DESCRIPTION AND LOCATION OF GUYANA 6
I. A LITERATURE R E V IEW ...... 10 1.0 The Scope of the Review ...... 10 1.1 Roadway Infrastructure M anagem ent ...... 11 1.1.1 Inventorization ...... 20 1.1.2 Condition Evaluation ...... 21 1.1.3 Performance Prediction ...... 23 1.2. The State of the A r t...... 23 1.3. Statem ent of the Problem ...... 33 1.4. Research Approach and Contribution ...... 36 1.4.1. Ranking Alternatives ...... 41
vi CHAPTER PAGE
1.5. The Goal...... 42 1.6. The O bjectives...... 42 1.7. The Scope ...... 43
II. DATA REQUIREMENTS...... 44 2.0 Supporting Data files ...... 44 2.0.1. Design and Construction Data ...... 44 2.0.2. Maintenance History D a t a ...... 45 2.0.3. Drainage Inform ation ...... 45 2.0.4. Geometries In fo rm atio n ...... 45 2.0.5.Climatological Information ...... 46 2.1. Record Keeping Strategies ...... 46 2.2. Netw ork C lassificatio n ...... 47 2.3. N etw ork Inventory and Characterization ...... 48 2.4. Design Standards ...... 49 2.5. Pavem ent Condition Evaluation ...... 50 2.5.1. Evaluation M ethodology ...... 50 2.6 Guyana’s Network Evaluation ...... 52
III. PAVEMENT PERFORMANCE AND PREDICTION...... 54 3.1. Performance Data ...... 54 3.2. Modeling Pavement D eterioration ...... 60 3.2.1. The Markov P rocess ...... 61 3.2.2. The B-Spline Estim ation ...... 64
v i i CHAPTER PAGE
3.2.3. The Constrained Least Squares ...... 70
IV. IMPROVEMENT NEEDS AND ECONOMIC EVALUATION...... 71 4.0. Improvement N eeds...... 71 4.1. Economic Evaluation ...... 75 4.1.1. Initial Construction Costs ...... 76 4.1.2. Periodic Maintenance Costs ...... 77 4.1.3. Road Users Cost ...... 78 4.1.4. Accident C o s ts ...... 80 4.1.5. Travel Time C o s t s ...... 80 4.2. Transportation Benefits ...... 81 4.2.1. Economic Development Benefits ...... 81 4.3. Measures of Economic Efficiency ...... 82
V. PRIORITIZATION...... 85 5.1 Socioeconomic and Institutional Considerations . . 88 5.2. Ranking of Alternative ...... 89 5.2.1. Attributes of the Priority Utility Model . . 90 5.2.2. Determination of Relative Weights .... 92
VI INVESTMENT BEHAVIOR MODELING...... 96 6.1. Model Specifications ...... 96 6.1.1 The dependent V ariables ...... 97 6.1.2 The Independent Variables ...... 98 6.13 The variable Descriptions...... 100 viii CHAPTER PAGE
6.2. Data Collection and P re p a ra tio n ...... 107 6.3. Preliminary Diagnostics ...... 109 6.4. Variable R eduction ...... 109 6.4.1 The General Development Indicators . . I l l 6.4.2 The Economic Development Indicators . . 112 6.5. Model Building ...... 116 6.5.1 Model refin em en t ...... 117 6.6. Further Modeling C o n s id e ra tio n s ...... 120 6.7. Statistical Behavioral findings ...... 125 6.7.1 Model Application ...... 125 6.8. The Project Selection P ro c e ss ...... 126
VII RESULTS AND DISCUSSIONS...... 128 7.0. G e n e ra l ...... 128
7.1. Results ...... 131 7.2. Sensitivity I s s u e s ...... 131 7.3. Study L im itations ...... 138
VIII CONCLUSIONS AND RECOMMENDATIONS:...... 141 8.1 C onclusion ...... 141 8.2 Recom m endations ...... 144
LIST OF REFERENCES ...... 147
APPENDICIES
A DETAILS ON DEFINITIONS AND CODES USED IN THE ROAD NETWORK INVENTORY...... 152
ix APPENDICIES PAGE
B ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION.(CTPU/U G. 1987) ...... 159
C DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS (Road Administration Division Ministry of Works) .... 168
D POLITICAL RATING MODELS...... 173
E SAS STATISTICAL OUTPUTS FOR INVESTMENT BEHAVIOR MODELS...... 176
X LIST OF TABLES
TABLE PAGE
1. Scope of Primary Studies on Road Deterioration and Maintenance in Non-Freezing Climates ...... 17
2. Guyana's Network Classification ...... 48
3. Expert Opinion on Deterioration of Paved R oads ...... 57
4. Expert Opinion on Deterioration of Lightly-Paved R o a d s ...... 58
5. Expert Opinion on Deterioration of Unpaved Roads ...... 59
6. Data on Hypothesized Influential Macroeconomic variables . 108
7. Data on Hypothesized Influential Macroeconomic variables Reconstructed After Principal Component Analysis ...... 115
8 Results of Autocorrelation Statistics ...... 124
9. Marginal Contribution of Variables to Variance in actual . . Fiscal Expenditure ...... 132
10. Relative Weights of Attributes in the Priority Model ...... 132
11. Hhpothetical Attributes of an Improvement Alternative . . . 133
12 Relative Change in Road Improvement Priority Index When Individual Attributes are Varied (Current Account A n a ly sis) ...... 134
xi TABLE PAGE
12. Relative Change in Road Improvement Priority Index When Individual Attributes are Varied (Capital Account A n a ly s is ) ...... 135
x ii LIST OF FIGURES
FIGURES PAGE
1. Conceptual Structure of PMS Using Multicriteria Prioritization Approach ...... 5
2. A Location Map of Guyana ...... 8
3. Guyana and its V icinity ...... 9
4. Critical Elements of the Road Improvement Decision Process for a Developing Country ...... 25
5. The Current Project Selection Process in Most Developing Countries ...... 26
6. The Socioeconomic Project Selection Process ...... 28
7. The socioeconomic and Institutional Multicriteria Approach to the Project Selection Process ...... 29
8. A Condition Evaluation Matrix ...... 52
9. A Diagrammatic Representation of a Transformation of a One Time Pavement Survey ...... 55
10. A Schematic Representation of a State, State Vector and Performance Cycle ...... 62
11. Performance Curves For Paved Roads ...... 66
12. Performance Curves For Lightly-paved Roads ...... 67
x iii FIGURES PAGE
13. Performance Curves for Unpaved Roads ...... 68
14. A Comparison of Performance Curves for Paved Lightly-Paved and Unpaved Roads ...... 69
15. Maintenance Cost Gradients for Different Roadway Conditions ...... 72
16. A hypothetical Representation of the Distribution of Improvement N eed s ...... 74
17. A Conceptual Chart of Country Specific Influential Macroeconomic Indicators ...... 106
18 Subgroup of General Development Indicators ...... 113
19 Subgroup of Economic Development Indicators ...... 114
20 Time Series Residual Plot (Capital Expenditure) ...... 122
21 Time Series Residual Plot (Current Expenditure) ...... 123
22 Change in Priority Index When Attributes are varied Independently (Current Expenditure Analysis). . . . 136
23 Change in Priority Index When Attributes are varied Independently (Capital Expenditure Analysis) ...... 137
x iv INTRODUCTION
In the planning of any type of road maintenance activity, developing countries have generally adopted the planning methodologies convention ally practiced by developed countries. Because of the mechanisms through which these planning processes have been applied, the results have not been optimal and sometimes not encouraging. It is evident to planners in developing countries that there is an urgent need to develop inexpensive and appropriate technologies to plan, assess and develop road improvement strategies. In this way they hoped to mitigate the consequences of inefficiencies in road maintenance and misguided priorities. In so doing, they can contribute towards the enhancement of the quality of life in these poor countries. Procedures and techniques for coordinating highway agency functions to yield optimal investment strategies, (systems approaches), are being exten sively researched and documented. Significant contributions to the advancement of these techniques in developing countries are such models as the HDM III (The World Bank 1986) and the RTIM (Transportation Road Research Laboratory 1982). These models enable the engineer to relate roadway deterioration to maintenance effort, and vehicle operating cost to road surface condition for various roadway types and geometries. Unfortunately, these models require vast amounts of supporting data files, and appropriate skills and technology to provide complete and meaningful application. The paradox many developing countries are faced with is that, although these models have been designed and tailored for use in their local environments, the data required for supporting such models are not available, and implementation skills are limited. In short, the technology is inappropriate. This poor resource endowment, coupled with unique management policy decisions, budgetary constraints, and some non- traditional political policies, present complicated systemic problems for the agencies concerned. More recently, there has been rapid development of modified systems approaches to facilitate some degree of appropriate technology transfer. In the absence of historical data on networks, probabilistic approaches have been applied to pavement management to account for conditions of uncertainty when predicting the resource-based factors (pavement conditions, traffic, and costs). Compromises are made by using less expensive, easily measured, road condition parameters and by making simplifying assumptions about traffic. The existing state of the facility, which greatly influences decisions on time, cost, and nature of maintenance, is assessed by some form of inspection. The identification and quantification of parameters of state/condition have received quite a lot of research attention. Methods of measuring these parameters and the levels of accuracy associated with these measurements have been, and are continuing to be, refined. 3
A pavement management system is useless to any agency if it cannot be adequately implemented and maintained. A major function of the agency is to create the environment, that is, the conditions and the logistical support, for the effective implementation of all road related activities. The scope of these activities must, therefore, be consistent with the implementation capacity of the agency. To select appropriate rehabilitation and maintenance technology, highway agencies are currently directing their resources towards identifying causes of pavement deterioration, condition data acquisition, and traffic studies. The investigations seek to determine the separate and interacting effects of the system's components on the maintenance decision. The decisions are, however, further complicated by cost considerations and factors unique to the environment within each agency. The particular response of agencies (implementation strategies) under a given set of technical, economic, and environmental conditions is what will be referred to as "agency behavior."
ON INVESTMENT BEHAVIOR Agencies concerned with decision making for pavement management in developing countries constantly fail to make decisions which reflect optimal use of their available resources. The search for intelligence in this form of decision-making imputes "systemic rationality" to observed behavioral choice, in an effort to rationalize the discrepancies in agency behavior. The assumption is that the action of the agency follows rules of 4 behavior that have evolved through sensible processes which are not fully understood and, hence, cannot be described by any specific rule (with present knowledge). In order to develop an aid to the decision making process, this investigation was undertaken., It does not, however, produce the relevant answers as to why and through what processes the implementation decisions differ from those of the normative optimal procedures. Part of the objective was to ascertain whether there is a systematic and persistent deviation of the behavioral implementation strategy from those which reflect the optimal use of available resources. An attempt was made to identify the factors which influence the decisions, and to assign relative weights to each factor in the overall decision process. The study focuses on issues of planning, developing and implementing a pavement management system for a developing country, and was done in two phases. The first phase was the systemization of maintenance functions for Guyana's network by adapting improved technologies for use under local conditions. The second phase focused on the identification of the critical social and institutional factors influencing the decision process and the development of decision models using a multicriteria approach, for evaluating alternatives funded by both capital and current budget. Figure 1 shows the conceptual structure of the pavement management system using a multicriteria prioritization approach. 5
NETWORK INVENTORIZATION & ( START CHARACTERIZATION
PAVEMENT CONDITION EVALUATION
PERFORMANCE & PREDICTION MODELING
IMPROVEMENT NEEDS ANALYSIS
SOCIOECONOMIC AND 1IA INSTITUTIONAL EVALUATION
PROGRAM PRIORITIZATION preparatior
FIG. 1 CONCEPTUAL STRUCTURE OF A PAVEMENT MANAGEMENT SYSTEM USING THE MULTICRITERIA APPROACH TO PRIORITIZATION DESCRIPTION AND LOCATION OF GUYANA The Co-operative Republic of Guyana lies off the east shoulder of the South American continent at a latitude between 1° and 9° and longitude between 57° and 61°. It covers an area of 83,000 square miles. The country has three distinct geographical areas: 1) The Coastal Belt; 2) The Forest Area; and 3) The Savannah Regions. The narrow coastal belt of the Atlantic seacoast extends for 270 miles and varies between 20 - 40 miles in width (four percent of the total land area). It lies 4ft - 5ft. below sea level at high tide and is dependent on an elaborate system of dams, walls and groynes to protect it from the sea. The coastal belt contains eighty percent of the country's total population (approximately one million), and most of the transportation network is concentrated within this zone, with the exception of a few unsurfaced roads which connect the capital city to the mining areas and timber industries of the interior.
The soil structure along the coast is basically weak, compressible soils - stratified marine deposits of silts and clays which overlay the stronger, slightly compressible clay formation referred to as the coropina clay. The depth of overburden for the coropina varies continuously throughout the coast, and there are even outcrops over small inland stretches . The climate in Guyana is hot and humid. There is little variation in the temperature throughout the year; it averages a maximum of 93°F inland and 88°F in the coastal area. The minimum average is approximately 75°F. The rainfall pattern reflects the prevailing winds and the topography. The prevailing winds come from the northeast to the southwest from the Atlantic Ocean, often carrying moisture. The mean annual rainfall averages from 80-100 inches. Figures 2-3 give some geographical perspective of the study area. '-A^'.-SviwHi* u*^iJ *1 !,vfiiJ/\ A Co X ii
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ORTH ^;^ f?.UNITEp^ST^ES » NORTH
PA ClL / PIC n u / ■ = r ' - ' ' " c OCEAN/ ; ^ OCEAN ■M**Sa, \ ‘ W ihcmkmku MCXICOi • ./ r ■ a»Ar?>. *om»ir«CA.-j The'Study Area \ < - • ‘HSTffrW'WHC \ *• “• ••• W -.J « * . Cvni&6ej/i • ( V. < CAtV A • | 1Wt9K* ■,r 1 .1 *.»•/. : 5 .. •*..i;v JWWJP * * I . v-\ V-->->rVx.-- *• : ■ ’ .-"V ■ - vcfcfzunV COLOMBIA «** *
. GHAZr -V;.- U v - V V ' SO U TH ^PACIFIC-, % - - V - ; ' P. . ■• OCEAN -. " ' V ' -.v'-'-v?•*••:••'• - ■"• 'h )
SOUTH
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HAUGEN TIMA
RG. 2 A LOCATION MAP OF GUYANA ATLANTIC OCCAH VENEZUELA
5*
SURINAM
BRA'Zt t
FIG 3 GUYANA AND ITS VICINITY CHAPTER 1 LITERATURE REVIEW
1.0 SCOPE OF REVIEW This review sets out to address the development and state of the art of the management of roadway infrastructure, focusing more specifically on roadway pavements and constrained in detail to flexible pavement systems. The general framework of roadway infrastructure management encompasses the following specializations: (a) Design - Here the objective is to design more economical and more durable roadway structures to carry the increasing traffic loads and volumes that are anticipated to use the facility. (b) Construction and Maintenance - The focus of this specialization is on the development of skills and techniques, using resources available (man, money, machine and materials) to produce more economically and efficiently, the designed roadway facility; and to preserve in an acceptable manner the roads already built.
(c) Management - This is the systematic coordination of the roadway related agency functions to achieve the most desirable investment strategy. (d) Technology Transfer - The successful implementation of new and improved technology depends on the capacity, awareness, willingness, and 10 11
socioeconomic conditions of the implementing agency. This area deals with the the information dissemination techniques and the evaluation of appropriateness of new technologies and materials for achieving sustainable technological development. This literature review, although acknowledging the works done in roadway design and construction, emphasizes the elements of management and technology transfer for which the state of the art in developing countries is to be established.
1.1 ROADWAY INFRASTRUCTURE MANAGEMENT: The idea of applying management principles to highway maintenance began in the 1950's. The literature on the first formal studies was on the research into management operations in Connecticut by the Bureau of Public Roads (now the Federal Highway Administration) in 1950. This study established that there is commonality of maintenance problems across many states.
In 1959 the Bureau of Public Roads in collaboration with the Iowa State Highway Commission conducted a study of larger scope. The objectives being, To produce facts which could be used by management for controlling and improving maintenance operations’ . The proceedings of this conference was published in Highway Research Board Special Report, No 65, 1961. The next major event was the Highway Research Board maintenance workshop in 1968 held at The Ohio State University. The general emphasis of the presentations was on research projects having to do with the 12 systematizing of maintenance operations. The proceedings of this workshop was published in Highway Research Board Special Report No.100, 1968. Once the maintenance management concept became formalized and understood it became the focus of highway agencies. Years of research were spent on proposing and validating approaches for systematization of maintenance operations. By 1967 other countries were also involved in the formalizing of maintenance management procedures. In Great Britain, highway authorities had set up a committee for highway maintenance, to investigate the management of maintenance and all its aspects. The state-of- the-art in Britain at the time was presented by Wingate, (1968) at the Highway Research Board workshop. As early as 1970 The Highway Research Board hosted a workshop to discuss solutions to problems encountered in implementing and using maintenance management programs. The objective of this workshop was to provide feedback information on existing procedures for possible modification based on field experience. By 1975 the entire developed world was adopting the systems approach for solving maintenance problems and the focus was then placed on the interface between the maintenance managers and the decision makers. As the roadway infrastructure in the underdeveloped countries of Africa, the Caribbean, and the Pacific increased in importance and became more developed, the problems of infrastructure management became more evident. The limited resources (material, money , machine and skills) posed further problems to the infrastructure management effort. LaBaugh et al, 13
(1975), reviewed the financing for construction and improvement of roadways in developing countries. They argued that a substantial amount of the financing is by low interest loans from developed countries or from international agencies such as the Inter-American Development Bank (IADB), The International Bank for Reconstruction and Development (IBRD), and the International Development Association (IDA). These agencies require that loans be measured with respect to the economic return that will be provided by the proposed budget. The International Bank of Reconstruction and Development was particularly popular with developing countries because it emphasized assistance for low volume roads in lower income countries. In these cases the trade-offs between initial construction costs and future maintenance and road user costs dictate the highway design and maintenance strategies which are different from those that obtain in North America and Europe. A comprehensive research project was financed by the Agency for International Development between September 1968 and November 1970 in Ghana; this study, ’Laterite , Lateritic Soils and Other Problem Soils of Africa’, (M oavaenzadeh, F. et al 1971) made a significant contribution to roadway design and specifications for roads in tropical environments. The study considered condition evaluation, traffic classification, deflection measure ments and structural capacity. The first phase of the study which began in 1968 was financed by the IBRD in collaboration with research institutions in the United States, the United Kingdom and France. The major focus was alternative design and 14 maintenance strategies for low volume roads. From this study evolved a conceptual framework for interrelating construction, maintenance and vehicle operating costs in order to minimize total highway costs. The authors pointed out that there was an absence of empirical evidence in this phase of the study and this led to further studies of an empirical nature. In 1971 the United Kingdom Transportation Road Research Laboratory (UK TRRL) in collaboration with the International Bank of Reconstruction and Development (IBRD) and The Kenya Ministry of Works conducted further studies in Kenya. The results of these studies were published in three volumes. The first volume of the Kenya Road Transport Study dealt with research on vehicle operating costs (Hide, et al 1973). The second volume addressed road deterioration (Hide et al 1973). The last phase of the study was the development of a road transport investment model for developing countries (Abayanaka, et al 1975). The Kenya studies may be summarized as "establishing new empirical data on the trade-offs among construction, maintenance and vehicle operating cost based on design and maintenance strategies." ( Harral and Agerwal, 1975). These findings were constrained by the applicability to countries with similar conditions and environments. The relationships derived - mostly linear in form - are between the various operating cost components (fuel, vehicle spares,depreciation and travel time), and the significant characteristics of the road (surface finish, roughness, alignment etc,). 15
Recognizing the inadequacy of the findings as they relate to the roads in different environments and with different geometries, a small-scale test was conducted in the Caribbean to further study the effects of geometry. The concepts were extended to mountainous and hilly terrain. An attempt was made to accommodate different comfort levels and different terminal road conditions by using very rough paved roads among the sample of test roads. (Hide, Morosiuk and Abayanaka, 1982). The study sites were in the islands of St. Lucia, Barbados, Dominica and St. Vincent. A combination of these findings with those of the Kenya Studies gave rise to a comprehensive modified model - The TRRL Road Investment Model for Developing Countries RTIM2 which - made another significant contribution to the state of the art of maintenance management, (Parsley and Robinson, 1982) Simultaneously a much larger study was being conducted in Brazil (1975-1984) by a team of specialists financed by The government of Brazil and the United Nations Development Programme. The team comprised The Empresa Brasiliera de Planejements de Transportes (GEIPOT), World Bank specialists and the Texas Research and Development Foundation. This study advanced the state of the art to the use of non- linear models for predicting relationships of vehicle speeds, fuel consumption etc. . . and introduced probabilistic approaches to provide a realistic array of actual outcomes in any specific speed-related situation. (W atanatada et al 1987). The modeling principles were extended to address vehicle operating cost (tire-wear and vehicle maintenance cost). Data and other limitations 16 prevented success in estimating mechanistic-empirical models for the operating cost component. Major criticisms of the preceding studies were: the uniformity of the vehicle types (traffic mix ) , the geometry of the sites (number of lanes) and wearing surface of sections. Further studies were done to investigate different roadway and traffic conditions. The Central Road Research Institute (CRRI - New Deli) undertook a Road User Costs Study in 1976 in India. The results of this study advanced the state of the art beyond basic studies of vehicle speeds, fuel-speed relationships and user costs. The study generated information on road accident costs, value of time savings and congestion sim ulation. (CRRI, 1982). Developing countries benefited in an enormous way from these studies, since their road networks were comprised of a large percentage of unsurfaced roads to which little or no attention was paid prior to these studies. A summary of the scope of the preliminary studies done in Kenya and Brazil is given in Table 1. The World Bank remained active in research and empirical validation of these models. Experience was gained from every new situation that some research aspect was applied. 17 TABLE 1. SCOPE OF PRIMARY STUDIES ON ROAD DETERIORATION AND MAINTENANCE IN NON-FREEZING CLIMATES SOURCES: GEIPOT(1982; PATERSON (1987) AND HODGES ET AL (1975) EXTRACTED FROM HDM-III VOL 1 (1986)
KENYA BRAZIL
A PAVED ROADS
Total no. of sections 49 116 No. of granular base sections 10 74 No. of cemented-base sections 39 11 No. of overlaid sections 0 33 Length of sections (m) 1000 720 Period of observations (year) 4 5 No. of observations - 5000,000 Traffic volum e (veh per dy.) 323-1618 73-5700 Equivalent axles (mil/lane/year) 0.012-3.6 0.0003-1.7 Equivalent axles per heavy vehicles 0.2-4.0 0.08-14 C um ulative equivalent axles (mil) 0.004-3.30 0.003-18 A nnual rainfall (mm) 400-2000 1200-2000 Pavem ent age (yrs) 0-14 0-24 M odified structural num ber. 2.5-5.1 1.5-7.0 Deflection (Ben. Beam) (mm) 0.18-1.12 0.20-2.02 Road roughness (m /km IRI) 2.9-6.0 1.8-10.2 Change of roughness (m /km IRI) 0.3-1.7 0-4.9 TABLE 1. (continued),
KENYA BRAZIL B UNPAVED ROADS
Total no. of sections 46 48 No. of gravel roads 37 37 No. of earth roads 9 11 Length of sections (m) 1000 320-720 Traffic volume (veh/day) 42-403 18-608 Truck volume (veh/day) 12-136 5-477 Annual rainfall (mm/year) 400-2000 1200-2000 Period of observations (year) 2 2.5 Road roughness (m /k m IRI) 4-17 1.5-29 19
In 1986 a comprehensive (third generation) model, HDM III (The Highway Design and Maintenance Model) was developed by the Massachusetts Institute of Technology, the Transport and Research Road Laboratory and the World Bank. This model drew on the experience of all the previous studies. The major focus was on predicting total life cycle costs, these comprise: Construction Costs = /(terrain, soils, rainfall, geometries, pavement design and unit costs). Maintenance Costs = / [road deterioration: ( pavement design,climate, traffic , time): maintenance standards and unit costs] Road Users Costs = / (geometries, surface condition, speed, vehicle type, and unit costs) The model is used to make comparative cost estimates for different scenarios of policy options, and it can efficiently handle a large number of alternatives. The model is organized to perform the following sequence of operations : diagnose input data and simulate traffic flows and changes in the roadway due to deterioration and maintenance. It also does the economic analyses and comparisons of alternative construction and maintenance policies. Meanwhile in the developed countries The National Cooperative Highway Research Program (NCHRP), The Federal Highway Administration (FHWA) and other transportation research agencies continued to add to the wealth of knowledge of maintenance management of low-volume roads by 20 conducting and encouraging research on the resource-based components (technical, economic, and financial) of maintenance management. As areas of maintenance management became more specialized it resulted in the evolution of management of sub-systems: pavement management, bridge management, and management of non-pavement roadway elements. With use of the personal computer becoming more popular, many countries and agencies developed customized pavement management and bridge management systems. These procedures vary in complexity depending on the agency and the intended use. The essential features of these systems are the same, they include: inventorization, condition evaluation, performance prediction, improvement needs analysis, economic evaluation, and prioritization. The desire to achieve more precision in decision making on the management of roadway infrastructure led to extensive research into each of the essential features.
