Study on Road Infrastructure Costs- Analysis of Unit Costs and Cost ...

Table of ContentsSummary 1

1. Introduction 3

2. Building a Database for the Analysis of Road Costs 42.1 Establishing the Baseline for a New Database on Road Costs 42.2 Consolidation of Databases from Previous Studies 42.3 Addition of Cost Data from Recent AfDB Projects 52.4 Master Database for Analysis of Unit Rates and Cost Overruns 6

3. Analytical Approach for Road Infrastructure Costs 83.1 Standardizing the Unit Rate 83.2 Analytical Approach 8

4. Results 104.1 Establishing the Unit Cost Curve on the Basis of Project Size 10

4.1.1 Construction/Upgrading of Paved Roads 104.1.2 Rehabilitation of Paved Roads 104.1.3 Periodic Maintenance of Paved Roads 114.1.4 Regraveling of Unpaved Roads 124.1.5 Summary Results 12

4.2 Other Major Potential Drivers of Unit Rates 124.2.1 Location of Road Projects 124.2.2 Origin of Contractors 144.2.3 Landlocked vs. Seaboard Countries 16

4.3 Determining Unit Cost Trends 184.3.1 Unit Rates over Time 184.3.2 Cost Overruns/Underruns 204.3.3 Median Rates 22

5. Conclusions 255.1 Typical Road Unit Costs 255.2 Unit Cost Overruns/Underruns 255.3 Trend in Unit Costs 265.4 General Conclusions 26

Appendix A: Detailed Description of Database Parts for AICD Initiative 27

Appendix B: Statistical Terms 29

Appendix C: Consolidated Data 30

AfDBMarket Study Series

Study on Road Infrastructure Costs:Analysis of Unit Costs and Cost Overruns of Road Infrastructure Projects in AfricaStatistics Department (ESTA)

May 2014www.afdb.org

A f r i c a n D e v e l o p m e n t B a n k

Acknowledgements: This report was prepared by a team compris-ing Maurice Mubila, Chief Statisti-cian, (Statistics Department), Altus Moolman, Consultant, (Statistics Department) and Willem Van Zyl, Consultant (Statistics Department) under the supervision of Beejaye Kokil, Manager, Economic and So-cial Statistics Division, and the di-rection of Charles Leyeke Lufumpa, Director Statistics Department.

This report was prepared by the Statistics Department in the Chief Economist Vice Presidency of the African Development Bank. Its findings reflect the opinions of the authors and not necessarily those of the African Development Bank, its Board of Directors, or the countries that they represent. Designations employed in this ar-ticle do not imply the expression of any opinion on the part of the

African Development Bank Group concerning the legal status of any country or territory, or the delimi-tation of its frontiers. While every effort has been made to present reliable information, the African Development Bank accepts no responsibility whatsoever for any consequences of its use.

Layout and production by Phoenix Design Aid A/S, Denmark

Mthuli NcubeChief Economist and Vice PresidentOffice of the Chief Economist

Charles Leyeka LufumpaDirectorStatistics Department

Steve Kayizzi MugerwaDirectorDevelopment Research Department

Victor MurindeDirectorAfrica Development Institute

SUMMARY

The African Development Bank (AfDB) commissioned a study during 2010/11 to analyze road unit costs and the extent of cost overruns in road infrastructure projects in Africa. The study focused on three main objec-tives, namely: (i) to determine unit costs for road infrastructure projects in Africa; (ii) to determine the prevalence and extent of cost overruns; and (iii) to determine the evolution of unit costs and cost overruns since the comple-tion of previous studies specif-ically the Africa Infrastructure Country Diagnostics (AICD) study and a related study for the AfDB on Road Maintenance and Construction Costs in Africa.

This represents the Final Report for the study and presents the findings of the analysis on unit costs and cost overruns of road infrastructure projects in Africa.

Research was undertaken during 2007 and 2008, under the auspices of the joint World BankAfDB Africa Infrastructure Country Diagnostic (AICD), into baseline unit cost data and the causes of cost overruns, specifically on road

projects. The research resulted in the compilation of databases on road projects in Africa, which were used as the point of depar-ture for the current study. The databases were consolidated into a new structure, and a total of 26 new AfDB projects (with Project Completion Reports (PCRs) dated 2004 or later) were added to the database. The final database con-sisted of a total of 172 projects.

The consolidated structure divided projects into four categories, based on the type of work that was undertaken, as follows:

1. Regraveling or periodic maintenance of unpaved roads: This involves reinstat-ing the surface layer of gravel roads;

2. Periodic maintenance of paved roads: This involves the repair of minor surface defects and a seal or thin overlay, but without structural improve-ments or geometric upgrades;

3. Rehabilitation of paved roads: This typically entails the reinstatement of roads to

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African Development Bank Group / Chief Economist Complex . May 2014 AfDBStudy on Road Infrastructure Costs:Analysis of Unit Costs and Cost Overruns of Road Infrastructure Projects in Africa

the original design standard, including structural repairs; and

4. Construction and upgrading of paved roads: This typically entails the upgrad-ing of gravel roads to paved standard, or the addition of lanes to existing paved roads.

Analysis was performed on the projects in the database. The sequence of enquiry followed three steps, namely (i) establishing the position of the unit cost curve, (ii) veri-fying whether rates that lie off the curve can be explained, and (iii) determining whether the position of the curve is as expected (overruns) and whether it is shifting over time (unit cost trends).

What this review has brought to the fore is that there is no such thing as a typical unit cost. This is because (i) unit costs are calculated by standardizing projects that are broadly similar but which differ in their design details and specific circumstances; and (ii) the size of the project invariably has an overriding effect on the unit rate (economy of scale). The first issue is largely overcome by excluding major project and location-specific factors (e.g. bridges, taxes). The second issue is something that anyone estimating or eval-uating roads costs should be vigilant about.

The table below provides a summary of the unit cost findings. It should be noted that the unit rates are all expressed per lane kilometer, i.e. a 50 km two-lane (single carriageway) road would have 100 lane km.

The analysis of unit cost overruns shows that (i) there appears to be a correlation between the over/underrun and the size of the project and (ii) the estimation error (i.e. PCR value minus Project Appraisal Report (or PAR) value) is likely to be an underestimate (48 percent) rather than an overestimate (-15 percent).

A typical observation is that the smaller the project, the larger the difference between the expected unit rate (PAR value) and the PCR value. The implication is that unit rates for small projects should be treated with some caution, although care should be taken not to spend more resources on refining designs, feasibility studies, and other work underlying PARs than the ben-efit that could be expected.

In the case of the rehabilitation of paved roads, the difference occurs both above (overrun) and below (underrun) the PAR value. In the case of construction or upgrading of paved roads, it appears that the pattern is for small projects to overrun,

rather than underrun. This may point to PARs being overly optimistic.

The finding with respect to an increase in unit cost over time is inconclusive. This may be purely because of data constraints, i.e. a limited sample size for a specific year and standardization issues across projects in the same class. The effect is that statisti-cally extrapolated unit cost curves (rather than rates) are compared.

Given these shortcomings, comparing the cost curves of those years for which such curves can indeed be constructed, shows that unit costs for large projects (>100 lane km) have not increased during the last dec-ade. It might even be inferred that they have reduced, although this is counterin-tuitive, given the field experience of AfDB task managers, which points to increasing unit rates.

The main conclusion from this study is that while lenders and national road agencies will clearly benefit from having a better understanding of unit costs and related issues, a more permanent road unit cost database should be established (possibly under the AICD initiative), which can then be analyzed on a systematic basis.

Summary of Unit Rate Statistics for Different Types of Road Infrastructure Investment (USD/lane km, rounded to 00)

Type of Road Infrastructure Investment

Regraveling/ Periodic Maintenance of Unpaved Roads

Periodic Maintenance of Paved Roads

Rehabilitation of Paved Roads

Construction and Upgrading of Paved Roads

< 100 lane km

Quartile 3 10,500 N/A 290,000 425,400

Median 9,600 N/A 180,300 227,800

Quartile 1 8,100 N/A 109,800 166,300

100 lane km

Quartile 3 12,800 72,200 130,500 162,000

Median 11,300 64,600 84,400 147,100

Quartile 1 9,600 56,900 47,400 115,900

Note: All values are given in 2006 USD.

