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Policy Research Working Paper 8602
Prioritizing Infrastructure Investments
A Comparative Review of Applications in Chile
Darwin MarceloSchuyler House
Aditi Raina
Infrastructure, PPPs & Guarantees Global Practice October 2018
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Produced by the Research Support Team
Abstract
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Policy Research Working Paper 8602
Governments worldwide face the difficult challenge of deciding which infrastructure projects to prioritize and select for implementation, given the limits of available funding and the need to attain their developmental goals. The key objective of this report is to conduct a compar-ative exercise between the World Bank’s Infrastructure Prioritization Framework, a multicriteria analysis–based methodology to project prioritization, and a more complex cost-benefit analysis–based approach. The report focuses on Chile, which has a well-institutionalized evaluation process that uses cost-benefit analysis to assess projects on their quality and ability to generate value for money. The analysis compares the results of the Infrastructure Priori-tization Framework alongside Chile’s current cost-benefit analysis–based and multicriteria analysis approaches to the same subsets of projects in the road transport and water
reservoir subsectors, respectively. The results show that the Infrastructure Prioritization Framework has application beyond its original proposition and can complement a tra-ditional cost-benefit analysis by directly considering social and environmental policy goals that are otherwise diffi-cult to quantify in a cost-benefit analysis. The analysis also finds that in Chile there is a discrepancy between the stated goals and objectives of the appraisal system and the actual implementation. In the case of transport sector projects, there is an evident deviation between cost-benefit analysis–based selection policy and actual decisions made for project implementation. In the case of water catchment selection, there is a bias toward projects with higher financial-eco-nomic performance as compared to social-environmental performance, despite policy intentions to afford consider-ation to environmental and social development goals.
This paper is a product of the Infrastructure, PPPs & Guarantees Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at [email protected].
Prioritizing Infrastructure Investments: A Comparative Review of Applications in Chile
Darwin Marcelo, Schuyler House and Aditi Raina
Keywords: Infrastructure prioritization, infrastructure planning, public investment, principal component analysis, multi-criteria analysis, transport, water
JEL Classification Codes: R42, O18, O21, O22, H54, C38
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Table of Contents Table of Contents ................................................................................................................................................ 2
Abbreviations ................................................................................................................................................... 4
Acknowledgements ........................................................................................................................................ 5
Chapter 1. Introduction ...................................................................................................................................... 6
Approaches to Infrastructure Appraisal and Selection ......................................................................... 6
Comparing Approaches: A Case Study of Chile ....................................................................................... 8
Chapter 2. Infrastructure Appraisal and Selection in Chile ..................................................................... 10
Evolution of Project Appraisal and Selection in Chile ........................................................................... 10
Chile’s Project Investment Cycle ................................................................................................................ 11
Project Appraisal and Selection in Chile .................................................................................................. 13
Extending Investment Decision Support in Chile .................................................................................. 14
Multi-Criteria Analysis to Support Investment Decision-Making .................................................... 15
Chapter 3. Infrastructure Prioritization Framework: An Alternative Approach ................................. 17
The IPF Process ............................................................................................................................................... 17
Step 1. Select Criteria ................................................................................................................................... 18
Step 2. Prepare Data .................................................................................................................................... 19
Step 3. Constructing Performance Indices – SEI and FEI .................................................................... 19
Step 4. Creating the Visual Interface: The Investment Prioritization Matrix ................................ 20
Evolution of the IPF ....................................................................................................................................... 21
Pre-Analytical Steps .................................................................................................................................... 21
Technical Improvements: Variable Specification and PCA Restrictions ........................................ 21
Sensitivity Analysis and Criteria Weighting .......................................................................................... 22
Organizational and Capacity Issues ......................................................................................................... 23
Chapter 4. Applying IPF to Chile Infrastructure Project Proposals ....................................................... 24
Water Catchment Projects in Chile .......................................................................................................... 24
Water Catchment Project Sample ........................................................................................................... 24
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Water Catchment Project Indicators ...................................................................................................... 25
IPF Results: Water Catchment ................................................................................................................. 29
Water Catchment IPF Matrix .................................................................................................................... 33
Comparing IPF to Selection of Water Catchment Projects ............................................................... 34
Applying IPF to Road Transport ................................................................................................................36
Transport Policy Goals and Road Project Criteria ................................................................................36
Road Transport Project Sample ...............................................................................................................36
Transport Project Indicators ..................................................................................................................... 37
IPF Results: Road Transport ..................................................................................................................... 38
Road Transport IPF Matrix ........................................................................................................................ 39
Comparing IPF to Funding of Transport Projects ................................................................................ 39
Chapter 5. Conclusion ..................................................................................................................................... 40
References .......................................................................................................................................................... 42
Annex 1. Chilean Law Relevant to Project Appraisal and SNI ............................................................. 44
Annex 2. Water Catchment Raw Project Data ...................................................................................... 45
Annex 3. Water Catchment Project SEI Calculations, by Region ...................................................... 47
Annex 4. Water Catchment Project FEI Calculations, by Region...................................................... 50
Annex 5. Road Transport Raw Project Data ........................................................................................... 53
Annex 6. Road Transport Project SEI Calculations, by Region (with standard poverty rate) .... 54
Annex 7. Road Transport Project SEI Calculations, by Region (with multidimensional index
poverty rate) ...................................................................................................................................................56
Annex 8. Road Transport Project FEI Calculations, by Region .......................................................... 58
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Abbreviations
BIP Integrated Project Bank
CBA Cost-Benefit Analysis
CFA Centralized Finance Agency
CORFO National Development Corporation
DIPRES Budget Office
ESP Social Project Evaluation
IDI Investment Initiative
IPF Infrastructure Prioritization Framework
IRR Internal Rate of Return
MDS Ministry of Social Development
MIDEPLAN Ministry of Planning and Cooperation
MCA Multi Criteria Analysis
MCEM Multi Criteria Evaluation Methodology
NPV Net Present Value
ODEPLAN National Planning Office
RATE Result of Technical-Economic Analysis
RS Recommended Favorably (according to RATE)
SCBA Social Cost-Benefit Analysis
SNI National Investment System
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Acknowledgments
This report was prepared by a team of experts from the World Bank's Infrastructure, PPPs and Guarantees Group with inputs and guidance from Pilar Contreras García, Head of Unit Public Investments and Non-Financial Assets (DIPRES) at Ministry of Finance, and Eduardo Koffman, Coordinator of the Planning and Development Department at Ministry of Transport and Telecommunications. We would also like to thank the Department of Irrigation Project at Ministry of Public Works for providing all the project-relevant data information for the water reservoirs analysis. The World Bank team included Darwin Marcelo (Task Team Leader), Schuyler House and Aditi Raina. Cledan Mandri-Perrott and Jordan Schwartz provided essential guidance and oversight. The team would also like to thank the World Bank Singapore Infrastructure Hub for its support.
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Chapter 1. Introduction Infrastructure services are significant determinants of economic and social development and are typically prominent components of national development plans. While national governments and their central finance agencies (CFAs) often consider numerous project proposals from agencies, line ministries, and various sub-national government units, financial resources are often insufficient to fund the full set of proposals, particularly in the short-term. Global estimates of infrastructure investments required to support economic growth and human development lie in the range of US$65 trillion to US$70 trillion by 2030, while the estimated pool of available funds is limited to approximately US$45 trillion.
Governments worldwide face a two-pronged challenge; to increase the pool of funding available for infrastructure development and to make difficult decisions about which projects to select for implementation, given the real limits of available funding. This paper deals with the latter challenge –namely the need for CFAs, ministries, and other relevant agencies to prioritize potential infrastructure projects aligning needs with fiscal constraints while attaining their respective economic and social development goals.
The key objective of this report is to conduct a comparative exercise between the World Bank’s Infrastructure Prioritization Framework (IPF), a multicriteria-based methodology to project prioritization, and a more complex Cost Benefit Analysis (CBA) based approach (i.e. more data and analytically intensive). To this end, the report focuses on the infrastructure prioritization process in Chile, a country that is recognized for the strength of its institutions and capacity of its public administration. Chile has a well-institutionalized evaluation process that uses CBA to assess projects on their quality and ability to generate value for money. The Ministry of Public Works (Ministerio de Obras Públicas – MOP) has been recognized for its capacity to prepare and implement high-quality infrastructure projects (OECD, 2017). This report explores the theoretical and practical challenges of prioritization; the robustness and integrity of current approaches; and the comparative outcomes of these approaches and their alternatives. This exercise is intended to serve practical ends.
The purpose is to progress discourse on project prioritization and selection in order to validate useful and productive public administration guidance, on the one hand, and create space for alternative approaches to support sound investment decision-making and responsiveness to non-monetizable policy aims, on the other.
Approaches to Infrastructure Appraisal and Selection
In addition to a growing infrastructure gap, the past 20 years have also seen a shift towards decentralized infrastructure planning. Many subnational governments, regional entities, and sector agencies have been delegated responsibility for infrastructure planning to promote local responsiveness. Moreover, while spending ceilings are defined by the centralized finance agency (CFA), allocation of funds for implementation remains at the line ministry level. At the national level, decision-makers must deal with numerous project proposals, each with varying amounts of attendant project information. These projects must be ideally appraised, compared, and selectively allocated funds for implementation.
The framework on Public Investment Management (PIM), proposed by Rajaram et al. (2014), is useful for guiding governments through the processes of infrastructure planning, appraisal, investment, and implementation, with an eye to increase the effectiveness of infrastructure
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investments. PIM identifies eight key “must-have” features of an effective public investment management system (see Figure 1). Project selection should follow first-level screening, project appraisal, and independent review.1
Making decisions as to which projects should be implemented implies grappling with efficiency and effectiveness of proposed investments, monetizable project costs and benefits, non-monetizable social and environmental impacts, and the relationship of these aspects to national and sub-national development plans. Because so many factors must be considered, the use of decision support frameworks and methods can help systematize appraisal and selection. Prioritization frameworks should be rigorous and comprehensive enough to accommodate multiple facets of infrastructure development, but also sufficiently practical to implement.
Best practice in public management and traditional policy analysis suggest that economic appraisals (preferably full social cost-benefit analysis when the main costs and benefits are measurable and there is an economic price available for them) and feasibility studies provide sound bases for project prioritization, using highest societal net present value (NPV) (or a variation thereof) as a ranking metric, along with assessing a project’s fit with infrastructure policy guidance (Rajaram, Tuan, Bileska, & Brumby, 2014, p. 20).2
In practice, however, capacity, resources, and time are often too short in supply to support extensive social cost-benefit analysis (SCBA) across full project sets and sectors. Also, in many cases, it is not possible to value the main benefits of a project, such as cultural or health investments, even if those benefits are identified and measured. Decision-makers often only have partial information on project costs and benefits, particularly since many are difficult to quantify and monetize. The PIM approach proposes that, in cases of restricted capacity or resources, basic elements of project appraisal should be applied. This includes a good justification for a project, clearly-specified objectives, comparison of alternatives, detailed analysis of the best options, fully-estimated project costs, and qualitative assessment of project benefits to justify costs (Rajaram et al., 2014, p. 8).
Facing restricted information and capacity, a risk arises of falling into unsystematic project selection. In these cases, decision frameworks based on multi-criteria analysis can help government decision-makers (a) systematize prioritization based on key development goals; (b) make best use of available information; and (c) formalize clear decision criteria to promote
1 First-level screening should be done to ensure that projects align with the development strategy and meet basic requirements for budget inclusion as a project (Rajaram et al., 2014). 2 In Chile, for example, analyses utilize the NPV index (SNPV), which equates to the NPV of future costs and benefits divided by the investment level. The ranking is then determined by sorting the highest SNVP to the lowest.
Figure 1. Key Features of a Public Investment Management System
Source: Power of Public Investment Management (Rajaram et al., 2014)
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accountability. The World Bank’s Infrastructure Prioritization Framework (IPF) is one such multicriteria analysis (MCA) approach that condenses government-selected project indicators into composite financial-economic and social-environmental indices. The analysis may incorporate the results of financial or partial social cost-benefit analysis, but does not require full SCBA.
Comparing Approaches: A Case Study of Chile
The IPF has been piloted in Vietnam, Panama, Argentina, and Sri Lanka. These pilots imparted methodological and practical lessons that have been used to adjust and improve the IPF. An important unanswered question remained, however, as to how effectively the IPF can substitute for the best practice of project appraisal and selection based on SCBA. Moreover, while IPF was designed as a ‘next-best’ prioritization approach based on ‘less-than-SCBA’ appraisal, IPF may nevertheless have something to offer countries where SCBA/CBA approaches are institutionalized.
For these reasons, the IPF was additionally piloted in Chile, where CBA-based analysis is a standard input to project acceptance for economic infrastructure such as roads, transfer ports, dams, railroads, etc. Chile stands apart from much of the world with respect to systematic, institutionalized project appraisal and evidence-based project selection. The Government of Chile (GoC) has a centrally-managed Public Investment System (SNI) that separates project proposal (initiated by line agencies and sub-national units) from appraisal, selection, and budget allocation performed by the Ministry of Social Development and the Ministry of Finance. The SNI is used to consolidate project information, subject proposals to policy filters, and appraise projects before inclusion in sector plans and budget requests. The Chilean SNI is likely the most systematically managed and consolidated investment appraisal system in Latin America (de Rus Mendoza, 2014) and is generally seen as a good example of a “structured and coherent framework for identifying, coordinating, evaluating and implementing public investments” (OECD, 2016, p. 93).
The ready availability of CBA appraisals in Chile proffered a valuable opportunity to compare IPF outcomes with CBA-based project selection. Moreover, the IPF is relevant in the Chilean context for other reasons. For one, the government recognizes the value of additional policy considerations alongside the results of CBA and has implemented a multi-criteria approach to project selection in the water sector, indicating recognition of the value of MCA even where cost-benefit analysis is widely applied. Second, the CBAs employed for sector-level project selection deviate somewhat from the academic policy approach to SCBA, due primarily to the realities of time and resource demands. Most appraisals – particularly for small- to medium-size projects – are partial (financial) CBAs that rely on highly standardized assumptions and often yield results with limited variance across projects. This exposed the possibility for MCA approaches to help fill in the missing considerations in partial CBAs.
This report presents an overview of the current system of project appraisal and selection in Chile, a summary of the IPF methodology and its evolution, and the comparative results from applying IPF alongside Chile’s current CBA and MCA approaches to the same subsets of projects in the road transport and water catchment subsectors. The report follows with a discussion of the findings of the exercise.
The results of this exercise show that the IPF has application beyond its original proposition of being a stop-gap measure until more sophisticated project appraisal methods can be implemented. This is because it can complement a traditional CBA by directly considering social
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and environmental policy goals that are otherwise difficult to quantify in a CBA analysis. In addition, this exercise led to two deeper findings that went beyond the initial aim of merely comparing the IPF and CBA results. The first was that CBA analyses are not used as the basis of prioritization in Chile. There was an evident deviation between CBA-based selection policy and actual decisions made for project implementation, in the case of transport sector projects. The second was the fairly consistent alignment of IPF- and MCA-based prioritization in the case of water catchment selection, but with a surprising inclination towards projects with higher financial-economic performance as compared to social-environmental performance, despite policy intentions to afford key consideration to security, environmental, and social development goals. Therefore, in both cases, there is a discrepancy between stated goals and objectives of the appraisal system and the actual implementation.
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Chapter 2. Infrastructure Appraisal and Selection in Chile The SNI is essentially a set of processes, data collection mechanisms, and appraisal functions that support project selection across multiple sectors. By design, and with the overarching policy goal of promoting economic growth, project selection is generally based on social net present value. Because SCBA does not consider distributional effects and regional or territorial inequalities, the SNI is complemented by a cost efficiency analysis when ‘desired but non-quantifiable’ social or environmental outcomes are deemed significant enough to justify project costs.
Chile has developed a CBA-based system for investment decisions (Candia et al., 2015). Over time, the investment system has evolved, most recently by extending the appraisal approach to consider additional factors via multi-criteria analysis (MCA). The following section provides an overview of the evolution of Chile’s infrastructure investment system and its current technical and institutional aspects.
Evolution of Project Appraisal and Selection in Chile
Chile’s Sistema Nacional de Inversiones (National Investment System) (SNI) is a centralized public investment system jointly administered by the Ministerio de Desarrollo Social (Ministry of Social Development) (MDS) and the Ministerio de Hacienda (Ministry of Finance), via the Dirección de Presupuestos (Budget Office) (DIPRES). MDS is responsible for ex-ante project appraisal and ex-post evaluation, as well as systematic data collection and reporting, while the Ministerio de Hacienda (through DIPRES) sets the public budget.
The SNI is the latest organizational arrangement in an extended history of formalized project appraisal and selection. The genesis of Chile’s investment system was the Corporación Nacional de Fomento (National Development Corporation) (CORFO) established in the 1950s, created to evaluate the financial and social impacts of national projects and units, with a strong emphasis on state enterprises. This agency’s role in investment decision-making was assumed in the 1960s by the Oficina de Planificación Nacional (National Planning Office) (ODEPLAN), which gave rise to the first formal project appraisal system. ODEPLAN also served as a platform for developing government capacity for project appraisal, leading to the creation of a specialized Social Project Evaluation unit (ESP). Through the 1970s, Chile developed extensive guidance, processes and methodologies for project appraisal and developed an integrated investment decision-making system that specified the roles and relationships among the ministries and other governmental units. Institutionally, this would be consolidated in the 1990s with the creation of the Ministry of Planning and Cooperation (MIDEPLAN), later renamed the Ministry of Planning in 2005, and replaced by the Ministry of Social Development (MDS) in 2011.