1.1.1 Inventorization. The identification and complex description which could now be attributed to each roadway link, notwithstanding the ability to store and retrieve this information, has greatly enhanced the task of compiling inventories. The use of geographical information systems to develop network inventories is presently considered the state of the art. The GIS has the additional advantage of providing a visually oriented display of network information. This technology is not yet popular in developing countries. 21 1.1.2 Condition Evaluation Years of research and field experiments resulted in comprehensive methods of conducting condition evaluations for both paved and unpaved roads in both developed and undeveloped countries. The technology thus far has provided four primary measures of pavement condition: (i) roughness or ride quality, (ii) distress, (iii) structural capacity, (iv) pavement friction. Each agency may have variations in the procedures used to evaluate these measures, but they all have the same purpose. It is essentially to have some means of determining how well pavements are serving their intended purpose during their service life. The repeatability of the procedures for each evaluation cycle during the life of the pavement is of much importance. Methods of evaluation range from observational to automated. Developing countries benefited significantly from methods developed from a combination of visual and simple mechanical procedures. These methods were consistent with their levels of sustainability and overall resource endowment. Some of the more significant contributions to the state-of-the- art in developing countries included research by The University of Wisconsin (1987 revised 1989). 'Pavement Surface Evaluation and Rating' (PASER). It covers in much detail, gravel, concrete, and asphalt roads and contains the salient elements of other pavement evaluation schemes. Eaton et al (1988) of The US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, also proposed a comprehensive method for evaluating condition of unpaved roads. These approaches were more compatible with the level of technology in developing countries and were often adapted to suit these local environments.' In 1990, the Organisation for Economic Cooperation and Development (OECD) Scientific Expert Group on 'Pavement Management for Developing Countries' collaborated with the World Bank in preparing a manual for road pavement inspection applicable to Third World countries. (OECD,1990) This exercise resulted in the documentation of a manual on road monitoring for maintenance management for developing countries, and a damage catalog The procedures outlined emphasized sustainable technology, applicability and relevancy to developing countries, and cost effectiveness. As is to be expected, these procedures were limited to direct measurement technologies involving visual inspection by humans. In the developed countries of North America and Europe improved technology by way of automation has preoccupied researchers in their quest to advance the state of the art of pavement condition evaluation. The measurement technology was extended to indirect measurement techniques involving optical and laser technologies. These procedures emphasized precision, speed and volume which usually had high cost associated with them .
Regardless of the level of technology the types of data collected may be classified into one of the following groups: ride, structural capacity, surface distress or skid resistance. As agencies became more and more comfortable with these parameters, the next logical step was the refinement of the associated data collection procedures. Since each procedure has its unique error structure there were further investigations into the analysis of measurement errors as presented by Humplick, F. (1989). 23
1.1.3 Performance Prediction Important to the roadway infrastructure management process is the ability to predict with some accuracy the condition of the pavement throughout its service life. The change in condition over the life of the facility is described by a broad term, "performance". There are various approaches for quantifying the performance measure. Some commonly used quantitative measures are: PSI (present serviceability index from the AASHTO road test), Pavement Condition Index (0 - 100 scale), Average Condition Index (0-5 scale) etc. Deterministic models were first used to predict the anticipated state of deterioration in roadways. In 1982 the first probability-based model was developed for the Arizona Pavement Management System. Details on this study is published by, Golabi, et al, (1982). The system was based on a Markov decision model and considered both short term and long term management objectives; as well as such factors as physical road conditions, traffic densities, environmental characteristics and types of roads.
1.2 STATE OF THE ART
The management technology for roadway infrastructure involves the process of decision making for investments in roadway improvements. These decisions are influenced by: (a) Resource use. (b) Community - (socioeconomic factors). (c) Politics. (d) Agency constraints. (e) Realities. 24
To date all approaches recognized resource use as separate to all other elements or group of elements influencing the decision process. Figure 4 shows the critical elements of the road improvement decision process for developing countries. The foregoing review on management of roadway infrastructure in developing countries reveals a dominant emphasis in research on resource use. Throughout the evolution of the art, the rationale has always been to begin with an economic analysis; select the alternative that makes the best use of limited resources, then check for financial, political and organizational viability. If the best choice from an economic point of view fails either of the other tests, the next best solution from the economic hierarchy is checked, this process is continued until a plausible alternative is obtained. (Ogelsby 1975) Figure 5 shows the project selection process where objective economic analysis is combined with the subjective checks for social and institutional viability.
Techniques of economic and financial analyses are well developed. Work has been extended from the project level to the systemwide or network level (life cycle costs). The categories of costs have been clearly established, these include: Construction costs - established through a relationship to terrain, soils, rainfall, geometric design, pavement design and unit costs. 25
Econom ic R esou rces T ech n ical F inancial
Income level Em ploym ent Education Social factors Population Ext. assistance e tc .
P o litic a l Institutional Factors Organisational
Fig .4. CRITICAL ELEMENTS OF THE ROAD IMPROVEMENT DECISION PROCESS FOR A DEVELOPING COUNTRY 26
IDENTIFY OBJECTIVES
ECONOMIC RANK OF ALL ANALYSIS ALTERNATIVES
SELECT BEST ALTERNATIVE
FINANCIAL POLITICAL AGENCY VIABILITY VIABILITY VIABILITY
SELECT FOR IMPLEMENTATIOI
Fig. 5 THE CURRENT PROJECT SELECTION PROCESS IN MOST DEVELOPOING COUNTRIES. 27
Maintenance costs -- established through a relationship to road deterioration (pavement design, climate, service life, traffic): maintenance standards and unit costs Vehicle operating costs -- established through a relationship to geometric design, road surface conditions, vehicle speeds, vehicle types and unit costs. Work on socioeconomic, political and agency factors have not had such comprehensive research attention. To continue to research exclusively the resource-based factors (labor, equipment, money, material etc.) would lead progressively to diminishing returns. The decision to improve infrastructure is based on multicriteria objectives, and in developing countries the social and institutional factors have significant weights relative to resource factors. Research is needed to add to the thin body of literature on the use of these factors and to gain knowledge of the effects of the complex interrelationships of these factors on the decision process. Figure 6 shows the socioeconomic approach to project evaluation and selection. This process uses economic and social measures of effectiveness (MOE’S) jointly in a multicriteria analysis to rank alternatives. Subjective checks are then made for institutional viability. Figure 7 shows the ideal project selection process where economic and social and institutional factors are used simultaneously to rank alternatives, thereby reducing the subjective component of the decision making to a minimum. Baum and Tolbert (1985), argued that most countries will have achieved a consensus at the national level on broad economic and social objectives - such as income levels and its distribution, employment level and IDENTIFY ALTERNATIVES I 1 ECONOMIC SOCIAL MOE'S M OE’S I r
SOCIO-ECONOMIC RANK OF ALL ANALYSIS ALTERNATIVES
SELECT BEST ALTERNATIVE
N POLITICAL N AGENCY VIABILITY VIABILITY ' INSTITUTIONAL
SELECT FOR CIMPLEMENTATION
Fig. 6 SOCIO-ECONOMIC PROJECT SELECTION PROCESS 29 IDENTIFY ALTERNATIVES
ECONOMIC SOCIAL AGENCY POLITICAL MOE’S MOE’S M O E’S MOE'S
SOCIO-ECONOMIC RANK OF ALL AND ALTERNATIVES INSTITUTIONAL ANALYSIS
SELECT BEST ALTERNATIVE
IMPLEMENTATION
Fig.7. SOCIO-ECONOMIC AND INSTITUTIONAL MULTICRITERIA APPROACH TO THE PROJECT SELECTION PROCESS 30 degree of dependence on external assistance. These objectives help in the formulation of development policy and influence the general direction of public investment. It is important that they be considered when goals are set and progress must be assessed from time to time. These objectives, however, cannot be used effectively in decision making for short and medium-term public investment programs. In order to make investment decisions governments rely on forecasts of the principal macroeconomic magnitudes (development indicators).Baum and Tolbert (1985) explicitly addressed this problem.
"Forecasts of such variables as GNP, savings, investment, public revenue, public expenditure, exports, imports and foreign capital inflow are needed to provide an informed framework for the decision making ...... This framework also supplies a basis for estimating the financial resources, both domestic and external, likely to be available for public sector investment in a given year."
They also argue that the forecasts of output trends must be based on past performance, and that the preparation of the public sector investment models must use feedback from one phase to make adjustments for the next, to obtain a satisfactory fit among variables. Reviews by the World Bank on investment programs show that in many developing countries capacity and intentions have been seriously mismatched. The financial resources and the managerial capacity of the public sector are continuously overloaded. (Baum and Tolbert 1985). With respect to road improvements, few attempts have been made to quantify the effect of the socioeconomic variables on the decision process. A 31 significant contribution to this body of knowledge whose time has come, is the determination in a formal and quantitative way, the effects of these variables on the decision process for each country. These effects are intuitively expected to be much more significant in developing countries than in developed countries of North America and Europe. Greenstein and Bonjack/ 1983), used population density and illiteracy rate in an empirically weighted relationship to develop a socio-economic priority index. The socioeconomic priority index is composed of 70% economic consideration and 30% socioeconomic consideration. This model was limited in scope to the data available to the researchers at the time of the investigation. The limitations of this model is easily conceived when one considers the wide range of socioeconomic variables that would influence the prioritization decision process but were, however, not used in the analysis. Later, these researchers revisited the socioeconomic problem and developed a model which evaluated the economic indicators jointly with some social factors namely, population and level of education. The objective was to determine both socioeconomic justification and construction prioritization. The study was published by Greenstein, Berger and Bonjack, (1989.) Sharma, (1989), investigated the socioeconomic effects as a result of investment on roads, in the past. He then developed a model for quantifying socioeconomic impact of the rural road network plan. 32
The lessons learned so far are that socioeconomic conditions significantly influence the prioritization decision process in roadway infrastructure management. These conditions are unique to each country and may even be stratified by regions within countries. On institutional analysis, Baum and Tolbert noted that the outcome of development projects is dependent on the quality of institutions responsible for them. This responsibility is not only limited to the implementing agencies but also to the sector and governmental institutions that affect the project's success - ministries, development banks, etc. Both institutional and social dimensions has received much less attention than the technical, economic and financial ones. There is a void of established knowledge on what works and what does not work in particular circumstances, more specifically in developing countries. The need for research in this area cannot be overemphasized. For road improvement projects, agency constraints have been addressed implicitly in the past in the context of resource use. One such way was by making maintenance targets compatible with the capacity of the agency, thereby constraining the total maintenance program. The literature review has produced no evidence of studies with an explicit quantification and evaluation of agency factors with respect to the road improvement decision process.
All development projects are affected by the government's macro- economic policies and by the legislative and regulatory framework embodying those policies (Baum and Tolbert 1985). The impact of the 33 political environment cannot be neglected when institutional performance is being considered. Political instability results in higher risks and uncertainty about the future of projects. For these same reasons it is hypothesized that the political environment in which maintenance management is practiced has some influence on the nature of prioritization decisions. The literature review has again produced no evidence of studies done to verify and/or quantify the effect of the political environment on the decision process. Expanding on the existing body of knowledge with studies of this nature would be a significant refinement to the current approaches and a positive contribution to the state of the art of infrastructure improvement decision making. If a relationship could be established which captures the individual amount of consideration given to economic, social, and institutional factors in a single model, it would be a step towards a more accurate description of how prioritization decisions on road improvements are made in developing countries.
1.3 STATEMENT OF THE PROBLEM
The growing concern among highway agencies to manage road systems effectively and economically has led to increased research into development of systematic maintenance management procedures. As refinements were made to the art, each specialization was extensively researched and special attention was given to pavement management systems. Pavement management is broadly described as the effective and efficient directing of the
t 34
following highway agency functions: data collection, planning, research, design, construction, maintenance and rehabilitation. To take advantage of state-of-the-art techniques, developing countries made rational adaptations and modifications to existing pavement management structures, and conducted a series of empirical investigations in an attempt to obtain a better understanding of the complex interrelationships of the resource-based factors in environments (economic, social, political, geographical, etc.) different from those of the developed countries of North America and Europe. Generally, the structure of these systems reflects the purpose for which they are used. In metropolitan societies, where the technology originated, the road networks are highly developed and the management concept is now narrowed down to the functions associated with maintenance and rehabilitation (basically preservation of road investment). The intrinsic structure of these management systems are not totally oriented towards the requirements of developing countries whose road networks are not fully developed an as such, require the agency to expend a substantial portion of its road-works budget on new construction, to expand for the improvement of their economic health. At the same time, they need to preserve the investment made in their existing networks. The priority planning, therefore, is much broaderin developing countries. At the network level, it must take into consideration the economic trade-offs of implementing new projects against doing needed maintenance/rehabilitation works. In a similar manner, at the project level, rehabilitation prioritization programming must be done to provide an 35
effective management tool; detailing both the projects which could be implemented at varying funding levels, and conversely the costs associated with different levels of service. Among all the factors which influence roadway decision making process, only 'resource use' has been extensively researched and quantified. No evidence was found of any objective research done on the social and institutional factors. Particularly for agencies in developing countries, the problem of formulating and implementing pavement management systems is in some respects more complex. In these countries, some notable systemic problems such as, inadequate funding, obsolete technology, and non-traditional management and political policies are encountered. For this reason individual agencies will also be unique in their implementation strategies. The persistence of these agencies in the investigation of the influence of resource use on the roadway improvement decision making process would be to concentrate valuable research effort on a single facet of the problem. There would be no convergence of the information gap in the management problem which is created by social and institutional factors also. The desire is to close this gap by deriving methods for generating reasonable apriori information on road investment decisions of agencies based on both systems (resource use; materials, money , machine, etc. .). and systemic (social, political, agency capacity etc. factors. .) The problem is, therefore, to develop a relationship which also captures the amount of consideration given to the social and institutional 36
factors on the road improvement decision process. These factors must be considered simultaneously with resource use to evaluate improvement alternatives, thereby providing a step towards a more realistic description of how prioritization decisions on road improvements are to be made in developing countries.
1.4 RESEARCH APPROACH AND CONTRIBUTION In order to contribute to the thin body of knowledge pertaining to the influence of social, political and institutional factors on the decision making process, this study is undertaken in two phases. The first phase seeks to develop a functional methodology for evaluating roadway improvement needs in Guyana. It is not intended to develop a new conceptual structure for proposing this methodology but rather to use existing state of the art techniques and adapt them to suit the needs of the country and the capacity of the agency responsible for roadway improvement in that country. A significant contribution in this process would be the establishing of performance curves for the road network in Guyana, based on the opinions of experts knowledgeable of the maintenance practices in that country and their subjective judgement of the performance of the roadways in the country. The second phase of the study seeks to make a significant contribution to the state of the art of the structure of the decision making process by identifying the critical social and institutional factors and attempting to quantify their effects on the decision making process in that country. The literature on the resource based factors for road improvement projects (technical, economical and financial analyses etc.) is overwhelming for both developed and developing countries. Research attempts to integrate the social factors in the decision making processare lacking. Bonjack and
Greenstein considered two macroeconomic factors namely, population and education. However,the literature review revealed that there are a number of other social and institutional factors which had been identified as influential to the decision process. Also, since the influence of these factors varies in magnitude from country to country and from sector to sector within countries, it is therefore inadequate to assume that two factors (population and education) would capture the full impact of the social factors in the decision making process in any country where road improvement is contemplated. By making an analysis of observed data on the sectors performance it is hoped that forecasts could be made of output trends. Firstly, analyses are required to verify the influence of hypothesized macroeconomic variables on output. Secondly there is need to establish a favorable weighting system by which these factors may be considered in the decision process. Thirdly, application of an appropriate multiattribute decision technique to rank and select projects is needed. With the presumption that agencies in developing countries concerned with roadway infrastructure management are exclusively in the public sector, the logic of the conceptual framework of the decision process has the following theoretical underpinnings:- 38
Consider the road improvement strategies and their related budgets for a given agency over a period of time (to - tn)/ the investment program in
any year 't' is expected to reflect some systematic association with the level of influential macro-economic factors in year't'. Assume that a priority utility is given based on the attributes of the influential macroeconomic factors; and that the utility function relating to each attribute is linear.
Then U(rij) = bi + amj (1 .1). rij = the value of the i**1 attribute of alternative j. ai > 0 (monotonically increasing with respect to overall utility).
Using the mathematical combination rule for individual utilities, the total utility of alternative j is;
Uj = SU(rij) (1.2). i
This equation assumes utility independence along with risk neutrality. (Goicoechea, Hansen and Duckstein 1982) By substitution of equation (1.1) in equation (1.2).
Uj = Xbi +X but Sbj is the same across all j. Therefore for a rank order utility of alternatives j = 1, 2, 3, . . k, the only parameters which need to be evaluated is ai, the relative weights on attributes. The priority utility function of alternative j may be written: 39 U j = a jrij + a2 r2j + a3r3j + ....+ an rnj or k £ a i rij (1.4) i=l k = number of attributes rij = the value of the i**1 attribute of alternative j. ai = the coefficient representing the % consideration given to attribute rij in the decision process, i = 1,2,3, . . n = 1 In any given year't* the alternative’s utility may be expressed as: U jt = a irijt + a2r 2jt + a3r3jt+. . . .+ anrnjt Ujt = utility in year 't'. rijt = the value of the i**1 attribute of alternative j in year ’t\ ai = the coefficient representing the % consideration given to attribute i of alternative j. i = 1,2,3, . . n j = 1,2,3, . . . k ZaA = 1 The equation may be written in the form: k U jt = Xai rijt ...... (1.5). i=l k = number of attributes This equation assumes no interaction between the attributes influencing the decision process. To relate these facts to our particular study is 40 to assume that economical, social and institutional factors influence the road improvement decision process but are considered to be independent of each other, (ie. no complementarity ). In any fiscal year the implementation choice is among several alternatives; the decision rule is to select and rank projects in order of 'w o rth '. Ujt* = Max^. Ujt (1.7). The alternative which maximizes Ujt is considered the most plausible for implementation in year *t'. The prioritization of projects or alternatives for implementation is therefore derived from this model. The expert based empirical model derived by Greenstein and Bonjack for Ecuador utilizes three attributes from two of the above stated categories:- Internal Rate of Return (IRR) (economic) Population Index (PI) (social) Education Index (El) (social) Their prioritization decision index was given as: SEPI = 0.70GRR) + 0.225(PI) + 0.075(ED ...... (1.8) By extending this argument to address all categories of macro-economic factors hypothesized to affect the decision process, the agency utility function in any given fiscal year't' may be expressed as follows: Ujt = ai(economic)t + a 2 (social)t + a.3 (political) t + m (organizational) t ...... (1.9) 41 where: (economic)t a measure of economic efficiency in year 't\ (sodal)t an index of social efficiency in year *t'. (political)t an index of political efficiency in year't'. (organizational) t a measure of organizational efficiency in y e a r ’t’. al, a2 . . .a4 relative weights 1.4.1 Ranking of Alternatives The determination of the percentage consideration attributed to each social or institutional factor in the decision process may be determined by two approaches: (1). By deriving the weights from observed historical data on the agency decision behavior or (2) using client explicated techniques. (Goicoechea, Hansen and Duckstein 1982). The latter approach, which includes Decision Analysis Lottery techniques, the Indifference Tradeoff Method, Ratio Questioning etc., solicits weights directly from the decision maker and ensures that evaluations of alternatives are consistent and rational. By deriving the weights from observed data on agency performance, the judgement of the agency’s decision makers are simulated. This approach is found suitable for the investigation of the agency's road improvement investment practices, which is one of the focal issues of this study. By using regression techniques the influence of the hypothesized influential factors on the road improvement decision process may be verified and from the regression statistics (r^ - coefficient of partial determination) appropriate weights are derived for estimating the percentage of 42 consideration each factor was allocated. By using the weights derived in the priority utility function of the agency in any fiscal year, a ranking for the selection of improvement alternatives may be obtained. 1.5 THE GOAL The goal of this study is to develop an appropriate, functional methodology for evaluating and programming road network improvements in a developing country - Guyana. This methodology must be sustainable by the country, in spite of its scarce resources and the limited capacity of the road administration. 