AfDB African Development Bank Group / Chief Economist Complex

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Study on Road Infrastructure Costs:Analysis of Unit Costs and Cost Overruns of Road Infrastructure Projects in Africa

1. Introduction

The African Development Bank (AfDB) and other development agencies are con-cerned about significant cost escalations of road projects under implementation. This is particularly important, given the AfDBs firm commitment to scale up its support to infrastructure development, as an opera-tional priority of its Ten Year Strategy 2013-2022. Investing in infrastructure is a prime way to boost economic growth, improve the social wellbeing of populations, and promote regional integration. However, excessive cost overrruns result in increased funding needs and act as a constraint to development; therefore a mechanism is needed to address this issue.

The imperative to scale up infrastruc-ture and improve the competitiveness of African economies is being constrained by limited data availability. Indeed, there is a scarcity of information regarding the costs of implementing road infrastructure projects in Africa, although significant data on the unit cost of projects exist, both in government records and those of devel-opment agencies in the region. There is therefore a need to systematically review and analyze these sources, to improve the generation of statistical data on the unit

costs of various types of road infrastructure investments.

Studies conducted about four years ago observed that cost overruns in road infra-structure projects had become increasingly common. The average cost overrun was 35 percent, but in a third of the cases it could be as high as 50 to 100 percent. The assumption has been that the increases are due to a variety of factors, including lack of competition in the bidding process, increases in fuel and bituminous product prices locally and internationally, technol-ogy used in road works, contract manage-ment practices, and the availability and quality of road construction materials.

The African Development Bank commis-sioned a study during 2010/11 to analyze road unit costs and the extent of cost overruns in road infrastructure projects in Africa. The study focused on three main objectives, namely: (i) to determine road unit costs for road infrastructure projects in Africa; (ii) to determine the occurrence and extent of cost overruns; and (iii) to determine the evolution of unit costs and cost overruns since the time of comple-tion of previous studies (specifically the

Africa Infrastructure Country Diagnostics (AICD) study and a related study for the AfDB on Roads Maintenance and Construction Costs in Africa). The cur-rent study represents the Final Report and presents the findings of the analysis on unit costs and cost overruns of road infrastruc-ture projects in Africa.

A four-step methodology was followed for the study: (i) first, the project databases from the previous studies were consoli-dated; (ii) new projects were then selected from the AfDB database, for comparison with the previous studies; (iii) data were extracted for each of the selected projects and added to the consolidated database; and (iv) analysis was performed to deter-mine unit rates and cost overruns.

This report contains four sections, includ-ing this introduction. Section 2 describes the development of a consolidated data-base of old (i.e. from previous studies) and newly selected projects, which were used for the analysis of unit rates. Section 3 provides an analysis of unit rates and cost overruns for all projects captured in the consolidated database. Section 4 sum-marizes the main findings of the report.

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2. Building a Database for the Analysis of Road Costs

Significant data on the unit cost of projects exist in the records of governments and donor agencies in the region. Under the auspices of the joint World BankAfDB initiative of the Africa Infrastructure Country Diagnostic (AICD), research was undertaken during 2007 and 2008 into unit cost data. This then served as a baseline against which future improve-ments in infrastructure services could be measured. The research also analyzed the causes of cost overruns, specifically on road projects.

The research resulted in the compilation of databases on road projects in Africa, containing cost data and allowing cate-gorization of the type of work conducted. These databases were used as the point of departure for the current study.

2.1 Establishing the Baseline for a New Database on Road CostsAfricon Limited completed a study on unit costs during 2008 as part of the joint World BankAfrican Development Bank AICD Initiative. The main aim of the study, which focused on 24 countries in Sub-Saharan Africa, was to provide a set of baseline unit cost data against which future infrastructure improvements could be measured. This was expected to provide a more solid and empirical foundation for prioritizing investments and design-ing policy reforms in the infrastructure sectors in Africa.

As part of this study, a database of infra-structure projects in Africa was compiled, covering roads, water, sanitation, and elec-tricity. The database was populated from Project Completion Reports (PCRs), con-tractual documents from donor agencies participating in the study, and publicly

available data from governments. For roads, the database covered 115 projects.

The database consisted of two parts, namely: (i) a general section common to all projects, which captured the major exogenous variables; and (ii) an infra-structure-specific section, adapted for each sector (e.g. roads, water, and elec-tricity) and for different infrastructures and technologies within each sector. A detailed description of these parts is presented in Appendix A.

In 2007, Africon Limited was appointed to carry out an addendum to the AICD assignment which was then underway. The topic of the addendum generally related to the main unit cost study, i.e. an investiga-tion into the causes of recent cost overruns, specifically on road projects.

Whereas the main AICD study sought to determine the final cost of projects that had already been completed, the Addendum examined the reasons for cost changes in ongoing projects, relative to the original engineers estimates. The purpose of the Addendum was not to analyze and improve the project appraisal and procurement cycle per se, but rather to identify and mitigate the causes of the recent wave of cost overruns (i.e. during the time of the AICD study).

A total of 24 AfDB projects were selected for the purposes of the study. Project details were obtained on the following: brief project description; timeframe (listing milestones such as Specific Procurement Notices (SPNs) issued, pre-qualification, tender issued, tender received, tender evaluation approved, contract signed); key project events; procurement; project com-ponent increases; changes in explanatory

variables; summary impacts on tender price; and classification.

Since the purpose of the 2007 AfDB study was to investigate cost overruns and not unit costs, it was not a requirement to enter these project details into a general database. Rather, the focus was on analyzing these projects on an individual basis.

2.2 Consolidation of Databases from Previous StudiesThe databases developed for the AICD study (2008) and the Addendum to AICD (2007), differed slightly in terms of their structure and the data captured. This was owing to the divergent objectives of each study. There was a need to consolidate these databases into one structure, taking due account of the specific objectives of this report. The consolidated structure con-sisted of nine major data fields, as listed below:

1. Project information (general infor-mation describing the project and its location);

2. Procurement information (e.g. International Competitive Bidding (ICB) or National Competitive Bidding (NCB));

3. Number of bidders, plus the name and nationality of the winning bidder

4. Project dates (dates for approval and signing of loan, signing of contract, expected and actual contract com-pletion dates);

5. Type of project (road type and sur-face type);


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6. Activity (category describing the type of work performed, as dis-cussed below);

7. Inputs & Costs: a) as per Project Appraisal Report (PAR) and b) as per Project Completion Report (PCR);

8. Size descriptors (number of lanes and road length); and

9. Currency (currency of costs, con-version factor to USD and USD CPI factor, for conversion to 2006 USD).

All projects from the 2008 AICD study, as well as from the 2007 AfDB study, were captured in the new database structure for the purposes of this report. It should how-ever be noted that some projects did not have data for all the fields in the database.

Projects listed in the database were further grouped into four categories, based on the type of work that was performed. These four categories, as well as a definition of each, are as follows:

1. Regraveling or periodic mainte-nance of unpaved roads. This is the activity of reinstating the sur-face layer of gravel roads. This term was also applied when there were substantial gravel road rehabilitation activities;

2. Periodic maintenance of paved roads. This involves the repair of

minor surface defects and a seal or thin overlay, but without struc-tural improvements or geometric upgrades. These contracts come in a range of light to heavy upgrades. Since the term periodic mainte-nance is used by road agencies for several different types of activity, some contracts with this activity in their title were reclassified (mostly to rehabilitation/reconstruction);

3. Rehabilitation of paved roads. This typically entails the reinstatement of roads to the original design stand-ard, including structural repairs; and

4. Construction and upgrading of paved roads. This covers the upgrading of gravel roads to paved standard, or the addition of lanes to existing paved roads.