The national appraisal system employed cost-benefit analysis from its earliest days to appraise proposed projects. During the 1970s, however, it was recognized that some projects – particularly in social sectors like health and education – involve social benefits that are difficult to estimate, but which may be assumed high enough to outweigh project costs. This led to the adoption of a ‘cost-efficiency’ approach to appraisal in some sectors, wherein effort is concentrated on minimizing costs to attain the desired outcome (often the provision of basic services), with qualitative assessment of expected benefits. The cost efficiency approach also allowed the government to deal with distributional effects and regional inequalities.
The government employed hybrid approaches to investment appraisal, including cost-efficiency and multi-criteria approaches, to support project prioritization. The cost-efficiency approach was
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often applied by combining expected benefits into a single composite indicator to be compared to project costs.3 While CBA remained the standard project appraisal method, cost-efficiency approaches were used to appraise 71% of projects proposed between 2000 and 2015, accounting for 47% of proposed investments (Agostini and Razmilic, 2015). Therefore, a spectrum of appraisal techniques has been in use since the 1970s. Over the past few decades, the CBA and MCA methodologies have developed with respect to valuation approaches, applied assumptions, and analytical sophistication, but the overall methodologies and institutional frameworks have remained quite the same.
Chile’s Project Investment Cycle
By law, except the armed forces (which have their own systems), any public-sector institution wishing to develop an investment project must do so via the SNI. Law pertinent to this process is described in Annex 1. The proponent unit initiates the process by submitting background project information to the SNI. This information is immediately available to the public via an open digital registry called the Banco Integrado de Proyectos (Integrated Project Bank) (BIP). The BIP provides a record of all project proposals in standardized format and tracks project development from initial proposal through ex-post project evaluation.
The proponent agency engages in an iterative process of submissions and approvals with MDS, via the SNI, that involves increasing levels of detail with respect to project appraisal as the project progresses through the system. Upon initial submission to the SNI, a project is assigned a unique identification code within the BIP, which can be used to track the projects’ progress through these stages. Except for some project types (e.g., projects with pre-approved designs), project proponents must submit project preparation information to the SNI, via the BIP platform, at the following stages:
Profile (concept): the policy problem is described, along with the purpose and context of the project, alternative solutions under consideration, and an assessment of the feasibility and impacts of various alternatives to inform the selection of the most viable alternative;
Prefeasibility: prefeasibility studies include additional project details, including tentative schedules, budgets, and more extensive information on expected benefits;
Feasibility: full feasibility studies, including CBA or cost-effectiveness analysis are provided; Design: technical architectural, engineering, and construction studies are done, and the
timings of investments and detailed budget are specified. Project execution plans are required to be based on specific estimates of the costs of equipment, personnel and supplies, as well as a realistic schedule to estimate the duration of the various activities required; and
Execution: the project is approved to seek funding. Some projects can apply to the design and execution stages simultaneously when the main sources of risk are known.
Simple projects may not include detailed information at every step, however, and may move directly to project execution from the project profiling stage.
At each stage, MDS assesses the project and approves or rejects its progression to further development, depending on whether it meets the requirements of each stage. It then issues a
3 Pilar Contreras, an economist with a long career in public investment in Chile (ODEPLAN) and currently serving as Chief of Investment (DIPRES), reported in an interview that, where CBA was not feasible for health and education projects, analysts used relevant variables common to all projects (e.g., malnutrition, education, infant mortality, etc.) to construct a weighted, combined single indicator reflecting each project’s projected impact.
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Resultado del Análisis Técnico Económico (Economic Technical Analysis Result) (RATE), which results in one of the following RATE results:
a) Recommended Favorably (RS); b) Missing Information (FI), to specify that records lack required information necessary to
secure favorable recommendation; c) Technical Objection (OT), reflecting a negative assessment; d) Reassessment (RE), wherein the project is recommended for additional analysis; or e) Breach of Regulations (IN), when spending is executed without the support of MDS.
Projects must attain a favorable RATE (RS) at each stage to move to the next stage of development.
For typical projects, the information required to pass each stage is summarized in Table 1.
Table 1. SNI Informational Requirements for Investment Project Assessment
Stage Transition Submission Requirements
Profile to Prefeasibility / Feasibility
Pre‐investment study containing:
Definition of the problem
Analysis of supply and demand
Study of solution alternatives
Initial cost estimates
Preliminary strategic and economic evaluation
Prefeasibility / Feasibility to Design Further specification of best solution
Detailed budget
Feasibility study
Design to Execution Detailed line‐item budget
Full engineering design
Draft bidding proposal
Sources: Government of Chile (2017). Standards, Instructions, and Procedures for the Public Investment Process (PIN); Presentation: Public Investment Management Conference: The Chilean Experience
Once a project moves to the Execution stage, the proponent may seek funding for the project (in the SNI, the designation ‘Execution’ simply reflects authorization to seek funding, but does not necessarily mean that the project is funded or under implementation).
Projects are typically funded from the proponent unit’s annual budget allocated by the Ministerio de Hacienda. Depending on the agency, funding may also come from other sources. For example, projects formulated by municipalities are funded by the regional government through the National Fund for Regional Development (FNDR). Projects funded solely by the national government do not have to necessarily have an RS from SNI, though projects that are regionally funded do require an RS RATE by law.4
Within the limits of their respective budget allocations, public entities (ministries, government agencies, etc.) then apply their own approaches to prioritize and select projects. In the case of nationally-funded projects, agencies must select from among projects that have been positively recommended by MDS for funding (assigned a RATE of RS). In the transport sector, projects are given ‘high’, ‘medium’, or ‘low’ priority by a largely qualitative consideration of the project’s
4 Organic Constitutional Law on Government and Regional Administration, Law 19175, Article 75.
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alignment with sectoral and national strategies, whereas in the case of developing small reservoirs, appraised projects are subject to a multi-criteria process to prioritize.
Project Appraisal and Selection in Chile
Project selection in Chile is notionally based on social cost-benefit analysis (SCBA), with an overarching goal of maximizing societal benefits using scarce public resources. SCBA requires the quantification and monetization of societal costs and benefits, including potential positive and negative externalities as well as social and environmental benefits and costs that may be difficult to quantify and monetize. All projected costs and benefits are discounted to determine the net present values (discounted benefits minus discounted costs) of proposed projects, from the societal point of view.5
Generally speaking, SCBA can be used either to eliminate projects whose costs outweigh benefits or that do not meet minimum internal rates of return (IRR), or to rank projects by highest net present value (NPV), benefit-cost ratio (BCR), or NPV index (the ratio of the net present value of benefits minus costs to the value of the initial investment). The great strength of SCBA is the ability to compare projects across sectors and regions based on a common metric of monetized value.
In Chilean practice, prior to considering CBA outcomes, projects must first pass initial screenings for legality and strategic alignment and meet the informational requirements for every stage of the SNI. SCBA is, thereafter, used to filter projects based on a minimum internal rate of return rather than to rank projects. In other words, CBA results help decide eligibility for further development but are not necessarily used to prioritize from among RS-rated projects.
Chile developed its capacity and processes for CBA appraisal involving sophisticated estimation techniques, including the use of shadow pricing, the application of various estimation assumptions and methods for various kinds of projects, and standardized use of social discount rates and conversions for values of various expenses and profits in analyses. Some recent advances in project appraisal that have been mentioned are:
• Consideration of the benefits associated with decreased road traffic accidents; • Consideration of the benefits associated with the reduction of greenhouse gas effects; • Evaluation of multipurpose projects (dams) or project networks (bike paths); • Consideration of increased traffic generated by transport projects (as a benefit); • Use of hedonic pricing in the evaluation of urban parks; and • Improved measurement of social prices (e.g., fuels, carbon dioxide, travel and leisure time).
Proposed investments in the transport; forestry, agricultural, and fisheries; and water sectors must be subjected to CBA to generate economic indicators such as Internal Rate of Return (IRR), Net Present Value (NPV), and Net Present Value Index (IVAN). These metrics are to be included in SNI project documentation and are used to guide investment decisions.
One of the most important calculated values – IRR – is used to filter projects. Following initial project appraisal and pre-feasibility, investments that do not meet a minimum IRR of 6% are eliminated from consideration (i.e., they do not receive RATEs of RS). Exceptions to this filtering rule are made for projects with low IRRs that are nevertheless deemed strategically significant (in
5 In Chile, the discount rate is approved by law and encoded in guidance on application of CBA to projects included in the SNI. See SNI instruction here (link).
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the case of water security, for example) and/or when it is recognized that CBAs are missing key information or are unlikely to capture important social benefits (e.g., closing regional income disparities, assuring future environmental quality, etc.). In these cases, a cost efficiency analysis or MCA are used to augment the appraisal (see discussion on MCA to follow).
Extending Investment Decision Support in Chile
While Chile has an extensive record in systematic project appraisal, there remain some technical, policy-oriented, and procedural shortfalls related to its current use as the basis of investment decision-making. These are also recognized internally and have served as an impetus for recent government efforts to extend the processes of project selection to include additional approaches to appraisal and comparison.
In the transport sector, for example, the Highway Design and Maintenance Model (HDM-III/4) has been applied since the late 1980s to extensively estimate expected full life-cycle costs (including construction and maintenance) and benefits (e.g., maintenance, fuel, and travel time savings) associated with road projects. Nevertheless, due to the time and resource demands inherent to SCBA, appraisers must employ extensive assumptions. This can reduce the variation of results across sets of similar projects, tempering the comparative power of estimated metrics.
Typically, it is also only feasible to account for select costs and benefits. Some key considerations may be excluded from analysis, especially costs and benefits that are strategic, environmental, or distributional in nature. Rooted in economic optimization and efficiency, CBA inherently favors projects that generate higher revenues and, therefore, cannot account for strategic or distributional issues. CBA also does not give weight to future-oriented goals such as national security or environmental preservation and privileges more profitable9 projects in metropolitan areas and low-cost regions (such as coastal metropolitan areas). As such, infrastructure funding in Chile is often concentrated in regions that are already more developed, exacerbating territorial inequalities (Ahmad & Viscarra, 2016).
It is increasingly recognized that infrastructure development must consider an extended set of goals beyond economic efficiency. As a recent OECD report on ‘Gaps and Governance Standards’ in Chile’s infrastructure development system states, “The project evaluation and prioritization system will need to accommodate transversal issues and multiple policy goals,” including sustainability commitments. The report points out that “the current system offers limited scope for incorporating transversal issues and other political objectives into the decision-making process in a transparent way. Nevertheless, changes to project evaluation methodologies and selection criteria must not come at the expense of value for money and efficiency considerations” (2017). For this reason, multi-criteria analysis can be helpful to incorporate multiple considerations in addition to maximization of economic benefits, including climate change, cost efficiency, and regional inequality, in a transparent manner (OECD, 2017).
Lastly, putting issues of methodological robustness aside, the outcomes of CBA analysis (e.g., IRR, NPV, IVAN) do not, in fact, strictly guide project selection or the order of fund allocation. As mentioned earlier, CBA is used to filter projects (e.g., removing those with IRRs of less than 6%) through to ‘Execution’ status, which explicitly confers SNI approval and allows the proponent to seek funding. The capital budget submitted to Congress by the Ministry of Finance may only include projects approved by SNI. Therefore, passing the CBA filter is a necessary condition of funding and implementation. But beyond this, calculated IRRs and other SCBA metrics (NPV, IVAN, BCR) are not necessarily used to prioritize within the set of projects that attain ‘Execution’
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status. Rather, proponent units may use any number of approaches (which are often undocumented or based on loosely-defined qualitative criteria) to select projects for implementation from the SNI-approved set. Moreover, a budget decree issued for a sector or unit does not bind the unit to developing the specific projects included in the budget proposal to the Ministerio de Hacienda.
Multi-Criteria Analysis to Support Investment Decision-Making
Some efforts have been made to extend the Chilean approach to project appraisal and investment decision-making to deal with the technical, policy-related, and implementation issues discussed above. In addition to the cost efficiency approach (which assumes that project benefits will be sufficient to justify estimated costs), Chile has also institutionalized the use of multi-criteria analysis (MCA) to support investment decisions for some sectors.
Specifically, MCA has been applied in the rural water sector to deal with an observed mismatch between the methodological outcomes of CBA for water catchments and strategic goals of the sector. More specifically, water security warrants the development of water catchments to ensure the long-term availability of water for agricultural, residential, and industrial use, but CBA appraisals of water catchments typically yield IRRs of less than 6%, which would result in the filtering out of most catchment projects under the prevailing SNI process. As such, a 2014 Decree on the Use of a Multi-Criteria Evaluation Methodology (MCEM) for Small Reservoirs was issued by the government, based on a set of criteria and weights approved by the National Irrigation Commission.
The MCEM applies the Analytic Hierarchy Process (AHP) to aggregate five criteria and 20 sub-criteria into an overall score associated with each water catchment project.6 The methodology is applied in the final stage of progression through the SNI, which starts with an initial filtering to eliminate projects that require resettlement, impose environmental threats, or exhibit various technical difficulties. Thereafter, projects pass through the pre-feasibility, feasibility, and design stages in SNI as in other infrastructure sectors. At any time, projects may be filtered out if major environmental, technical, legal, or political difficulties arise. In the last stage, catchment projects that pass filtering are subject to the small-reservoir MCEM analysis for final selection.
The criteria and sub-indicators applied to select reservoir projects are detailed in Table 2, along with weights used to combine criteria into a single score. Values associated with the sub-criteria are not measured as continuous variables. Rather, sub-criteria are scored ordinally. Most are given an ordinal score across a range (often 0, 5, or 10), though some are simple binaries [0,1].
An additional criterion – technical complexity – is used in the ultimate selection of projects, though this is not weighted along with the MCE criteria in Table 2. This additional consideration covers technical issues such as soil mechanics, location of the catchment with respect to natural channels, proximity of materials earthworks and dumping sites for excavated soils. The degree to which technical complexity influences the ultimate selection of projects, relative to the MCE scoring, is not specified.
6 For technical details of AHP, see Saaty, Thomas L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26.
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Table 2. Reservoir MCEM Criteria and Sub-Criteria Criteria / Weight Indicators / Sub‐criteria
Economic
18.4%
Social VAN ($ million)
Investment ($ million) / hectare
Investment ($ million) / land plots
Social
34.1%
% households under poverty line
Surface area of subsistence farms / small farms <12 ha
Number of beneficiaries (population in irrigable zone)
Indigenous communities in the territory [0,1]
% growth of rural population during last inter‐census period
Extreme zone, border region, or undeveloped area [0,1]
Strategic
22.6%
Number of water shortage decrees in past five years
Number of jobs generated (landowners and relatives)
Number of irrigation association systems that can be connected7
Electricity generation capacity (MWh/year)
Environmental / Territorial
9.2%
Number of people required to relocate
Number of archaeological site affected
Hectares of native forest in flood zone
Management
15.7%
Interest/support of beneficiaries
Economic contribution of regional government (regional government contribution / total project investment)
Organization (1‐4 indicating degree of incorporation / legal standing)
Number of land parcels required to expropriate
Source: Ministerio de Hacienda, 2014. Minuta Matriz Multicriterio Plan de Pequeños Embalses
7 This measures the number of ‘asociación pequeños regantes’ (APRs) or small irrigation associations that can be linked.
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Chapter 3. Infrastructure Prioritization Framework: An Alternative Approach The Infrastructure Prioritization Framework is a quantitative multi-criteria approach to within-sector infrastructure project comparison. The IPF condenses project-level indicators (selection criteria) into two composite indices – a financial-economic index (FEI) and a social-environmental index (SEI) – and considers these alongside the budget constraint for a particular sector. Results are displayed graphically to map the projects’ relative expected performance along these two dimensions.
While the IPF is quantitative in nature, it is also policy-responsive, since the government specifies the set of project selection criteria that reflect the sectoral and developmental policy goals. These criteria may include social, strategic, and environmental considerations alongside traditional financial and economic factors. In fact, the IPF was developed by an infrastructure team within the World Bank in response to government demand for alternative decision support approaches that could directly consider key policy goals; be feasibly applied across large projects sets within the resource means of the government; and remain systematic and evidence-based (Marcelo et al., 2016).
IPF is designed to employ quantitative measures to the greatest extent possible to systematize project comparison and limit subjectivity in selection. While the IPF is a multi-criteria approach, it can utilize the results of CBA analysis as a key decision factor. While the approach was initially envisaged for low-capacity governments, its relevance to the Chilean context is demonstrated in the results of this report. In this section, we summarize the IPF prior to presenting the results of its application to the transport and water catchment sectors in Chile.
The IPF Process
Implementing the IPF is relatively straightforward and follows five steps: (1) selecting decision criteria; (2) gathering related project indicator data; (3) calculating social-environmental and financial-economic indices; (4) plotting projects and budget limits; and (5) comparing projects (see Figure 2). In this section, we summarize IPF application in terms of these steps.8
8 An extensive technical description of the IPF methodology is detailed in Marcelo et al., 2016, and Marcelo et al., 2015.