1.6 THE OBJECTIVES Phase One: 1. To use state-of-the-art pavement management procedures to customize a pertinent road improvement evaluation methodology for a developing country - Guyana. 2. To identify the critical social and institutional factors that influence the decision process and the agency investment behavior. Phase Two: 3. To use these factors along with available empirical country-specific data, to develop models to aid the multiobjective road improvement decision process at the countrywide network level. 43 1.7 SCOPE Comprehensive road maintenance management systems are methodically subdivided into three categories: 1. Pavement management; this deals with the actual flexible or rigid load carrying roadway facility. 2. Roadway structures management; this comprises the management of all roadway structures including bridges, culverts, railings and other roadway-related structures. 3. Management of the non-pavement roadway elements; these comprise:- the right-of-way, side ditches and parapets, traffic control devices, etc. The study was, however, restricted to the category of pavement m anagem ent and addressed, specifically, flexible pavements. The methodology was targeted for operations at the network level and the models were essentially directed to decision making for overall road networks. Descriptive models were developed using the data available through the Ministry of Works, Guyana, the World Bank, and other agencies monitor ing infrastructural works in developing countries. CHAPTER n DATA REQUIREMENTS 2.0 SUPPORTING DATA FILES: In order to develop a complete maintenance management system it is necessary to gather other types of data in addition to pavement condition and performance. The quantity and type of data depends on the complexity of the system. The more complex systems generally require the following categories of data to support the pavement condition data: (a) Design and Construction (b) Rehabilitation and Maintenance History (c) Drainage (d) Geometries (e) Climatological and Geographical 2.0.1 Design and Construction Data The information in this category comprises data that relates design to life-cycle performance. Specific types include: records on materials character ization, materials inventories, construction dates,and initial design details. 44 45 For a developing country, materials characterization and inventorization programs are given high priority to make the best use of locally available materials, while, at the same time, satisfying structural design requirements. 2.0.2 Maintenance History Data This information traces the sequence of maintenance activities on the respective sections since construction. An evaluation of the sites at the end of each service cycle (time between maintenance operations) is also included. The specific data includes: surface type, maintenance dates and methods; rehabilitation dates, methods and types. Information on unit cost and trends of costs for performing economic analyses are also incorporated in these records. 2.0.3 Drainage Information Hydraulic data is usually maintained as separate records. In the case of critical water table and sub-sea level regions like Guyana, specifications must be formulated for the guidance of the designers. The information may cover general drainage requirements. Safe elevation for construction of embankments are also specified. 2.0.4 Geometries Information Geometric design standards based on class and type of road are necessary. The specific data includes: cross slope, longitudinal slope, number of lanes, segment lengths, and extent of right-of-way reserves. 46 2.0.5 Climatological Information It cannot be overemphasized that the performance of a roadway pavement is greatly influenced by its environment. Data on rainfall and other seasonal information is helpful in the planning of maintenance operations and determines structural and material suitability. 2.1 RECORD KEEPING STRATEGIES As a general rule, the process of data collection, storage and retrieval is expensive and difficult to manage. If the requirements become exhaustive, developing countries would find that their human and technological resources do not permit efficient collection and management of data. It would be prudent to develop a system which utilizes, as far as possible, data comprising simple and easy-to-measure parameters. The overriding principle is that unless data is absolutely necessary, it should not be included as a pavement record. Discrimination in the selection of data may be made by rough assessment using these possible minimum guidelines, ( Monismith et al., 1987). Investigate whether the data is: (1) An identifier of pavement section. (2) Required for establishing priorities. (3) Required for selection of improvement strategy. (4) Required for cost analysis 47 2.2 NETWORK CLASSIFICATION The study takes into consideration all significant roads and trails in the national network. Local roads that form part of the national network have also been included. The focus of this study is on flexible pavements. Rigid pavement technology has not been popular in the study area and there are no significant stretches of rigid pavements in the national roadway reticulation. For the purpose of this study the road network was divided into three categories of flexible pavement: (1) Surfaced roads - roads surfaced with asphalt concrete (2) Lightly surfaced roads - roads finished with bituminous surface treatment (single or double treatment) (3) Unsurfaced roads - roads that have been surfaced with selected materials, e.g., laterite or other local materials of relatively high stability. Each category of road is further classified according to its function. Primary Roads - These comprise major network links and are generally surfaced roads. Secondary Roads - These are usually major branches to the network links and are at least lightly surfaced. Tertiary Roads - These are primarily lesser developed branches in the network and may be lightly surfaced or unsurfaced. 48 Penetration and access roads are classified as trails and are always unsurfaced. Table 2. gives a classification of the existing roads and trails in the network. TABLE 2 GUYANA’S NETWORK CLASSIFICATION PavementPavement % Of Category Miles Type Class Network Primary 140 11 Asphalt conc. Secondary SURFACED Surface 3 5 0 2 7 treatm ent Secondary (double) surface treatm ent Tertiary (single) LIGHTLY 30 0 23 SURFACED laterite Tertiary Surfaced Un Earth surfaced T rails 5 0 0 39 roads 49 2.3 NETWORK INVENTORY AND CHARACTERIZATION Appendix B contains a generalized tabular road network inventory. This data has been extracted from Transport Plan for Guyana (1976) prepared by the Central Transport Planning Unit Ministry of Economic Development of Guyana, with the assistance of Israel Institute of Transportation Planning and Research. It has been modified and updated where necessary to reflect the existing network structure. Appendix A contains details on definitions and codes used in the road network inventory. Information is provided to explain the classification and characterization methodology for the network. The CTPU method has been retained as far as its relevancy and accuracy perm it. 2.4 DESIGN STANDARDS In order to make qualitative and quantitative judgement on the conditions of links in the network, an important requirement is the design standards to which the facilities were built. The standards for public roads and trails in Guyana are based upon the American Association of State Highway and Transportation Officials (AASHTO) Policy for Geometric Design for Rural Highways. Modifications were appropriately made to suit the local conditions and supplementary detail was included to account for tertiary roads and trails. The standards are organized under the following headings: (1) Roads in Urban Areas (2) Roads in Village Areas (3) Roads and Trails in Rural Areas 50 Subgroups within these categories include the following classes: (a) Primary divided /undivided (b) Secondary div ided/undivided (c) Tertiary (d) Trails The roadway classes reflect the traffic level and structural capacity. Appendix C gives the design standards adapted for public roads and trails by the Road Administration Division of Guyana. The source of this document is in Transport Plan for Guyana, (Central Transport Planning Unit, Guyana, 1976). 2.5 PAVEMENT CONDITION EVALUATION To obtain an overall picture of the condition on the roads and to identify and systematically prioritize candidates for maintenance and rehabilitation, road condition surveys are necessary. Guyana, with its unimproved technology and scarcity of technical skills, requires a system of monitoring that is inexpensive and utilizes easy to measure parameters to guarantee sustainability by the Road Administration. 2.5.1 Evaluation M ethodology The evaluation process includes two levels of inspection; a Preliminary Inspection and a Detailed Survey. Preliminary Inspection The Preliminary Inspection is primarily visual and is done to assess the general condition of the road network. It identifies those sections of the 51 network which are in poor condition and require remedial work. It also serves as a routine to detect severe areas which require emergency action. Detailed Surveys: The detailed surveys are done selectively on the sections identified from the preliminary surveys as candidates for major works. The purpose here is to record the type, extent and severity of damage for the determination of the causes of damage, and identification of the appropriate repair measures. The condition survey technique used accommodated both surfaced and unsurfaced roads. These states of deterioration are quantified in terms of the following measures. Paved Roads Unpaved Roads Rutting Rutting Corrugations Corrugations Depressions Potholes Cracking Improper Cross Section Potholes Gravel loss of wearing surface Stripping/Ravelling Inadequate roadside drainage Bleeding Depth of loose surface material The measures chosen were adopted from OECD, Road Monitoring for Maintenance Management, (1990). The concept of condition values developed therein were also utilized for the determination of the operating conditions of the roads. The condition values used for rating pavement damage were obtained from a systematic combination of two characteristics of 52 deterioration measures, namely extent and severity. Both characteristics were evaluated at three levels and a 5-point scale classification was used to establish a condition matrix for each parameter. Figure 8 gives a diagrammatic representation of a condition evaluation matrix, where severity and extent are evaluated in a 3x3 matrix. The details on the measurement of condition values will not be given in this study but the complete treatment is given in OECD, (1990). Severity 1 2 3 e 1 X t e 2 n entries based on a scale of 1-5 t 3 FIG 8 - A CONDITION EVALUATION MATRIX The average condition value is based upon the assumption that immediately after construction, the pavement is in its best possible condition and the condition value is considered to be (1). Over the range of 1-5 53 condition value points, the pavement will transgress down to its worst functional condition. A general idea of the conditions described by the 5- point scale is as follows: 1 Good 2 Acceptable 3 Unsatisfactory 4 Bad 5 Very Bad 2.5.1 GUYANA'S NETWORK CONDITION EVALUATION The condition values which are obtained on roadway sub-sections are combined into an arithmetic weighted average for each complete link. The weighted average condition values are tabulated and used as a present condition value for the performance and prediction modeling. (Note, Appendix B gives average condition values for all network links from an evaluation completed by the University of Guyana and the Delft Institute of Technology 1985-87. The procedure used was not detailed to that of the OECD method, but a similar 5-point scale was used.) CHAPTER m PAVEMENT PERFORMANCE AND PREDICTION 3.1 PERFORMANCE DATA Data files on construction history and pavement maintenance activities are non-existent for the network under study. It was, therefore, not an easy task to replicate the performance of the individual pavement types over time or to make reasonable predictions as to their possible future states. To obtain the information required for deterministic investigation of the pavements' performance would require the repetitive acquisition of condition data over an extensive period of time. Ideally, data collection would consist of complete histories and information collected from roads put into use at the same time so that the ages would be identical. In the absence of such ideal data conditions, several approaches were made to construct data files to support the predictive component of the system. The first conceptual approach was an attempt to consider a representa tive sample of pavement sections of various ages, and of each family (paved, lightly, paved, and unpaved) simultaneously. Assuming that these sections represent the condition of pavements of the same family at various ages, it is possible to deduce a probable relationship between condition values and age 54 55 simultaneous survey sections of a single 1 family. 2 age §3 T3 o4 o 5 age 0 6 1 2 1 8 24 30 years 1 transformation to single service life condition curve 2 for the representative family. §3 o 4 o 5 0 6 1 2 1 8 3024 y e a rs FIG. 9 - A DIAGRAMMATIC REPRESENTATION OF A TRANSFORMATION OF A ONE-TIME PAVEMENT SURVEY 56 by grouping all sections of the same family (different ages). It was necessary to make some simplifying assumptions about traffic and maintenance.Fig. 9 shows a diagrammatic representation of a transformation of average condition values of a simultaneous survey of different sections to a composite condition data versus age performance curve. The second approach was the construction of a condition data file by obtaining subjective performance data from the local experts. Interviews were conducted with several local engineers, superintendents, and technicians who, from their knowledge and experience, supplied subjective predictions on the changes in road condition over time for each family of pavement under a "do-nothing" condition. For consistency, a similar 5-point evaluation scale was used in the interviews. The general assumption was that immediately after construction (year 0) the pavement is in its best possible condition and the condition is evaluated at (1). Over the range of 1-5 condition value points, the pavement will transgress to its worst possible condition. Each expert was required to give a subjective evaluation for all three families of roads. Tables 3 -5 give the data sets generated from interviews with experts. The values are the respective subjective evaluations of pavement conditions over the corresponding service life. The data reconstruction methods by both approaches were evaluated for practicality and appropriateness. Consequently the method of subjective evaluation was selected. The first approach entailed more complexity and requires supporting information on traffic level and maintenance activity TABLE 3 DATA OF EXPERT OPINION ON DETERIORATON OF PAVED ROADS EXPERT OPINION ON DETERIORATION OF PAVED ROADS yr a B C D E F 0 H I J K L M N 0 ? Q R 3 T 0 V W XY Z AX A2 A3 A4 A5 A6 A7 A8 A9 AVCOt 0 1 1 1 1 1 1 1 1 1 1 1 1 X X XX X X 1 X 1 X X XX X X XX X X 1 XXX 1.00C 1 1 1 1 1 1 1 1 1 1 1 1 XX X X 1 X X 1 X 1 X X X XXX X X ' X X X X X X 1.00C 2 1 1 1 1 1 1 1 1 1 1 1 X X XXX 1 X 1 X 1 X X X 1 XXX XX X X X X X 1.00C 3 1 1 1 1 1 1 1 1 1 1 1 X X X X X X X X X X X XX XX X XX X X X XXX 1.00C 4 1 1 1 1 1 1 1 1 1 1 1 X X X X X X X X X X X XX XXXX X X X X XIX 1.00C 5 1 1 1 1 1 1 1 1 1 1 1 XX X XXX X X X X X XX X X 1 XX X 1 X XXX 1.00C 6 1 1 1 1 1 1 1 1 1 1 1 XXXX XX X X X 1 X 1 1 1 1 X . 1 X X X X XXX 1.00C 7 1 1 1 1 1 1 1 1 1 1 1 XXXX XX X X X 1 X 1 XXXX XX X X X XXX 1,000 8 1 1 1 1 1 1 1 1 1 1 1 XXXXX 1 X X X 1 X 1 1 X X X XX X X X XXX 1.00C 9 1 1 1 1 1 1 1 1 1 1 X X X X X X 1 X X X 1 X X X X X X X X X X X X X X 1.00C 10 1 1 2 1 2 1 2 2 2 2 2 2 X 1 X 2 X 2 2 2 2 X 2 X X 2 X 2 X 2 2 1.62E LI 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 X 2 2 2 X 2 2 2 2 X 2 X 2 2 2 X 2 1 2 2 1.742 12 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 X 2 2 2 2 2 2 2 2 2 X 2 2 2 X 2 1 2 2 1,800 13 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 X 2 2 2 2 2 2 2 2 2 2 X 2 2 2 X 2 2 2 2 1.82E 14 2 2 3 2 3 2 2 3 2 3 2 2 X 3 2 3 2 2 2 3 2 2 3 2 2 3 2 2 3 2 3 3 2.314 15 2 3 3 2 3 3 3 2 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 3 3 3 2 3 3 3 2 3 2 3 3 2.742 16 2 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 2.857 17 2 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 3 3 4 3 2 3 3 4 3 3.00C 18 2 3 3 3 4 3 4 3 3 4 3 4 3 3 3 3 4 4 3 3 3 3 4 3 4 3 3 3 4 4 3 4 3 4 4 3.342 19 3 4 4 3 4 4 4 3 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 4 4 3.800 20 3 4 4 3 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4. 4 4 4 4 4 4 4 4 4 3 4 3 4 4 3.857 21 3 4 4 3 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 4 4 3.885 22 3 4 4 4 5 4 S 4 4 5 5 5 4 4 4 4 5 5 4 4 4 4 5 4 5 4 4 4 5 4 4 4 5 4 4.314 23 3 5 S 4 5 4 S 4 4 5 5 5 4 5 4 4 5 5 5 5 4 5 5 5 5 5 4 5 5 5 4 5 4 5 5 4.628 24 4 s s 4 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 S 5 5 5 4 5 4 5 5 4.828 25 4 5 S 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 S 5 5 5 5 5 4 5 4 5 5 4.857 26 4 5 5 4 5 5 5 4 5 5 5 S 5 5 5 5 S 5 5 5 5 S 5 5 5 5 S 5 7 5 5 5 4 5 5 4.885 27 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 S 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.942 28 4 5 5 5 5 5 5 S S S 5 5 5 5 5 5 5 5 5 5 S 5 5 5 5 5 5 5 S S 5 5 5 5 5 4.971 29 5 5 5 5 5 5 S 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 S 5 s 5 5 5 5 5 5.000 30 5 5 5 5 5 5 5 5 5 5 S 5 5 S S 5 5 5 5 5 5 5 5 S 5 5 5 5 5 s 5 5 5 5 5 5.000 TABLE 4 DATA OF EXPERT OPINION ON DETERIORATON OF LIGHTLY PAVED ROADS EXPERT OPINION ON DETERIORATION OP LIGHTLY PAVED ROADS 10i25 THURSDAY, HAY 2, KR A B C D Z t a h I J X L H N O P Q R S TU VW X Y Z A1 A2 A3 A4 A5 A5 £7 A8 A9 AVCON i l l 1 1 1 1- 1 i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.000 i l l 1 1 1 1 1 l 1 1 1 1 1 1 1 1 1 1 1 1 1 1 i l l 1 1 1 1 1 l 1 1 1 1 1.000 1 1 1 1.1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.000 i l l 1 1 1 1 1 l 1 1 1 1 1 1 1 1 1 1 1 1 1 1 i l l 2 11 2 1 2 2 1 1 1 1 1 1.000 1 2 1 2 2 2 1 2 1 2 1' 2 2 2 1 1 1.428 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2. 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 2- 2 2 2 2 2 2 2 2,000 2 2 2 2 2 2 2 3 2 3 2 3 2 2 2.171 2 3 2 3 2 2 2 2 3 3 2 2 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 2 2 2.485 4 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 3 4 3 4 9 3 4 3 4 3 3 3 3 4 4 3 3 4 3 3 3 3 3.142 3 4 4 4 3 4 3 4 4 4 4 4 3 3 3.465 10 4 4 4 5 4 4 4 4 5 5 4 4 5 4 5 11 4 5 3 5 4 5 5 5 4 5 4 4 4.342 5 5 5 5 5 4 4 5 5 5 5 4 5 4 5 5 5 4 5 12 5 5 5 5 5 5 5 5 4.742 5 5 5 5 5 5 5 S 5 5 5 5 5 5 5 5 13 5 4 5 5 5 5 5 5 5 5 5 4.971 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 14 5 5 5 5 5 S 5 5 5 5 5 5.00C 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 S.00C TABLE 5 DATA OF EXPERT OPINION ON DETERIORATON OF UNPAVED ROADS EXPERT OPINION ON DETERIORATION OF UNPAVED ROADS 10128 THURSDAY, MAX 2, 1991 STR ABC D E F 0 H I J K I, M NOP Q RS T UVW X V Z A1 A2 A3 A4 A5 A6 A7 A8 A9 AVCOl 0 1 1 1 1111 1 11111111 11111111111 1 1 1 1 1 111 1.00C 1 2 2 1 2 2 2 1 1 2 1 2 2 2 2 1 1 2 1 2 1 2 2 2 2 1 2 2 1 2 1 2 2 2 1 2 1.657 2 3 3 2 3 3 3 2 2 3 2 3 3 3 3 2 2 3 2 3 2 3 3 3 3 2 3 2 2 3 2 3 3 3 2 3 2.62E 3 4 4 3 4 4 4 3 3 4 4 4 4 4 4 3 3 4 3 4 3 4 4 4 4 3 4 3 3 4 3 4 4 4 3 4 3.657 4 5 5 4 5 5 5 4 4 5 S 5 5 S 5 4 4 5 4 5 4 5 5 5 5 4 5 4 4 5 4 4 5 5 4 5 4,628 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5. 5 5S'55555 5 5 5 5 5 5 5 5 5.000 5 S 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00C Ln v£> 60 over the service life to make meaningful transformations. The data did not always display the intuitive progressive decrease in condition value with age, which would imply a monotonically decreasing data values for the predicted condition versus age curve. The subjective evaluation data, however, consistently gave results of worsening condition with time and required no screening. 3.2 MODELING PAVEMENT DETERIORATION Because of the favorable economic and advanced technological conditions in the developed countries of Europe and North America, the road administration agencies have better capabilities of effectively sustaining a pavement management system. Under these conditions most of the success of the system is attributed to the accuracy of the predictive components of the system, namely the ability to model and predict pavement condition accurately over time. In developing countries, where there is usually low sustainability characteristics, there is little temporal control over maintenance activities. Elaborate and complex predictive procedures might be counter productive if the implementation of projects in a timely fashion is highly uncertain. Three modeling approaches were considered for application to the Guyana network: (1) The M arkov Process (2) The constrained least squares estimate (3) The B-spline approxim ation 61 3.2.1 The Markov Process: The first modeling technique attempted was the Markov probability- based model.1 It describes the behavior of pavements whose rates of deterioration are uncertain. This method was first introduced in the network optimization system of Arizona and was later refined by Butt, Shahin, Fergham and Carpenter (1987). Model Specifications In order to apply the Markov concept to behavior of the pavements in the Guyana Network, the generic structure of the Arizona model was retained. (i) Pavement Classes (ii) Initial Pavement Conditions (iii) Pavement States (iv) Initial State Vector (v) Transition Matrices A design service life of 30 years was established. This time horizon was divided into three zones, each representing a period of (10) years. 1The Markov Process was reviewed and adapted in anticipation of condition data reconstructed from transformations of one time surveys on each family of roads. For each family, several roads of different ages were to be surveyed (as discussed in section 3.1). However, it was not possible to expedite this survey exercise, and the theory reviewed was not applied. No results are available for this method. 62 The initial pavement condition: This condition was measured by an average pavement condition value for both paved and unpaved roads (as discussed under Pavement Evaluation). The Pavement States: Immediately after construction, the pavement was considered to be in its best possible condition. The condition value is considered to be 1.0. Over the range of 0-5 value points, five (5) states were assumed, each state being one value point wide. To allow for changes in traffic loads and maintenance policies over time, a zoning scheme was used. During the life of the pavement it passes through three 10-year transition zones; over each zone it is subjected to five biennial performance cycles. The rate of deterioration within each zone was assumed constant, but the rate of deterioration was assumed to vary from one zone to another. The 10-year period chosen represents the most probable time span that a developing country like Guyana could complete two condition surveys of its network. (The Markov chain needs at least two points per zone for continuity.) State Vector: For each zone there will be developed a separate transition matrix and a homogeneous Markov Chain. It means, therefore, that transition from one zone to another will be achieved by a non-homogeneous Markov Chain. The construction year (year 0) will be the indicator of the beginning of zone (1), and starts in state (1). A state vector is used to indicate the probability of the pavement being in any of the five states at a performance cycle in a zone. The state vector in year 0 is: 1 0 0 0 0. 63 Zone two (2) takes the last state of zone one (1) as its starting state vector. This process is continued for all the zones over the life of the pavement. 1 s P o d ) 2 T state p0(2) 3 A vector p0(3) 4 T Po(4) 5 E Po(5) [p o (l) the probability of being in state 1 at performance cycle = 0.] FIG. 10 - SCHEMATIC REPRESENTATION OF STATE, STATE VECTOR AND PERFORMANCE CYCLE Transition Matrix The determination of the pavement's condition with time is represented by the Markov probability transition matrix. The assumption here is that the pavement's condition will not drop more than one state in two years. The probability matrix has the form p(l) q(l) 0 0 0 0 p(2) q(2) 0 0 P = 0 0 p(3) q(3) 0 0 0 0 p(4) q(4) 0 0 0 0 1 64 p(j) is the probability of the road staying in the state (j) during one duty cycle and q(i) = l-p(j) is the probability that the road deteriorates down to the next lower state (j+1) in two years. Given any performance cycle 't', the state vector is obtained by multiplying the initial state vector p(0)by the transition matrix P raised to the p o w e r’t'. p(0) = p(0) x P p(2) = p(l)xP = p(0)xP2 tf (I it if n ii p(5) = p(4)xP = p(0)xP5 By estimating the transition matrix probabilities, the future state of the road at any performance cycle't' can be predicted. A non-linear programming approach estimates the transition matrix probabilities by using the Fletcher Powell algorithm. The objective is to determine the values of the four parameters p(l) through p(4), which minimizes the absolute distance between the actual condition value versus age data points and the expected pavement condition for the corresponding age generated by the Markov chain using these parameters: the objective function may be written as follows: n m(t) Min . . X £ I Y(t ,j) - E[x(t ,p)] I . (3.1). t=l j=l n = total number of performance cycles for which index versus age data points are available. m(t) = total number of data points recorded at a performance cycle't'. 65 Y(t,j) = condition index ratings for each sample taken at a cycle age’t’. E[x(t,p)] = expected value of pavement condition at a duty cycle age't', as predicted by the current Markov values. 3.2.2 The B-Spline Approximation This curve fitting technique for plotting average condition versus age data utilizes a polynomial curve fitting procedure. The selected points of the data set are joined using a cubic spline method with continuous second derivatives. The specific routine (available on SAS/GRAPH software) uses a piecewise third degree polynomial for each set of two adjacent points and matches the first and second derivatives of neighboring segments at the points. The software produces a refinement to this procedure by fitting a smooth line to noisy data using a spline routine. The cubic spline minimizes a linear combination of the sum of squares of residuals of fit and the integral of the square of the second derivatives. The data set generated by subjective evaluation was averaged on an annual basis and the knots of the spline (selected points) were chosen by the special "smoothing" techniques of the SAS software. There was no possibility of the occurrence of a positive trend in the function since subjective expert evaluation always resulted in monotonically decreasing condition versus age data. Figures 11-14 show the performance curves reproduced for the three families of pavements using the B-Spline technique. 