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Title: Ranking and Measuring Efficiency of Middle East Cooperation Projects


1
Ranking and Measuring Efficiency of Middle East
Cooperation Projects
  • Bar-El Raphael, Hadad Yossi and Malul Miki

2
Background
  • Regional cooperation projects between countries
    in the Middle East are expected to provide
    economical and socio-political benefits to all
    parties involved.
  • However, in case of budget constraints the
    decision maker should select the best project
    composition, that is, the composition of projects
    that will maximize regional/world welfare.

3
Background
  • The benefits of these cooperation projects are
    the regular direct economic impacts and the
    externalities, which can be categorized into two
    groups as suggested by Bar-El (2005).
  • macro-economic externalities- includes the
    well-known type of economic benefits that are
    generated by a project.
  • socio-political externalities- includes benefits
    that are not necessarily measurable in economic
    terms, such as regional stability, the easing of
    social tensions, and the diminution of
    international conflicts (Bouillon, 2004 Forman
    et al., 2000 Isard, 2004 Weede, 2004).

4
Background
  • According to Forman et al. (2000), during the
    1990s the international donor community pledged
    more than one hundred billion dollars in aid to
    countries recovering from violent conflicts, and
    more recently the USA promised (by President Bush
    to President Abbas on May 26, 2005) 50 million
    dollars to fund infrastructure development in
    Gaza.

5
Background
  • The international community should use the funds
    in the most efficient way, that is, to maximize
    the net benefits under the budget constraint
  • these decisions require a credible method for
    recognizing the most efficient projects.
  • The ultimate tool in choosing the projects'
    composition is by the profit index which we will
    discuss in the next section.
  • However in these kinds of projects the benefits
    are not only strictly economic but also
    socio-political externalities which are not
    always transferable to regular profits, so
    different methods are required.

6
Methodology
  • In this paper we suggest a unique approach for
    dealing with those difficulties using the Data
    Envelopment Analysis (DEA).
  • we will use the DEA to identify the most
    efficient projects according to three inputs and
    three outputs, and then we will rank the projects
    using the Super Efficiency method.
  • We suggest that this ranking will be used as an
    alternative to the Profit Index ranking.
  • In addition, using a statistical approach we will
    build a simple ranking tool for future budgets
    that will probably flow into the region.

7
Profit Index, Data Envelopment Analysis and
Ranking Method
  • The profit index approach-
  • in order to maximize welfare (from the world
    point of view), each project that has a positive
    Net Present Value (NPV), should be implemented.
  • However, if there are limitations on the
    investing program that prevents the decision
    maker from undertaking all such projects for
    example, when capital is rationed,
  • we will need a method for selecting a package of
    projects that is within the limited resources and
    yet yields the highest possible accumulated NPV.
    In order to achieve this goal we should invest
    the budget step by step, that is, the first
    dollars in the project generate the highest NPV
    per dollar of initial outlay (investment), and so
    on until the budget is exhausted. This ratio
    known as the profit index.

8
  • Theoretically this method is the most efficient
    way to invest the limited resources that the
    international community provides to the region,
  • yet it is not applicable to Middle East projects
    because of the lack of complete economic data and
    the extra benefits (outputs) and costs (inputs)
    that are not measurable in money terms.

9
Data Envelopment Analysis
  • The DEA is a non-parametric methodology for
    evaluating the relative efficiency of
    Decision-Making Units (DMU) based on multiple
    inputs and multiple outputs. The efficiency score
    is measured as a ratio between weighted outputs
    and weighted inputs, even if the production
    function is unknown.
  • A project will be efficient if, and only if, the
    other project's performance does not satisfy a
    proof that its weighted output and inputs ratio
    can be better than the project ratio.

10
  • The question is How to select the weights if no
    standard unit of measure can be assigned to the
    inputs and outputs?
  • Here lies the cornerstone of DEA procedure. DEA
    permits each DMU to select any desirable
    (optimal) weight for each input and output,
  • If under the optimal weights the project is less
    efficient than other projects, then under
    different weights (for example the unknown real
    weights) it would be even less efficient.
  • The process should be repeated for each project
    so that each project will get its optimal
    weights, and than the efficient projects could be
    determined.

11
Technical Definitions
  • Consider n DMUs, where each DMU j (j1,,n) uses
    m inputs
  • for producing S outputs
  • The model is For each unit k we find the best
    weights and
  • that maximize the ratio between
    the weighted output and weighted input.
  • The objective function will be solved under the
    following constraints

12
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13
  • The weights are all positive and the ratios are
    bounded by 1 (100). Each unit k is assigned the
    highest possible efficiency score by choosing the
    optimal weights. If a unit reaches the maximum
    possible value of 100 it is efficient, otherwise
    it is inefficient.
  • This model should be solved (using linear
    programming) for each project.
  • The efficient projects will have a ratio of one
    and the inefficient projects will have a ratio
    that is less than one.