It should be noted that the above four cat-egories are fairly broad, and may contain a wide variance with regard to technical standards. For example, new road con-struction (category 4) can include stand-ards such as gravel base and asphalt layer, gravel base and seal layer, bitumen-treated base and asphalt layer, and concrete base. However, in order to conduct a statisti-cal analysis, a sufficiently large sample is required (i.e. data points), and a compro-mise was therefore reached between the number of categories and level of detail per category. Furthermore, the data available in PCRs often did not provide a detailed technical description of the type of design

and work conducted. For these reasons, the four categories indicated above were used for the purpose of this study.

2.3 Addition of Cost Data from Recent AfDB ProjectsThe previous studies (discussed above) contained projects up to about 2007. Therefore, in the identification of new projects for addition to the database, the focus was generally on post-2007 projects.

During 2010, the AfDB availed a list of 44 projects (approved since 2007) for consideration in the study. The list was then evaluated based on the availability of their project documents. For most of the projects, PARs were available but not PCRs. As the PARs did not contain all the data required for the database informa-tion that was also needed for the analysis of unit rates and cost overruns it was decided to investigate a wider range of projects.

Subsequently, it was decided to list all pro-jects for which PCRs dated 2004 or later (although the actual projects could have been completed much earlier) were availa-ble from DARMS (Documents and Records Management System). A further criterion was that such projects should not have been included in the previous studies. The list contained 26 projects and was approved by AfDB for addition to the database, and for use in analysis of road infrastructure costs and cost overruns. The list of 26 new projects is indicated in Table 2-1.

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PCRs and PARs for the 26 projects indi-cated above were extracted from DARMS. All relevant data were obtained from these documents and recorded in the database. Some of the projects comprised two or more types of road infrastructure invest-ments (e.g. rehabilitation of paved roads, as well as the upgrading of paved roads). In such cases, each road infrastructure

investment was captured separately in the database, resulting in more than one entry for the specific project.

2.4 Master Database for Analysis of Unit Rates and Cost OverrunsThe final database of projects used for the analysis of unit rates consisted of projects

from the 2008 AICD study, the 2007 AfDB study, as well as the new AfDB projects identified during 2010. A total of 172 pro-jects were included in the final database, as indicated in Table 2-2.

Table 2-1: List of New Projects Selected for Addition to the Database

Number Country Project Name

1 Benin CotonouPorto Novo Road Rehabilitation Project

2 Botswana Trans-Kgalagadi Road Project

3 Burkina Faso Second Road Program

4 Cameroon Road Improvement in the West, Littoral and South Provinces

5 Chad DjermayaMassaguet Road Construction Project

6 Chad Road Rehabilitation and Maintenance Project

7 Ethiopia AlemgenaButajira Road

8 Ethiopia Road Maintenance and Rehabilitation Project

9 Ghana AchimotaAnyinam Road Rehabilitation Project

10 Morocco Road Project III

11 Lesotho LikalanengThaba Tseka Road Upgrading - Lot1 (LikalanengCheches Pass)

12 Lesotho MpharaneBela Bela Road Upgrading Project

13 Madagascar Road Rehabilitation and Maintenance Project

14 Malawi MsuliraNkhotakota Road Project

15 Malawi Road Maintenance and Construction (ROMAC II) Project

16 Mauritius South-Eastern Highway Project

17 Mozambique PembaMontepuez Road Rehabilitation Project

18 So Tom and Principe Second Road Maintenance Project

19 Swaziland Transportation Sector Project

20 Swaziland Two International Roads Project

21 Tanzania HimoArusha Road Rehabilitation Project

22 Tunisia Classified Road Network Development Project II

23 Tunisia Classified State Road Network Development Project - Phase I

24 Tunisia Classified State Road Network Rehabilitation Project

25 Uganda KyoteraMutukula Road Upgrading Project

26 Uganda Rural Feeder Roads Maintenance Program


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Table 2-2: Projects Included in Final Database

Data Source

Number of Projects

Regraveling/ Periodic

Maintenance of Unpaved Roads

Periodic Maintenance of

Paved RoadsRehabilitation

of Paved Roads

Construction and Upgrading of Paved Roads Total

2008 AICD Study 37 4 51 23 115

2007 AfDB Study 2 0 7 13 22

New projects from AfDB identified during 2010 3 2 14 16 35

Total 42 6 72 52 172

Note: As described above, some of the new projects entailed more than one type of road infrastructure investment. In such cases, each investment was cap-tured separately, resulting in more than one entry for the specific project. Therefore a total of 35 new projects are reflected in Table 2-2, and not 26 as indicated in Table 2-1.

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3. Analytical Approach to Road Infrastructure Costs

3.1 Standardizing the Unit RateThe Terms of Reference for the study identified three topics that should be investigated, namely (i) what typical road unit costs are; (ii) whether the actual unit rates overran what was initially anticipated and if so, to what extent; and (iii) whether there is a trend in unit cost rates. The unit rate applied in this study is the cost per lane-kilometer. The lane-kilometer is the product of the number of lanes and the road length.

Road design standards differ between juris-dictions, and road works do not necessarily fall neatly into the four types of project clas-sifications identified above. Projects were classified based on the description of the works in the PCR and the project contract. The main adjustment made to compensate for physical differences was to exclude all major bridges and structures. Physical characteristics that were not standardized included the use of specific materials and differing geometric standards.

Data on lane widths were not always avail-able. The 3.50m appears to dominate the data, but there are lane width outliers from 3.25 m to 3.80 m. The unit rate does not compensate for width variations. For regraveling and periodic maintenance of paved roads, all roads were single carriage-way (two lanes). For the construction of paved roads, about 4 percent of projects were dual carriageway (four lanes), while for the rehabilitation of paved roads, dual carriageways comprised about 1 percent.

Financial adjustments made to standard-ize projects entailed excluding all (i) fea-sibility, environmental, design and other studies; (ii) social mitigation costs (e.g. relocation costs); (iii) supervision and audit services; and (iv) taxes. The value considered is the latest available contract

cost, either obtained from the PCR or the project contract. The 2008 AICD data and 2010 AfDB data (i.e. the current study) are typically based on PCR values. The 2007 AfDB data mostly entailed projects that were on-going and that had experienced unexpected increases from the point of project appraisal or loan approval, prior to the contract being completed. For the purposes of this report, contract values were standardized in the same manner as for the 2007 AfDB and 2008 AICD studies.

Unit rates are expressed as United States Dollar (USD) per lane kilometer. All con-tracts were standardized to 2006 USD value. This was the convention used in the previous two studies (which supply the bulk of the material considered in this study). The most common currency in the combined database is the UA (AfDB Unit of Account), followed by the FCFA and the USD, but the USD is favored as the currency to which most readers can readily relate.

3.2 Analytical ApproachIn analyzing the four types of road project, a similar sequence of enquiry is followed. It entails three steps: (i) establishing the position of the unit cost curve; (ii) verifying whether rates that lie off the curve can be explained; and (iii) determining whether the position of the curve is as expected (overruns) and whether it is shifting over time (unit cost trends). These steps are further elaborated below.

Step 1: The projects in the particular cat-egory are reviewed, highlighting the con-tribution of each of the three studies to the universum and indicating any major differences between the three data subsets. The convention applied is to plot unit rates

by showing the relationship between the rate and the project size (lane length). This is a crucial understanding: in most cases, the main explanatory variable for the unit rate is economies of scale. The smaller the project, the more disproportionate is the relative contribution of project overheads such as Preliminaries & General (site establishment).

Step 2: After project size, the other major potential drivers of the unit rate are con-sidered. It is expected that these variables could help to explain any deviation from the economies-of-scale curve already estab-lished. (a) Regional characteristics such as geography, climate, business practice and state of the contracting industry are cap-tured by grouping projects into Northern (Maghreb), Western (largely ECOWAS), Central (largely ECCAS), Eastern (EAC and surrounds), and Southern Africa (mostly SADC). (b) The origin (home country) of the main contractor is con-sidered, to determine whether outlier unit rates can be traced back to the prevalence of home country vs regional or contractors from further away. (c) It is often argued that a countrys accessibility to the sea affects its cost of construction. This prop-osition is tested by comparing unit costs between landlocked countries and those with seaboards.