66 CONDIN B - s p lin e Least squares -2 -3 0 10 20 30 YE HR FIG 11 PERFORMANCE CURVES FOR PAVED ROADS IN GUYANA 67 CONDTN B - s p lin e Least square 2 -3 4 5 0 2 3 5 6 7 8 9 10 1 11 12 13 14 TERR FIG 12 PERFORMANCE CURVES FOR LIGHTLY PAVED ROADS IN GUYANA 68 CONOTN B - s p lin e L east s q u a r e s -2 -3 3 4 5 6 TERR FIG 13 PERFORMANCE CURVES FOR UNPAVED ROADS IN GUYANA 69 CONDTN p av ed lig l paved un »ved 0 S 10 i 5 20 25 30 YEAR TABLE 14 A COMPARISON OF PERFORMANCE CURVES FOR PAVED, LIGHTLY PAVED AND UNPAVED ROADS 70 3.2.3 Constrained Least- Squares This method uses polynomial curve fitting techniques According to Shahin et al., 1987., validated research has shown that the third order polynomial gives relatively accurate results in the search for an operational response of the deterioration function. Using the mathematical constraint that the first derivative of the function at any age is ^ 0 The function below was adopted: n 2 Min £ [Yi-C(xi)] (3.2). i=l Subject to: C(0) = 1 (condition at construction). C'(xi) ^ 0 for any Xj between 0 and 30. C(xi) is the value of the response function at age xi C(xO = Po + PlXi + 02Xi2 + foxi3 ...... (3.3). By just averaging the subjective data on an annual basis the response data is always monotonically decreasing and the constraints are satisfied. The search for the optimal response was done by using the cubic curve fitting routine of the SAS/GRAPH software. Plots showing the average condition values, and fitted performance curves for the three families of pavements (paved, lightly paved, and unpaved) are presented in figures: 11 through 14.2 2The SAS program assumes 'O’ for end values of data and requires the data to be monotonically decreasing. The curves, therefore, display atypical characteristics at both end regions. These regions of the curves are, however, of no relevance to the prediction process for planning maintenance activities. CHAPTER IV IMPROVEMENT NEEDS AND ECONOMIC EVALUATION 4.0 IMPROVEMENT NEEDS Terminal roadway conditions are derived by combining the principles of acceptable maintenance practices with the levels of acceptability of the users, while at the same time giving consideration to the most economically advantageous period for maintenance in the service life of the roadway. In a developing country the level of conflict between these guidelines makes it difficult to develop stringent criteria for determination of the "need year" of the pavement - (the point in time when the pavement reaches its terminal acceptability). Taking advantage of the literature on the relationships between maintenance cost and pavement condition in developing countries, (Sharaf, El-Waquad and Saleh 1990), the principle of the three stage curve was investigated. From a relative conversion of the condition scales ( 0-100 to average condition values, a scale of 1-5), the concept of varying cost gradients were applied to the families of roads in the Guyana network. Figure 15 shows a diagram of restoration cost gradients for different roadway conditions. The deterioration process assumes progressive failure from surface to base and 71 PAVED AND LIGHTLY PAVED ROADS o> O) U) CM CO CONDITION UNPAVED ROADS h- CONDITiON FIG. 15 MAINTENANCE COST GRADIENTS FOR DIFFERENT ROADWAY CONDITIONS 73 sub-base; the maintenance assumes repair action to the same structural capacity with the same type of material, and ranges from patching and crack sealing to surface reconstruction. The gradient of the cost vs condition curve for unsurfaced roads show no significant rate of change with worsening condition until it is in very poor state where major rehabilitation - the last option must be applied. When scheduling maintenance, it would economically advantageous to do light maintenance in stage 1. (shown in Figure 15). However, user acceptability and scarcity of maintenance funds result in agencies delaying necessary remedial works to the second and third stages. For developing countries, a terminal condition value of '3' should result in maintenance being implemented at a stage when the restoration cost gradient is still small and the pavement condition is still acceptable to the travelling public. Using the performance curves developed and knowing the terminal condition, the annual accumulation of improvement needs for the existing network may be obtained. By monitoring the development plan of the road transportation sub-sector, information on the capital improvement (new links) for each fiscal year may also be obtained. A graphical representation using hypothetical values for the distribution of restoration costs and capital improvement needs for the network, is given in Figure 16. The following conditions are the constraints within which the decision maker must operate:- (1) Detailed condition surveys are few and far between, and the need year is more often estimated from the performance prediction curves, ie. aggregate I.6 HYPOTHETICALFIG.16 IMPROVEMENT10-YR. NEEDS miles of road 100 1990 1 991 1 992 1993 1994 year 1 995 1996 rew □ SURFACED UNSURF. L/SURF 1 997 1998 1999 75 models of individual families of pavements must be used to explain disaggregate performance of individual links and sections of the respective family classification. Using the performance curves, the need year is determined on the basis of time. (2) The decision to, 'do nothing’ is not uncommon over the service life of the pavement. 4.1 ECONOMIC EVALUATION One of the more critical prerequisites of the decision making process for roadway infrastructure improvements is the economic evaluation. Any such evaluation involves a comparison of the benefits and costs of different improvement alternatives during each year in the planning period. Principles of economic evaluation for roads in developing countries have been given exhaustive consideration. It is not the intention of this study to investigate any new approaches or strategies for evaluating the economics of road improvements in Guyana, but rather to use established procedures which matches the countries fiscal programme and financial disbursement schedules. The methodology that may be used in Guyana is one based on a comparison of alternatives by life-cycle costing procedures. The cost analysis is done using the Equivalent Uniform Annual Cost Method (EUAC). This method allows for evaluations over different analysis periods. The alternative with the lowest EUAC is considered the most plausible option. 4.1.0. TRANSPORTATION COSTS EVALUATION The total transportation cost is composed of: (1) Initial Construction Costs. (2) Periodic Maintenance Costs. (3) Road-User Costs. 4.1.1 Initial Construction Costs The initial cost is made up of two basic components (a) Fixed costs (b) Variable costs. The Fixed Cost component is a direct multiple of the local cost per unit measurement of facility (usually expressed as cost per unit area of facility). The fixed cost (Tk) of any maintenance and rehabilitation alternative (or new construction) has been appropriately expressed by Sharaf et al 1982, as: n Tk = IC ik * Fik (4.1) i=l where: Tk = Total unit area (sq. yd) fixed cost for the kth M&R alternative; Cik = Average area (sq. yd.) unit cost for the ith cost item in the kth alternative; Fik = Frequency of the ith cost item in the kth alternative; and n = Total number of cost items. The variable cost component comprise the expenses associated with site and pavement preparation for maintenance and construction works. Different pavements will be in different conditions of disrepair at the time 77 improvement is contemplated, also each job has a related specification which determines the preparation strategy. An equation representing the total surface preparation cost (SPCki) may be represented in a similar manner: n SPCki = ISC ikl * Fjki (4.2) i=l where: SPCki = Total surface preparation cost for the kth surface type in condition T; SCiki = Average unit cost of surface preparation for the ith cost item required to improve the kth surface type in condition T; Fikl Frequency of the ith cost item in the kth alternative; and n = Total number of cost items. The total Initial Construction Cost (ICC*) is therefore the sum of the total fixed cost and the total variable cost. ICC* = Tk + SPCki. (4.3) From the above relationship it is possible to obtain the initial construction cost per unit area of roadway at different conditions of disrepair. 4.1.2 Periodic Maintenance Cost Periodic maintenance, like surface preparation, depends on the state of deterioration of the facility at the time improvement is considered. The 78 equation of the periodic maintenance cost (PCki) takes the same form as the one derived for surface preparation. n PCki = ZPCikl * Fikl (4.4) i=l where: PCki = Total maintenance cost for the kth alternative of pavement in condition T; PCiki = Average unit cost of maintenance for the ith cost item required to improve the kth alternative in condition T; Fikl = Frequency of the ith cost item in the kth alternative of pavement in condition T; and n = Total number of cost items. 4.1.3 Road User Costs Methods for the determination of this cost component has presented quite some variability among agencies because of the three unique components of which it is comprised: (1) Vehicle operating costs; (2) Travel time costs; and (3) Accident costs The most significant component is vehicle operating cost, which has the following elements: fuel, oil, grease, spares, depreciation etc.. Extensive research has been done to establish relationships between this cost com 79 ponent and the following four factors: roadway factors, vehicle factors, traffic factors and environmental factors. An appropriate methodology for computing the total vehicle operating cost in developing countries has been summarized in five steps by Watanatada et al (1986). (1 ) Classify vehicles and determine average operating speed for each category. (2) Determine the consumption per vehicle mile for the following resources: (a) Fuel. (b) Tire wear. (c) Lubricants. (d) Crew. (e) Depreciation. (f) Interest. n £ O Q (4.5) i=l where: 80 OQ = Total operating cost of traffic group ’i'; i = 1 ,2 ,3 ,...... n; and n = number of traffic categories. 4.1.4 Accident Costs Accidents are as a result of three categories of causal factors; roadway characteristics, vehicle characteristics, and environmental factors. The complexity of the inter-relationships between these factors has resulted in large scale research throughout the world. Jacobs and Sayer, (1983), has shown that although accident costs are large cumulatively - approximately 1% of the Gross National Product in some developing countries, when compared with the Vehicle Operating Cost, this amount is negligible. Their analysis also suggested no sensitivity to roadway characteristics. In this study accident costs will not be included as an element in the computation of the Total Road User benefit/costs. 4.1.5 Travel Time Cost The persistence of low incomes and un/underemployment in developing countries makes the component of travel time costs of little importance in the overall assessment of road user costs. This study will not consider the value of travel time in the computation of road user costs. 81 4.2 TRANSPORTATION BENEFITS The significant benefits from improved transportation include any or all of the following ( Alder 1987): (1) Reduced operating expenses, (2) Increased comfort and safety, (3) Time savings (passenger and freight), (4) Reduction in accidents, and (5) Stim ulation of economic developm ent. In a similar manner as in the computation of transportation costs, not all benefits are present in every project, and whenever they exist they are in different magnitudes of importance. The benefit most readily expressed in monetary terms is the reduction in operation expenses. This benefit together with the stimulation of economic development has been most widely used for computing total transportation benefits in developing countries. The 'with' or 'without' comparison is the recommended approach for measuring economic benefits from roadway investments; ie. to consider what the road user costs with the project will be as compared to what they will be without the project, the difference will be the benefit attributable to the project or specific alternative considered. 4.2.1 Economic Benefits When a new road is constructed or an existing facility is upgraded, there is new potential for economic development from which benefits of the roadway facility may accrue. These benefits are due to additional production, 82 expanded markets for produce, or more efficient production because of improved accessibility resulting from the new project. Additional Production: Of particular importance to developing countries with unimproved road networks is the construction of new roads in areas with development potential. These new roads facilitate the rapid and extensive development of economic activity (eg. agricultural, mining etc.) which would not have normally taken place without improved access. The benefit attributable to the project would be the net value of the additional production. To quantify these benefits in monetary terms , it is required to estimate the produce according to the market price and deduct the expenses associated with its production (transportation, materials, labor, etc.) Expanded Market: Of lesser importance to developing countries are the benefits which accrue from widening of markets for new and existing production. New and improved facilities enable the rapid transportation of produce to selective markets. The benefits are calculated by the difference in quantities and value of the commodities in the old and new markets minus the transport costs. 4.3 MEASURES OF ECONOMIC EFFICIENCY To justify the implementation of a project, it is necessary to compare the benefits and costs for each year over the useful life of the project. Naturally these amounts are discounted at some appropriate discount rate to account for the value of time on money, before the comparison is made. 83 Since it is impossible to implement every feasible project because of financial and other constraints, it is helpful to know the levels of economic efficiency that is associated with each feasible project. Commonly used measures of economic efficiency of projects include the net present value, the internal rate of return, and the benefit cost ratio Each method has advantages and disadvantages and the choice would depend on the purpose of the economic evaluation. The Net Present Value (NPV): This is the measure of the difference between the discounted costs and benefits of a project. A common limitation of this and other methods is the determination of the true opportunity cost of capital - this more often is not known and has to be estimated based on the interest rate being paid by the banks. The Internal Rate of Return (IRR): This is the discount rate at which the difference between the discounted benefits and costs equals zero. This method eliminates some of the problems associated with the NPV but has its own limitations. It may lead to poor ranking of projects if the following conditions are not satisfied (Weiss 1974): (1) The stream of benefit flows for all alternatives are proportional to each other year by year. (2) All alternatives have identical life spans. (3) No large expenditures occur during the project life after the initial investment such that a negative cash flow occurs in any future years 84 The Benefit Cost Ratio (B/C ratio) is the ratio of the total discounted benefits to the discounted costs, and is dependent on the selected discount rate because investments are in the present and benefits are derived in the future. The higher the interest rate the lower would be the future benefits. The First Year Benefit Ratio (FYBR) and The first Year Rate of Return (FYRR): These measures of efficiency are of special importance to the road improvement plan, since projects which are rejected from the road improvement program in any given year are still candidates for consideration in following year. It is therefore necessary to know which project is most suitable for immediate implementation; hence necessary to investigate the First Year Benefit. Details on the economic appraisal of projects in developing countries may be obtained from the large collection of literature prepared by the the World Bank and other agencies. CHAPTER V PRIORITIZATION As a general rule, the requirements for roadway infrastructure improvement in developing countries exceed the resources available (money, manpower, machines etc.)- The task of establishing priorities among the various proposals and ultimately producing a works program presents a formidable challenge to the engineers and planners. In Guyana like many other developing countries, the roadworks rest exclusively in the domain of the public sector. The authority to utilize public funds for potential projects is obtained by way of a development budget. This budget, when submitted, is a reflection of the roadway infrastructure improvement needs; however, the instrument, which is finally approved and used to schedule project investments, is generally not so generous. This budget (as passed) limits the implementation program which in itself requires some form of prioritization of the various improvement alternatives. This study seeks to establish methods of setting priorities for the road improvement program by considering all the primary factors that influence the decision process. The primary factors may be grouped under one of the following headings: 85 86 (1). Economy, (resource factors). (2). Com m unity (socio-economic factors). (3). Politics. (4). Organizational Capacity. Intuitively, the extent to which each factor contributes to the final ranking varies uniquely with the country within which the planning is done. In developed countries where there are reasonably well developed roadway infrastructure, the bulk of the road improvement budget is expended on maintenance operations. The prioritization process is adequately established on the basis of economic justification; of little concern are the systemic problems like unavailability of skills, obsolete machines, inadequate cash flow, and political instability. The economic evaluation revolves around the maintenance alternatives and the effect of time on these investments. On the other hand, as this study emphasizes, that because of unfortunate social, political and organizational conditions in developing countries, attempts must be made to combine economic evaluation with social, political and organizational considerations in the evaluation of improvement priorities. Traditional approaches for ranking alternatives began with an economic analysis; selecting the alternative which made the best use of the limited resources. This alternative was then checked for financial, social and institutional viability. If the best choice, from an economic point of view, fails any of the other tests, the next best solution from the economic hierarchy 87 is checked until a plausible alternative is obtained (Ogelsby 1975). This procedure involves the combination of the objective economic analyses with the extremely subjective checks for political and institutional viability. This study attempts to reduce the high input of subjectivity into the evaluation of improvement priorities. Attempts to reduce the scope of subjectivity in the evaluation dates back to 1983 when the international financing agencies particularly the IBRD, IADB and The World Bank collaborated with South American governments on the idea of evaluating economic factors jointly with some social factors. A formal methodology to combine economic evaluation together with social factors was proposed by Greenstein and Bonjack (1983) in Ecuador. This study was conducted on a limited number of social factors, namely population density and rate of illiteracy. The choice was founded on the rationale that the higher the population density, the greater would be the demand for transportation between various origins and destinations. It follows that for a given investment the total social benefits to be derived from road improvements will tend to be greater as population density increases. Greenstein et al 1983 defined this social factor by a population index (PI). P I = population in the road's influence area .... (5.1) construction cost The social factor which captured rate of illiteracy was defined as an education index (El). El = (RI) * (PI) (5.2) where : RI = the rate of illiteracy of the population in the area of influence of the road (as a percentage); PI = population index. 88 In order to combine the economic and social factors together in the decision process, they derived a socioeconomic priority index (SEPI) by assigning empirical weights to represent the extent to which each factor contributes to the decision process. A discussion of this expert based empirical model is given in section 1.4. The relationship of the decision index to the economic and social factors is given in equation ( 1 .8 ) 5.1 SOCIOECONOMIC AND INSTITUTIONAL CONSIDERATIONS Primarily due to the involvement of the World Bank and other agencies monitoring development in developing countries a comprehensive database of macroeconomic development indicators are available to planners in these countries. The empirical model considered two macro-economic factors, population and education, but the literature in the foregoing chapters revealed that there are a number of other social and institutional factors which are important to the determination of the country's output trends. It is therefore necessary to account for them in the investment decision process. The influence of these factors vary in magnitudes from country to country and from sector to sector within countries. As a result it is inadequate to assume that two factors (ie. population and education) would capture the full social impact of the decision making process in any country where road improvement is contemplated. In Guyana the maintenance and improvement of the roadway infrastructure is solely the responsibility of the public sector. Considering the 89 road improvement strategies and their related budgets for the country over a fixed period of time, the investment program in any given year is expected to reflect some systematic association with the magnitude of influential macro- economic index in that year. Using the theory of establishing priorities developed in equations 1.1 through 1.9 a road improvement priority index (RIPI) will be developed. This index will express the amount of individual consideration given to economic social and institutional factors in the road investment decision process. Chapter six gives a detailed statistical analysis on the social, economic and institutional indicators which were assumed to be influencing road investment decisions in Guyana. From the analysis, a relationship between the proposed expenditure and the actual expenditure is derived for both current and capital works programs. The proposed expenditure is the annual estimate calculated directly from the network improvement needs in any given fiscal year. The actual expenditure is the total capital outlay on works executed calculated at the end of the fiscal year. The models express actual expenditure as a function of proposed expenditure, social indicators and institutional factors. The data suggested that the political factor was not statistically significant in a test for influence on actual expenditure. 5.2 RANKING OF ALTERNATIVES A brief overview on the method of determining the amount of consideration attributed to each influential factor is given in section 1.4.1. 90 Here it was recommended that the regression approach be used to investigate road improvement investment behavior. 5.2.1 Attributes of The Priority Utility Model In order to utilize the information on the general development, economic, and organizational factors in a priority model, there is need to establish quantifiable measures of efficiency based on these factors. The evaluation, therefore, requires a number of threshold analyses to obtain these criteria. Specific to each group of influential factors, there may be more than one criteria established. Depending on the nature of the project alternatives (eg maintenance or new facility, local or foreign funded). It may be necessary to select the most appropriate criteria to be used in the priority model. The following paragraphs present the methods used in the study for establishing measures of efficiency based on the economic, development, and organizational factors. Economic efficiency: This criterion is a measure of the effective and efficient utilization of money. In chapter four the most commonly used measures of economic efficiency were discussed. Appropriate selection will be made from among these measures for use in the priority model depending upon the nature of the project alternatives. Of importance to lending agencies is the relative efficient utilization of available funds by competing 91 agencies. As a general rule, a minimum First Year Rate of Return of twelve percent is used as a cut off point for consideration of potential projects. Efficiencies of Development: This study groups attributes of development into two categories: (a) attributes of general development and (2) attributes of economic development. The derived quantitative measure that describes development performance must be obtained from information on all significant general development indicators. This information has been combined by way of the principal component analysis to produce a quantitative variable "Develop 1". This measure has '$US per capita 1 as its units and may be multiplied by the population in the road's area of influence to produce a general development index (GDI) which is used in the priority model as the attribute of general development. The attribute is therefore defined as: GDI = (Develop 1) x (population in influence area) . . . .(5.4) The measure of economic development obtained from the principal component analysis is 'Econo 1', which has '$US'.as its units . To be used as an attribute in the priority model this factor is converted to an economic development index (EDI) by multiplying it by the ratio of the population in the road's area of influence to the total country's population. The attribute is therefore defined as: EDI = Econo. 1] x (population in influence area) . . . .(5.5) Population Efficiencies of Organizational Capacity: The significant criteria by which performance of the organization is measured is defined as the 92 management capacity (MC), this is expressed as a percentage of the number of engineers per 1 0 0 miles of primary road network to the number considered adequate for improvement works in Guyana (twelve). This measure is used unadjusted as the attribute of organizational efficiency in the priority model. MC = (No. of engineers per 100 mis, of improvement.) x jqq (5 6 ) 12 5.2.2 Determination of Relative Weights In order to complete the requirements for the priority model it is necessary to determine the percentage of consideration allocated to each attribute in the model. From a regression analysis of the actual expenditure against the proposed expenditure and values of influential macroeconomic factors, the coefficient of partial determination 'r2, for each independent variable can be obtained. This coefficient measures the marginal contribution of the respective variable when all other variables are in the model. In relation to the actual expenditure, it gives the percentage of the variability in actual expenditure explained by use of one of the variables in the model when all other influential factors are already in the model. Since the data represents a time series of actual expenditures and macroeconomic magnitudes, the percentage of variance accounted for by each variable simulates the judgement of the decision maker and may be used as a measure of consideration given to each of the attributes. To present some background and theory on the coefficient of partial determination, refer to the first order multiple regression model (discussed in chapter six) of the capital account analysis. 