Ranking and Measuring Efficiency of Middle East Cooperation Projects

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Ranking and Measuring Efficiency of Middle East
Cooperation Projects

• Bar-El Raphael, Hadad Yossi and Malul Miki

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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.

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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).

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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.

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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.

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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.

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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.

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• 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.

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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.

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• 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.

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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

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• 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.

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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.

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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.

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• 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.

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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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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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).

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• 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.

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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)
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• 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.

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• 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.

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• 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.

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

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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
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• 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.

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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.

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• 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.