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Figure 2. IPF Process Map
Step 1. Select Criteria
The first IPF step is to identify and select criteria used to compare projects. Selected variables may vary in different contexts based on the policy goals shaping decision-making, but will generally include indicators of value, efficiency, and social and environmental impact. This step is an opportunity to leverage professional knowledge and allow policy makers, experts, and other key stakeholders to reach consensus on the decision-making factors most important to project selection. In this way, this step helps crystallize a government’s infrastructure policy goals.
Variables are organized into two general categories: social-environmental and financial-economic. Infrastructure projects are meant to improve quality of life; therefore, several direct social and environmental benefits are relevant, including factors like improved access to public services and job creation. These benefits come at a cost, however. Engineering works may require clearing forested areas, polluting and endangering natural environments, or resettling communities. The IPF directly considers these relevant social and environmental benefits and costs without requiring their monetization. In Panama, for example, the SEI initially consisted of five indicators: the number of direct beneficiaries; direct jobs created; people affected by repurposing of land use; poverty rates; and environmental impact (categorized as negative, neutral, or positive), all measured in their ‘natural’ units.
The financial and economic effects of a project are also central to infrastructure decision-making. These can be assessed using outcomes of CBA or partial analyses that, at the very least, estimate project costs. In Panama, for example, four indicators were initially selected to comprise the financial-economic index (FEI): the internal rate of return, economic multiplier effects, monetizable externalities, and implementation risk. In other cases, a single economic indicator (e.g., NPV or IRR) may constitute the FEI.
One key lesson from past pilots is that the set of selected indicators may require adjustment. An indicator may be found to be analytically problematic due to lack of sufficient data, calculation problems, or other issues, such as imprecision in variable specification. This is an iterative process, and indicator problems are likely to be discovered during data collection or index calculation.
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Step 2. Prepare Data
The second IPF step is to gather and transform raw data so that they are usable for calculating SEI and FEI project scores. A simple Excel environment can be constructed to populate a prioritization database with raw data. Because selection criteria variables may have different units of measurement (i.e., they are not all dollarized costs and benefits) but are combined in additive models, two types of data transformation are required. First, any qualitative data must be transformed into either ordinal or scalar data.9 Second, observations are standardized to deal with disparate units of measure, transforming all measurements to have a transformed value between [-1] and [1], with the set having a zero mean and unit variance.10
Step 3. Constructing Performance Indices – SEI and FEI
Indices are used to combine information from multiple variables into composite indicators. In IPF, variables are organized into two classes to construct the social-environmental index (SEI) and the financial-economic index (FEI). The FEI may be the standardized value of a single component derived from CBA (e.g., NPV or BCR) or a combination of several factors, but the SEI is typically constructed by combining a number of key social and environmental variables. This is done via an additive model, wherein each indicator’s contribution to the overall index score is determined by weights (the coefficients associated with each variable).
For example, if the SEI variables selected include (a) number of beneficiaries (BEN), (b) number of poor served (POOR), and (c) number of jobs created, the function may be expressed as
𝑆𝐸𝐼 𝑤 𝐵𝐸𝑁 𝑤 𝑃𝑂𝑂𝑅 𝑤 𝐽𝑂𝐵𝑆 ,
where weights, 𝑤 , are associated with each social-environmental indicator.
The weights used to combine variables can be set subjectively or objectively, such as using some form of Principal Component Analysis (PCA). PCA is an information reduction procedure that seeks redundancies in sets of variables. These redundancies can be expressed as linear combinations or ‘principal components’ of the variables comprising the set. One key characteristic of PCA is the ability to calculate coefficients (weights) based solely on the statistical relationship between variables. While other weighting schemes may be used, PCA is particularly useful when there is a preference to objectively assign weights.
Some significant advances have been made with respect to using PCA to determine weights. These changes have been made to deal with policy preferences regarding the relative importance of criteria. Over the course of the Sri Lanka and Chile pilots, for example, calculation methods were developed to add restrictions to PCA that can attain the following: require a particular coefficient sign (+/-) associated with specified variables; require that criteria are weighted to reflect a pre-set order of importance (a weighting order); and require a minimum weight for a variable.
9 This can be done with either scaling methods or via approaches like ALSOS. The transformation of categorical and ordinal qualitative and quantitative data into usable numerical data may be done using the Alternating Least Squares Optimal Scaling (ALSOS) algorithm, a widely-accepted transformation approach. Within a quantified ordinal variable, the numbers assigned by the ALSOS algorithm to each category reflect the distance between categories, revealing the implicit metric of the variable (Perreault & Young, 1976). 10 Numerical values are standardized via a standardization formula that can be coded into Excel. The standard score z of a raw score x is z_ij= (x_ij-μ_j)/ _j, where μ is the sample mean and is the standard deviation of the variable j.
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After SEI and FEI variables are combined in the additive model, resulting values are normalized and rescaled to generate SEI and FEI scores between 0 and 100 for each project. The rescaled score 𝑧 can be expressed as
𝑧 𝑧 𝑍 / 𝑍 𝑍 100,
where 𝑍 is the minimum value for variable 𝑧 and 𝑍 is the maximum value. These rescaled scores are used as the SEI and FEI scores for plotting in Step 4.
Step 4. Creating the Visual Interface: The Investment Prioritization Matrix
To create a visual comparison, projects are plotted on a two-dimensional Cartesian plane, with axes representing the SEI and FEI. The budget limit for the sector is also imposed along each axis (intercepting the axis where funds are exhausted). First, however, the budget must be hypothetically allocated separately to the SEI- and FEI-ranked project lists to determine the fundable sets in each. In other words, the budget limit is hypothetically allocated to the top-ranked projects on each list (as if selection were based only on SEI or only on FEI) until resources are exhausted. The resulting fundable sets are compared simultaneously on the investment matrix.
A ‘good’ project in terms of financial and economic performance may nevertheless be undesirable from a social and environmental perspective, and vice versa. As such, decision-makers must consider projects along both dimensions. Projects can be compared by their respective SEI and FEI scores on a visual interface called the Infrastructure Prioritization Matrix (Figure 3). Once projects are plotted, the budget limit is imposed onto the plane – perpendicular to each axis – at the point where the budget would be exhausted if funding were determined solely by each index. The plane is intersected by the dually-imposed budget limit, creating four quadrants.
Figure 3. Example Investment Prioritization Matrix, Panama Water and Sanitation Pilot, 2015
Source: Prioritizing Infrastructure Investments in Panama: Pilot Application of the World Bank Infrastructure Prioritization Framework (April 2016)
P3
P4
P5
P6
P7
P10
P11
P12
P13
P14
P15
P16P17
P18P19
P20
P21
P22
P23
P24
P25P26
P27
P28
P29
P30
P31
P32
P33
P34
P35
P1
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50 60 70 80 90 100
SEI
FEI
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Projects that fall inside the budget constraint along each axis represent the ‘Investment Possibilities Set’ for each dimension. Projects in the upper right quadrant fall in the Investment Possibilities Set for both SEI and FEI and are then categorized as ‘High Priority’ projects.
Evolution of the IPF
Through pilot applications of the IPF in Vietnam, Panama, Argentina, Sri Lanka, and Chile, the framework has been refined. This section discusses key areas of progression, including lessons learned on important pre-analytical steps and capacity and institutional requirements, technical aspects of variable specification and weighting, and the improved use of sensitivity analysis.
Pre-Analytical Steps
Pre-analytical processes can help filter projects to reduce the analytical burden of prioritization and ensure sufficient comparability of data during project comparison. One of the challenges of early pilots was that some data, even from within feasibility studies, was either opaquely determined or had limited comparability across projects (Mandri-Perrott, Marcelo, and Haddon, 2015). Feasibility studies should follow clear rules, guidelines, and standards of appraisal to ensure quality and comparability of data (particularly financial estimations) across projects.
Additionally, filters are helpful to ensure that projects meet basic informational, policy, or strategic requirements and/or align with key sector goals. Filters may also be useful when there are inherent biases observed in the set of projects proposed or where the government aims to break regressive patterns. In Vietnam, for example, it was observed that projects in poorer regions tended to score lower on inputs to the FEI or SEI. This observation justified use of an initial filter to target areas with higher poverty rates (Mandri-Perrott, Marcelo, & Haddon, 2015).
Technical Improvements: Variable Specification and PCA Restrictions
A second set of lessons is that special consideration should be given to the selection and definition of variables as well as the weights assigned via PCA. For one, metrics must be carefully specified to deal with regressive biases. As in Vietnam, Panama revealed an inherent bias towards infrastructure projects in wealthier urban regions due to better scoring on project indicators. If development plans aim to improve rural areas, however, this can yield adverse results. Alternative to using a filter, this problem can be overcome by careful indicator specification and/or the inclusion of additional indicators to capture development goals.
Variable specification is also important to balancing considerations of efficiency and efficacy. For example, one could use the absolute number of beneficiaries as an input to the SEI to consider policy effectiveness where service expansion is a priority. On the other hand, ‘beneficiaries per dollar spent’ may be more appropriate if the key goal is fiscal efficiency. In the case of Panama, where development of rural services is an important policy goal, the decision was made not to control indicators by project size to avoid privileging urban projects with greater economies of scale (Marcelo, Mandri-Perrott, & House, 2015). Another lesson on variable specification relates to the appropriate use of financial and economic indicators under conditions of low information, particularly regarding project benefits. If only project costs can be estimated, additional variables must be considered to construct the FEI (i.e., FEI should not be based on cost only).
Another technical issue arose regarding the use of PCA for weighting. Since PCA synthetizes information based on correlations between variables (which may yield positive or negative
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coefficient signs), it is important to make sure that weights reflect the desired relationship between a variable and the composite indicator. A problem arises, for example, if PCA assigns a negative weight to a variable that should be positively rewarded in selection. In some cases, this can be resolved by alternative specifications of a component variable, but an important methodological development has been the imposition of a coefficient sign restriction in PCA. This development allows the user to restrict PCA results to ensure that variables that should positively contribute to the SEI are assigned positive-signed coefficients, and vice-versa for variables that should be scored negatively.
Sensitivity Analysis and Criteria Weighting
Another important improvement has been the addition of sensitivity analysis to test the robustness of results with different variable specifications and criteria weightings. In the Chile pilot, for example, there was an expressed goal of focusing transport investments in areas with higher poverty. Since poverty rates may be measured in several ways, however, a sensitivity analysis applied two alternative poverty rate approaches to compare IPF results.
Since criteria weighting is one of the most important methodological decisions for building indices, it is another important area of sensitivity analysis. The results of IPF are determined, in part, by the weights associated with each indicator. Though a significant lesson from Panama was that composite indices were far more sensitive to indicator values than to the weights used to combine them.11 While this must be further tested, this suggests that PCA may be a useful way to weight variables if time and objectivity are important factors in selection.
In practice, the use of subjective weighting can give rise to several problems, including lack of transparency, manipulation of weights to privilege ‘pet projects’, or index scores with low variation (and then, limited value for comparison). On the other hand, subjective weighting is more intuitive and directly responsive to policy preferences. Developments have focused on finding a compromise between the responsiveness of subjective weighting and the objectivity of PCA.
In addition to the sign restrictions discussed above, the need arose to adjust the mechanics of PCA to better capture policy preferences. The use of PCA was further refined to allow several additional restrictions on coefficients. These include the ability to specify a minimum value for a coefficient and the ability to specify the order of weighting (order of importance to the overall score). These restrictions define a spectrum from purely objective to purely subjective weighting (Figure 4).
Figure 4. Spectrum of Index Weighting Approaches
11 A sensitivity analysis was performed to compare PCA indices against composite indices using subjectively established weights. Two subjective weighting schemes (equal weighting and hypothetical policy-determined) were tested to calculate alternative SEI composite indices. The categorization of projects changed only minimally when using policy-determined or equal weights (Marcelo, Mandri-Perrott, & House, 2015).
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Organizational and Capacity Issues
To improve robustness of results and foster concurrent application with other supportive analytical tools (including CBA and expert assessment), users must have sufficient technical capacity to understand the mechanics and implications of key decisions regarding the use of IPF, including decisions about criteria selection, indicator specification, and weighting. Further, to extoll the benefits of the responsiveness inherent to the tool, the proposed methodology should not be a one-off exercise. Rather, it should be utilized as a progressive approach, intended to ‘live and grow’ with the country's infrastructure needs and policy objectives. As such, the prioritization program should involve continuous refinement of the decision-support tool, based on informed deliberation regarding criteria selection and any pre-decisions of a policy nature (Mandri-Perrott, Marcelo, & Haddon, 2015). Last, planning offices and decision makers must be familiarized with the multi-criteria approach to build credibility of the decision support tool itself, establish familiarity with its use, and legitimize the results of analysis.
Normative
Subjective
Positive
Objective
Neutral Responsive
Sign-constrained PCA PCA
Subjective weighting
Preference-ordered PCA
Minimum-value PCA
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Chapter 4. Applying IPF to Chile’s Infrastructure Project Proposals This section documents the results of the IPF-based project prioritization for water catchment and road transport projects and compares these results with actual project selection outcomes. The greatest value of the Chile pilot is the opportunity to compare IPF results to project selection informed by CBA to test the analytical demands of IPF inputs, the robustness of outputs, and degree of alignment of IPF results with the outcomes of other approaches. The comparative results show that prioritization outcomes are affected not only by the methodologies in use, but also by the practices and policies of project selection – i.e., how the results of analyses are applied in decision-making.
In this chapter, we first present the IPF mechanics and comparative results of IPF- and MCE-based prioritization for water catchments and follow with IPF construction and comparative results of IPF- and SCBA-based prioritization for road transport projects.
Water Catchment Projects in Chile
Water resource management is an important policy priority in Chile with direct impacts on rural and agricultural development and environmental sustainability. Despite an abundance of water resources (overall availability of around 50,000 m3 per capita per year), Chile faces water stress due to geographic distribution patterns. Most of Chile’s population lives in arid and semi-arid areas where water availability is low (less than 1,000 m3 per capita year) and demand exceeds surface water supply. An increasing need to offset unmet demand by groundwater extraction has led to a significant increase in annual freshwater withdrawals, which has become a key sustainability concern.12 Moreover, Chile is projected to move from a level of medium water stress to extremely high stress in 2040 due to the impacts of climate change.13
The erosion and desertification of soils also present a recognized sustainability challenge related to development of the Chilean forestry and agriculture sectors. Deforestation, overgrazing, inadequate crop management, and irrigation practices have resulted in soil degradation affecting nearly half of the territory and 75 percent of productive soils. This increased water stress and soil degradation disproportionally affects the poorest populations, who rely on small-scale agriculture as a critical income source; depend on natural resources for food, fuel, and building materials; and are typically located in arid rural regions most affected by climate variability and drought.
As such, there is increased pressure to better manage water resources to ensure water security, meet growing agricultural and industrial demand on water resources, support adequate provision to the poorest communities, and deal with increased water stress associated with the effects of climate change.
Water Catchment Project Sample
Projects are typically prioritized within regions (as opposed to the national level) to ensure the disbursement of funding across regions. Since projects are allocated funds region by region, the IPF was also applied at the regional level in four areas: Biobio, Maule, O’Higgins, and Valparaiso.
12 Chile’s annual freshwater withdrawals as percentage of internal resources went from 2.3 percent in 1992 to 4.0 percent in 2014. 13 Maddocks, Young, & Reig (2015).
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The regions and samples of proposed projects considered via MCE and IPF are summarized in Table 3.
Table 3. Water Catchment Project Sample
Region Number of Projects in IPF Sample
Biobio 14 Maule 13
O’Higgins 22 Valparaiso 12
To compare IPF and MCE prioritization outcomes, projects were first assigned scores and ranked in regional groups, as in practice. They are also presented in ranked order as a pooled (all regions combined) group in the IPF analysis.
Water Catchment Project Indicators
The selection of indicators used to construct the SEI reflects some of the key policy goals of the government with respect to developing water catchments. As discussed in Chapter 2, many of the intended benefits associated with improving water resources are strategic, environmental, and social – and these are often difficult to quantify and monetize. Therefore, economic analyses often result in calculated internal rates of return (IRRs) below the 6% threshold required for favorable recommendation. These low IRRs are likely due to undervaluation of some long-term benefits. Recognizing the importance of water resource development, however, the government implemented the Multi-Criteria Evaluation approach to assess these projects.
The IPF draws from the MCE’s components to select the sets of input indicators for the SEI and FEI. Data previously gathered for the MCE were simply re-organized to fit the format of the IPF approach, with three important changes. First, the selection of input variables required paring down the extended list of MCE indicators. The resulting IPF indicator set excluded some MCE variables either because (a) they brought redundant information to the analysis or (b) exhibited no variation across projects. Second, some indicators used in IPF drew directly on project data in natural units rather than the ordinal score (e.g., 0, 5, 10) assigned to projects in the MCE approach. Third, some values maintain their ordinal scores, but are transformed so that more positive scores are recorded with higher values (in the MCE, lower scores are attributed to better performance on sub-indicators). The resulting indicators are described in Table 4, and relevant transformations are described in Table 5.