93 $ Actual expenditure = 14.5 + 1.13 (Programmed expenditure) + 1.9 (Developl) + 3.9 (Econol) + 2 0 (management capacity) (5.7) For simplicity of notation, consider that this model is of the form: (5.8) From the sum of squares regression statistics;SSE(Xi) measures the variation in Y when Xi is included in the model. SSE(Xi,X 2 ,X3 ,X4 ) measures the variation in Y when all four variables are included in the model. The relative reduction in the variation of Y associated with Xi when X2, X3 and X 4 are already in the model is: sse (x 2,X3,x4) - sse(x 1,x 2,x 3,x4) sse(x 2,x 3,x4) s s r (x 2,x 3,x 4 / x 1) . . .(5.9). sse(x 2,x 3,x4) This is the measure of the coefficient of partial determination between Y and X1 given that X2 ,X3,and X 4 are in the model. This measure is represented by the notation r2yl 234 • From the sum of squares regression statistics given in outputs 11 - 12 of Appendix E, the coefficients of partial determination between ’Actual Expenditure’ and 'Programmed Expenditure' when all other factors are in the model, were calculated as: r2yl 234 = 0.37 (capital acct.); 0.89 (current acct). 94 The coefficients of partial determination between 'Actual Expenditure' and 'General Development variables' when all other factors are in the model, were calculated as: r 2y2 134 . = 0.05 (capital acct.); 0.46 (current acct.). The coefficients of partial determination between 'Actual Expenditure' and 'Economic Development variables’ when all other factors are in the model, were calculated as: r 2y 3 124 • = 0.17(capital acct.); 0.42(curent acct.) The coefficients of partial determination between 'Actual Expenditure’ and 'Organizational Variables' when all other factors are in the model,were calculated as: r 2y4 123 . = 0.35 (capital acct.); 0.16(current acct.) Since the influence of these attributes are to be compared against each other, there is need to obtain relative weights for use in the priority model, the relative weights are obtained by applying the following standardization procedures: 2 r . i a i = (5.10) * 2t where, aj = the relative weight of attribute i in the priority model, and £ai = 1 The relative weights derived by this method using the respective coefficients of partial determination are as follows: 95 Capital Account Analysis Economic 0.40 (a) general development 0.05 Social (b) economic development 0.18 organizational 0.37 Current Account Analysis Economic 0.46 (a) general development 0.24 Social (b) economic developm ent 0.22 organizational 0.08 CHAPTER VI INVESTMENT BEHAVIOR MODELING The research hypothesis is that a number of development and organizational characteristics are systematically associated with investment behavior (road improvement decision making and response). The task at hand was to express in a formal and quantitative way the nature and extent of these relationships and also to determine the effects on behavior when these characteristics prevail at different levels. The technique which was used to investigate these relationships is Regression Analysis. 6.1 MODEL SPECIFICATIONS The investigation considers a model linear in the parameters (i.e. not necessarily in shape and response surface). Consider a case of p-1 independent variables Xi, X 2 , X3 , ... ,Xp-i, the regression model can be written as: Yi = Po + P i X i l + P2Xi2 + . . . •+P(p-l)Xi(p-i) + ei . . . . ( 6 . 1 ) in which: Po, Pi, P2, . . .,P(p-l) are parameters; Xn, Xj 2, . . . ,Xi(p-i) are known constants, or namely, the values of the independent variables in the ith observation; and 96 97 Et is the independent random error term with mean E{et) = 0 . By setting Xio = 1, the model may be expressed in the following form: Yi = PoXiO + PlXii + p 2 Xi2 + • . . .+P(p-l)Xi(p-i) + Ei. ..(6 .2 ) or p -1 XPkXik + £i (6.3) k = 0 w here: X jo = 1 The above model assumes that there are no interaction effects, i.e. change in mean response with a unit increase in any X i is independent of all other X j in which J is not equal to i. If interaction effects are considered, the model will contain cross- products of terms of all combinations of Xij and Xjk where j is not equal to k and j = 1 ,2 ,3 ... p-1; k = 1,2,3 ... p-1. For example, considering two independent variables Xi and X 2 and assuming interaction effects; the general regression equation becomes: Yi = Po + PlXii + P2Xj2 + P3Xii Xi2 + Ei (6.4) Here, the change in the mean response with a unit change in X 2 w hen X\ is held constant is: P 2 + P3X1 Similarly, the change in mean response with a unit change in X\ w hen X2 is held constant is: Pi + P 3 X2 6.1.1 The D ependent Variable The traditional framework of pavement management systems (i.e. from the condition evaluation to the point of prioritization) is designed, to produce an optimal investment strategy from the following two points of view : 98 (1) Selecting projects in such a way as to maximize total benefits as a result of road improvements, without violating constraints. (2) Selecting projects in such a way to minimize total cost without violating pre-spedfied performance targets. Nowhere in the structure are the social and institutional factors accommodated in the attempts to maximize benefits or minimize cost. The model therefore investigates whether there is a systematic and persistent relationship among some hypothesized influential macroeconomic indicators and significant output trends of the road improvement program. A significant indicator which reflects the output of the improvement program in any fiscal year is the actual (as-executed) improvement costs .3 This indicator is chosen as the response or dependent variable of the model. It will be measured in Guyana dollars and, for simplidty, the costs measured will be finandal costs. The variable name used is ’ACAP' for capital expenditure and 'ACUR' for current expenditure. 6 .1 .2 The Independent Variables Considering the postulate that a number of social and institutional characteristics are assodated with agency investment behavior, the potential 3It should be noted here that inflation would result in a mis-representation of the actual output in terms of volume of road-work. The unavailability of 'as built' road improvement data resulted in the use of the readily available but less accurate 'as executed ' financial data. 99 independent variables of the model include the proposed expenditure .4 The names used for this variable are 'PCUR' in current expenditure analysis and 'PCAP' for capital expenditure analysis. The other variables were chosen from among the following macroeconomic groups: G roup 1 - DEVELOPMENT INDICATORS (a) Basic Indicators Gross National Product (GNP) Xi Population (POP) X 2 Gross Natural Income (GNI) X 3 (b) Social Indicators Employment (EMP) X 4 Education (ED) X 5 (c) Economic Indicators (i) External Debt Total Ext Debt (EXD) X$ International Resources (IRE) X 7 (ii) Balance of Payments Current Account Bal.(CAB) Xs (iii) Foreign Trade Fuel (FU) X9 Manufacturers (MAN) X 10 (iv) Central Govt Finances Government Deficit or Surplus (GDFCT) Xn 4The ’Proposed Expenditure’ is the cost of road improvement work estimated by the Road Administration Division for each fiscal year. This amount is submitted to the Budget Committee for approval. 100 Group 2 - ORGANIZATIONAL INDICATORS Construction Policy (CPOL) XU Management Capacity (MC) X13 Group 3 - POLITICAL INDICATORS Political Rating (POLR) X14 6.1.2 The Variable Descriptions The development indicators were adopted from a document of world tables, extracted from the data files of the World Bank (39). The definitions and descriptions are consistent with the aforementioned source and are listed hereunder. GENERAL DEVELOPMENT INDICATORS The general development indicators initially selected are Gross National Product, Population, Gross National Income, and Foreign Exchange Rate. These represent the traditional, internationally used indicators to evaluate levels of development of countries. The world bank report (38) indicates that they are used extensively throughout developing countries. Population This is a measure of the country's population estimated at the middle of the stated year (thousands). Gross National Product The total value of goods and services produced over the stated year by the country in dollars (US $). 101 Gross National Income This is generally an estimate per capita stated in dollars (US $). Foreign Exchange Rate This may be described as the annual averages of market exchange rates for the country and is quoted in units of Guyana dollars per U.S. dollars. SOCIAL INDICATORS The social indicators chosen are Employment and Education. E m ploym ent The size and composition of labor force within a non-industrial non oil producing developing country is indicative of the capacity of construction agencies and the possible rates of execution of their projects. This variable is usually reported as a percentage of population of working age. Education ( Secondary School Enrollment Ratio ) This is the gross enrollment of all ages at the secondary level as a percentage of children in the country’s secondary school-age group (including pupils enrolled in vocational, or teacher-training secondary schools). The secondary school age is considered to be 12-17 years. The level and quality of decision making within an agency in a developing country will be associated in some way with the education, training and expertise of personnel that it possesses. The basis for such skills is a secondary education, hence this variable is intuitively expected to reflect some association. 102 ECONOMIC DEVELOPMENT INDICATOR The Economic development Indicators chosen were grouped into the sub categories of External Debt, Balance of Payments, Foreign Trade and Central Government Finances. The specific indicators chosen are those which would intuitively make a significant impact on the country's cash flow on maintenance projects. External Debt Total External Debt This variable gives the amounts disbursed and outstanding expressed in dollars at the official exchange rate at the end of each year (US $). International Reserve This reflects the states of the country’s monetary authorities (central banks, currency boards, etc.) holdings of special drawing rights under the International Monetary Fund (SDR's) quoted in thousands of US dollars. Balance of Payments Current Account Balance This factor is described as the sum of the net exports of goods and nonfactor services, net factor service income, and net transfers. (US $) Foreign Trade (Value of imports c.i.f) Fuels Non-oil producing developing countries are always at a serious disadvantage for acquiring foreign exchange, and therefore for the acquisition of road maintenance materials (asphalt, cement). This indicator comprises mineral fuels and lubricants, and selected materials. This measure is a value 103 of imports, [cost, insurance and freight (c.i.f)] in millions of current U.S. dollars. Manufactures Included under this category are chemicals and related products, basic manufacturers, machinery, and transportation equipment. This variable reflects the degree of mechanization of the country's construction agencies and is intuitively expected to have some impact on output. (This factor is measured in million of U.S. dollars import c.i.f.) Central Government Finances Government Deficit or Surplus This is a basic measure of the excess or deficit of the sum of current and capital revenue, including all grants received, over-current and capital expenditure. The World Bank in estimating this factor considers current revenue to comprise both tax and non-tax revenue. Tax received includes income tax, social security, profits property taxes, domestic taxes on goods and services, etc. Non-tax revenue includes grants, property income and operating surpluses of departmental enterprises, administrative fees, charges, etc. Current expenditure, on the other hand, is basically expenditure for goods and services, interest payments and subsidies. Capital payments are excluded. The measure is stated in millions of Guyana dollars. INSTITUTIONAL INDICATORS The institutional indicators were chosen from two subgroups, namely Organizational and political. 104 Organizational Indicators An important organizational indicator is Construction Policy. W ith special reference to implementation, experience with road administration agencies in developing countries has shown that the methods of execution, either force account or contractual services, yields different results for outputs of production and project duration. It is the view of the road administration experts in Guyana, that the differences in annual output become significant if the road administration contracts out more then 30% of its annual program. In order to represent the difference in levels of output, indicator variables were introduced into the model. For any annual improvement program if 30% or more of the volume of works was contracted out the indicator '1' is used otherwise it is represented by 'O'. Another significant organizational indicator is Management Capacity. The capacity of the agency to implement its programs depends to a large extent on the quality and quantity of technical skills available. In developing countries there is usually an adequate supply of unskilled labor and a scarcity of trained technical personnel. Standard road maintenance practice specifies what the critical numbers of "engineers" for given amounts of "network mileage."should be, for effective organizational management. The generally accepted practice, according to Guyana's standards, is twelve engineers per 100 miles of primary networks. Dummy variables are used in the model to represent the difference in staffing levels in any given fiscal year on primary road projects. For adequately staffed organizations, ie. 12 or more engineers 105 per 100 mis. of improvement, a dummy variable of ' 1' is used in the model, otherwise a variable of 'O' is used. Political Indicators: Political Rating : Developing countries have traditionally depended upon international lending agencies for funding their major road rehabilitation works. The ability of these countries to service their debts has been a major concern to these lending agencies, and many approaches have been sought to quantify and compare risk among countries. It was recognized that risk is comprised of both economic and political factors. The economic indicators measure the country's ability to repay its debt, this measure was adequately captured by the aforementioned variables in the economic category. The political indicators which reflect the country's willingness to pay debt have not been captured. Using a combination of two groups of characteristics, namely characteristics of political stability, and governmental characteristics, a political rating scheme was represented by the Texas Commerce Bank and reported by John B. Morgan, (1985). This checklist approach for tracing the political rating of the country was adopted and a subjective index (long term rating) was deduced for the model. Appendix D shows the checklist for political rating by Texas Commerce Bank. 106 INFLUENTIAL MACROECONOMIC INDICATORS DEVELOPMENT ORGANISATIONAL POLITICAL POPULATION EMPLOYMENT CONSTRUCTION G.N.P. POLICY G.N.I. EDUCATION MANAGEMENT F. EXC. CAPACITY BALANCE OF FOREIGN CENTRAL GOVT EXTERNAL DEBT PAYMENTS TRADE FINANCES TOTAL EXTERNAL FUELS DEBT CURRENT GOVERNMENT ACCOUNT DEFICIT INTERNATIONAL BALANCE MANUFACTURES RESERVES FIG. 17 - CONCEPTUAL CHART OF COUNTRY SPECIHC INFLUENTIAL MACROECONOMIC INDICATORS. 107 Having discussed all the groups of indicators, and described each factor considered influential to the road improvement decision process, Figure 16 is presented to show a conceptual chart of the country specific macroeconomic groups and the political and organizational factors considered. 6.2. DATA COLLECTION AND PREPARATION The study period represents twenty-five years of the country’s independent rule from 1966 - 1990. The scope of the model was restricted to this period. Data prior to this period was not used for the model's development. The intuitive argument here is that the agency behavior under colonial rule would not be consistent with that of an independent country. The social and economic development data were obtained from the data files of the World Bank - World Tables (39). The organizational and network budget data were obtained from the files of the Ministry of Communications Road Division and the Ministry of Finance. The political data was derived according to the guidelines of the Texas Commerce Bank country risk assessment model,(see Appendix D). From the list of conceptually useful independent variables a screening process based on the quality of data obtained was done. The variables describing employment and government deficit were screened out owing to the inadequate and incomplete nature of the observations collected. Table 6 shows a complete data set of the potential influential variables used in the study. This data represents the annual magnitudes of the macroeconomic indices for Guyana over the period 1966-1989 TABLE 6. - DATA ON HYPOTHESIZED INFLUENTIAL MACROECONOMIC VARIABLES POP CNP GNI ED EXD IRE CAB FXC FU HAN CPOL HC POLR PCUR ACUR 609 325 « * 66.9 • • 1.7 •• 0 1 42 1.5 1.1 624 348 • 72.9 18.85 -22.70 1.7 10.48 93.10 ,0 1 42 1.5 1.4 636 309 866 55 62.0 23.55 -15.70 2.0 10.48 75.00 0 1 42 1.8 1.2 654 325 937 55 70.9 20.55 -12.70 2.0 10.69 84.26 0 1 42 1.8 1.2 670 380 710 55 82.7 20.40 -21.80 2.0 11.52 99.99 0 1 42 2.0 1.4 683 420 780 55 156.4 26.16 -6.62 2.0 11.89 97.57 0 1 39 2,0 1.6 696 400 710 55 157.7 36.75 -16.29 2.1 13.52 105.65 0 1 39 1.8 .1.2 710 409 736 55 178.1 13.97 -64.49 2.1 23.02 122.31 0 1 39 1.9 1.2 724 500 860 55 219.8 62.57 -10.70 2.2 46.52 164.08 0 1 39 2.2 1.5 730 640 1060 54 295.5 100.50 -24.65 2.4 57.30 236,29 0 1 39 2.1 1.3 735 660 850 61 383.8 27.28 -142,81 2.6 54.19 252.48 0 1 35 3.0 2.4 742 620 800 61 481.4 22.98 -97.53 2.6 63.08 200.35 0 1 35 2.0 1.6 747 610 760 60 563.1 58.27 -29.57 2.6 68,75 159.36 0 1 35 2.6 1.0 754 650 690 59 629.1 17.53 -82.90 2.6 69.19 167.51 0 0 35 3.0 3.0 760 720 720 60 767.5 12.70 -128.51 2.6 80.88 215.58 0 0 35 3.9 4.0 766 730 670 60 847.4 69.11 -183.50 2.8 97.79 257.75 1 0 43 4.9 1 772 620 550 57 930.9 10.56 -141.31 3.0 62,70 166.10 1 0 43 fl • 779 550 480 55 1180.7 6.49 -157.49 3.0 54.72 144.91 1 0 43 ■ • 785 500 430 55 1242.7 5.85 -94.62 3.8 47,08 125.78 1 0 43 ■ • 790 480 430 54 1452.8 6.47 - -96.64 4.3 52.33 151.00 1 0 43 • • 794 470 460 54- 1583.1 9.00 -122.70 4.3 25.23 118.22 1 0 44 6.5 8.2 797 390 420 53 1677.9 8.43 -170.00 9.7 34.61 117.94 1 0 44 4.9 799 420 4.0 510 53 1647.4 4.04 • 9.9 42.52 75.80 1 0 44 6.9 8.2 109 6.3. PRELIMINARY DIAGNOSTICS The data population comprised fifteen independent variables on which twenty-five measurements were recorded. To identify the influential influential factors and to deduce functional forms in which these variables should be put in the regression, a number of diagnostics were employed. Scatter plots of each independent variables against the dependent variable were made and preliminary regressions, using ’all possible' variables, were done. Residual plots were also done for each independent variable. Two separate models were considered. One model was constructed on the current account disbursement, from which routine maintenance is usually done, and another was developed on the capital account disbursement from which major rehabilitation and new projects were financed. The Preliminary linear regression models using all variables explained 99% of the variance in Actual expenditure for the case of the Current Account Analysis (output 1 of Appendix E) and 98% (output 2) of the variance in the case of the Capital Account Analysis. 6.4 VARIABLE REDUCTION In order to develop a valid model, the rule of thumb is that there should be at least five observations for each variable included in the model. The general approach was to use the most parsimonious model with respect to the independent variables. A subset selection in the range of four to six variables was targeted for this model. 110 The variables were drawn from a population which consisted of four broad categories of indicators 'Financial', 'Development','Organizational' and 'Political.' The model was expressed in the general form: (AE)j = / [(DEV)j, (ORG)j, (POL)i, (PE)il .... (6.5). in which, (AE) = Actual expenditure; (DEV) = A vector of development variables; (ORG) = A vector of organizational variables; (POL) = A variable indicating the country's political rating; and (PE) = The proposed expenditure based on the improvement needs analysis. These broad categories were themselves subdivided into subcategories. The development indicators comprise "general", "social", and "economic" variables. The Economic variables were further subdivided into four groups of variables which are widely used indicators of economic performance, these include "external debt," "balance of payments," "foreign trade," and "central government finances." The strategy towards a subset selection was to combine intuitive reasoning with three formal multivariate analytical procedures. Firstly, a I ll formal check for multicolliniarity among the independent variables was made to determine whether these variables are unduly effecting the parameter estimates. Secondly, an analysis of principal components was done within large subgroups, to determine optimal numbers of variables within these groups that would be appropriate to measure the effect of the entire group without much loss of information. Thirdly, finding the best subset from among all subgroups, by the process of "grouping dominant variables" from different subgroups. The summary of multicolliniarity statistics for the Current Expenditure analysis is given in output 1 of Appendix E. The variance inflation factor (VIF) for each variable is significantly greater than 10, and the mean variance inflation factor (VIF) is m uch greater than 1, ( VIF = 151.4). The data suggests high multicolliniarity among the variables. The summary of the multicolliniarity statistics for the Capital Expenditure Analysis (all variables) is given in output 2 of Appendix E. Again, the variance inflation factor for most variables are significantly greater than 10, the mean variance inflation factor is greater than 1 (VIF=30.5). Here again, the data suggests serious multi colliniarity among the variables. The subgroups which were considered for reduction were: the General Development indicators and the Economic indicators. 6.4.1 The General Development Indicators The reduction of this subgroup (Figure 18) was done by creating variables from a combination of information on the individual factors. The 112 effects of the variable 'population' was accounted for by using per capita values of GNI and GNP: GNP = GNP/Capita POP GNI = GNI/Capita POP A principal component analysis to examine the relationship among these variables was done. The eigenvalues tend to suggest that two components accounted for 87% of the standardized variance. The results tend to suggest that the effect of the subgroup of general development variables may be captured by two new variables ,'developl' and 'develop2', without much loss of information. The results of the principal component analysis is given in output 3 of Appendix E. These new variables are a linear combination of the general indicators. The combination is obtained according to the theory of principal component analysis which will not be addressed in this study. Details of this procedure may be obtained in Miller (1980), Johnson and Wichern (1988) and other texts on multivariate statistical analysis. The observations for the two components (developl and develop2) are generated by a SAS program and given in table 7 6.4.2 Economic Development Indicators This large subgroup of economic variables (Figure 19) was reduced by way of a principal component analysis similar to the group of general indicators. From the results it was deduced how many components could provide a good summary of the data group, without much loss of information. 113 DEVELOPMENT INDICATORS I _____ GENERAL ECONOMIC Population GNI GNP FX FIG. 18. SUBGROUP OF GENERAL INDICATORS The results of the Principal Component Analysis is given in output 4 of appendix E. The eigenvalues indicate that two components 'econol' and 'econo2 ’ accounted for 8 8% (cumulatively) of the standardized variance. The effect of this group of five variables could therefore be adequately measured by these two ( 2) new variables without much loss of information. The observations generated from a SAS program for the respective principal components econol and econo2 are given in table 7. The table shows the annual magnitudes of the macroeconomic factors for Guyana, over the years 1966 - 1989. It however replaces the data on individual factors, in the large sub-groups, with the data calculated on their principal components. DEVELOPMENT INDICATORS I - ^ GENERAL ^ ECONOMIC External Balance of Foreign Debt Payments Trade Total Ext Fuels Debt Current Account International Balance Manufacturers Reserve FIG. 19 SUBGROUP OF ECONOMIC INDICATORS TABLE 7. - DATA ON HYPOTHESIZED INFLUENTIAL MACROECONOMIC VARIABLES RECONSTRUCTED AFTER PRINCIPAL COMPONENT ANALYSIS ED CPOL HC POLR PC UR ACUR PCAP ACAP ECONOl ECON02 DEVELOP1 1 • 0 1 42 1.5 1.1 9.4 7 4 2 • 0 X 42 1.5 1.4 10.7 8 -2.12070 -0.14643 • 3 53 1 0 44 4.9 4.0 14.1 10 1.06549 -2.24014 -3.19130 4 54 1 0 43 • « 14.3 11 0.92335 -1.33952 -1.29687 5 55 1 0 43 « 10.2 3 0.41167 -1.38033 -1.09888 6 54 1 0 44 6.5 8.2 11.6 9 0.38768 -1.97178 -1.21031 7 55 1 0 43 • 9.2 3 1.24939 -1.43661 -0.56078 8 53 1 0 44 6.9 8.2 25.0 17 • • -2.85966 9 57 1 0 43 t 11.7 9 1.29008 -0.80572 -0.15134 10 60 1 0 43 4.9 24.0 10 3.28744 1.60801 0.58973 11 59 0 0 35 3.0 3.0 25.4 IS 0.89999 0.15939 0.54584 12 55 0 1 42 2.0 1.4 14.1 11 -2.03418 -0.05067 0.20242 13 55 0 1 39 1.8 1.2 6.6 5 -1.89796 0.42269 0.21618 14 60 0 35 3.9 4.0 22.6 18 1.88900 -0.04058 0.81023 IS 55 0 1 39 1.9 1.2 17.6 16 -1.16165 -0.28377 0.33046 16 60 0 1 35 2.6 1.0 26.5 22 0.21244 1.42741 0.71076 17 55 0 1 39 2.0 1.6 9.9 9 -2.11351 0.10941 0.54517 18 61 0 1 35 2.0 1.6 21.3 36 0.91662 0.40430 0.87804 19 61 0 1 35 3.0 2.4 35.4 62 1.52954 0.66509 1.14826 20 55 0 1 39 2.2 1.5 15.8 14 -0.62409 1.79588 0.95169 21 55 0 I 42 1,8 1.2 16.0 12 -2.34201 -0.09590 0.61029 22 55 0 1 42 1.8 1.2 15.9 14 -2.28073 -0.10546 0.90332 23 54 0 1 39 2.1 1.3 26.0 42 0.51213 3.30474 1.92673 116 6.5. MODEL BUILDING The general model is developed by combining the subsets of variables from each group to obtain a corresponding additive regression sum of squares. A general overview on additive regression sum of squares is presented in the subsequent paragraphs. Consider two variables Xj and Xj, each from one of the groups into which the population of potential variables were divided. Let SSj be the reduction in regression sum of squares resulting from adding Xj to a given preselected subset. Then, if SSj is the corresponding reduction due to adding Xj instead of Xj, then the reduction when both are added is SSj + SSj. Conditions for Additive Sum of Squares as described by Miller in Subset Selection (26). The regression sum of squares are the squares of the length of the projection of Y on Xi and Xj, respectively: these may be written as follows; SSj = (X'iY)2/X'iXi (6 .6 ) SSj = (X*jY)2/X'jXj (6.7) If Xj is added after Xj and the regression sum of squares be given as SSj.j, the relationship may be written as: (6 .8) where Xj.j is the part of Xj orthogonal to Xj, This represents the vector of residuals when Xj is regressed against Xj. Let Xi.j = Xj - BjjXj and Y j be that part of Y orthogonal to Xj, where Byj is the regression coefficient then 117 [(Xj - Bj.jXj)’ {Y - By.jXj )]2 S S • = ______[ >Q “ BjjXj ]' [ Xj- BijXj ] ( X'i Y— BijX’j Y)2 ______.... (6.9) [ X’iXj - BijX’j Xj ]■ If we now consider the correlation coefficients among dependent and independent variables r^, riy, r^ (direction cosines), it can be shown that: SSi.j = SSj (the regression sum of squares is additive) w hen, 1 - Tjp = (1 - Tjj Tjy/riy)2 . . . ,(6.10) With some mathematical manipulations it can be shown that this condition is satisfied if Tij = o;or ...... (6 .11) r ij = 2Tij rjy / (fiy2 + rjy2) . . . .