Step 3: Movement of the unit cost curve could be short-term, i.e. the unit rate achieved exceeding the rate expected by the roads agency; or longer-term, i.e. that rates are drifting upward over time. In the short-term, overrun refers to a project completion amount exceeding the sum originally foreseen. Completion amounts can be the actual completion, as recorded in the PCR, or the contracted amount. The original amounts can be either as stated in the PAR or the loan agreement. This


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study applied the PCR values for comple-tion and the PAR values for the expected amount. In other words, overrun defines the extent to which the contracted amount was exceeded.

The short-term overrun issue relies heavily on relevant project documentation being available. Of the three studies, only the 2007 AfDB investigation attempted to unpack the reasons for overruns and the current report therefore largely reflects overrun findings of that study. Here, cost overruns were assessed against the reference project amount as per the loan approval, or where this was unavailable, the PAR.

Road projects considered were contracted through 20002010. For the unit rate trend investigation, for most of the project types, there are insufficient data points to explain possible long-term upward trends in unit rates. In no cases are there directly com-parable projects (in terms of size, country/location, etc.) which would enable a firm conclusion to be drawn on changes in unit rates. Unit rate trends are therefore cal-culated by comparing best-fit statistical simulations of different time periods.

The unit rate data are subject to large var-iances. This may be the result of projects being incongruously categorized together,

even though they may have some unique characteristics. Or it may be due to vari-ances in contracting conditions; or a num-ber of other considerations. This report therefore presents the unit rates not as a firm value, but as ranges of values that indi-cate the confidence interval around the calculated rate. The unit rates are presented as median (not average) rates, bounded by first and third quartile intervals. One set of statistics is provided for projects smaller than 100 lane km (typically subject to a large variance) and those larger than 100 lane km (typically demonstrating fairly small variance).

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4. Results

4.1 Establishing the Unit Cost Curve on the Basis of the Project Size4.1.1 Construction/Upgrading of Paved RoadsThe database includes 52 construction or upgrading of paved roads projects, about a quarter each from the 2007 AfDB and AfDB 2010 studies, and about half from the 2008 AICD study. Of these, 47 have usable data (i.e. contracted values).

Table 4-1: Construction/Upgrade of Paved Road Projects Sample

Study/Region North West Central East South Total

2007 AfDB - 3 - 7 2 12

2008 AICD - 4 2 14 2 22

2010 AfDB 1 1 2 3 6 13

Total 1 8 4 24 10 47

Table 4-2: Rehabilitation of Paved Road Projects Sample


2007 AfDB 1 5 - 1 - 7

2008 AICD - 8 2 8 33 51

2010 AfDB 3 3 3 2 2 13

Total 4 16 5 11 35 71

As shown in Figure 4-1, for large projects ( 100 lane km), the unit cost rates of the three studies distribute similarly. For smaller projects (< 100 lane km), there are some outlier projects, especially in the 2010 AfDB and 2007 AfDB studies that result in very spiky curves compared with the AICD 2008 study curve, which shows a less prominent diseconomy of scale.

Figure 4-1: Construction/Upgrading of Paved Road Projects per Study

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0 100 200 300 400 500

USD/lane km

lane km

AfDB 2007

AICD 2008

AfDB 2010

Swaziland MR3 Bypass

Mauritius South-Eastern Highway

4.1.2 Rehabilitation of Paved RoadsA total of 72 projects were classified under the rehabilitation of paved roads inter-vention. The majority (two-thirds) were from the 2008 AICD study, with 14 added in this study. Unit rates could not be calcu-lated for one project, leaving a universum of 71 projects, as indicated in Table 4-2.


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4.1.3 Periodic Maintenance of Paved RoadsThe combined data set includes six pro-jects for the periodic maintenance of paved roads (Table 4-3). Four of these are from the 2008 AICD study and two were intro-duced in the 2010 AfDB review.

Figure 4-2: Distribution of Paved Rehabilitation Projects per Study

Figure 4-3: Periodic Maintenance of Paved Road Projects per Study

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

AfDB 2007

AICD 2008

AfDB 2010

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

AfDB 2007

AICD 2008

AfDB 2010

Table 4-3: Periodic Maintenance of Paved Road Projects Sample

Study/Region North West Central East Southern Total

2007 AfDB - - - - - -

2008 AICD - 3 1 - - 4

2010 AfDB 1 - - - 1 2

Total 1 3 1 - 1 6

From Figure 4-3, it can be seen that the unit rates of the two 2010 AfDB projects are substantially lower than the rates from the 2008 AICD study. The lower of the two 2010 unit rates is a resurfacing project in Morocco and the other a resealing project in Malawi.

The projects that were added to the overall database by the 2010 AfDB study distribute very similarly to those of the 2008 AICD study (Figure 4-2). The projects from the 2007 AfDB study tended to be more expensive if smaller than 500 lane km. It should be recalled that the 2007 projects were specifically selected because they had resulted in overruns.

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4.1.4 Regraveling of Unpaved RoadsFor unpaved roads, the major intervention identified in the three studies is regrave-ling. There are 42 projects in the database, of which 37 yield useful unit rates (Table 4-4). The bulk of projects are from the 2008 AICD study, while two were added in the AfDB 2010 review. Reliable unit rates could not be calculated for the two regraveling projects from the 2007 AfDB study.

Table 4-4: Regraveling of Unpaved Roads Projects Sample


2007 AfDB - - - - - -

2008 AICD - 31 3 1 - 35

2010 AfDB - 2 - - - 2

Total - 33 3 1 - 37

Figure 4-4: Regraveling Projects per Study

Figure 4-5: Regional Distribution of Construction/Upgrading of Paved Road Projects

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 200 400 600 800

USD/lane km

lane km

AfDB 2007

DRC projects

AICD 2008

AfDB 2010

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0 100 200 300 400 500

USD/lane km

lane km

North

West

Central

East

South

The addition of the two AfDB 2010 projects enhances an understanding of the cluster of unit rates below 100 lane km (Figure 4-4). Even with the addition of these pro-jects, the same economy-of-scale curve that appears for other project types still does not appear here. Even if the outlier projects are ignored, the curve remains flat.

4.1.5 Summary ResultThe unit cost curve confirms the econ-omies of scale proposition, that the smaller the project, the more dispro-portionate is the relative contribution of project. In other words, smaller con-tracts with road size < 100 lane Km have higher unit costs compared to larger projects.

4.2 Other Major Potential Drivers of Unit Rates4.2.1 Location of Road Projects4.2.1.1 Construction/Upgrading of Paved RoadsFor construction/upgrading of paved roads, half of the projects are in East Africa and a fourth in Southern Africa (Figure 4-5). The unit rate variance is large (i.e. there is a high degree of scatter in the data) for small projects in both these regions. The most outlying small project is the Mbabane bypass (Swaziland) a project that over-spent notoriously. The outlier small project from the 2007 AfDB study is an 11 km project in East Africa (Mauritius). That country has a sizable contracting industry, which means that the unit rate cannot be explained simply by it being an island state.


13



4.2.1.2 Rehabilitation of Paved RoadsFor rehabilitation of paved roads, half of the projects are in Southern Africa, 22 percent in West Africa, and 15 percent in East Africa (Figure 4-6). The distribution of unit rates reflects the typical econo-mies-of-scale curve, with higher unit rates for small projects and lower rates for larger ones. As was previously noted in the 2007 AfDB and 2008 AICD reports, the variance is particularly high for projects of fewer than 100 lane-km. For paved rehabilita-tion projects, the initial peakiness (unit rates from about USD500,000/lane km) is ascribed to projects in the West Africa subregion, specifically four in Benin and two in Ghana.