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Table 4: Variables Included as SEI and FEI Indicators
Type Indicator Included Variables English Use / Relevant Calculations
Other Inputs
Investment ($m) Inversión inicial Initial Investment Direct from MCE data
Predios (u) Número de predios # Land Plots Direct from MCE data
Surface (ha) Superficie equivalente Surface Direct from MCE data
SEI
Poverty Porcentaje de hogares en pobreza comunal
Poverty
Drew from MCE data, but directly used the percentage value rather than an assigned ordinal score
Beneficiaries # Predios / superficie # Land Plots / Surface Area
Direct from MCE data
Jobs
Comuna extrema, fronteriza o rezagada
Underdeveloped Community
See Table 5 Generación de empleo agrícola
Generation of Agricultural Employment
Territorial Relocalización de vivienda Household Relocation
See Table 5 Afecta bosque nativo Native forest affectation
FEI
i_NPV VAN social / inversión inicial NPV / Investment Direct from project data
Expropriations Expropiaciones Expropriations See Table 5
Endorsement Interés de beneficiarios Community Endorsement
See Table 5
Legal Desarrollo organizacional Legal Standing of Organizations
See Table 5
27
Table 5. Calculation of Select SEI and FEI Indicators Indicator Calculation / Indexation Rule
Jobs
(a) For Comuna Extrema (underdeveloped community 'UC') (i) If UC = Yes assign 1 (ii) If UC = No assign 0
(b) Generación de Empleo Agrícola (Agricultural employment 'AE') (i) If AE = Yes assign 1 (ii) If AE = No assign 0
(c) Jobs Index = a + b
Original MCE Score Converted
Underdeveloped Community Agricultural Employment
Yes 1 1 1
No 10 0 0
Territorial
(a) For Relocalizacion de vivienda (household relocation 'HR') (i) If HR = Yes assign 0 (ii) If HR = No assign 1
(b) Afecta Bosque Nativo (native forest affected 'FA') (i) If FA = Yes assign 0 (ii) If FA = No assign 1
(c) Environmental = a + b
Original MCE Score
Converted Relocation Required Affects Native Forests
Yes 1 0 0
No 10 1 1
Expropriations
(a) The more expropriations 'EX', the higher the risk to the project. (b) We use the following formula for expropriations: 𝐸𝑋 𝐼𝑛𝑑𝑒𝑥 𝐸𝑋 𝑚𝑎𝑥 / 𝐸𝑋′𝑠 𝑟𝑎𝑛𝑔𝑒 Therefore, the larger the number of expropriation in original data, the smaller value taken in the expropriations index.
Interest (community
endorsement of project)
(a) This stands for interest of beneficiaries 'IB' in the project (i) If IB = YES (the community endorse the project) Assign 1 (ii) If IB = No (the community doesn't endorse the project) assign 0
Legal
(a) This variable stands for the soundness of legal standing of organizations (i) If original value = 1, legally constituted organization (best) assign 3 (ii) If original value = 5, "de facto" constituted organization assign 2 (iii) If original value = 10, non‐constituted/ non‐existing organization assign 1.
Legal Standing of Organizations (in English) Original Converted
Organización con personalidad jurídica Legally constituted organization 1 3
Organización de hecho constituida "De Facto" constituted 5 2
Organización de hecho no constituida "De Facto" non‐constituted 10 1
Inexistencia de organización Non‐existing organization 10 1
Water Catchment Project Indicator Weighting
While projects were ranked region by region, the weights associated with criteria were calculated via PCA based on the full (combined) sample of projects. This decision was made due to the low degree of variation of many variables within each region. In other words, criteria weights were
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calculated via PCA (or a restricted PCA) using all projects across these four regions. This common set of weights was applied to score projects in Biobio, Maule, O’Higgins, and Valparaiso, separately, based on the following formulas:
𝑆𝐸𝐼 𝑤 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 𝑤 𝐵𝑒𝑛𝑒𝑓𝑖𝑐𝑖𝑎𝑟𝑦 𝑤 𝐽𝑜𝑏𝑠 𝑤 𝑇𝑒𝑟𝑟𝑖𝑡𝑜𝑟𝑖𝑎𝑙 , and
𝐹𝐸𝐼 𝑤 𝐼𝑉𝐴𝑁 𝑤 𝐸𝑥𝑝𝑟𝑜𝑝𝑟𝑖𝑎𝑡𝑖𝑜𝑛𝑠 𝑤 𝐸𝑛𝑑𝑜𝑟𝑠𝑒𝑚𝑒𝑛𝑡 𝑤 𝐿𝑒𝑔𝑎𝑙,
where 𝑤 , …, 𝑤 are the weights associated with each criterion.
The weights 𝑤 , …, 𝑤 used to combine SEI and FEI indicators are described in Tables 6 and 7.
Table 6 includes four weighting schemes for SEI. The first is determined by PCA with no restrictions; the second is determined by PCA with the restriction that all weights must have a positive value; the third is determined by PCA with the restriction that all weights must be positive and have a minimum value of .10; and the fourth is a simple equal weighting of all variables.
Table 6. Weighting and % Variance Explained, Water Catchment SEI Calculations Factor loadings (x vector) and % of variance explained
No restriction PCA weights >=0
PCA weights, minimum requirement
(10%) Simple average
Poverty 0.011 0.011 0.316 0.500
Beneficiary 0.619 0.619 0.642 0.500
Jobs 0.416 0.416 0.400 0.500
Territorial 0.666 0.666 0.573 0.500
Retained variance 1.608 1.608 1.559 1.455
% explained 40% 40% 39% 36%
The weighting adopted to calculate the SEIs for all regions is that determined by PCA with the restriction that all criteria contribute at least 10% to the overall score. Then, the additive model is:
𝑆𝐸𝐼 .316 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 .642 𝐵𝑒𝑛𝑒𝑓𝑖𝑐𝑖𝑎𝑟𝑖𝑒𝑠 .400 𝐽𝑜𝑏𝑠 .573 𝑇𝑒𝑟𝑟𝑖𝑡𝑜𝑟𝑖𝑎𝑙 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑
Similarly, Table 7 includes four weighting schemes for FEI.
Table 7. Weighting and % Variance Explained, Water Catchment FEI Calculations Factor loadings (x vector) and % of variance explained
No restriction PCA weights >=0
PCA weights, minimum requirement (10%)
Simple average
NPV_inv ‐0.047 0.000 0.316 0.500
Expropriations 0.395 0.369 0.316 0.500
Endorsement 0.711 0.710 0.635 0.500
Legal 0.580 0.600 0.630 0.500
Retained variance 1.808 1.807 1.732 1.457
% explained 45% 45% 43% 36%
Again, the weighting adopted to calculate the FEIs was that determined by PCA with a 10% minimum contribution restriction, with the resulting additive model:
𝐹𝐸𝐼 .316 𝐼𝑉𝐴𝑁 .316 𝐸𝑥𝑝𝑟𝑜𝑝𝑟𝑖𝑎𝑡𝑖𝑜𝑛𝑠 .635 𝐸𝑛𝑑𝑜𝑟𝑠𝑒𝑚𝑒𝑛𝑡 .630 𝐿𝑒𝑔𝑎𝑙 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑
29
IPF Results: Water Catchment
After calculating the SEI and FEI scores for each catchment project according to the weighted additive models above, the projects are ranked by each score, by region. The calculations for SEI and FEI scores are detailed in Annexes 3 and 4, while the graphical results of rankings by each index are summarized in Figures 5, 6, 7, and 8. Figures 5 (SEI) and 7 (FEI) display results region by region, whereas Figures 6 (SEI) and 8 (FEI) display the full set of projects pooled into a single ranked group according to each index.
In each ranking chart, the projects that were actually approved for funding by the government are marked with a checkmark above the project’s score. Generally speaking, irrigation projects with higher SEI and FEI scores tended to be those approved for funding, though some projects with outlying low SEI scores were among the approved set.
Water Catchment SEI Results
Project rankings by SEI for each region are detailed in Figure 5. In each ranking chart, the projects actually selected for funding are marked with a checkmark.
Figure 5. Water Catchment Project SEI Scores and Ranking, by Region
✔ - Project approved for funding by GoC
Source: Authors’ calculations
✔ ✔ ✔
✔
0102030405060708090
100
5 3 4 2 10 9 7 11 13 12 8 1 6 14
Project ID
Biobio SEI
✔ ✔
✔ ✔
✔
0102030405060708090
10020 22 15 24 21 17 16 26 23 25 27 19 18
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Maule SEI
✔
✔✔ ✔
✔✔
0102030405060708090
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36 46 35 29 30 31 37 41 42 43 32 33 34 28 47 48 39 40 38 44 45 49
Project ID
O'Higgins SEI
✔ ✔ ✔ ✔
0102030405060708090
100
56 58 57 51 50 55 54 60 61
52 53 59
Project ID
Valparaiso SEI
30
Pooled SEI Results Figure 6. Water Catchment Project SEI Scores and Ranking, Pooled Regions
Biobio 14 projects O’Higgins 22 projects
Maule 13 projects Valparaiso 12 projects
Source: Authors’ calculations
0
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20 22 23 5 56 36 46 58 57 15 16 47 48 26 51 24 21 4 44 45 49 50 27 2 10 9 7 11 13 35 17 19 55 3 54 25 29 30 31 37 41 42 43 39 40 60 12 8 61 32 33 34 28 181
52 53 38 6 14 59
Project ID
Pooled Region SEI
31
Water Catchment FEI Figure 7. Water Catchment Project FEI Scores and Ranking, by Region
✔ - Project approved for funding by GoC
Source: Authors’ calculations
✔ ✔
✔
✔
0102030405060708090
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5 7 13 4 14 12 11 1 9 2 10 6 8 3
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Biobio FEI
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✔
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24 20 21 18 16 15 22 25 27 23 17 19 26
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Maule FEI
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✔
✔ ✔ ✔
✔
0102030405060708090
100
35 33 36 32 34 42 43 44 31 29 30 39 40 41 37 38 48 49 46 45 47 28
Project ID
O'Higgins FEI
✔✔
✔ ✔
0102030405060708090
100
50 59 51 56 53 52 57 55 61
58 54 60
Project ID
Valparaiso FEI
32
Figure 8. Water Catchment Project FEI Scores and Ranking, Pooled Regions
Biobio 14 projects O’Higgins 22 projects
Maule 13 projects Valparaiso 12 projects
Source: Authors’ calculations
0
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60
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1005
50 59 35 33 36 32 34 51 56 53 52 247
44 14 13 4 21 12 11 201 9 2 10 6 18 16 15 42 43 22 25 31 29 30 57 55 8 39 40 41 3 27 23 37 38 48 49 17 61 58 46 45 47 54 28 26 19 60
Project ID
Financial and Economic Indicator (FEI)
33
Water Catchment IPF Matrix
Figure 9 shows the results of plotting projects (by their SEI and FEI scores as x and y coordinates) onto a water catchment prioritization matrix, region by region. Yellow project points denote projects that were actually approved for funding. Interestingly, these results suggest different patterns with respect to funding preferences. In Maule and Valparaiso, for example, approved projects are those that had the highest FEIs, whereas project selection in O’Higgins and Biobio does not directly reflect high FEI or SEI.
Figure 9. Water Catchment IPF Matrices, by Region
Source: Authors’ calculations
P5
P3P4
P2P10
P9
P7 P11P13
P12P8
P1
P6
P14
0
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30
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FEI
Biobio
P20 P22
P15P24 P21 P17
P16 P26P23
P25 P27P19
P18
0
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30
40
50
60
70
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0 10 20 30 40 50 60 70 80
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100
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FEI
Maule
P36 P46
P35
P42
P43 P31P29
P30P41
P37
P33P32
P34
P28
P48 P47P39P40
P38P44
P49P45
0
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20
30
40
50
60
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FEI
O'Higgins
P56 P58P57P51
P50P55 P54
P60P61
P52
P53
P59
0
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60
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90
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SEI
FEI
Valparaiso
34
Comparing IPF to Selection of Water Catchment Projects
Table 8 offers more detail on the comparative outcomes of IPF versus MCE ranking in each region. The MCE-ranked projects marked with an asterisk are those that were not recommended for implementation due to location in an area where environmental restrictions were in place. Highlighted projects are those that were recommended for funding.
Table 8. Comparing IPF Maps and MCE Project Rankings, by region Biobio
IPF Mapping MCE Ranking
ID Name Score
1 Kaiser 3.0
9 Quidico 1 3.3
3 Ranquil 3.5
8 Mirihue 3.5
14 Leoneras 3.5
2 Las Puentes 4.1
10 Quidico 2 4.3
6 Pichi Bureo* 4.4
5 Laguna El Pillo* 4.6
12 Vegas de Itata 4.6
7 Rumena 5.0
13 Tauco 5.3
4 Tranaquepe* 5.4
11 Perales 5.9
Maule
IPF Mapping MCE Ranking
ID Name Score
19 La Bruja 2.38
17 Peralito* 3.12
18 Peralito 2 3.17
25 El Molino 3.22
26 Derivado Porvenir 2 3.31
21 Sauzal 4.50
24 Manantiales 5.01
27 Limávida 5.11
23 Vaquería 5.45
20 El Guindo 5.55
22 Botacura 6.20
15 Huencuecho I 6.65
16 Huencuecho II 6.65
* Not recommended due to environmental restriction
P5
P3P4
P2P10
P9
P7 P11P13
P12P8
P1
P6
P14
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
100
SEI
FEI
Biobio
P20 P22
P15P24 P21 P17
P16 P26P23
P25 P27P19
P18
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
100
SEI
FEI
Maule
35
O’Higgins
IPF Mapping MCE Ranking
ID Name Score
29 San Francisco 1 (Zonada) 6.2
30 San Francisco 2 (Zonada) 6.2
31 San Francisco 3 (Zonada) 6.2
28 Codegua (CFGD) 6.4
37 Manquehue 1 (Zonada) 6.5
38 Manquehue 2 (Zonada) 6.5
41 Estero Seco (CFGD) 6.7
36 Ucúquer (CFRD) 7.6
32 El Maiten 1 (Zonada)* 7.8
33 El Maiten 2 (Zonada)* 7.8
34 El Maiten 3 (Zonada)* 7.8
35 Huehuinco (CFRD)* 7.8
40 Las Palmas 2 (RCC) 8.2
42 Cementerio 1 (Hormigón) -
43 Cementerio 2 (Hormigón) -
Valparaiso
IPF Mapping MCE Ranking
ID Name Score
55 Pullally 3.04
54 Santa Marta 3.26
60 Santa Julia 4.48
59 Valle Hermoso* 4.85
56 Cuncumen 1 5.11
58 El Zaino 2* 5.18
52 Chalaco* 5.60
57 Lo Zárate 2 5.62
53 Las Carditas 2* 6.18
50 Vitahue* 6.31
51 Paihuen* 6.50
* Not recommended due to environmental restriction Note: Some projects that entered the database later do not have a corresponding MCE ranking, such as Project 39, 44-49 in O’ Higgins and Project 61 in Valparaiso
P36 P46
P35
P42
P43 P31P29
P30P41
P37
P33P32
P34
P28
P48 P47P39P40
P38
P44P49
P45
0
10
20
30
40
50
60
70
80
90
1000 10 20 30 40 50 60 70 80 90
100
SEI
FEI
O'Higgins
P56 P58P57P51
P50P55 P54
P60P61
P52
P53
P59
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
100
SEI
FEI
Valparaiso
36
Applying IPF to Road Transport
Regional proposals for transport projects are initiated with the submission of draft regional investment pre-project proposals (pre-ARI), which are developed at the regional level with the participation of representatives of the Ministerio de Obras Públicas (Ministry of Public Works) (MOP) subnational offices, Secretarías Regionales Ministeriales (SEREMI). These initial proposals are considered jointly by regional and national planners. SEREMI sends several regional projects on for consideration to MDS to follow the process of ongoing project appraisal and RATE. While the national government may decide to fund projects with a RATE other than RS, regional projects require an RS designation.14
The MOP determines which of the projects that received an MDS favorable recommendation (RS) will be sent further for funding to the Ministry of Finance. In the MOP's prioritization methodology, projects are classified as medium, high or low priority based on considerations of strategic relevance, completeness of information, and alignment with regional development plans. Projects selected by the MOP are sent to the Ministry of Finance for review. On approval, a decree is issued by the Ministry of Finance to commit financial resources and initiate project execution. To modify the list of projects, MOP must send its revised prioritized list to the Ministry of Finance to be approved and included in a new decree.
Transport Policy Goals and Road Project Criteria
The processes of transport project prioritization described above hinge on two primary inputs: project cost-benefit analysis and qualitative assessment of project alignment with medium- and long-term regional and national transport development plans. The former – CBA – is based on a sophisticated approach to assessing road project lifecycle costs, namely the Highway Development and Management (HDM-III/4) tool.
The latter involves consideration of Chile’s transport policy goals. Road density in Chile lags behind other Latin America and OECD countries, partly due to low overall population density but high concentration in Santiago and Valparaiso. The road network’s size (approximately 78,000 kms in 2010) has not significantly changed since 1990, though the share of paved roads has increased from 13.8 to 23.3 percent. This pilot application of IPF considers inter-urban road projects intended to extend the road network and link urban areas. Specifically, the pilot was applied to 50 projects in the BIP that met basic requirements (mentioned below) to be included in the IPF pilot, including HDM-3 data and the existence of a CBA.15 Road repositioning projects were excluded from the pilot.
Road Transport Project Sample
The sample of 50 interurban road transport projects to which IPF was applied includes only a set that has reached Execution status in the SNI. Additionally, this set has passed the basic profitability test of having IRRs of at least 6% and have been judged to align with transport policy goals.