(6.12) 6.5.1. Model Refinement The following variables were chosen for developing the behavioral model: (1) The Proposed Expenditure (PCAP or PCUR) . Xi (2) Development variables: (i) Developl X 2 (ii) Develop2 X 3 (3) Education - A secondary school enrollment ratio (ED) X 4 118 (4) Economic Variables (i) Econol x5 (ii) Econo2 X6 (5) Political Rating (POLR) x7 (6 ) Agency Variables (i) Construction Policy (CPOL) Xs (ii) Management Capacity (MC) X9 The dependent variables for both capital and current analysis was stated as the actual expenditure on improvement works over a given fiscal year (ACAP or ACUR). The Capital Account Analyses Model The potential list of variables after the first process of variables reduction, for the Capital Account behavioral analysis was: Dependent variable - ACAP Independent variables - (PCAP, DEVELOP1, DEVELOP2, ECONOi, EC0N02, ED, MC, CPOL, POLR) In the regression analysis, it was found that this model explained a high percentage of the variation in actual expenditures 91.5%. Tests for significance of variables in the model suggested that the variables "construction policy" and "political rating" was not statistically significant. A check for multicolliniarity among the variables revealed that there was high multicolliniarity among the other macroeconomic factors. The unstable nature of these parameter estimates was also evident from a ridge trace of these variables from a ridge analysis (outputs 5 and 6 of Appendix E). After 119 further model reduction and refinement, the final selection of variables was expressed by the equation. ACAP = /(PCAP, DEVELOP1, ECONOl, MC) (6.13) The parameter estimates of this model is given in output 9 of Appendix E. The model suggested no signs of serious multicolliniarity among variables and explains 80% of the variance in Actual Capital expenditures. The Current Account Analysis The candidates for the regression model after the first reduction of variables were: Dependent variable - ACUR Independent variables - (PCAP, DEVELOPi, DEVELOP2, ECONOi, EC 0N02, ED, MC, CPOL, POLR) Again, this model explained a high percentage of variance in the Actual Current Expenditure (98%). The results tend to suggest that "political rating", is not statistically significant and that there is high multicolliniarity among the other variables ( output 7 of appendix E). A ridge trace of the variables shows the unstable nature of most of the parameter estimates, (output 8 of Appendix E). The final selection of variables for the current account model was: ACUR = /(PCUR, DEVELOPI, ECONOI, MC.) (6.14) The parameter estimates of this model are given in output 10 of Appendix E. The model explains 96% of the variance in actual expenditure when the above stated variables are used in a regression. 120 6.6 FURTHER MODELING CONSIDERATIONS The models developed were done under the assumption that the random error terms ei are independent normal random variables or uncorrelated random variables. This assumption may be inappropriate for the data used since it is time series data. The dropping of variables from the model to acquire parsimony (given the small number of observations) may lead to positively autocorrelated error terms over time. A graphical diagnostic to examine the possible effect of autocorrelated error.was done by making a plot of residuals against time (years). Figures 19 and 20 show plots of residuals versus time for Capital and Current Analysis. The plots indicate no systematic pattern in the error terms and therefore suggested no autocorrelation existed. A formal test was then conducted to determine whether or not the error terms were serially correlated (The Durbin-Watson Test) . This test assumes a first order autoregressive multiple regression m odel: Yt = P0 + piXti + P2Xt2 + . . . + P(p-i)Xt(p-i) + e t. (6.15) w here, £t = peM + Pt- • • -(6.16) when I p I <1; and pt are independent N(0, a2) The analysis is based on the following assumptions on the error terms. Considering the relationships: et = pet-i + p t- . . . .(6.17) Et-i = pet-2 +Pt-1- • • • .(6.18) 121 Substituting, (6.17) in (6.18) w e obtain: £t = P(pet-2 + Pt-l ) + Pt = P^t-2 + PPt-l* + Pt . . (6.19) Similarly, et = p3et-3 + f o t - i + PPt-l + Pt ... .(6.20) The general equation may therefore be written as follows: Et = Xpcet-c (6.21) c=0 This equation represents the condition that the weight of the disturbance term in determining et reduces as we go further back in time, when 0 < p < 1. The test hypothesis is to test whether the autocorrelation parameter p of equations (6.13) is zero. The argument is that if p = 0 then et = pt< Therefore the error terms are not correlated. The test hypotheses are stated as follows. Ho: p = 0. H i: p > 0. The Durbin-Watson test statistic 'D'is obtained by using ordinary least squares to fit the data and calculating the residuals: A et = Yt - Yt Then calculating the statistic: n Z(et - et-i )2 t-2 D = ------. . . .(6.22) n K et)2 t-i where n is number of years. RESIDUALS 20 0 -10 SO 70 80 90 YEAR FIG 19 - TIME SERIES RESIDUAL PLOT (CAPITAL EXPENDITURE) FIG 20 - TIME SERIES RESIDUAL PLOT (CURRENT EXPENDITURE) 124 The decision rule is not based on an exact procedure but Durbin and Watson have obtained lower (dO and upper (du) bounds for decision making. A value of 'D' outside these limits leads to a definite decision. The decision rule is as follows: If 'D' > du conclude Ho; if D < dL conclude Hi; and if dL ^ D £ du (inconclusive) The test was conducted at .05 level of significance and the following results were obtained from analyses using SAS reference (outputs 9 and 10 of Appendix E). The results reveal no significant autocorrelation when compared against the Durbin-Watson limits at .05 significance level (see Table 8). TABLE 8 - RESULTS OF AUTOCORRELATION STATISTICS DW-Limits 1st Order Auto Model Type D 4-D du dL Correlation Capital 1.118 2.882 1.8 0.96 0.284 Current 2.116 1.884 1.9 0.73 -0.089 125 6.7. STATISTICAL BEHAVIORAL FINDINGS The final models of statistically significant indicators, in both cases of capital and current expenditure, tend to suggest the same general influential indicators on investment behavior. The data tend to suggest that development indicators, economic indicators, and agency characteristics influence the investment decision by a measure which may be quantified by the parameters of the regression equations. For Capital Works: $ Actual expenditure = -14.5 + 1.13 (Proposed expenditure) + 1.9 (Developl) + 3.9 (Econol) + 20(management capacity) . . . .(6.23) For Current Works: $ Actual expenditure = -1.9 + 1.6 (Proposed expenditure) + 0.04 (Developl) - 0.3 (Econol) - 0.7(management capacity) .... (6.24) 6.7.1 Model Application These findings can now be explicitly applied to the proposed works budget to estimate the actual expenditure in any given year. The proposed works budget is obtained by accumulating the cost of the road improvement needs for the specified year. The actual expenditures are estimated for both capital and current work programs by substituting the appropriate annual values of the respective variables in the above equations. The Road Administration is now equipped with some apriori information, in the form of ( 1) the estimated actual budget expenditure and 126 (2) a prioritized list of improvement projects (with related costs) which are competing for that budget. The Administration requires to make the most prudent selection of projects for implementation within its budget constraints. 6.8 THE PROJECT SELECTION PROCESS The strategy of ranking projects for implementation based on the broader objectives (economic, social, and institutional) is consistent with the central government's overall consideration for improving social welfare. It would be prudent of the Road Administration to select projects for implementation in a manner which maximizes social welfare . A social welfare function was developed using the Road Improvement Priority Indices (RIPI discussed in chapter 5), and the estimated actual budget expenditure for the need year. For a given budget, the group of improvement alternatives that maximizes social welfare (SW) is considered the most plausible for implementation. The social welfare function may be expressed in the following mathematical form: Capital Expenditure Analysis Maximize SW I (RIPI)j Xj (6.25) s.t. ICjXj < (ACAP) for Xj (0,1) all j 127 Current Expenditure Analysis Maximize SW X (RIPI)j Xj .(6.26) j s.t. XCjXj (ACUR) for Xj (0, 1) all j w here, (RlPI)j the Road Improvement Priority Index for alternative j. 1 if selected Xj the decision variable: = 0 otherwise q = the cost of implementing alternative j. ACAP = the estimated actual capital budget expenditure. ACUR = the estimated actual current budget expenditure. j = project index (j = 1,2,3 . . . n). The above project selection technique serves as an aid to the Road Administration for identifying the most plausible projects for implementation. This project list with the associated costs are submitted to the central government’s budget committee for approval. The anticipated result is that the projects will be more acceptable to the committee than in the past, and the proposed cost will match, in a reasonable way, the approved budget. CHAPTER VH RESULTS AND DISCUSSIONS 7.0 GENERAL The goal of the study was to customize a pertinent road improvement evaluation methodology for Guyana, giving special consideration to sustainability and institutional capacity. The organization of the study into two phases permitted extensive use of state of the art techniques. In the first phase the focus was on transfer of pavement management technology, adapting these methods to suit local conditions. The second phase presented a conceptually new investigation into the critical social and institutional factors that influence the road improvement decision process. These influential factors were used simultaneously to develop a model for ranking improvement alternatives and a method of project selection was then proposed. This method was fashioned to select improvement alternatives for each fiscal year which match the interest and the approved budget of the central government. The major contribution of the first phase is the development of performance curves by using the subjective condition ratings of experts, who are knowledgeable on the deterioration trends of the roadways in Guyana and the maintenance practices of the local road administration. 128 129 The analyses conducted in the second phase was done at three levels. The first level made use of multivariate statistical techniques to test for significance of social and institutional factors on the investment trends in roadway improvement. Special regression techniques via. variable grouping and principal component analysis were employed to reduce the number of independent variables assumed to be influential, without much loss of information. The second level entailed, (a) assigning relative weights to the social and institutional factors based on the amount of variability each one explained in the regression model and (b) constructing a priority utility model based on economic, social and institutional attributes to rank improvement alternatives. At the third level, a function which optimizes the rank order subject to budget constraints was developed as an aid to the decision process. 7.1 RESULTS Two data sets were analyzed to ascertain whether there is systematic association between economic, social and institutional factors, and the investment behavior of the road administration. This relationship was investigated separately for two different types of funding mechanisms: capital account funding and current account funding. The data used in both cases suggest that there is systematic association with most of the influential factors assumed. The variables found to be statistically significant are: proposed expenditure, econol (a variable of economic development), developl (a variable of general development), and management capacity. The data, however did not support the hypothesis on the influence of political factors. A summary of the parameter estimates and statistical tests on variables for 130 both the capital and current account analyses are presented in the outputs 9 and 10 of Appendix E. Having deduced that there is a systematic association between investment behavior and the variables aforementioned, the degree of association of these variables was then determined. In the regression procedure the coefficient of partial determination (r2) measures the degree of association of independent variables with the dependent variable (when all other variables are in the model). This coefficient was calculated for each variable in the model and a summary of results is presented in Table 9. The table shows the coefficient of partial determination for each significant independent variable for both capital and current analyses. The relative weights (ai, a2, ^3, and a 4) were derived. These weights are measures of the amount of consideration given to each of the influential factors/groups in the priority decision model. The method used was discussed in section 5.2.2. A summary of results is presented in Table 10. The table gives the relative weights of each attribute for both the capital and current priority models. From the results obtained, the models of the Road improvement priority index (RIPI) using the prescribed measures of efficiency may be written in the form: Capital account analysis: RIPI = 0.40(IRR)* + 0.05(GDI) + 0.18(EDI) + 0.37(MC). . .(6.27) Current account analysis: RIPI = 0.46GRR)* + 0.24(GDI) + 0.22(EDI) + 0.08MC).. . . (6.28) w here, RIPI = Road improvement priority index. GDI = An Index measuring general development. EDI = An index measuring economic development MC = A measure of management capacity. IRR* = The internal rate of return, a measure of economic efficiency which may be substituted by more appropriate economic efficiency measures: NPV, FYBR, FYRR etc. depending upon the nature of the project and administrative policy. 7.2 SENSITIVITY ISSUES The evaluation methodology was formulated on the premise that the attributes of the the decision model are truly independent. The logical deduction is that there is no change in one attribute associated with changes in the others. The technique used to investigate the sensitivity of the model to changes in the values of the attributes was by varying one variable at a time and keeping the others constant. By using hypothetical values of attributes chosen from the data of a randomly selected year, an investigation of the sensitivity of the model was conducted. Consider an improvement alternative with the following attributes as shown in Table 11. Possible values of road improvement priority index when individual attributes are varied and all others kept constant are given in 132 Table 9 - Marginal Contribution of variables to variance in actual fiscal expenditure Coeff. of Partial determination INDEPENDENT VARIABLES C apital Current 2 Econom ic 0 .3 7 0 .8 9 r y 1.234 2 D evelopl r y2.134 0 .0 5 0 .4 6 2 Econol 0 .1 7 0 .4 2 r y3.1 24 2 0 .3 5 0 .1 6 MC r y4.1 23 Table 10 - Relative Weights of attributes in the priority model Weighting for weighting for A ttrib u tes CAPITAL works CURRENT Works Econom ic 0 .4 0 0 .4 6 General Development 0.05 0.24 Economic Development 0.18 0 .2 2 Organisational 0.37 0 .0 8 133 tables 11 and 12, for both current and capital account analyses. Figures 21 and 22 show the family of curves for change in attribute versus RIPI. The road improvement priority index was calculated for each 5% change in value of individual attributes while the values of all other attributes are held constant. Table 11 HYPOTHETICAL ATTRIBUTES OF AN IMPROVEMENT ALTERNATIVE. IRR FYRR GDI EDI M3 20% 41% (0.5897*1 0,000) (3.287*1 0/777) (7/12*1 00) 5,897 0.042 53.88% The gradients of the curves indicate that the model is most sensitive to changes in the general development attributes, and is less sensitive to changes in economic and organizational attributes. It is not responsive to changes in economic development attributes. These characteristics were consistent for both capital and current analyses. 134 TABLE 12 - MARGINAL CHANGE IN ROAD IMPROVEMENT PRIORITY INDEX WHEN INDIVIDUAL ATTRIBUTES ARE VARIED - (CURRENT ACCOUNT ANALYSIS) ROAD IMPROVEMENT PRIORITY INDEX (RIPI) % IRR MC GDI EDI CHANGE -5 0 1423.0 1426.7 720.2 1428.9 -40 1424.5 1427,1 862.4 1428.9 -30 1425.4 1427.5 1004.6 1428.9 -2 5 1425.9 1427.7 1075.1 1428.9 -20 1426.4 1427.9 1146.2 1428.9 -1 5 1426.8 1428.2 1216.8 1428.9 -10 1427.3 1428.4 1287.6 1428.9 -5 1427.7 1428.6 1358.4 1428.9 0 1428.2 1428.9 1429.2 1428.9 5 1428.7 1429.1 1499.7 1428.9 10 1429.1 1429.3 1570.5 1428.9 15 1429.6 1429.6 1641.6 1428.9 20 1430.0 1429.8 1712.1 1428.9 25 1430.5 1430.0 1782.9 1428.9 30 1430.9 1430.3 1853.7 1428.9 40 1431.2 1430.7 1995.7 1428.9 50 1432.8 1431.1 2137.9 1428.9 135 TABLE 13 - MARGINAL CHANGE IN ROAD IMPROVEMENT PRIORITY INDEX WHEN INDIVIDUAL ATTRIBUTES ARE VARIED - (CAPITAL ACCOUNT ANALYSIS) ROAD IMPROVEMENT PRIORITY INDEX (RIPI) % IRR MC GDI EDI CHANGE -5 0 320.5 314.6 177.2 324.5 -4 0 321.3 316.8 206.6 324.5 -3 0 322.1 319.1 236.4 324.5 -2 5 322.5 320.2 252.4 324.5 -2 0 322.9 321.2 267.1 324.5 -1 5 323.3 322.3 281.8 324.5 -1 0 323.7 323.3 296.5 324.5 -5 324.1 324.5 312.7 324.5 0 324.5 325.6 325.9 324.5 5 324.9 326.6 340.5 324.5 10 325.3 327.7 355.3 324.5 15 325.7 328.8 361.0 324.5 20 326.1 329.9 384.8 324.5 25 326.5 330.0 399.5 324.5 30 326.9 331.0 414.2 324.5 40 327.7 333.2 443.6 324.5 50 328.5 335.4 473.0 324.5 136 2000 RRENT ACCOUNT ANALYSIS -0 - RIPI(nR-CUF) RIPI(11C-CUR | 1800 -H —«IP4<< 1DI-GUF f -«• RIPI(EiDI-CUF) 1600 1200 1000 -60 -40 -20 20 6040 % CHANGE IN ATTRIBUTE FIG. 21 CHANGE IN PRIORITY INDEX WHEN ATTRIBUTES ARE VARIED INDEPENTLY 137 500 CAPITAL ACCOUNT ANALYAIS a- RIPI [IRR-CA 3) r i p ;m c -c a i >) r i p ;gdi -cap ) RIPI [EDI-CA *) 400 300 200 -60 -40 -20 20 40 60 % CHANGE IN ATTRIBUTE FIG. 22 -CHANGE IN PRIORITY INDEX WHEN ATTRIBUTES ARE VARIED INDEPENDENTLY 138 7.3. STUDY LIMITATIONS Data Quality Limitations of the first phase of the study include absence of historical data on the condition of Guyana's network links. There was also no data on the maintenance history of these roads. The use of expert opinion cannot account for atypical conditions which may occur on sections during the service life of the pavement. Secondly, the road network was classified into three broad categories; paved roads, lightly paved roads, and unpaved roads. Within these groups there are a number of pavement types which exhibit both functional and structural disparity. The prediction method requires the decision maker to use curves and analyses based on aggregate data to describe the performance of individual network segments. This procedure could lead to erroneous results under atypical conditions and therefore requires field verification before final decisions on improvement programs are made. In the second phase of the study the data used to estimate and test the significance of macroeconomic and institutional variables were obtained from the data files of the World Bank (39). The available data covered a period of twenty five years, which reflects the age of Guyana as an independent nation. This comprised a relatively small sample of annual observations on each macroeconomic index on which regression analysis was performed. The amount of independent variables kept in the final model had to be reduced drastically from an original amount of fourteen to four, in order to validate the regression procedure. Undoubtedly, this procedure reduced the accuracy of the investigation, since there is no perfect variable 139 reduction technique which would result in absolutely no loss of information on the original data set. Unfortunate also, is the fact that the actual expenditure on annual road works was used to represent the quantity of physical works done. The method did not take into account inflation in future years which would result in a misrepresentation of actual output in terms of quantity of roadworks. The inflation over the range of data used was relatively small but it is expected to increase significantly in the future. As a result adjustments for inflation will have to be factored into the predicted actual output. Variable Description The development indicators were defined according to the conventional World Bank method. The annual magnitudes of these indicators for Guyana, were appropriate for use in regression procedures. However, the political variable (political rating) derived exogenously according to the Texas Commerce Bank Method; when applied to Guyana, produced values which exhibited very little change in the respective annual magnitudes over the twenty five years for which it was estimated. Therefore it was not unusual, that in a regression analysis, the data suggested that political rating was not significant in a test for association with the dependent variable. The measures of efficiency established for attributes in the priority model, although theoretically sound, does not reflect individual sensitivity in the model for ranking of alternatives. Apart from the general development and economic attributes the total numerical change in Road Improvement Priority Index produced by changes in values (up to 50%) of the other individual attributes are insignificant. The use of these measures to rank alternatives present operational problems and the models may not be considered operationally efficient. CHAPTER VIII CONCLUSIONS AND RECOMMENDATIONS 8 .1 CONCLUSIONS In order to have a deeper appreciation of the results and the extent to which the objectives of the study were achieved, one needs to have an overview of the hierarchy of decisions, for roadway improvement projects within Guyana. Of importance also are the lines of communication between the technical and institutional policy makers. The models were designed to serve as decision aids to senior technical road administrators who are generally vested with the responsibility of efficiency of resource use on road improvement projects. The current practice is for these technical officers to make improvement recommendations to the institutional policy makers who use this information with current information on social factors, political factors and development to make budgetary appropriations. The combination of the social, development, and political information in the decision process is purely judgmental and therefore the results cannot be reasonably predicted. The source of the problem is that the information base on which the technical officers make recommendations is usually limited to resource use. This is generally much smaller than that used by the institutional policy makers to maximize social welfare and make budget appropriations. 141 142 Under the general conditions of austerity which prevail in developing countries the approved annual improvement budget requires further refinement (trimming) of the technical proposals. These proposals, after a few cycles of refinement, describes crisis or emergency works and does not lend itself to the planning of maintenance and improvement operations, for medium or long term objectives. The expansion of the data base of the technical policy makers coupled with a method of simulating the judgement of the institutional policy makers will allow these technical officers who are usually aware of their organizational capabilities, to make reasonable budgetary projections and hence more pragmatic work programs. The proposed work programs are expected to be more acceptable to the central government and timely planning could be made for the implementation of maintenance activities. The additional information would also help in the monitoring of these programs and ultimately lead to the identification of systemic problems of the organization which are not in the manageable interest of the technical officer. It will provides a basis for collaborative work between the technical and institutional policy makers to address these problems at the national level. Within the limitations of data availability and data quality The study achieved the following objectives: 1. Use of state-of-the-art pavement management procedures to customize a pertinent road improvement evaluation methodology for Guyana. 143 2, The study established objective proof of the influence of development and organizational factors on the road improvement investment behavior in Guyana. It also established a quantifiable measure by which each factor is weighted in the decision process. 