Figure 4-6: Regional Distribution of Paved Rehabilitation Projects

Figure 4-7: Regional Distribution of Periodic Maintenance of Paved Road Projects

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

North

West

Central

East

South

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

North

West

Central

East

South

4.2.1.3 Periodic Maintenance of Paved RoadsFor periodic maintenance, as shown in Figure 4-7, the difference between the rates of the 2010 and 2008 studies may be attributable to the fact that the projects are widely distributed over the continent. A further explanatory factor could be that the two AfDB 2010 projects predate 2000 (i.e. it is expected that unit rates have increased, even in real terms). Nevertheless, the data sample (six projects) is so small that it can-not be concluded with certainty that these projects unit rates are in fact outlying.

14



4.2.1.4 Regraveling of Unpaved RoadsFor regraveling roads, the three apparent outlier projects are all in Central Africa, specifically the Democratic Republic of the Congo (DRC). These were all emergency projects in Katanga Province. The cluster of small (less than 25 km road length) West African projects relates to the rural access program in Burkina Faso. For purposes of calculating the summary statistics below, this program is excluded because it has a dominating effect in the overall calculation.

Figure 4-8: Regional Distribution of Periodic Maintenance of Paved Road Projects

Figure 4-9: Paved Road Construction/Upgrading Projects by Origin of Contractor

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 200 400 600 800

USD/lane km

lane km

AfDB 2007

AICD 2008

AfDB 2010

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0 100 200 300 400 500

USD/lane km

lane km

Home Country

Regional Country

Europe

China

Other East

Other

N/A

4.2.1.5 Summary ResultIn all four test scenarios, the location of the project does not seem to signifi-cantly influence the distribution of unit rates.

4.2.2 Origin of Contractors4.2.2.1 Construction/Upgrading of Paved RoadsFor construction/upgrading of paved roads (Figure 4-9), Chinese contractors account for a significant share of projects. However, except for one possible project (the 11.1 lane km Mauritius South-Eastern Highway Project), they are not obviously responsible for outlier contracting values. The widest variance in contract values may rather be ascribed to local contractors.


15



4.2.2.2 Rehabilitation of Paved RoadsAlthough there is no discernible pat-tern in contractor origin versus unit rate (Figure 4-10), three of the outlier con-tracts identified above were carried out by European contractors and one by a Chinese contractor.

Figure 4-10: Paved Rehabilitation Projects by Origin of Contractor

Figure 4-11: Paved Road Periodic Maintenance Projects by Origin of Contractor

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

Home Country

RegionalCountry

Europe

China

Other East

Other

N/A

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

Home Country

RegionalCountry

Europe

China

Other East

Other

N/A

4.2.2.3 Periodic Maintenance of Paved RoadsSimilarly for periodic maintenance of paved roads (Figure 4-11), it is unlikely that the origin of the contractor played a significant role in the unit rate variance.

16



4.2.2.4 Regraveling of Unpaved RoadsAs might be expected for a type of inter-vention that does not require a high degree of sophistication or major capital invest-ment, regraveling contracts are dominated by domestic contractors (Figure 4-12). When the three outlier projects (DRC) are excluded, projects in landlocked coun-tries have very similar unit rates to those in seaboard countries (Figure 4-13). There is however still no clear pattern to make a conclusion.

Figure 4-12: Regraveling Projects by Origin of Contractor

Figure 4-13: Construction/Upgrading of Paved Road Projects Landlocked vs. Seaboard Countries

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 200 400 600 800

USD/lane km

lane km

Home Country

Regional Country

Europe

China

Other East

Other

N/A

2,000,000

1,800,000

1,600,000

1,400,000

1,200,000

1,000,000

800,000

600,000

400,000

200,000

0

0 100 200 300 400 500

USD/lane km

lane km

Landlocked

Seaboard

Power (Landlocked)

Power (Seaboard)

4.2.2.5 Summary ResultIn all four test scenarios, the origin of the contractor does not seem to sig-nificantly influence the distribution of unit rates.

4.2.3 Landlocked vs. Seaboard Countries4.2.3.1 Construction/Upgrading of Paved RoadsFor construction/upgrading of paved roads, there is no apparent difference in the distribution of unit costs for landlocked countries versus those with sea coasts.


17



4.2.3.2 Rehabilitation of Paved RoadsFor rehabilitation of paved road projects, the unit rate patterns are very closely cor-related between landlocked and seaboard countries (Figure 4-14), with no difference in the distribution of unit costs.

Figure 4-14: Paved Rehabilitation Projects Landlocked vs. Seaboard Countries

Figure 4-15: Paved Rehabilitation Projects by Landlocked vs. Seaboard

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

Landlocked

Seaboard

Power (Landlocked)

Power (Seaboard)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

Landlocked

Seaboard

4.2.3.3 Periodic Maintenance of Paved RoadsThe project sample is too small to draw a conclusion about the unit cost comparison between landlocked and seaboard coun-tries (Figure 4-15).

18



4.2.3.4 Regraveling of Unpaved RoadsFor regraveling roads, there is no clear pat-tern (Figure 4-16).

Figure 4-16: Regraveling Projects Landlocked vs. Seaboard Countries

Figure 4-17: Construction/Upgrading of Paved Road Projects Unit Rates over Time

USD/lane km

lane km

Landlocked Seaboard Power (Landlocked) Power (Seaboard)

0

5,000

10,000

15,000

20,000

25,000

30,000

0 200 400 600 800

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0 100 200 300 400 500

USD/lane km

lane km

pre-2000

2000

2001

2002

2003

2004

2005

2006

2007

4.2.3.5 Summary ResultFor at least two test cases, it appears unit rates between landlocked and seaboard countries strongly correlate without any major influence on unit cost distribution.

4.3 Determining Unit Cost Trends4.3.1 Unit Rates over Time4.3.1.1 Construction/Upgrading of Paved RoadsFigure 4-17 shows the trend in unit rates over time (i.e. the movement of the unit cost curves). Good-fit curves can be plotted for the period prior to 2000 (grey), and for 2003 (green), 2004 (yellow), and 2006 (orange). For projects larger than 400 lane km, the 2006 curve exceeds the others, but the occurrence of projects of this length is limited, and firm statistical conclusions cannot be drawn. For projects of 200 lane km and less, the variance of unit rates is high, resulting in an apparent decrease in unit costs over time.


19



4.3.1.2 Rehabilitation of Paved RoadsFigure 4-18 demonstrates the evolution of unit rates over time for rehabilitation of roads. Three trend lines are shown for years that had a relatively large number of projects: pre-2000 (grey), 2004 (dark yellow), and 2006 (orange). The 2004 and 2006 curves virtually overlie. Unexpectedly, they are both slightly lower than the pre-2000 curve.

Figure 4-18: Paved Rehabilitation Projects Unit Rates over Time

Figure 4-19: Paved Road Periodic Maintenance Projects Unit Rates over Time

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

pre-2000

2000

2001

2002

2003

2004

2005

2006

2007

2008

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

pre-2000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

4.3.1.3 Periodic Maintenance of Paved RoadsThe project sample is too small to make a conclusion about the unit cost comparison over time (Figure 4-19).

20



4.3.1.4 Regraveling of Unpaved RoadsThe projects universum is dominated by projects from 2005 and 2006, with no subsequent projects recorded. There is no clear pattern of unit rate changes over time (Figure 4-20).

Figure 4-20: Regraveling Projects Unit Rates over Time

Figure 4-21: Construction/Upgrading of Paved Road Projects Unit Rate Overruns/Underruns (PCR vs. PAR)

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 200 400 600 800

USD/lane km

lane km

pre-2000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

-120%

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

0 50 100

150

200

250

300

350

400

450

500

% Over/Under (Contract vs PAR)

lane km

4.3.1.5 Summary ResultIn cases with wide distribution of sam-ple data, it appears that for smaller pro-jects the unit cost rate reduces over time while for larger projects it increases.

4.3.2 Cost Overruns/UnderrunsThe summary result above is borne out by comparing the expected unit rate (as per the PAR) with the actual contracted rate.