14 The Organic Law on Financial Administration of the State, Article 19, requires only that a project’s profitability be analyzed, but does not require project profitability. The Organic Constitutional Law on Government and Regional Administration (19175), Article 75 states that projects financed with regional funds require an RS RATE. 15 Special classifications exempt some projects from requiring CBA (e.g., projects in extreme zones, projects with expected volumes of less than 400 vehicles per day).
37
Transport Project Indicators
SEI indicators were selected to consider beneficiaries, job creation, extension of service to areas with higher poverty levels, and savings associated with reduced gas consumption.16 Each of these input indicators is controlled for project size, yielding the following criteria inputs: beneficiaries per dollar invested (Ben_inv), jobs per dollar invested (Jobs_inv), and gas savings per dollar invested (Savings_inv). Beneficiaries is calculated by estimating traffic volumes, considering the occupancy rates for different types of vehicles. This information is taken directly from project proposal data included in the BIP. Data on the creation of jobs directly associated with construction and operation are also calculated based on BIP data, which were in turn derived from an estimation model based on the macroeconomic input-output matrix. Gas savings is an estimate of the annual savings in gas, measured in liters. The initial set of indicators also considered the extension of service to indigenous populations, but this criterion was later dropped due to limited variation.
Lastly, the SEI includes the poverty rate and tests the use of two alternative specifications to measure poverty. One takes a simple poverty rate (Pov_c) reflecting the percentage of people in the service area classified as “income poor”. It is important to note that it is not necessary that the users of the road are the same as the population that lives nearby the road that is under appraisal. It is possible that the locals could have a low vehicle per capita ratio, and the usage of the road is composed by transportation trucks, tourists, drive through or long-haul trips, etc. The other applies a multi-dimensional composite poverty index (MPI-CL) developed by MDS (Pov_multi) that accounts for five poverty dimensions: education, health, employment and social security, housing, and networks and social cohesion.
The FEI takes into account only one measure – the net present value index (IVAN) – derived from project CBAs. The IVAN is equivalent to a ratio of the net present value of future project costs and benefits to the project’s initial investment cost.
The formulas for SEI and FEI are as follows:
𝑆𝐸𝐼 𝑤 𝐵𝑒𝑛_𝑖𝑛𝑣 𝑤 𝐽𝑜𝑏𝑠_𝑖𝑛𝑣 𝑤 𝑆𝑎𝑣𝑖𝑛𝑔𝑠_𝑏𝑒𝑛 𝑤 𝑃𝑜𝑣_𝑐/𝑚𝑢𝑙𝑡𝑖 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑠𝑒𝑑
𝐹𝐸𝐼 𝐼𝑉𝐴𝑁 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑
where 𝑤 , …, 𝑤 are the weights associated with each SEI criterion. Input project data are detailed in Annex 5.
Transport Project Indicator Weighting
The criteria weighting of SEI variables was done using PCA, with the two specifications for measuring poverty. Table 9 gives the weights associated with each variable using each approach.
Table 9. Criteria Weighting, Transport SEI
Simple poverty rate Ben_inv Jobs_inv Savings_inv Pov_c
0.614 0.224 0.724 0.224
Multi‐dimensional poverty index
Ben_inv Jobs_inv Savings_inv Pov_multi
0.442 0.224 0.839 0.224
Note: The weights have been determined by PCA with the restriction that all weights must be positive and have a minimum contribution of 10%
16 In economic ($) terms, this would likely be considered in the FEI; however, here, our interest is to reduce consumption for environmental reasons. Therefore, the savings is measured in liters and included in the SEI.
38
IPF Results: Road Transport
As with water catchment projects, transport projects are ranked by SEI and FEI scores separately. The calculations for SEI and FEI scores are detailed in Annexes 6, 7, and 8, while the graphical results by each index are summarized in Figures 10, 11 and 12. Figures 10 and 11 show the SEI ranking based on the simple poverty indicator and the multi-criteria poverty index, respectively.
Road Transport SEI and FEI Rankings Figure 10. Road Transport SEI Ranking Results from Input and Weighting with Pov_c
Figure 11. Road Transport SEI Ranking Results from Input and Weighting with Pov_multi
Figure 12. Road Transport Project FEI Ranking Results
Source: Authors’ calculations
0
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P31
P12
P10
P26
P34 P4 P46
P19
P23
P13
P39
P28
P14
P17
P44 P5 P21
P25
P18
P29 P8 P16
P30 P6 P9 P43
P22
P20
P35
P49
P11
P32
P42
P48 P3 P33 P2 P41
P47 P1
P50
P15
P38
P36
P27
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P45 P7
P24
Social and Environmental Indicator (SEI) - Pov_c
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P31
P19
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P10
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P26 P5 P18
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P17
P4 P25
P21 P1 P13
P20
P11
P35 P6
P30
P22
P9 P48
P29
P49
P50 P3 P42 P2 P8 P16
P41
P32
P33
P47
P43
P15
P27
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P36
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P45 P7
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Social and Environmental Indicator (SEI) - Pov_multi
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P46
P42
P8 P14
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P27
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P10
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P12
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P18
P48 P7
P23
P49
P44
P19 P9 P32
P45
P40
P35 P2 P6 P4 P33
P28
P15
P34
P41
P30
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P11
P29
P43
P13
P20
P37
P24 P1
P38
P36
Financial and Economical Indicator (FEI)
39
Road Transport IPF Matrix
Figure 13 shows projects by their SEI and FEI scores. Projects are color coded according to the duration between a project reaching Execution status in the SNI and the allocation of project execution funding. This indication of urgency with respect to funding is taken as a loose proxy of project prioritization. Projects coded in yellow were allocated funds within a year of reaching Execution status, and projects coded in orange were funded between one and two years of reaching Execution status. Green projects were either funded two or more years after reaching Execution status or are still awaiting funding, indicating lowest priority.
Figure 13. Road Transport Project Prioritization Matrix
Funded <1 year ⏺ ; Funded within 1-2 years ⏺ ; Funded in 2+ years or not yet funded ⏺
Comparing IPF to Funding of Transport Projects
Figure 13 demonstrates no clear pattern with respect to the link between project prioritization and the outcomes of CBA analysis. If prioritization of projects that met the basic requirements were decided according to highest profitability, this would have resulted in high-FEI projects being funded first and, therefore, a concentration of yellow- and orange-coded projects towards the upper limits of the x (FEI) axis. Nor does the figure demonstrate prioritization of projects’ funding order by joint consideration of CBA with other social and environmental goals. Prioritization based on CBA plus alignment with social and environmental goals would have resulted in a concentration of yellow and orange projects in the northeast quadrant of the matrix.
Interestingly, three projects –24, 36, and 38– with the highest SEI and FEI scores were not allocated funds for implementation until 2, 9, and 9 years, respectively, after having reached Execution status in the SNI.
These results suggest that, while CBA is an important filtering mechanism for road transport, it is not necessarily the basis of prioritizing investments for projects that pass the profitability test. Moreover, while the government does engage in consultation to discuss the social, environmental, and strategic implications of various projects to inform selection, much of this discussion and consultation is informal and largely unstructured.
P1
P2P3
P4
P5P6
P7
P8
P9
P10
P11
P12
P13P14
P15
P16
P17
P18
P19
P20P21
P22
P23
P24
P25
P26
P27
P28P29
P30
P31
P32
P33
P34
P35
P36P37
P38
P39
P40
P41
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P48P49
P50
0
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SEI
FEI
40
Chapter 5. Conclusion The pilot of the IPF in Chile produced valuable overarching findings beyond the outcomes of the actual prioritization exercise. First, the outputs of CBA were found to be useful inputs to the FEI – inputs that can also be effectively complemented by the simultaneous and direct consideration of social and environmental policy goals that are otherwise difficult to quantify and value. The extensive economic appraisals (partial SCBAs) in place are useful to generate important economic measures associated with projects, including IRRs and NPV index (IVAN) scores, which should be used as key inputs to project prioritization and, in the case of IPF, inputs to the FEI. In this case, the IPF complements rather than substitutes traditional CBA appraisal.
The Chile pilot has shown that IPF has application beyond the 'stepping stone' approach previously proposed (Marcelo, et al, 2016), which couched IPF as a stop-gap measure until more sophisticated project appraisal methods could be implemented. Such CBA outputs as benefit-cost ratios, internal rates of return, and net present value indexes can be used to construct the FEI – either as the sole input or in combination with other relevant financial and economic factors, such as risk. As such, the IPF has relevance to a wide array of government capacity levels. It can serve to systematize consideration of key factors, where only limited project information is available, or complement full economic appraisals with additional policy considerations.
Moreover, CBA analyses are not necessarily used as the basis of prioritization. In Chile, for example, CBA analysis is used as a filter to eliminate projects with IRRs below a profitability threshold (6%). Once projects pass this filter and are given a positive endorsement for development (RS) within the SNI, they may be prioritized in a number of ways (often unspecified) by the proponent agency. The reservoir MCE directly responds to the inability of the CBA to fully account for critical yet undervalued strategic issues, which resulted in poor CBA results for projects that were required to ensure water security. So then, even where CBA is extensively applied and used to filter out ‘bad’ projects, there is a demand for systematic consideration of other important policy factors in the actual prioritization of investments.
Also, in the last couple of years, some road projects that were traditionally appraised using the CBA methodology have been appraised instead by the CEA approach, due to political pressures and considerations, because for those low traffic flows were not allowing the project to satisfy the positive NVP criteria. These kinds of projects were not considered in the prioritization exercise, as the main economic efficiency index (IVAN) values were not possible to get. This can be seen as a signal of how economic efficiency is losing influence in the investment and prioritization decision by the authorities, as the objectives are politically driven.
IPF recognizes that, in addition to economic considerations, projects may be valued by governments and other stakeholders due to non-economic considerations such as reducing income disparity or territorial inequality, promoting social cohesion, safeguarding the environment, preserving culture, or managing disaster and climate risks. CBA analyses are often unable to capture such developmental policy goals, which are inherently difficult to valuate. While SCBA should certainly be maintained as the gold standard in project appraisal and a key input for decision making, IPF can help prioritize projects under a variety of analytical conditions and according to multiple policy goals.
41
Recognizing the importance of these additional facets to decision making related to infrastructure development, there is an expressed demand to shift away from purely efficiency-based approaches to approaches that can consider strategic and social goals, that are often undervalued in traditional CBA-like assessments. For example, the Governments of the United Kingdom, Australia, and Chile, and many U.S. state governments have published notes and guidance on the application of multi-criteria decision analysis (MCDA), expanding the discourse to suggest structured ways of employing MCDA to incorporate key policy criteria. Some countries, such as Ireland, have imposed thresholds to guide when government should apply SCBA, multi-criteria analysis, or more simple assessments, depending on the size of the proposed investment.
In relation to the PIM framework, the IPF tool is meant to be one possible option in a basket of analytical tools that can be used to gather the best appraisal data available and organize them to support prioritization. In Chile, where multi-criteria approaches to project selection have already been developed and institutionalized, this benefit is clearly recognized.
42
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Marcelo, D., Mandri-Perrott, C., and House, S. (2015). Prioritizing infrastructure investments in Panama: Pilot application of the World Bank Infrastructure Prioritization Framework. Report to the Panama Ministry of Economy and Finance. The World Bank.
Marcelo, D., Mandri-Perrott, X. C., House, S., and Schwartz, J. (2016), Prioritizing infrastructure investment: A framework for government decision-making. Policy Research Working Paper, 7674. Washington, DC: World Bank.
MDS (2014b). Socio-Economic Assessment Methodology for rail transport projects, Division of Social Investment Evaluation (Ministry of Social Development) and Ministry of Transportation Planning - SECTRA (Ministry of Transport and Telecommunications). Retrieved September, 2016, from http://goo.gl/EQHc6w.
Minestario de Hacienda (2014). Minuta Matriz Multicriterio Plan de Pequenos Embalses.
OECD (2016). OECD Development Pathways Multi-dimensional Review of Uruguay Volume 2: In-depth Analysis and Recommendations. OECD Development Pathways. OECD Publishing, Paris. http://dx.doi.org/10/1787/9789264251663-en.
OECD (2017). Gaps and governance standards of public infrastructure in Chile. http://www.oecd.org/gov/ethics/public-infrastructure-in-chile-2017.htm
Perreault, W., and Young, F. (1980). Alternating least squares optimal scaling: Analysis of nonmetric data in marketing research. Journal of Marketing Research, 1-13.
43
Rajaram, A., Le, T., Biletska, N., Brumby, J. (2010). A diagnostic framework for assessing Public Investment Management. World Bank Policy Research Working Paper, 5397. Washington, DC: World Bank.
Rajaram, Anand; Minh Le, Tuan; Kaiser, Kai; Kim, Jay-Hyung; Frank, Jonas. 2014. The Power of Public Investment Management : Transforming Resources into Assets for Growth. Directions in Development--Public Sector Governance;. World Bank Group, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/20393
Ruiz-Nuñez, F., & Wei, Z. (2015). Infrastructure investment demands in emerging markets and developing economies. World Bank Policy Research Working Paper, 7414. Washington, DC: World Bank.
Saaty, Thomas L. (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26
44
Annex 1. Chilean Law Relevant to Project Appraisal and SNI National Investment System According to current legislation, investment initiatives to be financed with public funds must have a Ministry of
Social Development record and approval, which must be based on a technical and economic assessment to analyze profitability.
"The institutions authorized to directly present the SNI investment initiatives are those that are part of the public sector, in particular services and institutions as defined in Article 2 of the Organic Law of Financial Administration."
Process for Submission of Investment Initiatives (projects)
Projects submitted to SNI are initially assessed in the following ways:
Admissibility
Analysis and issuance of the First RATE
Analysis and emission RATE
The presentation of investment initiatives can be made continuously throughout the calendar year.
Investment initiatives (independent of the source of funding proposed) whose area of influence is regional, provincial or communal, and for which competition analysis is regional, must apply via the Ministerial Regional Secretariat of Social Development.
Investment initiatives whose area of influence is national, international or interregional, and for which competition analysis is a national must apply to the central‐level Ministry of Social Development.
Investment Programs (Item 03): "These are the expenses for investment initiatives designed to increase, maintain or regain the ability to generate profits from human or physical resources, and which do not correspond to those inherent in the institution formulates the program. "
Guidance on Issuance of Economic Analysis and Technical Result (RATE)
Ministry of Social Development, Undersecretary of Social Investment Evaluation
Ministry of Finance Budget Office Rules, Instructions and Procedures of the National Investment System
The technical economic analysis process begins with the receipt of the investment initiative and culminates with the issuance of the result of analysis by the Ministry of Social Development. This analysis reviews whether the initiative was properly formulated and evaluated, and if it contains the required technical and economic background indicated in the rules of SNI and sectoral information requirements. The responsibility for this process lies with the Ministry of Social Development at the central or regional level, as appropriate.
The analysis of investment initiatives must prove the technical economic benefit of carrying them out, based on an assessment to analyze their social and economic returns, issuing for that purpose a report in terms set out in Article 19a of Decree Law 1,263, 1975, which is expressed through the Result of Technical Analysis ‐ Economic (RATE) in the IDI tab of BIP.
Assessments of favorably recommended projects (RS) should include:
a. The problem to be solved and / or addressed, b. Analyzed alternatives that would allow to solve the problem, with their corresponding indicators, c. Alternative selected, d. Assumptions, results and estimates incorporated in the evaluation, e. Sensitization of variables, where appropriate, f. Estimated operating costs and annual maintenance (separately) taken into consideration for evaluation, g. Certifications pending and that make possible the next step, and h. The regional sectoral policy strategy to which the initiative contributes.
Projects are given a Technical Objection (OT) RATE due to one of the following reasons:
a. The initiative is badly formulated, b. The initiative does not use the general evaluation methodology or specific sector concerned, c. It does not conform to the policies defined for the sector, institution and / or region, i. The initiative is not a profitable investment (socially or economically) or is technically not feasible, j. The information presented does not adequately support the quantification and / or valuation of benefits
and / or costs, k. The background includes simultaneous support for more than one type and / or stage fit, or l. The investment initiative postulated doubles with an initiative previously entered into the system.