3. A project selection methodology was developed which took into consideration economic, social and organizational objectives. It was designed to rank and select projects in a manner which is compatible with the interest and budget of the central government. The most significant contributions of the study are: (a) The development of performance curves for the three broad classes of roads in the Guyana network (using expert knowledge). These curves characterize the 'family' rather than the specific roadway performance under the prevailing conditions of traffic, but are inexpensive predictors of conditions of state. They can be used to estimate the need year for each roadway in the network, within reasonable limits of accuracy. This information may be used to plan effectively for maintenance activities and also for the estimation of more accurate road improvement budgets. In general it is a significant improvement to the tools of pavement management presently used by the road administration in Guyana. (b) The identification and quantification of the development (economic and general) and organizational factors which influence the road 144 improvement decision process. To date no approach has been proposed which incorporates in a quantifiable way the degree of consideration given to each of these factors in the evaluation process. Apart from adding more completeness to the theory of decision making in developing countries, the ranking of alternatives is achieved through a multiobjective approach considering all the factors simultaneously. This method presents a closer representation of the manner in which decision making is done in these countries. (c) An objective process of project ranking and selection was established. Because of the severe austerity which prevails in developing countries this method is unique. It allows for the optimization of the rank order of improvement alternatives subject to the estimated budget. This application is expected to significantly reduce the incompatibility between the projects and budget proposed by he road administration and those approved by the central government. The study and its findings provide a framework within which other agencies may use data available to them, to conduct empirical research. Continuing research in this direction will permit developing countries to develop more accurate objective methods of evaluating road improvement alternatives for implementing maintenance management. 8.2 RECOMMENDATIONS On the basis of the results of the empirical study of the road investment decision process in Guyana, the following recommendations are made: 145 (1) Extreme caution should be taken when attempting to use the findings of this study. These models were developed from social, economic and institutional indicators specific to Guyana and their relevance therefore, can only be supported for that country. (2) Lack of experience in the use of social and institutional factors in decision analysis led to close adherence to descriptions formulated by the world bank and other credible sources. In many cases the data values were not in a form which may be efficiently used in the statistical method applied. For example, the political and education variables which were obtained for Guyana, did not exhibit large enough variations in annual values to be significant in the behavioral regression model. It is recommended that these variables be redefined in a form more appropriate for regression modeling; or, alternatively, another analytical approach may be applied. (3) Refinement is also needed to be done on the measures of efficiency of the attributes of the prioritization model. There is need to describe these attributes in a form which makes the model operational, ie. jointly and separably they should constitute an unambiguous discriminator for evaluating improvement alternatives. The lack of sensitivity to the individual attributes which was displayed by the priority models will pose serious operational problems. (4) In order to improve the efficiency of the model it is recommended that the models be updated as data becomes available. (5) It is recommended that further work be done to investigate the effect of inflation on the dependent variable (actual expenditure) of the investment behavior model. If 'as built' data is available, it would be more accurate to use the actual quantities of road improvement works executed during the fiscal year as data for the dependant variable, this would eliminate the problem associated with inflation. REFERENCES (1) Alder, H. "Economic Appraisal of Transport Projects - a manual with case studies," Published for the World Bank, John Hopkins University Press 1987. (2) Anderson, D. "Maintenance Management systems", National Cooperative Highway Research Program, Synthesis of Highway Practice No.110 /Transportation Research Board, National Research Council, Wash. DC. 1984. (3) The ASSHO Road Test, Report No. 5: Pavement Research, In Special Report 61E, HRB, National Research Council, Wash. DC., 1962. (4) Baum W. C. and S. M. Tolbert 'Investing in Development, Lessons of the World Bank experience. The World Bank Wash. DC. 1985. (5) Bulman, J. N. , and R. M. Weatherell, "A Practical Method for Regional Road Maintenance Planning," Proceedings of the 6 th Conference, Road Engineering Association of Asia and Australia, Kuala, Lumpur, March 1990. (6 ) Butt, A. A. , M.Y. Shahin, K.J. Feighen and S.H. Carpenter, "Pavement Performance Prediction Model Using the Markov Process," Transportation Research Record 1123, 1987. (7) Caldwell, G. "Development of Statewide Maintenance Management System for Queensland, Australia," Proceedings of the 6 th Conference, Road Engineering Association of Asia and Australia, Kuala, Lumpur, M arch 1990. 147 148 (8 ) Caroff, G. and P. Ley cure, "Pavement Management Systems Objectives and Functions," Proceedings of the 6 th Conference, Road Engineering Association of Asia and Australia, Kuala, Lumpur, March 1990. (9) Central Transport Planning Unit, Ministry of Economic Development, and The Israel Institute of Transportation Planning and Research, "Transport Plan for Guyana", Ministry of Economic Development Guyana 1976. (10) Eaton, R.A., S. Gerard and D.W. Cate, "Rating Unsurfaced Roads Unsurfaced Roads-A Field Manual for Measuring Maintenance Problems," USACE-CRREL Special Report 78-15, 1988. (11) Goicoechea, A., D. R. Hansen, and L. Duckstein, 'Multiobjective Decision Analysis With Engineering and Business Applications’, John Wiley and Sons Inc. 1982. (12) Golabi, K., R. B. Kulkarni, and G. B. Way, "A Statewide Pavement Management System," Interfaces 12, Providence, RI, Dec. 1982. (13) Greenstein, J. and H. Bonjack, "Socio-economic Evaluation and Upgrading of Rural Roads in Agricultural Areas of Ecuador",Transportation Research Record 898, Transportation Research Board, National Research Council, pp 88-94. Wash. DC 1983. (14) Greenstein, J. and H. Bonjack, "Socio-economic Methodology for Rural Road Construction",Transportation Research Record 1229, Transportation Research Board, National Research Council, pp 118-126. Wash. DC 1989. (15) Groski, M.B., P. Vervenne and V. Veverka, "Gersec: A Pavement Management System for Secondary Roads," Proceedings of the 6 th Conference, Road Engineering Association of Asia and Australia, Kuala, Lumpur, March 1990. 149 (16) Harral, C. and S. Agerwal, "Highway Design Standards Study, In Special Report 160 TRB„ National Research Council, pp 17-24, Wash. DC 1975. (17) H ide, H. , S. A bayanaka, I. Sayer and R. W yatt, " The K enya Road Transportation Study: research on vehicle operating costs, Department of the Environment, Transport and Road Research Laboratory Report LR 672 C row thorne 1975. (18) Hide, H., G. Morosiuk and S. Abayanaka, 'Vehicle Operating Costs in The Caribbean. Transportation Research Record 898, Transportation Research Board, National Research Council, pp 65-72. Wash. DC 1983. (19) Hodges, J. , J. Rolt and T. Jones, " The Kenya Road Transportation Study: research on road deterioration, Department of the Environment, Transport and Road Research Laboratory Report LR 673 C row thorne 1975. (20) Hodgkins, E. A. "Technology Transfer in Selected Highway Agencies",National Cooperative Highway Research Program, Synthesis of Highway Practice No.150 /Transportation Research Board, National Research Council, Wash. DC. 1989. (21) Hudson,W. R., R. Haas, and R. D. Pedigo, "Pavement Management System Development," National Cooperative Highway Research Program Report 215,1979. (22) Humplick, F.F. "Theory and Methods of Analyzing Infrastructure Inspection Output: Application to Highway Pavement Surface Condition Evaluation," Dissertation M.I.T., Dept, of Civil Engineering, A ugust 1989. (23) Johnson R. and D. W. Wichern, "Applied Multivariate Statistical Analysis", Prentice Hall, Englewood Cliffs New Jersy 1988. 150 (24) Kitma, R., H. Zhao and A. Gibby, "Development of a Pavement Maintenance Cost Allocation Model," Research Report UCD-TRG-89-3, DOT, May 1989. (25) La Baugh, W. "Roads in developing countries"In Special Report 160 Transportation Research Board, National Research Council, pp 25-30, Wash. DC 1975. (26) Miller, A. J., "Subset Selection in Regression Analysis", Chapman and Hall 1990. (27) Moavenzadeh S. , J. Suhrbier and J. Alexander, "Highway Design Study Phase 1: The Model," IBRD Economics Dept, working paper No. 96, Wash. DC. 1971 (unpublished). (28) Neter, J. , W. Wassermar and M. Kutner, "Applied Linear Regression Models", Richard Irwin Inc. 1989. (29) Ogelsby, C. "Dilemmas in the Administration, Planning, Construction and Maintenance of Low-Volume Roads,In Special Report 160 Transportation Research Board, National Research Council, pp 7-17, Wash. DC 1975. 30) Organisation for Economic Cooperation and Development, "Road Monitoring for Maintenance Management, - Vol.l, a manual for developing countries: Vol. 2, a damage catalogue, Paris, 1990. (31) Parsley, L. and R. Robinson "The TRRL Road Investment Model For Developing Countries (RTIM2)", United Kingdom Transport and Road Research Laboratory, report No. LR 1057, Crowthorne 1982. (32) Peterson, D. "Pavement Management Practices",National Cooperative Highway Research Program, Synthesis of Highway Practice No.135 Transportation Research Board, National Research Council, Wash. DC. 1987. 151 (33) Records W. N., "An Overview of the Highway Maintenance Management Research Program," In Special Report 100, Highway Research Board, National Research Council, pp 14-21, Wash. DC 1968. (34) Robinson, R. , H. Hide, J. Hodges and J. Rolt, "The Kenya Road Transportation Study", A road transport investment model for developing countries, Department of the Environment, Transport and Road Research Laboratory Report LR 674 Crowthorne 1975. (35) Sharaf, E. A ., E. Richelt, M.V. Shahin and K.C. Shina, "Development of a Methodology to Estimate Pavement Maintenance and Repair Costs for Different Ranges of Pavement Condition Index," Transportation Research Record 1123,1987. (36) Watanatada, T., C. Harral, W. Paterson,A. Dhareshwar, A. Bhandari, and K. Tsunokawa. 'The Highway Design and Maintenance Standards Model’ vol. 1. Description of the HDM-m Model, vol. 2. User's Manual for the HDM-IH Model. The World Bank Wash. DC. 1986. (37) Wingate P. J. F., "A Concept of the Maintenance Management Problem Insofar as it has been Established by Preliminary Investigations in Great Britain," In special Report 100, HRB, National Research Council, pp 22-26, Wash. DC 1968. (38) The World Bank, "World Development Report - 1987" Oxford University Press 1987. (39) The World Bank, "World Tables 1989 - 1990", John Hopkins University Press 1990. 40) Wyatt, R. , R. Harrison, B. Moser and L. de Quadros, "The Effect of Road Design and Maintenance on Vehicle Operating Cost. - Field research in Brazil. Transportation Research Record 898, Transportation Research Board, National Research Council, pp 80-87. Wash. DC 1983. A PPEN D IX A DETAILS ON DEFINITIONS AND CODES USED IN THE ROAD NETWORK INVENTORY 152 153 Details on Definitions and Codes used in the Road Network Inventory (adopted from the files of The Ministry of Transport, Guyana) This scheme provides for classification of links in terms of 13 elements which are reported in their respective columns as follows: 1. Link/Node Codes 2. Link/Node Names 3. Functional Classification 4. Statutory Classification 5. Road Design Code 6. Lengths 7. Number of Lanes 8. Lane Width 9. Shoulder W idth 10. Surface 11. Soil 12. Terrain 13. The condition values included are values obtained from a survey done by the University of Guyana and the Delft Institute of Technology, 1985-1987. Definitions and codes for these elements are presented below. The term "road" in the following definitions, in most places, also includes trails. 1. Link/Node Codes: A pair of six-digit numbers that identify the beginning and ending points (nodes) of a link. The first two digits of each node number represent the administrative region; the second two, the transport zone (within the administrative region); and the third two, the point within the transport zone at which the beginning 154 or ending node of a link is located. For example, the Link/Node Codes 080101-080402 signify a link whose beginning node is located at point 080101 and ending node at point 080402; or more specifically, the link whose beginning node is located at point 01, in transport zone 01, in administrative region 08 and whose ending node is located at point 02, in transport zone 04, in region 08. 2. Link/Node Names: A pair of names that identify the beginning and ending points (nodes) of a link. For example, the Link/Node Names Georgetwon-Turkeyen signify the link beginning at Georgetown and ending at Turkeyen. 3. Functional Classification: A two-character code that describes, first, whether a link is part of a national, regional, or local road, and second, whether it is part of an arterial, collector, or access road. Codes and definitions are as follows: First Character - Network Level 1 . . . Part of a N ational Road 2 ... Part of a Regional Road 3 ... Part of a Local Road National Road: a road linking nationally important settlements or centers of activity in different regions, or a nationally- important road in a single region. Regional Road: a road linking settlements or centers of activity in the same region, linking settlements or centers of activity to a national road, or linking settlements or centers of activity to a regional-arterial or a regional-collector road. Local Road: a road or street in a city, town, or village which is not part of a national or regional road. Second Character - Type of Road A ... Part of an Arterial Road B ... Part of a Collector Road C ... Part of an Access Road Arterial Road: a main national or regional road. Collector Road: a road that collects traffic from other roads and carries it to an arterial road. Access Road: a road linking a settlement or center of activity to a national or regional arterial or collector road, or linking individual properties to a local arterial or collector road. For example, an entry under Functional Classification of 1A signifies a national-arterial road; 2B a regional-collect-road; and 3C a local-access road. 4. Statutory Classifications: A one-character code that describes the statutory or jurisdictional classification of a link. Codes and definitions are as follows: 1 ... Part of a Declared Public Road 2 ... Part of an Undeclared National/Regional Road Declared Public Road: a road included in the Schedule of Declared Public Roads issued pursuant to the Roads Act and accordingly under the jurisdiction of the Ministry of Works, Road Administration Division. 156 Undeclared National/Regional Road: a road which is not a declared road but is under the jurisdiction of either the Road Administration division or one of the regional administrations. 5. Road Design Code: a five-character code indicating four design characteristics of a link as follows: the type of area the link passes through, the geometries of the link, the type of link, and the design speed of the link. Codes and definitions are as follows: First Character - Type of Area U ... Urban V . . . Village R ... Rural Urban: a link located within the boundaries of an urban (city or town) area. Village: a link located within the boundaries of a village area. Rural: a link that passes through a rural area. Second Character - Geometric Design P ... Part of a Primary Road S ... Part of a Secondary Road T ... Part of a Tertiary Road W ... Part of a Trail Detailed Geometric design standards for primary, secondary and tertiary roads and for trails are included in Appendix 3. These standards apply to links that are parts of primary, secondary, and tertiary roads, or parts of trails. 157 Third Character - Type of Link D ... Part of a Divided Road U ... Part of an Undivided Road A ... Part of an All-weather Trail F ... Part of a Fair-weather Trail These codes indicate whether a link is part of a divided or undivided road or a part of an all-weather or fair-weather trail. Fourth and Fifth Characters - Design Speed These characters (numeric values such as 20,25, 30, 40, 50, etc.) signify the design speed of a link in miles per hour. For example, a Road Design Code RSU 40 signifies the link passes through a rural area and that it is part of a secondary undivided road with a design speed of 40 miles per hour. 6. Length: a two- or three-digit number indicating the length of a link in miles and tenths of a mile. For example, a length entry 25.2 signifies the link is 25.2 miles in length from beginning to ending node. 7. Number of Lanes: a one-digit number signifying the number of lanes that a link has, counting all lanes in both directions. For example, an entry of 2 signifies the link has a total of two lanes. 8. Lane Width: a one- or two-digit number signifying the width in feet of each lane in the link. For example, an entry of 12 signifies the link has twelve-foot-wide lanes. 9. Shoulder Width: a one- or two-digit number signifying the width of the shoulder either side of the roadway of a link. For example, an entry 158 of 4 signifies that the roadway has a four-foot-wide shoulder on each side. 10. Surface: a one- to four-character code signifying the type of surface that a link has. Codes are as follows: AC Asphalt Concrete BE Burnt Earth BW Bauxite Waste CS Crushed Stone DBST Double Bituminous Surface Treatment L Laterite MW Manganese Waste S Sand SC Sand Clay SL Sand Loam For example, an entry AC signifies the link has Asphalt Concrete surface. 11. Soil: a one- or two-character code signifying the primary type of soil on which a link is built. Codes are as follows: C .. Clay L Laterite S .. Sand SL .. Sand Loam For example, an entry C signifies the link is built on a Clay soil. 12. Terrain: a one- to three-character code signifying the predominant type of terrain on which a link is situated. F Flat R Rolling H Mountainous F /R . Flat and Rolling For example, an entry F/R signifies the link is located on flat and rolling terrain. APPENDIX B ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987 159 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987 (continued) Link/Node Codes LinkyNode Names (from-to) from to Node Node (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) 3. East Bank Berbice (South from New Amsterdam) 060102 060103 New Amsterdam Providence 1A 1 VSU40 1.5 2 10 0 DBST C F 3 060103 060408 Providence • Everton 1A 1 VSU30 2.0 2 10 0 DBST C F 3 060408 060405 Eveiton Schepmoed 1C 1 RSU30 19.5 2 9 0 SC C F 2 060405 060404 Schepmoed Mara 1C 1 RTU30 2.0 1 12 3 L CF 2 060404 060403 Mara Brandwagt Sari 2C 2 RTU30 7.0 1 10 2 L c F 3 4. Corentyne (East from New Amsterdam) 060101 060401 New Amsterdam Sandvoort IB 1 RSU40 3.5 2 10 0 L c F 4 060401 060402 Sadvoort Anna Xlementia IB 1 RSU30 2.0 2 9 0 L c F 4 060402 060407 Anna dementia Philadelphia IB 1 RTU30 1.0 1 12 0 L c F 4 060104 060701 New Amsterdam Cumberland 1A 1 VPU50 1.5 2 11 8 AC c F 2 060701 060502 Cumberland Rosehall Estate IB 1 VSU40 2.5 2 10 0 DBST c F 4 060502 060501 Rosehall Estate De Voedsten IB 1 RSU30 6.0 2 9 0 L c F 4 060501 061405 De Voedsten Harmony 2B 1 RTU30 0.5 1 12 3 L c F 4 060701 060601 Cumberland Bohemia 1A 1 RPU50 4.0 2 11 8 AC c F 2 060601 060801 Bohemia Fyrish 1A 1 RPU30 6.0 2 11 8 AC c F 2 060801 060802 Fyrish Albion 1A 1 RPU50 1.0 2 11 8 AC c F 2 060802 060901 Albion Rosehall Town 1A 1 RPU50 2.0 2 11 8 AC c F 2 060901 060902 Rosehall Town Tain 1A 1 RPU50 2.0 2 11 8 AC c F 2 060902 061001 Tain Bloomfield 1A 1 RPU50 1.0 2 11 8 AC c F 2 061001 061002 Bloomfield Whim 1A 1 RPU50 1.0 2 11 8 AC c F 2 061002 061003 Whim Adventure 1A 1 RPU50 2.5 2 11 8 AC c F 2 061003 061007 Adventure No. 31 Phillippi 1A 1 RPU50 3.0 2 11 8 AC c F 2 061007 061004 No. 31 Phillippi No. 43 Joppa 1A 1 RPU50 6.0 2 11 8 AC c F 2 061004 061005 No. 43 Joppa No. 51 1A 1 RPU50 ' 5.0 2 11 8 AC c F 2 061005 061101 No. 51 No. 63 Benab 1A 1 RPU50 5.0 2 11 8 AC c F 2 061101 061102 No. 63 Benab No. 74 Stockholm 1A 1 RPU50 4.5 2 11 8 AC c F 2 061102 061201 No. 74 Stockholm Springlands 1A 1 RPU50 1.5 2 11 8 AC c F 2 061201 061204 Springlands Stelling IB 1 RPU30 0.2 2 10 3 DBST c F 4 061201 061203 Springlands Skelton 1A 1 RPU50 2.0 2 11 8 AC c F 2 061203 061202 Skeldon Crabwood Creed 1A 1 RPU50 4.0 2 11 8 AC c F 2 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987 Link/Node Codes Link/Node Names (from-to) from to Node Node (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) A. COASTAL AREA - MAJOR ROADS 1. East Coast Demerara (East of Georgetown) 040101 040402 Georgetown Turkeyen 1A 1 UPD40 2.8 4 10 0 AC C F 2 040402 040404 Turkeyen University of Guyana 2A 2 RSU40 2.0 2 8 3 AC C F 2 040402 040301 Turkeyen Ogle 1A 1 RPD50 1.5 4 10 0 AC c F 2 040301 040401 Ogle Plaisance 1A 1 RPD50 0.9 4 10 0 AC c F 2 040401 040501 Plaisance LaBonne Intention 1A 1 RPU50 1.8 2 10 0 AC c F 3 040501 040601 La Bonne Intention Triumph 1A 1 RPU50 0.5 2 10 0 AC c F 3 040601 040603 Triumph Mon Repos 1A 1 RFU50 0.5 2 10 2 AC c F 3 040603 040801 Mon Repos Buxton 1A 1 RFU50 2.5 2 10 2 AC c F 3 040801 040304 Buston Bachelor's Adventure 1A 1 RPU30 2.5 2 10 2 AC c F 3 040804 040901 Bachelor's Adventure Enmore 1A 1 RPU30 1.0 2 10 2 AC c F 3 040901 041002 Enmore Golden Grove 1A 1 RPU40 1.0 2 10 3 AC c F 3 041002 041004 Golden Grove Belfield 1A 1 RPU40 1.3 2 10 3 DBST c F 3 041004 041006 Belfield Clonbrook 1A 1 RFU30 1.6 2 10 2 DBST c F 3 051006 051102 Clonbrook Helena No. 2 1A 1 RPU30 6.0 2 10 0 DBST c F 3 051102 051203 Helena No. 2 Bygeval 1A 1 RPU50 1.5 2 11 4 AC c F 2 051203 051201 Bygeval De Kinderen 1A 1 RPU40 4.5 2 11 3 AC c F 2 051201 051203 De Kinderen Mahicony 1A 1 RPU40 4.5 2 11 4 AC c F 2 2. West Coast Berbice (East from M/cony) 061203 061301 Mahaicony Calcutta 1A 1 RSU 40 45 2 11 4 AC c F 2 061301 061305 Calcutta Abary River 1A 1 RSU40 1.0 2 11 4 AC c F 2 051305 051601 Abary River Belladrum 1A 1 RSU 40 4.0 2 11 3 AC c F 2 051601 051604 Belladrum Lichfield 1A 1 RSU 40 4.0 2 11 4 AC c F 2 051604 051702 Lichfield Fort Wellington 1A 1 RPU40 7.0 2 11 4 AC c F 2 051702 051701 Fort Wellington Rosignol 1A 1 RPU30 9.0 2 11 4 AC c F 2 050701 051704 Rosignol Ferry Stelling 1A 1 RPU50 0.4 2 11 4 S c F 2 051701 051802 Rosignol Blairmont 1C 1 RSU 30 1.0 2 9 0 DBST c F 3 051802 051801 Blairmont Ithaca 1C 1 RSU 30 2.5 2 9 0 DBST c F 3 161 051801 050302 Ithaca Schumacker Lust 1C 1 RSU 30 1.5 2 8 0 L c F 4 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987(continued) Link/Node Codes Link/Node Names (from-to) from to Node Node (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) 5. East Bank Demerara (South from Georgetown) 040101 040301 Georgetown Ruimveldt 1A 1 VPU40 1.0 2 11 6 AC C F 3 040301 040203 Ruimveldt Ramsburg 1A 1 VPU40 3.0 2 11 6 AC C F 2 040203 040202 Ramsburg Prospect 1A 1 VSU30 1.8 2 10 0 AC CF 3 040202 040201 Prospect Diamond 1A 1 VSU40 1.0 2 10 0 AC C F 3 040201 040205 Diamond Golden Grove 1A 1 VSU40 1.0 2 10 3 AC C F 3 040205 040102 Golden Grove Craig 1A 1 VSU40 1.0 2 10 3 AC c F 3 040102 040101 Craig Friendship 1A 1 VSU40 2.5 2 10 3 AC c F 2 040101 140101 Friendship Soesdyke 1A 1 RSU 40 8.2 2 10 3 AC c F 2 140101 140201 Soesdyke Timehri IB 1 RSU 40 4.0 2 10 3 AC c F 2 140201 140102 Timehri Timehri Junction IB 1 VSU40 4.5 2 9 0 AC c FR 2 6. West Bank Demeiaia (South from Vreed-en-Hoop) 030703 030705 Vreed-en-Hoop Versailles 1A 1 VSU30 1.5 2 11 3 DBST c F 1 030705 030804 Versailles Bagotville 1A 1 VSU30 3.0 2 11 3 DBST c F 1 030804 030802 Bagotville La Retraite 1A 1 VSU30 1.5 2 11 3 DBST c F 1 030302 030903 La Retraite Wales 1A 1 VSU30 2.4 2 10 4 DBST c F 1 030903 030902 Wales Patentia 1A 1 RSU 30 0.4 2 10 3 SC c F 1 030902 030901 Patentia Vreede Stein 1A 1 RSU 30 6.5 2 9 3 SC c F 1 7. West Coast Demerara (West from Vreed-en-Hoop) 030704 030703 Stelling Vreed-en-Hoop 1A 1 VPU40 0.2 2 11 8 AC c F 1 030703 030710 Vreed-en-Hoop The Best Road 1A 1 VSU30 2.0 2 10 4 AC •c F 1 030710 030702 The Best Road Rotterdam 1A 1 VSU30 2.0 2 10 2 AC c F 1 030702 030708 Rotterdam Windsor Forest 1A 1 VSU30 2.0 2 10 2 AC c F 1 030708 030706 Winsor Forest Den Amstel 1A 1 VSU30 2.0 2 10 2 AC c F 1 303706 030602 Den Amstel Leonora 1A 1 VSU30 3.0 2 10 2 AC c F 1 030602 030601 Leonora Stewartville 1A 1 VSU30 0.8 2 10 2 AC c F 1 030601 030601 Stewartville Uitvlugt 1A 1 VSU30 0.7 2 10 2 AC c F 1 030501 030406 Uitvlugt Tuschen 1A 1 RPU50 3.0 2 11 8 AC c F 1 030406 030403 Tuschen Parika 1A 1 RPU50 5.8 2 11 8 AC c F 1 030403 040404 Parika Stelling 1A 1 VSU40 0.2 2 11 8 AC c F 1 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987 (continued) Link/Node Codes Link/Node Names (from-to) front to Node Node (1) (2) (3) (4) (S) (6) (7) (8) (9) (10) (11) (12) (13) 8. East Coast Essequibo (South from Parika) 030403 030401 Parika Hubu IB 1 RSU 40 5.0 2 11 6 DBST C F 3 030401 030405 Hubu Maripa IB 1 RTU30 3.5 2 9 2 LC FR 5 9. Essequibo Islands (West of Parika) 020101 020102 Wakenaam Island Roads 2B 1 RTU30 18.0 1 12 3 L C F 4 020201 020202 Leguan Island Roads 2B 1 RTU30 15.5 1 12 3 LC F 4 10. Essequibo Coast (West of Supenaam) 020304 020305 Supenaam Adventure 1A 1 RSU 40 8.2 2 9 3 SC c F 3 020303 020305 Stelling Adventure 1A 1 VSU 30 0.1 2 11 4 AC c F 3 02030S 020302 Acventure Suddie 1A 1 VSU 30 2.0 2 11 4 AC c F 3 020302 020301 Suddie perseverance 1A 1 VSU 30 2.7 2 10 4 AC c F 3 020301 020102 Perseverance Queenstown 1A 1 VSU 30 4.0 2 10 4 AC c F 3 020102 020101 Queenstown Anna Regina 1A 1 VSU 30 4.7 2 11 4 DBST c F 3 020101 020201 Anna Regina Lima 1A 1 VSU 30 1.8 2 10 3 DBST c F 3 020201 020301 Lima Charity 1A 1 RSU 30 13.7 2 10 0 SC c F 3 B. COASTAL AREA-MINOR ROADS 1. East Coast Demerara (East of Georgetown) 040101 040102 Georgetown Kitty Railway Sta. 2C 1 USU30 0.3 2 9 0 AC c F 3 040401 040402 Plaisance Railway Station 3C 1 RSU 30 1.0 2 9 3 DBST c F 3 040605 040602 Betervergwagting Railway Station 3C 1 RSU 30 1.4 2 9 3 DBST c F 3 040801 040803 Buston Railway Station 3C 1 RSU 30 1.2 2 9 2 DBST c F 3 040901 040902 Enmore Railway Station 3C 1 RSU 30 0.5 2 10 2 DBST c F 3 041002 041003 Golden Grove Railway Station 3C 1 RSU 30 1.0 2 9 0 DBST c F 3 041004 041005 Belfield Railway Station 3C 1 RSU 30 1.4 2 9 0 DBST c F 3 041006 041007 Clonbrook Railway Sation 3C 1 RSU 30 1.0 2 9 0 DBST c F 3 041102 041101 Helena No. 2 Cane Grove 2C 1 RSU 30 6.0 2 9 0 DBST c F 4 051207 051208 Bygeval Broeken Waterland 2C 1 RSU 30 2.0 2 9 0 DBST c F 4 051203 051202 Mahaicony Wash Clothes 2C 1 RSU 30 55 2 9 0 DBST c F 4 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987(continued) Link/Node Codes Link/Node Names (from-to) from to Node Node (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) 2. West Coast Berbice (East of Mahakony) 051301 051304 Calcutta Mards 2C 1 RSU 40 4.5 2 9 4 DBST C F 3 051601 051602 Belladrum Railway Station 3C 1 RTU3Q 1.0 1 12 8 L CF 3 051604 051605 Lichfield Railway Station 3C 1 RTU30 1.0 1 12 8 LCF 3 051620 051621 Onverwagt Abary River 3C 1 RTU30 7.0 2 9 0 DBST c F 3 /BE c F 3 051702 051703 Fort Wellington Railway Station 3C 1 RTU30 1.0 1 12 8 DBST c F 3 3. Corentyne (East of New Amsterdam) 060801 060803 Fyrish Fyrish Village 3B 1 USU30 1.0 2 9 2 DBST c F 3 060903 061423 PortMourant 4th Depth 3C 1 RTU30 18.0 2 9 0 BE c F 3 061002 061006 Whim Whim Police Sta. 3C 1 RTU30 0.2 1 12 8 DBST c F 2 061003 061501 