Ranking and Measuring Efficiency of Middle East

Cooperation Projects

Bar-El Raphael, Hadad Yossi and Malul Miki

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.

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).

1. macro-economic externalities- includes the well-known type of economic benefits that are generated by a project.

2. 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).

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.

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.

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.

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.

• 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.

• 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.

Data Envelopment Analysis

• 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.

Technical Definitions

• Consider n DMUs, where each DMU j (j=1,…,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:

X X ,X ,.....,X 0T

j 1j 2j mj

Y Y ,Y ,.....,Y 0T

j 1j 2j sj

r 1,2,..,S (i 1, 2,..,m)VkiUk

r

sk

k r rkr 1

s mk kr rj i ij

r 1 i 1

mki ik

i 1

kr

ki

h Max U Y

s.t.

U Y V X 0 for j 1,2,..,n (1)

V X 1

U 0 r 1,2,..,s

V 0 i 1,2,...,m

Where is a non-Archimedean infinitesimal

• 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.

The Super Efficiency Method

• Anderson and Peterson (A&P) (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,

• A&P suggest allowing the efficient units to receive a score greater than 1 by dropping the constraint that bounds the score of the evaluated project.

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.

• 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 Sea–Dead 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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

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 A&P ranking method, that ranks all the projects from 1 (the most efficient project) to 250 (the most inefficient one).

• 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.

Table 1: t-test for means

Y1Y2Y3X1X2X3

MeanUpper third3.713.742.651.231.1395.97

Lower third3.733.982.862.542.84537.42

standard deviationUpper third0.740.631.280.570.49165.45

Lower third0.630.510.930.50.362364.34

P(T<=t)0.400.010.10<0.01<0.010.046

• 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 isn’t 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.

• 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.

• 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 A&P and the explanatory variables are the inputs: EX- Extent, INT- Intensity, INV- Investment and Y- score according to A&P.

• We found that the regression is significant and that each of the explanatory variables is significant.

• Table 2- Regression results, F=163.1827, n=250

INTEXINVAY *

VariablesElasticityp-value

X1(investment)

-0.05053<0.01

X2 (extent)

-0.12640.0355

X3 (intensity)

-0.5604<0.01

Adjusted R2=0.662

• 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 isn’t sufficient to compensate the increase in the inputs, and therefore projects with a high level of inputs are less attractive.

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 A&P 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.

• 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.