Information Requirements for Projects
In the state of pre‐investment, proponents must prepare and evaluate the project to determine whether it is desirable to implement. The pre‐investment stage includes preparation and appraisal of a project. Analysis must include market research (supply and demand), as well as technical, economic, environmental, legal and financial appraisal. Notably, an investment project will not necessarily go through each sub‐stage of pre‐investment status; this will depend on the complexity and the amounts involved in the project to execute. The requirements to apply for the stages of pre‐investment status may be different depending on the sector and type of project; therefore, it is important to review the specific requirements for investment projects contained in the respective sector
http://sni.ministeriodesarrollosocial.gob.cl/evaluacion/exante/requisitos/
45
Annex 2. Water Catchment Raw Project Data
Region
ID
Nam
e
SRegion
Comarca
Investmen
t
Approved
LPlots
NPV
Surface
Poverty
Benef
Jobs
Territorial
i_NPV
Exprop
Endorse
Legal
Biobio
1 Kaiser Ñuble Coihueco, Pinto
4578 ✔ 82.00 ‐1069 140.00 32.86 0.586 2 0 ‐0.23 0.50 0 1
2 Las Puentes Arauco Arauco 14147 87.00 ‐860 537.56 24.32 0.162 1 0 ‐0.06 0.50 0 1
3 Ranquil Ñuble Ranquil 6349 ✔ 38.00 ‐4510 46.00 15.10 0.826 2 0 ‐0.71 0.75 1 1
4 Tranaquepe Concepción Hualqui 12388 20.00 ‐7464 54.23 30.81 0.369 0 0 ‐0.60 0.50 0 1
5 Laguna El Pillo Biobío Laja 1713 10.00 ‐4419 12.67 18.55 0.789 0 0 ‐2.58 0.50 0 1
6 Pichi Bureo Biobío Mulchén 13507 120.00 ‐101 1282.32 22.95 0.094 2 1 ‐0.01 0.50 0 1
7 Rumena Arauco Arauco 18087 11.00 ‐19352 30.54 24.32 0.360 1 0 ‐1.07 0.50 0 1
8 Mirihue Biobío Antuco 3244 ✔ 57.00 ‐2937 10.60 22.21 5.377 2 0 ‐0.91 0.50 1 1
9 Quidico 1 Arauco Arauco 4793 ✔ 57.00 ‐831 188.58 24.32 0.302 1 0 ‐0.17 0.50 0 1
10 Quidico 2 Arauco Arauco 20913 293.00 ‐602 1134.95 24.32 0.258 1 0 ‐0.03 0.50 0 1
11 Perales Ñuble Coelemu 11294 21.00 ‐3582 103.01 24.80 0.204 1 0 ‐0.32 0.50 0 1
12 Vegas de Itata Ñuble Coelemu 7641 19.00 ‐2746 90.92 24.80 0.209 2 0 ‐0.36 0.50 0 1
13 Tauco Ñuble Coelemu 4077 16.00 ‐2667 14.67 24.82 1.090 1 0 ‐0.65 0.50 0 1
14 Leoneras Ñuble Coelemu 7128 189.00 ‐1326225 5.69 24.80 33.193 2 1 0.50 0 1
Mau
le
15 Huencuecho I Talca Pelarco 11949 69.00 ‐829 301.33 12.10 0.229 1 0 ‐0.07 0.00 0 2
16 Huencuecho II Talca Pelarco 8646 69.00 ‐687 180.84 12.10 0.382 1 0 ‐0.08 0.00 0 2
17 Peralito Talca San Clemente 2451 ✔ 46.00 609 327.00 14.90 0.141 2 0 0.25 0.00 1 3
18 Peralito 2 Talca San Clemente 7322 46.00 ‐666 148.40 14.90 0.310 2 1 ‐0.09 0.00 0 2
19 La Bruja Talca San Clemente 2863 ✔ 75.00 ‐1778 172.00 14.90 0.436 2 0 ‐0.62 0.75 1 3
20 El Guindo Talca Río Claro 3286 26.00 ‐1314 131.35 8.40 0.198 0 0 ‐0.40 0.00 0 2
21 Sauzal Talca Empedrado 4539 17.00 90.40 17.30 0.188 1 0 0.00 0 2
22 Botacura Linares San Javier 7130 8.00 140.02 17.30 0.057 0 0 0.25 0 2
23 Vaquería Linares San Javier 3655 ✔ 130.00 ‐2768 280.00 17.30 0.464 0 0 ‐0.76 0.00 1 3
24 Manantiales Linares San Javier 2050 31.00 ‐2850 177.78 17.30 0.174 1 0 ‐1.39 0.00 0 2
25 El Molino Linares Retiro 4585 144.00 4546 803.97 15.40 0.179 1 1 0.99 0.00 0 2
26 Derivado
Porvenir 2 Linares Parral‐Retiro 6198 ✔ 123.00 2762 362.00 15.40 0.340 1 0 0.45 0.50 1 3
27 Limávida Talca Curepto 10577 ✔ 58.00 ‐507 163.28 22.90 0.355 1 0 ‐0.05 0.25 1 2
46
Region
ID
Nam
e
SRegion
Comarca
Investmen
t
Approved
LPlots
NPV
Surface
Poverty
Benef
Jobs
Territorial
i_NPV
Exprop
Endorse
Legal
O'Higgins
28 Codegua (CFGD) Cachapoal Codegua 10477 ✔ 1200.00 ‐8944 1113.00 8.00 1.078 1 2 ‐0.85 0.75 1 3
29 San Francisco 1 (Zonada) Cardenal Caro Litueche 8355 156.00 ‐4654 156.00 15.00 1.000 1 1 ‐0.56 0.25 1 1
30 San Francisco 2 (Zonada) Cardenal Caro Litueche 9324 142.00 ‐5186 142.00 15.00 1.000 1 1 ‐0.56 0.25 1 1
31 San Francisco 3 (Zonada) Cardenal Caro Litueche 11261 148.00 ‐7215 148.00 15.00 1.000 1 1 ‐0.64 0.25 1 1
32 El Maiten 1 (Zonada) Cardenal Caro Navidad 14980 183.00 ‐
12461 183.00 8.00 1.000 1 2 ‐0.83 0.25 0 1
33 El Maiten 2 (Zonada) Cardenal Caro Navidad 6552 182.00 ‐5474 182.00 8.00 1.000 1 2 ‐0.84 0.25 0 1
34 El Maiten 3 (Zonada) Cardenal Caro Navidad 17838 133.00 ‐
14819 133.00 8.00 1.000 1 2 ‐0.83 0.25 0 1
35 Huehuinco (CFRD) Cardenal Caro Navidad 3945 400.00 ‐3363 400.00 8.00 1.000 1 1 ‐0.85 0.25 0 1
36 Ucúquer (CFRD) Cardenal Caro Navidad 5518 73.00 ‐4592 73.00 8.00 1.000 1 0 ‐0.83 0.25 0 1
37 Manquehue 1 (Zonada) Cardenal Caro Litueche 14980 133.00 ‐6945 133.00 15.00 1.000 1 1 ‐0.46 0.00 1 3
38 Manquehue 2 (Zonada) Cardenal Caro Litueche 17838 84.00 ‐6122 84.00 15.00 1.000 1 2 ‐0.34 0.00 1 3
39 Los Tricahues (RCC) Colchagua Lolol 7776 ✔ 1317.00 ‐6753 1317.00 18.00 1.000 1 1 ‐0.87 0.50 1 1
40 Las Palmas 2 (RCC) Colchagua Lolol 12252 370.00 ‐
10243 370.00 18.00 1.000 1 1 ‐0.84 0.50 1 1
41 Estero Seco (CFGD) Cardenal Caro La Estrella 6292 80.00 ‐5235 80.00 15.00 1.000 1 1 ‐0.83 0.25 1 2
42 Cementerio 1 (Hormigón)
Cardenal Caro La Estrella 5518 30.00 ‐4582 30.00 15.00 1.000 1 1 ‐0.83 0.75 0 1
43 Cementerio 2 (Hormigón)
Cardenal Caro La Estrella 5518 30.00 ‐4582 30.00 15.00 1.000 1 1 ‐0.83 0.75 0 1
44 Manquehua (Zonada) Cardenal Caro Litueche 1122 ✔ 25.00 ‐9305 5.00 15.00 5.000 1 0 ‐8.29 0.00 1 3
45 La Virgen (Zonada) Cardenal Caro La Estrella 1045 ✔ 30.00 ‐868 6.00 15.00 5.000 1 0 ‐0.83 0.50 1 3
46 La Palmera (Zonada) Cachapoal Pichidegua 1031 ✔ 123.00 ‐883 123.00 8.00 1.000 1 0 ‐0.86 0.50 1 3
47 Pulin (Zonada) Cardenal Caro Litueche 1013 ✔ 20.00 ‐841 4.00 8.00 5.000 1 0 ‐0.83 0.50 1 3
48 Rapel (Zonada) Cachapoal Rapel 594 10.00 ‐493 2.00 8.00 5.000 1 0 ‐0.83 0.75 1 2
49 Licancheu (Zonada) Cardenal Caro Navidad 987 10.00 ‐819 2.00 15.00 5.000 1 0 ‐0.83 0.75 1 2
Valparaiso
50 Vitahue Petorca Cabildo 14865 40.00 ‐
13779 9.14 12.24 4.378 0 1 ‐0.93 0.25 0 1
51 Paihuen Petorca Cabildo 16685 6.00 ‐8130 17.38 12.24 0.345 0 1 ‐0.49 0.25 0 1
52 Chalaco Petorca Petorca 33316 22.00 ‐9794 36.48 15.49 0.603 2 1 ‐0.29 0.25 0 1
53 Las Carditas 2 Petorca Petorca 14022 54.00 ‐4853 24.47 15.49 2.207 2 1 ‐0.35 0.25 0 1
54 Santa Marta Petorca La Ligua 3992 ✔ 79.00 ‐602 23.00 26.31 3.435 1 0 ‐0.15 0.50 1 3
55 Pullally Petorca La Ligua 4529 170.00 ‐522 93.10 26.31 1.826 1 0 ‐0.12 0.25 0 3
56 Cuncumen 1 San Antonio San Antonio 6245 8.00 ‐2347 28.53 20.41 0.280 0 0 ‐0.38 0.25 0 1
57 Lo Zárate 2 San Antonio Cartagena 6891 57.00 ‐1110 66.59 24.40 0.856 0 0 ‐0.16 0.25 0 3
58 El Zaino San Felipe Sta María 5013 ✔ 164.00 ‐1878 48.00 7.00 3.417 1 0 ‐0.37 0.25 1 3
59 Valle Hermoso Petorca La Ligua 4618 562.00 ‐4024 9.72 26.31 57.819 1 1 ‐0.87 0.25 0 1
47
60 Santa Julia Petorca Petorca 2444 ✔ 73.00 ‐1370 13.00 15.50 5.615 2 0 ‐0.56 0.75 1 3
61 Pedegua Petorca Petorca 2024 ✔ 179.00 ‐1149 17.00 15.50 10.529 2 0 ‐0.57 0.25 1 3
Annex 3. Water Catchment Project SEI Calculations, by Region VARIABLES POVERTY BENEFICIARY JOBS TERRITORIAL
APPLIED WEIGHTS 0.316 0.642 0.400 0.573
ID Project Name Poverty Beneficiary Jobs Territorial pov_std ben_std job_std terr_std SEI_STD SEI
Biobio
0 15.1 0.094 0 0 ‐2.12 ‐0.35 ‐1.77 ‐0.39 ‐1.83 0
1 Kaiser 32.86 0.586 2 0 2.01 ‐0.29 0.98 ‐0.39 0.62 42
2 Las Puentes 24.32 0.162 1 0 0.02 ‐0.34 ‐0.39 ‐0.39 ‐0.59 21
3 Ranquil 15.1 0.826 2 0 ‐2.12 ‐0.26 0.98 ‐0.39 ‐0.67 20
4 Tranaquepe 30.81 0.369 0 0 1.53 ‐0.32 ‐1.77 ‐0.39 ‐0.65 20
5 Laguna El Pillo 18.55 0.789 0 0 ‐1.32 ‐0.27 ‐1.77 ‐0.39 ‐1.52 5
6 Pichi Bureo 22.95 0.094 2 1 ‐0.29 ‐0.35 0.98 2.36 1.43 56
7 Rumena 24.32 0.360 1 0 0.02 ‐0.32 ‐0.39 ‐0.39 ‐0.58 21
8 Mirihue 22.21 5.377 2 0 ‐0.47 0.26 0.98 ‐0.39 0.19 35
9 Quidico 1 24.32 0.302 1 0 0.02 ‐0.32 ‐0.39 ‐0.39 ‐0.58 21
10 Quidico 2 24.32 0.258 1 0 0.02 ‐0.33 ‐0.39 ‐0.39 ‐0.59 21
11 Perales 24.8 0.204 1 0 0.14 ‐0.33 ‐0.39 ‐0.39 ‐0.55 22
12 Vegas de Itata 24.8 0.209 2 0 0.14 ‐0.33 0.98 ‐0.39 0.00 31
13 Tauco 24.82 1.090 1 0 0.14 ‐0.23 ‐0.39 ‐0.39 ‐0.49 23
14 Leoneras 24.8 33.193 2 1 0.14 3.43 0.98 2.36 3.99 100
Mau
le
0 8.4 0.057 0 0 ‐2.03 ‐1.68 ‐1.41 ‐0.41 ‐2.52 0
15 Huencuecho I 12.1 0.229 1 0 ‐0.96 ‐0.30 0.00 ‐0.41 ‐0.73 39
16 Huencuecho II 12.1 0.382 1 0 ‐0.96 0.94 0.00 ‐0.41 0.06 57
17 Peralito 14.9 0.141 2 0 ‐0.15 ‐1.01 1.41 ‐0.41 ‐0.36 47
18 Peralito 2 14.9 0.310 2 1 ‐0.15 0.36 1.41 2.25 2.04 100
19 La Bruja 14.9 0.436 2 0 ‐0.15 1.38 1.41 ‐0.41 1.17 81
20 El Guindo 8.4 0.198 0 0 ‐2.03 ‐0.55 ‐1.41 ‐0.41 ‐1.79 16
21 Sauzal 17.3 0.188 1 0 0.55 ‐0.63 0.00 ‐0.41 ‐0.46 45
22 Botacura 17.3 0.057 0 0 0.55 ‐1.68 ‐1.41 ‐0.41 ‐1.71 18
23 Vaquería 17.3 0.464 0 0 0.55 1.60 ‐1.41 ‐0.41 0.40 64
24 Manantiales 17.3 0.174 1 0 0.55 ‐0.74 0.00 ‐0.41 ‐0.53 44
25 El Molino 15.4 0.179 1 1 0.00 ‐0.70 0.00 2.25 0.84 74
26 Derivado Porvenir 2 15.4 0.340 1 0 0.00 0.60 0.00 ‐0.41 0.15 59
48
27 Limávida 22.9 0.355 1 0 2.18 0.72 0.00 ‐0.41 0.92 75
49
ID Project Name Poverty Beneficiary Jobs Territorial pov_std ben_std job_std terr_std SEI_STD SEI
O’Higgins
0 8 1.000 1 0 ‐1.15 ‐0.53 ‐1.21 ‐1.40 0
28 Codegua (CFGD) 8 1.078 1 2 ‐1.15 ‐0.49 1.45 0.16 74
29 San Francisco 1 (Zonada) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
30 San Francisco 2 (Zonada) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
31 San Francisco 3 (Zonada) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
32 El Maiten 1 (Zonada) 8 1.000 1 2 ‐1.15 ‐0.53 1.45 0.13 73
33 El Maiten 2 (Zonada) 8 1.000 1 2 ‐1.15 ‐0.53 1.45 0.13 73
34 El Maiten 3 (Zonada) 8 1.000 1 2 ‐1.15 ‐0.53 1.45 0.13 73
35 Huehuinco (CFRD) 8 1.000 1 1 ‐1.15 ‐0.53 0.12 ‐0.63 36
36 Ucúquer (CFRD) 8 1.000 1 0 ‐1.15 ‐0.53 ‐1.21 ‐1.40 0
37 Manquehue 1 (Zonada) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
38 Manquehue 2 (Zonada) 15 1.000 1 2 0.67 ‐0.53 1.45 0.70 100
39 Los Tricahues (RCC) 18 1.000 1 1 1.45 ‐0.53 0.12 0.19 75
40 Las Palmas 2 (RCC) 18 1.000 1 1 1.45 ‐0.53 0.12 0.19 75