Adventure Lesben Holden 2A 1 RTU30 5.3 1 12 2 AC c F 1 061501 061502 Lesben Holden Yakusari 2A 1 RTU30 9.3 1 12 2 AC c F 1 061502 061004 Yakusari No. 43 Joppa 2A 1 RTU30 7.2 1 12 2 AC c F 1 061020 061421 No. 47/48 4th Depth 3C 1 RTU30 5.0 3 10 0 BE c F 3 061120 061422 No. 57/58 Canje River 3C 1 RTU30 15.0 3 12 0 C c F 3 061101 061102 No. 63 Guest House 3C 1 USU30 0.2 2 10 2 DBST c F 2 061122 061123 No. 72 Sea Forth Canal 3C 1 RTU30 4.0 3 12 0 C c F 3 4. East Bank Demerara (South from Georgetown) 040203 040204 Ramsburg Mocha Village 2B 1 RSU 40 2.1 2 9 2 DBST c F 2 5. West Bank Demerara (South from Vreed-en-Hoop) 030804 030803 Bagotville Studley Park 1C 1 RTU40 7.0 1 12 ‘ 3 SC c F 3 030802 030801 La Retraite New Annlegt 1C 1 RTU30 6.7 1 12 3 SC c F 3 6. West Coast Demerara (West from Vreed-en-Hoop) 030711 030710 Best Hospital The Best Road 3C 1 RSU 30 0.1 2 10 0 DBST c F 3 030709 030708 Railway Station Windsor Forest 3C 1 RSU 30 0.5 2 10 2 CSBE c F 3 030707 030706 Railway Station Den Amstel 3C 1 RTU30 0.5 1 12 4 CSBE c F 3 030407 030406 Railway Station Tuschen 3C 1 RTU30 0.2 1 12 4 CSBE c F 3 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987 (continued) LinWNode Codes Link/Node Names (from-to) from to Node Node (1 ) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) 7. Essequibo Coast (West from Supenaam) 020302 030306 Suddie Hospital 3C RTU30 0.2 12 DBST C F 030101 030103 Anna Regina Red Lock Bend 2C RTU30 3.8 12 AC C F C PERIPHERAL AREA-ROADS 1. Mapenna-Orealla (South from the Corentyne) 160103 160101 Mapenna Orealla 2C 2 RTU30 5.0 1 12 4 S S FR 3 2. Soesdyke-Linden (South from East Bank Demerara) 140101 140102 Soesdyke Timehri Junction 1A 1 RPU50 8.0 2 11 4 AC S FR 2 140102 100303 Timehri Junction Linden Junction 1A 1 RPU60 34.0 2 11 4 AC S FR 2 100303 100301 Linden Junction Linden 1A 1 RPU60 3.5 2 11 4 AC S F 2 100301 100302 Linden Linden Bridge 1A 1 VSU 40 1.0 2 10 4 AC S F 3 D. INTERIOR AREA - ROADS AND TRAILS 1. Linden-Ituni-Kwakwanl-Klbilibiri (South from Linden) 100301 100104 Linden Ituni 1A 2 RSU 30 36.0 2 10 0 S s FR 1 100104 100102 Ituni Ituni Junction IB 2 RWF 8.0 1 12 0 s s FR 3 100102 100103 Ituni Junction Kwakwani IB 2 RWF 16.0 1 9 0 s s FR 3 100102 100101 Ituni Junction Kibilibiri 2B 2 RWF 16.0 1 9 0 s s FR 3 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987(continued) Link/Node Codes Link/Node Names (from-to) from to Node Node (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) 2. Linden-Mabura Hill-Annai-Lethem- Aishalton (South from Linden) 100302 100202 Linden Bridge Wismar 7 1A 2 RSU 30 7.0 2 9 0 BW S F 2 100202 100203 Wismar 7 Mabura Hill 1A 2 RWA 68.0 1 12 0 SL SL FR 2 100203 100202 Mabura Hill Kurupukari (E bank) 1A 2 RWA 65.0 1 9 0 SL SL FR 3 100204 100205 Kurupukari(W bank) Burro Burro Junction 1A 2 RWF 33.0 1 9 0 SL SL SW 3 100205 190201 Burro Burro Junction Annai 1A 2 RWF 37.0 1 9 0 SL SL FR 3 190201 190203 Annai Good Hope 1A 2 RWA 28.0 1 9 0 SL SL F 3 190203 190101 Good Hope Lethem 1A 2 RWA 42.0 1 9 0 SL SL F 2 190101 190208 Lethem Karanambo B2 2 RWF 60.0 1 9 0 L SL FR 3 190101 190201 Lethem Aishalton B2 2 RWF 99.0 1 9 0 L SL FR 2 3. Linden-Rockstone-Kumaka Sherima- Allsopp Point (West from Linden 100202 100201 Wismar 7 Rockstone Junction 1A 2 RSU 30 12.0 2 9 0 BW S FR 2 100201 100105 Rockstone Junction Rockstone 2B 2 RWF 5.0 1 12 0 S S FR 2 100201 100103 Rockstone Junction Arawai (E bank) IB 2 RWA 16.0 1 12 0 S S FR 2 170212 170202 Kumaka Sherima Allsopp Point IB 2 RTU20 12.0 1 12 0 SL S FR 2 4. Bartica-Allsopp Point-Issano-Mahdia- Tumatumari (South from Bartica) 170101 170201 Bartica Mile 21 Potaro 1A 1 RSU 40 21.0 2 10 3 SL S FR 4 170201 170202 Mile 21 Potaro Allsopp Point 1A 1 RWA 14.0 1 9 0 SL S FR 4 170202 170203 Allsopp Point Mile 72 Potaro 1A 1 RWA 37.0 1 9 0 S S FR 4 170203 170204 Mile 72 Potaro Issano Junction 1A 1 RWA 5.0 1 9 0 S S FR 4 180204 180205 Issano Junction Mahdia 1A 1 RWA 36.0 1 9 0 SL S FR 4 180204 180206 Issano Junction Issano IB 1 RWA 51.0 1 9 0 SL SL FR 4 180205 180210 Mahdia Haiari 1A 2 RTU20 20.0 1 12 0 SL SL FR 4 180205 180207 Mahdia Tumatumari 2B 2 RWA 2.5 1 9 0 S S FR 4 180207 180208 Tumatumari Tumatumari Junction 2B 2 RWA 16.0 1 9 0 SL SL FR 4 ROAD TRANSPORT NETWORK INVENTORY AND CONDITION EVALUATION: 1987(continued) Link/Node Codes Link/Node Names (from-to) from to Node Node (1) (2) (3) (4) (S) (6) (7) (8) (9) (10) (11) (12) (13) 5. Kumaka-Kwabanna (South of Kumaka) 010104 020205 Kumaka Kwabanna 2A 2 RWA 20.0 1 9 0 S s FR 2 6. Mabaruma-Port Kaituma-Arakaka- Matthews Ridge (Sough of Morawhanna) 010102 010101 Morawhanna Mabaruma 2A 1 RSU 30 6.0 2 9 0 DBST SL FR 4 010101 010107 Mabaruma Wanaina 2A 1 RTU30 7.0 1 12 2 CS C F 4 010107 010108 Wanaina Yarakita 2A 2 RWF 17.0 1 9 0 SL SL FR 4 010108 010106 Yarakita Port Kaituma 2A 2 RWF 20.0 1 9 0 SL SL FR 4 010106 010105 Port Kaituma Arakaka 2A 2 RSU 30 19.0 2 10 5 SL SL FR 4 010105 010102 Arakaka Matthews Ridge 2A 2 RSU 30 20.0 2 10 5 MW SL FR 4 010105 010204 Arakaka Towakaima 2B 2 RWF 29.0 1 9 0 SL SL FR 4 010105 010103 Arakaka Five Star 2B 2 RWF 30.0 1 9 0 S SL FR 4 010104 010403 Towakaima Makapa 2B 2 RWF 50.0 1 9 0 S SL FR 4 APPENDIX C DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS: ROAD ADMINISTRATION DIVISION MINISTRY OF WORKS 168 DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS: ROAD ADMINISTRATION DIVISION MINISTRY OF WORKS Road Maximum Gradient (percent) Max. Super* Max. Min. Stopping Min. Passing No. of Lane Shoulder Min. Road Design Flat Rolling Mountainous Elevation Horizontal Sight Sight Lanes Width Width Median Reserve Code up/down up/down up/down (feet per foot) Curvature Distance Distance (feet) (feet) Width Width (degrees) (feet) (feet) (feet) (feet) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) A. ROADS IN URBAN AREAS Primary Divided Roads UPDSO 4 5 5 6 7 8 0.06 7.0 350 1,800 4 12 8-10 4 100-150 UPD40 5 6 6 7 8 9 0.06 115 275 1,500 4 12 8-10 4 100-150 Erimarv Undivided Roads UPU60 4 4 5 5 7 7 0.06 7.0 350 1,800 2 10-12 8-10 80-130 UPU40 5 5 6 6 8 8 0.06 115 2.75 1,500 2 10-12 8-10 80-130 UPU30 6 6 7 7 9 9 0.06 21.0 200 1,100 2 10-12 8-10 80-130 Secondary Divided Roads USD 40 5 6 6 7 8 9 0.06 115 275 1,500 4 11-12 6-8 4 80-130 USD 30 6 7 7 8 9 10 0.06 21.0 200 1,100 4 11-12 608 4 80-130 S«s>ndaiy.,Undivided Read? USU 40 5 5 6 6 8 8 0.06 115 275 1,500 2 10-12 6-8 80-130 USU30 6 6 7 7 9 9 0.06 21.0 200 1,100 2 10-12 6-8 80-130 Source: Transport Plan for Guyana O' 10 DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS: ROAD ADMINISTRATION DIVISION MINISTRY OF WORKS Road Maximum Gradient (percent) Max. Super- Max. Min. Stopping Min. Passing No. of Lane Shoulder Min. Road Design Flat Rolling Mountainous Elevation Horizontal Sight Sight Lanes Width Width Median Reserve Code up/down up/down up/down (feet per foot) Curvature Distance Distance (feet) (feet) Width Width (degrees) (feet) (feet) (feet) (feet) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) B. ROADS IN VILLAGE AREAS Primary Divided Road* VPD40 5 6 6 7 8 9 0.06 115 275 1500 4 10-12 6-10 4 100-130 VPD30 6 7 7 8 9 10 0.06 21.0 200 1,100 4 10-12 6-10 4 100-130 Primary Undivided Roads VPU40 5 5 6 6 8 8 0.06 11.0 275 1,500 2 10-12 6-10 80-130 VPU30 6 6 7 7 9 9 0.06 21.0 200 1,100 2 10-12 6-10 80-130 Secondary Undivided Roads VSU 30 8 8 9 9 11 11 0.06 21.0 200 1,100 2 10 4-6 - 60 Source: Transport Plan for Guyana DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS: ROAD ADMINISTRATION DIVISION MINISTRY OF WORKS Road Maximum Gradient (percent) Max. Super- Max. Min. Stopping Min. Passing No. of Lane Shoulder Min. Road Design Flat Rolling Mountainous Elevation Horizontal Sight Sight Lanes Width Width Median Reserve Code up/down up/down up/down (feet per foot) Curvature Distance Distance (feet) (feet) Width Width (degrees) (feet) (feet) (feet) (feet) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) C ROADS & TRAILS IN RURAL AREAS Primary Divided Road? RPD60 3 4 4 5 6 7 0.08 5.0 475 2,100 4 12 6-8 4 120-200 RPD50 4 5 5 6 7 8 0.08 75 350 1,800 4 12 6-8 4 120-200 RPD40 5 6 6 7 8 9 0.08 12 5 275 1,500 4 12 6-8 4 120-200 Primary Undivided Roads RPU60 3 3 4 4 6 6 0.08 5.0 4.75 2,100 2 10-12 6 80-130 RPU60 5 5 6 6 8 8 0.06 75 350 1,800 2 10-12 6 -• 80-130 RPU40 5 6 6 7 8 9 0.08 12 5 275 1,500 2 10-12 6 80-130 Secondary Undivided Roads RSU 50 5 5 6 6 8 8 0.08 75 350 1,800 2 10 4 80 RSU 40 6 6 7 7 9 9 0.08 12.5 275 1,500 2 10 4 ,* 80 RSU 30 8 8 9 9 *11 11 0.08 23.0 200 1,100 2 10 4 80 Source: Transport Plan for Guyana DESIGN STANDARDS FOR PUBLIC ROADS AND TRAILS: ROAD ADMINISTRATION DIVISION MINISTRY OF WORKS Road Maximum Gradient (percent) Max. Super- Max. Min. Stopping Min. Passing No. of Lane Shoulder Min. Road Design Flat Rolling Mountainous Elevation Horizontal Sight Sight Lanes Width Width Median Reserve Code up/down up/down upfdown (feet per foot) Curvature Distance Distance (feet) (feet) Width Width (degrees) (feet) (feet) (feet) (feet) (1) (2) (3) (4) (5), (6) (7) (8) (9) (10) (11) IsrtlaQLUndMdfidJSaidi RTU40 6U 6- 0.08 7 275 1,500 1 10-12 3-4 — 80-200 RTU3Q 10^ 10^ 10- 10- 0.08 23 200 1,100 1 10-12 3-4 -- 80-200 RTU20 i i - 0.08 57 125 500 1 10-12 3-4 — 80-200 All-Weather Trails 1 RWA No delsgn standards. Must be passable by 4-wheel-c irive vehicles:n all weather Fair-Weather Trails 1 RWF No design standards. Musb be passable by 4-wheel-< drive vehicles n all weather L L...... Source: Transport Plan for Guyana 1/ 8%for 200feet 2/ 12% for 750 feet 3/ 14%for 500 feet 172 APPENDIX D POLITICAL RATING MODELS 173 174 POLITICAL RATING MODELS Government Characteristics, Country ABC What Classification best describes the current government? Government type (0 to 10) i 0 Despot, dictator 2 Military director 4 Monarchy, family rule 6 One-party democracy or nonviable multiparty democracy 8 Multiparty (coalition) democracy 10 Viable two-party democracy Latest change in government (0 to 10) I 0 Bloody and violent coup d'etat 2 Bloodless coup d'etat 4 Peaceful dictator change 6 Monarch change, change in colonial status 8 Elections, one candidate only 10 Peaceful elections, two or more candidates Relations w ith United States (0 to 10) ‘ 0 Considered a threat to US security 2 Anti-American policies 4 Nonaligned but leaning to the East 5 Nonaligned 6 Nonaligned but leaning to the West 8 Supports most US foreign policies 10 Strongly pro-American, supports all US policies Government's role in economy (0 to 10) < 0 Government controls all aspects of economy (communism) 2 Government influences all aspects of economy 4 Socialistic type o economy 6 General agreement between capitalists and government 8 Capitalism with minor government intervention 10 Strongly capitalistic, free enterprise Stability of present government (0 to 10) ( 0 Violent coup d-etat imminent 2 Overthrow of government likely 4 Unexpected change in government possible (i.e., death of leader) 6 Government could lose in next election 8 Likely change in government, political power remains intact 10 Government unlikely to lose in next election POLITICAL RATING MODELS (continued) Political stability, Country ABC What are the chances of the following events in the short term and medium term? Short Medium Term Term Destabilizing riots, civil unrest 3 2 Increased terrorist activities 3 2 Guerrilla activity, armed rebels 3 2 Civil war 4 4 Government overthrow, coup d'etat 4 3 Foreign war, border skirmishes 4 4 Political moratorium of debt 4 4 Nationalization of major industries 3 3 Socialistic party comes to power 3 3 Communist party comes to power 4 4 Total 35 31 Probabilities 5 Extremely unlikely 4 U nlikely 3 Neutral 2 Likely 1 Extremely likely 0 Present situation Governm ent Characteristics 30 Political Stability (short term/medium term) 35/31 Total 65/61 Short term is within 1 year; long term is between 1 and 5 years Source: John Morgan, "Assessing Country Risk at Texas Commerce," BANKERS MAGAZINE May-June 1985, Vol. 168, No. 3 APPENDIX E SAS STATISTICAL OUTPUTS FOR INVESTMENT BEHAVIORAL MODELS 176 CURRENT ACCOUNT ANALYSIS Model: MODELi Dependent Variablei ACUR OUTPUT NO.l Analyii* of variance Sum of Mean Source or Square* Square F Value Prob>F Model 11 SO.96013 3.93001 46.741 0.1140 Error 1 0,08387 0.08387 C Total 14 S I .04400 Root MSE 0.38960 R-square 0.9984 Dep Mean 3.33000 AdJ R-iq 0.9770 C a V* 13.48364 Parameter Eitlmate* Parameter Standard T for HOi Variance Variable DFDF Estimate Error Parameter^ Prob > |T| In fla tio n INTERCEP 1 43.503739 37.01630940 1.148 0.4561 PCUR 0,00000000 1 0.783187 0.38301439 3.043 0.3899 45.46774633 POP 1 •0.036309 0.03395438 -1.143 0.4579 GNI 198.55499377 1 •0.003191 0.00373368 -0.805 0.5687 33.74871300 CM? 1 •0.010899 0.00834104 •1.307 0.4159 317.16968166 ED 1 •0.365146 0.34647995 -1.076 EXD 0.4768 80.93888835 1 0.006835 0.00309509 3.305 0.3710 419.59985306 IRE 1 •0.046791 0.03673304 -1.751 0.3303 74.76339478 CAB 1 0.038064 0.01717937 1.634 0.3497 FXC 156.39163053 1 •0.635173 0.31781785 -3.916 0.3103 30.49433799 FU 1 -0.001363 0.03445097 -0.056 MAN 0.9646 63.94158044 1 0.050304 0.03580337 1.950 0.3017 368.63984879 MC 1 0.331553 1.11658311 0.397 0.8163 POLR 43.60677999 1 •0.167396 0.33340534 •0.843 0.5544 93.55854497 Colllnearity Oiagnottioe Condition Var Prop Var Prop Var Prop Var Prop Var Prop ir Prop Var Prop Var Prop Number Eigenvalue Number INTERCEP Var Prop Var Prop PCUR POP GNX GNP ED EXD IRE CAB FXC 1 11.73063 1.00000 0.0000 0.0000 0.0000 0.0000 0.0000 3 0.0000 0.0000 0.0000 0.0000 0 0001 1.41376 2.87931 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0003 3 0.41444 5.31795 0.0005 0.0003 0 0006 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0,0000 0.0008 0.0001 0 4 0.35179 6.83368 0.0000 0.0001 0.0000 0.0001 0.0000 0031 5 0.10627 0.0000 0.0006 0.0108 0.0O13 0 0039 10.50220 0.0000 0.0077 0.0000 0.0001 0.0001 0.0000 0.0003 6 0.05275 14.90626 0.0000 0.0005 0.0053 0 0357 0.0113 0.0000 0.0003 0.0000 0.0000 0.0004 0.0058 0.0080 0 0390 7 0.02867 20.21871 0.0000 0.0000 0.0000 0.0046 0,0000 8 0.0000 0.0075 0.0058 0.0032 0 0058 0.00591 44.52470 0.0000 0.0064 0.0003 0.1144 0,0086 0.0001 9 0.00289 63,70184 0.0031 0.0003 0.0063 0 0133 0.0000 0.4976 0.0000 0.0433 0.0063 0.0000 0,1387 10 0,00178 81.22246 0.0001 0.0113 0.0064 0 0959 0.37S8 0,0010 0.0394 0.0195 0.0001 0.0038 0.1813 0.2132 0 5735 11 0.0007319 126.55053 0.0000 0.0003 0,0004 0.03S6 0.0876 12 0.0040 0.0161 0.3465 0,3383 0 0000 0.0002359 222.89910 0.0002 0.1145 0.0337 0.0863 0.1754 0.0005 13 0.0001533 276.50843 0.0034 0.0009 0.0147 0, 0043 0.0000 0.0533 0.0316 0.0976 0.3510 0.1144 0.0576 0.0254 14 2 ,9530BE-6 1992 0.9996 0.0441 0 0647 0.0337 0.9440 0.5885 0.4515 0.8809 0.7783 0.5101 0.4585 0, 1707 7 7 1 CAPITAL ACCOUNT ANALYSIS Model! MODEL1 Dependent Variable! ACAP Analysis of Variance OUTPUT No.2 Sun of Mean Source OF Squares Square F Value Prob>F Model 14 3933.92102 280.99436 31.202 0.0006 Error 5 45.02898 9.00580 C Total 19 3978.95000 Root MSE 3.00097 R-square 0.9887 De^ Mean 16.55000 AdJ R-iq 0.9570 18.13272 Parameter Estimates Parameter Standard T for HOi Variance Variable DF Estimate Error Param eters Prob > |T| In fla tio n INTERCEP 1 98.031154 155.09164095 0,632 0.5551 0.00000000 PCAP 1 0.886896 0.24230493 3.660 0.0146 6.69454789 POP 1 •0.111682 0.10957390 -1.019 0.3548 58.45932244 CNI 1 0.032334 0.01639019 1.973 0.1056 18.99223730 ON? 1 0.076153 0.03790328 2.009 0.1008 51.79052369 ED 1 •1.332727 1.08630410 -1.227 0.2745 17.66325007 EXD 1 0.014954 0.00851348 1.757 0.1393 43.99018630 IRE 1 -0.338070 0,07970725 -4.241 0.0082 8.67849878 CAS 1 0.064250 0.04596362 1.398 0.2210 16.33372671 FXC 1 1.716898 1.09523260 1.568 0.1778 7.50498536 FU 1 -0.316322 0.11680552 •2.708 0.0424 19.40986858 KAN 1 0.210358 0.06485744 3.243 0.0229 27.36683096 CPOL 1 18.837595 11.50617861 1,637 0,1625 66.88850827 MC 1 25.400320 5.63906197 4.504 0.0064 17.47820483 POLR 1 •1.017219 1.34495583 •0,756 0.4835 43.97834527 Collinearlty Diagnostics Condition Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Number Eigenvalue Number Var Prop Var Prop INTERCEP PCAP POP CNI GNP ED EXD IRE CAB FXC 12.18730 1.00000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0001 0, 0002 1.80214 2.60052 0.0000 0.0003 0.0000 0.0001 0.0000 0.0000 0.45024 0.0008 0.0028 0.0011 0, 0007 5.20275 0.0000 0.0020 0.0000 0.0001 0.0001 0.0000 0.0002 0.0167 0.26351 6.80072 0.0000 0.0015 0. 0074 0.0059 0,0000 0.0000 0.0001 0.0000 0.0002 0.1085 0.0072 0, 0010 0.13214 9.60354 0.0000 0.0122 0.0000 0.0003 0.0002 0.0000 0.0031 0.0112 6 0.07685 12.59321 0.0001 0, 1264 0.0000 0.0036 0.0000 0.0002 0.0004 0.0000 0.0127 0.0025 0.1192 0, 0000 7 0.03341 19.09786 0.0000 0.0951 0.0000 0,0068 0.0000 0.0000 0.0672 0.0319 0.0514 0. 0680 0 0.02644 21.46804 0,0000 0.3786 0.0000 0.0078 0.0011 0.0000 0.0006 0.0038 0.0100 0. 0308 9 0.01595 27.63851 0.0000 0.0807 0.0000 0.0001 0.0027 0.0000 0.1289 0.0826 0.0727 0. 1544 10 0,00598 45.14126 0.0000 0.0148 0.0000 0.0001 0.0020 0.0006 0.0906 0.3810 0,4910 0. 2670 11 0.00406 54.77406 ' 0.0002 0.1384 0.0009 0.5299 0,0134 0.0016 0.1144 0.1332 0.0030 0. 0069 12 0.00151 89.75540 0,0003 0.0376 0.0005 0.0757 0.4483 0.0000 0.0675 0.0190 0,0373 0. 2783 13 0.0002507 220.48934 0.0000 0.2115 0.0630 0.0025 0.1025 0.3145 0.0825 14 0.0001910 252.61480 0.0685 0.1186 0. 0073 0.0023 0.0113 0.1386 0.2407 0.3887 0.0077 0.2910 0.1288 0.0267 0. 15 0.0000135 951.11999 0.9971 0491 0.0078 0.7969 0.1357 0.0407 0.6755 0.1403 0.0092 0.0600 0. 0024 1-78 OUTPUT N o .3 Principal Component Analysis 21 Observations 3 Variables Simple Statistics GNI ON? FXC Mean 687.0952381 514.4285714 3.361904762 sto 182.9786613 129.2314093 2.249772475 Correlation Matrix GNI GNP FXC GNI 1.0000 0.0792 -.6 0 3 4 GNP 0.0792 1.0000 -.1 9 9 4 FXC -.6 0 3 4 -.1 9 9 4 1.0000 Eigenvalues of the Correlation Matrix Eigenvalue Difference Proportion C um ulative DEVELOP1 1.66255 0.709272 0.554184 0.554184 DEVELOP2 0.95328 0.317760 0.871944 Eigenvectors DEVELOP1 DEVELOP2 GNI 0.663522 -.3 0 5 1 9 0 GNP 0.287264 0.946958 FXC -.6 9 0 8 0 9 0.100645 OUTPUT NO.4 Principal Component Analysis 21 Observations 5 Variables Simple Statistics EXD IRE CAB FU MAN Mean 620.7809524 27.52238095 -78.24904762 43 .14142857 151.2014286 StD 537.6388254 24.88055223 60.36024800 26 .39331246 55.7382452 Correlation Matrix EXD IRE CAB FU MAN EXD 1.0000 -.3766 -.7544 0.3870 0.1427 IRE -.3 7 6 6 1.0000 0.3011 0.2928 0.4738 CAS -.7 5 4 4 0.3011 1.0000 -.6139 -.5070 FU 0.3870 0.2928 -.6139 1.0000 0.8677 MAN 0.1427 0.4738 -.5070 0.8677 1.0000 Eigenvalues of the Correlation : M a trix E ig e n v a lu e Difference Proportion C um ulative ECONOl 2.66678 0.914938 0 .533356 0.533356 ECON02 1.75184 0 .350369 0.883725 Eigenvectors ECONOl ECON02 EXD 0.398531 -.4 7 3 8 6 4 IRE 0.054537 0.692320 CAB -.5 2 7 6 1 6 0.312329 FU 0.556689 0.218784 MAN 0.499917 0.388239 OUTPUT NO.5 A CHECK FOR MULTICOLLINIARITY AMONG INDEP. VARIABLES Modeli MODELI Dependent Variable! ACUR Analysis of Variance Sum of Meann Source DF Squares Square F Value Prob>F Model 9 50.43163 5.60351 45.753 0.0003 Error 5 0.61237 0.12247 C Total 14 51.04400 Root MSE 0.34996 R-square 0.9880 De^ Mean 2.32000 AdJ R-sq 0.9664 15.08457 Parameter Estimates Parameter Standard T for K0i Variance Variable DF Estimate Error Param eters Prob > 1T| In fla tio n INTERCEP 4.689890 10.05153039 0.467 0,6604 0.00000000 PCUR 0.817462 0.39182258 2.086 0.0913 33.58360206 DEVILOP1 1.231167 0.31625273 3.893 0,0115 15.98213512 DEVELOP! 0.988250 0.61701489 1.602 0.1701 44.11478616 ECONOl -0.413760 0.34932211 •1.656 0,1587 16.38700069 EC0N02 •0.715983 0,24928228 -2.872 0.0349 13.78624335 POLR 0.030958 0.14319040 0,216 0.8374 26.37348986 MC -0.310965 0.57161596 -0.537 0.6146 8.04637534 ED •0.108947 0.11601195 •0.939 0.3908 13.37853187 CPOL 2.761948 1.81390563 1.523 0.1883 46.56607910 Collinearity Diagnostics Condition Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Var Prop Number Eigenvalue Number Var Prop Var Prop INTERCEP PCUR DEVELOP! DEVELOP! ECONOl ECON02 POLR MC ED CPOL 5.00286 1.00000 0.0000 0.0002 0.0003 0,0001 2.69355 0.0003 0.0002 0.0000 0.0011 0.0000 0.0001 1.36285 0.0000 0.0002 0.0057 0.0001 0.0010 0.0069 0.0000 0.0005 1.86821 1.63642 0.0000 0.0021 0.0000 0.0001 0.0015 0.0049 0.0120 0,0034 0.0000 0.0000 0.0000 0.0000 0.23708 4.59365 0.0000 0.0001 0.0171 0.0027 0.0002 0,10052 0.1763 0.0000 0.0013 0.0000 0.0225 7.05468 0.0000 0.0070 0.2148 0.0016 0.0283 0.0001 0.0000 0.06058 9.08772 0.0992 0.0000 0.0213 0.0000 0.0009 0.1562 0.0394 0.2235 0.1080 0.0001 0.1307 0.0001 0.0234 0.03306 12.30181 0.0001 0.0018 0.0095 0.2189 0.00367 0.4446 0.0325 0.0004 0.2195 0.0001 0.0291 36.93921 0.0013 0.9310 0.5644 0.1946 0,1586 0.1562 0.0029 0.1724 0.0024 9 0.0004243 108.59070 0.7311 0.0005 0.0073 0.0033 0.4349 0.0039 0.3899 0.3324 0.3135 0.1958 0,1704 10 0,0000523 309.42827 0.99B0 0.0515 0.0272 0.1027 0.1277 0.1267 0.6642 0.0619 0.8016 0.0000 T 8 T A RIDGETRACE OF INDEPENDENT VARIABLES CURRENT ACCOUNT ANALYSIS OUTPUT NO.6 PCUR 0.00 0.02 0.08 0.12 0.14 0.18 0.20 0.24 0.26 RIDGE K VALUE 182 A CHECK FOR HULTICOLLINIARITY AMONG INDEP. VARIABLES Modeli MODEL1 Dependent Variablei ACAP Analysis of variance OUTPUT No,7 Sum of Mean Source DF Squares Square F Value Prob>F Model 9 3649.80555 405.53395 12.321 0.0003 Error 10 329.14445 32.91445 c Total 19 3978.95000 Root KSE 5.73711- R -square 0.9171 Dep Mean 1$.55000' Adj R-sq 0.842B C.V. 34.65532 Parameter Estimates Parameter Standard T for H0t Variance Variable DF Estimate Error Param eters Prob > |TI In fla tio n INTERCEP 167.566457 99.31124244 1.687 0.1224 0.00000000 PCAP 1.418391 0.40566162 3.496 0.0058 5.13405072 DEVELOP1 5.676141 3.18204972 1.784 0.1048 7.58751508 DSVELOP2 6.273581 8.09822688 0.775 0.4565 37.96508122 ECONOl 6.106653 4.28045146 1.427 0.1842 27.06127177 EC0N02 •8.497282 2.70110196 •3.146 0.0104 7.76142262 POLR •0.232849 1.84166853 -0.126 0.9019 22.56219474 MC 33.346194 7.73821451 4.309 0.0015 9.00534155 ED -3.298449 1.11341204 -2.962 0.0142 5.07709371 CPOL ■ •0.416459 15.75924124 -0.026 0.9794 34,33171230 Colllnearlty Diagnostics Condition Var Prop Var Prop Var Prop Var Prop Var Prop Number Eigenvalue Number Var Prop Var Prop Var Prop INTERCEP PCAP ECONOl ECON02 POLR HC ED CPOL 4.82620 1.00000 0.0000 0.0011 0.0003 0.0000 0.0000 0.0001 0.0000 0.0013 0.0000 0.0002 2.79027 1.31516 0.0000 0.0000 0.0092 0.0013 0.0024 0.0071 0.0000 1.92134 0.0021 0.0000 0.0014 .58490 0,0000 0.0003 0.0112 0.0042 0.0045 0.0158 0.0000 0.0002 0.0000 0.0002 0.20918 .80337 0,0000 0.0105 0.0135 0.0075 0.0001 0,2691 0.0000 0.13661 0.0002 0.0000 0.0351 .94375 0.0000 0.0172 0.4987 0.0004 0.0129 0.0773 0,0000 0.0428 0.0000 0.016S 0.05483 .38170 0.0001 0.0931 0.1469 0.1805 0.1578 0.0581 0.0001 0.0028 0.0003 0.0086 0. 04828 .99822 0.0001 0.0527 0.0447 0.0160 0.0209 0.1693 0.0002 0.6463 0,0002 0.0257 0.01278 19.43300 0,0008 0.7895 0.0006 0.1769 0.7706 0.0000 0.0014 0.0006 0.0018 0.1649 9 0.0004087 108.66222 0.0039 0.0344 0.0143 0.3997 0.0001 0.4016 0.3069 0.2651 0.5684 0.3273 10 0.0001073 212.06246 0.9951 0.0011 0.2608 0.2135 0.0307 0.0016 0.6914 0.0386 0.4293 0.4201 183 A RIDGETRACE OF INDEPENDENT VARIABLES CAPITAL ACCOUNT ANALYSIS OUTPUT N o .8 PCAP 0.08 0.10 0.14 0.26 184 RIDGE K VALUE CAPITAL EXPENDITURE ANALYSIS Model: MODEL1 Dependent Variable< ACAP Analysis of Variance O u tp u t N o. 9 . Sum o f Mean S ource DF S q u ares Square F Value Prob>F Model 4 3202.78567 800.69642 15.474 0.0001 E rro r 15 776.16433 51.74429 C T o ta l 19 3978.95000 R oot MSE 7.19335 R -sq u a re 0.8049 Dep Mean 16.55000 Adj R -sq 0.7529 C.V. 43.46435 Parameter Estimates P ara m e te r S ta n d a rd T f o r HO: V a ria n c e Variable DP E stim a te E rro r P a r a m e te r s P rob > IT| I n f l a t i o n INTERCEP -14.455851 5.84769503 -2 .4 7 2 0.0259 0.00000000 PCAP 1.134671 0.38511485 2.946 0.0100 2.94331645 DEVELOP1 -1 .9 3 0 8 6 3 2.18118264 -0.885 0.3900 2.26774296 ECONOl 3.944950 2.22799009 1.771 0.0969 4.66358269 nc 20.198191 7.04S439S6 2 .8 6 7 0.0118 4.74852760 Collinearity Diagnostics C o n d itio n Va r P rop V ar P rop V ar Prop V ar P rop V ar Prop Number Eigenvalue Number INTERCEP PCAP DEVELOP1 ECONOl MC 2.77725 1.00000 0.0079 0.0055 0.0141 0.0003 0.0105 1.32438 1.44811 0.0039 0.0035 0.0482 0.0991 0.0053 0.80192 1.86098 0.0086 0.0000 0.3704 0.0555 0.0018 0.06682 6.44702 0.3364 0.0004 0.4535 0.3194 0.7554 0.02963 9.68167 0.6432 0.9907 0 .1138 0.5257 0.2270 Durbin-Watson D 1.826 (For Number of Obs.) 20 1st Order Autocorrelation 0.039 CURRENT ACCOUNT ANALYSIS Model: MODEL1 Dependent Variable: ACUR Analysis of Variance Output No. 10. Sum o f Mean S ource DF S q u ares S quare F V alue Prob>F Model 4 49.19626 12.29907 66.563 0.0001 E rro r 10 1.84774 0.18477 C T o ta l 14 51.04400 R oot MSE 0.42985 R -sq u are 0.9638 Dep Mean 2.32000 Adj R -sq 0.9493 C.V. 18.52814 Parameter Estimates Parameter Standard T f o r HO: V a ria n c e V a ria b le DF E s tim a te E rro r P a r a m e te r ^ P rob > IT| I n f l a t i o n INTERCEP 1 -1 .8 7 5 1 1 7 0.76240560 -2.459 0.0337 0.00000000 PCUR 1 1.598862 0.17759661 9.003 0 .0001 4.43702197 DEVELOP1 1 0.435302 0.14907866 2 .9 2 0 0.0153 2.35396097 ECONOl 1 -0 .2 9 0 6 4 9 0.10686830 -2 .7 2 0 0 .0216 1.98344173 MC 1 -0.657473 0.47247636 -1 .3 9 2 0.1942 3.54390334 Collinearity Diagnostics C o n d itio n V ar P rop V ar P rop V ar P rop V ar P rop V ar Prop Number Eigenvalue Number INTERCEP PCUR DEV8L0P1 ECONOl MC 1 2.82180 1.00000 0.0025 0 .0031 0.0112 0.0097 0 .0086 2 1.24035 1.50831 0.0010 0.0101 0.0867 0.1294 0 .0019 3 0 .82801 1.84606 0.0000 0.0002 0.2322 0.2885 0.0000 4 0.09675 5.40041 0.0006 0.0860 0.5575 0.5341 0 .4278 5 0.01307 14.69083 0 .9960 0 .9006 0.1124 0.0383 0 .5618 Durbin-Watson D 2.116 (For Number of Obs.) 15 1st Order Autocorrelation -0.089 186 CAPITAL EXPENDITURE ANALYSIS Models M0DEL1 Dependent Variable! ACAP Analysis of Variance , Output No. ] Sum of Mean Source DF Squares Square F Value Prob>F Model 4 3202.78567 800.69642 15.474 0,0001 Error 15 776.16433 51.74429 C Total 19 3978.95000 Root MSE 7.19335 R-square 0.8049 De^ Mean 16.55000 AdJ R-sq 0.7529 43.46435 Parameter Estimates Squared Squared Parameter Standard T for HOl Variable DF P artial Partial Estimate Error Param eters Prob > 1T| Type I 88 Type II 88 Corr Type I Corr Type II INTERCEP 1 •14.455851 5.84769503 -2.472 0.0259 5478.050000 316.213447 PCAP 1 1.134671 0.38511485 2.946 0.0100 2691.530055 449.181503 0.67644229 0l366S7S29 DEVELOP1 1 -1.930863 2.18118264 -0.885 0.3900 45.480007 40.549120 ECONOl 1 0.03532647 0.04964914 3.944950 2.22799009 1.771 0.0969 40.499300 162.225390 0.03260971 MC 1 20.198191 7.04543956 0.17287635 2.867 0.0118 425.276310 425.276310 0.35397197 0.35397197 CURRENT ACCOUNT ANALYSIS Model: MODEL1 Dependent Variable: ACUR Analyels of Variance Output No. 1 Sum of Mean Source DF Squares Square F Value Prob>F Model 4 49.19626 12.29907 66.563 0.0001 Error 10 1.84774 0.18477 C Total 14 61.04400 Root MSE 0.42985 R -square 0.9638 Dep Mean 2.32000 AdJ R -sq 0.9493 C.V. 18.52814 Parameter Estimates Squared Squared Parameter Standard T for K0i P artial P a r t i a l Variable DF Estimate Error ParameteraO Prob > |T| Type 1 S3 Type II S3 Corr Type I Corr Type II INTERCEP -1.875117 0.76240560 -2.459 0.0337 80.736000 1.117698 PCUR 1.598862 0.17759661 9.003 0.0001 47.164402 14.975881 0.92379912 0189017016 DEVZLOPl 0.435302 0.14907866 2.920 0.0153 0.612205 1.575397 0.15739535 0.46022097 ECONOl -0,290649 0.10686830 -2.720 0.02X6 1.071862 1.366717 0.32704707 0.42517887 MC •0.657473 0.47247636 -1.392 0.1942 0.357796 0.357796 0.16222672 0.16222672