Title: Ranking and Measuring Efficiency of Middle East Cooperation Projects
1Ranking and Measuring Efficiency of Middle East
Cooperation Projects
- Bar-El Raphael, Hadad Yossi and Malul Miki
2Background
- 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.
3Background
- 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).
4Background
- 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.
5Background
- 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.
6Methodology
- 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.
7Profit 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.
9Data 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.
11Technical 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(No Transcript)
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.
14The 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.
15A 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.
17The 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.
23The 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.
25Table 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
30p-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.