41 Estero Seco (CFGD) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
42 Cementerio 1 (Hormigón) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
43 Cementerio 2 (Hormigón) 15 1.000 1 1 0.67 ‐0.53 0.12 ‐0.06 64
44 Manquehua (Zonada) 15 5.000 1 0 0.67 1.80 ‐1.21 0.68 99
45 La Virgen (Zonada) 15 5.000 1 0 0.67 1.80 ‐1.21 0.68 99
46 La Palmera (Zonada) 8 1.000 1 0 ‐1.15 ‐0.53 ‐1.21 ‐1.40 0
47 Pulin (Zonada) 8 5.000 1 0 ‐1.15 1.80 ‐1.21 0.10 71
48 Rapel (Zonada) 8 5.000 1 0 ‐1.15 1.80 ‐1.21 0.10 71
49 Licancheu (Zonada) 15 5.000 1 0 0.67 1.80 ‐1.21 0.68 99
Valparaiso
0 7 0.280 0 0 ‐1.70 ‐0.46 ‐1.17 ‐0.81 ‐1.76 0
50 Vitahue 12.24 4.378 0 1 ‐0.90 ‐0.20 ‐1.17 1.13 ‐0.23 32
51 Paihuen 12.24 0.345 0 1 ‐0.90 ‐0.45 ‐1.17 1.13 ‐0.39 28
52 Chalaco 15.49 0.603 2 1 ‐0.40 ‐0.44 1.17 1.13 0.71 51
53 Las Carditas 2 15.49 2.207 2 1 ‐0.40 ‐0.34 1.17 1.13 0.77 53
54 Santa Marta 26.31 3.435 1 0 1.26 ‐0.26 0.00 ‐0.81 ‐0.23 32
55 Pullally 26.31 1.826 1 0 1.26 ‐0.36 0.00 ‐0.81 ‐0.30 30
56 Cuncumen 1 20.41 0.280 0 0 0.35 ‐0.46 ‐1.17 ‐0.81 ‐1.11 14
57 Lo Zárate 2 24.4 0.856 0 0 0.97 ‐0.42 ‐1.17 ‐0.81 ‐0.90 18
58 El Zaino 7 3.417 1 0 ‐1.70 ‐0.26 0.00 ‐0.81 ‐1.17 12
59 Valle Hermoso 26.31 57.819 1 1 1.26 3.12 0.00 1.13 3.05 100
60 Santa Julia 15.5 5.615 2 0 ‐0.40 ‐0.12 1.17 ‐0.81 ‐0.20 32
50
61 Pedegua 15.5 10.529 2 0 ‐0.40 0.18 1.17 ‐0.81 0.00 37
Annex 4. Water Catchment Project FEI Calculations, by Region VARIABLES NPV EXPROPRIATIONS ENDORSEMENT LEGAL
APPLIED WEIGHTS 0.316 0.316 0.635 0.630
ID Project Name NPV_inv Expropriations Endorsement Legal NPV_std exp_std endorse_std Legal_std FEI_STD FEI
Biobio
0 ‐2.579 1 0 1 ‐2.89 ‐0.27 ‐0.39 ‐1.25 0.0
1 Kaiser ‐0.233 1 0 1 0.52 ‐0.27 ‐0.39 ‐0.17 28.5
2 Las Puentes ‐0.061 1 0 1 0.77 ‐0.27 ‐0.39 ‐0.09 30.6
3 Ranquil ‐0.710 1 1 1 ‐0.17 3.47 2.36 2.54 100.0
4 Tranaquepe ‐0.603 1 0 1 ‐0.01 ‐0.27 ‐0.39 ‐0.34 24.0
5 Laguna El Pillo ‐2.579 1 0 1 ‐2.89 ‐0.27 ‐0.39 ‐1.25 0.0
6 Pichi Bureo ‐0.007 1 0 1 0.85 ‐0.27 ‐0.39 ‐0.07 31.2
7 Rumena ‐1.070 1 0 1 ‐0.69 ‐0.27 ‐0.39 ‐0.55 18.3
8 Mirihue ‐0.905 1 1 1 ‐0.46 ‐0.27 2.36 1.27 66.4
9 Quidico 1 ‐0.173 1 0 1 0.61 ‐0.27 ‐0.39 ‐0.14 29.2
10 Quidico 2 ‐0.029 1 0 1 0.82 ‐0.27 ‐0.39 ‐0.08 30.9
11 Perales ‐0.317 1 0 1 0.40 ‐0.27 ‐0.39 ‐0.21 27.4
12 Vegas de Itata ‐0.359 1 0 1 0.34 ‐0.27 ‐0.39 ‐0.23 26.9
13 Tauco ‐0.654 1 0 1 ‐0.09 ‐0.27 ‐0.39 ‐0.36 23.4
14 Leoneras 1 0 1 ‐0.27 ‐0.39 ‐0.33 24.1
Mau
le
0 ‐1.390 0 0 2 ‐1.94 ‐0.56 ‐0.76 ‐0.64 ‐1.67 0.0
15 Huencuecho I ‐0.069 0 0 2 0.14 ‐0.56 ‐0.76 ‐0.64 ‐1.02 15.9
16 Huencuecho II ‐0.079 0 0 2 0.13 ‐0.56 ‐0.76 ‐0.64 ‐1.02 15.8
17 Peralito 0.248 0 1 3 0.64 ‐0.56 1.22 1.44 1.71 81.8
18 Peralito 2 ‐0.091 0 0 2 0.11 ‐0.56 ‐0.76 ‐0.64 ‐1.03 15.7
19 La Bruja ‐0.621 1 1 3 ‐0.72 2.54 1.22 1.44 2.25 95.1
20 El Guindo ‐0.400 0 0 2 ‐0.38 ‐0.56 ‐0.76 ‐0.64 ‐1.18 11.9
21 Sauzal 0 0 2 ‐0.56 ‐0.76 ‐0.64 ‐1.06 14.8
22 Botacura 0 0 2 0.48 ‐0.76 ‐0.64 ‐0.73 22.7
23 Vaquería ‐0.757 0 1 3 ‐0.94 ‐0.56 1.22 1.44 1.21 69.7
24 Manantiales ‐1.390 0 0 2 ‐1.94 ‐0.56 ‐0.76 ‐0.64 ‐1.67 0.0
25 El Molino 0.992 0 0 2 1.82 ‐0.56 ‐0.76 ‐0.64 ‐0.49 28.7
26 Derivado Porvenir 2 0.446 1 1 3 0.96 1.51 1.22 1.44 2.46 100.0
27 Limávida ‐0.048 0 1 2 0.18 0.48 1.22 ‐0.64 0.58 54.4
51
52
O’Higgins
0 ‐8.291 0 0 1 ‐4.46 ‐1.53 ‐1.43 ‐0.84 ‐3.33 0.0
28 Codegua (CFGD) ‐0.854 1 1 3 0.15 1.44 0.67 1.33 1.77 100.0
29 San Francisco 1 (Zonada) ‐0.557 0 1 1 0.34 ‐0.54 0.67 ‐0.84 ‐0.17 62.1
30 San Francisco 2 (Zonada) ‐0.556 0 1 1 0.34 ‐0.54 0.67 ‐0.84 ‐0.17 62.1
31 San Francisco 3 (Zonada) ‐0.641 0 1 1 0.29 ‐0.54 0.67 ‐0.84 ‐0.18 61.7
32 El Maiten 1 (Zonada) ‐0.832 0 0 1 0.17 ‐0.54 ‐1.43 ‐0.84 ‐1.55 34.9
33 El Maiten 2 (Zonada) ‐0.835 0 0 1 0.17 ‐0.54 ‐1.43 ‐0.84 ‐1.55 34.8
34 El Maiten 3 (Zonada) ‐0.831 0 0 1 0.17 ‐0.54 ‐1.43 ‐0.84 ‐1.55 34.9
35 Huehuinco (CFRD) ‐0.852 0 0 1 0.16 ‐0.54 ‐1.43 ‐0.84 ‐1.56 34.8
36 Ucúquer (CFRD) ‐0.832 0 0 1 0.17 ‐0.54 ‐1.43 ‐0.84 ‐1.55 34.9
37 Manquehue 1 (Zonada) ‐0.464 0 1 3 0.40 ‐1.53 0.67 1.33 0.90 83.1
38 Manquehue 2 (Zonada) ‐0.343 0 1 3 0.47 ‐1.53 0.67 1.33 0.93 83.5
39 Los Tricahues (RCC) ‐0.868 1 1 1 0.15 0.45 0.67 ‐0.84 0.08 67.0
40 Las Palmas 2 (RCC) ‐0.836 1 1 1 0.17 0.45 0.67 ‐0.84 0.09 67.1
41 Estero Seco (CFGD) ‐0.832 0 1 2 0.17 ‐0.54 0.67 0.25 0.46 74.4
42 Cementerio 1 (Hormigón) ‐0.830 1 0 1 0.17 1.44 ‐1.43 ‐0.84 ‐0.93 47.1
43 Cementerio 2 (Hormigón) ‐0.830 1 0 1 0.17 1.44 ‐1.43 ‐0.84 ‐0.93 47.1
44 Manquehua (Zonada) ‐8.291 0 1 3 ‐4.46 ‐1.53 0.67 1.33 ‐0.63 52.9
45 La Virgen (Zonada) ‐0.831 1 1 3 0.17 0.45 0.67 1.33 1.46 93.9
46 La Palmera (Zonada) ‐0.856 1 1 3 0.15 0.45 0.67 1.33 1.45 93.8
47 Pulin (Zonada) ‐0.830 1 1 3 0.17 0.45 0.67 1.33 1.46 93.9
48 Rapel (Zonada) ‐0.830 1 1 2 0.17 1.44 0.67 0.25 1.09 86.7
49 Licancheu (Zonada) ‐0.830 1 1 2 0.17 1.44 0.67 0.25 1.09 86.7
Valparaiso
0 ‐0.927 0 0 1 ‐1.87 ‐0.40 ‐0.68 ‐0.96 ‐1.75 0.0
50 Vitahue ‐0.927 0 0 1 ‐1.87 ‐0.40 ‐0.68 ‐0.96 ‐1.75 0.0
51 Paihuen ‐0.487 0 0 1 ‐0.20 ‐0.40 ‐0.68 ‐0.96 ‐1.22 13.4
52 Chalaco ‐0.294 0 0 1 0.54 ‐0.40 ‐0.68 ‐0.96 ‐0.99 19.2
53 Las Carditas 2 ‐0.346 0 0 1 0.34 ‐0.40 ‐0.68 ‐0.96 ‐1.05 17.7
54 Santa Marta ‐0.151 1 1 3 1.08 1.21 1.35 0.96 2.19 99.6
55 Pullally ‐0.115 0 0 3 1.22 ‐0.40 ‐0.68 0.96 0.43 55.2
56 Cuncumen 1 ‐0.376 0 0 1 0.23 ‐0.40 ‐0.68 ‐0.96 ‐1.09 16.8
57 Lo Zárate 2 ‐0.161 0 0 3 1.04 ‐0.40 ‐0.68 0.96 0.38 53.8
58 El Zaino ‐0.375 0 1 3 0.23 ‐0.40 1.35 0.96 1.41 79.9
59 Valle Hermoso ‐0.871 0 0 1 ‐1.65 ‐0.40 ‐0.68 ‐0.96 ‐1.68 1.7
60 Santa Julia ‐0.561 1 1 3 ‐0.47 2.82 1.35 0.96 2.20 100.0
ID Project Name NPV_inv Expropriations Endorsement Legal NPV_std exp_std endorse_std Legal_std FEI_STD FEI
53
61 Pedegua ‐0.568 0 1 3 ‐0.50 ‐0.40 1.35 0.96 1.18 74.0
Annex 5. Road Transport Raw Project Data Project ID Stage Allocation Allocation2 Invest2 Length BEN Ben_inv Jobs Jobs_inv Savings Savings_inv POV POV2 Indig IVAN
P1 30106685 1 0 1 11,698 16.0 15,605 1.334 481 0.041 321,133 27.5 28.45 22.90 1 1.72 P2 30080831 1 3 1 21,371 29 5,891 0.276 1,649 0.077 2,090,057 97.8 25.24 26.27 0 0.57 P3 30096463 1 2 1 1,776 7.8 1,427 0.803 122 0.069 94,958 53.5 23.60 29.20 1 0.06 P4 30076931 1 0 1 12,262 6.0 3,614 0.295 510 0.042 1,104,082 90.0 7.10 20.10 1 0.63 P5 30172722 1 2 1 4,898 14 1,597 0.326 313 0.064 273,980 55.9 17.60 19.20 0 0.09 P6 30107176 1 0 1 9,530 20 1,525 0.160 635 0.067 683,701 71.7 23.60 29.20 0 0.59 P7 30124028 1 9 0 625 12 1,949 3.118 444 0.710 45,796 73.3 17.60 19.20 0 0.30 P8 30090914 1 9 0 26,913 32.1 1,727 0.064 1,530 0.057 1,491,948 55.4 26.83 36.62 1 0.05 P9 30083777 1 1 1 6,747 14 6,010 0.891 444 0.066 598,069 88.6 9.70 21.00 0 0.49 P10 30071390 1 1 1 14,026 35 754 0.054 994 0.071 323,581 23.1 16.80 22.60 0 0.12 P11 30131878 1 2 1 3,735 3.8 4,907 1.314 237 0.063 145,979 39.1 16.10 23.20 1 1.15 P12 30137944 1 9 0 28,397 29.5 4,784 0.168 1,986 0.070 988,411 34.8 5.05 19.18 1 0.23 P13 30427024 1 9 0 36,791 110.0 1,495 0.041 2,534 0.069 4,482,591 121.8 5.40 17.20 0 1.35 P14 30104149 1 0 1 6,123 9.0 4,039 0.660 380 0.062 299,852 49.0 12.00 18.20 1 0.06 P15 30073279 1 0 1 893 6 1,843 2.064 56 0.063 108,178 121.2 21.90 22.49 0 0.76 P16 30091314 1 1 1 4,982 15.5 2,560 0.514 211 0.042 217,797 43.7 23.27 36.13 1 0.11 P17 30078400 1 0 1 4,513 7.4 2,298 0.509 234 0.052 261,893 58.0 14.93 22.79 1 0.18 P18 30070422 1 5 1 4,130 7.8 3,117 0.755 218 0.053 157,887 38.2 17.60 19.20 1 0.26 P19 30393223 1 9 0 5,202 8.8 1,432 0.275 366 0.070 223,331 42.9 14.54 15.79 1 0.47 P20 30135925 1 0 1 9,056 2.8 10,919 1.206 637 0.070 823,735 91.0 6.55 13.47 0 1.38 P21 30276122 1 9 0 8,221 16.0 1,687 0.205 545 0.066 299,093 36.4 23.60 29.20 1 0.01 P22 30064808 1 9 0 3,775 8.4 2,509 0.665 266 0.070 177,032 46.9 22.38 28.91 1 0.12 P23 30071804 1 2 1 3,970 12.1 1,944 0.490 237 0.060 211,746 53.3 10.77 21.18 1 0.33 P24 30089927 1 2 1 16,483 33 9,668 0.587 1,107 0.067 13,594,274 824.7 5.40 17.20 0 1.70 P25 30080601 1 1 1 14,921 23 6,640 0.445 999 0.067 923,499 61.9 16.80 22.60 0 0.10 P26 30080632 1 2 1 22,698 40 1,202 0.053 1,199 0.053 835,561 36.8 17.67 23.31 0 0.08 P27 30275022 1 0 1 280 2 1,102 3.934 20 0.071 14,321 51.1 16.10 23.20 0 0.08 P28 30083882 1 2 1 11,133 20 4,430 0.398 1,056 0.095 584,572 52.5 11.12 18.85 0 0.72 P29 30106248 1 0 1 14,297 39.0 2,786 0.195 955 0.067 2,167,634 151.6 5.40 17.20 1 1.25 P30 30106756 1 0 1 26,905 44 6,212 0.231 1,885 0.070 3,523,633 131.0 11.12 18.85 0 0.95 P31 30122219 1 2 1 11,453 9 3,084 0.269 813 0.071 213,608 18.7 4.40 9.10 0 0.13 P32 30061863 1 1 1 25,894 21 24,354 0.941 1,602 0.062 3,220,696 124.4 7.10 20.10 0 0.53 P33 30081072 1 1 1 5,903 12 6,222 1.054 492 0.083 634,376 107.5 9.70 21.00 0 0.65 P34 30106138 1 0 1 5,784 3.0 2,018 0.349 352 0.061 81,484 14.1 16.80 22.60 0 0.83 P35 30044558 1 1 1 12,246 24.3 1,808 0.148 825 0.067 1,046,948 85.5 25.20 25.58 1 0.56 P36 20079319 1 9 1 26,910 28.2 9,218 0.343 1,716 0.064 8,659,859 321.8 17.60 19.20 1 3.19 P37 30091479 1 0 1 12,611 11.2 39,110 3.101 653 0.052 2,151,890 170.6 8.86 18.05 1 1.57 P38 30381293 1 9 1 8,004 19.0 10,313 1.288 508 0.063 1,982,596 247.7 13.80 23.90 1 2.53 P39 20184422 1 1 1 7,428 16 827 0.111 479 0.064 128,461 17.3 23.60 29.20 0 0.07 P40 30134178 1 1 1 1,569 2.8 6,951 4.430 112 0.071 143,378 91.4 8.86 18.05 1 0.54 P41 30108830 1 0 1 23,258 28 20,502 0.882 1,573 0.068 3,387,199 145.6 9.93 15.53 0 0.94 P42 30080314 1 1 1 1,719 4.5 2,174 1.265 97 0.056 98,927 57.6 15.82 25.38 1 0.02 P43 30077537 1 2 1 15,406 29 7,062 0.458 890 0.058 2,560,370 166.2 4.62 27.82 0 1.29 P44 30131496 1 1 1 10,821 12.0 10,000 0.924 761 0.070 675,083 62.4 6.50 16.90 0 0.44 P45 30218272 1 1 1 10,540 4 61,374 5.823 544 0.052 837,548 79.5 12.00 18.20 0 0.53 P46 30081190 1 0 1 2,968 8 1,454 0.490 198 0.067 87,199 29.4 13.70 23.00 1 0.02 P47 30070762 1 1 1 15,225 26 9,619 0.632 1,020 0.067 2,092,546 137.4 16.10 23.20 0 1.04