4.3.2.1 Construction/Upgrading of Paved RoadsCost over/underrun data are available for 24 of the projects (Figure 4-21). Significant overruns (>20%) occurred in seven pro-jects and significant under-expenditure (< -20%) in five projects. The major overrun-ning projects were: the Cameroon Road Improvement Project; Swaziland Two International Roads Projects; the Lesotho MpharaneBela Bela Road Upgrading Project; Swaziland M3 Bypass Project; and the Ghana Akatsi-Dzodze (Akatsi-Akanu Section) Project. The Tanzania KigomaLusahunga Project (308lane km) is the only large project that overran significantly.

It would therefore appear that small pro-jects are more susceptible to cost overruns than are larger projects.


21



4.3.2.2 Rehabilitation of Paved RoadsOver/underruns were generally not signifi-cant except for very small contracts, which in any case are subject to much wider price variances. In the case of rehabilitation of paved roads (Figure 4-22), there appears to be a clear correlation between the degree of over/underrun and the size of the pro-ject: the smaller the project, the larger the difference between the expected unit rate (PAR value) and the contracted value.

Figure 4-22: Paved Rehabilitation Projects Unit Rate Overruns/Underruns (PCR vs. PAR)

Figure 4-23: Paved Road Periodic Maintenance Projects Unit Rate Overruns/Underruns (PCR vs. PAR)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

0 200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000


lane km

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

0 50 100

150

200

250

300

350

400


lane km

4.3.2.3 Periodic Maintenance of Paved RoadsFrom Figure 4-23, it can be seen that con-tracts for the two 2010 AfDB projects were concluded substantially below the rates originally estimated.

4.3.2.4 Regraveling of Unpaved RoadsIt was only possible to calculate cost over-runs for the two 2010 AfDB projects. In both cases, the projects had underruns of less than 1 percent compared with the PAR estimate. It was therefore not possible to draw any conclusions from the limited sample size.

4.3.2.5 Summary Results Small projects are more susceptible

to cost overruns than larger projects.

Construction/Upgrading of roads is more susceptible to cost overruns than underruns.

Rehabilitation of roads exhibits both characteristics (overrun/underrun).

22



4.3.3 Median Rates4.3.3.1 Construction/Upgrading of Paved RoadsSmall construction/upgrade of paved road projects have a median unit rate (i.e. the unit rate in the middle of the dataset) of about USD 228,000/lane km, but ranging from USD 61,000 below to USD198,000 above. For large projects, the expected rate is USD 147,000 but ranging from USD 31,000 lower to USD 15,000 higher, as indicated in Figure 4-24.

Table 4-5: Unit Rate Statistics for Construction/Upgrading of Paved Road Projects (USD/lane km, rounded to 000)

Metric < 100 lane km 100 lane km

Quartile 3 425,400 162,000

Median 227,800 147,100

Quartile 1 166,300 115,900

Figure 4-24: Representative Dataset for Construction/Upgrading of Paved Road Projects

Figure 4-25: Representative Dataset for Paved Road Rehabilitation Projects

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 100 200 300 400 500

USD/lane km

lane km

Data

Median100km

0

100,000

200,000

300,000

400,000

500,000

600,000

0 500 1,000 1,500 2,000

USD/lane km

lane km

Data

Median100km

For both small and large projects, the inclu-sion of the 2010 AfDB dataset exerts an augmenting effect on unit rates. For small projects, the 2008 AICD median unit rate was USD 201,000/lane km. The 2008 AICD median unit rate for large projects was USD 145,000/lane km.

4.3.3.2 Rehabilitation of Paved RoadsFigure 4-25 shows the dataset for paved rehabilitation projects after correcting for the major outliers. Outliers includes two 2007 projects that exceeded the PAR sig-nificantly, as well as the other very peaky West African projects.


23



Accommodating the wide variance in unit rates for projects below 100 lane km, the summary statistics for paved road rehabili-tation projects are shown in Table 4-6. For small rehabilitation of paved road projects, the unit rate is expected to be about USD 180,000/lane km, but it can range from USD 70,000 lower to USD110,000 higher. For large projects, the expected rate is USD 84,000, but ranging from USD 37,000 lower to USD 46,000 higher.

The median unit rates in the 2008 AICD study were USD 150,000/lane km for small projects and USD 79,000/lane km for large projects. (The unit rate was not calculated in the 2007 AfDB study.)

Table 4-6: Unit Rate Statistics for Paved Road Rehabilitation Projects (USD/lane km, rounded to 000)


Quartile 3 290,000 130,500

Median 180,300 84,400

Quartile 1 109,800 47,400

Table 4-7: Unit Rate Statistics for Periodic Maintenance of Paved Road Projects (USD/lane km, rounded to 00)


Quartile 3 N/A 72,200

Median N/A 64,600

Quartile 1 N/A 56,900

Figure 4-26: Representative Dataset for Periodic Maintenance of Paved Road Projects

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 100 200 300 400

USD/lane km

lane km

Data

Median100km

4.3.3.3 Periodic Maintenance of Paved RoadsTwo 2010 AfDB outlier projects (Figure 4-26) are excluded, leaving three fairly tightly spaced unit rate data points for periodic maintenance of paved roads.

The median unit rate for projects larger than 100 lane km is USD 65,000. The first quartile is about USD8,000/lane km lower, while the upper quartile is about USD 8,000/lane km higher than the median. Since only the 2008 AICD projects are used to calculate the summary statistics, the median rate is the same as calculated in that in the study.

24



4.3.3.4 Regraveling of Unpaved RoadsWith the exclusion of two outlier projects, the DRC and Burkinab program, the regraveling dataset is graphically presented in Figure 4-27.

Table 4-8: Unit Rate Statistics for Regraveling Projects (USD/lane km, rounded to 00)


Quartile 3 10,500 12,800

Median 9,600 11, 300

Quartile 1 8,100 9,600

Figure 4-27: Representative Dataset for Regraveling Projects

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 100 200 300 400 500

USD/lane km

lane km

Data

Median100km

This is the only type of road project reviewed that does not demonstrate a clear pattern of economy of scale. The median unit rate for small projects (less than 100 lane km) is about USD 10,000, ranging downward by USD 2,000 and upward by USD 1,000/lane km. The median rate for large regraveling projects ( 100 lane km) is USD 11,000, minus or plus USD 2,000/lane km.

In the 2008 AICD study, the median rate was calculated for all (i.e. small and large projects) at USD7,800/lane km.

4.3.3.5 Summary ResultThe median unit rates clearly demon-strate a pattern of economy of scale, i.e. smaller contracts have higher unit costs compared to larger projects.


25



Conclusions

The analysis for this report was performed on a total of 172 projects (sourced from the 2008 AICD study, the 2007 AfDB study, and newly selected projects obtained from AfDB during 2010), to determine unit rates for different types of road infrastructure investments. Of these 172 projects, unit rates could be calculated for 161 projects in this study.

In response to the studys Terms of Reference, the following conclusions are drawn:

5.1 Typical Road Unit CostsOne very important conclusion from this review is that there is no such thing as a typical unit cost. This is because (i) unit costs are calculated through a process of standardizing projects that are broadly sim-ilar but which differ in their design details and specific circumstances, and (ii) the

Table 5-1: Summary of Unit Rate Statistics for Different Types of Road Infrastructure Investment (USD/lane km, rounded to 00)

Type of Road Infrastructure Investment

Regraveling/ Periodic Maintenance of Unpaved

Roads

Periodic Maintenance of

Paved RoadsRehabilitation of

Paved Roads

Construction and Upgrading of Paved

Roads

< 100 lane km

Quartile 3 10,500 N/A 290,000 425,400

Median 9,600 N/A 180,300 227,800

Quartile 1 8,100 N/A 109,800 166,300

100 lane km

Quartile 3 12,800 72,200 130,500 162,000

Median 11,300 64,600 84,400 147,100

Quartile 1 9,600 56,900 47,400 115,900

Note: All values are given in 2006 USD.

size of the project invariably has an over-riding effect on the unit rate (economy of scale). The first issue is largely overcome by excluding major project and location-spe-cific factors (e.g. bridges, taxes). The second issue is something that anyone estimating or evaluating roads costs should be vigilant about.