14
The Super Efficiency Method
  • Anderson and Peterson (AP) (1993) view the DEA
    score for the inefficient units as their rank
    scale.
  • Actually, the same model that we have used for
    the DEA method will be solved again in order to
    rank scale the efficient units However,
  • AP suggest allowing the efficient units to
    receive a score greater than 1 by dropping the
    constraint that bounds the score of the evaluated
    project.

15
A Case Study on Cooperation Projects
  • The Data
  • We will use a data base that includes a list
    compiled by the Ministry of Finance of 250 ideas
    of regional cooperation which have been raised
    since 1993, and a second stage with data compiled
    by the Ministry of Regional Cooperation in
    Israel.
  • We should mention that in the last couple of
    years this data base has not been updated, due to
    the closing of the Ministry of Regional
    Cooperation, and some of the information about
    the projects is still missing.
  • The data base includes projects that cover a wide
    variety of areas of cooperation, and are at
    various stages of processing.

16
  • About a quarter of the projects are still at the
    phase of idea consideration, with no active steps
    taken yet for implementation these include many
    desalination projects, tourism ideas, and
    research projects.
  • The second quarter includes projects at the
    stage of feasibility studies and planning, such
    as the big "Jordan Rift Valley" projects
    (including the Red SeaDead Sea canal project),
    industrial zones, and a joint airport.
  • The third quarter of the projects is at the stage
    of active implementation, such as certain
    research projects, ecology projects, and
    industrial parks.
  • The last quarter comprises projects which have
    already been implemented, such as trade
    agreements, air transport agreements, and energy
    agreements.

17
The variables
  • The outputs (benefits) - Y
  • Y1 - Micro-economic viability
  • Micro-economic viability is measured in terms of
    evaluation of expected return for capital
    investments, or business profitability.
  • In relation to public projects that are not
    expected to be evaluated in terms of business
    profitability, micro-economic viability is
    evaluated in terms of public utility achieved by
    given amounts of investment.

18
  • Y2 - Macro-economic externalities (MEE)
  • These are defined in principle as the gap between
    the "regional impact" and the "micro-economic
    viability".
  • This difference can actually be accounted as the
    externalities of the project extra benefits that
    are not perceived within the project itself, but
    by the economy of the region or the world as a
    whole.
  • Actually, economic policy should consider the
    macro-economic benefits as the major objective,
    and provide incentives to all projects that
    create externalities.
  • A project with a low level of micro-economic
    viability may not be implemented because its
    benefits to the investor are low, but if it has a
    high level of regional or global impact public
    policy and external bodies should support it for
    the benefits it brings to the region and to the
    world as a whole.

19
  • Y3 - Socio-political externalities (SPE)
  • These are extra benefits (that are not economic)
    that stem from the projects, such as reduction of
    the tension in the region, lessening of the
    probability of war, and reduction of the country
    risk premium.
  • These three outputs were assessed by a group of
    experts that ranked them on a 1-5 scale, where 5
    represent the highest output in each variable.

20
  • The inputs (costs) X
  • X1 - Investment
  • The size of investment that the project requires
    in US dollars.
  • X2 - Extent
  • This variable reflects the extent of the projects
    in terms of number of countries involved we
    categorized the projects into three groups
  • Single country projects, counting only on support
    from other countries, but with no actual
    cooperation in the implementation of the project.
  • Bilateral projects, involving two countries
    together.
  • Multi-lateral projects, with the participation of
    three or more countries in the cooperation
    project.
  • We assume that projects that require cooperation
    between many countries are more complex and
    require more resources (not necessarily economic
    ones) to implement them.

21
  • X3 -Intensity
  • The intensity of cooperation in the project is
    defined as the extent to which the project is
    conducted under a close joint action. Intensity
    of cooperation is also classified into three main
    groups
  • Low intensity, for projects where cooperation
    takes the form of mere coordination of actions,
    with no active cooperation. These may be projects
    that are conducted by one of the countries for
    its own interests, but may affect another country
    as well, such as the construction of dams,
    development of tourism structures in neighboring
    regions, installation of basic infrastructures in
    one country such as a neighboring sea port,
    environmental projects in a neighboring region,
    etc.

22
  • Medium intensity, for projects based on
    agreements between the countries, when the actual
    implementation is mostly performed by each of the
    countries separately. Projects in this category
    include the creation of joint frameworks in
    various fields (such as joint investment funds,
    and joint professional organizations and forums),
    the signing of formal agreements (such as trade
    agreements, air transport agreements, water
    distribution, etc.), and the implementation of
    training programs (mainly in areas of
    agriculture).
  • High intensity, for projects that are actively
    jointly planned or managed. Such projects may be
    joint industrial parks, joint infrastructures
    (bridges between two countries, trans-regional
    roads, etc.), joint industrial enterprises,
    environment care, administration of water
    distribution, etc.
  • We assume that projects that require more
    intensive cooperation between the countries
    involved are more complex and need more resources
    (which are not necessarily economic ones) to
    implement them.