54
P48 30123462 1 1 1 1,526 6.5 1,724 1.130 97 0.064 101,585 66.6 16.10 23.20 1 0.26 P49 30122528 1 1 1 4,942 21.48 2,809 0.568 348 0.070 289,395 58.6 23.60 29.20 1 0.42 P50 30217972 1 9 0 1,650 4 3,083 1.868 89 0.054 111,427 67.5 12.00 18.20 1 0.25
Annex 6. Road Transport Project SEI Calculations, by Region (with standard poverty rate) BEN_INV JOBS_INV SAVINGS_INV POV_C
0.614 0.224 0.724 0.224
Project ID Ben_inv Jobc_inv Savings_inv Pov_c Ben_std Jobs_std Savings_std Pov_std SEI_STD SEI
P1 30106685 1.334 0.041 27.5 28.4 0.31 ‐0.39 ‐0.57 2.03 0.15 26.37
P2 30080831 0.276 0.077 97.8 25.2 ‐0.58 0.00 0.02 1.55 0.01 23.57
P3 30096463 0.803 0.069 53.5 23.6 ‐0.13 ‐0.09 ‐0.35 1.31 ‐0.06 22.18
P4 30076931 0.295 0.042 90.0 7.1 ‐0.56 ‐0.38 ‐0.05 ‐1.15 ‐0.72 9.15
P5 30172722 0.326 0.064 55.9 17.6 ‐0.53 ‐0.14 ‐0.33 0.41 ‐0.51 13.40
P6 30107176 0.160 0.067 71.7 23.6 ‐0.67 ‐0.11 ‐0.20 1.31 ‐0.29 17.68
P7 30124028 3.118 0.710 73.3 17.6 1.81 6.89 ‐0.19 0.41 2.61 75.25
P8 30090914 0.064 0.057 55.4 26.8 ‐0.75 ‐0.22 ‐0.34 1.79 ‐0.36 16.40
P9 30083777 0.891 0.066 88.6 9.7 ‐0.06 ‐0.12 ‐0.06 ‐0.76 ‐0.28 17.97
P10 30071390 0.054 0.071 23.1 16.8 ‐0.76 ‐0.07 ‐0.61 0.30 ‐0.86 6.50
P11 30131878 1.314 0.063 39.1 16.1 0.30 ‐0.15 ‐0.47 0.19 ‐0.15 20.48
P12 30137944 0.168 0.070 34.8 5.0 ‐0.67 ‐0.08 ‐0.51 ‐1.45 ‐1.12 1.28
P13 30427024 0.041 0.069 121.8 5.4 ‐0.77 ‐0.09 0.22 ‐1.40 ‐0.65 10.54
P14 30104149 0.660 0.062 49.0 12.0 ‐0.25 ‐0.16 ‐0.39 ‐0.42 ‐0.57 12.20
P15 30073279 2.064 0.063 121.2 21.9 0.93 ‐0.16 0.21 1.05 0.92 41.75
P16 30091314 0.514 0.042 43.7 23.3 ‐0.38 ‐0.38 ‐0.43 1.26 ‐0.35 16.56
P17 30078400 0.509 0.052 58.0 14.9 ‐0.38 ‐0.27 ‐0.31 0.02 ‐0.52 13.18
P18 30070422 0.755 0.053 38.2 17.6 ‐0.17 ‐0.26 ‐0.48 0.41 ‐0.42 15.13
P19 30393223 0.275 0.070 42.9 14.5 ‐0.58 ‐0.07 ‐0.44 ‐0.04 ‐0.70 9.62
P20 30135925 1.206 0.070 91.0 6.6 0.20 ‐0.07 ‐0.04 ‐1.23 ‐0.19 19.61
P21 30276122 0.205 0.066 36.4 23.6 ‐0.64 ‐0.12 ‐0.49 1.31 ‐0.48 13.90
P22 30064808 0.665 0.070 46.9 22.4 ‐0.25 ‐0.07 ‐0.41 1.13 ‐0.21 19.25
P23 30071804 0.490 0.060 53.3 10.8 ‐0.40 ‐0.19 ‐0.35 ‐0.60 ‐0.68 10.06
P24 30089927 0.587 0.067 824.7 5.4 ‐0.32 ‐0.11 6.06 ‐1.40 3.86 100.00
P25 30080601 0.445 0.067 61.9 16.8 ‐0.43 ‐0.11 ‐0.28 0.30 ‐0.43 14.95
P26 30080632 0.053 0.053 36.8 17.7 ‐0.76 ‐0.26 ‐0.49 0.43 ‐0.79 7.83
P27 30275022 3.934 0.071 51.1 16.1 2.50 ‐0.06 ‐0.37 0.19 1.29 49.12
P28 30083882 0.398 0.095 52.5 11.1 ‐0.47 0.19 ‐0.36 ‐0.55 ‐0.63 10.95
P29 30106248 0.195 0.067 151.6 5.4 ‐0.64 ‐0.11 0.46 ‐1.40 ‐0.40 15.58
P30 30106756 0.231 0.070 131.0 11.1 ‐0.61 ‐0.08 0.29 ‐0.55 ‐0.31 17.41
P31 30122219 0.269 0.071 18.7 4.4 ‐0.58 ‐0.07 ‐0.64 ‐1.55 ‐1.18 0.00
P32 30061863 0.941 0.062 124.4 7.1 ‐0.02 ‐0.16 0.24 ‐1.15 ‐0.13 20.84
P33 30081072 1.054 0.083 107.5 9.7 0.08 0.07 0.10 ‐0.76 ‐0.04 22.73
P34 30106138 0.349 0.061 14.1 16.8 ‐0.52 ‐0.18 ‐0.68 0.30 ‐0.78 7.96
55
P35 30044558 0.148 0.067 85.5 25.2 ‐0.68 ‐0.10 ‐0.09 1.54 ‐0.16 20.28
P36 20079319 0.343 0.064 321.8 17.6 ‐0.52 ‐0.14 1.88 0.41 1.10 45.32
P37 30091479 3.101 0.052 170.6 8.9 1.80 ‐0.27 0.62 ‐0.88 1.29 49.15
P38 30381293 1.288 0.063 247.7 13.8 0.27 ‐0.15 1.26 ‐0.15 1.02 43.62
P39 20184422 0.111 0.064 17.3 23.6 ‐0.72 ‐0.14 ‐0.65 1.31 ‐0.65 10.57
P40 30134178 4.430 0.071 91.4 8.9 2.91 ‐0.06 ‐0.04 ‐0.88 1.55 54.22
P41 30108830 0.882 0.068 145.6 9.9 ‐0.07 ‐0.10 0.41 ‐0.73 0.07 24.92
P42 30080314 1.265 0.056 57.6 15.8 0.25 ‐0.22 ‐0.32 0.15 ‐0.09 21.66
P43 30077537 0.458 0.058 166.2 4.6 ‐0.42 ‐0.21 0.59 ‐1.51 ‐0.22 19.07
P44 30131496 0.924 0.070 62.4 6.5 ‐0.03 ‐0.07 ‐0.28 ‐1.24 ‐0.51 13.28
P45 30218272 5.823 0.052 79.5 12.0 4.09 ‐0.28 ‐0.14 ‐0.42 2.25 68.18
P46 30081190 0.490 0.067 29.4 13.7 ‐0.40 ‐0.11 ‐0.55 ‐0.17 ‐0.71 9.47
P47 30070762 0.632 0.067 137.4 16.1 ‐0.28 ‐0.11 0.35 0.19 0.10 25.42
P48 30123462 1.130 0.064 66.6 16.1 0.14 ‐0.15 ‐0.24 0.19 ‐0.08 21.89
P49 30122528 0.568 0.070 58.6 23.6 ‐0.33 ‐0.07 ‐0.31 1.31 ‐0.15 20.47
P50 30217972 1.868 0.054 67.5 12.0 0.76 ‐0.25 ‐0.24 ‐0.42 0.15 26.39
56
Annex 7. Road Transport Project SEI Calculations, by Region (with multidimensional index poverty rate) BEN_INV JOBS_INV SAVINGS_INV POV_MULTI
0.442 0.224 0.839 0.224
Project ID Ben_inv Jobs_inv Savings_inv Pov_multi Ben_std Jobs_std Savings_std Pov_std SEI_STD SEI
P1 30106685 1.334 0.041 27.5 22.9 0.31 ‐0.39 ‐0.57 0.16 ‐0.39 15.92
P2 30080831 0.276 0.077 97.8 26.3 ‐0.58 0.00 0.02 0.80 ‐0.06 21.32
P3 30096463 0.803 0.069 53.5 29.2 ‐0.13 ‐0.09 ‐0.35 1.35 ‐0.07 21.15
P4 30076931 0.295 0.042 90.0 20.1 ‐0.56 ‐0.38 ‐0.05 ‐0.37 ‐0.46 14.83
P5 30172722 0.326 0.064 55.9 19.2 ‐0.53 ‐0.14 ‐0.33 ‐0.54 ‐0.67 11.37
P6 30107176 0.160 0.067 71.7 29.2 ‐0.67 ‐0.11 ‐0.20 1.35 ‐0.19 19.24
P7 30124028 3.118 0.710 73.3 19.2 1.81 6.89 ‐0.19 ‐0.54 2.06 56.28
P8 30090914 0.064 0.057 55.4 36.6 ‐0.75 ‐0.22 ‐0.34 2.76 ‐0.05 21.54
P9 30083777 0.891 0.066 88.6 21.0 ‐0.06 ‐0.12 ‐0.06 ‐0.20 ‐0.15 19.91
P10 30071390 0.054 0.071 23.1 22.6 ‐0.76 ‐0.07 ‐0.61 0.11 ‐0.84 8.58
P11 30131878 1.314 0.063 39.1 23.2 0.30 ‐0.15 ‐0.47 0.22 ‐0.25 18.23
P12 30137944 0.168 0.070 34.8 19.2 ‐0.67 ‐0.08 ‐0.51 ‐0.54 ‐0.86 8.21
P13 30427024 0.041 0.069 121.8 17.2 ‐0.77 ‐0.09 0.22 ‐0.91 ‐0.39 16.00
P14 30104149 0.660 0.062 49.0 18.2 ‐0.25 ‐0.16 ‐0.39 ‐0.73 ‐0.64 11.84
P15 30073279 2.064 0.063 121.2 22.5 0.93 ‐0.16 0.21 0.09 0.57 31.72
P16 30091314 0.514 0.042 43.7 36.1 ‐0.38 ‐0.38 ‐0.43 2.67 ‐0.02 22.03
P17 30078400 0.509 0.052 58.0 22.8 ‐0.38 ‐0.27 ‐0.31 0.14 ‐0.46 14.74
P18 30070422 0.755 0.053 38.2 19.2 ‐0.17 ‐0.26 ‐0.48 ‐0.54 ‐0.66 11.51
P19 30393223 0.275 0.070 42.9 15.8 ‐0.58 ‐0.07 ‐0.44 ‐1.18 ‐0.91 7.45
P20 30135925 1.206 0.070 91.0 13.5 0.20 ‐0.07 ‐0.04 ‐1.62 ‐0.32 17.04
P21 30276122 0.205 0.066 36.4 29.2 ‐0.64 ‐0.12 ‐0.49 1.35 ‐0.42 15.44
P22 30064808 0.665 0.070 46.9 28.9 ‐0.25 ‐0.07 ‐0.41 1.30 ‐0.18 19.41
P23 30071804 0.490 0.060 53.3 21.2 ‐0.40 ‐0.19 ‐0.35 ‐0.16 ‐0.55 13.28
P24 30089927 0.587 0.067 824.7 17.2 ‐0.32 ‐0.11 6.06 ‐0.91 4.72 100.00
P25 30080601 0.445 0.067 61.9 22.6 ‐0.43 ‐0.11 ‐0.28 0.11 ‐0.43 15.27
P26 30080632 0.053 0.053 36.8 23.3 ‐0.76 ‐0.26 ‐0.49 0.24 ‐0.76 9.92
P27 30275022 3.934 0.071 51.1 23.2 2.50 ‐0.06 ‐0.37 0.22 0.83 35.95
P28 30083882 0.398 0.095 52.5 18.9 ‐0.47 0.19 ‐0.36 ‐0.60 ‐0.60 12.41
P29 30106248 0.195 0.067 151.6 17.2 ‐0.64 ‐0.11 0.46 ‐0.91 ‐0.12 20.28
P30 30106756 0.231 0.070 131.0 18.9 ‐0.61 ‐0.08 0.29 ‐0.60 ‐0.18 19.41
P31 30122219 0.269 0.071 18.7 9.1 ‐0.58 ‐0.07 ‐0.64 ‐2.45 ‐1.36 0.00
P32 30061863 0.941 0.062 124.4 20.1 ‐0.02 ‐0.16 0.24 ‐0.37 0.07 23.53
P33 30081072 1.054 0.083 107.5 21.0 0.08 0.07 0.10 ‐0.20 0.09 23.77
P34 30106138 0.349 0.061 14.1 22.6 ‐0.52 ‐0.18 ‐0.68 0.11 ‐0.81 8.95
P35 30044558 0.148 0.067 85.5 25.6 ‐0.68 ‐0.10 ‐0.09 0.67 ‐0.25 18.25
P36 20079319 0.343 0.064 321.8 19.2 ‐0.52 ‐0.14 1.88 ‐0.54 1.20 42.00
P37 30091479 3.101 0.052 170.6 18.1 1.80 ‐0.27 0.62 ‐0.75 1.09 40.22
P38 30381293 1.288 0.063 247.7 23.9 0.27 ‐0.15 1.26 0.35 1.23 42.53
P39 20184422 0.111 0.064 17.3 29.2 ‐0.72 ‐0.14 ‐0.65 1.35 ‐0.59 12.60
57
P40 30134178 4.430 0.071 91.4 18.1 2.91 ‐0.06 ‐0.04 ‐0.75 1.07 40.01
P41 30108830 0.882 0.068 145.6 15.5 ‐0.07 ‐0.10 0.41 ‐1.23 0.02 22.66
P42 30080314 1.265 0.056 57.6 25.4 0.25 ‐0.22 ‐0.32 0.63 ‐0.06 21.29
P43 30077537 0.458 0.058 166.2 27.8 ‐0.42 ‐0.21 0.59 1.09 0.50 30.59
P44 30131496 0.924 0.070 62.4 16.9 ‐0.03 ‐0.07 ‐0.28 ‐0.97 ‐0.48 14.42
P45 30218272 5.823 0.052 79.5 18.2 4.09 ‐0.28 ‐0.14 ‐0.73 1.47 46.47
P46 30081190 0.490 0.067 29.4 23.0 ‐0.40 ‐0.11 ‐0.55 0.18 ‐0.62 12.08
P47 30070762 0.632 0.067 137.4 23.2 ‐0.28 ‐0.11 0.35 0.22 0.19 25.51
P48 30123462 1.130 0.064 66.6 23.2 0.14 ‐0.15 ‐0.24 0.22 ‐0.13 20.27
P49 30122528 0.568 0.070 58.6 29.2 ‐0.33 ‐0.07 ‐0.31 1.35 ‐0.12 20.37
P50 30217972 1.868 0.054 67.5 18.2 0.76 ‐0.25 ‐0.24 ‐0.73 ‐0.08 21.03
58
Annex 8. Road Transport Project FEI Calculations, by Region Project ID IVAN IVAN_std FEI_STD FEI
P1 30106685 1.718 1.62 1.62 53.66
P2 30080831 0.568 ‐0.10 ‐0.10 17.49
P3 30096463 0.060 ‐0.86 ‐0.86 1.50
P4 30076931 0.634 0.00 0.00 19.56
P5 30172722 0.086 ‐0.82 ‐0.82 2.34
P6 30107176 0.591 ‐0.07 ‐0.07 18.21
P7 30124028 0.303 ‐0.50 ‐0.50 9.16
P8 30090914 0.054 ‐0.87 ‐0.87 1.32
P9 30083777 0.489 ‐0.22 ‐0.22 15.01
P10 30071390 0.115 ‐0.78 ‐0.78 3.24
P11 30131878 1.152 0.77 0.77 35.87
P12 30137944 0.230 ‐0.61 ‐0.61 6.86
P13 30427024 1.353 1.08 1.08 42.17
P14 30104149 0.055 ‐0.87 ‐0.87 1.36
P15 30073279 0.758 0.18 0.18 23.46
P16 30091314 0.107 ‐0.79 ‐0.79 3.00
P17 30078400 0.184 ‐0.68 ‐0.68 5.42
P18 30070422 0.261 ‐0.56 ‐0.56 7.84
P19 30393223 0.474 ‐0.24 ‐0.24 14.54
P20 30135925 1.385 1.12 1.12 43.19
P21 30276122 0.012 ‐0.93 ‐0.93 0.00
P22 30064808 0.125 ‐0.77 ‐0.77 3.55
P23 30071804 0.326 ‐0.46 ‐0.46 9.86
P24 30089927 1.698 1.59 1.59 53.05
P25 30080601 0.105 ‐0.80 ‐0.80 2.91
P26 30080632 0.081 ‐0.83 ‐0.83 2.18
P27 30275022 0.084 ‐0.83 ‐0.83 2.26
P28 30083882 0.716 0.12 0.12 22.16
P29 30106248 1.254 0.93 0.93 39.07
P30 30106756 0.953 0.48 0.48 29.62
P31 30122219 0.130 ‐0.76 ‐0.76 3.70
P32 30061863 0.528 ‐0.16 ‐0.16 16.24
P33 30081072 0.654 0.03 0.03 20.20
P34 30106138 0.830 0.29 0.29 25.74
P35 30044558 0.559 ‐0.12 ‐0.12 17.19
P36 20079319 3.191 3.83 3.83 100.00
P37 30091479 1.574 1.41 1.41 49.16
P38 30381293 2.532 2.84 2.84 79.29
P39 20184422 0.071 ‐0.85 ‐0.85 1.84
P40 30134178 0.539 ‐0.14 ‐0.14 16.58
P41 30108830 0.943 0.46 0.46 29.27
P42 30080314 0.025 ‐0.92 ‐0.92 0.40
P43 30077537 1.291 0.98 0.98 40.23
P44 30131496 0.443 ‐0.29 ‐0.29 13.57
P45 30218272 0.533 ‐0.15 ‐0.15 16.40
P46 30081190 0.023 ‐0.92 ‐0.92 0.35
P47 30070762 1.044 0.61 0.61 32.47
P48 30123462 0.261 ‐0.56 ‐0.56 7.84
P49 30122528 0.415 ‐0.33 ‐0.33 12.68
P50 30217972 0.250 ‐0.58 ‐0.58 7.49