Although it may be advantageous, from a statistical analysis perspective, to recom-mend that road project costs should be cast into a standard format (e.g. standard bills of quantities) that will facilitate ex post comparison, this could add to the administrative burden of national road agencies. Rather, it is recommended that lenders through programs such as AICD classify relevant data into standardized databases, as extracted by task team leaders at PAR and subsequent stages.

Table 5-1 provides a summary of the unit cost findings.

5.2 Unit Cost Overruns/UnderrunsNearly a third of the projects are from the 2008 AICD Study, where cost overrun was not an area investigated. This implies that the conclusions on overruns are based on a smaller dataset than the rest of the analysis.

Analysis of cost overruns indicates that for all the projects where data on over/under-runs are available, the overruns amount to 48 percent and the underruns to -15 percent of the PAR values.

The main conclusions are that: (i) there appears to be a correlation between the over/underrun and the size of the pro-ject and (ii) the estimation error (i.e. PCR value minus PAR value) is likely to be an underestimate (48 percent) rather than an overestimate (-15 percent). These two conclusions are further elaborated upon in the next paragraphs.

26



In terms of conclusion (i), the analysis shows that the smaller the project, the larger the difference between the expected unit rate (PAR value) and the contracted value. This may be because larger contrac-tors are more sophisticated in their costing, and/or that funding agencies expend more effort on the price estimates of larger pro-jects. The implication is that unit rates for small projects should be treated with some caution. However, care should be taken not to spend more resources on refining designs, feasibility studies, and other work underlying PARs than might be expected to accrue in terms of the project benefits.

In terms of (ii), the estimation error varies according to the type of project under-taken. For the rehabilitation of paved roads, the difference occurs both above (overrun) and below (underrun) the PAR value. In the case of the construction or upgrading of paved roads, it appears that the pattern is for small projects to overrun, rather than underrun. This suggests that PARs may have a tendency to be overly optimistic.

5.3 Trend in Unit Costs The finding on an increase in unit cost over time is inconclusive. This may be

purely because of data constraints, i.e. the limited sample size for a specific year and standardization issues across projects in the same class. The effect is that statistically extrapolated unit cost curves (rather than rates) are compared.

Given these shortcomings, where com-parisons can be made, these indicate that unit costs for large projects (>100 lane km) have not increased during the last decade. It could even be inferred that they have reduced, although this is counterintuitive, given the field experience of AfDB task managers, which points to an upward trend in unit rates.

5.4 General ConclusionsIt is important for lending agencies and national road agencies to track road unit cost trends and issues so that these can be reflected in their planning. Their ability to influence the rates is probably fairly limited, other than in the case of contractor market distortions (e.g. collu-sion). However, a greater understanding of issues and trends will allow planners and lending agencies to make more accurate projections.

Two general conclusions can be drawn, the first related to the implications of the study findings and the second regarding the analysis process:

1. The study findings point out that many of the traditional causes of high road costs (e.g. geographic location, origin of contractor) are not very useful to explain unit cost rates. The principal explanatory factor appears to be project size and related issues, e.g. that small contracts get less attention from lend-ers. Teasing out deeper statistical rela-tionships will require a more thorough and larger database.

2. In terms of the analysis of road unit costs, much greater effort is required in two areas, namely: (i) the management of documentation (enabling ex post analysis) and (ii) proactively extract-ing relevant data, while issues are still recent and well-understood. Although the data may only be analyzed periodi-cally, the database itself should be kept up to date. The AICD initiative, based on collaboration among the World Bank, AfDB, and other lenders, is a useful platform to establish a perma-nent database.

REFERENCES

Africon Limited. 2008. Africa Infrastructure Country Diagnostic Study: Consulting Services for the Evidence on Unit Costs of Infrastructure Projects in Sub-Saharan Africa. Washington, DC: World Bank Group.

Africon Limited. 2007. Africa Infrastructure Country Diagnostic Study: Study on Unit Costs of Infrastructure Projects in Sub-Saharan Africa, Addendum 2: African Development Bank Study on Roads Maintenance and Construction Costs in

Africa. Washington, DC: World Bank Group.


27



APPENDIX A: Detailed Description of Database Parts for AICD Initiative

Africa Infrastructure Country Diagnostic Database (2008)

Africon Limited carried out a study on unit costs during 2008 under the auspices of the joint World BankAfrican Development Bank initiative on Africa Infrastructure Country Diagnostic (AICD).

As part of this study, a database of infra-structure projects in Africa was compiled, including roads, water, sanitation, and electricity. The database was designed to consist of two parts, namely: a) a general section common to all projects, capturing the major exogenous variables; and b) an infrastructure-specific section, adapted for each sector (e.g. roads, water, and elec-tricity) and for different infrastructures and technologies within each sector.

A short description of these parts is given in the next sections.

a) General Contract Information The following general information was

obtained for all contracts:

Project and contract information, including project/program title, pro-ject/program unique number, contract title, and contract unique number.

Task Team Leader information, including name and contact details (email, telephone number).

Geographic information (country/ies, district/s and location/s) and whether urban, rural, or deep rural. An attempt was made to standardize the use of urban for cities, towns, and large set-tlements, rural for small settlements, and deep rural for locations totally

isolated from towns and villages. In the case of roads, the term inter-urban was used for roads through rural areas but connecting towns, and rural access for rural feeder roads.

Implementing agency/ies, includ-ing the main funding Development Finance Institution (DFI), other DFIs and local funders and role players.

Procurement, which may be National Competitive Bidding (NCB), International Competitive Bidding (ICB) or direct appointment.

Bidders information, including the number of bidders (at Request for Proposal/tender stage), the name of the winning bidder, and nationality of the winning bidder.

Key dates, including the date the con-tract was signed, the contracted com-pletion date, and the actual completion date (in as far as these are available).

b) Specific Contract Information The following information for each

infrastructure (e.g. roads) was obtained:

Infrastructure type, i.e. what tech-nology. Road projects were differen-tiated according to whether they were urban, inter-urban and rural access, whether with or without a shoulder, and whether paved or unpaved.

Activity, including preservation, reha-bilitation, improvement, and/or new construction.

Basis of costing, i.e. whether a pre-con-tract estimate, contracted value, or the contract value as modified in the course of the contract.

Input costs. This subsection divided the infrastructures into their major components. This allowed some com-parison to be made between technol-ogies, but importantly, it provided the basis on which to isolate components of costs, which appeared to be out-of-the-norm in the course of standardizing contract information. The major com-ponents considered were:

Studies, design, land acquisition and environmental costs prior to construction (note that for roads, environment is isolated as a stan-dalone item to facilitate the use of the study data in ROCKS);

Contractors establishment and mobilization (for roads, demolition, dismantling, site clearance and other site preparation are again isolated for use in ROCKS);

Mass earthworks;

Civils and structures. These were fur-ther subdivided into the following:

Major bridges and structures; Minor bridges, culverts and

drainage; Accesses and junctions; Pavement courses; Shoulder works; and Ancillary road works;

28



In the calculation of unit costs, the cost of major structures and bridges was not included, as these were very contract-specific and did not cast light on average road costs.

Mechanical, electrical, and control equipment;

Installation and commissioning (including dayworks);

Other (non-categorized costs);

Taxes;

Contingencies; and

Supervision (owners engineering costs).

Output descriptors, i.e. the number of units purchased with the contract

amount, on the basis of which output unit costs are calculated. For road projects, the key descriptors were the number of lanes and road length (km).

Currency-related information, including the contract currency/ies, the conversion date, the conversion factor/s (to USD) and USD CPI factor (for conversion to 2006 USD).