23
The results
  • We implemented the DEA method on all of the 250
    projects, thus classifying them into two groups
    efficient and inefficient.
  • Fifteen projects came out as efficient, which
    constitutes 6 of the projects. Among the
    efficient projects 67 related to infrastructure
    and public services, while the ratio among the
    inefficient projects was 49.
  • A very interesting result is that among the
    efficient projects, the international community
    was involved with only 40 of the projects, and
    among the inefficient projects the figure is 65.
  • In order to impose an efficient capital rationing
    on the projects we ran the AP ranking method,
    that ranks all the projects from 1 (the most
    efficient project) to 250 (the most inefficient
    one).

24
  • Can the differences in input and outputs explain
    the ranks of the projects?
  • We checked whether there was a significant
    difference between the amount of either inputs or
    outputs in the best projects and with respect to
    the worst projects. We categorized the projects
    into thirds and then compared the means of the
    upper third in relation to the lowest third.

25
Table 1 t-test for means
X3 X2 X1 Y3 Y2 Y1
95.97 1.13 1.23 2.65 3.74 3.71 Upper third Mean
537.42 2.84 2.54 2.86 3.98 3.73 Lower third Mean
165.45 0.49 0.57 1.28 0.63 0.74 Upper third standard deviation
2364.34 0.36 0.5 0.93 0.51 0.63 Lower third standard deviation
0.046 lt0.01 lt0.01 0.10 0.01 0.40 P(Tltt)
26
  • A very interesting result is that in general the
    projects that ranked in the upper third use fewer
    inputs on average than those that ranked in the
    lower third, and it is significant. In addition,
    in two out of three outputs the difference in the
    means isnt significant. For Y2, in which the
    difference is significant, the upper third
    projects produce on average less output than
    those in the lowest third.
  • As one can see, inefficient projects even
    though they produce higher outputs use more
    inputs, which means that an increase in inputs is
    subject to substantial diminishing marginal
    productivity.

27
  • At this point we want to suggest a simple tool
    that can be used for the ranking of future
    projects.
  • We assume that the easiest data to collect and
    estimate are the inputs of each project in
    addition, the outputs data estimation bounds with
    a relatively high level of uncertainty, and
    therefore its estimation is more complex and less
    accurate.
  • Therefore if a significant relation is found
    between the score of the DEA and the inputs of
    the project, together with high Adjusted R2, then
    we suggest that decision makers should use that
    rule as a preliminary estimator.

28
  • In order to find the statistic relation between
    the ranking of each project and the inputs that
    the project requires, we estimated a production
    function in which the dependent variable is the
    score of AP and the explanatory variables are
    the inputs EX- Extent, INT- Intensity, INV-
    Investment and Y- score according to AP.

29
  • We found that the regression is significant and
    that each of the explanatory variables is
    significant.
  • Table 2- Regression results, F163.1827, n250

30
p-value Elasticity Variables
lt0.01 -0.05053 X1(investment)
0.0355 -0.1264 X2 (extent)
lt0.01 -0.5604 X3 (intensity)
Adjusted R20.662
31
  • The elasticity of the score of the project
    respective to all of the inputs is negative.
  • The elasticity of the score in relation to the
    required level of intensity of cooperation is
    -0.56, which means that a one percent increase in
    the intensity decreases the score of the
    project by 0.56 investment and extent also have
    a negative elasticity of -0.05 and -0.12,
    respectively.
  • According to this, a project that uses a high
    level of inputs is likely to have a low score.
  • However, we found a positive correlation between
    the inputs and the outputs, which means that as
    the project uses more inputs the outputs increase
    as well.
  • However, that isnt sufficient to compensate the
    increase in the inputs, and therefore projects
    with a high level of inputs are less attractive.

32
Summary
  • The international community needs a credible tool
    for evaluating cooperation projects in the Middle
    East.
  • The conventional Profit Index method is obsolete
    in most of these projects.
  • we suggest that using the DEA approach together
    with the AP ranking is a systematic method that
    will facilitate the efficient use of a limited
    budget.
  • We can see from our results that about 6 of the
    250 projects came out as efficient most of these
    are related to infrastructure and public services.

33
  • The score of the project has a negative
    elasticity in relation to the inputs however the
    outputs are positively correlated with the
    inputs. This means that as the input rises the
    output rises, but at lower rates than the inputs.
    Thus the rise in the outputs does not compensate
    for the rise in the input.
  • According to our results, we suggest that the
    international community invest in projects that
    require a low level of inputs and especially a
    low level of intensity. In addition, we propose a
    significant statistical equation that can be used
    as a preliminary tool for project ranking, since
    it requires only the estimation of the inputs
    that the project requires.
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