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APPENDIX B: Statistical Terms

Note on Statistical Terms

This report employs a number of statis-tical terms to describe the methodology and findings. These all have to do with the distribution of unit costs, i.e. how unit rates distribute around a value that indi-cates the norm for the particular type of project (construction, rehabilitation, etc.). In the analysis, there are two major steps to interpret the data statistically:

Best-fit curve. The first step is to determine a formula that eliminates the noise of the scattered unit cost data, so that the data can be shown as a curve. The best-fit curve is the mathematical

formula that reduces most noise. The formula used in the analysis is a nega-tive exponential curve (y = a.x-b). The best-fit curve allows conclusions to be drawn about the typical unit rates across all project sizes.

Statistics of distribution. The best-fit curve allows projects to be divided into large (> 100 lane km) and small projects. For each group, the central value is determined with reference to the median. The central value shows a point around which the data cluster. The median is the value in the middle

of the data range, i.e. there are as many values smaller than the median as val-ues larger than it. The median is gener-ally accepted to be a truer reflection of the central value than the average, as it gives a lower importance to outlier val-ues (very high or very low values in the data set). The data clustering or noise around the median is measured by the data variance. The more scattered the data, the higher the variance and the lower therefore the confidence that can be placed in the median.

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APPENDIX C: Consolidated Data

Source Class Project Title Country/iesContractor Nationality

Dates Contract Price DimensionsOriginal Currency

Exchange Rate

USD CPI

Factor USD/lkmPAR Contract Completion PCR PAR PCR Lanes Length

Periodic Maintenance/Regravel (Unpaved roads)

AICD 2008 Regravel & related reinstatement

Transport Sector Project

Burkina Faso NA 12/1/2006 - 0.049 2 0.30 FCFA 1.00 1.00 -



Burkina Faso NA 12/1/2006 - 0.035 2 1.00 FCFA 1.00 1.00 17,485.40





Village Communities Support Program (Phase 1)

Guinea Guinea 10/22/2002 3/1/2003 - 0.067 2 4.20 FG 1.00 1.00 8,004.86

AICD 2008 Regravel Nakivubo Channel Rehabilitation Project

Uganda Uganda 2/2/2004 10/13/2004 - 0.976 2 4.25 USH 1.00 1.00 -









Burkina Faso Burkina Faso 3/21/2005 6/7/2006 - 0.101 2 6.66 FCFA 1.00 1.00 7,603.35









Guinea Guinea 11/5/2002 3/24/2003 - 0.161 2 8.40 FG 1.00 1.00 9,590.55




AICD 2008 Regravel Second Transport Sector Program

Senegal Senegal 11/23/2001 - 0.300 2 10.00 FCFA 1.00 1.00 14,985.06






Guinea Guinea 11/5/2002 3/26/2003 - 0.196 2 11.50 FG 1.00 1.00 8,532.42












Guinea Guinea 10/22/2002 3/24/2003 - 0.228 2 16.30 FG 1.00 1.00 6,994.72

AfDB 2007 Pobe-Ketou-Illara (regraveling)

0 Benin Benin 9/1/2004 8/1/2006 4.000 4.312 2 16.50 UA 1.47 0.99 -


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Exchange Rate

USD CPI




















Guinea Guinea 10/28/2002 3/24/2003 - 0.277 2 25.10 FG 1.00 1.00 5,520.62





Guinea China 10/24/2002 2/12/2004 - 0.435 2 26.60 FG 1.00 1.00 8,178.04












AICD 2008 Regravel Burkina Faso Burkina Faso Burkina Faso 7/31/2001 - 1.501 2 75.00 FCFA 1.00 1.00 10,009.68

AICD 2008 Regravel Emergency Recovery and Rehabilitation Project

Ethiopia Ethiopia 9/11/2003 11/29/2006 - 2.704 2 120.00 ETB 1.00 1.00 11,264.74

AICD 2008 Regravel Emergency Living Conditions Improvement Support

DRC DRC 1/18/2006 2/27/2006 - 13.665 2 168.00 USD 1.00 1.00 40,668.29

AfDB 2007 Wacha-Maji (upgrade to gravel)

0 Ethiopia China 8/11/2003 5/1/2007 23.310 51.102 2 173.00 UA 1.53 0.97 -


DRC DRC 12/20/2005 2/27/2006 - 16.347 2 208.00 USD 1.00 1.00 39,296.73


DRC DRC 1/18/2006 2/27/2006 - 18.072 2 224.00 USD 1.00 1.00 40,339.22

AfDB 2010 Second Road Program

Periodic Maintenance on Earth Roads - Lot 1

Burkina Faso Burkina Faso 11/14/2001 4/28/2004 6/1/2008 9/1/2008 5.011 4.978 2 276.00 UA 1.48 1.07 14,347.32

AfDB 2010 Second Road Program

Periodic Maintenance on Earth Roads - Lot 2

Burkina Faso Burkina Faso 11/14/2001 5/6/2004 6/1/2008 9/1/2008 3.825 3.799 2 377.80 UA 1.48 1.07 7,953.41

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Exchange Rate

USD CPI


AfDB 2010 Rural Feeder Roads Maintenance Program

Rural Feeder Roads Maintenance Program

Uganda ? 10/30/1991 2/1/1996 6/1/2001 11/1/2005 8.860 8.360 2 4,003.00 UA 1.45 1.30 -

Periodic Maintenance (Paved roads)

AfDB 2010 Road Maintenance and Construction (ROMAC II) Project

Resealing of two paved roads (Benga-Nkhotakota-Dwangwa)

Malawi UK 2/15/1990 11/1/1993 10/1/1998 10/1/2005 6.362 3.747 2 109.00 UA 1.40 1.38 33,243.18

AfDB 2010 Road Project III Resurfacing of main roads (132 km)

Kingdom of Morocco

Morocco 12/15/1994 11/1/1995 8/31/2001 9/1/2002 4.869 3.516 2 126.00 UA 1.52 1.31 27,863.11

AICD 2008 Paved: Inter-urban Periodic Maintenance

Second Transport Sector Program

Senegal Senegal 7/29/2004 11/16/2006 - 9.986 2 89.00 - 1.00 1.00 56,098.72

AICD 2008 Paved: Inter-urban Light Reconstruction

Emergency Multisector Rehabilitation amd Reconstruction Project (EMRRP)

DRC NA 9/27/2006 - 13.526 2 93.85 FCFA 1.00 1.00 72,064.04


Transport Corridors Improvement Project

Mali France 10/4/2005 3/29/2006 - 21.725 2 150.00 USD 1.00 1.00 72,418.14


0 Burkina Faso Other 1/19/1999 - 19.801 2 173.00 FCFA 1.00 1.00 57,229.57

Rehabilitation (Paved roads)

AICD 2008 Paved: Inter-urban Rehab/Reconstruct

Roads and Bridges Management and Maintenance Program

Mozambique Italy 11/11/2004 10/5/2006 - 1.841 2 1.95 MT 1.00 1.00 472,098.28

AICD 2008 Paved: Urban Rehab/Reconstruct

Road Maintenance and Rehabilitation Project

Malawi Malawi 4/28/2004 - 1.192 2 2.60 MKW 1.00 1.00 229,284.64


Urban Development and Decentralization Project

Mali Senegal 12/29/2000 3/7/2005 - 1.753 4 1.48 FCFA 1.00 1.00 296,722.27



Malawi Malawi; Zimbabwe

10/5/2005 - 1.048 2 3.50 MKW 1.00 1.00 149,762.87



Malawi Malawi 4/11/2004 - 1.715 2 3.64 MKW 1.00 1.00 235,535.15

AICD 2008 Paved: Improvement/Upgrade

Nakivubo Channel Rehabilitation Project

Uganda Uganda 3/2/2004 10/13/2004 - 0.743 2 3.70 USH 1.00 1.00 100,338.29

AICD 2008 Paved: Inter-urban Rehab/Reconstruct

Roads Sector Invetment Program

Zambia South Africa 12/16/2003 4/12/2004 - 3.251 2 4.10 ZMK 1.00 1.00 396,479.45

AICD 2008 Paved:

Study on Road Infrastructure Costs- Analysis of Unit Costs and Cost ...

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